Fire risk early warning method and system, medium and electronic equipment
By integrating and analyzing the status data of fire sites through the Internet of Things and fire risk warning models, the problem of traditional fire risk warning relying on manual inspections is solved, early prediction and timely warning of fire hazards are achieved, and the level of fire safety is improved.
Patent Information
- Application Number
- CN202410442457.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-04-12
- Publication Date
- 2025-10-21
AI Technical Summary
Traditional fire risk warnings mainly rely on manual inspections, which are easily affected by subjective factors and cannot achieve early prediction of fire hazards. Alarms can only be issued after a fire actually occurs, which is detrimental to human safety and property protection.
Fire status data of fire-fighting sites is obtained through the Internet of Things network, and the fire risk warning model is used to integrate and analyze the data to determine the degree of fire safety risk, including comprehensive monitoring and evaluation of electricity consumption data, fire risk data, fire system status data, fire alarm data and crowd flow data.
It has achieved comprehensive monitoring and early warning of risks in fire-fighting sites, improved the level of fire safety, and can timely identify fire risk points and take preventive measures to reduce personal injury and property losses.
Smart Images
Figure CN120822816A_ABST
Abstract
Description
Technical Field
[0001] The present application belongs to the field of fire safety technology and relates to a fire risk early warning method, system, medium and electronic equipment. Background Art
[0002] Fire safety risk warnings play a vital role in public spaces, commercial buildings, residential communities, and industrial production areas. The primary purpose of fire risk warnings is to promptly issue an alert when a fire occurs, enabling the implementation of necessary preventive measures to avoid or minimize harm to people and property damage. Furthermore, an efficient fire risk warning system can help fire departments quickly locate the source of a fire, enabling more effective firefighting.
[0003] Traditional fire risk warnings mainly rely on manual inspections, which are easily affected by subjective factors and have limitations. They cannot achieve early prediction of fire hazards and can only issue alarms after a fire actually occurs. This is very detrimental to human safety and property protection. Summary of the Invention
[0004] The purpose of this application is to provide a fire risk warning method, system, medium and electronic equipment for providing stable and effective fire risk warnings for fire sites.
[0005] In a first aspect, the present application provides a fire risk warning method, which includes: obtaining fire status data of fire sites through an Internet of Things network; integrating and analyzing the fire status data; processing the fire status data through a fire risk warning model to determine the degree of fire safety risk.
[0006] In an implementation of the first aspect, the fire status data includes: electricity consumption data obtained by an intelligent circuit breaker, fire risk data obtained by a fire risk detection device, fire system status data obtained by a fire sensor, fire alarm data obtained by a fire alarm device, and / or crowd flow data obtained by an intelligent camera.
[0007] In an implementation of the first aspect, the process of integrating and analyzing the fire status data includes: monitoring the power usage status based on the power usage data; evaluating the fire risk based on the fire risk data; monitoring and early warning analysis of the fire system based on the fire system status data; identifying and processing the fire situation based on the fire alarm data; real-time monitoring of the personnel density and evacuation situation of the fire place based on the crowd flow data; monitoring the potential fire risk points in the fire place based on the power usage data, the fire risk data, the fire system status data, the fire alarm data and the crowd flow data.
[0008] In one implementation of the first aspect, the fire status data is processed by a fire risk warning model, and the process of determining the degree of fire safety risk includes: obtaining the number and timeliness of fire incident processing; obtaining the linkage status between fire status data acquisition equipment; obtaining the usage of high-current equipment; obtaining the triggering status of fire facilities and equipment; and obtaining the population density of the crowd.
[0009] In an implementation of the first aspect, the fire risk warning method further includes: performing fire safety supervision on the fire site according to the fire safety risk level.
[0010] In an implementation of the first aspect, the process of conducting fire safety supervision on the fire-fighting site according to the fire safety risk level includes: monitoring the offline status and self-inspection status of the fire status data acquisition equipment; monitoring the protection and troubleshooting status of the fire-fighting system; monitoring the concentration of people in the fire-fighting site; and determining the number of the fire status data acquisition equipment according to the fire safety risk level.
