Early warning processing method and system for intelligent security and disinfection

Abstract

The invention provides an early warning processing method and system for intelligent security and disinfection, and belongs to the technical field of early warning devices.The method specifically comprises the steps that hidden danger fire fighting devices in associated fire fighting devices are determined according to analysis results of historical monitoring data of the associated fire fighting devices, distribution data of hidden danger fire-fighting devices in the associated fire-fighting devices of the different fire-fighting hidden danger sub-regions are obtained, and the fire-fighting hidden danger sub-regions with fire-fighting management and control risks not meeting requirements do not exist in combination with the spacing distances between the different fire-fighting hidden danger sub-regions and the different associated fire-fighting devices; the spacing distance between different fire-fighting hidden danger sub-areas is determined, and whether an early warning signal is sent out is determined by combining the fire-fighting management and control risks of the different fire-fighting hidden danger sub-areas and the fire-fighting hidden danger coefficient, so that the reliability of fire-fighting risk management and control is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of early warning devices, and in particular, relates to an early warning processing method and system for smart firefighting. Background Art

[0002] In order to achieve early warning of fire hazards, the invention patent application CN202111086614.2 "A safety early warning system for electric vehicle charging areas" monitors the temperature signal and smoke signal in the charging area in real time and transmits them to the control module. The control module monitors the charging current and power consumption of the charging pile in real time, and specifically controls the power off of the charging pile to minimize the possibility of harm. However, there are the following technical problems: In the process of identifying fire hazards, in addition to fire hazard signals such as temperature signals and smoke signals, the operating status of the water pressure monitoring data of the fire-fighting equipment, the fire-fighting linkage early warning device, etc. are also crucial. Therefore, if the monitoring and analysis of multi-dimensional data cannot be achieved, the accuracy of the identification and processing of fire hazards cannot be guaranteed.

[0003] In response to the above technical problems, the present invention provides an early warning processing method and system for smart fire protection. Summary of the invention

[0004] To achieve the purpose of the present invention, the present invention adopts the following technical solutions: According to one aspect of the present invention, a warning processing method for smart fire safety is provided.

[0005] A warning processing method for smart fire prevention and control, specifically comprising: S1 determines the control sub-areas in the fire control area based on the distribution data of the fire hazard devices, obtains the setting data of the fire monitoring devices in different control sub-areas, and determines the fire hazard sub-areas and fire hazard coefficients in different control sub-areas based on the usage data of the fire hazard devices in different control sub-areas; S2 determines associated fire-fighting devices of different fire-hazard sub-areas according to the distribution positions of different fire-hazard sub-areas, and determines fire-fighting devices with hidden dangers among the associated fire-fighting devices by using the analysis results of historical monitoring data of the associated fire-fighting devices; S3 obtains the distribution data of the hidden danger fire-fighting devices in the associated fire-fighting devices of different fire-hazard sub-areas, and when it is determined that there is no fire-hazard sub-area where the fire control risk does not meet the requirements, based on the interval distances between different fire-hazard sub-areas and different associated fire-fighting devices, proceeds to the next step; S4 determines the interval distances between different fire hazard sub-areas, and determines whether to issue an early warning signal based on the fire control risks and fire hazard coefficients of the different fire hazard sub-areas.

[0006] The beneficial effects of the present invention are: The analysis results of the historical monitoring data of the associated fire-fighting devices are used to determine the fire-fighting devices with hidden dangers in the associated fire-fighting devices, thereby realizing the identification of fire-fighting devices with hidden dangers based on the abnormal conditions of the historical monitoring data of the associated fire-fighting devices. This also lays the foundation for further identifying the fire risks in the fire hazard sub-areas based on the hidden danger conditions of the associated fire-fighting devices, thereby improving the accuracy of the fire risk identification and processing results.

[0007] Whether to issue a warning signal is determined based on the interval distances between different fire hazard sub-areas, the fire management and control risks of different fire hazard sub-areas, and the fire hazard coefficient. This not only takes into account the distribution density of the fire hazard sub-areas caused by the interval distances between the fire hazard sub-areas, but also takes into account the risk conditions of fire-fighting equipment in different fire hazard sub-areas and the hidden dangers of fire risks. It realizes the generation of warning signals from multiple angles and improves the reliability of fire risk identification and processing.

