Cloud on-duty intelligent fire alarm emergency processing method, system and device and storage medium

Abstract

The invention relates to a cloud on-duty intelligent fire alarm emergency processing method, system and device and a storage medium, and the method comprises the steps: obtaining an image shot by an area in real time, and analyzing whether the image shot by the area has a suspected fire point or not through an image recognition technology; under the condition that the suspected fire point is detected, obtaining a specific position of the suspected fire point and a suspected point position image of the surrounding environment, and uploading the suspected point position image to a cloud on-duty system; under the condition that a fire confirmation message of the cloud on-duty system is obtained, an emergency processing flow of a region corresponding to the suspected fire point is started, and fire fighting equipment at a position corresponding to the suspected fire point is remotely controlled based on multiple wire coils according to the emergency processing flow; and acquiring state information of the fire-fighting equipment in real time, detecting whether the state information of the fire-fighting equipment has an abnormal condition, and generating a corresponding voice alarm instruction according to the abnormal condition. The method has the effect of improving the fire response speed in the area.

Description

Technical Field

[0001] The present application relates to the technical field of fire safety, and in particular to a cloud-based intelligent fire emergency response method, system, equipment and storage medium. Background Art

[0002] At present, with the acceleration of urbanization and the increase in high-rise buildings, fire prevention and control work has become more complicated. Traditional fire protection systems rely on real-time monitoring and manual operation by on-site personnel. These methods not only require a lot of human resources, but are also prone to human errors or slow response in emergency situations, resulting in the failure to control the fire in time. To solve these problems, existing fire alarm automation processing systems have begun to introduce technologies such as cloud computing and the Internet of Things to improve the efficiency of fire emergency response through remote monitoring and automatic control. However, these systems still have problems such as insufficient fire identification accuracy and slow equipment linkage response speed.

[0003] The above-mentioned existing technical solutions have the following defects: the linkage method between manual duty and on-site fire-fighting equipment has a slow response speed when dealing with fire situations, so there is room for improvement. Summary of the invention

[0004] In order to improve the fire response speed in the area, the present application provides a cloud-based intelligent fire emergency response method, system, equipment and storage medium.

[0005] The above-mentioned invention objective of the present application is achieved through the following technical solutions: A cloud-based intelligent fire alarm emergency processing method, the cloud-based intelligent fire alarm emergency processing method comprising: Acquire images taken in the jurisdiction in real time, and use image recognition technology to analyze whether there are suspected fire spots in the images taken in the jurisdiction; When the suspected fire point is detected, the specific location of the suspected fire point and the suspected point image of the surrounding environment are obtained, and the suspected point image is uploaded to the cloud duty system; When a fire confirmation message is obtained from the cloud duty system, the emergency treatment process of the area corresponding to the suspected fire point is started, and the fire-fighting equipment at the corresponding position of the suspected fire point is remotely controlled based on the multi-line disk according to the emergency treatment process; The fire-fighting equipment status information is obtained in real time, and the fire-fighting equipment status information is detected to see if there is any abnormality, and a corresponding voice alarm instruction is generated according to the abnormality.

[0006] By adopting the above technical solution, by acquiring images taken in the jurisdiction in real time, and using image recognition technology to analyze whether there are suspected fire points in the images taken in the jurisdiction, potential fire risks can be identified in a timely and accurate manner, thereby reducing false alarms and missed alarms and improving the accuracy of fire detection; by acquiring the specific location of the suspected fire point and the suspected point image of the surrounding environment when a suspected fire point is detected, and uploading the suspected point image to the cloud duty system, key image information can be quickly transmitted to ensure that the cloud duty personnel can remotely confirm the fire at the first time, thereby accelerating the fire response speed; by starting the emergency treatment process of the area corresponding to the suspected fire point when the fire confirmation message of the cloud duty system is obtained, and remotely controlling the fire-fighting equipment at the corresponding position of the suspected fire point based on the multi-line disk according to the emergency treatment process, the corresponding fire-fighting equipment can be started quickly and efficiently, ensuring that the fire is handled in time, thereby reducing fire losses; by acquiring the status information of the fire-fighting equipment in real time, detecting whether there are abnormal conditions in the status information of the fire-fighting equipment, and generating corresponding voice alarm instructions according to the abnormal conditions, the equipment operation status can be monitored in real time, and equipment failures can be discovered in time, thereby avoiding the failure of the equipment to start normally during a fire, and improving the reliability of the fire-fighting system.

[0007] In one example, the present application may be further configured as follows: the use of image recognition technology to analyze whether there is a suspected fire point in the image taken in the jurisdiction specifically includes: A dense smoke detection algorithm is used to monitor the smoke condition in the captured image of the area in real time, and by identifying the smoke form, smoke color and smoke diffusion speed of the smoke condition, it is determined whether there is a suspected fire point; The flame conditions in the captured images of the area are analyzed by a flame detection algorithm, the flame shape, flame color and flame brightness characteristics of the flame conditions are identified, and it is determined whether there is a suspected fire point.

