Real-time data collection of abnormal sensors in pipe gallery intelligent operation and maintenance method based on AI technology

Abstract

The present invention belongs to the technical field of abnormal operation and maintenance of pipe corridors, and discloses an intelligent operation and maintenance method for pipe corridors based on AI technology and real-time data collection by abnormal sensors. When analyzing the abnormal state of each monitoring section of the target pipe corridor, the present invention analyzes the environmental factors of the sensor data collection of each monitoring section, and then analyzes the abnormal operation of the comprehensive sensor equipment. This analysis method can eliminate the influence of environmental factors, thereby improving the accuracy of sensor data. When judging the existence of abnormal conditions, the present invention corrects the abnormal operation of the comprehensive sensor equipment of each monitoring section based on the ventilation performance evaluation of each monitoring section, and then judges the existence of abnormal conditions. This analysis method introduces ventilation performance evaluation to more comprehensively understand the actual operating environment of the monitoring section. Combining ventilation performance evaluation for abnormal judgment can more accurately identify abnormal conditions caused by environmental factors.

Description

Technical Field

[0001] The present invention belongs to the technical field of abnormal operation and maintenance of pipe corridors, and relates to an intelligent operation and maintenance method for pipe corridors using abnormal sensors based on AI technology to collect real-time data. Background Art

[0002] The pipe gallery, or the urban underground integrated pipe gallery, refers to a tunnel space built underground in the city, which integrates various engineering pipelines such as electricity, communications, gas, heating, water supply and drainage. The internal environment of the pipe gallery is complex, and there may be many changing factors such as temperature, humidity, and concentration of harmful gases. By monitoring these environmental parameters in real time, abnormal conditions can be discovered in time to prevent the occurrence of potential risks such as fire, explosion, and corrosion. AI technology can realize real-time monitoring of the internal environment of the pipe gallery, including key parameters such as temperature, humidity, and gas concentration, to ensure that any abnormalities can be discovered in time. Therefore, it is of great significance for abnormal sensors based on AI technology to collect intelligent operation and maintenance of the pipe gallery in real time.

[0003] In the prior art, there are also related solutions for abnormal operation and maintenance of pipe gallery environment, for example, the Chinese invention patent application for the method, system, equipment and storage medium for automatic monitoring and early warning of urban integrated pipe gallery and surrounding geological environment with publication number CN114185959A, which includes: collecting data and determining the assembly method of monitoring devices, and determining the installation method and communication method of monitoring devices; determining monitoring points and data acquisition interfaces; installation and trial operation, installing monitoring devices and protection devices, operating monitoring devices, and determining data analysis models and data display models based on the data output by the monitoring devices; correction of monitoring data and abnormal alarm, analyzing the monitoring data output by the monitoring device through the data analysis model; if it is an error, correct it through historical data and current data; if it is an abnormal situation, output an alarm signal, and at the same time output the abnormal signal and the data category corresponding to the abnormal signal. This application has the purpose of simultaneously completing the monitoring of the integrated pipe gallery and the surrounding environment, and realizing the purpose of automatic monitoring and analysis and early warning.

[0004] Although the above scheme proposes some solutions for the quality analysis of diode packaging interfaces, it still has certain limitations: On the one hand, the existing pipeline corridor environment abnormality analysis process judges abnormal conditions based on the data information collected by sensors, but ignores the analysis of the accuracy of sensor data collection. This analysis method may cause the collected data to be inaccurate due to environmental factors or aging, failure, etc. If the accuracy of the sensor data is not corrected, these inaccurate data may directly lead to false alarms or missed abnormal conditions, affecting the judgment and handling of operation and maintenance personnel. At the same time, frequent false alarms will waste the time and energy of operation and maintenance personnel.

[0005] On the other hand, the existing abnormal operation and maintenance process of the pipe gallery environment is usually only designed to analyze the abnormal location, without further automatic processing of abnormal situations at abnormal locations. This kind of analysis method of manual processing of abnormalities is often limited by factors such as the number, skills and experience of personnel, which may lead to low processing efficiency and failure to quickly solve the problem. If the abnormal situation is not handled in time, it may further deteriorate and even cause more serious safety problems. For example, if abnormal situations such as fire and leakage are not controlled in time, they may spread rapidly and cause greater damage to the pipe gallery facilities and the surrounding environment. Summary of the invention

[0006] In view of this, in order to solve the problems raised in the above background technology, a method for intelligent operation and maintenance of a pipeline corridor using abnormal sensors based on AI technology to collect real-time data is proposed.

[0007] The purpose of the present invention can be achieved through the following technical solutions: S1. Monitoring section division: the target corridor is divided into monitoring sections based on equal interval lengths to obtain several monitoring sections, and the midpoint of each monitoring section is recorded as a monitoring point.

[0008] S2. Ventilation performance evaluation: Conduct ventilation performance tests on each monitoring section and obtain ventilation performance evaluation data for each monitoring section, including wind speed, wind speed response adjustment amount, wind speed response adjustment time, air duct outlet area and reference distance. Analyze the ventilation performance evaluation of each monitoring section and determine whether there are any abnormalities in the ventilation facilities of each monitoring section.