[0008] A further technical solution is that the method for determining the fire hazard sub-area in the control sub-area is: Determine the accumulated usage time of the fire hazard devices in the control sub-area based on the usage data of the fire hazard devices in the control sub-area; Determining defect data of the fire monitoring device during the accumulated usage time, and determining defective usage time in the accumulated usage time using the defect data; The fire hazard coefficient of the control sub-area is determined according to the ratio of the defective usage time in the control sub-area to the cumulative usage time, and the fire hazard coefficient is used to determine whether the control sub-area is a fire hazard sub-area.

[0009] A further technical solution is that the defective usage time is the time during which the operating state of the fire monitoring device is abnormal in the accumulated usage time.

[0010] A further technical solution is to determine whether to issue an early warning signal, specifically including: Based on the fire control risks and fire hazard coefficients of different fire hazard sub-areas, the comprehensive risk coefficients of different fire hazard sub-areas are determined; Based on the interval distances between different fire hazard sub-areas, the number of fire hazard sub-areas with interval distances within a preset interval distance interval is determined, and the distribution clustering coefficient is determined according to the proportion of the number of fire hazard sub-areas with interval distances within the preset interval distance interval; The weights of the comprehensive risk coefficients of different fire hazard sub-areas are used to determine the risk coefficient, and the fire risk coefficient is determined in combination with the distribution clustering coefficient, and whether to issue an early warning signal is determined based on the fire risk coefficient.

[0011] A further technical solution is that the fire risk coefficient is determined based on the product of the risk coefficient and the distribution clustering coefficient.

[0012] A further technical solution is that when the fire risk coefficient is less than a preset fire risk coefficient threshold, it is determined that an early warning signal needs to be issued.

[0013] In a second aspect, the present invention provides a computer system comprising: a memory and a processor that are communicatively connected, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the above-mentioned early warning processing method for smart fire protection when running the computer program.

[0014] Other features and advantages will be described in the following description. The objects and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description and drawings.

[0015] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings.

[0017] Figure 1 It is a flow chart of an early warning processing method for smart security and firefighting; Figure 2 It is a flow chart of a method for determining a fire hazard sub-area in a control sub-area; Figure 3 is a flow chart of a method for determining a hidden danger fire-fighting device in an associated fire-fighting device; Figure 4 It is a flow chart of a method for determining the fire control risk of a fire hazard sub-area; Figure 5 It is a framework diagram of a computer system. DETAILED DESCRIPTION

[0018] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this specification.

[0019] In the process of identifying fire hazards, in addition to fire hazard signals such as temperature signals and smoke signals, the operating status of the water pressure monitoring data of the fire-fighting equipment, the fire-fighting linkage early warning device, etc. are also crucial. Therefore, it is necessary to monitor and analyze multi-dimensional data to realize the identification and processing of fire hazards.

[0020] Fire hazard coefficient in the control sub-area: The fire hazard coefficient of the control sub-area is determined by the ratio of the defective use time of the fire hazard devices in the control sub-area to the cumulative use time, and the control sub-area with a fire hazard coefficient greater than 0.6 is defined as a fire hazard sub-area.

[0021] Associated fire-fighting equipment: Fire-fighting equipment whose distance from the fire hazard sub-area is within the preset distance interval.

[0022] Fire-fighting devices with hidden dangers in associated fire-fighting devices: The time when the historical monitoring data in the associated fire-fighting devices are defective is taken as the time of defective monitoring data, and the distribution data of the time of defective monitoring data is used to determine the period of defective monitoring data. When the number of defective monitoring data periods is greater than 10, the associated fire-fighting device is determined to be a fire-fighting device with hidden dangers.

[0023] Fire control risk: determine the control impact weight coefficients of different associated fire protection devices based on the distance between the fire hazard sub-area and different associated fire protection devices, obtain the proportion of the number of hidden danger fire protection devices in the associated fire protection devices, and use the proportion of the number of hidden danger fire protection devices in the associated fire protection devices to determine the hidden danger proportion coefficient. Determine the fire control risk of the fire hazard sub-area based on the sum of the control impact weight coefficients of different associated fire protection devices and the product of the hidden danger proportion coefficient. When the fire control risk is below 0.3, it is determined that the fire control risk of the fire hazard sub-area meets the requirements. When the fire control risk is above 0.3, it is determined that the fire control risk of the fire hazard sub-area does not meet the requirements.