[0008] By adopting the above technical solution, by using a thick smoke detection algorithm to monitor the smoke conditions in the images taken in the jurisdiction in real time, it is possible to accurately identify smoke signals in the early stages of a fire, thereby giving early warning of the fire and reducing the risk of the fire expanding; by analyzing the flame conditions in the images taken in the jurisdiction through a flame detection algorithm, it is possible to effectively identify the flame characteristics that appear in the fire, thereby making a timely response in the early stages of the fire and preventing the fire from spreading further.

[0009] In one example, the present application may be further configured as follows: in obtaining the specific location of the suspected fire point and the suspected point image of the surrounding environment, specifically including: Using a positioning algorithm based on visual SLAM technology, a three-dimensional environmental model is constructed by taking images of the area corresponding to the suspected fire point, and the positioning coordinates of the suspected fire point are realized in combination with GPS data; According to the positioning coordinates, a captured image of the area in which the positioning coordinates can be obtained is reversely determined as the suspected point image.

[0010] By adopting the above-mentioned technical scheme, by using a positioning algorithm based on visual SLAM technology, a three-dimensional environmental model is constructed through the images taken in the jurisdiction corresponding to the suspected fire point, and the positioning coordinates of the suspected fire point are realized in combination with GPS data. The specific location of the fire point can be accurately located, thereby improving the efficiency of firefighters in reaching the fire point; by reversely determining the images taken in the jurisdiction that can obtain the positioning coordinates based on the positioning coordinates, as the suspected point image, the fire situation can be accurately grasped from different angles and positions, thereby providing more comprehensive information for firefighting decisions.

[0011] In one example, the present application may be further configured as follows: when a fire confirmation message from the cloud duty system is obtained, an emergency response process for the area corresponding to the suspected fire point is initiated, specifically including: Based on the preset emergency handling rules under the fire alarm situation, select the emergency handling process for the corresponding area according to the severity of the fire; Real-time dispatch and prioritization through intelligent dispatch algorithms, parallel and / or sequential control of multiple firefighting equipment based on the urgency of the fire and the availability of resources.

[0012] By adopting the above technical solution, by selecting the emergency handling process of the corresponding area according to the severity of the fire based on the preset emergency handling rules under the fire alarm situation, it is possible to flexibly adjust the emergency measures according to the actual fire situation, thereby improving the emergency response capability of the fire protection system; through real-time scheduling and priority sorting by intelligent scheduling algorithms, multiple fire-fighting equipment can be controlled in parallel and / or sequentially according to the urgency of the fire and the availability of resources, which can optimize the use efficiency of fire-fighting equipment, thereby maximizing the fire-fighting effect under limited resources.

[0013] In one example, the present application may be further configured as follows: the real-time acquisition of fire equipment status information and the detection of whether the fire equipment status information is abnormal include: Use an embedded sensor network to monitor the water pressure information of the fire pump in real time, and combine the state detection algorithm to determine whether the fire pump has abnormal conditions such as leakage or blockage through water pressure fluctuations; The operating current of the smoke exhaust fan is monitored in real time through the motor current sensor, and the current mode analysis technology is used to determine whether there are any abnormal fan conditions such as overload or no-load in the equipment operation status.

[0014] By adopting the above technical solution, the water pressure information of the fire pump is monitored in real time by using an embedded sensor network, and the state detection algorithm is combined to determine whether the fire pump has water leakage, blockage or other water pump abnormalities through water pressure fluctuations. The working status of the water pump can be monitored in real time, and hidden faults can be discovered in advance, thereby avoiding water pump failure at critical moments; the operating current of the smoke exhaust fan is monitored in real time through the motor current sensor, and the current mode analysis technology is used to determine whether the equipment operating status has fan abnormalities such as overload or no-load. The fan operation abnormalities can be effectively identified, thereby ensuring the normal operation of the smoke exhaust system during a fire and ensuring the safety of on-site personnel.

[0015] In one example, the present application may be further configured as follows: generating a corresponding voice alarm instruction according to the abnormal situation specifically includes: Obtaining the serial number and location information corresponding to the fire-fighting equipment where the water pump abnormality and / or the fan abnormality occurs; An intelligent speech synthesis system based on natural language processing technology is used to generate and broadcast corresponding voice alarm instructions according to the number position information; By utilizing an event-driven voice reminder mechanism, when no processing feedback is received after the first broadcast, a second reminder will be automatically made according to a preset time period until the abnormal situation is processed.

[0016] By adopting the above technical solution, by obtaining the numbered position information corresponding to the fire-fighting equipment where the water pump abnormality and / or the fan abnormality occurs, the position of the faulty equipment can be quickly located, thereby reducing the troubleshooting time and speeding up the maintenance response; by adopting an intelligent speech synthesis system based on natural language processing technology, corresponding voice alarm instructions are generated and broadcast according to the numbered position information, which can remind the on-duty personnel to handle the equipment abnormality in the most intuitive way, thereby ensuring that the problem is solved in a timely manner; by utilizing an event-driven voice reminder mechanism, when no processing feedback is received after the first broadcast, a second reminder is automatically made according to a preset time period until the abnormal situation is handled, which can prevent the fault from being handled in a timely manner due to negligence of personnel, thereby ensuring the normal operation of the fire-fighting equipment.

