Energy-saving optimization method for tunnel smoke outlet fire monitoring system

Abstract

The invention relates to the technical field of tunnel safety monitoring, in particular to an energy-saving optimization method for a tunnel smoke outlet fire monitoring system. According to the method, an analytic hierarchy process (AHP) is adopted to process sensor weight distribution, a judgment matrix is flexibly adjusted according to a fire scene and a non-fire scene, and the importance weight of the sensor is dynamically calculated. Meanwhile, in combination with a dynamic storage capacity calculation model, the storage requirements of a fire state and a non-fire state are distinguished by integrating factors such as the acquisition frequency, the data volume and the operation time of a sensor, the total storage capacity of the cloud platform is accurately estimated through a mathematical model, and storage resources are reasonably planned. According to the method, the use efficiency of storage resources of the cloud platform can be effectively improved, energy consumption and storage requirements are reduced, the economical efficiency and long-term operation sustainability of the monitoring system are improved, and the method is suitable for optimization requirements in complex monitoring scenes.

Description

Technical Field

[0001] The present invention relates to the technical field of tunnel safety monitoring, and particularly relates to an energy-saving optimization method for a fire monitoring system at a tunnel smoke exhaust port. Background Art

[0002] With the acceleration of the urbanization process, public transportation infrastructure such as subways has been widely constructed. As an important public place, the safety of subway tunnels directly affects the operation efficiency and public safety of the city. The smoke exhaust port, as an important part of tunnel environment control and fire emergency handling, plays a key role in fire early warning, emergency disposal and daily maintenance. In recent years, the development of Internet of Things technology has provided many solutions for monitoring.

[0003] Patent No. CN218768341U discloses an NB-IoT-based intelligent fire monitoring system for laboratories. The system realizes the collection, analysis and fire monitoring of laboratory environment data by setting up an environment collection module, a control module, an NB-IoT wireless transmission module, an alarm module, etc. When abnormal environment data is detected, the system generates an alarm signal and transmits it wirelessly to the cloud platform for storage through NB-IoT, and at the same time triggers an alarm prompt to achieve timely fire early warning.

[0004] Patent No. CN119203193A discloses an Internet of Things sensor monitoring data encryption and storage processing method, which solves the problems of encrypted storage and efficient retrieval of massive sensor time series data. This method combines the MD5 information digest algorithm, array rearrangement calculation and CRC cyclic redundancy check algorithm for multiple confusion encryption, and uses a TDengine time series database to store real-time monitoring data and a MySQL relational database to store abnormal data to improve data storage efficiency and data security.

[0005] Currently, tunnel fire monitoring systems usually rely on multiple sensors to monitor the environmental changes in the tunnel in real time, such as temperature, smoke concentration, carbon monoxide concentration, etc. With the application of Internet of Things technology, the number of sensors has increased significantly, and the data collection frequency has also been greatly improved. Although this method effectively enhances the fire monitoring ability, it also faces problems such as energy consumption, storage pressure and data redundancy. The general method usually adopts a fixed sensor collection strategy. Whether a fire occurs or in the daily monitoring state, the collection frequency and data volume of the sensors are basically the same.

[0006] However, although this method can ensure comprehensive monitoring during a fire, it often leads to excessive resource consumption, such as unnecessary energy consumption and storage pressure. In a non-fire state, the monitoring requirements of the sensors are relatively low, and the waste of resources is particularly significant. Therefore, how to scientifically allocate sensor weights, improve energy efficiency, and efficiently manage storage resources has become an urgent problem to be solved. By integrating multiple types of sensors and combining the big data storage and processing capabilities of the cloud platform, comprehensive monitoring of the tunnel environment can be achieved. Reasonably planning data storage requirements, preferentially storing key data during a fire and focusing on storing basic monitoring data in a non-fire state, while optimizing energy consumption management and reducing unnecessary energy consumption, is of great significance for improving the reliability, intelligence level, and energy efficiency of the system.

