An intelligent community fire big data analysis system and method based on artificial intelligence

Abstract

The present invention relates to the technical field of intelligent data analysis, and particularly to an intelligent community fire big data analysis system and method based on artificial intelligence. The system includes: a data acquisition module, an artificial intelligence module, a screening module, a first determination module, a calculation module, a second determination module, a judgment module, an alarm module, and an adjustment module. By continuously monitoring the combustible piles, fire hydrant water levels, and water tank water pressures in each grid within the community, the system of the present invention can quickly identify potential fire risks and issue alarms in a timely manner, reducing the probability of fires. The dynamic adjustment function of the system flexibly optimizes the grid spacing according to the number of abnormal grids and preset standards, improving the allocation efficiency of fire protection resources, enabling the community to accumulate and analyze fire protection data, and effectively solving the problems of low accuracy and slow response in fire safety analysis results caused by single monitoring means and insufficient real-time processing capabilities.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent data analysis, and particularly to an intelligent community fire fighting big data analysis system and method based on artificial intelligence. Background Art

[0002] With the acceleration of the urbanization process, the population density in communities is continuously increasing, and fire safety issues are becoming increasingly prominent. Traditional fire safety monitoring means often rely on manual inspections and regular checks, and cannot achieve real-time monitoring and rapid response to potential fire hazards. Therefore, there is an urgent need for an advanced intelligent system to effectively integrate big data analysis and artificial intelligence technology to improve the efficiency and accuracy of fire safety management.

[0003] The patent document with the publication number CN117132434A discloses a building fire safety supervision system based on big data analysis. The system includes: a safety supervision platform, which is communicatively connected to a passage supervision module, a pipeline monitoring module, a safety rating module, and a storage module; the passage supervision module is used for monitoring and managing the smoothness of the building fire passage: recording the fire passage of the building through a camera, decomposing the recorded video into frames of images and marking them as monitored images, performing image processing on the monitored images to obtain smooth grids and blocked grids; performing aggregation analysis on the blocked grids and marking the monitored images as blocked images or smooth images; the pipeline monitoring module is used for monitoring and analyzing the safety of the building fire water pipes: obtaining the water pressure data SY, differential pressure data YC, and pressure stability data YW of the building fire water pipes and performing numerical calculations to obtain the pipeline coefficient GD of the fire water pipes; obtaining the pipeline threshold GDmax through the storage module, comparing the pipeline coefficient GD with the pipeline threshold GDmax, and determining whether the safety of the fire water pipes meets the requirements based on the comparison result; the safety rating module is used for performing grade evaluation and analysis on the building fire safety.

[0004] It can be seen that the system has a delay in image processing and analysis, resulting in insufficient response to emergencies such as fires. The process of decomposing the video into image frames and processing them takes a long time, increasing the complexity and maintenance cost of the system. Image marking and analysis require manual intervention, increasing the possibility of human error. Summary of the Invention

[0005] Therefore, the present invention provides an intelligent community fire fighting big data analysis system and method based on artificial intelligence to overcome the problems of low accuracy and slow response of fire safety analysis results caused by single monitoring means and insufficient real-time processing ability in the prior art.

[0006] To achieve the above object, on the one hand, the present invention provides an intelligent community fire fighting big data analysis system based on artificial intelligence, including:

[0007] A data acquisition module for acquiring real-time images within each grid in an acquisition area constructed based on a preset grid spacing in the community, the real-time average water level of all water tanks, and the real-time average water pressure of all fire hydrants;

[0008] An artificial intelligence module, connected to the data acquisition module, for extracting the real-time pile quantity of combustibles in each of the real-time images;

[0009] A screening module, connected to the artificial intelligence module, for screening out a number of risk grids according to the real-time pile quantity;

[0010] A first determination module, respectively connected to the screening module and the data acquisition module, for determining the fluctuation consistency according to the real-time average water level and the real-time average water pressure in any two adjacent risk grids within a preset calculation duration, and determining a number of first temporary grids according to the fluctuation consistency;

[0011] A calculation module, respectively connected to the data acquisition module and the first determination module, for calculating the water flow deviation according to the real-time average water level and the real-time average water pressure at adjacent moments of each of the first temporary grids;

[0012] A second determination module, connected to the calculation module, for determining a number of second temporary grids according to the water flow deviation;

[0013] A determination module, respectively connected to the second determination module and the data acquisition module, for determining a number of abnormal grids according to the distribution degree of the second temporary grids and recording the duration;

[0014] An alarm module, connected to the determination module, for sending an alarm according to the duration;

[0015] An adjustment module, respectively connected to the determination module and the data acquisition module, for adjusting the preset grid spacing according to the number of grids in each of the abnormal grids within a preset adjustment duration and a preset standard quantity fluctuation value.

