Real-time monitoring method and system for operating status of skid-mounted fire station

Abstract

The present invention relates to the technical field of fire operation management, and in particular to a real-time monitoring method and system for the operation status of a skid-mounted fire station, which can improve the efficiency and reliability of the skid-mounted fire station in emergency response and management, while reducing the losses caused by fire; the method comprises: real-time acquisition of a fire facility status parameter set and a fire facility image data set; mapping in a pre-built first emergency response time database according to the fire facility status parameter set to obtain a first emergency response time; feature extraction of the fire facility image data set to obtain a fire facility image feature set; mapping in a pre-built second emergency response time database according to the fire facility image feature set to obtain a second emergency response time; comparing the first emergency response time with the second emergency response time, and taking the largest time as the predicted emergency response time of the skid-mounted fire station.

Description

Technical Field

[0001] The present invention relates to the technical field of fire operation management, and in particular to a method and system for real-time monitoring of the operation status of a skid-mounted fire station. Background Art

[0002] With the acceleration of urbanization and the in-depth development of industrialization, fire safety issues have become increasingly prominent, especially in densely populated areas, chemical parks, large storage bases and other places. Efficient emergency response has become the key to reducing fire losses. As a new type of firefighting facility, the skid-mounted fire station modularly installs key equipment such as fire pumps, fire extinguishing agent storage tanks, and fire trucks on one or more transportation platforms for easy movement and rapid deployment. This design not only shortens the time it takes for firefighting equipment to arrive at the scene, but also realizes real-time monitoring and remote management of the status of firefighting facilities through an integrated automated control system, thereby ensuring a rapid response in an emergency and reducing losses caused by fire.

[0003] However, to achieve efficient operation and rapid response of skid-mounted fire stations, it is necessary to overcome the limitations of traditional fire monitoring systems, especially real-time perception and intelligent analysis of the status of fire facilities. Traditional fire monitoring methods often rely on passive alarms from equipment, which has problems such as delayed response and incomplete information, making it difficult to accurately monitor the emergency response time of skid-mounted fire stations. Summary of the invention

[0004] To solve the above technical problems, the present invention provides a method and system for real-time monitoring of the operating status of a skid-mounted fire station, which can improve the efficiency and reliability of the skid-mounted fire station in emergency response and management, while reducing the losses caused by fire.

[0005] In a first aspect, the present invention provides a method for real-time monitoring of the operating status of a skid-mounted fire station, the method comprising:

[0006] Real-time collection of fire-fighting facility status parameter sets and fire-fighting facility image data sets;

[0007] According to the fire-fighting facility status parameter set, mapping is performed in a pre-built first emergency response duration database to obtain the first emergency response duration;

[0008] Extracting features from a fire-fighting facility image data set to obtain a fire-fighting facility image feature set;

[0009] According to the fire-fighting facility image feature set, mapping is performed in a pre-built second emergency response time database to obtain the second emergency response time;

[0010] Compare the first emergency response time and the second emergency response time, and take the longest time as the predicted emergency response time of the skid-mounted fire station;

[0011] The predicted emergency response time is compared with the preset response time threshold. If the predicted emergency response time does not exceed the preset response time threshold, no action is taken; if the predicted emergency response time exceeds the preset response time threshold, an abnormal alarm is issued at the fire station.

[0012] Furthermore, the firefighting facility status parameter set includes a firefighting pump status, a fire extinguisher tank storage status, and a firefighting vehicle status; and the firefighting facility image data set includes firefighting pump image data, fire extinguisher tank image data, and firefighting vehicle image data.

[0013] Furthermore, the method for constructing the first emergency response duration database includes:

[0014] Collect historical data on firefighting facility status parameters, including fire pump operating status, fire extinguisher tank storage status, and fire truck standby status;

[0015] Clean the collected data, remove invalid outliers, and standardize the state parameters;

[0016] Analyze the relationship between the state parameters and the actual emergency response time to obtain a state parameter set;

[0017] Design the database architecture, including status parameter identifiers, parameter values, and corresponding emergency response duration fields;

[0018] Each state parameter set obtained through analysis and its corresponding predicted emergency response time are recorded in the database to form a one-to-one mapping relationship;

[0019] Update the database regularly, add the corresponding relationship between new status parameter sets and response time, and keep the database up to date.

[0020] Furthermore, the method for extracting features from the firefighting facility image data set includes:

[0021] Use filters to remove noise from images;

[0022] Convert color images to grayscale images;

[0023] Rotate, scale and translate images;

[0024] Using edge detection algorithms to detect edge information in the image, the edge information corresponds to the shape and outline of the firefighting facilities;

[0025] Locate the fire-fighting facilities in the image based on their shape characteristics;

[0026] Segment the image into different regions to analyze different components of the firefighting facility;

[0027] Apply shape feature extraction algorithm to describe the shape of firefighting equipment components;

[0028] Analyze the texture information on the surface of fire-fighting facilities through texture feature extraction method;

[0029] The extracted features are converted into feature vectors in numerical form. Each fire-fighting facility image is represented as a feature vector. The feature vectors of each fire-fighting facility are fused to obtain a fire-fighting facility image feature set.

