A subway station ventilation alarm method and system

Abstract

The present invention discloses a subway station ventilation alarm method and system, which relates to the technical field of intelligent monitoring and alarm devices for subway station ventilation equipment, including real-time monitoring of equipment status data, using a Petri net model based on FMECA analysis to build dependencies between different ventilation equipment in a subway station, and dynamically updating fault propagation paths; dynamically evaluating the priority of the fault according to the location, impact range, and propagation speed of the equipment fault, combined with the importance and working status of the equipment; automatically triggering a fault alarm through an alarm device according to the evaluated priority, and sending a repair instruction to maintenance personnel through an instruction sending device. The method of the present invention improves the intelligence level of the system by combining dynamic evaluation and priority-driven alarm device design, and the design of a multi-factor comprehensive evaluation method and linkage repair instructions shortens the processing time of high-priority faults, effectively improving the safety and operating efficiency of the subway ventilation system.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent monitoring and alarm devices for subway station ventilation equipment, and in particular to a subway station ventilation alarm method and system. Background Art

[0002] As an important equipment for maintaining air circulation in the station and the comfort of the passenger environment, the subway station ventilation system has been driven by intelligent and automated technologies in recent years. In modern subway stations, ventilation equipment usually includes fans, air ducts, exhaust fans and other devices. These devices realize data collection and status evaluation of key operating parameters (such as fan speed, air duct pressure and air quality index) through real-time monitoring systems. The current technological development focuses on real-time monitoring of the operating status of a single device and preliminary data analysis based on sensor networks. Some research methods, such as fault tree analysis (FTA) and equipment status detection models, have been able to help operation and maintenance personnel to determine whether the equipment is operating normally to a certain extent. However, with the expansion of the scale of subway stations and the increase in the number of ventilation system equipment, the existing monitoring devices have shown obvious limitations in the linkage analysis between equipment, dynamic fault assessment and automatic alarm capabilities. More efficient solutions are urgently needed to meet the intelligent needs in complex environments.

[0003] Although the existing subway station ventilation equipment monitoring and alarm devices can collect equipment operating parameters, they still have the following shortcomings in terms of overall linkage and intelligence level: most devices lack the ability to model the dependencies between ventilation equipment, especially in the linkage operation of equipment. The existing monitoring devices cannot accurately describe the interactions between multiple devices and the fault propagation paths, which leads to low reliability of fault source location and propagation warning; the existing devices have limited ability to evaluate fault priorities, and are mostly based on static thresholds and single device status judgments. They fail to make full use of the collaborative relationship between equipment and dynamic factors such as fault propagation speed and impact range, and cannot meet the needs of accurate priority assessment in complex environments. The separate design of the alarm device and the repair instruction device leads to a high time cost for manual judgment and processing, and the existing devices cannot achieve the linkage between fault alarm and automatic repair process. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problems solved by the present invention are: the existing subway ventilation system monitoring and alarm methods have insufficient equipment dependency analysis, the fault priority assessment is not dynamic and flexible enough, the alarm and repair processes are not automatically linked, and there is also the problem of how to achieve accurate fault alarms and efficient repair command triggering in a complex subway station environment.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a subway station ventilation alarm method, comprising real-time monitoring of equipment status data, using a Petri net model based on FMECA analysis to construct the dependency relationship between different ventilation equipment in the subway station, and dynamically updating the fault propagation path; dynamically evaluating the priority of the fault according to the location, impact range, and propagation speed of the equipment fault, combined with the importance and working status of the equipment; automatically triggering a fault alarm through an alarm device according to the evaluated priority, and sending a repair instruction to maintenance personnel through an instruction sending device.