[0011] In an implementation of the first aspect, the process of monitoring the offline status and self-test status of the fire status data acquisition device includes any one or more of the following methods: obtaining the power supply and connection status of the fire status data acquisition device, and confirming the power status of the fire status data acquisition device; obtaining the network line and server configuration of the fire status data acquisition device, and confirming the network connection status of the fire status data acquisition device; controlling the fire status data acquisition device to power off and restart to confirm whether the online status of the fire status data acquisition device is restored; and confirming the working status and working position of the fire status data acquisition device.
[0012] In the second aspect, the present application provides a fire risk warning system, which includes: a data acquisition module for acquiring fire status data of fire sites through the Internet of Things network; a data processing module for integrating and analyzing the fire status data; and a risk estimation module for processing the fire status data using a fire risk warning model to determine the degree of fire safety risk.
[0013] In a third aspect, the present application provides an electronic device, characterized in that the electronic device includes: a memory on which a computer program is stored; and a processor, communicatively connected to the memory, for executing the computer program to implement any of the fire risk warning methods described above.
[0014] In a fourth aspect, the present application provides a computer-readable storage medium on which a computer program is stored, characterized in that when the program is executed by an electronic device, it implements any of the fire risk warning methods described above.
[0015] As described above, the fire risk warning method, system, medium, and electronic equipment described in this application have the following beneficial effects:
[0016] The fire risk warning method provided in this embodiment can obtain fire status data from fire sites through the Internet of Things (IoT) network and process the integrated fire status data using a fire risk warning model to determine the degree of fire safety risk. By conducting in-depth data analysis on the fire status data of IoT devices, comprehensive monitoring and early warning of fire site risks are achieved, improving the overall fire safety level of fire sites. BRIEF DESCRIPTION OF THE DRAWINGS
[0017] Figure 1 Shown is a flow chart of the fire risk warning method described in an embodiment of the present application.
[0018] Figure 2 Shown is a schematic diagram of the process of integrating and analyzing fire status data as described in an embodiment of the present application.
[0019] Figure 3 Shown is a schematic diagram of the process of determining the degree of fire safety risk as described in an embodiment of the present application.
[0020] Figure 4 Shown is a schematic diagram of the process of fire safety supervision of the fire-fighting site as described in an embodiment of the present application.
[0021] Figure 5 Shown is a structural diagram of the fire risk warning system described in an embodiment of the present application.
[0022] Figure 6 Shown is a structural schematic diagram of an electronic device described in an embodiment of the present application.
[0023] Component number description
[0024] 1 Fire risk early warning system
[0025] 11 Data Acquisition Module
[0026] 12 Data processing module
[0027] 13 Risk Assessment Module
[0028] 5 Electronic devices
[0029] 51 Memory
[0030] 52 processors
[0031] Steps S21 to S24
[0032] Steps S31 to S36
[0033] Steps S41 to S45
[0034] Steps S51 to S54 DETAILED DESCRIPTION
[0035] The following describes the embodiments of the present application through specific examples. Those skilled in the art can easily understand the other advantages and effects of the present application from the content disclosed in this specification. The present application can also be implemented or applied through other different specific embodiments. The details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments can be combined with each other unless they conflict.
[0036] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present application. Therefore, the illustrations only show components related to the present application and are not drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component can be changed at will, and the component layout type may also be more complicated.
[0037] Fire safety risk warnings play a vital role in public spaces, commercial buildings, residential communities, and industrial production areas. The primary purpose of fire risk warnings is to promptly issue an alert when a fire occurs, enabling the implementation of necessary preventive measures to avoid or minimize harm to people and property damage. Furthermore, an efficient fire risk warning system can help fire departments quickly locate the source of a fire, enabling more effective firefighting.
[0038] Traditional fire risk warnings mainly rely on manual inspections, which are easily affected by subjective factors and have limitations. They cannot achieve early prediction of fire hazards and can only issue alarms after a fire actually occurs. This is very detrimental to human safety and property protection.
[0039] At least to address the above-mentioned issues, the following embodiments of the present application provide a fire risk warning method, system, medium and electronic equipment. The technical solutions in the embodiments of the present application will be described in detail below in conjunction with the accompanying drawings in the embodiments of the present application.