[0030] Furthermore, the method for constructing the second emergency response duration database includes:

[0031] Collect image data of firefighting facilities, including images of fire pumps, fire extinguisher tanks, and fire trucks in different states;

[0032] Annotate the collected images and record the corresponding firefighting facility status and the emergency response time at that time;

[0033] Perform feature extraction on the collected firefighting facility image data, and the extracted features include shape, color distribution and texture;

[0034] Pair and annotate the extracted image features with the known emergency response duration to form an initial image feature response duration dataset;

[0035] Design a database structure that consists of two parts: one for storing detailed information on image features and the other for recording the corresponding emergency response time;

[0036] The image feature response time dataset is entered into the database, and the database is regularly updated and optimized based on newly collected data and actual emergency response performance.

[0037] Furthermore, when the fire station abnormal alarm is performed, the method for determining the abnormal cause is:

[0038] Automatically trace back the firefighting facility status parameter set and image feature set when the alarm was triggered, compare and analyze the differences between the firefighting facility status parameter set and image feature set and the historical normal operation status, and preliminarily determine the source of the abnormality;

[0039] Check the fire-fighting facilities that triggered the alarm in real time and confirm the abnormal conditions;

[0040] Analyze the real-time data stream of sensors, look for abnormal and fluctuating data patterns, and locate the problem;

[0041] Comprehensively analyze all collected data, diagnose the cause of the anomaly, and propose solutions and maintenance recommendations.

[0042] Furthermore, factors affecting the setting of the response time threshold include site characteristics, firefighting facility configuration, historical data analysis, geographical traffic conditions, weather and environmental factors, and community risk assessment.

[0043] On the other hand, the present application also provides a skid-mounted fire station operation status real-time monitoring system, the system comprising:

[0044] A data acquisition module, for real-time acquisition of a fire-fighting facility status parameter set and a fire-fighting facility image data set; the fire-fighting facility status parameter set includes a fire-fighting pump status, a fire-extinguishing tank storage status, and a fire-fighting vehicle status; the fire-fighting facility image data set includes fire-fighting pump image data, fire-extinguishing tank image data, and fire-fighting vehicle image data;

[0045] A first emergency response duration mapping module is used to map the first emergency response duration database built in advance according to the fire protection facility status parameter set to obtain the first emergency response duration;

[0046] An image feature extraction module is used to extract features from a fire-fighting facility image data set to obtain a fire-fighting facility image feature set;

[0047] The second emergency response time mapping module is used to map the second emergency response time database built in advance according to the firefighting facility image feature set to obtain the second emergency response time.

[0048] The response time fusion module is used to compare the first emergency response time and the second emergency response time, and take the longest time as the predicted emergency response time of the skid-mounted fire station;

[0049] The early warning and response module is used to compare the predicted emergency response time with the preset response time threshold. If the predicted emergency response time does not exceed the preset response time threshold, no action is taken; if the predicted emergency response time exceeds the preset response time threshold, an abnormal alarm is issued by the fire station.

[0050] In a third aspect, the present application provides an electronic device, comprising a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected via the bus, and the computer program, when executed by the processor, implements the steps of any one of the above methods.

[0051] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps in any one of the above-mentioned methods when executed by a processor.

[0052] Compared with the prior art, the present invention has the following beneficial effects: the method provides real-time monitoring of the operation status of the fire station by collecting firefighting facility status parameters and image data in real time, which greatly reduces the response delay and ensures the timely grasp of the changes in the status of firefighting facilities compared with the traditional method of relying on passive alarms;

[0053] The method combines state parameters and image features to predict the emergency response time. This method not only considers the direct operating status of the equipment, but also captures visual clues that may affect the response speed through image recognition technology, thus improving the comprehensiveness and accuracy of the prediction model.

[0054] Relying on two pre-built emergency response time databases, this method can use historical data for pattern recognition and learning, and the mapping relationship between different state parameter sets and image feature sets and actual emergency response time makes the prediction more scientific and reasonable, reflecting the response efficiency in real situations;

[0055] By setting a preset response time threshold, it can proactively identify potential emergency response delays and immediately trigger abnormal alarms. This mechanism effectively improves the initiative of problem discovery, buys valuable time for timely corrective measures, and reduces the risk of fire losses.

[0056] This method integrates automatic control and intelligent analysis, which not only optimizes the allocation and dispatch of fire protection resources, but also promotes the development of fire protection management towards intelligence and refinement, and adapts to the higher requirements for fire protection safety in the process of urbanization and industrialization.

[0057] In summary, this method effectively makes up for the shortcomings of traditional fire monitoring systems through innovative data collection, intelligent analysis and early warning mechanisms, improves the efficiency and reliability of skid-mounted fire stations in emergency response and management, and thus significantly reduces the losses that may be caused by fire. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 is a flow chart of the present invention;

[0059] Figure 2 It is a structural diagram of the real-time monitoring system for the operation status of a skid-mounted fire station. DETAILED DESCRIPTION

[0060] In the description of the present application, those skilled in the art should know that the present application can be implemented as a method, an apparatus, an electronic device, and a computer-readable storage medium. Therefore, the present application can be specifically implemented in the following forms: complete hardware, complete software (including firmware, resident software, microcode, etc.), a combination of hardware and software. In addition, in some embodiments, the present application can also be implemented in the form of a computer program product in one or more computer-readable storage media, and the computer-readable storage medium contains computer program code.