[0007] Therefore, developing an energy-saving optimization method for the fire monitoring system of tunnel smoke exhaust outlets, which can dynamically adjust the working mode and weight allocation of sensors for different scenarios, and at the same time achieve refined management of storage resources in combination with the cloud platform, optimize energy consumption, reduce unnecessary consumption, and improve the overall performance of the tunnel monitoring system. Summary of the Invention

[0008] The object of the present invention is to provide an energy-saving optimization method for the fire monitoring system of tunnel smoke exhaust outlets, which dynamically adjusts the working state and weight allocation of sensors to adapt to different monitoring requirements.

[0009] The object of the present invention is achieved through the following means: An energy-saving optimization method for the fire monitoring system of tunnel smoke exhaust outlets, the steps are as follows:

[0010] S1. Calculate the weights of different types of sensors according to the analytic hierarchy process;

[0011] S2. Calculate the node density adjustment factor according to the total monitoring area of the tunnel and the fire impact area;

[0012] S3. Dynamically calculate the number of each sensor participating in the acquisition during a fire according to the weights of different types of sensors and the node density adjustment factor;

[0013] S4. Estimate the storage capacity of the cloud platform in a fire state and a non-fire state.

[0014] As a further limitation of this solution, the specific method of S1 is as follows:

[0015] Assume that there are n types of sensors in the system, and construct an n×n judgment matrix A, where the element a ij represents the importance of the i-th type of sensor relative to the j-th type of sensor:

[0016]

[0017] Among them, a ijThe value of ij a ij = 1 indicates that the two types of sensors are equally important; a ij > 1 indicates that the i-th type of sensor is more important than the j-th type of sensor;

[0018] Calculate the weight vector. Using the arithmetic mean method, calculate the geometric mean of each row element:

[0019]

[0020] Normalize the geometric mean to obtain the weights of different types of sensors:

[0021]

[0022] And perform a consistency check to ensure the consistency of the judgment matrix A. Calculate the consistency index (CI):

[0023]

[0024] where λ max represents the maximum eigenvalue of the judgment matrix;

[0025] Calculate the consistency ratio (CR):

[0026]

[0027] where RI is the random consistency index, which depends on the order of the matrix; when CR < 0.1, the consistency of the judgment matrix is acceptable, otherwise the judgment matrix needs to be adjusted.

[0028] As a further limitation of this solution, the specific method of S2 is as follows:

[0029] The length of the fire area is determined by the distance where the data monitored by the sensors at both ends do not reach the fire threshold:

[0030] L fire = (K - 1)·d + 2R

[0031] In the formula, K represents the number of sensors triggered; d represents the distance between sensors; R represents the monitoring radius of the sensor;

[0032] The area of the fire affected area is:

[0033] A fire = L fire ·W

[0034] In the formula, L fireDenoted as the length of the area affected by the tunnel fire; W is the width of the tunnel;

[0035] The node density adjustment factor is:

[0036]

[0037] In the formula, A tunnel is the total monitored area of the tunnel; L tunnel Denoted as the length of the total monitored area of the tunnel.

[0038] As a further limitation of this solution, the specific method of S3 is as follows:

[0039] According to the weights of different types of sensors and the node density adjustment factor, dynamically calculate the number M of each sensor participating in the acquisition i :

[0040] M i = β·w i ·N i

[0041] In the formula, β is the node density adjustment factor, that is, the ratio of the fire-affected area to the total monitored area of the tunnel; w i is the weight of the i-th type of sensor; N i is the total number of the i-th type of sensor.

[0042] As a further limitation of this solution, the specific method of S4 is as follows:

[0043] The storage capacity model of the cloud platform is divided into fire state and non-fire state:

[0044] Fire state

[0045]

[0046] Non-fire state

[0047]

[0048] Assume that the fire-free time in a year accounts for 99% and the fire time accounts for 1%, and the total storage capacity is obtained as:

[0049] D total =(0.01D fire +0.99D normal )×365

[0050] In the formula, n represents the sensor type; P i represents the sensor frequency; S i represents the amount of data collected per single time; M i represents the number of the i-th type of sensor dynamically participating in the acquisition; T fireIndicates the fire occurrence time; T normal Indicates the non - fire occurrence time; N i is the total number of the i - th type of sensors.

