Fire-fighting equipment remote monitoring system integrated with Internet of Things technology

Abstract

The invention belongs to the technical field of fire-fighting equipment monitoring, and provides a fire-fighting equipment remote monitoring system integrated with the Internet of Things technology, which comprises an Internet of Things data acquisition module, a data transmission module, a remote monitoring center, an intelligent analysis module and a control execution module, the Internet of Things data acquisition module, the data transmission module, the remote monitoring center, the intelligent analysis module and the control execution module are electrically connected in sequence; according to the invention, the working state data and environmental data of the fire-fighting equipment are accurately acquired in real time through the Internet of Things data acquisition module, and the accuracy and reliability of the data are improved by using a Kalman filtering algorithm; the data transmission module adopts a wireless communication technology to realize real-time transmission of data, so that the data transmission delay is reduced; the remote monitoring center receives and processes the data and predicts a future trend by using a time sequence prediction algorithm, and the intelligent analysis module adopts a machine learning algorithm to deeply mine and analyze the data so as to accurately identify fire hazards.

Description

Technical Field

[0001] The present invention belongs to the technical field of fire equipment monitoring, and specifically relates to a remote monitoring system for fire equipment integrating Internet of Things technology. Background Art

[0002] In the field of fire equipment monitoring, most current monitoring systems rely on manual inspections and on-site monitoring. This method is not only inefficient but also difficult to detect and respond to fire hazards in a timely manner. With the rapid development of Internet of Things technology, more and more fire equipment has started to introduce Internet of Things technology to achieve remote monitoring.

[0003] However, there are still some deficiencies in existing Internet of Things fire equipment monitoring systems; for example, the data collection of some systems is inaccurate and the data transmission delay is relatively high, resulting in the monitoring center being unable to obtain the accurate status of the equipment in a timely manner, thus affecting the speed of fire warning and emergency response; in addition, existing systems also have limitations in data processing and analysis and are unable to deeply mine and analyze massive data, thus restricting the accuracy and timeliness of fire hazard identification.

[0004] Therefore, those skilled in the art have proposed a remote monitoring system for fire equipment integrating Internet of Things technology to solve the problems raised in the background art. Summary of the Invention

[0005] In order to solve the above technical problems, the present invention provides a remote monitoring system for fire equipment integrating Internet of Things technology to solve the problems existing in the prior art.

[0006] A remote monitoring system for fire equipment integrating Internet of Things technology includes:

[0007] An Internet of Things data collection module for collecting the working status data and environmental data of fire equipment;

[0008] A data transmission module for transmitting the data collected by the Internet of Things data collection module to a remote monitoring center;

[0009] A remote monitoring center for receiving and processing the data and sending out a warning message or a control instruction according to the processing result;

[0010] An intelligent analysis module integrated in the remote monitoring center for intelligently analyzing the received data to identify fire hazards or abnormal situations;

[0011] A control execution module connected to the fire equipment for performing corresponding fire operations according to the control instruction of the remote monitoring center.

[0012] Preferably, the Internet of Things data acquisition module includes various types of sensors for collecting the working state data of different types of fire-fighting equipment and environmental data. For the data collected from different sensors, the Kalman filtering algorithm is introduced to improve the accuracy and reliability of the data.

[0013] Preferably, the data transmission module uses wireless communication technology to achieve real-time data transmission.

[0014] Preferably, in the remote monitoring center, for the processing of data, the time series prediction algorithm is used to predict the future working state of fire-fighting equipment and environmental data trends, so as to issue early warning information or adjust control instructions in advance.

[0015] Preferably, the remote monitoring center further includes a data storage module for storing historical data and warning records.

[0016] Preferably, the intelligent analysis module uses machine learning algorithms to perform intelligent analysis on the received data to improve the accuracy of fire hazard identification.

[0017] Preferably, the control execution module further includes a feedback mechanism for feeding back the execution result to the remote monitoring center to verify the execution effect of the control instruction. The feedback mechanism includes a feedback control algorithm.

[0018] A processor configured to execute a remote monitoring system for fire-fighting equipment integrating the Internet of Things technology as described above.

[0019] A computer-readable storage medium having a computer program stored thereon, where the computer program, when executed by a processor, implements the remote monitoring system for fire-fighting equipment integrating the Internet of Things technology as described above.

[0020] Compared with the prior art, the present invention has the following beneficial effects:

[0021] 1. By introducing the Internet of Things data acquisition module, the present invention can collect the working state data of fire-fighting equipment and environmental data in real time and accurately, providing a reliable data source for remote monitoring. At the same time, the Kalman filtering algorithm is used to process the collected data, further improving the accuracy and reliability of the data, and providing strong data support for subsequent fire warning and emergency response.