[0017] The second object of the invention is achieved by the following technical solutions: A cloud-based intelligent fire alarm emergency processing system, the cloud-based intelligent fire alarm emergency processing system comprising: An image acquisition module is used to acquire images taken in the jurisdiction in real time, and use image recognition technology to analyze whether there are suspected fire spots in the images taken in the jurisdiction; An image uploading module is used to obtain the specific location of the suspected fire point and the suspected point image of the surrounding environment when the suspected fire point is detected, and upload the suspected point image to the cloud duty system; An emergency processing module is used to start the emergency processing process of the area corresponding to the suspected fire point when the fire confirmation message of the cloud duty system is obtained, and remotely control the fire-fighting equipment at the corresponding position of the suspected fire point based on the multi-line disk according to the emergency processing process; The equipment monitoring module is used to obtain the fire equipment status information in real time, detect whether there is any abnormality in the fire equipment status information, and generate corresponding voice alarm instructions according to the abnormality.

[0018] By adopting the above technical solution, by acquiring images taken in the jurisdiction in real time, and using image recognition technology to analyze whether there are suspected fire points in the images taken in the jurisdiction, potential fire risks can be identified in a timely and accurate manner, thereby reducing false alarms and missed alarms and improving the accuracy of fire detection; by acquiring the specific location of the suspected fire point and the suspected point image of the surrounding environment when a suspected fire point is detected, and uploading the suspected point image to the cloud duty system, key image information can be quickly transmitted to ensure that the cloud duty personnel can remotely confirm the fire at the first time, thereby accelerating the fire response speed; by starting the emergency treatment process of the area corresponding to the suspected fire point when the fire confirmation message of the cloud duty system is obtained, and remotely controlling the fire-fighting equipment at the corresponding position of the suspected fire point based on the multi-line disk according to the emergency treatment process, the corresponding fire-fighting equipment can be started quickly and efficiently, ensuring that the fire is handled in time, thereby reducing fire losses; by acquiring the status information of the fire-fighting equipment in real time, detecting whether there are abnormal conditions in the status information of the fire-fighting equipment, and generating corresponding voice alarm instructions according to the abnormal conditions, the equipment operation status can be monitored in real time, and equipment failures can be discovered in time, thereby avoiding the failure of the equipment to start normally during a fire, and improving the reliability of the fire-fighting system.

[0019] The third objective of the present application is achieved through the following technical solutions: A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned cloud-based intelligent fire alarm emergency processing method are implemented.

[0020] The fourth objective of the present application is achieved through the following technical solutions: A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned cloud-on-duty intelligent fire alarm emergency processing method.

[0021] In summary, this application includes the following beneficial technical effects: 1. By acquiring images taken in the jurisdiction in real time and using image recognition technology to analyze whether there are suspected fire points in the images taken in the jurisdiction, potential fire risks can be identified in a timely and accurate manner, thereby reducing false alarms and missed alarms and improving the accuracy of fire detection; by acquiring the specific location of the suspected fire point and the suspected point image of the surrounding environment when a suspected fire point is detected, and uploading the suspected point image to the cloud duty system, key image information can be quickly transmitted to ensure that the cloud duty personnel can remotely confirm the fire at the first time, thereby speeding up the fire response speed; by starting the emergency treatment process of the area corresponding to the suspected fire point when the fire confirmation message of the cloud duty system is obtained, and remotely controlling the fire-fighting equipment at the corresponding position of the suspected fire point based on the multi-line disk according to the emergency treatment process, the corresponding fire-fighting equipment can be started quickly and efficiently to ensure that the fire is handled in time, thereby reducing fire losses; by acquiring the status information of the fire-fighting equipment in real time, detecting whether there are abnormal conditions in the status information of the fire-fighting equipment, and generating corresponding voice alarm instructions according to the abnormal conditions, the equipment operation status can be monitored in real time, and equipment failures can be discovered in time, thereby avoiding the failure of the equipment to start normally during a fire, and improving the reliability of the fire-fighting system; 2. By using the embedded sensor network to monitor the water pressure information of the fire pump in real time, and combining the state detection algorithm to determine whether the fire pump has abnormal conditions such as leakage or blockage through water pressure fluctuations, it can monitor the working status of the pump in real time and discover potential faults in advance, thereby avoiding pump failure at critical moments; the operating current of the smoke exhaust fan is monitored in real time through the motor current sensor, and the current mode analysis technology is used to determine whether the equipment has abnormal fan conditions such as overload or no load in the operating status, which can effectively identify the abnormal operation of the fan, thereby ensuring the normal operation of the smoke exhaust system during a fire and ensuring the safety of on-site personnel; 3. By obtaining the numbered position information corresponding to the fire-fighting equipment where the water pump abnormality and / or the fan abnormality occurs, the position of the faulty equipment can be quickly located, thereby reducing the troubleshooting time and speeding up the maintenance response; by adopting an intelligent speech synthesis system based on natural language processing technology, corresponding voice alarm instructions are generated and broadcast according to the numbered position information, which can remind the on-duty personnel to handle the equipment abnormality in the most intuitive way, thereby ensuring that the problem is solved in a timely manner; by utilizing an event-driven voice reminder mechanism, when no processing feedback is received during the first broadcast, a second reminder is automatically made according to a preset time period until the abnormal situation is handled, which can prevent the failure from being handled in a timely manner due to negligence of personnel, thereby ensuring the normal operation of the fire-fighting equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 This is a flow chart of a cloud-on-duty intelligent fire alarm emergency handling method in one embodiment of the present application; Figure 2 It is a flowchart for implementing step S10 in the cloud-on-duty intelligent fire alarm emergency handling method in one embodiment of the present application; Figure 3 It is a flowchart for implementing step S20 in the cloud-on-duty intelligent fire alarm emergency handling method in one embodiment of the present application; Figure 4 It is a flowchart for implementing step S30 in the cloud-on-duty intelligent fire alarm emergency processing method in one embodiment of the present application; Figure 5 , Figure 6 It is a flowchart for implementing step S40 in the cloud-on-duty intelligent fire alarm emergency processing method in one embodiment of the present application; Figure 7 This is a principle block diagram of a cloud-based intelligent fire emergency response system in one embodiment of the present application; Figure 8 It is a schematic diagram of a device in an embodiment of the present application. DETAILED DESCRIPTION