[0016] Further, the first determination module includes:

[0017] A standard deviation calculation unit for calculating the standard deviation of the real-time average water level to obtain a water level standard deviation, and for calculating the standard deviation of the real-time average water pressure to obtain a water pressure standard deviation;

[0018] A consistency calculation unit, connected to the standard deviation calculation unit, for calculating the relative deviation between the water level standard deviation and the water pressure standard deviation to form the fluctuation consistency;

[0019] A first determination unit, which is connected to the consistency calculation unit, is configured to determine a number of first temporary grids according to a comparison result between the fluctuation consistency and a preset standard consistency.

[0020] Further, the calculation module includes:

[0021] A deviation calculation unit, configured to calculate a relative deviation of the real-time average water level to obtain a water level deviation, and configured to calculate a relative deviation of the real-time average water pressure to obtain a water pressure deviation;

[0022] A water flow calculation unit, which is connected to the deviation calculation unit, is configured to calculate a relative deviation of the water level deviation and the water pressure deviation to form the water flow deviation.

[0023] Further, the second determination module includes:

[0024] A water flow comparison unit, configured to compare the water flow deviation with a preset standard water flow deviation to form a water flow comparison result;

[0025] A second determination unit, which is connected to the water flow comparison unit, is configured to determine a number of second temporary grids according to the water flow comparison result.

[0026] Further, the determination module includes:

[0027] A distribution degree calculation unit, configured to confirm a distribution degree according to the second temporary grids and all adjacent second temporary grids within a preset radius;

[0028] A determination unit, which is connected to the distribution degree calculation unit, is configured to determine that the second temporary grid is an abnormal grid according to a comparison result between the distribution degree and a preset standard distribution degree, and form a number of abnormal grids;

[0029] A recording unit, which is connected to the determination unit, is configured to record the duration of the abnormal grid.

[0030] Further, the distribution degree calculation unit includes:

[0031] A distance calculation sub-unit, configured to calculate an average distance between each of the second temporary grids and all adjacent second temporary grids within the preset radius;

[0032] A distribution degree calculation sub-unit, which is connected to the distribution degree confirmation unit, is configured to calculate a relative deviation of the average distance and a preset standard distance to form a distribution degree.

[0033] Further, the adjustment module includes:

[0034] A quantity fluctuation calculation unit, configured to calculate a standard deviation of the grid quantity to form a quantity fluctuation value;

[0035] An adjustment unit for adjusting the preset grid spacing according to the comparison result between the quantity fluctuation value and the preset standard quantity fluctuation value, the number of grids, the preset standard quantity fluctuation value, and a preset adjustment coefficient.

[0036] Further, the alarm module includes:

[0037] An alarm determination unit for determining whether an alarm needs to be sent according to the comparison result between each of the duration and the preset standard duration, and forming an alarm determination result;

[0038] An alarm sending unit connected to the alarm determination unit for sending an alarm according to the alarm determination result.

[0039] Further, the screening module includes:

[0040] A material pile comparison unit for comparing the real-time material pile quantity and the preset standard quantity, and forming a material pile comparison result;

[0041] A screening unit connected to the material pile comparison unit for screening out a number of grids according to the material pile comparison result to form a number of risk grids.

[0042] On the other hand, the present invention also provides an artificial intelligence-based intelligent community fire big data analysis method, including:

[0043] Collecting real-time images in each grid within the collection area constructed based on the preset grid spacing in the community, the real-time average water level of each fire hydrant, and the real-time average water pressure of each water tank;

[0044] Extracting the real-time material pile quantity of combustibles in each of the real-time images;

[0045] Screening out a number of risk grids according to the real-time material pile quantity;

[0046] Determining the fluctuation consistency according to the real-time average water level and the real-time average water pressure in any two adjacent risk grids within the preset calculation duration, and determining a number of first temporary grids according to the fluctuation consistency;

[0047] Calculating the water flow deviation according to the real-time average water level and the real-time average water pressure at adjacent moments of each of the first temporary grids;

[0048] Determining a number of second temporary grids according to the water flow deviation;

[0049] Determining a number of abnormal grids according to the distribution degree of the second temporary grids, and recording the duration;

[0050] Sending an alarm according to the duration;

[0051] Adjust the preset grid spacing according to the number of grids in each of the abnormal grids within the preset adjusted duration and the fluctuation value of the preset standard quantity.

[0052] Compared with the prior art, the beneficial effects of the present invention are as follows. The intelligent community fire fighting big data analysis system based on artificial intelligence significantly improves the efficiency and accuracy of community fire safety management. By real-time monitoring of the combustible piles, fire hydrant water levels, and water tank water pressures in each grid within the community, the system can quickly identify potential fire risks and issue alarms in a timely manner, reducing the probability of fires. The dynamic adjustment function of the system flexibly optimizes the grid spacing according to the number of abnormal grids and preset standards, improving the allocation efficiency of fire fighting resources. The continuous monitoring and data analysis function of the system enables the community to accumulate and analyze fire fighting data, thereby providing a scientific basis for future safety decisions, effectively solving the problems of low accuracy and slow response in fire safety analysis results caused by single monitoring means and insufficient real-time processing capabilities.