[0061] The above-mentioned computer-readable storage medium may adopt any combination of one or more computer-readable storage media. Computer-readable storage media include: electrical, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or devices, or any combination of the above. More specific examples of computer-readable storage media include: portable computer disks, hard disks, random access memories, read-only memories, erasable programmable read-only memories, flash memories, optical fibers, optical disc read-only memories, optical storage devices, magnetic storage devices, or any combination of the above. In the present application, computer-readable storage media can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device, or device.

[0062] The acquisition, storage, use, and processing of data in the technical solution of this application comply with the relevant provisions of national laws.

[0063] The present application describes the provided methods, devices, and electronic devices through flowcharts and / or block diagrams.

[0064] It should be understood that each box in the flowchart and / or block diagram and the combination of boxes in the flowchart and / or block diagram can be implemented by computer-readable program instructions. These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer or other programmable data processing device to produce a machine, and these computer-readable program instructions are executed by a computer or other programmable data processing device to produce a device that implements the functions / operations specified by the boxes in the flowchart and / or block diagram.

[0065] These computer-readable program instructions may also be stored in a computer-readable storage medium that enables a computer or other programmable data processing device to work in a specific manner. In this way, the instructions stored in the computer-readable storage medium produce an instruction device product including functions / operations specified in the blocks in the flowchart and / or block diagram.

[0066] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device, so that a series of operational steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby enabling the instructions executed on the computer or other programmable data processing apparatus to provide a process for implementing the functions / operations specified in the blocks in the flowchart and / or block diagram.

[0067] The present application is described below in conjunction with the drawings in the present application.

[0068] Embodiment 1: Figure 1 As shown, the real-time monitoring method of the skid-mounted fire station operation status of the present invention specifically includes the following steps:

[0069] S1, real-time collection of fire-fighting facility status parameter sets and fire-fighting facility image data sets; the fire-fighting facility status parameter sets include fire pump status, fire extinguisher tank storage status and fire-fighting vehicle status; the fire-fighting facility image data sets include fire pump image data, fire extinguisher tank image data and fire-fighting vehicle image data;

[0070] Step S1 effectively realizes the real-time and accurate collection of key facility status parameters and image data in the skid-mounted fire station, laying a solid foundation for subsequent emergency response time prediction and abnormal alarm;

[0071] Install various sensors, including pressure sensors, liquid level sensors and GPS positioning devices, on facilities such as fire pumps, fire extinguisher tank storage areas, and fire trucks; the pressure sensors are used to monitor the working pressure of fire pumps, the liquid level sensors are used to monitor the storage volume of fire extinguishing agents, and the GPS positioning devices are used to track the location of fire trucks;

[0072] Deploy high-definition cameras at key locations in fire stations, including fire pump operation areas, fire extinguisher tank areas, and vehicle parking areas, to ensure all-round, no-blind-angle monitoring of firefighting facilities. High-definition cameras have night vision, wide dynamic range, and high resolution, and can clearly capture facility images under a variety of lighting conditions.

[0073] Use wired or wireless communication technology to transmit the data collected by sensors and cameras to the central processing unit or cloud server in real time; on the server side, establish a data integration platform to uniformly manage the state parameters and image data and pre-process them before analysis;

[0074] Implement data quality monitoring mechanisms, including data integrity checks and outlier detection, to eliminate or correct erroneous data and ensure that the information entered into the emergency response duration database is reliable and accurate.

[0075] In this step, by deploying high-sensitivity sensors and high-performance cameras on key fire-fighting facilities, real-time and accurate monitoring of the working status of fire pumps, the storage of fire extinguishing agents and the location of fire trucks is achieved, providing instant and accurate data support for emergency response, which is significantly better than the traditional monitoring method that relies on passive alarms; the night vision and wide dynamic range characteristics of high-definition cameras ensure that the images of fire-fighting facilities can be clearly obtained in different time periods and complex lighting environments, achieving 24-hour uninterrupted and blind-spot-free monitoring coverage, and improving the reliability of fire safety; the use of modern communication technology to achieve rapid data transmission, combined with the data integration platform of the cloud or central processing unit, not only speeds up the flow of information, but also Through preprocessing and quality monitoring mechanisms, the integrity and accuracy of the data are effectively guaranteed, laying a high-quality data foundation for subsequent analysis and prediction; the diversified data collected in real time provides a rich source of information for the intelligent analysis of the status of fire-fighting facilities, which can not only support the accurate prediction of emergency response time, but also timely discover and respond to potential problems through abnormal alarm mechanisms, providing a scientific basis for the optimal allocation and efficient scheduling of fire-fighting resources; step S1 significantly enhances the intelligent monitoring level of skid-mounted fire stations through the deep integration of high-tech equipment and information technology, ensuring real-time and accurate grasp of the status of fire-fighting facilities, and providing strong technical support for improving emergency response speed and efficiency and reducing fire losses.