[0051] Compared with the prior art, the beneficial effects of an energy - saving optimization method for a fire monitoring system at a tunnel smoke exhaust port of the present invention are as follows:

[0052] 1. In the prior art, the working mode and data acquisition method of sensors are fixed, without considering the importance differences of sensors in different scenarios. For example, during a fire, temperature and smoke sensors respond more quickly and significantly, while the change of carbon monoxide sensors is relatively lagging. However, the existing system does not dynamically adjust the working state or weight allocation of each sensor according to these differences, resulting in low response efficiency and resource utilization rate of the system in the fire emergency state. The present invention uses the Analytic Hierarchy Process (AHP) to dynamically adjust the working state and weight allocation of sensors according to the functions and importance of different sensors, especially for differential processing in fire and non - fire scenarios. In the fire emergency, the acquisition and processing of key sensors are preferentially guaranteed, effectively improving the response speed of the system in emergencies and ensuring the high efficiency of the system in dealing with emergencies and daily monitoring.

[0053] 2. After the data in the prior art is pre - processed, it is transmitted by the data transmission module to the edge device processing module through wireless communication technology for local storage, and at the same time, the data is also transmitted to the cloud platform processing center for further analysis. However, the prior art fails to dynamically adjust according to the storage requirement differences in fire and non - fire states, resulting in resource waste. Moreover, the prior art fails to effectively optimize energy consumption management, and sensors are often in an unnecessary high - energy - consumption state, increasing the operation cost of the system. The present invention proposes a storage amount model for the cloud platform and combines it with the Analytic Hierarchy Process, improving the utilization efficiency of storage resources, with a storage optimization rate reaching 79%, and at the same time achieving 76% energy consumption optimization by reducing unnecessary energy consumption. The present invention can flexibly adapt to fire and non - fire scenarios, ensure the high - efficiency operation of the system, avoid resource waste, and provide economic support for the long - term sustainable operation of the Internet of Things system. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The present invention will be further described in detail below with reference to the drawings:

[0055] Figure 1 is the flow block diagram of the present invention;

[0056] Figure 2 is the flow chart of the Analytic Hierarchy Process of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0057] Such as Figure 1As shown, an energy-saving optimization method for a fire monitoring system of a tunnel smoke exhaust port in the present invention is proposed, and a dynamic storage calculation model based on the acquisition frequency, data volume, and running time of sensors is presented. This model dynamically estimates the storage requirements of the cloud platform according to the data acquisition modes in fire and non-fire states. The present invention can reasonably plan the storage resources of the cloud platform, ensure its efficient operation, and avoid resource waste.

[0058] To meet the requirements of dynamically adjusting the working states and weight distributions of sensors in the subway tunnel monitoring scenario, for specific monitoring situations, in the scenario of the present invention, the analytic hierarchy process will be used to handle the weight distribution of sensors.

[0059] Assume there are n types of sensors in the system, and an n×n judgment matrix A is constructed, where the element a ij represents the importance of the i-th type of sensor relative to the j-th type of sensor:

[0060]

[0061] Among them, the value of a ij usually follows the 1-9 scale method (scores range from 1 to 9, and the higher the score, the more important), a ij =1 indicates that the two types of sensors are equally important; a ij >1 indicates that the i-th type of sensor is more important than the j-th type of sensor; a ij <1 indicates that the i-th type of sensor is less important than the j-th type of sensor.

[0062] Calculate the weight vector. Using the arithmetic mean method, calculate the geometric mean of each row of elements:

[0063]

[0064] Normalize the geometric mean to obtain the weights of different types of sensors:

[0065]

[0066] And perform a consistency check to ensure the consistency of the judgment matrix A. Calculate the consistency index (CI):

[0067]

[0068] Among them, λ max represents the maximum eigenvalue of the judgment matrix.