[0040] Figure 1 Shown is a flow chart of a fire risk warning method in one embodiment of the present application. Figure 1 As shown, the fire risk early warning method includes:
[0041] S21: Obtain fire status data for a fire-fighting location via an IoT network. The fire-fighting location is equipped with IoT devices, such as smart circuit breakers, fire risk detection equipment, fire sensors, fire alarms, and / or smart cameras. The IoT devices are capable of transmitting data from each IoT device to a central server or cloud platform via the IoT network.
[0042] S22, integrating and analyzing the fire status data. For example, the acquired fire status data can be input into a relevant software system for data integration and analysis, and multi-dimensional data correlation analysis can be performed using intelligent algorithms and models to comprehensively assess the fire safety status of the fire site area.
[0043] S23: Process the fire status data using a fire risk warning model to determine the degree of fire safety risk. A fire risk warning model is constructed based on assessment rules. The fire safety risk level is analyzed and determined by processing the operating conditions of IoT devices and fire status data.
[0044] As can be seen from the above description, the fire risk warning method provided in the embodiments of this application can obtain fire status data of fire sites through the Internet of Things network, and process the integrated fire status data using a fire risk warning model to determine the degree of fire safety risk. By conducting in-depth data analysis on the fire status data of IoT devices, comprehensive monitoring and early warning of fire site risks are achieved, thereby improving the overall fire safety level of fire sites.
[0045] In one embodiment of the present application, the fire status data includes: electricity consumption data obtained through intelligent circuit breakers, fire risk data obtained through fire risk detection equipment, fire system status data obtained through fire sensors, fire alarm data obtained through fire alarm equipment and / or crowd flow data obtained through intelligent cameras.
[0046] For example, electricity usage data obtained from smart circuit breakers may include the current, voltage, and / or power of the smart circuit breaker. Fire risk data obtained from fire risk detection equipment may include smoke detected by smoke sensors, combustible gas detected by combustible gas detectors, and sparks detected by arc detection devices. Fire protection system status data obtained from fire sensors may include fire water system data collected by water sensors, water pressure sensors, and liquid level sensors. Fire alarm data obtained from fire alarm equipment may include fire scene image data collected by fire alarm cameras and fire alarm data.
[0047] Fire scene image data is used for real-time identification and verification of fire conditions. Fire alarm data is used for real-time processing and coordinated response to fire alarm signals. Smart cameras collect traffic flow data from entrances and other channels, which can be used to monitor crowd density and evacuation conditions at fire sites in real time. This allows for evacuation scenario simulations and plan optimization, improving evacuation efficiency.
[0048] Furthermore, by integrating and analyzing various data types, such as electricity usage data, fire risk data, fire system status data, fire alarm data, and / or crowd flow data, potential fire risk points in fire-fighting sites can be identified. Examples of fire risk points include electrical equipment anomalies, combustible gas leaks, and / or fire hotspots.
[0049] Figure 2 The figure shows a flow chart of integrating and analyzing fire status data in one embodiment of the present application. Figure 2 As shown, the process of integrating and analyzing the fire status data includes:
[0050] S31, monitoring the power usage status according to the power usage data.
[0051] S32: Evaluate the fire risk according to the fire risk data.
[0052] S33: Monitor and analyze the fire protection system based on the fire protection system status data.
[0053] S34: Identify and process the fire situation according to the fire alarm data.
[0054] S35: Monitor the density of people and the evacuation situation of the fire-fighting site in real time based on the crowd flow data.
[0055] S36: Monitor potential fire risk points in the fire-fighting site based on the electricity usage data, the fire risk data, the fire-fighting system status data, the fire alarm data, and the crowd flow data.
[0056] It should be noted that the above reference numerals S31 to S36 are only used to represent different steps, and are not used to limit the order in which these steps are executed. For example, in some embodiments, step S31 may be executed after step S32, and in other embodiments, step S31 may be executed simultaneously with step S32.
[0057] By monitoring fire status data, conducting early warning analysis, and conducting risk assessments, the fire protection system can be monitored, fire risks can be assessed, and fire situations can be identified and addressed promptly, avoiding potential fire risk points in firefighting areas. Real-time monitoring of the density of people in firefighting areas allows for safe and early evacuation of personnel.