[0076] S2. Mapping the first emergency response time database built in advance according to the fire-fighting facility status parameter set to obtain the first emergency response time;

[0077] In the first emergency response duration database, all firefighting facility status parameter sets in historical data are stored, and different firefighting facility status parameter sets correspond to a first emergency response duration;

[0078] The method for constructing the first emergency response duration database includes:

[0079] Collect historical data on firefighting facility status parameters from existing firefighting logs, emergency response records, facility maintenance reports, etc., including the working status of firefighting pumps, the storage status of fire extinguisher tanks, and the standby status of firefighting vehicles;

[0080] Clean the collected data, remove invalid outliers, and ensure data quality; at the same time, standardize the state parameters to ensure that data from different sources and types can be compared and analyzed on the same scale;

[0081] Use statistical methods or machine learning models to analyze the relationship between state parameters and actual emergency response time to determine which parameters have a significant impact on response time;

[0082] Design the database architecture, including fields such as status parameter identifier, parameter value, and corresponding emergency response duration;

[0083] Each state parameter set obtained through analysis and its corresponding predicted emergency response time are recorded in the database to form a one-to-one mapping relationship;

[0084] As new data accumulates, the database is updated regularly, and the corresponding relationship between new state parameter sets and response times is added to maintain the timeliness of the database;

[0085] The specific structure of the first emergency response duration database is:

[0086] Facility status parameter table: records various status parameters of fire-fighting facilities, including fire pump ID, pump working status, fire extinguisher tank ID, storage volume percentage, fire vehicle ID, vehicle location status, etc.

[0087] Response time mapping table: a core table that records the predicted emergency response time corresponding to each state parameter combination; the field includes one or more state parameter identifiers and the corresponding response time;

[0088] Parameter definition table: Detailed definition and explanation of storage status parameters, helping to understand the meaning of parameters in the database and facilitating maintenance and query.

[0089] In this step, by analyzing the complex relationship between status parameters and emergency response time through statistical methods or machine learning models, the key factors affecting response time can be identified, thereby providing more accurate prediction results; it is helpful to plan resources in advance and optimize emergency response strategies; the construction of the database makes effective use of historical data and forms an emergency response time prediction model based on a large number of actual cases; managers can quickly judge the combat readiness of fire stations based on this, providing strong data support for immediate decision-making; once the status parameters of firefighting facilities are collected in real time, the corresponding emergency response time can be immediately queried in the database, which greatly shortens the cycle from monitoring to decision-making and buys precious time for actual emergency actions; the regular update mechanism of the database ensures the timeliness of the model. With the continuous addition of data, the prediction model can be continuously optimized and learn the response rules in more scenarios, which will improve the adaptability and accuracy of the entire system in the long run; through standardized processing and parameter definition tables, the consistency and comparability of the data are ensured, which facilitates cross-regional and cross-time data integration and analysis, and is conducive to the standardization and scientificization of fire management; the database is not only a tool for emergency response, but also a reference for facility maintenance management; by analyzing the response time under different parameter combinations, the weak links in facility maintenance can be identified, maintenance work can be guided, and the occurrence of failures can be prevented; building the first emergency response time database is a key step in achieving efficient and intelligent management of fire stations. It not only improves the speed and efficiency of emergency response, but also promotes the continuous optimization and intelligent upgrading of the fire safety management system.

[0090] S3, extracting features from the fire-fighting facility image data set to obtain a fire-fighting facility image feature set;

[0091] Step S3 can effectively extract valuable feature information from the firefighting facility image data, providing accurate data support for subsequent emergency response time prediction and abnormal alarm, thereby improving the operating efficiency and response capability of the skid-mounted fire station;

[0092] The method for extracting features from a firefighting facility image data set includes:

[0093] Use filters to remove noise from images and improve image quality;

[0094] Convert color images into grayscale images to reduce the amount of data to be processed and simplify subsequent processing steps;

[0095] Rotate, scale, and translate the image to ensure that the firefighting facilities are in the same position in the image, which is convenient for feature extraction;

[0096] Using edge detection algorithms to detect edge information in the image, the edge information corresponds to the shape and outline of the firefighting facilities;

[0097] Based on the shape characteristics of fire-fighting facilities, a shape detection algorithm is used to locate the fire-fighting facilities in the image;

[0098] The image is segmented into different regions by threshold segmentation, morphological operations and other methods, which facilitates the analysis of different components of firefighting facilities;

[0099] Apply shape feature extraction algorithms, such as circularity, rectangularity, Hu moment, etc., to describe the shapes of key components of fire protection facilities;

[0100] The texture information of the surface of fire-fighting facilities is analyzed by using texture feature extraction methods such as gray-level co-occurrence matrix and local binary pattern.

[0101] The extracted features are converted into feature vectors in numerical form. Each fire-fighting facility image will be represented as a feature vector. The feature vectors of each fire-fighting facility are fused to obtain a fire-fighting facility image feature set.