[0024] Determine whether to issue a warning signal: determine the comprehensive risk coefficients of different fire hazard sub-areas based on the product of the fire control risks and the fire hazard coefficients of different fire hazard sub-areas, determine the number of fire hazard sub-areas with interval distances within a preset interval distance range based on the interval distances between different fire hazard sub-areas, determine the distribution clustering coefficient based on the proportion of the number of fire hazard sub-areas with interval distances within the preset interval distance range, determine the risk coefficient using the weighted sum of the comprehensive risk coefficients of different fire hazard sub-areas, determine the fire risk coefficient using the weighted sum of the risk coefficient and the distribution clustering coefficient, and determine whether to issue a warning signal based on the fire risk coefficient. Embodiment 1 To solve the above problem, according to one aspect of the present invention, Figure 1 As shown, a first aspect is provided, and the present invention provides an early warning processing method for smart fire prevention and control, specifically comprising: S1 determines the control sub-areas in the fire control area based on the distribution data of the fire hazard devices, obtains the setting data of the fire monitoring devices in different control sub-areas, and determines the fire hazard sub-areas and fire hazard coefficients in different control sub-areas based on the usage data of the fire hazard devices in different control sub-areas; S2 determines associated fire-fighting devices of different fire-hazard sub-areas according to the distribution positions of different fire-hazard sub-areas, and determines fire-fighting devices with hidden dangers among the associated fire-fighting devices by using the analysis results of historical monitoring data of the associated fire-fighting devices; S3 obtains the distribution data of the hidden danger fire-fighting devices in the associated fire-fighting devices of different fire-hazard sub-areas, and when it is determined that there is no fire-hazard sub-area where the fire control risk does not meet the requirements, based on the interval distances between different fire-hazard sub-areas and different associated fire-fighting devices, proceeds to the next step; S4 determines the interval distances between different fire hazard sub-areas, and determines whether to issue an early warning signal based on the fire control risks and fire hazard coefficients of the different fire hazard sub-areas.

[0025] Further, such as Figure 2 As shown, the method for determining the fire hazard sub-area in the control sub-area is: Determine the number of fire monitoring devices in the control sub-area according to the setting data of the fire monitoring devices in the control sub-area; Determine the accumulated usage time of the fire hazard devices in the control sub-area based on the usage data of the fire hazard devices in the control sub-area; The fire hazard coefficient of the control sub-area is determined according to the accumulated usage time of the fire hazard devices in the control sub-area and the number of fire monitoring devices set up, and the fire hazard coefficient is used to determine whether the control sub-area is a fire hazard sub-area.

[0026] Optionally, the method for determining the fire hazard sub-area in the control sub-area is: Determine the accumulated usage time of the fire hazard devices in the control sub-area based on the usage data of the fire hazard devices in the control sub-area; Determining defect data of the fire monitoring device during the accumulated usage time, and determining defective usage time in the accumulated usage time using the defect data; The fire hazard coefficient of the control sub-area is determined according to the ratio of the defective usage time in the control sub-area to the cumulative usage time, and the fire hazard coefficient is used to determine whether the control sub-area is a fire hazard sub-area.

[0027] Furthermore, the defective usage duration is the duration during which the operating state of the fire monitoring device is abnormal in the accumulated usage duration.