[0058] In one embodiment of the present application, a fire risk model can be used to process the fire status data to determine the degree of fire safety risk. This fire risk model includes various assessment dimensions, such as the number and timeliness of fire incident handling, the linkage between fire status data collection devices, the use of high-current equipment, the triggering of firefighting facilities and equipment, and the density of people in the crowd, and includes corresponding weights for each dimension. In some implementations, scores for each assessment dimension can be determined using corresponding rules. The degree of fire safety risk can be determined by combining the weights and scores of each assessment dimension.
[0059] Figure 3 Shown is a flow chart of determining the degree of fire safety risk in one embodiment of the present application. Figure 3 As shown, the process of processing the fire status data by the fire risk early warning model to determine the fire safety risk level includes:
[0060] S41: Obtain the number and timeliness of fire incident handling. The number and timeliness of fire incident handling can be monitored and calculated in real time through the fire alarm system. The fire alarm system can automatically record and summarize the number of fire incidents and handling timeliness each month, calculate regional scores based on preset scoring rules, and generate corresponding scoring reports and statistical data. By analyzing and comparing the scoring reports and statistical data, problems can be promptly identified and corresponding improvements can be made to improve fire safety levels.
[0061] For example, the process of obtaining the number and timeliness of fire incident handling includes: obtaining a score for the number of fire incidents handled according to the fire incident handling number evaluation rules, and obtaining a score for the average fire incident handling speed according to the fire incident average handling speed evaluation rules. The timeliness of fire incident handling can be measured by the average fire incident handling speed. The evaluation of the number and timeliness of fire incident handling accounts for 30% of the fire risk early warning model.
[0062] The score of fire incident handling quantity obtained according to the evaluation rules of fire incident handling quantity is as follows:
[0063] If fewer than three fire incidents occur in a month (per month), the score is 1.0. If 30 or fewer fire incidents are handled (per month), the score is 0.7. If 100 or fewer fire incidents are handled (per month), the score is 0.6. If 150 or fewer fire incidents are handled (per month), the score is 0.3. If more than 150 fire incidents are handled (per month), the score is 0 (denoted as N1).
[0064] The score of the average fire incident handling speed obtained according to the evaluation rules of the average fire incident handling speed is as follows:
[0065] The average processing speed is required to be less than 1 hour (from receipt of the alarm to completion of the processing), with a score of 1.0. If the processing speed is between 1 and 3 hours, the score is 0.7. If the processing speed is between 3 and 8 hours, the score is 0.6. If the processing speed is between 8 and 24 hours, the score is 0.3. If the processing speed exceeds 24 hours, the score is 0 (denoted as N2).
[0066] S42, obtain the linkage status between fire status data acquisition devices. Integrate the real-time fire monitoring alarms of fire status data acquisition devices such as the power alarm of the intelligent circuit breaker, smoke sensor, combustible gas detector, arc detection device fire alarm and / or fire camera. When the camera alarm is triggered and any other type of alarm is added, it is defaulted to a real fire and linkage is performed, thereby achieving a rapid response of fire linkage. Other types of alarms are, for example, one-button fire alarms. When a fire is discovered, the alarm system is automatically triggered to sound an alarm, thereby improving the efficiency of fire detection and handling and reducing fire losses.
[0067] Exemplarily, the process of obtaining the inter-device linkage status of fire status data collection includes obtaining a score for the inter-device linkage status according to an evaluation rule for the inter-device linkage status. The evaluation of the inter-device linkage status of fire status data collection accounts for 20% of the fire risk warning model.
[0068] According to the evaluation rules for the linkage between fire status data collection devices, the score of the linkage between fire status data collection devices is obtained as follows:
[0069] If light monitoring equipment, fire cameras, and other equipment can automatically trigger the fire alarm system to issue an alarm after detecting a fire, a score of 1 point will be awarded (recorded as C). If the automatic triggering of the fire alarm system is not achieved, a score of 0 will be awarded.
[0070] S43, obtain the usage status of high-current equipment. Record the usage status of high-current equipment and compare the timing of high-current equipment. Determine whether the equipment has timed out based on 90% of the rated current of the high-current equipment. Through timely monitoring and management of high-current equipment, potential fire hazards caused by excessive use of high-current equipment can be avoided. Among them, high-current equipment can be an electrical appliance that uses a current greater than the normal current consumption, and its current range exceeds the standard current. Causes of high current include tinning, component burnout, circuit false welding, and circuit short circuit.