[0102] In this step, the preprocessing steps such as removing noise through filters and converting to grayscale images significantly improve the quality of image data and ensure the accuracy of subsequent analysis; not only does it reduce unnecessary data complexity, but it also improves the efficiency of feature extraction; through edge detection, shape detection, and texture analysis, it can accurately identify and locate the key components and states of fire-fighting facilities, providing intuitive and detailed information for status assessment; the image features are vectorized to form a set of fire-fighting facility image features, and the complex image information is converted into a data form that is easy to process and analyze, which can more directly and comprehensively reflect the real-time status of fire-fighting facilities and obtain more accurate information through mapping databases. Prediction of emergency response time; through the automated feature extraction process, human intervention is reduced, processing speed and decision-making efficiency are improved; intelligent analysis of image data enables fire stations to identify potential problems more quickly, take measures in advance, avoid response delays, and reduce losses from fire accidents; this method can adapt to the status monitoring of fire facilities in different environments and conditions through flexible image processing and feature extraction technology, ensuring the stability and reliability of the monitoring system; the S3 step greatly improves the intelligence and accuracy of skid-mounted fire station operation status monitoring through meticulous and in-depth image feature extraction technology, providing strong technical support for achieving efficient emergency response and safety management.

[0103] S4. Mapping the image feature set of the fire-fighting facilities in a pre-built second emergency response duration database to obtain the second emergency response duration;

[0104] In the second emergency response duration database, all firefighting facility image feature sets in historical data are stored, and different firefighting facility image feature sets correspond to a first emergency response duration;

[0105] The method for constructing the second emergency response duration database includes:

[0106] Collect a large amount of firefighting facility image data, including images of fire pumps, fire extinguisher tanks, fire trucks, etc. in different states;

[0107] Label these images, record the corresponding firefighting facility status and emergency response time at that time, and ensure the accuracy of the data;

[0108] Using image processing and computer vision technology, the collected firefighting facility image data is subjected to feature extraction, including shape, color distribution, texture, etc.;

[0109] Pair and annotate the extracted image features with the known emergency response duration to form an initial image feature response duration dataset;

[0110] Design a database structure that consists of two parts: one part is used to store detailed information about image features, including the type and value of the feature; the other part records the corresponding emergency response time;

[0111] Enter the image feature response time data set into the database, and regularly update and optimize the database based on newly collected data and actual emergency response performance;

[0112] The structure of the second emergency response duration database is:

[0113] Image feature data table:

[0114] Feature ID: uniquely identifies each feature record;

[0115] Facility type: fire pump, fire extinguisher tank or fire truck, etc.;

[0116] Feature type: describes the category of features, such as shape features, texture features, etc.

[0117] Eigenvalue: a specific value or vector representing the quantitative description of the feature;

[0118] Image source: metadata such as the time and location of the image;

[0119] Response duration mapping table:

[0120] Mapping ID: associated with the feature ID, uniquely identifies the mapping record;

[0121] Feature ID: points to a specific record in the image feature data table;

[0122] Emergency response time: emergency response time obtained based on feature prediction or historical data;

[0123] Confidence: The confidence level of the model's prediction duration, used to assess the reliability of the prediction.

[0124] In this step, by associating and mapping the image features of fire-fighting facilities with the emergency response time, it not only relies on direct equipment status parameters, but also uses the additional dimension of image features to comprehensively analyze the readiness status of the fire station; it helps to predict the emergency response time more comprehensively and accurately, and improve the accuracy of fire response; the use of image processing and computer vision technology for feature extraction reflects the intelligent trend of modern fire management; it can automatically identify changes in facility status, reduce the subjectivity and errors of human judgment, and improve the efficiency and quality of data processing; the structural design of the database supports regular updates and optimizations, and can continuously learn and improve with the accumulation of new data, adapt to the natural changes in facility status and the diversity of environmental conditions, and ensure that the prediction model timeliness and effectiveness; by pairing feature ID with emergency response time, managers can quickly query the response time corresponding to a specific facility status or feature, provide data support for making emergency plans and optimizing resource allocation, and make the decision-making process more scientific and efficient; detailed image feature data tables and response time mapping tables not only record the current status of fire-fighting facilities, but also retain historical records, providing valuable information for facility maintenance, troubleshooting, and event review, and enhancing the transparency and traceability of management; the S4 step and the construction of the second emergency response time database involved, through integrated image analysis and intelligent prediction, significantly enhance the intelligent level of emergency response of skid-mounted fire stations, and improve the efficiency and reliability of fire safety management.

[0125] S5. Compare the first emergency response time and the second emergency response time, and take the longest time as the predicted emergency response time of the skid-mounted fire station;

[0126] Step S5 can compare the emergency response times obtained by two different approaches and select the longest time as the final predicted emergency response time; this ensures that the emergency response capability of the fire station is estimated most conservatively and most likely to be close to the actual situation, taking into account both equipment status parameters and intuitive facility images.