[0053] Furthermore, by calculating the relative deviation of the standard deviation of water level and water pressure, the regional grids with abnormal fluctuations can be quickly screened out. This can effectively identify potential risk grids, improve the early warning and response efficiency of the fire fighting system, and promptly investigate abnormal water pressure and water level conditions.

[0054] Furthermore, by real-time feedback of the deviation of water level and water pressure, abnormal situations can be quickly identified.

[0055] Furthermore, by comparing with the preset standards, potential fire safety hazards can be quickly identified. After promptly determining the second temporary grid, the system can respond quickly.

[0056] Furthermore, by monitoring the distribution degree of the grids, false alarms can be effectively reduced, and the accuracy of the system can be improved. In addition, recording the duration of abnormal grids provides data support for subsequent decision-making, facilitating the formulation of more effective preventive measures.

[0057] Furthermore, by calculating the distribution degree, the uniformity of the grid distribution can be effectively quantified, providing data support for the subsequent determination of abnormal grids.

[0058] Furthermore, when the fluctuation of the number of grids exceeds the preset range, timely adjustment of the grid spacing can ensure the comprehensiveness and accuracy of monitoring and reduce blind spots.

[0059] Furthermore, by monitoring the duration of abnormal grids, it is possible to ensure that countermeasures are taken quickly before the problem worsens.

[0060] Furthermore, by comparing the real-time data with the preset standards, the accuracy of fire fighting monitoring can be effectively improved, and suspicious areas can be identified in a timely manner.

[0061] Furthermore, through real-time data monitoring and intelligent analysis, potential fire risks within the community can be effectively identified, enhancing the early warning ability and response efficiency of fire safety. By accurately screening out risk grids, the likelihood of false alarms and missed alarms is reduced. Meanwhile, the grid spacing is dynamically adjusted to ensure the flexibility and accuracy of the monitoring system. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 It is a schematic diagram of the intelligent community fire big data analysis system based on artificial intelligence in this embodiment;

[0063] Figure 2 It is a determination logic diagram for the first determination module in this embodiment to determine the first temporary grid;

[0064] Figure 3 It is a determination logic diagram for the second determination module in this embodiment to determine the second temporary grid;

[0065] Figure 4 It is a flowchart of the intelligent community fire big data analysis method based on artificial intelligence in this embodiment. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0066] In order to make the objectives and advantages of the present invention clearer, the present invention will be further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only for explaining the present invention and are not used to limit the present invention.

[0067] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only for explaining the technical principles of the present invention and do not limit the protection scope of the present invention.

[0068] It should be noted that in the description of the present invention, the terms indicating directions or positional relationships such as "upper", "lower", "left", "right", "inner", "outer", etc. are based on the directions or positional relationships shown in the drawings. This is only for convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention.

[0069] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installation", "connection", and "coupling" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two elements. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0070] Please refer to Figure 1As shown, it is a schematic diagram of the intelligent community fire big data analysis system based on artificial intelligence in this embodiment;

[0071] This embodiment provides an intelligent community fire big data analysis system based on artificial intelligence, including:

[0072] A data acquisition module for acquiring real-time images of each grid within the acquisition area constructed based on a preset grid spacing in the community, the real-time average water level of all water tanks, and the real-time average water pressure of all fire hydrants;

[0073] An artificial intelligence module, which is connected to the data acquisition module, for extracting the real-time pile quantity of combustibles in each of the real-time images;

[0074] A screening module, which is connected to the artificial intelligence module, for screening out a number of risk grids according to the real-time pile quantity;

[0075] A first determination module, which is respectively connected to the screening module and the data acquisition module, for determining the fluctuation consistency according to the real-time average water level and the real-time average water pressure in any two adjacent risk grids within a preset calculation duration, and determining a number of first temporary grids according to the fluctuation consistency;

[0076] A calculation module, which is respectively connected to the data acquisition module and the first determination module, for calculating the water flow deviation according to the real-time average water level and the real-time average water pressure at adjacent moments of each of the first temporary grids;

[0077] A second determination module, which is connected to the calculation module, for determining a number of second temporary grids according to the water flow deviation;

[0078] A determination module, which is respectively connected to the second determination module and the data acquisition module, for determining a number of abnormal grids according to the distribution degree of the second temporary grids and recording the duration;

[0079] An alarm module, which is connected to the determination module, for sending an alarm according to the duration;

[0080] An adjustment module, which is respectively connected to the determination module and the data acquisition module, for adjusting the preset grid spacing according to the number of grids in each of the abnormal grids within a preset adjustment duration and a preset standard quantity fluctuation value;

[0081] The artificial intelligence module extracts the quantity of combustible piles in the real-time image through its built-in YOLO model.