[0071] For example, the process of obtaining the usage of high-current equipment includes obtaining a score for the high-current equipment according to the evaluation rules for the usage of high-current equipment. The evaluation of the usage of high-current equipment accounts for 10% of the fire risk warning model.
[0072] The score of high-current equipment is obtained according to the evaluation rules of high-current equipment usage as follows:
[0073] If the product is not used with high current (i.e. 90% of the rated current) for more than 3 hours, the score is 1 point. If the product is used with high current for more than 3 hours, the score is 0 point (recorded as D).
[0074] S44, obtain the triggering status of fire-fighting facilities and equipment. Carry out regular inspections of fire-fighting facilities, and set up scheduled self-inspections of IoT devices such as circuit breaker self-inspections, smoke detector self-inspections, and / or combustible gas detector self-inspections through the cloud platform. The system can issue self-inspection commands to IoT devices and monitor whether there are alarm messages reported within a short period of time. The time can be defined as 30 seconds, but this application is not limited to this. The system performs self-inspections by triggering equipment such as fire emergency broadcasting equipment, fire water systems, and water pressure and water level detection equipment to check whether the relevant data meets the index requirements. The self-inspection process of fire-fighting facilities and equipment can be automatically triggered after being set up on the cloud platform, so that timely repairs and / or replacements can be carried out when there are problems with the triggering of fire-fighting facilities and equipment, thereby ensuring fire safety.
[0075] For example, the process of obtaining the triggering status of fire protection facilities and equipment includes: obtaining the score of the triggering status of fire protection facilities and equipment according to the evaluation rules of the triggering status of fire protection facilities and equipment. Among them, the evaluation of the triggering status of fire protection facilities and equipment accounts for 35% of the fire risk warning model.
[0076] The scores of fire protection facilities and equipment triggering conditions are obtained according to the evaluation rules of fire protection facilities and equipment triggering conditions as follows:
[0077] 1 point (E) for indoor and outdoor fire hydrants. 1 point (F) for fire emergency broadcasting equipment. 1 point (G) for intelligent circuit breakers, smoke detectors, and combustible gas detectors functioning properly. 0.5 point (H) for fire water system pressure and water level meeting specified standards.
[0078] S45: Obtaining crowd density. Intelligent cameras collect traffic data at entrances and other channels, and conduct real-time monitoring and statistics of the crowd density within the building to obtain crowd density. Simultaneously, the building's fire protection level is assessed based on fire regulations and actual conditions, and a comprehensive assessment is conducted based on the building's fire safety measures and the use of firefighting facilities and equipment.
[0079] For example, the process of obtaining the crowd density includes obtaining a crowd density score according to a crowd density evaluation rule, wherein the crowd density evaluation accounts for 5% of the fire risk warning model.
[0080] The personnel density score is obtained according to the evaluation rules of personnel density as follows:
[0081] If there are more than 4 people per square meter in a building, the score is -3 points (recorded as N3). If the building's fire protection level is lower than Class B, the score is -2 points (recorded as N4).
[0082] In the embodiment of the present application, the degree of fire safety risk can be calculated according to the following formula:
[0083]
[0084] Please continue reading Figure 1 The fire risk warning method further includes: S24, performing fire safety supervision on the fire site according to the fire safety risk level.
[0085] Figure 4 Shown is a flow chart of fire safety supervision of the fire-fighting site in one embodiment of the present application. Figure 4 As shown, the process of conducting fire safety supervision on the fire-fighting site according to the fire safety risk level includes:
[0086] S51, monitoring the offline status and self-test status of the fire status data acquisition equipment.
[0087] S52, monitors the protection and troubleshooting status of the fire protection system.
[0088] In some implementations, the fire risk warning method may further include: performing fire safety supervision on fire sites based on the protection and troubleshooting status of the fire protection system, which may be specifically manifested as:
[0089] By verifying the sensors, instruments and / or pipe connections, confirm whether the water pressure and / or water level are abnormal. When an abnormality occurs, check whether the working status of the fire water system and / or pipeline is normal. Among them, the working status includes the operation of the water pump, the switching of the valve, the sealing of the pipeline and the water supply of the water source. When the main water supply source fails, it is switched to the backup water supply source. The backup water supply source can be a water tanker and a fire water pool, but this application is not limited to this. The backup water supply source can provide the fire protection system with a stable water source with sufficient water pressure, providing a second layer of protection for fire safety.