[0028] In another embodiment, the method for determining the fire hazard sub-area in the control sub-area is: S11 determines the number of fire monitoring devices in the control sub-area according to the setting data of the fire monitoring devices in the control sub-area, and determines the monitoring hidden danger coefficient of the control sub-area in combination with the defect duration of different fire monitoring devices; Optionally, the above step S11 includes the following contents: S111 determines the number of fire monitoring devices in the control sub-area according to the setting data of the fire monitoring devices in the control sub-area. When the number of fire monitoring devices in the control sub-area meets the requirement, the process proceeds to step S112. When the number of fire monitoring devices in the control sub-area does not meet the requirement, the process proceeds to step S113. S112 determines the defect duration of different fire monitoring devices based on the defect data of the fire monitoring devices in different control sub-areas. When there is no fire monitoring device with a defect duration longer than the preset defect duration, it is determined that the control sub-area does not belong to the fire hazard sub-area. When there is a fire monitoring device with a defect duration longer than the preset defect duration, the process proceeds to step S113. S113 determines the monitoring hidden danger coefficient of the control sub-area according to the number of fire monitoring devices in the control sub-area and the defect duration of different fire monitoring devices. When the monitoring hidden danger coefficient of the monitoring sub-area is less than the preset hidden danger coefficient threshold, it is determined that the control sub-area does not belong to the fire hidden danger sub-area. When the monitoring hidden danger coefficient of the monitoring sub-area is not less than the preset hidden danger coefficient threshold, proceed to step S12.

[0029] S12: determining the cumulative usage time of the fire hazard devices in the control sub-area based on the usage data of the fire hazard devices in the control sub-area, and determining the fire defect coefficient of the control sub-area based on the defect usage time in the cumulative usage time of the fire hazard devices in the control sub-area; Optionally, the above step S12 includes the following contents: S121: When the monitoring hidden danger coefficient of the monitoring sub-area is within the preset hidden danger coefficient interval, the process proceeds to step S122; when the monitoring hidden danger coefficient of the monitoring sub-area is not within the preset hidden danger coefficient interval, the process proceeds to step S124; S122 determines the cumulative usage time of the fire hazard devices in the control sub-area based on the usage data of the fire hazard devices in the control sub-area. When the cumulative usage time of the fire hazard devices in the control sub-area is not greater than the preset usage time, it is determined that the control sub-area does not belong to the fire hazard sub-area. When the cumulative usage time of the fire hazard devices in the control sub-area is greater than the preset usage time, the process proceeds to step S123. S123: obtaining the defective use time in the accumulated use time of the fire hazard device in the control sub-area; when the defective use time in the accumulated use time of the fire hazard device is less than the preset defective time threshold, determining that the control sub-area does not belong to the fire hazard sub-area; when the defective use time in the accumulated use time of the fire hazard device is not less than the preset defective time threshold, proceeding to step S124; S124 determines the fire defect coefficient of the control sub-area according to the cumulative use time of the fire hazard devices in the control sub-area and the defective use time of the fire hazard devices in the control sub-area. When the fire defect coefficient and the monitoring hazard coefficient of the control sub-area are both within the preset coefficient range, it is determined that the control sub-area does not belong to the fire hazard sub-area. When any one of the fire defect coefficient and the monitoring hazard coefficient of the control sub-area is not within the preset coefficient range, proceed to step S13.

[0030] S13 determines the fire hazard coefficient of the control sub-area according to the monitoring hazard coefficient and the fire defect coefficient in the control sub-area, and uses the fire hazard coefficient to determine whether the control sub-area is a fire hazard sub-area.

[0031] It should be noted that the fire-fighting device associated with the fire hazard sub-area is a fire-fighting device whose interval distance from the fire hazard sub-area is within a preset interval of interval distance.

[0032] Specifically, Figure 3 As shown, the method for determining the hidden danger fire-fighting device in the associated fire-fighting device is: Based on the analysis result of the historical monitoring data of the associated fire-fighting device, determine the time when the historical monitoring data in the associated fire-fighting device has defects, and use it as the monitoring data defect time; Determine the monitoring data defect period based on the distribution data of the monitoring data defect time; According to the number of defective time periods of the monitoring data, it is determined whether the associated fire-fighting device is a fire-fighting device with hidden dangers.

[0033] Further, the monitoring data defect period is determined according to the number of monitoring data defect moments within a preset time length, and specifically the preset time length in which the number of monitoring data defect moments is greater than the preset number of moments is used as the monitoring data defect period.

[0034] It should also be noted that when the number of defective time periods of the monitoring data is greater than the number of preset time periods, the associated fire-fighting device is determined to be a fire-fighting device with hidden dangers.

