Family fire early warning system and method based on environmental perception

Abstract

The invention discloses a home fire early warning system and method based on environmental perception, and belongs to the technical field of home fire monitoring and early warning. The system comprises a home environment sensing module, an intelligent processing module, a multi-node networking data analysis module and an alarm information delivery module. The output end of the home environment sensing module is connected with the input end of the intelligent processing module; the output end of the intelligent processing module is connected with the input end of the multi-node networking data analysis module; the output end of the multi-node networking data analysis module is connected with the input end of the alarm information delivery module. According to the invention, through combination of an environment sensing technology and an intelligent household technology, fire monitoring and analysis in the intelligent household are realized, the problem that point location installation is not fine and intelligent enough is solved, the situations of missing report, error report and the like caused by monitoring point locations are prevented, and the household fire monitoring capability is greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of household fire monitoring and early warning, and in particular to a household fire early warning system and method based on environment perception. Background Art

[0002] Environmental perception is the dynamic perception and cognition of the environment through various sensors, providing digital known environmental information and providing input for decision-making modules. It is a prerequisite for realizing digital intelligent monitoring. Combining environmental perception technology with smart home technology can realize data monitoring and early warning inside the home.

[0003] In the current field of smart home fire warning technology, various types of sensors installed inside the smart home are usually used to monitor environmental information data in real time, such as environmental temperature and humidity, temperature and humidity at fire hazard points, smoke and flame images, etc. However, in the installation of monitoring points, a fixed and identical layout is basically adopted, which only ensures that the total coverage of each monitoring point can achieve full house coverage. However, in actual use, some fire-prone areas or areas where people are almost inactive may be at the edge of the monitoring point, or only under the monitoring of one monitoring point. In such a situation, firstly, poor monitoring effect and inaccurate monitoring are likely to occur; secondly, once a monitoring device has a line problem or a network problem, the entire monitoring system will be affected. Summary of the invention

[0004] The purpose of the present invention is to provide a household fire warning system and method based on environment perception to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a household fire early warning method based on environment perception, the method comprising the following steps:

[0006] S1. Build a home environment perception cloud platform, classify data, and separate first high-quality data and second planned data. The first high-quality data refers to the home activity history data existing in the home environment perception cloud platform, and the second planned data refers to the home in the planning stage. There is no home activity history data in the home environment perception cloud platform;

[0007] S2. Use the first high-quality data to build a home activity area analysis model, analyze the activity ignored area, and mark the installation point of the fire monitoring device;

[0008] S3, substituting the second plan data into the household activity area analysis model, replacing the activity ignored area with the plan ignored area, and marking the installation point of the fire monitoring device;

[0009] S4. Based on the marked fire monitoring device installation points, a node network is constructed to feed back real-time monitoring data to the home environment perception cloud platform.

[0010] According to the above technical solution, the home activity history data in the home environment perception cloud platform refers to the activity data of the user's activities in the home after the user has moved in and authorized the registration of the home environment perception cloud platform service;

[0011] The home is in the planning stage, and there is no historical home activity data in the home environment perception cloud platform, which means that the user has not checked in or the activity data of the user's activities in the home cannot be obtained.

[0012] In the above technical solution, different situations are mainly handled. The first is installation when the person has moved in, the other is installation when the person has not moved in, and also includes installation under privacy.

[0013] According to the above technical solution, constructing the home activity area analysis model includes:

[0014] Obtain the user's authorization information data, and obtain the user's stay location in the smart home based on the authorization information data, wherein the stay location includes:

[0015] According to the data collected by the smart home, a spatial coordinate system is formed in the user's home, with unit 1 as the coordinate spacing, and the user's stay position is obtained at a fixed time interval T0. The stay position is selected based on the edge of the user's body. During the selection process, the minimum interval between two adjacent selected points is set, and a three-dimensional array is formed for all the selected points. The three-dimensional array includes the spatial coordinate data of each selected point, and the three-dimensional data is named with the current time and stored in the database;

[0016] Analyze all three-dimensional arrays in the database, select points with more than N repetitions as high-frequency points, set high-frequency distance D, group the selected points, and record the points whose distances from high-frequency points do not exceed high-frequency distance D as the same group as the high-frequency points. Any point that belongs to two or more groups will be randomly included in any group; the distance between any two high-frequency points in any group does not exceed M, where M, N, and D are all constant values ​​set by the system.