[0090] Fire safety supervision also includes ongoing safety maintenance. Regular maintenance of the fire water system involves checking equipment such as pumps, pipes, and sensors to ensure proper operation and accuracy. Any equipment exhibiting persistent abnormalities requires prompt contact with the fire equipment manufacturer or professional maintenance personnel for repair, inspection, or replacement. During repair or replacement, necessary safety measures, such as notice boards or warning signs, must be implemented to prevent intrusion into the maintenance area, ensuring the safety of both personnel and equipment.
[0091] S53, monitoring the crowd density of the fire-fighting site.
[0092] In some implementations, the fire risk warning method may further include: monitoring the density of people in a fire scene to determine the degree of fire safety risk and supervise fire safety. When the density of people in a fire scene exceeds the estimated density of people based on the fire safety risk, the following measures may be taken for evacuation:
[0093] ① Issue a reminder: Issue a notice to the personnel on site through public broadcasting, alarm system or staff reminder, reminding them to pay attention to the situation of excessive crowd density and take measures to control the number of people.
[0094] ② Control entrances: Limit the number of people at the entrance, such as controlling the number of people entering buildings, stores or other public places to avoid excessive density of people in the venue.
[0095] ③ Arrange queuing areas: For places where people need to queue, set up clear queuing areas and keep distance between people during the queuing process.
[0096] ④ Designate staff to direct and guide people: Arrange staff to direct and guide people inside the venue, guide people to queue in an orderly manner or divert the flow of people to avoid crowded areas.
[0097] ⑤ Provide online reservation service: If a special service or activity requires people to make reservations in advance, the number of people entering can be controlled through online reservations to avoid crowded situations.
[0098] ⑥ Provide remote service options: For some public service places, remote service options can be provided, allowing some personnel to complete service needs remotely and reduce the actual presence of personnel.
[0099] ⑦ Promote social distance: Promote social distance through propaganda, ideological education, announcements, etc., so that people can consciously maintain a certain distance.
[0100] ⑧ Consider adjusting the layout of the venue: Adjusting the layout of the venue, such as adding entrances and exits, adding pedestrian passages, and replanning activity areas, will help disperse the density of people.
[0101] S54: Determine the number of fire status data collection devices according to the fire safety risk level.
[0102] In some implementations, the fire risk warning method may also include: purchasing and installing fire-fighting equipment required by the fire-fighting site according to the number of the fire status data acquisition equipment determined in step S54, such as fire alarms, fire extinguishers, fire hydrants, sprinkler systems and / or smoke detectors.
[0103] In one embodiment of the present application, the process of monitoring the offline status of the fire status data acquisition device includes any one or more of the following methods:
[0104] Verify the power supply and connection status of the fire status data collection device. Check the power supply and connection status of the sensors to confirm that the device is receiving power and is functioning properly. If the fire protection equipment is battery-powered, verify that the batteries are replaced or fully charged.
[0105] Obtain the network line and server configuration of the fire status data acquisition device and confirm the network connection status of the fire status data acquisition device. Check whether the network connection of the sensor using the network connection is normal, such as whether the network line, router connection, networking function and / or server configuration are correct.
[0106] The fire status data collection device is controlled to power off and restart to confirm whether the online status of the fire status data collection device is restored. The restart method of the sensor device includes power off and restart and / or the operation of the sensor device manual, but the application is not limited thereto.
[0107] Verify the operating status and location of the fire status data collection device. Observe the sensor's status indicator to confirm that the device is connected to the network and / or operating. Check to see if the device has been accidentally moved or damaged. Ensure the device is securely installed and that there are no obstacles that could hinder its normal operation.
[0108] If the fire status data collection device remains offline after the above steps, contact the device vendor or a professional repair service provider for technical support or repair services. The fire status data collection device must promptly record and report offline conditions to facilitate follow-up actions after tracking and recording the device failure.