[0007] As a preferred solution of the subway station ventilation alarm method described in the present invention, wherein: the construction of the dependency relationship between different ventilation equipment in the subway station includes building the dependency relationship between the equipment through the real-time status data of the different ventilation equipment in the subway station, analyzing the interdependence between the equipment and the importance of each equipment through the FMECA analysis method, and using the Petri net model to describe the fault propagation path between the equipment; the ventilation equipment includes fans, air ducts and exhaust fans; the real-time status data includes fan speed, air duct pressure and air quality index.

[0008] As a preferred solution of the subway station ventilation alarm method described in the present invention, the dynamic update of the fault propagation path includes updating the equipment fault status in real time through the real-time collected equipment operation data. The change of the equipment status will trigger the dynamic adjustment of the fault propagation path. In the Petri net model, the mark position is automatically updated based on the change of the equipment status, and the latest path of the equipment fault propagation is output. The propagation path is adjusted according to the type, location and impact range of the equipment fault.

[0009] As a preferred solution of the subway station ventilation alarm method described in the present invention, the dynamic evaluation of fault priority includes analyzing the operating status and load of the equipment based on the location, fault type and impact range of the equipment fault, and evaluating the fault priority in combination with the dependency between the equipment and the speed of fault propagation. The fault type is determined by the real-time monitoring data of the equipment and the preset threshold.

[0010] As a preferred solution of the subway station ventilation alarm method described in the present invention, the dynamic evaluation of fault priority also includes real-time adjustment of fault priority according to external environmental factors through a dynamic algorithm; the external environmental factors include passenger flow and air quality.

[0011] As a preferred solution of the subway station ventilation alarm method described in the present invention, the automatic triggering of the fault alarm by the alarm device includes automatically adjusting the intensity and type of the alarm signal according to the impact and priority of the fault based on the priority of the equipment fault; when it comes to equipment with a higher fault priority, a red flashing light and a high-volume alarm are used, and an emergency notification is sent to the operator through a calling device; when it comes to equipment with a lower fault priority, a yellow indicator light and a low-volume alarm are used to remind the operator to conduct a follow-up inspection.

[0012] As a preferred solution of the subway station ventilation alarm method described in the present invention, the automatic triggering of the fault alarm by the alarm device also includes that when the fault alarm signal is triggered, the instruction sending device automatically generates a repair instruction, and sends the instruction to the maintenance personnel or the control center through the wireless network; the instruction content includes the location of the faulty equipment, the type of fault, the recommended handling measures and the impact of the fault on the ventilation system. The instruction sending device tracks the maintenance progress based on the repair status of the equipment, and updates the equipment status according to the maintenance feedback data.

[0013] Another object of the present invention is to provide a subway station ventilation alarm system, which can monitor equipment status data in real time, use a Petri net model based on FMECA analysis to build the dependency relationship between different ventilation equipment in the subway station, and dynamically update the fault propagation path, thereby solving the problem that the current subway station ventilation equipment intelligent monitoring and alarm device has insufficient equipment dependency modeling capabilities.

[0014] As a preferred solution of the subway station ventilation alarm system of the present invention, it includes:

[0015] A computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement a subway station ventilation alarm method.

[0016] A computer-readable storage medium stores a computer program, which implements the steps of a subway station ventilation alarm method when executed by a processor.

[0017] Beneficial effects of the present invention: The subway station ventilation alarm method provided by the present invention collects equipment status data in real time and dynamically adjusts the transmission path. Each information processing is based on the most real and accurate data at present, ensuring the accuracy of fault judgment and processing. Combined with the design of dynamic evaluation and priority-driven alarm device, the whole process automation from fault monitoring, priority evaluation to alarm triggering and repair instruction generation is realized, and the intelligence level of the system is improved. The design of multi-factor comprehensive evaluation method and linkage repair instruction shortens the processing time of high-priority faults, optimizes the allocation of maintenance resources, and effectively improves the safety and operation efficiency of the subway ventilation system. The present invention achieves better results in terms of accuracy, automation and efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0019] Figure 1 This is an overall flow chart of a subway station ventilation alarm method provided by the first embodiment of the present invention.