[0022] 2. The present invention realizes real-time data transmission through the data transmission module, effectively reducing the data transmission delay, enabling the monitoring center to timely obtain the accurate state of the equipment, and thus improving the speed of fire warning and emergency response. In addition, the data transmission module uses wireless communication technology, avoiding the complexity of wired transmission methods in wiring, maintenance, etc., and improving the flexibility and scalability of the system.

[0023] 3. The present invention introduces an intelligent analysis module in the remote monitoring center, which uses machine learning algorithms to deeply mine and analyze the received data, can accurately identify fire hazards or abnormal situations, and improves the accuracy and timeliness of fire early warning; at the same time, the intelligent analysis module can also learn from historical data and continuously optimize the algorithm model for identifying fire hazards, further enhancing the intelligence level of the system.

[0024] 4. The present invention realizes the execution of remote control instructions through the control execution module, can operate the fire-fighting equipment in a timely manner according to the instructions issued by the monitoring center, so as to effectively respond to emergencies such as fires; in addition, the control execution module also includes a feedback mechanism, which can feedback the execution result to the remote monitoring center to verify the execution effect of the control instruction, further improving the reliability and stability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 is a framework diagram of a fire-fighting equipment remote monitoring system integrating Internet of Things technology according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0026] The following further describes in detail the embodiments of the present invention in conjunction with the drawings and embodiments. The following embodiments are used to illustrate the present invention, but cannot be used to limit the scope of the present invention.

[0027] Embodiment: The present invention provides a fire-fighting equipment remote monitoring system integrating Internet of Things technology, as Figure 1 shown, including an Internet of Things data acquisition module, a data transmission module, a remote monitoring center, an intelligent analysis module and a control execution module, and the Internet of Things data acquisition module, the data transmission module, the remote monitoring center, the intelligent analysis module and the control execution module are electrically connected in sequence:

[0028] The Internet of Things data acquisition module is used to acquire the working state data and environmental data of the fire-fighting equipment;

[0029] The data transmission module is used to transmit the data acquired by the Internet of Things data acquisition module to the remote monitoring center;

[0030] The remote monitoring center is used to receive and process the data, and issue a warning message or a control instruction according to the processing result;

[0031] The intelligent analysis module is integrated in the remote monitoring center and is used to perform intelligent analysis on the received data to identify fire hazards or abnormal situations;

[0032] The control execution module is connected to the fire-fighting equipment and is used to perform corresponding fire-fighting operations according to the control instructions of the remote monitoring center.

[0033] As can be seen from the above, the system collects the working state data and environmental data of fire-fighting equipment in real time and accurately through the Internet of Things data acquisition module, and uses the Kalman filtering algorithm to improve the accuracy and reliability of the data. The data transmission module uses wireless communication technology to achieve real-time data transmission, reducing the data transmission delay. The remote monitoring center receives and processes the data, uses the time series prediction algorithm to predict future trends, and the intelligent analysis module uses machine learning algorithms to deeply mine and analyze the data to accurately identify potential fire hazards. The control execution module performs fire-fighting operations according to instructions and includes a feedback mechanism to verify the execution effect. It effectively improves the speed and accuracy of fire warning and emergency response, and enhances the reliability and stability of the system.

[0034] Furthermore, the Internet of Things data acquisition module includes various types of sensors for collecting the working state data and environmental data of different types of fire-fighting equipment. For the data collected from different sensors, the Kalman filtering algorithm is introduced to improve the accuracy and reliability of the data. The formula of the Kalman filtering algorithm includes:

[0035]

[0036] where is the state estimate, A is the state transition matrix, B is the control matrix, u k is the control vector, K k is the Kalman gain, z k is the observation value, and H is the observation matrix.

[0037] As can be seen from the above, by introducing various types of sensors, the working state data and environmental data of different types of fire-fighting equipment can be comprehensively collected. At the same time, for the data from different sensors, the Kalman filtering algorithm is used for processing, effectively improving the accuracy and reliability of the data. It not only provides strong data support for subsequent data analysis and warning, but also enhances the stability and credibility of the entire monitoring system, providing a more accurate and reliable information basis for fire warning and emergency response.

[0038] Furthermore, the data transmission module uses wireless communication technology to achieve real-time data transmission. The wireless communication technology converts the fire-fighting equipment data into electromagnetic wave signals and uses transmission media such as air to transmit the signals from the sending end to the receiving end. The receiving end then restores the electromagnetic wave signals to the original data, thereby realizing remote wireless transmission of the data.