[0082] The preset grid spacing refers to the distance between grids when dividing the monitoring area within the community. It depends on the layout of the community, the distribution of combustibles, and the density of fire-fighting facilities. It is usually set between 5 meters and 10 meters. In this embodiment, it is set to 8 meters, which can simplify data processing while ensuring the accuracy of information collection, is suitable for urban dense areas, and facilitates effective monitoring of fire safety.

[0083] The preset calculation duration refers to the time period used for statistical analysis of water level and water pressure data fluctuations. It depends on the response speed and operating conditions of the fire-fighting system. It is usually set between 1 minute and 5 minutes. In this embodiment, it is set to 3 minutes, which can effectively capture short-term fluctuations, timely identify equipment failures or water source problems, and improve the response efficiency.

[0084] The preset adjustment duration refers to the duration of monitoring abnormal grids to determine whether to adjust the grid spacing. It depends on the frequency of environmental changes and the requirements of fire safety management. It is usually set between 5 minutes and 10 minutes. In this embodiment, it is set to 7 minutes, which can effectively monitor abnormal states, give managers enough time for evaluation, and avoid unnecessary interference caused by frequent adjustments.

[0085] The preset standard quantity fluctuation value refers to the range used to define quantity changes when judging abnormal grids. It depends on historical data analysis and the actual situation of the community. It is usually set between 3 and 5. In this embodiment, it is set to 4, which can improve the sensitivity to real abnormal situations, avoid false alarms caused by small fluctuations, and enhance the reliability of the system.

[0086] Real-time monitoring and analysis of community safety are achieved through a series of modules. First, the data acquisition module obtains real-time images, real-time average water levels, and real-time average water pressure data within each grid. The artificial intelligence module processes the real-time images, extracts the quantity of combustible piles, and passes the results to the screening module to identify risk grids. Then, the first determination module determines the first temporary grids based on the fluctuation consistency of the real-time average water levels and real-time average water pressures of the risk grids. The calculation module then analyzes based on the water flow deviation of these temporary grids, and the second determination module further screens out the second temporary grids. The determination module identifies abnormal grids by calculating the distribution degree and records their duration. Finally, the alarm module issues an alarm based on the duration, and the adjustment module adjusts the grid spacing according to the quantity of abnormal grids and the preset standard.

[0087] The AI-based intelligent community fire big data analysis system significantly improves the efficiency and accuracy of community fire safety management. By real-time monitoring the combustible piles, fire hydrant water levels, and water tank water pressures in each grid of the community, the system can quickly identify potential fire risks, issue alarms in a timely manner, and reduce the probability of fires. The system's dynamic adjustment function flexibly optimizes the grid spacing according to the number of abnormal grids and preset standards, improving the allocation efficiency of fire resources. The system's continuous monitoring and data analysis functions enable the community to accumulate and analyze fire data, providing a scientific basis for future safety decisions, effectively solving the problems of low accuracy and slow response in fire safety analysis caused by single monitoring means and insufficient real-time processing capabilities.

[0088] Please continue to refer to Figure 2 as shown, which is the decision logic diagram of the first temporary grid determined by the first determination module of this embodiment;

[0089] Specifically, the first determination module includes:

[0090] The standard deviation calculation unit is used to calculate the standard deviation of the real-time average water level to obtain the water level standard deviation, and is also used to calculate the standard deviation of the real-time average water pressure to obtain the water pressure standard deviation;

[0091] The consistency calculation unit is connected to the standard deviation calculation unit and is used to calculate the relative deviation between the water level standard deviation and the water pressure standard deviation to form the fluctuation consistency;

[0092] The first determination unit is connected to the consistency calculation unit and is used to determine a number of first temporary grids when the fluctuation consistency is greater than the preset standard consistency.

[0093] The preset standard consistency is a reference value used to measure the relative fluctuation between the water level standard deviation and the water pressure standard deviation, which depends on the configuration of fire protection facilities, environmental conditions, and statistical analysis of historical monitoring data in the community. It is usually set between 0.1 and 0.5, and is set to 0.2 in this embodiment to ensure moderate sensitivity to water level and water pressure fluctuations.

[0094] First, the standard deviation calculation unit calculates the standard deviations of the real-time average water level and the real-time average water pressure respectively to obtain the water level and water pressure standard deviations; then, the consistency calculation unit calculates the relative deviation between the water level standard deviation and the water pressure standard deviation to obtain the fluctuation consistency; finally, the first determination unit screens and determines a number of first temporary grids according to the comparison result between the fluctuation consistency and the preset standard consistency, identifying areas where abnormal fluctuations in water level and water pressure may exist.