[0017] Performing regional processing on the data of each group, wherein the regional processing refers to forming a region in space to include all data points in the group, selecting the region with the smallest spatial area as the preferred region under regional processing, and after all the preferred regions are selected, the remaining regions in the smart home are recorded as activity ignored regions;

[0018] Get the total area of ​​the smart home, get the maximum monitoring area of ​​a single monitoring device, and set the number of monitoring devices to:

[0019]

[0020] Among them, K0 represents the number of monitoring devices; Represents the rounding function operation. When the decimal part is less than or equal to 0.5, the integer part is directly taken; when the decimal part is greater than 0.5, the integer value is rounded plus 1;

[0021] Based on the number of monitoring devices, a full coverage monitoring drawing is automatically generated by software, wherein the full coverage means that the monitoring areas of all monitoring devices can cover the total area of ​​the smart home, and the overlapping area parameter is input into the generated monitoring drawing, and the overlapping areas between the monitoring devices are sorted, and a basic value P is set, and the first P groups of monitoring drawings are analyzed, and the active ignored areas are called, and the active ignored areas are marked in the overlapping area of ​​each group of monitoring drawings, and the area ratio of the active ignored areas of each group of monitoring drawings in the total active ignored areas is calculated, and the largest area ratio is selected as the output drawing;

[0022] If the maximum area percentages are the same, the activity ignored areas marked in the overlapping area of ​​the same monitoring drawing are retrieved respectively, and the areas of the multiple monitoring areas in the marked activity ignored areas are calculated. The multiple monitoring areas refer to an area where there are more than two overlapping monitoring processes.

[0023] According to the above technical solution, replacing the active ignored area with the planned ignored area includes:

[0024] Obtain home device data in the smart home, mark the home devices, call the usage data of other users using home devices in the historical big database, the usage data includes the average usage time within a fixed time, and analyze the usage time of any home device:

[0025] Construct a set of data sets, which include the usage time of the same home appliance by different users, denoted as {a1, a2, ..., a n}, where a1, a2, …, a n They refer to the usage time of n identical household devices, where n represents a constant;

[0026] Using the grey prediction method, {a1, a2, ..., a n}After gray accumulation generation processing, perform whitening differentiation and output the development coefficient and gray action;

[0027] The least square method is constructed based on the development coefficient and the grey action to achieve the solution:

[0028]

[0029] Among them, a n+1 represents the output value; L represents the development coefficient; h represents the gray action amount;

[0030] According to each device's n+1 Sort the home appliances in the order of size, set a threshold b0, delete the home appliances whose output value is less than the threshold b0 directly, mark the remaining home appliances and then give feedback;

[0031] After receiving the feedback data, the system performs regional processing of the home equipment. The regional processing of the home equipment refers to forming an area in the space that includes all data points of the home equipment. Under the regional processing, the area with the smallest spatial area is selected as the preferred area. After all the preferred areas are selected, the remaining areas in the smart home are recorded as planned ignored areas.

[0032] According to the above technical solution, the node networking includes:

[0033] According to the marked fire monitoring device installation points, data nodes are installed on each monitoring device to realize data transmission and communication with the home environment perception cloud platform.

[0034] A home fire warning system based on environment perception, the system comprises: a home environment perception module, an intelligent processing module, a multi-node networking data analysis module and an alarm information delivery module;

[0035] The home environment perception module is used to build a home environment perception cloud platform, perform data classification, and separate the first high-quality data and the second planned data; the intelligent processing module is used to build different regional analysis models according to different data types and mark the installation points of the fire monitoring device; the multi-node networking data analysis module is used to perform home multi-node networking according to the marked fire monitoring device installation points and monitor the home environment data in real time; the alarm information delivery module is used to connect to the home environment perception cloud platform, receive the home environment data fed back by the multi-node networking data analysis module, and when a fire warning occurs, send the warning data back to the administrator port and the fire port;

[0036] The output end of the home environment perception module is connected to the input end of the intelligent processing module; the output end of the intelligent processing module is connected to the input end of the multi-node networking data analysis module; the output end of the multi-node networking data analysis module is connected to the input end of the alarm information delivery module.