[0039] As can be seen from the above, by efficiently converting fire-fighting equipment data into electromagnetic wave signals and using transmission media such as air for remote wireless transmission, real-time data transmission is achieved, greatly reducing the latency of data transmission. This wireless transmission method not only improves the flexibility and scalability of the system, avoiding the complexity of wired transmission methods in aspects such as wiring and maintenance, but also ensures that the monitoring center can promptly obtain the accurate status of the equipment, thus effectively enhancing the speed of fire warning and emergency response, and strengthening the practicality and efficiency of the entire remote monitoring system for fire-fighting equipment.

[0040] Furthermore, in the remote monitoring center, for data processing, a time series prediction algorithm is used to predict the future working status of fire-fighting equipment and environmental data trends, so as to issue early warning information or adjust control instructions in advance. The formula of the time series prediction algorithm includes:

[0041] φ(B)(Y t -μ)=θ(B)ε t ;

[0042] where φ(B) and θ(B) are lag operator polynomials, Y t is the time series data, μ is the mean value, and ε t is white noise.

[0043] As can be seen from the above, in the remote monitoring center, using the time series prediction algorithm to process the received data can accurately predict the future working status of fire-fighting equipment and environmental data trends. This not only enables the system to issue early warning information or adjust control instructions in advance, thus effectively preventing the occurrence of fire accidents, but also improves the intelligent level and response speed of the entire monitoring system. Through the application of the time series prediction algorithm, the remote monitoring center can formulate more scientific and reasonable fire prevention and emergency response strategies, providing more powerful guarantees for the safe operation of fire-fighting equipment.

[0044] Furthermore, the remote monitoring center also includes a data storage module for storing historical data and warning records.

[0045] As can be seen from the above, the data storage module included in the remote monitoring center can comprehensively store historical data and warning records. This not only helps the staff to consult and analyze past data at any time, understand the operation status of fire-fighting equipment and the historical situation of fire warnings, but also provides valuable data support for the subsequent optimization and improvement of the system. At the same time, the data storage module also enhances the traceability and auditability of the system, ensuring that the cause of the problem can be quickly located in case of a fire accident, providing a strong basis for the investigation and handling of the accident.

[0046] Furthermore, the intelligent analysis module uses machine learning algorithms to perform intelligent analysis on the received data, improving the accuracy of fire hazard identification. The formula of the machine learning algorithm includes:

[0047]

[0048] where y i is the true label, p i is the predicted probability, and N is the number of samples.

[0049] As can be seen from the above, by using machine learning algorithms, it is possible to perform in-depth learning and intelligent analysis on the received data, improving the accuracy of fire hazard identification. Through the application of machine learning algorithms, the intelligent analysis module can continuously learn from historical data and optimize the algorithm model for fire hazard identification, thereby more accurately identifying potential fire risks. This not only enhances the early warning ability of the system, reduces the probability of false alarms and missed alarms, but also provides a scientific basis for the timely maintenance and servicing of fire-fighting equipment, further improving the safety and reliability of the entire remote monitoring system for fire-fighting equipment.

[0050] Furthermore, the control execution module also includes a feedback mechanism for feeding back the execution result to the remote monitoring center to verify the execution effect of the control instruction. The feedback mechanism includes a feedback control algorithm, and the formula of the feedback control algorithm includes:

[0051]

[0052] where u(t) is the control output, e(t) is the error signal, and K p , K i and K d are the proportional, integral, and derivative gains respectively.

[0053] As can be seen from the above, the feedback mechanism included in the control execution module can feed back the execution result to the remote monitoring center in real time to verify the execution effect of the control instruction. By introducing the feedback control algorithm, precise control and adjustment of the operation process of fire-fighting equipment are achieved. The application of the feedback mechanism not only ensures that the control instruction can be executed accurately without error, but also improves the response speed and stability of the system. By continuously feeding back and adjusting according to the execution result, the control execution module can more accurately control the operating state of fire-fighting equipment, thereby effectively coping with various fire emergencies and further enhancing the reliability and practicality of the entire remote monitoring system for fire-fighting equipment.