[0069] Calculate the consistency ratio (CR):

[0070]

[0071] Among them, RI is the random consistency index, which depends on the order of the matrix. When CR < 0.1, the consistency of the judgment matrix is acceptable; otherwise, the judgment matrix needs to be adjusted.

[0072] If there is inconsistency in the judgment matrix, re-scoring and adjustment are required. The adjustment can be carried out in the following ways:

[0073] 1. Lower the overly high scores. If the scores of some sensors are too high, it may lead to an imbalance in the relative weights of other sensors, and appropriate reduction of the scores should be considered.

[0074] 2. Raise the overly low scores. If the scores of some sensors are too low, it may result in their too small proportion in the overall weight, and the scores should be appropriately increased.

[0075] 3. Adjust the score gap. Between some sensors, if the score gap is too large (such as between 3 and 9), the gap needs to be narrowed. According to the actual situation and requirements, the overly high score can be adjusted to a more reasonable intermediate value (such as adjusting 3 to 5).

[0076] 4. Ensure that the relative comparison between sensors is reasonable. For example, temperature sensors and smoke sensors are usually very important in a fire scenario, so the scores between them should be relatively high and reasonably distributed. If the score gap between the two is too large, adjustment should be made.

[0077] The adjusted matrix needs to be subjected to a consistency test, calculating the new consistency index (CI) and consistency ratio (CR). If CR is still greater than 0.1, the scores need to be adjusted continuously. If CR is less than 0.1, it indicates that the matrix consistency passes and the re-scoring is completed.

[0078] In a fire scenario, there is usually an increase in temperature, generation of smoke, increase in carbon monoxide concentration, and the need to activate the opening of the smoke exhaust port. A quick response to the fire is crucial, and the sensors that reach the threshold first and trigger the alarm have higher scores; the sensors mainly used for backup or auxiliary purposes have lower scores. The scoring criteria are as

[0079] shown in Table 1:

[0080]

[0081] In a non-fire scenario, the goal of the sensors is to monitor and maintain the equipment status daily. They are used to monitor the normal operation of the system, trigger equipment maintenance and adjustment operations. The sensors that are crucial for daily monitoring have higher scores; the sensors with low relevance to daily monitoring and mainly used for backup or auxiliary purposes have lower scores. The scoring criteria are shown in Table 2:

[0082]

[0083] The beneficial effects of using the Analytic Hierarchy Process are that complex problems can be decomposed into multiple levels and solved step by step. In the present invention, the goal is to evaluate the fire risk, the criteria are the importance of each sensor, and the solution is the weight allocation of the sensors; it can flexibly adapt to dynamic scenarios, as there are different requirements in fire and non-fire scenarios, allowing for flexible adjustment of the judgment matrix in different scenarios; it improves the accuracy of the judgment results, ensures that the weight allocation is logical, and avoids the fixed influence of human errors on the results; it is easy to expand, as this method is not only applicable to the current four types of sensors but can also incorporate other sensors, and only the judgment matrix needs to be updated. The structured nature of the method makes it applicable to any complex multi-factor decision-making problem.

[0084] According to the weights of different types of sensors, dynamically calculate the number M of each sensor participating in data collection i :

[0085] M i = β·w i ·N i

[0086] In the formula, β is the node density adjustment factor; w i is the weight of the i-th type of sensor; N i is the total number of the i-th type of sensor.

[0087] The node density adjustment factor is the ratio of the area of the fire-affected area to the total monitored area of the tunnel. After a period of time after a fire occurs, when the data monitored by the sensors at both ends do not trigger the fire threshold, the range of the fire-affected area can be determined. If the sensors are installed on both sides of the tunnel and their monitoring ranges cover a certain area, the width of the fire-affected area is equal to the width of the tunnel. The length of the fire area is determined by the distance at which the data monitored by the sensors at both ends do not reach the fire threshold:

[0088] L fire =(K - 1)·d + 2R

[0089] In the formula, K represents the number of sensors triggered; d represents the distance between sensors; R represents the monitoring radius of the sensor.