[0037] According to the above technical solution, the home environment perception module includes an environment perception unit and a data classification unit;

[0038] The environment perception unit is used to build a home environment perception cloud platform; the data classification unit is used to perform data classification and separate the first high-quality data and the second planned data; the first high-quality data refers to the home activity history data existing in the home environment perception cloud platform, and the second planned data refers to the home being in the planning stage, and there is no home activity history data in the home environment perception cloud platform;

[0039] The output end of the environment perception unit is connected to the input end of the data classification unit.

[0040] According to the above technical solution, the intelligent processing module includes a regional analysis unit and a device installation unit;

[0041] The regional analysis unit is used to construct different regional analysis models according to different data types, and separate the planned ignored area and the active ignored area; the device installation unit is used to mark the fire monitoring device installation point according to the planned ignored area or the active ignored area;

[0042] The output terminal of the area analyzing unit is connected to the input terminal of the device installing unit.

[0043] According to the above technical solution, the multi-node networking data analysis module also includes:

[0044] Build different data nodes, install data nodes on each fire monitoring device, connect to the home environment perception cloud platform, realize data transmission and communication, and monitor home environment data in real time.

[0045] According to the above technical solution, the alarm information delivery module includes a sensing unit and an early warning delivery unit;

[0046] The sensing unit is used to connect to the home environment sensing cloud platform and receive the home environment data fed back by the multi-node networking data analysis module; the warning delivery unit is used to deliver the warning data to the administrator port and the fire port when a fire warning occurs;

[0047] The output end of the sensing unit is connected to the input end of the early warning delivery unit.

[0048] Compared with the prior art, the beneficial effects achieved by the present invention are: by combining environmental perception technology with smart home technology, fire monitoring and analysis inside the smart home is realized, and by processing and analyzing different data, the problem of monitoring point installation under different data is realized, solving the pain point problem of insufficient refinement and intelligence of point installation, preventing omissions and false alarms caused by monitoring points, and greatly improving the ability of home fire monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0050] Figure 1 It is a flow chart of a household fire early warning system and method based on environment perception of the present invention. DETAILED DESCRIPTION

[0051] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0052] See also Figure 1 In the first embodiment, a household fire early warning method based on environment perception is provided, the method comprising: constructing a household environment perception cloud platform, performing data classification, and separating first high-quality data and second planned data, wherein the first high-quality data refers to the household activity history data existing in the household environment perception cloud platform, and the second planned data refers to the household being in the planning stage, and there is no household activity history data in the household environment perception cloud platform;

[0053] The home environment perception cloud platform contains home activity history data, which means that the user has moved in and authorized to register for the home environment perception cloud platform service, and the user's activity data in the home after moving in;

[0054] The home is in the planning stage, and there is no historical home activity data in the home environment perception cloud platform, which means that the user has not checked in or the activity data of the user's activities in the home cannot be obtained.

[0055] Use the first quality data to build a home activity area analysis model, analyze the activity ignored areas, and mark the installation points of fire monitoring devices;

[0056] The constructing of the household activity area analysis model comprises:

[0057] Obtain the user's authorization information data, and obtain the user's stay location in the smart home based on the authorization information data, wherein the stay location includes:

[0058] According to the data collected by the smart home, a spatial coordinate system is formed in the user's home, with unit 1 as the coordinate spacing, and the user's stay position is obtained at a fixed time interval T0. The stay position is selected based on the edge of the user's body. During the selection process, the minimum interval between two adjacent selected points is set, and a three-dimensional array is formed for all the selected points. The three-dimensional array includes the spatial coordinate data of each selected point, and the three-dimensional data is named with the current time and stored in the database;

[0059] Analyze all three-dimensional arrays in the database, select points with more than N repetitions as high-frequency points, set high-frequency distance D, group the selected points, and record the points whose distances from high-frequency points do not exceed high-frequency distance D as the same group as the high-frequency points. Any point that belongs to two or more groups will be randomly included in any group; the distance between any two high-frequency points in any group does not exceed M, where M, N, and D are all constant values ​​set by the system.