[0127] The first emergency response time obtained in step S2 and the second emergency response time obtained in step S4 are combined; these two time periods respectively represent the emergency response potential of the fire-fighting facilities analyzed through state parameters and image features;

[0128] Compare these two durations; if the first emergency response duration is longer, directly take it as the predicted emergency response duration; if the second emergency response duration is longer, take the second emergency response duration as the standard;

[0129] The larger of the two durations is selected as the final prediction value based on the principle of conservative estimation, which assumes that under the most unfavorable circumstances, the fire station will need the longest time to complete emergency response preparations.

[0130] In this step, by comprehensively analyzing the data of the two dimensions of firefighting facility status parameters and image features, a more comprehensive and actual emergency response time prediction can be provided, which effectively avoids misjudgment caused by one-sided information; the longest of the two predicted time periods is selected as the final emergency response time, which reflects the principle of safety first; it ensures that the fire station is always based on the worst-case scenario when planning and dispatching resources, thereby reserving sufficient response buffer time and reducing the risks caused by insufficient preparation in emergency situations; it enables the fire station to take measures in advance to optimize the facility status or adjust the response process, which substantially improves the efficiency and effectiveness of the emergency response; based on the longest time prediction, the fire manager can allocate and dispatch resources more specifically, ensuring that the fire station can quickly reach the best combat readiness state when a rapid response is really needed; step S5 comprehensively compares and selects the longest emergency response time as the predicted value, which not only improves the accuracy of the fire station's emergency preparedness, but also strengthens the preventive nature of safety management, which is an indispensable part of achieving efficient and safe fire response.

[0131] S6. Compare the predicted emergency response time with the preset response time threshold. If the predicted emergency response time does not exceed the preset response time threshold, no action is taken; if the predicted emergency response time exceeds the preset response time threshold, an abnormal alarm is issued to the fire station;

[0132] Step S6 is the decision-making and response stage in the real-time monitoring method of the skid-mounted fire station operation status. It aims to timely detect and respond to abnormal situations that may affect the rapid response of the fire station by comparing the predicted emergency response time with the preset standard;

[0133] The factors affecting the setting of the response time threshold include:

[0134] Location characteristics: Different types of locations have different requirements for fire response speed. In high-risk areas such as densely populated areas, chemical parks, and large storage bases, due to the high potential fire hazard and high difficulty in evacuating personnel, the response time threshold should be set more strictly to ensure rapid response.

[0135] Firefighting facility configuration: The configuration level, quantity, maintenance status, and automation level of firefighting facilities in the fire station will affect the response speed. Firefighting facilities with high configuration and good maintenance status can respond faster, so the threshold setting can be appropriately relaxed;

[0136] Historical data analysis: Emergency response times, successful cases, and lessons learned from previous fire accidents are important references for setting thresholds. By analyzing historical emergency response times, we can determine the average response time and the longest response time in extreme cases, and use this as a benchmark to set thresholds.

[0137] Geographical and traffic conditions: factors such as the location of the fire station, surrounding road conditions, and traffic flow will affect the actual response time. Remote areas or areas with frequent traffic congestion require shorter response time thresholds to compensate for the geographical disadvantages.

[0138] Weather and environmental factors: Extreme weather conditions or special environments can increase the difficulty of emergency response, so these uncontrollable factors should be considered when setting thresholds;

[0139] Community risk assessment: Based on the community risk assessment results, the risk level of potential fires is assessed by taking into account factors such as flammable materials, population density, and building structure in the community. The response time threshold is set accordingly to ensure that areas with higher risks receive priority and rapid response.

[0140] When an abnormal alarm is made at a fire station, the method for determining the cause of the abnormality is as follows:

[0141] Automatically trace back the state parameter set and image feature set of the fire protection facilities when the alarm was triggered, and compare and analyze the differences between these data and the historical normal operation status to preliminarily determine the possible source of the abnormality;

[0142] Use the high-definition video surveillance system in the fire station to view the firefighting facilities that trigger the alarm in real time, directly observe the status of the facilities through the video stream, and intuitively confirm abnormal conditions;

[0143] In-depth analysis of real-time data streams from relevant sensors, including key parameters such as temperature, pressure, and flow, to find abnormal fluctuations or data patterns that do not conform to normal operating modes, and pinpoint the problem;

[0144] Combined with artificial intelligence algorithms and expert systems, all collected data are comprehensively analyzed to intelligently diagnose the specific causes of abnormalities and propose possible solutions or maintenance suggestions;

[0145] Keep detailed records of all abnormal alarms and their handling processes, including alarm time, triggering factors, handling measures and results, etc. These records provide valuable data support for future fault analysis, system optimization and training.