[0090] The area of the fire-affected area is:

[0091] A fire = L fire ·W

[0092] In the formula, L fire represents the length of the tunnel fire-affected area; W is the width of the tunnel.

[0093] The node density adjustment factor is:

[0094]

[0095] In the formula, A tunnel is the total monitored area of the tunnel; L tunnel represents the total monitored length of the tunnel.

[0096] In order to optimize the data management of the Internet of Things cloud platform in the monitoring system, the present invention proposes a storage capacity model for the cloud platform. This model dynamically estimates the data storage requirements of the Internet of Things cloud platform according to the acquisition frequency, acquisition data volume, running time, etc. of the sensors. This model not only considers the acquisition mode in the fire state, but also takes into account the basic monitoring state in the non-fire state, so as to realize the reasonable planning of cloud storage resources.

[0097] The storage capacity model of the cloud platform is divided into a fire state and a non-fire state:

[0098] Fire state

[0099]

[0100] Non-fire state

[0101]

[0102] Assume that the time without fire in a year accounts for 99%, and the fire time accounts for 1%, and the total storage capacity is obtained as:

[0103] D otal =(0.01D fire +0.99D normal )×365

[0104] In the formula, n represents the type of sensor; P i represents the sensor frequency; S i represents the amount of data collected each time; M i represents the number of the i-th type of sensors dynamically participating in the acquisition; T fire represents the fire occurrence time; T normal represents the non-fire occurrence time. N i is the total number of the i-th type of sensors.

[0105] Embodiment:

[0106] According to the type of sensors used in the present invention, the scores of each sensor in the fire state are set as shown in Table 3:

[0107] Sensor type Trigger priority Score Temperature sensor Primary (rapid temperature rise) 9 Smoke sensor Second (significant smoke) 8 CO sensor Auxiliary (increasing poisonous gas) 6 Magnetic induction sensor Auxiliary (indirect reflection) 3

[0108] Set the scores of each sensor in the non-fire state as shown in Table 4:

[0109] Sensor type Importance of daily monitoring Score Temperature sensor Medium importance 6 Smoke sensor Standby monitoring 2 CO sensor Auxiliary detection 4 Magnetic induction sensor Maintenance core 7

[0110] In a fire scenario, temperature sensors, smoke sensors, and CO sensors are the core feature monitoring devices for fires, used to directly judge the occurrence and severity of a fire. The task of the magnetic induction sensor is to monitor the status of the smoke exhaust outlet, which belongs to the auxiliary part of the subsequent response to a fire rather than the key to fire judgment. Therefore, the scoring score of the magnetic induction sensor is lower than that of the other three sensors. During a fire, the temperature rises rapidly, with a significant and rapid response. Smoke is one of the prominent features of a fire, but the concentration change may be slightly slower than the temperature rise. The change in CO concentration is an auxiliary feature of a fire, usually lagging behind the changes in temperature and smoke.

[0111] In a non-fire scenario, the focus of monitoring is mainly on the maintenance of the smoke exhaust outlet equipment to ensure its normal operation. Therefore, the scoring score of the magnetic induction sensor is lower than that of the other three sensors. The temperature sensor is used for ambient temperature monitoring during daily monitoring, providing early signals of fire hazards. The CO sensor is used for auxiliary ventilation monitoring. The role of the smoke sensor in daily monitoring is limited. The smoke concentration is generally within the normal range, and the probability of sensor triggering is low. It is usually set to the standby state.

[0112] Assume there are 4 types of sensors in the system, construct a 4×4 matrix, and based on the above scores, the judgment matrices A and B for the fire state and non-fire state are as follows:

[0113]

[0114] Calculate the geometric mean of each row element of matrices A and B:

[0115]

[0116] Normalize the geometric mean to obtain the weights of different types of sensors:

[0117]

[0118] According to the formula, the weights of each sensor in the fire state and non-fire state are obtained as shown in Table 4:

[0119] Sensor type <![CDATA[Fire state w i > <![CDATA[Non-fire state w i > Temperature sensor 0.35 0.32 Smoke sensor 0.30 0.11 CO sensor 0.23 0.21 Magnetic induction sensor 0.12 0.36

[0120] And the maximum eigenvalue of matrices A and B is obtained as: λ A =4, Perform a consistency check to ensure the consistency of judgment matrix A, and calculate the consistency index (CI): CI A =0, CI B = -0.0696

[0121] Since the matrix is of order 4, take RI = 0.89, and calculate the consistency ratio (CR): CR A =0, CRB = -0.078. The consistency ratios of matrices A and B are both less than 0.1, so the consistency of the judgment matrix is acceptable.