[0035] Optionally, the method for determining the fire-fighting device with hidden dangers in the associated fire-fighting devices is: Based on the analysis result of the historical monitoring data of the associated fire-fighting device, determine the time when the historical monitoring data in the associated fire-fighting device has defects, and use it as the monitoring data defect time; Determine the number of monitoring data defect moments based on the distribution data of the monitoring data defect moments; According to the number of defect moments of the monitoring data, it is determined whether the associated fire-fighting device is a fire-fighting device with hidden dangers.

[0036] Optionally, the method for determining the fire-fighting device with hidden dangers in the associated fire-fighting devices is: S21, based on the analysis result of the historical monitoring data of the associated fire-fighting device, determining the time when the historical monitoring data in the associated fire-fighting device has defects, and using the time when the historical monitoring data in the associated fire-fighting device has defects; S22 determines the monitoring data defect period based on the distribution data of the monitoring data defect time, and determines the monitoring data abnormality coefficient of different monitoring data defect periods based on the distribution data of the monitoring data defect time in different monitoring data defect periods and the interval length between the monitoring data defect period and the adjacent monitoring data defect period; S23 determines a comprehensive data anomaly coefficient based on the monitoring data anomaly coefficients of different monitoring data defect periods, and uses the comprehensive data anomaly coefficient to determine whether the associated fire-fighting device is a fire-fighting device with hidden dangers.

[0037] Specifically, Figure 4 As shown, the method for determining the fire control risk of the fire hazard sub-area is: Determine the control influence weight coefficients of different associated fire-fighting devices based on the interval distance between the fire hazard sub-area and different associated fire-fighting devices; Obtaining the ratio of the number of fire-fighting devices with hidden dangers in the associated fire-fighting devices, and determining the hidden danger proportion coefficient by using the ratio of the number of fire-fighting devices with hidden dangers in the associated fire-fighting devices; The fire control risk of the fire hazard sub-area is determined according to the sum of the control influence weight coefficients of different associated fire-fighting devices and the hidden danger proportion coefficient.

[0038] Furthermore, the fire control risk of the fire hazard sub-area is determined according to the product of the sum of the control influence weight coefficients of different associated fire protection devices and the hazard proportion coefficient.

[0039] It should also be noted that the value range of the fire control risk of the fire hazard sub-area is between 0 and 1. When the fire control risk of the fire hazard sub-area is greater than the preset control risk coefficient, it is determined that the fire control risk of the fire hazard sub-area does not meet the requirements.

[0040] Determine that there are no fire hazard sub-areas where the fire control risk does not meet the requirements Furthermore, when there is a fire hazard sub-area where the fire control risk does not meet the requirements, it is determined that an early warning signal needs to be issued.

[0041] Optionally, the method for determining the fire control risk of the fire hazard sub-area is: The number of associated fire-fighting devices in the fire hazard sub-area is obtained, and when the number of associated fire-fighting devices is not within a preset device number range, it is determined that the fire control risk of the fire hazard sub-area does not meet the requirements; When the number of associated fire-fighting devices is within the preset device number range: Obtaining a ratio of hidden danger fire-fighting devices in the associated fire-fighting devices, and when the ratio of hidden danger fire-fighting devices in the associated fire-fighting devices is greater than a preset ratio, determining that the fire control risk of the fire hazard sub-area does not meet the requirements; When the proportion of the number of hidden danger fire-fighting devices in the associated fire-fighting devices is not greater than the preset proportion: the associated fire-fighting devices excluding the hidden danger fire-fighting devices are regarded as normal fire-fighting devices, and when the number of the normal fire-fighting devices does not meet the requirement, it is determined that the fire control risk of the fire hazard sub-area does not meet the requirement; When the number of normal fire-fighting devices meets the requirements: Determine the control influence weight coefficients of different associated fire-fighting devices based on the interval distances between the fire hazard sub-area and different associated fire-fighting devices; when the sum of the control influence weight coefficients of the normal fire-fighting devices does not meet the requirements, it is determined that the fire control risk of the fire hazard sub-area does not meet the requirements; When the sum of the control influence weight coefficients of the normal fire protection device meets the requirements: Obtaining the ratio of the number of fire-fighting devices with hidden dangers in the associated fire-fighting devices, and determining the hidden danger proportion coefficient by using the ratio of the number of fire-fighting devices with hidden dangers in the associated fire-fighting devices; The fire control risk of the fire hazard sub-area is determined according to the sum of the control influence weight coefficients of different associated fire-fighting devices and the hidden danger proportion coefficient.