[0146] In this step, by comparing the predicted emergency response time with the preset threshold in real time, potential response delays can be identified immediately to ensure that when an emergency such as a fire occurs, the fire station can take prompt action to reduce response time and reduce fire losses. Setting a response time threshold that takes into account a variety of factors ensures a reasonable response to firefighting needs in areas with different risk levels. Especially in high-risk areas, stricter threshold settings improve the ability to respond to fires and protect people’s lives and property. When the abnormal alarm mechanism is triggered, multi-dimensional data analysis and backtracking can quickly locate the source of the problem, guide the precise allocation and maintenance of resources, avoid waste of resources, and improve the maintenance efficiency and overall operation of firefighting facilities. efficiency; combining artificial intelligence algorithms with expert systems for intelligent diagnosis not only improves the accuracy of abnormal cause judgment, but also can propose targeted solutions, enhance the intelligent management level of fire stations, and reduce the burden of manual monitoring; detailed records of each abnormal alarm and handling process provide rich first-hand information for subsequent fault analysis, system optimization and personnel training, forming a closed loop of continuous improvement, and constantly improving the emergency response mechanism and management level of fire stations; S6 steps significantly improve the emergency response speed and management efficiency of skid-mounted fire stations through efficient identification of abnormalities, rapid location of problems, intelligent decision support and continuous optimization mechanisms, which is an important guarantee for ensuring fire safety and reducing fire risks.

[0147] Embodiment 2: Figure 2 As shown, the skid-mounted fire station operation status real-time monitoring system of the present invention specifically includes the following modules:

[0148] A data acquisition module, for real-time acquisition of a fire-fighting facility status parameter set and a fire-fighting facility image data set; the fire-fighting facility status parameter set includes a fire-fighting pump status, a fire-extinguishing tank storage status, and a fire-fighting vehicle status; the fire-fighting facility image data set includes fire-fighting pump image data, fire-extinguishing tank image data, and fire-fighting vehicle image data;

[0149] A first emergency response duration mapping module is used to map the first emergency response duration database built in advance according to the fire protection facility status parameter set to obtain the first emergency response duration;

[0150] An image feature extraction module is used to extract features from a fire-fighting facility image data set to obtain a fire-fighting facility image feature set;

[0151] The second emergency response time mapping module is used to map the second emergency response time database built in advance according to the firefighting facility image feature set to obtain the second emergency response time.

[0152] The response time fusion module is used to compare the first emergency response time and the second emergency response time, and take the longest time as the predicted emergency response time of the skid-mounted fire station;

[0153] The early warning and response module is used to compare the predicted emergency response time with the preset response time threshold. If the predicted emergency response time does not exceed the preset response time threshold, no action is taken; if the predicted emergency response time exceeds the preset response time threshold, an abnormal alarm is issued by the fire station.

[0154] The system acquires the status parameters and image data of fire-fighting facilities in real time through the data acquisition module, ensuring comprehensive monitoring of the operation status of the fire station. Compared with the traditional passive alarm system, it greatly improves the timeliness and integrity of information collection;

[0155] The system uses image feature extraction technology and two independent emergency response time mapping modules, combined with machine learning or artificial intelligence algorithms, to achieve intelligent analysis of the status of firefighting facilities, improving the accuracy and reliability of predicting emergency response time. The system can instantly identify factors that may affect the efficiency of emergency response, and quickly issue alarms through the early warning and response modules, allowing managers to take measures at the first time to avoid the expansion of fire losses due to slow response.

[0156] The system not only monitors passively, but also establishes an active early warning mechanism by comparing the predicted emergency response time with the preset threshold, which helps to detect and solve potential problems in advance, and fundamentally improve the emergency preparedness level of the fire station;

[0157] The modular design of the skid-mounted fire station and the intelligent management of the system jointly promote the efficient allocation and utilization of firefighting resources, which can more effectively reduce fire risks and protect people’s lives and property.

[0158] In summary, the system effectively makes up for the shortcomings of traditional fire monitoring systems through innovative data collection, intelligent analysis and early warning mechanisms, improves the efficiency and reliability of skid-mounted fire stations in emergency response and management, and thus significantly reduces the losses that may be caused by fire.

[0159] The various variations and specific embodiments of the skid-mounted fire station operating status real-time monitoring method in the aforementioned embodiment 1 are also applicable to the skid-mounted fire station operating status real-time monitoring system in this embodiment. Through the aforementioned detailed description of the skid-mounted fire station operating status real-time monitoring method, those skilled in the art can clearly understand the implementation method of the skid-mounted fire station operating status real-time monitoring system in this embodiment. Therefore, for the sake of brevity of the specification, it will not be described in detail here.

[0160] In addition, the present application also provides an electronic device, including a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor. The transceiver, the memory, and the processor are respectively connected via a bus. When the computer program is executed by the processor, each process of the above-mentioned method for controlling output data is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be described here.