[0020] Figure 2 This is an overall module diagram of a subway station ventilation alarm system provided in the third embodiment of the present invention. DETAILED DESCRIPTION

[0021] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0022] Example 1, reference Figure 1 , as an embodiment of the present invention, provides a subway station ventilation alarm method, comprising:

[0023] S1: Monitor equipment status data in real time, use the Petri net model based on FMECA analysis to build the dependency relationship between different ventilation equipment in the subway station, and dynamically update the fault propagation path.

[0024] Furthermore, building the dependency relationship between different ventilation equipment in the subway station includes building the dependency relationship between equipment through the real-time status data of different ventilation equipment in the subway station, analyzing the interdependence between equipment and the importance of each equipment through the FMECA analysis method, and using the Petri net model to describe the fault propagation path between equipment; ventilation equipment includes fans, air ducts, and exhaust fans; real-time status data includes fan speed, air duct pressure, and air quality index.

[0025] It should be noted that the analysis of the interdependence between devices and the importance of each device includes the construction of a dependency matrix: defining the dependencies between devices, for example, the dependency of the fan on the air duct can be quantified by the degree of coupling between the air volume supply and the pressure feedback, and constructing a dependency matrix ,in Indicates the device and equipment The dependency strength (value range is 0 to 1) is used to identify key dependencies: combined with equipment operation data, focus on analyzing the dependencies that have the greatest impact on the overall operation of the system; Failure mode analysis: through the FMECA analysis method, list the possible failure modes of each device (such as fan speed drop, duct pressure is too low, etc.), and evaluate the severity, probability of occurrence and detectability of each mode; Importance scoring: The severity of the failure mode is , probability of occurrence and detectability Combined, calculate the risk priority number , expressed as:

[0026]

[0027] According to the device Value and dependency matrix weights to calculate the comprehensive importance of each device .

[0028] Using the Petri net model to describe the fault propagation path between devices includes defining the basic elements of the Petri net model, including markers (Places), which represent the operating status of the equipment, such as normal operation of the fan, abnormal duct pressure, etc.; Transitions: represent changes in equipment status, such as a decrease in fan speed causing a change in duct pressure; Arc Weights: represent the degree of dependence between equipment states, and define arc weights in combination with the dependency matrix in FMECA analysis; construct a Petri net model, with the state of each device as a marker and the mutual dependence between devices as transitions; establish corresponding markers and transition relationships in the Petri net based on the importance and dependency of the equipment; initialize the Petri net state, the initial state: all devices are in normal operation, and the number of markers is a normal value; update the number of markers and transition rules based on the real-time collected status data.

[0029] It should be noted that the dynamic update of the fault propagation path includes updating the equipment fault status in real time through the real-time collection of equipment operation data. The change of equipment status will trigger the dynamic adjustment of the fault propagation path. In the Petri net model, the mark position is automatically updated based on the change of equipment status, and the latest path of equipment fault propagation is output. The propagation path is adjusted according to the type, location and impact range of the equipment fault.

[0030] It should also be noted that the dynamic update of the fault propagation path also includes, when the monitoring data indicates that the state of a certain device is abnormal (such as the fan speed is lower than the set threshold), the state update is triggered, and the number of corresponding marked positions in the Petri net is reduced or changed to an abnormal state; when the state of the device changes, the transition rule is triggered to update the state of other affected devices. For example, when the fan failure causes the duct pressure to drop, after the Petri net transition is triggered, the corresponding marked state of the duct pressure is adjusted, and the propagation impact of different faults (such as fan shutdown and fan speed drop) is analyzed by fault type, and the weights and transition rules of the Petri net are adjusted. According to the physical position of the equipment in the ventilation system, the propagation path is adjusted. For example, the failure of equipment near the exhaust port will directly affect the exhaust fan state; the number of devices that may be affected by the fault propagation is evaluated, and the corresponding marked transition rules are adjusted. After each state update, the dynamic path of the fault propagation is calculated through the Petri net; the output results include: a list of affected devices, possible propagation paths, the scope of the fault, and potential risk assessment.