[0109] In this embodiment, the process of self-checking the abnormality monitoring of the fire status data acquisition device includes any one or more of the following methods:
[0110] Obtain the self-test anomaly status of the fire status data acquisition device to ensure that the self-test anomaly is not caused by a false alarm or interference. Confirm whether the sensor has experienced a self-test anomaly by checking the anomaly information. Anomaly information includes alarm information from the alarm system display or related equipment, self-test reports, and / or fault codes.
[0111] Based on the abnormal self-test results, check for interference factors, including odor, dust, and dirt around the sensor and / or objects blocking the sensor ventilation holes.
[0112] Manually test the fire status data acquisition equipment. Use software programs and / or testing instruments to manually test the sensor under abnormal conditions such as smoke and combustible gas to verify the accuracy and sensitivity of the sensor.
[0113] Obtain the power supply status of the fire status data acquisition device. Check the power supply and connection status of the sensor to confirm whether the device is connected to the power supply and is supplying power normally.
[0114] Persistent self-test abnormalities can be maintained and repaired by replacing batteries, cleaning sensors, calibrating sensitivity and / or repairing faulty circuits.
[0115] If a self-test anomaly cannot be resolved, technical support or repair services can be obtained from the equipment supplier or a professional maintenance service provider. Fire status data collection equipment must promptly record and report offline conditions to facilitate staff to track and record equipment failures and take subsequent measures.
[0116] The protection scope of the fire risk warning method described in the embodiment of the present application is not limited to the execution order of the steps listed in this embodiment. All solutions implemented by adding, reducing, or replacing steps in the existing technology based on the principles of the present application are included in the protection scope of the present application.
[0117] An embodiment of the present application also provides a fire risk warning system, which can implement the fire risk warning method described in the present application. However, the implementation device of the fire risk warning method described in the present application includes but is not limited to the structure of the fire risk warning system listed in this embodiment. All structural deformations and replacements of the existing technology made according to the principles of the present application are included in the protection scope of the present application.
[0118] Figure 5 Shown is a schematic diagram of the structure of a fire risk warning system in one embodiment of the present application. Figure 5As shown, the fire risk warning system includes a data acquisition module 11, a data processing module 12, and a risk assessment module 13. The data acquisition module 11 is used to acquire fire status data of fire sites through the Internet of Things network; the data processing module 12 is used to integrate and analyze the fire status data; and the risk assessment module 13 is used to process the fire status data using a fire risk warning model to determine the degree of fire safety risk.
[0119] It should be noted that the data acquisition module 11, data processing module 12 and risk estimation module 13 in the fire risk warning system 1 are Figure 1 Steps S21 to S23 in the fire risk warning method shown correspond to each other and are not described in detail here.
[0120] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices or methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of modules / units is only a logical function division. There may be other division methods in actual implementation. For example, multiple modules or units can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules or units, which can be electrical, mechanical or other forms.
[0121] The modules / units described as separate components may or may not be physically separate, and the components displayed as modules / units may or may not be physical modules, that is, they may be located in one place or distributed across multiple network elements. Some or all of the modules / units may be selected according to actual needs to achieve the purpose of the embodiments of the present application. For example, the functional modules / units in the various embodiments of the present application may be integrated into a processing module, or each module / unit may exist physically separately, or two or more modules / units may be integrated into a single module / unit.
[0122] Those skilled in the art should further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the composition and steps of each example according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.
[0123] The embodiment of the present application also provides a computer-readable storage medium. Those skilled in the art will understand that all or part of the steps in the method for implementing the above embodiment can be completed by instructing the processor through a program, and the program can be stored in a computer-readable storage medium, and the storage medium is a non-transitory medium, such as a random access memory, a read-only memory, a flash memory, a hard disk, a solid-state drive, a magnetic tape, a floppy disk, an optical disc, and any combination thereof. The above storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that includes one or more available media. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a digital video disc (DVD)), or a semiconductor medium (for example, a solid-state drive (SSD)), etc.
[0124] An embodiment of the present application may also provide an electronic device. Figure 6 The diagram shows the structure of the electronic device 5 in one embodiment of the present application. Figure 6 As shown, in this embodiment, the electronic device 5 includes a memory 51 and a processor 52. The memory 51 is used to store computer programs. In some possible implementations, the memory 51 may include various media that can store program codes, such as ROM, RAM, a magnetic disk, a USB flash drive, a memory card, or an optical disk.