[0060] Performing regional processing on the data of each group, wherein the regional processing refers to forming a region in space to include all data points in the group, selecting the region with the smallest spatial area as the preferred region under regional processing, and after all the preferred regions are selected, the remaining regions in the smart home are recorded as activity ignored regions;

[0061] Get the total area of ​​the smart home, get the maximum monitoring area of ​​a single monitoring device, and set the number of monitoring devices to:

[0062]

[0063] Among them, K0 represents the number of monitoring devices; Represents the rounding function operation. When the decimal part is less than or equal to 0.5, the integer part is directly taken; when the decimal part is greater than 0.5, the integer value is rounded plus 1;

[0064] Based on the number of monitoring devices, a full coverage monitoring drawing is automatically generated by software, wherein the full coverage means that the monitoring areas of all monitoring devices can cover the total area of ​​the smart home, and the overlapping area parameter is input into the generated monitoring drawing, and the overlapping areas between the monitoring devices are sorted, and a basic value P is set, and the first P groups of monitoring drawings are analyzed, and the active ignored areas are called, and the active ignored areas are marked in the overlapping area of ​​each group of monitoring drawings, and the area ratio of the active ignored areas of each group of monitoring drawings in the total active ignored areas is calculated, and the largest area ratio is selected as the output drawing;

[0065] If the maximum area percentages are the same, the activity ignored areas marked in the overlapping area of ​​the same monitoring drawing are retrieved respectively, and the areas of the multiple monitoring areas in the marked activity ignored areas are calculated. The multiple monitoring areas refer to an area where there are more than two overlapping monitoring processes.

[0066] Substituting the second plan data into the household activity area analysis model, replacing the activity ignored area with the plan ignored area, and marking the installation point of the fire monitoring device;

[0067] The replacing of the active ignored area with the planned ignored area comprises:

[0068] Obtain home device data in the smart home, mark the home devices, call the usage data of other users using home devices in the historical big database, the usage data includes the average usage time within a fixed time, and analyze the usage time of any home device:

[0069] Construct a set of data sets, which include the usage time of the same home appliance by different users, denoted as {a1, a2, ..., a n}, where a1, a2, …, a n They refer to the usage time of n identical household devices, where n represents a constant;

[0070] Using the grey prediction method, {a1, a2, ..., a n}After gray accumulation generation processing, perform whitening differentiation and output the development coefficient and gray action;

[0071] The least square method is constructed based on the development coefficient and the grey action to achieve the solution:

[0072]

[0073] Among them, a n+1 represents the output value; L represents the development coefficient; h represents the gray action amount;

[0074] According to each device's n+1 Sort the home appliances in the order of size, set a threshold b0, delete the home appliances whose output value is less than the threshold b0 directly, mark the remaining home appliances and then give feedback;

[0075] After receiving the feedback data, the system performs regional processing of the home equipment. The regional processing of the home equipment refers to forming an area in the space that includes all data points of the home equipment. Under the regional processing, the area with the smallest spatial area is selected as the preferred area. After all the preferred areas are selected, the remaining areas in the smart home are recorded as planned ignored areas.

[0076] The node networking includes:

[0077] According to the marked fire monitoring device installation points, data nodes are installed on each monitoring device to realize data transmission and communication with the home environment perception cloud platform.

[0078] In the second embodiment, a home fire warning system based on environment perception is provided, which includes: a home environment perception module, an intelligent processing module, a multi-node networking data analysis module and an alarm information delivery module;

[0079] The home environment perception module is used to build a home environment perception cloud platform, perform data classification, and separate the first high-quality data and the second planned data; the intelligent processing module is used to build different regional analysis models according to different data types and mark the installation points of the fire monitoring device; the multi-node networking data analysis module is used to perform home multi-node networking according to the marked fire monitoring device installation points and monitor the home environment data in real time; the alarm information delivery module is used to connect to the home environment perception cloud platform, receive the home environment data fed back by the multi-node networking data analysis module, and when a fire warning occurs, send the warning data back to the administrator port and the fire port;

[0080] The output end of the home environment perception module is connected to the input end of the intelligent processing module; the output end of the intelligent processing module is connected to the input end of the multi-node networking data analysis module; the output end of the multi-node networking data analysis module is connected to the input end of the alarm information delivery module.

[0081] The home environment perception module includes an environment perception unit and a data classification unit;

[0082] The environment perception unit is used to build a home environment perception cloud platform; the data classification unit is used to perform data classification and separate the first high-quality data and the second planned data; the first high-quality data refers to the home activity history data existing in the home environment perception cloud platform, and the second planned data refers to the home being in the planning stage, and there is no home activity history data in the home environment perception cloud platform;

[0083] The output end of the environment perception unit is connected to the input end of the data classification unit.