[0031] It should also be noted that by monitoring the real-time status of the equipment (such as fan speed, duct pressure and air quality index), the timeliness of the data is guaranteed, providing accurate basic information for subsequent steps; the FMECA analysis method deeply explores the coupling relationship between equipment and can effectively identify the operating status of key equipment and its impact on the overall system; the introduction of the Petri net model makes the fault propagation path of the complex system visualized and precise.

[0032] S2: Dynamically evaluate the priority of the fault based on the location, impact range, and propagation speed of the equipment fault, combined with the equipment importance and working status.

[0033] Furthermore, dynamic fault priority assessment includes analyzing the operating status and load of the equipment based on the location, type and impact range of the equipment fault, and evaluating the fault priority in combination with the dependencies between equipment and the speed of fault propagation. The fault type is determined by the real-time monitoring data of the equipment and the preset threshold.

[0034] It should be noted that the dynamic assessment of fault priority also includes real-time adjustment of fault priority according to external environmental factors through dynamic algorithms; external environmental factors include passenger flow and air quality.

[0035] It should also be noted that the process of dynamically evaluating fault priority starts with the real-time collection of equipment status data and external environment data, combined with the established equipment dependency and fault propagation path, to comprehensively analyze the equipment operation status and fault impact range; specifically, when the monitored value of the fan speed is lower than 50% of the rated speed (for example, the rated speed is 1200 RPM, and the current speed is lower than 600 RPM), it is judged as a "speed too low" fault type; when the duct pressure exceeds ±20% of the normal range (for example, the normal range is 50-80 Pa, and the current pressure is lower than 40 Pa or higher than 96 When the air quality index (AQI) exceeds 150 (moderate pollution), the ventilation efficiency is determined to be obstructed. By comparing the above thresholds, the specific equipment that has failed and its failure type are determined. Then, combined with the calculated equipment importance, the impact of the failed equipment on the system operation is evaluated. For example, the "too low speed" of the fan will reduce the air flow rate in the air duct, directly affecting the working efficiency of the exhaust fan. This chain reaction is clarified through the Petri net propagation path model. The propagation range and speed of the fault need to be evaluated in combination with the physical location and dependency of the equipment. For example, the abnormality of fan A will cause the pressure of the nearest air duct B to change, and the propagation speed of the pressure change is determined by the actual wind speed of the system. Assuming the wind speed is 5 m / s, the fault impact will be transmitted to the exhaust fan 10 meters away within 2 seconds; according to the equipment dependency matrix, the number and type of directly affected equipment are calculated to further determine the impact of the fault on the entire ventilation system; in addition, the equipment's operating load (such as the ratio of the fan's actual power to the rated power) will be used as an important reference. For example, the current load of fan A reaches 80%, but the load drops sharply to 50% after the abnormality, indicating that the equipment performance has been significantly reduced and needs to be processed with higher priority.

[0036] It should also be noted that the real-time adjustment of fault priority includes the dynamic adjustment of fault priority directly affected by external environmental data such as passenger flow and air quality index; when a fault occurs, the passenger flow in the station (for example, through infrared sensor statistics, the current flow is 200 people per minute) and air quality (the current AQI is 120) are collected in real time; if the passenger flow is higher than the normal value (for example, the normal value is 100 people per minute), the fault priority of equipment related to exhaust efficiency (such as exhaust fans) is prioritized; if the AQI exceeds the set threshold (for example, the threshold is 150), the response priority to fan and duct pressure faults is increased; in specific operations, when the passenger flow is twice the normal value, the priority weight will increase by 50%; if the AQI reaches 180, the priority weight is increased by 100%; for example, duct C is judged as medium priority due to abnormal pressure, but when the passenger flow in the station reaches 3 times the normal value, its priority is directly upgraded to high priority; according to the adjustment of the dynamic algorithm, the priority calculation will be updated in real time; the initial priority of the equipment is calculated according to the fault type and propagation range, for example, fan A is judged as medium priority due to "too low speed", but after combining the high importance score and wind speed influence, its priority is adjusted to the second highest priority; after comprehensive external environmental factors, the priority ranking will be dynamically rearranged, and the final ranking result will be passed to the alarm module to ensure that high-priority faults are handled in the first time; this process ensures the accuracy of the evaluation, the timeliness of the response, and the rationality of resource allocation.