[0023] The present application is further described in detail below in conjunction with the accompanying drawings.

[0024] In one embodiment, if Figure 1 As shown, the present application discloses a cloud-based intelligent fire emergency response method, which specifically includes the following steps: S10: Acquire images taken in the jurisdiction in real time, and use image recognition technology to analyze whether there are suspected fire spots in the images taken in the jurisdiction.

[0025] Specifically, images captured by the camera are preprocessed, including noise removal, grayscale and edge detection, to improve image clarity. For example, images taken at night or in bad weather can still retain sufficient detail information. After preprocessing, important features in the image, such as smoke and flames, are analyzed through a deep learning model. The pre-trained convolutional neural network can automatically extract the shape, color and dynamic changes of smoke and flames from the image, and further combine historical data analysis to determine whether there is a fire point. For example, when the smoke diffusion rate exceeds the preset value or the flame brightness reaches a specific threshold, the area is immediately identified as a suspected fire point and an alarm message is sent.

[0026] S20: When a suspected fire point is detected, obtain the specific location of the suspected fire point and the suspected point image of the surrounding environment, and upload the suspected point image to the cloud duty system.

[0027] Specifically, the three-dimensional environmental model of the suspected fire point is constructed through visual SLAM technology using the camera's location information and the preset image coordinate system, and the suspected fire point is accurately located in combination with the GPS coordinates. The spatial position of the fire point is extracted in the three-dimensional model, and multiple angle images of the surrounding environment are collected at the same time. These images are integrated through an image stitching algorithm to form a complete three-dimensional view, ensuring that the status of the suspected fire point can be accurately monitored from multiple directions. For example, if the fire point is in a complex area of ​​a building, the fire situation can be further confirmed by integrating images from different cameras. The processed images are uploaded to the cloud duty system after compression and optimization, which not only ensures image quality but also improves transmission efficiency.

[0028] S30: When the fire confirmation message is obtained from the cloud duty system, the emergency handling process of the area corresponding to the suspected fire point is started, and the fire-fighting equipment at the corresponding position of the suspected fire point is remotely controlled based on the multi-line disk according to the emergency handling process.

[0029] Specifically, after the staff at the cloud duty center checks and confirms the fire, they select the best fire-fighting equipment operation plan through the preset emergency handling rules. The emergency handling process is automatically adjusted according to the severity of the fire. In case of minor fire, only the water pump may be started for preliminary fire extinguishing. When the fire spreads, the smoke exhaust fan, fire shutter door and other fire-fighting equipment will be started at the same time. All operation instructions are converted into control signals through the multi-disk system and sent to each fire-fighting equipment. The multi-disk has redundant communication links to ensure that the equipment can respond in time in an emergency and avoid the failure of fire-fighting equipment to start correctly due to communication failure. For example, in a multi-story structure of a building, the fire-fighting equipment on the corresponding floor will be automatically triggered after the fire is confirmed, ensuring that all relevant equipment near the fire point responds at the same time, thereby improving the fire-fighting effect.

[0030] S40: Obtain the fire-fighting equipment status information in real time, detect whether there is any abnormality in the fire-fighting equipment status information, and generate a corresponding voice alarm instruction according to the abnormality.

[0031] Specifically, various embedded sensors are used to monitor the working status of fire-fighting equipment in real time, including the water pressure of the water pump and the current changes of the fan. The water pressure sensor can accurately detect the water pressure fluctuations of the water pump under high load and provide timely feedback on whether there is a leak or blockage. The current sensor of the fan determines whether the fan is overloaded or idling by monitoring the current changes of the motor. When an equipment abnormality is found, an abnormality report is immediately generated, and a voice alarm instruction is generated according to the specific number and location of the equipment. For example, if it is detected that the water pump pressure on a certain floor drops too fast, the voice alarm will notify the on-duty personnel to check. In addition, if the equipment is not repaired or responded to within the set time, the system will give a secondary voice reminder to ensure that the fault is handled in time.