[0084] The intelligent processing module includes a regional analysis unit and a device installation unit;

[0085] The regional analysis unit is used to construct different regional analysis models according to different data types, and separate the planned ignored area and the active ignored area; the device installation unit is used to mark the fire monitoring device installation point according to the planned ignored area or the active ignored area;

[0086] The output terminal of the area analyzing unit is connected to the input terminal of the device installing unit.

[0087] The multi-node networking data analysis module also includes:

[0088] Build different data nodes, install data nodes on each fire monitoring device, connect to the home environment perception cloud platform, realize data transmission and communication, and monitor home environment data in real time.

[0089] The alarm information delivery module includes a sensing unit and an early warning delivery unit;

[0090] The sensing unit is used to connect to the home environment sensing cloud platform and receive the home environment data fed back by the multi-node networking data analysis module; the warning delivery unit is used to deliver the warning data to the administrator port and the fire port when a fire warning occurs;

[0091] The output end of the sensing unit is connected to the input end of the early warning delivery unit.

[0092] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0093] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A household fire early warning method based on environmental perception, characterized in that: The method comprises the following steps: S1. Build a home environment perception cloud platform, classify data, and separate first high-quality data and second planned data. The first high-quality data refers to the home activity history data existing in the home environment perception cloud platform, and the second planned data refers to the home in the planning stage. There is no home activity history data in the home environment perception cloud platform; S2. Use the first high-quality data to build a home activity area analysis model, analyze the activity ignored area, and mark the installation point of the fire monitoring device; S3, substituting the second plan data into the household activity area analysis model, replacing the activity ignored area with the plan ignored area, and marking the installation point of the fire monitoring device; S4. Based on the marked fire monitoring device installation points, a node network is constructed to feed back real-time monitoring data to the home environment perception cloud platform.

2. A household fire early warning method based on environment perception according to claim 1, characterized in that: The home environment perception cloud platform contains home activity history data, which means that the user has moved in and authorized to register for the home environment perception cloud platform service, and the user's activity data in the home after moving in; The home is in the planning stage, and there is no historical home activity data in the home environment perception cloud platform, which means that the user has not checked in or the activity data of the user's activities in the home cannot be obtained.

3. A household fire early warning method based on environment perception according to claim 1, characterized in that: The constructing of the household activity area analysis model comprises: Obtain the user's authorization information data, and obtain the user's stay location in the smart home based on the authorization information data, wherein the stay location includes: According to the data collected by the smart home, a spatial coordinate system is formed in the user's home, with unit 1 as the coordinate spacing, and the user's stay position is obtained at a fixed time interval T0. The stay position is selected based on the edge of the user's body. During the selection process, the minimum interval between two adjacent selected points is set, and a three-dimensional array is formed for all the selected points. The three-dimensional array includes the spatial coordinate data of each selected point, and the three-dimensional data is named with the current time and stored in the database; Analyze all three-dimensional arrays in the database, select points with more than N repetitions as high-frequency points, set high-frequency distance D, group the selected points, and record the points whose distances from high-frequency points do not exceed high-frequency distance D as the same group as the high-frequency points. Any point that belongs to two or more groups will be randomly included in any group; the distance between any two high-frequency points in any group does not exceed M, where M, N, and D are all constant values ​​set by the system. Performing regional processing on the data of each group, wherein the regional processing refers to forming a region in space to include all data points in the group, selecting the region with the smallest spatial area as the preferred region under regional processing, and after all the preferred regions are selected, the remaining regions in the smart home are recorded as activity ignored regions; Get the total area of ​​the smart home, get the maximum monitoring area of ​​a single monitoring device, and set the number of monitoring devices to: Among them, K0 represents the number of monitoring devices; Represents the rounding function operation. When the decimal part is less than or equal to 0.5, the integer part is directly taken; when the decimal part is greater than 0.5, the integer value is rounded plus 1; Based on the number of monitoring devices, a full coverage monitoring drawing is automatically generated by software, wherein the full coverage means that the monitoring areas of all monitoring devices can cover the total area of ​​the smart home, and the overlapping area parameter is input into the generated monitoring drawing, and the overlapping areas between the monitoring devices are sorted, and a basic value P is set, and the first P groups of monitoring drawings are analyzed, and the active ignored areas are called, and the active ignored areas are marked in the overlapping area of ​​each group of monitoring drawings, and the area ratio of the active ignored areas of each group of monitoring drawings in the total active ignored areas is calculated, and the largest area ratio is selected as the output drawing; If the maximum area percentages are the same, the activity ignored areas marked in the overlapping area of ​​the same monitoring drawing are retrieved respectively, and the areas of the multiple monitoring areas in the marked activity ignored areas are calculated. The multiple monitoring areas refer to an area where there are more than two overlapping monitoring processes.