[0037] It should also be noted that the combination of the importance of the equipment (obtained through FMECA), real-time status and external environmental influence makes the calculation of fault priority more in line with actual needs; especially in complex subway station environments, the application of dynamic algorithms can respond to changes in equipment status in real time and provide more accurate priority assessments; it overcomes the limitations of single threshold judgment and static evaluation in traditional methods and improves the accuracy and flexibility of fault priority; by accurately distinguishing between high-priority and low-priority faults, it not only optimizes resource allocation, but also shortens the response time of high-priority faults, thereby improving the overall operating efficiency and safety of the system.

[0038] S3: According to the assessed priority, the fault alarm is automatically triggered through the alarm device, and a repair instruction is sent to the maintenance personnel through the instruction sending device.

[0039] Furthermore, automatically triggering a fault alarm through an alarm device includes automatically adjusting the intensity and type of the alarm signal according to the impact and priority of the fault based on the priority of the equipment fault; when it comes to equipment with a higher fault priority, a red flashing light and a high-volume alarm are used, and an emergency notification is sent to the operator through a calling device; when it comes to equipment with a lower fault priority, a yellow indicator light and a low-volume alarm are used to remind the operator to conduct follow-up inspections.

[0040] It should be noted that automatically triggering a fault alarm through an alarm device also includes when a fault alarm signal is triggered, the instruction sending device automatically generates a repair instruction, and sends the instruction to the maintenance personnel or the control center through the wireless network; the instruction content includes the location of the faulty equipment, the type of fault, the recommended handling measures and the impact of the fault on the ventilation system. The instruction sending device tracks the repair progress based on the equipment's repair status and updates the equipment status according to the maintenance feedback data.

[0041] It should also be noted that the priority-driven alarm system makes the transmission of fault information more timely and intuitive; for example, high-priority faults use red flashing lights and high-volume alarms, while low-priority faults use yellow indicator lights and low-volume alarms. This hierarchical alarm method can quickly guide the operator's attention; in addition, the instruction sending device can directly send repair instructions to maintenance personnel, while tracking the progress of maintenance and dynamically updating the equipment status; the intelligence level of the alarm is improved, and delays and errors in manual judgment are avoided; through the linkage of alarms and repair processes, the response time of fault handling is reduced, and the efficiency and accuracy of equipment repair are improved; in particular, the automatic generation and tracking function of repair instructions ensures the efficient management of the maintenance process, thereby improving the safety and operation and maintenance efficiency of the subway station ventilation system.

[0042] Example 2 is an embodiment of the present invention, which provides a subway station ventilation alarm method. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0043] Firstly, a typical subway station ventilation system was selected as the experimental object, including 3 fans, 2 air ducts and 1 exhaust fan; the key parameters for monitoring the equipment status include fan speed (unit: RPM), duct pressure (unit: Pa) and air quality index (unit: AQI), a speed sensor was installed on the output shaft of each fan, pressure sensors were installed at different positions in the air duct, and air quality monitors were set up at key points of air circulation in the station; a data acquisition platform was built, and all sensor data were collected and transmitted to the central database in seconds; the rated speed of the fan is 1200 RPM, the normal range of duct pressure is 50-80Pa, and the normal value of the air quality index is 0-100.