[0032] In one embodiment, if Figure 2 As shown, in step S10, image recognition technology is used to analyze whether there are suspected fire spots in the images taken in the jurisdiction, which specifically includes: S11: Use the smoke detection algorithm to monitor the smoke conditions in the images taken in the jurisdiction in real time, and determine whether there are suspected fire points by identifying the smoke shape, smoke color and smoke diffusion speed.

[0033] Specifically, the color segmentation technology is used to divide the smoke in the image into regions and separate smoke of different shapes and colors. For example, light gray smoke may indicate an early fire, while thick black smoke means that the fire has spread. By analyzing the speed and direction of smoke diffusion, the severity of the fire point can be further determined. If the smoke spreads rapidly in a short period of time and is accompanied by irregular changes in shape, it is judged as a suspected fire point. In addition, the concentration and optical properties of the smoke will also be used as judgment criteria. For example, smoke with low transparency and irregular diffusion direction may indicate that the fire is intensifying.

[0034] S12: Analyze the flame conditions in the images taken in the jurisdiction through the flame detection algorithm, identify the flame shape, flame color and flame brightness characteristics of the flame conditions, and determine whether there are suspected fire points.

[0035] Specifically, by extracting features of the brightness, color and shape of the flame, the dynamic changes of the flame in the image are analyzed. For example, an orange-red flame is relatively stable and may be a normal light source, while a bright yellow and irregularly shaking flame may indicate a fire. When the flame brightness exceeds the set threshold or the flame shape changes rapidly, it is marked as a suspected fire point. The flame detection is combined with the changes in ambient light in the image for correction to ensure that the flame can be accurately identified even in strong light conditions. For example, as the flame extension range gradually expands, the changes in the flame shape can be monitored through continuous frames, and the fire condition can be determined in real time based on the dynamic characteristics of the flame.

[0036] In one embodiment, if Figure 3 As shown, in step S20, that is, obtaining the specific location of the suspected fire point and the suspected point image of the surrounding environment, specifically includes: S21: Using a positioning algorithm based on visual SLAM technology, a three-dimensional environmental model is constructed by taking images of the area corresponding to the suspected fire point, and the positioning coordinates of the suspected fire point are realized in combination with GPS data.

[0037] Specifically, through visual SLAM technology, feature points are extracted from continuously captured images, and these feature points are used to generate a three-dimensional spatial model of the fire point. This model can not only display the exact location of the fire point, but also dynamically track the changes of the fire point. The GPS positioning is combined with the three-dimensional model to ensure that the location of the fire point is accurately determined in real time over a large area. This is especially effective in complex areas of buildings. For example, in a multi-story structure of a building, the specific location of the fire point on the floor and in the room can be accurately located, which helps firefighters respond quickly.

[0038] S22: Determine, based on the positioning coordinates, the captured image of the area where the positioning coordinates can be obtained as the suspected point image.

[0039] Specifically, by calculating the spatial relationship between the positioning coordinates and the shooting range of the cameras in the jurisdiction, it is determined which cameras can cover the fire point, and the images taken by these cameras are automatically screened as suspected point images. The selection of images not only takes into account the position of the camera, but also combines the camera's viewing angle and distance factors to ensure that the surrounding environment of the fire point can be fully covered. For example, when one camera cannot capture clearly, the system will automatically retrieve images from multiple cameras and stitch them together to generate a more complete image of the fire point.

[0040] In one embodiment, if Figure 4 As shown, in step S30, when the fire confirmation message of the cloud duty system is obtained, the emergency treatment process of the area corresponding to the suspected fire point is started, which specifically includes: S31: Based on the preset emergency handling rules in the fire alarm scenario, select the emergency handling process for the corresponding area according to the severity of the fire.

[0041] Specifically, the emergency response process is divided into different levels according to the severity of the fire. When the fire is relatively minor, only basic fire-fighting equipment such as water pumps need to be started. When the fire spreads, a more complex combination of equipment will be started. The preset rules specify in detail the order and operation methods of the equipment to be started in each fire scenario. For example, in a large commercial building, when the fire is located on the middle floor, the system will first start the water pump and fire shutter door to ensure that the fire does not spread to other floors. When the fire deteriorates further, the smoke exhaust fan and emergency lighting equipment will also be started according to the rules to ensure the evacuation of personnel and on-site control.

[0042] S32: Real-time dispatch and prioritization through intelligent dispatch algorithms, parallel and / or sequential control of multiple firefighting equipment according to the urgency of the fire and the availability of resources.

[0043] Specifically, through the intelligent scheduling algorithm, real-time scheduling is carried out according to the urgency of the fire point and the availability of equipment resources. The algorithm first evaluates the severity of the fire, for example, determining the fire level through parameters such as flame spread speed and smoke concentration, and then sorts the fire-fighting equipment according to the preset priority rules. When resources are tight, equipment that has a direct impact on fire control, such as water sprinkler systems and smoke exhaust fans, will be activated first. When resources are sufficient, multiple devices can be controlled in parallel. For example, in large industrial parks, fire points may involve multiple areas at the same time. The scheduling algorithm will give priority to activating equipment close to the fire point and gradually expand it to fire-fighting equipment in distant areas to ensure that fire-fighting facilities in each area respond on demand and avoid waste of resources and response delays.