[0095] By calculating the relative deviation of the standard deviation of water level and water pressure, the area grids with abnormal fluctuations can be quickly screened out. This can effectively identify potential risk grids, improve the early warning and response efficiency of the fire protection system, and timely investigate abnormal water pressure and water level conditions.

[0096] Specifically, the calculation module includes:

[0097] A deviation calculation unit for calculating the relative deviation of the real-time average water level to obtain a water level deviation, and for calculating the relative deviation of the real-time average water pressure to obtain a water pressure deviation;

[0098] A water flow calculation unit connected to the deviation calculation unit for calculating the relative deviation of the water level deviation and the water pressure deviation to form the water flow deviation.

[0099] Since the water flow will fluctuate due to unstable water pressure or changes in water consumption during the use of the water tank or fire hydrant, the calculation module first obtains the relative deviations of the real-time average water level and water pressure through the deviation calculation unit to obtain the water level deviation and the water pressure deviation respectively. Subsequently, the water flow calculation unit combines these two deviations to calculate the water flow deviation. This process enables the system to dynamically monitor the changes in water flow and identify potential problems in a timely manner.

[0100] By providing real-time feedback on the deviations of water level and water pressure, abnormal situations can be quickly identified.

[0101] Please continue to refer to Figure 3 as shown, which is the decision logic diagram for the second determination module of this embodiment to determine the second temporary grid;

[0102] Specifically, the second determination module includes:

[0103] A water flow comparison unit for comparing the water flow deviation with a preset standard water flow deviation to form a water flow comparison result;

[0104] A second determination unit connected to the water flow comparison unit for determining a number of second temporary grids according to the water flow comparison result when the water flow deviation is greater than the preset standard water flow deviation.

[0105] The preset standard water flow deviation refers to the allowable water flow fluctuation range of the system under normal operating conditions, which is used to evaluate the normality of the real-time water flow. It depends on the historical data of the water pressure and water level in the community and the dynamic changes in fire protection requirements, ensuring that it can reflect the real operating state. It is usually set between 1% and 5%, and is set to 3% in this embodiment, which can effectively capture slight abnormal changes, reduce false alarms, and at the same time is not overly sensitive to normal fluctuations.

[0106] The second determination module compares the water flow deviation obtained by real-time calculation with the preset standard water flow deviation through the water flow comparison unit to obtain the water flow comparison result. Subsequently, the second determination unit identifies a number of second temporary grids based on this result for further analysis of the status and risks of these grids.

[0107] By comparing with the preset standard, potential fire safety hazards can be quickly identified, and after the second temporary grids are determined in a timely manner, the system can respond quickly.

[0108] Specifically, the determination module includes:

[0109] A distribution degree calculation unit for confirming the distribution degree according to the second temporary grid and all adjacent second temporary grids within the preset radius;

[0110] A determination unit, which is connected to the distribution degree calculation unit, for determining that the second temporary grid is an abnormal grid when the distribution degree is less than the preset standard distribution degree, forming a number of abnormal grids;

[0111] A recording unit, which is connected to the determination unit, for recording the duration of the abnormal grid.

[0112] The preset radius is used to determine the range of adjacent grids around the second temporary grid for calculating the distribution degree. Its setting depends on the actual layout of the community, the distribution density of fire-fighting facilities and the possible fire spread speed, and is usually set between 5 meters and 10 meters. In this embodiment, it is set to 8 meters, which helps to ensure the accurate evaluation of the relationship between grids within a reasonable range, thereby improving the recognition accuracy of abnormal grids.

[0113] The preset standard distribution degree is a reference value for judging whether the grid distribution is normal, depending on the layout of fire-fighting facilities in the community and historical data analysis, and is usually set between 0.1 and 0.3. In this embodiment, it is set to 0.2, which can help the system identify grids with obviously uneven distribution, so as to detect potential fire risk areas earlier.

[0114] The determination module analyzes the spatial distribution of the second temporary grid and all adjacent second temporary grids within its preset radius through the distribution degree calculation unit to confirm the distribution degree. Then, the determination unit compares the calculated distribution degree with the preset standard distribution degree to judge which grids are identified as abnormal grids. Finally, the recording unit is responsible for recording the duration of these abnormal grids for subsequent analysis and processing.

[0115] By monitoring the distribution degree of the grids, false alarms can be effectively reduced and the accuracy of the system can be improved. In addition, recording the duration of the abnormal grids provides data support for subsequent decision-making, which is convenient for formulating more effective preventive measures.