[0161] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for real-time monitoring of the operating status of a skid-mounted fire station, characterized in that: The method comprises: Real-time collection of fire-fighting facility status parameter sets and fire-fighting facility image data sets; According to the fire-fighting facility status parameter set, mapping is performed in a pre-built first emergency response duration database to obtain the first emergency response duration; Extracting features from a fire-fighting facility image data set to obtain a fire-fighting facility image feature set; According to the fire-fighting facility image feature set, mapping is performed in a pre-built second emergency response time database to obtain the second emergency response time; Compare the first emergency response time and the second emergency response time, and take the longest time as the predicted emergency response time of the skid-mounted fire station; The predicted emergency response time is compared with the preset response time threshold. If the predicted emergency response time does not exceed the preset response time threshold, no action is taken; if the predicted emergency response time exceeds the preset response time threshold, an abnormal alarm is issued to the fire station; The method for constructing the first emergency response duration database includes: Collect historical data on firefighting facility status parameters, including fire pump operating status, fire extinguisher tank storage status, and fire truck standby status; Clean the collected data, remove invalid outliers, and standardize the state parameters; Analyze the relationship between the state parameters and the actual emergency response time to obtain a state parameter set; Design the database architecture, including status parameter identifiers, parameter values, and corresponding emergency response duration fields; Each state parameter set obtained through analysis and its corresponding predicted emergency response time are recorded in the database to form a one-to-one mapping relationship; Update the database regularly, add the corresponding relationship between new status parameter sets and response time, and keep the database up to date; The method for extracting features from a firefighting facility image data set includes: Use filters to remove noise from images; Convert color images to grayscale images; Rotate, scale and translate images; Using edge detection algorithms to detect edge information in the image, the edge information corresponds to the shape and outline of the firefighting facilities; Locate the fire-fighting facilities in the image based on their shape characteristics; Segment the image into different regions to analyze different components of the firefighting facility; Apply shape feature extraction algorithm to describe the shape of firefighting equipment components; Analyze the texture information on the surface of fire-fighting facilities through texture feature extraction method; The extracted features are converted into feature vectors in numerical form. Each fire-fighting facility image is represented as a feature vector. The feature vectors of each fire-fighting facility are fused to obtain a fire-fighting facility image feature set. The method for constructing the second emergency response duration database includes: Collect image data of firefighting facilities, including images of fire pumps, fire extinguisher tanks, and fire trucks in different states; Annotate the collected images and record the corresponding firefighting facility status and the emergency response time at that time; Perform feature extraction on the collected firefighting facility image data, and the extracted features include shape, color distribution and texture; Pair and annotate the extracted image features with the known emergency response duration to form an initial image feature response duration dataset; Design a database structure that consists of two parts: one for storing detailed information on image features and the other for recording the corresponding emergency response time; Enter the image feature response time data set into the database, and regularly update and optimize the database based on newly collected data and actual emergency response performance; When an abnormal alarm is made at a fire station, the method for determining the cause of the abnormality is as follows: Automatically trace back the firefighting facility status parameter set and image feature set when the alarm was triggered, compare and analyze the differences between the firefighting facility status parameter set and image feature set and the historical normal operation status, and preliminarily determine the source of the abnormality; Check the fire-fighting facilities that triggered the alarm in real time and confirm the abnormal conditions; Analyze the real-time data stream of sensors, look for abnormal and fluctuating data patterns, and locate the problem; Comprehensively analyze all collected data, diagnose the cause of the anomaly, and propose solutions and maintenance recommendations.

2. The method for real-time monitoring of the operating status of a skid-mounted fire station according to claim 1, characterized in that: The firefighting facility status parameter set includes a firefighting pump status, a fire extinguisher tank storage status, and a firefighting vehicle status; the firefighting facility image data set includes firefighting pump image data, fire extinguisher tank image data, and firefighting vehicle image data.

3. The method for real-time monitoring of the operating status of a skid-mounted fire station according to claim 1, characterized in that: The factors affecting the setting of the response time threshold include site characteristics, fire facility configuration, historical data analysis, geographical traffic conditions, weather and environmental factors, and community risk assessment.

4. A detection system applied to the real-time monitoring method of the operating status of a skid-mounted fire station as claimed in any one of claims 1 to 3, characterized in that: The system comprises: A data acquisition module, for real-time acquisition of a fire-fighting facility status parameter set and a fire-fighting facility image data set; the fire-fighting facility status parameter set includes a fire-fighting pump status, a fire-extinguishing tank storage status, and a fire-fighting vehicle status; the fire-fighting facility image data set includes fire-fighting pump image data, fire-extinguishing tank image data, and fire-fighting vehicle image data; A first emergency response duration mapping module is used to map the first emergency response duration database built in advance according to the fire protection facility status parameter set to obtain the first emergency response duration; An image feature extraction module is used to extract features from a fire-fighting facility image data set to obtain a fire-fighting facility image feature set; The second emergency response time mapping module is used to map the second emergency response time database built in advance according to the firefighting facility image feature set to obtain the second emergency response time. The response time fusion module is used to compare the first emergency response time and the second emergency response time, and take the longest time as the predicted emergency response time of the skid-mounted fire station; The early warning and response module is used to compare the predicted emergency response time with the preset response time threshold. If the predicted emergency response time does not exceed the preset response time threshold, no action is taken; if the predicted emergency response time exceeds the preset response time threshold, an abnormal alarm is issued by the fire station.

5. A skid-mounted fire station operation status real-time monitoring electronic device, comprising a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected via the bus, and characterized in that: When the computer program is executed by the processor, the steps in the method according to any one of claims 1 to 3 are implemented.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps in the method according to any one of claims 1 to 3 are implemented.

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