[0044] In one embodiment, if Figure 5 As shown, in step S40, the fire-fighting equipment status information is obtained in real time, and whether the fire-fighting equipment status information has any abnormality is detected, which specifically includes: S41: Use an embedded sensor network to monitor the water pressure information of the fire pump in real time, and combine the state detection algorithm to determine whether the fire pump has abnormal conditions such as leakage or blockage through water pressure fluctuations.

[0045] Specifically, embedded sensors are installed at key pipes of fire pumps and can collect water pressure data in real time. The water pressure sensor will record the water pressure value of the pump under different working conditions. When the water pressure suddenly drops or rises above the preset threshold, the system will determine that the pump may have problems such as leakage, blockage or pipe rupture. The state detection algorithm further analyzes the fluctuation pattern of water pressure and confirms the specific type of problem. For example, if the water pressure drops and the flow rate decreases at the same time, the system will infer that there may be a pipe blockage and generate a corresponding fault report, while triggering an alarm to prompt maintenance personnel to perform maintenance. This water pressure monitoring method can detect potential problems with the pump at an early stage and avoid the pump from failing to work properly when a fire occurs.

[0046] S42: The operating current of the smoke exhaust fan is monitored in real time through the motor current sensor, and the current mode analysis technology is used to determine whether there are any abnormal fan conditions such as overload or no load in the equipment operation status.

[0047] Specifically, the current sensor is installed on the motor of the smoke exhaust fan to detect the current conditions of the motor when it is running in real time. The current pattern analysis technology is used to identify the fluctuation trend of the motor current. By monitoring the changes in the current, it can be determined whether the fan is in an overloaded, no-loaded or short-circuited state. For example, during normal operation, the current should be kept within a stable range. When the current suddenly rises or decreases, the system will immediately trigger an alarm to indicate that the fan may be faulty. If the current drops to near zero, the fan is judged to be idling. It may be because the fan blades are stuck by foreign objects or the motor rotor has failed. After detecting these anomalies, a detailed fault type report is generated and the on-site personnel are notified through the voice system to quickly troubleshoot the problem.

[0048] In one embodiment, if Figure 6 As shown, in step S40, a corresponding voice alarm instruction is generated according to the abnormal situation, specifically including: S43: Obtain the number and location information corresponding to the fire-fighting equipment where the water pump and / or fan abnormality occurs.

[0049] Specifically, when an abnormality is detected in equipment such as a water pump or fan, the system automatically retrieves the number and geographic location information of the faulty equipment. The number and location of the fire-fighting equipment are associated through a pre-set database, so the location of the specific faulty equipment can be quickly located. For example, in a large building, if a smoke exhaust fan on a certain floor fails, the system will retrieve the specific installation location of the fan by number, including the floor, room number, etc., and store this information in the fault report. In addition, the location information can be directly displayed on the system operation panel through the map interface, which is convenient for maintenance personnel to quickly find the problem equipment on site.

[0050] S44: An intelligent speech synthesis system based on natural language processing technology is used to generate and broadcast corresponding voice alarm instructions according to the number location information.

[0051] Specifically, based on natural language processing technology, the generated fault report content is automatically converted into voice alarms. The speech synthesis system will generate clear alarm information based on the equipment number and location information, and broadcast it through the on-site broadcasting system. For example, if a water pump on a certain floor fails, the speech synthesis system will broadcast "Warning, abnormal pressure in the water pump system on the second floor, please check immediately", and repeat key information to ensure that the on-duty personnel can respond quickly. In addition, the content of the voice alarm can be dynamically adjusted according to the severity of the fault. In case of emergency, the broadcast frequency will be increased and the prompt sound will be increased.

[0052] S45: Using the event-driven voice reminder mechanism, when no processing feedback is received after the first broadcast, a second reminder is automatically made according to a preset time period until the abnormal situation is processed.

[0053] Specifically, the voice reminder mechanism adopts an event-driven mode. When an abnormal device is detected, the system will automatically trigger a voice alarm. If no processing feedback is received within the set time, the system will issue a second reminder according to the preset time period. For example, if no processing confirmation is received within 5 minutes after the first voice alarm, the system will broadcast "The water pump system on the second floor is abnormal, please check as soon as possible" and repeat the reminder every few minutes until the abnormal situation is resolved or the manual feedback processing is completed. This mechanism ensures that any equipment abnormality can be paid attention to in time to prevent the spread of fire due to negligence and delay in equipment maintenance.