[0044] At the beginning of the experiment, all ventilation equipment was started and kept running; the status data of each device was collected in real time, including the actual speed of the fan, the duct pressure and the air quality index in the station; the dependency weights between the devices were calculated based on the FMECA analysis method; the dependency relationship between the fan and the duct (the fan provides airflow, the duct transmits airflow), as well as the feedback relationship between the exhaust fan and the duct pressure were determined; the dependency weights were used to construct the arc weights in the Petri net model; the possible failure modes of the equipment were analyzed; for example, the possible failure modes of fan A include speed drop or stop, and its risk priority number (RPN) was calculated based on severity, probability of occurrence and detectability, and the importance of the equipment in the system was calculated comprehensively; when the monitoring data showed that the equipment status was abnormal (such as the speed of fan A was lower than 600 RPM), triggering the state update in the Petri net model, recalculating the fault propagation path and the affected equipment; prioritizing the equipment based on its importance, fault type, fault propagation speed and external environment (such as passenger flow and air quality index); for example, when the pressure of duct B exceeds the normal range and the air quality index is higher than 150, its priority is increased; referring to Table 1, the collected equipment status data, fault type and priority score are organized into a table for further analysis.

[0045] Table 1 Experimental data record table

[0046]

[0047] The data shows that the speed of fan A dropped to 50% of the rated value, triggering a "speed too low" fault; according to FMECA analysis, its importance is high (RPN score is significant), and the fault has a greater impact on the duct pressure and exhaust efficiency, so the priority score is 8.5; in contrast, exhaust fans C and F are in normal operation, with priority scores of 5.0 and 4.5 respectively; this result shows that the method of the present invention can distinguish different types of faults and accurately calculate their impact; None in the table means that the data does not exist or is not applicable in a certain device or experiment; when the air quality index (AQI) increases (such as the AQI recorded by exhaust fan C is 80), the priority will be dynamically adjusted according to environmental requirements; for example, the initial priority of "pressure too high" in duct B is 7.0, but due to its abnormal pressure It may have a chain reaction on the exhaust efficiency, and the air quality index is close to the abnormal value, and its priority is dynamically adjusted to 7.8; this real-time adjustment combined with environmental data is difficult to achieve with the existing technology, which reflects the innovation of the present invention; traditional methods usually rely on static thresholds or periodic inspections, while the present invention dynamically updates priorities based on real-time data to ensure timeliness of processing; through the automatic triggering mechanism of the Petri net model, the present invention can quickly identify the fault propagation path and optimize the weight of equipment dependencies; this intelligent analysis improves the accuracy of priority evaluation; dynamic adjustment of priority sorting in combination with external environmental factors such as passenger flow and air quality provides a more flexible strategy for resource allocation; for example, when the passenger flow exceeds twice the normal value, the priority of exhaust-related equipment is improved.

[0048] Example 3, reference Figure 2 , as an embodiment of the present invention, provides a subway station ventilation alarm system, including a state analysis module, a fault assessment module, and a fault alarm module.

[0049] The status analysis module is used to monitor the equipment status data in real time, use the Petri net model based on FMECA analysis to build the dependency relationship between different ventilation equipment in the subway station, and dynamically update the fault propagation path; the fault assessment module is used to dynamically evaluate the priority of the fault based on the location, impact range, and propagation speed of the equipment fault, combined with the equipment importance and working status; the fault alarm module is used to automatically trigger the fault alarm through the alarm device according to the evaluation priority, and send repair instructions to maintenance personnel through the instruction sending device.