[0116] Specifically, the distribution degree calculation unit includes:

[0117] A distance calculation sub-unit, which is used to calculate the average distance between each of the second temporary grids and all adjacent second temporary grids within the preset radius;

[0118] A distribution degree calculation sub-unit, which is connected to the distribution degree confirmation unit and is used to calculate the relative deviation between the average distance and the preset standard distance to form a distribution degree.

[0119] The preset standard distance refers to the ideal or reference distance value set during the process of evaluating the uniformity of the distribution between the second temporary grids, which reflects the expected distribution state between the grids, helps to judge whether the actual distribution meets the expectations, depends on the requirements of the specific application scenario, the structural characteristics of the community, historical data, and fire safety standards, and is usually set between 5 meters and 20 meters to ensure coverage of key monitoring areas. In this embodiment, it is set to 10 meters to adapt to the distribution characteristics of the grids in the community and ensure that the status of each grid can be evaluated in a timely and effective manner.

[0120] The distribution degree calculation unit calculates the average distance between each second temporary grid and its adjacent grids within the preset radius around it through the distance calculation sub-unit, so as to obtain the relative positional relationship between these grids. Subsequently, the distribution degree calculation sub-unit compares this average distance with the preset standard distance, calculates the relative deviation, and forms the final distribution degree value.

[0121] By calculating the distribution degree, the uniformity of the grid distribution can be effectively quantified, providing data support for the subsequent determination of abnormal grids.

[0122] Specifically, the adjustment module includes:

[0123] A quantity fluctuation calculation unit, which is used to calculate the standard deviation of the grid quantity to form a quantity fluctuation value;

[0124] An adjustment unit, which is used to adjust the preset grid spacing according to the grid quantity, the preset standard quantity fluctuation value, and a preset adjustment coefficient when the quantity fluctuation value is greater than the preset standard quantity fluctuation value. The new preset grid spacing is equal to the preset grid spacing multiplied by an adjustment factor, and this adjustment factor is 1 plus the preset adjustment coefficient multiplied by the relative deviation between the grid quantity and the preset standard quantity fluctuation value.

[0125] The preset adjustment coefficient is a value used to adjust the grid spacing, and its function is to dynamically adjust the grid spacing according to the actual situation. It depends on the characteristics of the community and the distribution density of fire-fighting facilities, and is usually set between 0.05 and 0.15. In this embodiment, it is set to 0.1, which can ensure that when the relative deviation between the grid quantity and the standard quantity fluctuation value is small, the adjustment amplitude will not be too large, guaranteeing the stability of the system.

[0126] The quantity fluctuation calculation unit is responsible for calculating the standard deviation of the grid quantity in real time, forming a quantity fluctuation value, which reflects the change of the current grid quantity. When the quantity fluctuation value exceeds the preset standard quantity fluctuation value, the adjustment unit dynamically adjusts the preset grid spacing according to the current grid quantity, the preset standard quantity fluctuation value and the set adjustment coefficient. This process ensures that the system can adapt to the changing community environment.

[0127] When the grid quantity fluctuation exceeds the preset range, adjusting the grid spacing in time can ensure the comprehensiveness and accuracy of monitoring and reduce blind spots.

[0128] Specifically, the alarm module includes:

[0129] An alarm determination unit, which is used to determine that an alarm needs to be sent when the continuous duration is greater than the preset standard continuous duration, and forms an alarm determination result;

[0130] An alarm sending unit, which is connected to the alarm determination unit and is used to send a maintenance alarm according to the alarm determination result.

[0131] The preset standard continuous duration is a time period set by the system. When the monitored abnormal state lasts for more than this time period, the system will consider that this state has a certain risk and trigger an alarm. It depends on the requirements of fire safety management, the historical data of the community and the assessment of potential risks, and is usually set between 5 minutes and 20 minutes. In this embodiment, it is set to 10 minutes, which can effectively balance the sensitivity and false alarm rate of the alarm, ensure that the alarm is sent quickly when it is really needed, and reduce false alarms caused by short-term fluctuations.

[0132] The alarm module receives the continuous duration data from each abnormal grid through the alarm determination unit and compares it with the preset standard continuous duration. When the continuous duration exceeds the preset standard, the alarm determination unit will determine that an alarm needs to be sent and generate an alarm determination result. Subsequently, the alarm sending unit performs corresponding alarm sending operations according to this determination result to notify relevant personnel or the system to take necessary measures.

[0133] By monitoring the continuous duration of abnormal grids, it is possible to ensure that corresponding measures are taken quickly before the problem worsens.

[0134] Specifically, the screening module includes:

[0135] A material pile comparison unit, which is used to compare the real-time material pile quantity with the preset standard quantity and form a material pile comparison result;

[0136] A screening unit, which is connected to the pile comparison unit, is used to screen out a number of grids according to the pile comparison result when the real-time pile quantity is greater than the preset standard quantity, forming a number of risk grids.