[0122] According to the monitoring requirements in the case of the subway tunnel in the fire state and the non-fire state, assume that the acquisition frequency of the sensors is as shown in Table 5:

[0123] Sensor type Fire acquisition frequency Non-fire acquisition frequency Data volume (single acquisition) Temperature sensor 100 times per second 10 times per second 8 bytes (floating point number) Smoke sensor 100 times per second 10 times per second 8 bytes (floating point number) CO sensor 100 times per second 10 times per second 8 bytes (floating point number) Magnetic induction sensor 1 time per second 1 time per 10 seconds 1 byte (boolean)

[0124] To meet the requirements of real-time performance and reducing the occupancy of network bandwidth, assume that the upload interval and acquisition accuracy are as shown in Table 6:

[0125] Sensor type Upload interval (fire / non-fire) Data accuracy requirement Display value calculation method (fire / non-fire) Temperature sensor Every 1 second / every 10 seconds ±0.1℃ Average value of every 100 data / every 10 Smoke sensor Every 1 second / every 10 seconds ±0.1ppr1 Average value of every 100 data, every 10 CO sensor Every 1 second / every 10 seconds ±1%FS Average value of every 100 data / every 10 Magnetic induction sensor Every 5 seconds / every 30 seconds None None

[0126] Assume that the fire duration is 86 seconds (accounting for about 0.1% of the total time of a day), and the non-fire occurrence time is about 86,400 seconds. There are 500 sensors of each type in the whole section of the tunnel.

[0127] The storage capacity without using the analytic hierarchy process to calculate the weights of each sensor is as shown in Table 8

[0128]

[0129] The calculated energy consumption is as shown in Table 9:

[0130] Sensor type Energy consumption Temperature sensor 0.5×500＝250W Smoke sensor 0.6×500＝300W CO sensor 0.4×500＝200W Magnetic induction sensor 0.3×500＝150W Total E=(50 + 300 + 200 + 150)×3686 = 3.32MW

[0131] The storage capacity using the analytic hierarchy process to calculate the weights of each sensor is as shown in Table 10:

[0132] Assume that the number K of sensor triggers is 480, the distance d between sensors is 10, R is 5, and the total length L of the tunnel monitoring area tunnel is 8000. The length of the fire affected area and the node density adjustment factor are:

[0133] L fire = (480 - 1)×10 + 2×5 = 4800

[0134] β = 4800 / 8000 = 0.6

[0135] According to the weights of different types of sensors, dynamically calculate the number of each sensor participating in the acquisition in the fire state:

[0136] Temperature sensor: M = 0.6×0.35×500 = 105

[0137] Smoke sensor: M = 0.6×0.3×500 = 90

[0138] CO sensor: M = 0.6×0.23×500 = 69

[0139] Magnetic induction sensor: M = 0.6×0.12×500 = 36

[0140]

[0141]

[0142] The energy consumption is calculated as shown in Table 11:

[0143]

[0144] Compare according to the calculated results:

[0145]

[0146] By adopting the Analytic Hierarchy Process (AHP) for dynamic allocation of sensor weights, the working states of various sensors in the system can be adjusted more reasonably, thus optimizing energy consumption and storage capacity while meeting the monitoring requirements. Compared with the case without using the Analytic Hierarchy Process, the total energy consumption and storage capacity of the system are reduced.

[0147] Finally, by adopting the Analytic Hierarchy Process (AHP) for dynamic allocation of sensor weights, the annual data volume is approximately 0.76 PB. To reserve for future expansion, the storage capacity of the Internet of Things cloud platform can be ≥5 PB / year.