[0050] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0051] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0052] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk case (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0053] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logical function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc. It should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not limited. Although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

[0054] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A subway station ventilation alarm method, characterized in that: include: Monitor equipment status data in real time, use the Petri net model based on FMECA analysis to build the dependency relationship between different ventilation equipment in the subway station, and dynamically update the fault propagation path; Dynamically evaluate the priority of equipment failures based on their location, impact range, and propagation speed, combined with equipment importance and working status; According to the assessed priority, the fault alarm is automatically triggered through the alarm device, and the repair instruction is sent to the maintenance personnel through the instruction sending device; Constructing the dependency relationship between different ventilation equipment in the subway station includes building the dependency relationship between the equipment through the real-time status data of different ventilation equipment in the subway station, analyzing the interdependence between the equipment and the importance of each equipment in the system through the FMECA analysis method, and using the Petri net model to describe the fault propagation path between the equipment; Analyze the interdependencies between devices and the importance of each device in the system including building a dependency matrix , Indicates the device and equipment The strength of the dependencies is analyzed to identify the key dependencies and, combined with the equipment operation data, to analyze the dependencies that have the greatest impact on the overall operation of the system; Failure mode analysis, through the FMECA analysis method, lists the possible failure modes of each equipment and evaluates the severity, probability of occurrence and detectability of each mode; Importance score, which classifies the severity of the failure mode , probability of occurrence and detectability Combined, calculate the risk priority number , expressed as: According to the device Value and dependency matrix weight to calculate the comprehensive importance of each device in the system ; Using the Petri net model to describe the fault propagation path between devices includes defining the basic elements of the Petri net model, including labels, transitions, and arc weights; Construct a Petri net model, with the state of each device as a tag and the interdependence between devices as transitions; establish corresponding tags and transition relationships in the Petri net according to the importance and dependency of the devices; initialize the Petri net state, with the initial state that all devices are in normal operation and the number of tags is a normal value; Update the number of tags and transition rules based on the status data collected in real time; Dynamically updating the fault propagation path includes automatically updating the marked position in the Petri net model based on the change of the device status, outputting the latest path for the device fault propagation, and adjusting the propagation path according to the type, location and impact range of the device fault; Dynamically assessing fault priority includes analyzing the equipment's operating status and load based on the equipment's fault location, fault type, and impact range, and assessing fault priority based on the dependencies between devices and the speed at which faults propagate. Dynamically evaluating fault priorities also includes adjusting fault priorities in real time based on external environmental factors through dynamic algorithms.

2. The subway station ventilation alarm method according to claim 1, characterized in that: The automatic triggering of the fault alarm by the alarm device includes automatically adjusting the intensity and type of the alarm signal according to the impact and priority of the fault according to the priority of the equipment fault; When the equipment has a higher fault priority, use red flashing lights and high-volume alarms, and send emergency notifications to operators via a paging device; When the fault priority is low, the yellow indicator light and low volume alarm are used to remind the operator to perform follow-up inspection.

3. The subway station ventilation alarm method according to claim 2, characterized in that: The automatic triggering of the fault alarm by the alarm device also includes that when the fault alarm signal is triggered, the instruction sending device automatically generates a repair instruction and sends the instruction to the maintenance personnel or the control center through the wireless network; The instruction content includes the specific location of the faulty equipment, the type of fault, the recommended handling measures and the impact of the fault on the ventilation system. The instruction sending device tracks the maintenance progress based on the equipment's repair status and updates the equipment status based on the maintenance feedback data.

4. A system using the subway station ventilation alarm method according to any one of claims 1 to 3, characterized in that: Including status analysis module, fault assessment module and fault alarm module; The state analysis module is used to monitor the equipment state data in real time, use the Petri net model based on FMECA analysis to build the dependency relationship between different ventilation equipment in the subway station, and dynamically update the fault propagation path; The fault assessment module is used to dynamically assess the priority of the fault according to the location, impact range, and propagation speed of the equipment fault, combined with the importance and working status of the equipment; The fault alarm module is used to automatically trigger a fault alarm through an alarm device according to the evaluated priority, and send a repair instruction to a maintenance person through an instruction sending device.

5. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the subway station ventilation alarm method described in 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 of the subway station ventilation alarm method according to any one of claims 1 to 3 are implemented.

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