[0137] The preset standard quantity refers to the standard threshold of the combustible pile quantity set by the system under specific environments or conditions, which is used as the benchmark for evaluating the potential fire risk in the grid and depends on the characteristics of the community environment, historical fire data, fire safety regulations, and specific needs within the community. It is usually set between 1 and 5, and is set to 3 in this embodiment, which can effectively cover the risk assessment in general cases.

[0138] The screening module compares the real-time monitored combustible pile quantity with the preset standard quantity through the pile comparison unit to generate a pile comparison result. Subsequently, the screening unit determines which grids have a pile quantity exceeding the standard according to this result, screens out these grids, and forms a number of risk grids. This process ensures that the system can accurately identify the areas with potential fire risks.

[0139] By comparing the real-time data with the preset standard, the accuracy of fire monitoring can be effectively improved, and suspicious areas can be identified in a timely manner.

[0140] Please continue to refer to Figure 4 as shown, which is the flowchart of the intelligent community fire big data analysis method based on artificial intelligence in this embodiment;

[0141] An intelligent community fire big data analysis method based on artificial intelligence, based on the above-mentioned intelligent community fire big data analysis system, includes:

[0142] Collect the real-time images in each grid within the collection area constructed based on the preset grid spacing in the community, the real-time average water level of all water tanks, and the real-time average water pressure of all fire hydrants;

[0143] Extract the real-time pile quantity of combustibles in each of the real-time images;

[0144] Screen out a number of risk grids according to the real-time pile quantity;

[0145] Determine the fluctuation consistency according to the real-time average water level and the real-time average water pressure in any two adjacent risk grids within the pre-set calculation duration, and determine a number of first temporary grids according to the fluctuation consistency;

[0146] Calculate the water flow deviation according to the real-time average water level and the real-time average water pressure at adjacent moments of each of the first temporary grids;

[0147] Determine a number of second temporary grids according to the water flow deviation;

[0148] Determine a number of abnormal grids according to the distribution degree of the second temporary grid, and record the duration;

[0149] Send an alarm according to the duration;

[0150] Adjust the preset grid spacing according to the grid quantity of each of the abnormal grids within a preset adjustment duration and the preset standard quantity fluctuation value.

[0151] By collecting the real-time images, average water levels and water pressure data of each grid in the community, extract the quantity of combustible piles and screen out the risk grids. Then, determine the fluctuation consistency by using the water level and water pressure changes of adjacent risk grids, and form the first temporary grid according to this consistency. Subsequently, calculate the water flow deviation to determine the second temporary grid, and determine the abnormal grids according to the distribution degree of these grids, and record their duration. When the abnormal duration exceeds the preset standard, the system will automatically send an alarm. At the same time, dynamically adjust the preset grid spacing according to the quantity of abnormal grids and the standard fluctuation value within the adjustment duration to optimize the monitoring and response mechanism.

[0152] Through real-time data monitoring and intelligent analysis, it can effectively identify potential fire risks in the community, improve the early warning ability and response efficiency of fire safety. By accurately screening out risk grids, it reduces the possibility of false alarms and missed alarms. At the same time, dynamically adjusting the grid spacing ensures the flexibility and accuracy of the monitoring system.

[0153] So far, the technical solution of the present invention has been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the protection scope of the present invention.

[0154] The above are only the preferred embodiments of the present invention and are not used to limit the present invention; for those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent substitution, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A smart community fire big data analysis system based on artificial intelligence, characterized in that: include: A data collection module is used to collect real-time images of each grid in the collection area based on the preset grid spacing within the community, the real-time average water level of all water tanks, and the real-time average water pressure of all fire hydrants; An artificial intelligence module, connected to the data acquisition module, for extracting the real-time pile quantity of combustibles in each of the real-time images; A screening module connected to the artificial intelligence module for screening out a number of risk grids according to the real-time number of material piles; A first determination module, which is connected to the screening module and the data acquisition module respectively, is used to determine the fluctuation consistency according to the real-time average water level and the real-time average water pressure in any two adjacent risk grids within a preset calculation time, and determine a number of first temporary grids according to the fluctuation consistency; a calculation module, which is connected to the data acquisition module and the first determination module respectively, and is used to calculate the water flow deviation according to the real-time average water level and the real-time average water pressure at adjacent moments of each of the first temporary grids; a second determination module, connected to the calculation module, for determining a plurality of second temporary grids according to the water flow deviation; A determination module, which is connected to the second determination module and the data acquisition module respectively, and is used to determine a number of abnormal grids according to the distribution degree of the second temporary grid, and record the duration; an alarm module, connected to the determination module, for sending an alarm according to the duration; An adjustment module is connected to the determination module and the data acquisition module respectively, and is used to adjust the preset grid spacing according to the grid quantity of each abnormal grid within a preset adjustment time and a preset standard quantity fluctuation value.