[0042] Further, determine whether to issue an early warning signal, including: Based on the fire control risks and fire hazard coefficients of different fire hazard sub-areas, the comprehensive risk coefficients of different fire hazard sub-areas are determined; Based on the interval distances between different fire hazard sub-areas, the number of fire hazard sub-areas with interval distances within a preset interval distance interval is determined, and the distribution clustering coefficient is determined according to the proportion of the number of fire hazard sub-areas with interval distances within the preset interval distance interval; The weights of the comprehensive risk coefficients of different fire hazard sub-areas are used to determine the risk coefficient, and the fire risk coefficient is determined in combination with the distribution clustering coefficient, and whether to issue an early warning signal is determined based on the fire risk coefficient.

[0043] Specifically, the fire risk coefficient is determined according to the product of the risk coefficient and the distribution clustering coefficient.

[0044] It should be noted that when the fire risk coefficient is less than a preset fire risk coefficient threshold, it is determined that an early warning signal needs to be issued. Embodiment 2 In the second aspect, as Figure 5 As shown, the present invention provides a computer system, comprising: a memory and a processor that are communicatively connected, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the above-mentioned early warning processing method for smart fire protection when running the computer program.

[0045] Optionally, the above step S21 includes the following contents: S211: based on the analysis result of the historical monitoring data of the associated fire-fighting device, determine the time when the historical monitoring data in the associated fire-fighting device has defects, and use the time when the historical monitoring data in the associated fire-fighting device has defects as the monitoring data defect time; S212: When the number of defective moments of the monitoring data does not meet the requirement, the associated fire-fighting device is determined to be a fire-fighting device with hidden dangers. When the number of defective moments of the monitoring data meets the requirement, the process proceeds to step S213; S213 obtains the number of monitoring data defect moments within the most recent preset time period. When the proportion of the number of monitoring data defect moments within the most recent preset time period does not meet the requirements, the associated fire-fighting device is determined to be a hidden danger fire-fighting device. When the proportion of the number of monitoring data defect moments within the most recent preset time period meets the requirements, proceed to step S22.

[0046] Optionally, the above step S22 includes the following contents: S221 determines the monitoring data defect period based on the distribution data of the monitoring data defect time. When the number of monitoring data defect periods does not meet the requirement, the associated fire-fighting device is determined to be a hidden danger fire-fighting device. When the number of monitoring data defect periods meets the requirement, the process proceeds to step S222; S222: obtaining the interval durations between different monitoring data defect periods, and using the interval durations to determine the continuous defect periods in the monitoring data defect periods; when the number of continuous defect periods does not meet the requirement, determining that the associated fire-fighting device is a hidden danger fire-fighting device; when the number of continuous defect periods meets the requirement, proceeding to step S223; S223 determines the monitoring data anomaly coefficients of different monitoring data defect periods based on the distribution data of the monitoring data defect moments in different monitoring data defect periods and the interval length between the monitoring data defect period and the adjacent monitoring data defect period. When there is a monitoring data defect period whose monitoring data anomaly coefficient does not meet the requirements, the process proceeds to step S224. When there is no monitoring data defect period whose monitoring data anomaly coefficient does not meet the requirements, the process proceeds to step S23. S224 regards the defective period of monitoring data in which the abnormal coefficient of monitoring data does not meet the requirements as the abnormal period. When the number of abnormal periods does not meet the requirements, the associated fire-fighting device is determined to be a hidden danger fire-fighting device. When the number of abnormal periods meets the requirements, it goes to step S23.

[0047] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and non-volatile computer storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0048] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0049] The above description is only one or more embodiments of this specification and is not intended to limit this specification. For those skilled in the art, one or more embodiments of this specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included in the scope of the claims of this specification.