[0125] In the embodiment of the present application, the memory 51 may include a computer system readable medium in the form of a volatile memory, such as RAM and / or cache memory. The electronic device 5 may further include other removable / non-removable, volatile / non-volatile computer system storage media. The memory 51 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the various embodiments of the present application.
[0126] The processor 52 is connected to the memory 51 and is used to execute the computer program stored in the memory 51 so as to enable the electronic device 5 to execute the fire risk early warning method.
[0127] Exemplarily, the processor 52 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc. In other embodiments, the processor 52 may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.
[0128] The descriptions of the processes or structures corresponding to the above figures have different emphases. For parts that are not described in detail in a certain process or structure, please refer to the relevant descriptions of other processes or structures.
[0129] The above embodiments are merely illustrative of the principles and effects of this application and are not intended to limit this application. Anyone skilled in the art may modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or alterations made by one of ordinary skill in the art without departing from the spirit and technical concepts disclosed in this application shall be covered by the claims of this application.
Claims
1. A fire risk early warning method, characterized in that: The fire risk early warning method includes: Obtain fire status data of fire-fighting places through the Internet of Things network; Integrating and analyzing the fire status data; The fire status data is processed through a fire risk early warning model to determine the degree of fire safety risk.
2. The fire risk early warning method according to claim 1, characterized in that: The fire status data includes: electricity consumption data obtained through intelligent circuit breakers, fire risk data obtained through fire risk detection equipment, fire system status data obtained through fire sensors, fire alarm data obtained through fire alarm equipment and / or crowd flow data obtained through intelligent cameras.
3. The fire risk early warning method according to claim 2, characterized in that: The process of integrating and analyzing the fire status data includes: monitoring the power usage status according to the power usage data; Assessing the fire risk based on the fire risk data; Performing monitoring and early warning analysis on the fire protection system based on the fire protection system status data; identifying and processing the fire situation according to the fire alarm data; Performing real-time monitoring of the personnel density and evacuation situation of the fire-fighting site based on the crowd flow data; The potential fire risk points in the fire-fighting site are monitored based on the electricity consumption data, the fire risk data, the fire-fighting system status data, the fire alarm data and the crowd flow data.
4. The fire risk early warning method according to claim 1, characterized in that: The process of processing the fire status data using a fire risk early warning model to determine the fire safety risk level includes: Obtain the number and timeliness of fire incident handling; Obtain the linkage status between fire status data collection devices; Obtain usage information of high current devices; Obtain the triggering status of fire-fighting facilities and equipment; Get the population density of the crowd.
5. The fire risk early warning method according to claim 1, characterized in that: The fire risk warning method further includes: performing fire safety supervision on the fire site according to the fire safety risk level.
6. The fire risk early warning method according to claim 5, characterized in that: The process of conducting fire safety supervision on the fire-fighting sites according to the fire safety risk level includes: Monitor the offline status and self-test status of fire status data acquisition equipment; Monitor the protection and troubleshooting status of the fire protection system; Monitoring the density of people at the fire-fighting sites; The number of the fire status data acquisition devices is determined according to the fire safety risk level.
7. The fire risk early warning method according to claim 6, characterized in that: The process of monitoring the offline status and self-test status of the fire status data acquisition equipment includes any one or more of the following methods: Obtaining the power supply and connection status of the fire status data acquisition device, and confirming the power supply status of the fire status data acquisition device; Obtaining the network line and server configuration of the fire status data acquisition device, and confirming the network connection status of the fire status data acquisition device; Controlling the fire status data acquisition device to power off and restart to confirm whether the online status of the fire status data acquisition device is restored; Confirm the working status and working position of the fire status data acquisition equipment.
8. A fire risk early warning system, characterized in that: The fire risk early warning system includes: A data acquisition module is used to obtain fire status data of fire-fighting places through the Internet of Things network; A data processing module, for integrating and analyzing the fire status data; The risk estimation module is used to process the fire status data using a fire risk warning model to determine the degree of fire safety risk.
9. An electronic device, characterized in that: The electronic device comprises: a memory having a computer program stored thereon; A processor is communicatively connected to the memory, and is used to execute the computer program to implement the fire risk early warning method according to any one of claims 1 to 7.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by an electronic device, the fire risk early warning method according to any one of claims 1 to 7 is implemented.