Claims

1. A method for energy saving optimization of a tunnel smoke exhaust port fire monitoring system, characterized in that: Here are the steps: S1. Calculate the weights of different types of sensors according to the analytic hierarchy process; S2. Calculate the node density adjustment factor based on the total monitoring area of ​​the tunnel and the fire impact area; S3. Dynamically calculate the number of sensors involved in fire collection according to the weights of different types of sensors and node density adjustment factors; S4. Estimate the cloud platform storage capacity in fire and non-fire states.

2. The energy-saving optimization method for a tunnel smoke exhaust port fire monitoring system according to claim 1, characterized in that: S1 specific method is as follows: Assuming there are n types of sensors in the system, construct an n×n judgment matrix A, where element a ij Indicates the importance of the i-th sensor relative to the j-th sensor: Among them, a ij The value of is usually based on a 1-9 scale (the score is 1-9, the higher the score, the more important it is). ij =1 means that the two types of sensors are equally important; a ij >1 means that the i-th sensor is more important than the j-th sensor; a ij <1 means that the i-th sensor is less important than the j-th sensor; Calculate the weight vector and use the arithmetic mean method to calculate the geometric mean of the elements in each row: Normalize the geometric mean to get the weights of different types of sensors: And perform consistency check to ensure the consistency of the judgment matrix A and calculate the consistency index (CI): Among them, λ max represents the maximum eigenvalue of the judgment matrix; Calculate the consistency ratio (CR): Among them, RI is the random consistency index, which depends on the order of the matrix; when CR<0.1, the consistency of the judgment matrix is ​​acceptable, otherwise the judgment matrix needs to be adjusted.

3. The energy-saving optimization method for a tunnel smoke exhaust port fire monitoring system according to claim 1, characterized in that: The specific method of S2 is as follows: The length of the fire zone is determined by the distance at which the data monitored by the front and rear sensors do not reach the fire threshold: L fire =(K-1)·d+2R Where K represents the number of sensor triggers; d represents the distance between sensors; R represents the monitoring radius of the sensor; The area affected by the fire is: A fire =L fire ·W Where, L fire It is expressed as the length of the tunnel fire affected area; W is the width of the tunnel; The node density adjustment factor is: In the formula, A tunnel is the total monitoring area of ​​the tunnel; L tunnel Expressed as the total monitoring area length of the tunnel.

4. The energy-saving optimization method for a tunnel smoke exhaust port fire monitoring system according to claim 1, characterized in that: The specific method of S3 is as follows: According to the weights of different types of sensors and the node density adjustment factor, the number of sensors involved in the collection Mi is dynamically calculated: M i =β·w i ·N i Where β is the node density adjustment factor, which is the ratio of the fire affected area to the total monitoring area of ​​the tunnel; w i is the weight of the i-th sensor; N i is the total number of sensors of the i-th category.

5. The energy-saving optimization method for a tunnel smoke exhaust port fire monitoring system according to claim 1, characterized in that: The specific method of S4 is as follows: The cloud platform storage capacity model is divided into fire state and non-fire state: Fire status Non-fire status Assuming that there is no fire for 99% of the time in a year and fire for 1%, the total storage capacity is: <h2 style=";text-align:left;direction:ltr">D<h2 style=";text-align:left;direction:ltr"> total <h2 style=";text-align:left;direction:ltr"> =(0.01D<h2 style=";text-align:left;direction:ltr"> fire <h2 style=";text-align:left;direction:ltr"> +0.99D<h2 style=";text-align:left;direction:ltr"> normal <h2 style=";text-align:left;direction:ltr"> )×365 Where n represents the sensor type; P i Indicates the sensor frequency; S i Indicates the amount of data collected in a single time; M i represents the number of sensors of the i-th type participating in dynamic acquisition; T fire Indicates the time when the fire occurred; T normal Indicates non-fire time; N i is the total number of sensors of the i-th category.

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