Claims

1. A warning processing method for smart fire safety, characterized in that: Specifically include: Determine the control sub-areas in the fire control area based on the distribution data of the fire hazard devices, obtain the setting data of the fire monitoring devices in different control sub-areas, and determine the fire hazard sub-areas and fire hazard coefficients in different control sub-areas based on the usage data of the fire hazard devices in different control sub-areas; Determine the associated fire-fighting devices of different fire-hazard sub-areas according to the distribution positions of different fire-hazard sub-areas, and determine the fire-fighting devices with hidden dangers in the associated fire-fighting devices according to the analysis results of the historical monitoring data of the associated fire-fighting devices; Obtain the distribution data of the hidden danger fire-fighting devices in the associated fire-fighting devices of different fire-hazard sub-areas, and when it is determined that there is no fire-hazard sub-area where the fire control risk does not meet the requirements, combining the interval distances between different fire-hazard sub-areas and different associated fire-fighting devices, proceed to the next step; Determine the interval distances between different fire hazard sub-areas, and determine whether to issue an early warning signal based on the fire control risks and fire hazard coefficients of different fire hazard sub-areas.

2. The early warning processing method for smart fire safety according to claim 1, characterized in that: The method for determining the fire hazard sub-area in the control sub-area is: Determine the accumulated usage time of the fire hazard devices in the control sub-area based on the usage data of the fire hazard devices in the control sub-area; Determining defect data of the fire monitoring device during the accumulated usage time, and determining defective usage time in the accumulated usage time using the defect data; The fire hazard coefficient of the control sub-area is determined according to the ratio of the defective usage time in the control sub-area to the cumulative usage time, and the fire hazard coefficient is used to determine whether the control sub-area is a fire hazard sub-area.

3. The early warning processing method for smart fire safety according to claim 2, characterized in that: The defective usage duration is the duration during which the operating state of the fire monitoring device is abnormal in the accumulated usage duration.

4. The early warning processing method for smart fire safety according to claim 1, characterized in that: The fire-fighting device associated with the fire hazard sub-area is a fire-fighting device whose interval distance from the fire hazard sub-area is within a preset interval of interval distance.

5. The early warning processing method for smart fire safety according to claim 1, characterized in that: The method for determining the hidden danger fire-fighting device in the associated fire-fighting device is: Based on the analysis result of the historical monitoring data of the associated fire-fighting device, determine the time when the historical monitoring data in the associated fire-fighting device has defects, and use it as the monitoring data defect time; Determine the monitoring data defect period based on the distribution data of the monitoring data defect time; According to the number of defective time periods of the monitoring data, it is determined whether the associated fire-fighting device is a fire-fighting device with hidden dangers.

6. The early warning processing method for smart fire safety according to claim 5, characterized in that: The monitoring data defect period is determined according to the number of monitoring data defect moments within a preset time period, and specifically, a preset time period in which the number of monitoring data defect moments is greater than the preset number of moments is used as the monitoring data defect period.

7. The early warning processing method for smart fire safety according to claim 5, characterized in that: When the number of defective time periods of the monitoring data is greater than the number of preset time periods, it is determined that the associated fire-fighting device is a fire-fighting device with hidden dangers.

8. The early warning processing method for smart fire safety according to claim 1, characterized in that: Determine whether to issue an early warning signal, including: Based on the fire control risks and fire hazard coefficients of different fire hazard sub-areas, the comprehensive risk coefficients of different fire hazard sub-areas are determined; Based on the interval distances between different fire hazard sub-areas, the number of fire hazard sub-areas with interval distances within a preset interval distance interval is determined, and the distribution clustering coefficient is determined according to the proportion of the number of fire hazard sub-areas with interval distances within the preset interval distance interval; The weights of the comprehensive risk coefficients of different fire hazard sub-areas are used to determine the risk coefficient, and the fire risk coefficient is determined in combination with the distribution clustering coefficient, and whether to issue an early warning signal is determined based on the fire risk coefficient.

9. The early warning processing method for smart fire safety according to claim 8, characterized in that: The fire risk coefficient is determined based on the product of the risk coefficient and the distribution clustering coefficient.

10. A computer system comprising: A memory and a processor that are communicatively connected, and a computer program stored in the memory and capable of running on the processor, characterized in that when the processor runs the computer program, an early warning processing method for smart fire safety as described in any one of claims 1-9 is executed.

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