[0054] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0055] In one embodiment, a cloud-based intelligent fire emergency response system is provided, which corresponds one-to-one to the cloud-based intelligent fire emergency response method in the above embodiment. Figure 7 As shown, the cloud duty intelligent fire alarm emergency processing system includes an image acquisition module, an image upload module, an emergency processing module, and an equipment monitoring module. The detailed description of each functional module is as follows: The image acquisition module is used to acquire images taken in the jurisdiction in real time and use image recognition technology to analyze whether there are suspected fire spots in the images taken in the jurisdiction; An image uploading module is used to obtain the specific location of the suspected fire point and the suspected point image of the surrounding environment when a suspected fire point is detected, and upload the suspected point image to the cloud duty system; The emergency handling module is used to start the emergency handling process of the area corresponding to the suspected fire point when the fire confirmation message from the cloud duty system is obtained, and remotely control the fire-fighting equipment at the corresponding position of the suspected fire point based on the multi-line disk according to the emergency handling process; The equipment monitoring module is used to obtain the fire equipment status information in real time, detect whether there are any abnormal conditions in the fire equipment status information, and generate corresponding voice alarm instructions according to the abnormal conditions.

[0056] Optionally, the image acquisition module specifically includes: The smoke judgment submodule is used to use the dense smoke detection algorithm to monitor the smoke conditions in the images taken in the jurisdiction in real time, and to determine whether there are suspected fire points by identifying the smoke shape, smoke color and smoke diffusion speed of the smoke situation; The flame recognition submodule is used to analyze the flame conditions in the images taken in the jurisdiction through the flame detection algorithm, identify the flame shape, flame color and flame brightness characteristics of the flame conditions, and determine whether there are suspected fire points.

[0057] Optionally, the image upload module specifically includes: The 3D modeling submodule is used to use the positioning algorithm based on visual SLAM technology to build a 3D environmental model by taking images of the area corresponding to the suspected fire point, and realize the positioning coordinates of the suspected fire point in combination with GPS data; The image screening submodule is used to reversely determine the images taken in the jurisdiction where the positioning coordinates can be obtained as suspected point images based on the positioning coordinates.

[0058] Optionally, the emergency handling module specifically includes: The dispatch control submodule is used to select the emergency handling process of the corresponding area according to the severity of the fire based on the preset emergency handling rules under the fire alarm situation; The equipment scheduling submodule is used for real-time scheduling and prioritization through intelligent scheduling algorithms, and parallel and / or sequential control of multiple fire-fighting equipment according to the urgency of the fire and the availability of resources.

[0059] Optionally, the equipment monitoring module specifically includes: The water pressure monitoring submodule is used to monitor the water pressure information of the fire pump in real time using an embedded sensor network, and to determine whether the fire pump has abnormal conditions such as leakage or blockage through water pressure fluctuations in combination with a state detection algorithm; The current monitoring submodule is used to monitor the operating current of the smoke exhaust fan in real time through the motor current sensor, and use the current mode analysis technology to determine whether there are any abnormal fan conditions such as overload or no load in the equipment operation status.

[0060] The equipment positioning submodule is used to obtain the number and location information corresponding to the fire-fighting equipment with abnormal water pump and / or abnormal fan conditions; The voice alarm submodule is used to generate and broadcast corresponding voice alarm instructions according to the number position information by using an intelligent speech synthesis system based on natural language processing technology; The reminder mechanism submodule is used to utilize the event-driven voice reminder mechanism. When no processing feedback is received after the first broadcast, a second reminder will be automatically made according to a preset time period until the abnormal situation is processed.

[0061] For the specific definition of the cloud-based intelligent fire alarm emergency response system, please refer to the definition of the cloud-based intelligent fire alarm emergency response method above, which will not be repeated here. Each module in the above-mentioned cloud-based intelligent fire alarm emergency response system can be implemented in whole or in part through software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0062] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 8 As shown. The computer device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a cloud-based intelligent fire alarm emergency processing method is implemented.

[0063] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program: Acquire images taken in the jurisdiction in real time, and use image recognition technology to analyze whether there are suspected fire spots in the images taken in the jurisdiction; When a suspected fire point is detected, obtain the specific location of the suspected fire point and the suspected point image of the surrounding environment, and upload the suspected point image to the cloud duty system; When the fire confirmation message is obtained from the cloud duty system, the emergency response process of the area corresponding to the suspected fire point is initiated, and the fire-fighting equipment corresponding to the suspected fire point is remotely controlled based on the multi-line disk according to the emergency response process; Obtain fire equipment status information in real time, detect whether there are any abnormalities in the fire equipment status information, and generate corresponding voice alarm instructions based on the abnormalities.

[0064] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented: Acquire images taken in the jurisdiction in real time, and use image recognition technology to analyze whether there are suspected fire spots in the images taken in the jurisdiction; When a suspected fire point is detected, obtain the specific location of the suspected fire point and the suspected point image of the surrounding environment, and upload the suspected point image to the cloud duty system; When the fire confirmation message is obtained from the cloud duty system, the emergency response process of the area corresponding to the suspected fire point is initiated, and the fire-fighting equipment corresponding to the suspected fire point is remotely controlled based on the multi-line disk according to the emergency response process; Obtain fire equipment status information in real time, detect whether there are any abnormalities in the fire equipment status information, and generate corresponding voice alarm instructions based on the abnormalities.

[0065] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0066] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above.