[0054] Furthermore, the fire-fighting equipment remote monitoring system integrating Internet of Things technology in the embodiment is compared with the existing Internet of Things fire-fighting equipment monitoring system (comparative example) in terms of effects, and the following table is obtained:

[0055]

[0056]

[0057] As can be seen from the above table, a fire equipment remote monitoring system integrating Internet of Things technology in this embodiment is superior to the existing Internet of Things fire equipment monitoring systems in terms of data acquisition accuracy, data transmission delay, data processing and analysis capabilities, warning and response speed, system flexibility and scalability, control instruction execution effect, data storage and retrieval, and system intelligence level.

[0058] Working principle: The working state data and environmental data of fire equipment are collected through the Internet of Things data acquisition module, and the collected data is transmitted to the remote monitoring center in real time by using the data transmission module. The remote monitoring center receives and processes these data, uses the time series prediction algorithm to predict future trends, and at the same time the intelligent analysis module uses machine learning algorithms to deeply analyze the data to identify fire hazards. According to the analysis results, the remote monitoring center issues warning messages or control instructions, and the control execution module performs corresponding fire operations according to the instructions, and feeds back the execution results to the remote monitoring center through the feedback mechanism to verify the execution effect. The entire system realizes the remote monitoring and intelligent management of fire equipment.

[0059] The embodiment of the present application provides an electronic device, which is applicable to the above-mentioned fire equipment remote monitoring system integrating Internet of Things technology, including:

[0060] A memory, used to store computer programs and data;

[0061] A processor, used to run the system program.

[0062] The embodiment of the present application provides a computer storage medium, which is applicable to the above-mentioned fire equipment remote monitoring system integrating Internet of Things technology, and performs hierarchical confidentiality management on the above system and data according to the confidentiality management requirements.

[0063] Those skilled in the art should understand that the embodiments of the present application can be provided as a system or a computer program product. Therefore, the present application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes.

[0064] This application is described with reference to the flowcharts and / or block diagrams of devices (systems) and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, as well as the combination of flows and / or blocks in the flowchart and / or block diagram. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in the Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.

[0065] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in the Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.

[0066] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in the Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.

[0067] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and a memory.

[0068] The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM), and / or non-volatile memory, such as read-only memory (ROM) or flash memory (flash RAM). The memory is an example of computer-readable media.

[0069] Computer readable media include permanent and non-permanent, removable and non-removable media, and can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include transitory media such as modulated data signals and carrier waves.

[0070] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, commodity or device including the elements.

[0071] The embodiments of the present invention are provided for the purpose of illustration and description. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations of the present invention. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A remote monitoring system for fire fighting equipment integrating Internet of Things technology, characterized in that: include: IoT data collection module, used to collect working status data and environmental data of fire-fighting equipment; A data transmission module, used to transmit the data collected by the Internet of Things data collection module to a remote monitoring center; A remote monitoring center is used to receive and process the data and issue warning information or control instructions according to the processing results; Intelligent analysis module, integrated into the remote monitoring center, is used to intelligently analyze the received data and identify fire hazards or abnormal conditions; The control execution module is connected to the fire-fighting equipment and is used to execute corresponding fire-fighting operations according to the control instructions of the remote monitoring center.

2. A remote monitoring system for fire fighting equipment integrating Internet of Things technology as claimed in claim 1, characterized in that: The Internet of Things data acquisition module includes multiple types of sensors for collecting working status data and environmental data of different types of fire-fighting equipment, and introduces a Kalman filter algorithm for collecting data from different sensors.

3. A remote monitoring system for fire fighting equipment integrating Internet of Things technology as claimed in claim 1, characterized in that: The data transmission module adopts wireless communication technology.

4. A remote monitoring system for fire fighting equipment integrating Internet of Things technology as claimed in claim 1, characterized in that: In the remote monitoring center, time series prediction algorithms are used for data processing to predict future fire equipment working status and environmental data trends.

5. A remote monitoring system for fire fighting equipment integrating Internet of Things technology as claimed in claim 1, characterized in that: The remote monitoring center also includes a data storage module for storing historical data and warning records.

6. A remote monitoring system for fire fighting equipment integrating Internet of Things technology as claimed in claim 1, characterized in that: The intelligent analysis module uses a machine learning algorithm to perform intelligent analysis on the received data.

7. A remote monitoring system for fire fighting equipment integrating Internet of Things technology as claimed in claim 1, characterized in that: The control execution module also includes a feedback mechanism for feeding back the execution result to the remote monitoring center, and the feedback mechanism includes a feedback control algorithm.

8. A processor, characterized in that: A fire-fighting equipment remote monitoring system configured to execute an integrated Internet of Things technology according to any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, a remote monitoring system for fire-fighting equipment integrating Internet of Things technology as described in any one of claims 1 to 7 is implemented.

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