4. A household fire early warning method based on environment perception according to claim 3, characterized in that: The replacing of the active ignored area with the planned ignored area comprises: Obtain home device data in the smart home, mark the home devices, call the usage data of other users using home devices in the historical big database, the usage data includes the average usage time within a fixed time, and analyze the usage time of any home device: Construct a set of data sets, which include the usage time of the same home appliance by different users, denoted as [a1, a2, ..., a n }, where a1, a2, …, a n They refer to the usage time of n identical household devices, where n represents a constant; Using the grey prediction method, {a1, a2, ..., a n }After gray accumulation generation processing, perform whitening differentiation and output the development coefficient and gray action; The least square method is constructed based on the development coefficient and the grey action to achieve the solution: Among them, a n+1 represents the output value; L represents the development coefficient; h represents the gray action amount; According to each device's n+1 Sort the home appliances in the order of size, set a threshold b0, delete the home appliances whose output value is less than the threshold b0 directly, mark the remaining home appliances and then give feedback; After receiving the feedback data, the system performs regional processing of the home equipment. The regional processing of the home equipment refers to forming an area in the space that includes all data points of the home equipment. Under the regional processing, the area with the smallest spatial area is selected as the preferred area. After all the preferred areas are selected, the remaining areas in the smart home are recorded as planned ignored areas.

5. The household fire early warning method based on environment perception according to claim 1 is characterized by: The node networking includes: According to the marked fire monitoring device installation points, data nodes are installed on each monitoring device to realize data transmission and communication with the home environment perception cloud platform.

6. A household fire warning system based on environmental perception, characterized in that: The system includes: a home environment perception module, an intelligent processing module, a multi-node networking data analysis module and an alarm information delivery module; The home environment perception module is used to build a home environment perception cloud platform, perform data classification, and separate the first high-quality data and the second planned data; the intelligent processing module is used to build different regional analysis models according to different data types and mark the installation points of the fire monitoring device; the multi-node networking data analysis module is used to perform home multi-node networking according to the marked fire monitoring device installation points and monitor the home environment data in real time; the alarm information delivery module is used to connect to the home environment perception cloud platform, receive the home environment data fed back by the multi-node networking data analysis module, and when a fire warning occurs, send the warning data back to the administrator port and the fire port; The output end of the home environment perception module is connected to the input end of the intelligent processing module; the output end of the intelligent processing module is connected to the input end of the multi-node networking data analysis module; the output end of the multi-node networking data analysis module is connected to the input end of the alarm information delivery module.

7. A household fire warning system based on environmental perception according to claim 6, characterized in that: The home environment perception module includes an environment perception unit and a data classification unit; The environment perception unit is used to build a home environment perception cloud platform; the data classification unit is used to perform data classification and separate the first high-quality data and the second planned data; the first high-quality data refers to the home activity history data existing in the home environment perception cloud platform, and the second planned data refers to the home being in the planning stage, and there is no home activity history data in the home environment perception cloud platform; The output end of the environment perception unit is connected to the input end of the data classification unit.

8. The household fire warning system based on environment perception according to claim 6 is characterized by: The intelligent processing module includes a regional analysis unit and a device installation unit; The regional analysis unit is used to construct different regional analysis models according to different data types, and separate the planned ignored area and the active ignored area; the device installation unit is used to mark the fire monitoring device installation point according to the planned ignored area or the active ignored area; The output terminal of the area analyzing unit is connected to the input terminal of the device installing unit.

9. The household fire warning system based on environment perception according to claim 6 is characterized by: The multi-node networking data analysis module also includes: Build different data nodes, install data nodes on each fire monitoring device, connect to the home environment perception cloud platform, realize data transmission and communication, and monitor home environment data in real time.

10. The household fire warning system based on environment perception according to claim 6, characterized in that: The alarm information delivery module includes a sensing unit and an early warning delivery unit; The sensing unit is used to connect to the home environment sensing cloud platform and receive the home environment data fed back by the multi-node networking data analysis module; the warning delivery unit is used to deliver the warning data to the administrator port and the fire port when a fire warning occurs; The output end of the sensing unit is connected to the input end of the early warning delivery unit.

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