[0067] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A cloud-based intelligent fire emergency response method, characterized in that: The cloud-based intelligent fire alarm emergency handling method comprises: Acquire images taken in the jurisdiction in real time, and use image recognition technology to analyze whether there are suspected fire spots in the images taken in the jurisdiction; When the suspected fire point is detected, the specific location of the suspected fire point and the suspected point image of the surrounding environment are obtained, and the suspected point image is uploaded to the cloud duty system; When a fire confirmation message is obtained from the cloud duty system, the emergency treatment process of the area corresponding to the suspected fire point is started, and the fire-fighting equipment at the corresponding position of the suspected fire point is remotely controlled based on the multi-line disk according to the emergency treatment process; The fire-fighting equipment status information is obtained in real time, and the fire-fighting equipment status information is detected to see if there is any abnormality, and a corresponding voice alarm instruction is generated according to the abnormality.

2. The cloud-based intelligent fire alarm emergency handling method according to claim 1 is characterized in that: The use of image recognition technology to analyze whether there are suspected fire spots in the images taken in the jurisdiction specifically includes: A dense smoke detection algorithm is used to monitor the smoke condition in the captured image of the area in real time, and by identifying the smoke form, smoke color and smoke diffusion speed of the smoke condition, it is determined whether there is a suspected fire point; The flame conditions in the captured images of the area are analyzed by a flame detection algorithm, the flame shape, flame color and flame brightness characteristics of the flame conditions are identified, and it is determined whether there is a suspected fire point.

3. The cloud-based intelligent fire emergency response method according to claim 1 is characterized in that: The obtaining of the specific location of the suspected fire point and the suspected point image of the surrounding environment specifically includes: Using a positioning algorithm based on visual SLAM technology, a three-dimensional environmental model is constructed by taking images of the area corresponding to the suspected fire point, and the positioning coordinates of the suspected fire point are realized in combination with GPS data; According to the positioning coordinates, a captured image of the area in which the positioning coordinates can be obtained is reversely determined as the suspected point image.

4. The cloud-based intelligent fire emergency response method according to claim 1 is characterized in that: When the fire confirmation message of the cloud duty system is obtained, the emergency handling process of the area corresponding to the suspected fire point is started, which specifically includes: Based on the preset emergency handling rules under the fire alarm situation, select the emergency handling process for the corresponding area according to the severity of the fire; Real-time dispatch and prioritization through intelligent dispatch algorithms, parallel and / or sequential control of multiple firefighting equipment based on the urgency of the fire and the availability of resources.

5. The cloud-based intelligent fire emergency response method according to claim 1 is characterized in that: The real-time acquisition of fire-fighting equipment status information and the detection of whether the fire-fighting equipment status information is abnormal specifically include: Use an embedded sensor network to monitor the water pressure information of the fire pump in real time, and combine the state detection algorithm to determine whether the fire pump has abnormal conditions such as leakage or blockage through water pressure fluctuations; The operating current of the smoke exhaust fan is monitored in real time through the motor current sensor, and the current mode analysis technology is used to determine whether there are any abnormal fan conditions such as overload or no-load in the equipment operation status.

6. The cloud-based intelligent fire emergency response method according to claim 5 is characterized in that: Generating a corresponding voice alarm instruction according to the abnormal situation specifically includes: Obtaining the serial number and location information corresponding to the fire-fighting equipment where the water pump abnormality and / or the fan abnormality occurs; An intelligent speech synthesis system based on natural language processing technology is used to generate and broadcast corresponding voice alarm instructions according to the number position information; By utilizing an event-driven voice reminder mechanism, when no processing feedback is received after the first broadcast, a second reminder will be automatically made according to a preset time period until the abnormal situation is processed.

7. A cloud-based intelligent fire emergency response system, characterized in that: The cloud duty intelligent fire alarm emergency processing system includes: An image acquisition module is used to acquire images taken in the jurisdiction in real time, and use image recognition technology to analyze whether there are suspected fire spots in the images taken in the jurisdiction; An image uploading module is used to obtain the specific location of the suspected fire point and the suspected point image of the surrounding environment when the suspected fire point is detected, and upload the suspected point image to the cloud duty system; An emergency processing module is used to start the emergency processing process of the area corresponding to the suspected fire point when the fire confirmation message of the cloud duty system is obtained, and remotely control the fire-fighting equipment at the corresponding position of the suspected fire point based on the multi-line disk according to the emergency processing process; The equipment monitoring module is used to obtain the fire equipment status information in real time, detect whether there is any abnormality in the fire equipment status information, and generate corresponding voice alarm instructions according to the abnormality.

8. The cloud-based intelligent fire emergency response system according to claim 7 is characterized in that: The image acquisition module specifically includes: The smoke judgment submodule is used to use a dense smoke detection algorithm to monitor the smoke condition in the image captured in the jurisdiction in real time, and to determine whether there is a suspected fire point by identifying the smoke shape, smoke color and smoke diffusion speed of the smoke condition; The flame recognition submodule is used to analyze the flame conditions in the image captured in the jurisdiction through a flame detection algorithm, identify the flame shape, flame color and flame brightness characteristics of the flame conditions, and determine whether the suspected fire point exists.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the cloud-based intelligent fire alarm emergency handling method as described in any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by the processor, the steps of the cloud-based intelligent fire alarm emergency handling method as described in any one of claims 1 to 6 are implemented.

Images