2. According to the artificial intelligence-based smart community fire big data analysis system of claim 1, it is characterized in that: The first determining module comprises: a standard deviation calculation unit, used for calculating the standard deviation of the real-time average water level to obtain the water level standard deviation, and for calculating the standard deviation of the real-time average water pressure to obtain the water pressure standard deviation; a consistency calculation unit connected to the standard deviation calculation unit, for calculating the relative deviation between the water level standard deviation and the water pressure standard deviation to form the fluctuation consistency; A first determination unit is connected to the consistency calculation unit and is used to determine a plurality of first temporary grids according to a comparison result between the fluctuation consistency and a preset standard consistency.

3. The smart community fire protection big data analysis system based on artificial intelligence according to claim 2 is characterized in that: The calculation module comprises: a deviation calculation unit, used for calculating the relative deviation of the real-time average water level to obtain the water level deviation, and for calculating the relative deviation of the real-time average water pressure to obtain the water pressure deviation; A water flow calculation unit is connected to the deviation calculation unit and is used to calculate the relative deviation between the water level deviation and the water pressure deviation to form the water flow deviation.

4. The smart community fire protection big data analysis system based on artificial intelligence according to claim 3 is characterized in that: The second determining module comprises: A water flow comparison unit, used to compare the water flow deviation with a preset standard water flow deviation to form a water flow comparison result; A second determining unit is connected to the water flow comparison unit and is used to determine a plurality of second temporary grids according to the water flow comparison result.

5. The artificial intelligence-based smart community fire big data analysis system according to claim 4 is characterized in that: The determination module comprises: a distribution degree calculation unit, configured to determine the distribution degree according to the second temporary grid and all adjacent second temporary grids within a preset radius; a determination unit connected to the distribution degree calculation unit, for determining the second temporary grid as an abnormal grid according to a comparison result between the distribution degree and a preset standard distribution degree, and forming a plurality of abnormal grids; A recording unit is connected to the determination unit and is used to record the duration of the abnormal grid.

6. The smart community fire protection big data analysis system based on artificial intelligence according to claim 5 is characterized in that: The distribution degree calculation unit comprises: a distance calculation subunit, used for calculating an average distance between each of the second temporary grids and all adjacent second temporary grids within the preset radius; The distribution degree calculation subunit is connected to the distance calculation subunit and is used to calculate the relative deviation between the average distance and a preset standard distance to form a distribution degree.

7. The smart community fire protection big data analysis system based on artificial intelligence according to claim 6 is characterized in that: The adjustment module comprises: A quantity fluctuation calculation unit, used to calculate the standard deviation of the grid quantity to form a quantity fluctuation value; An adjustment unit is used to adjust the preset grid spacing according to a comparison result between the quantity fluctuation value and the preset standard quantity fluctuation value, the grid quantity, the preset standard quantity fluctuation value and a preset adjustment coefficient.

8. The smart community fire protection big data analysis system based on artificial intelligence according to claim 7 is characterized in that: The alarm module comprises: An alarm determination unit, configured to determine whether an alarm needs to be sent according to a comparison result between each of the durations and a preset standard duration, and form an alarm determination result; An alarm sending unit is connected to the alarm determination unit and is used to send an alarm according to the alarm determination result.

9. The smart community fire protection big data analysis system based on artificial intelligence according to claim 8 is characterized in that: The screening module comprises: A material pile comparison unit, used to compare the real-time material pile quantity with a preset standard quantity to form a material pile comparison result; A screening unit is connected to the object pile comparison unit and is used to screen out a plurality of grids according to the object pile comparison result to form a plurality of risk grids.

10. A method for analyzing big data of firefighting in a smart community based on artificial intelligence, based on the big data analysis system of firefighting in a smart community based on artificial intelligence according to claims 1-9, characterized in that: include: Collect real-time images of each grid in the collection area based on preset grid spacing within the community, the real-time average water level of each fire hydrant, and the real-time average water pressure of each water tank; Extracting the real-time pile quantity of combustible materials in each of the real-time images; Screening out a number of risk grids according to the real-time number of material piles; Determine the fluctuation consistency according to the real-time average water level and the real-time average water pressure in any two adjacent risk grids within a preset calculation time, and determine a number of first temporary grids according to the fluctuation consistency; Calculating the water flow deviation according to the real-time average water level and the real-time average water pressure of each of the first temporary grids at adjacent moments; Determine a plurality of second temporary grids according to the water flow deviation; Determine a number of abnormal grids according to the distribution degree of the second temporary grid, and record the duration; Sending an alert according to the duration; The preset grid spacing is adjusted according to the grid quantity of each abnormal grid within a preset adjustment time and a preset standard quantity fluctuation value.

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