[0009] S3. Identification of abnormal monitoring sections: Collect the operating data of the comprehensive sensor equipment in each monitoring section, analyze the abnormal operation of the comprehensive sensor equipment in each monitoring section, and then combine the ventilation performance evaluation of each monitoring section to determine whether each monitoring section is in an abnormal state, and record the monitoring section that is judged to be in an abnormal state as an abnormal monitoring section.

[0010] S4. Abnormal direction analysis: Identify the specific abnormal direction of the abnormal monitoring section.

[0011] S5. Abnormal state control: perform abnormal state control on abnormal sections.

[0012] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) When analyzing the abnormal status of each monitoring section of the target tunnel, the present invention analyzes the environmental factors of the sensor data collected in each monitoring section, and then analyzes the abnormal operation of the comprehensive sensor equipment. This analysis method can eliminate or weaken the influence of environmental factors, thereby improving the accuracy of sensor data. Monitoring and correcting the abnormal operation of the sensor equipment itself can eliminate erroneous data caused by equipment failure or instability, ensuring that the data based on the analysis is true and reliable. Based on accurate data and correction considering environmental factors, the abnormal status of each monitoring section of the tunnel can be judged more accurately, reducing the false alarm and missed alarm rate caused by inaccurate data.

[0013] (2) When judging the existence of abnormal conditions, the present invention corrects the abnormal operation of the comprehensive sensor equipment in each monitoring section based on the ventilation performance evaluation of each monitoring section, and then judges the existence of abnormal conditions. This analysis method can more comprehensively understand the actual operating environment of the monitoring section by introducing ventilation performance evaluation. The quality of ventilation performance directly affects the distribution of parameters such as gas concentration and temperature in the corridor, and thus affects the accuracy of sensor data. Combining ventilation performance evaluation for abnormal judgment can more accurately identify abnormal conditions caused by environmental factors and reduce misjudgments.

[0014] (3) The present invention can quickly determine the specific location and possible causes of the abnormality by performing abnormality pointing analysis and automatic abnormal state control on the abnormal monitoring section. This precise positioning helps operation and maintenance personnel to quickly understand the root cause of the problem and avoid blind investigation and waste of resources. The present invention introduces an automatic control mechanism that can automatically take corresponding control measures after detecting an abnormal state. This automated processing method not only reduces the need for manual intervention and reduces labor costs, but also improves response speed and processing efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing 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.

[0016] Figure 1 It is a schematic diagram of the implementation steps of the method of the present invention.

[0017] Figure 2 The present invention provides a logic flow chart for determining the existence of an abnormal monitoring section.

[0018] Figure 3 This is a schematic diagram of the structure of the comprehensive sensor device provided by the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] See also Figure 1 As shown, the present invention provides an intelligent operation and maintenance method for a pipeline corridor using abnormal sensors in real time based on AI technology, and the specific steps are as follows: S1. Monitoring section division: The target pipeline corridor is divided into monitoring sections based on equal interval lengths to obtain a number of monitoring sections, and the midpoint of each monitoring section is recorded as a monitoring point.

[0021] It should be noted that the reason for the division of monitoring sections is that when an abnormality or failure occurs in the tunnel, the problem can be quickly located by dividing the monitoring sections. Each monitoring section has clear boundaries and signs, and the operation and maintenance personnel can quickly determine the specific location of the abnormality based on the monitoring data, thereby shortening the troubleshooting time. After locating the problem, the operation and maintenance personnel can quickly mobilize relevant resources and personnel to go to the scene for processing. The division of monitoring sections can ensure that the operation and maintenance team can respond quickly after receiving the alarm, reducing the impact of the failure on the operation of the tunnel.

[0022] It should be noted that the monitoring point is used for equipment integrated sensor equipment.

[0023] S2. Ventilation performance evaluation: Conduct ventilation performance tests on each monitoring section and obtain ventilation performance evaluation data for each monitoring section, including wind speed, wind speed response adjustment amount, wind speed response adjustment time, air duct outlet area and reference distance. Analyze the ventilation performance evaluation of each monitoring section and determine whether there are any abnormalities in the ventilation facilities of each monitoring section.

[0024] It should be noted that the reasons for selecting wind speed, wind speed response adjustment, wind speed response adjustment time, duct outlet area and reference distance as ventilation performance evaluation data are as follows: 1. Wind speed is the most intuitive reflection of ventilation system performance, which directly determines the speed of air flow and the quality of ventilation effect. 2. Wind speed response adjustment refers to the ability of the ventilation system to automatically adjust the wind speed to maintain the set conditions under certain conditions. This indicator reflects the flexibility and stability of the system. 3. Wind speed response adjustment time refers to the time required from the system receiving the adjustment instruction to the actual wind speed reaching the set value. This indicator reflects the response speed and control accuracy of the system. 4. The size of the duct outlet area directly affects the distribution and diffusion range of the airflow. Reasonable duct outlet area design can ensure that the air is evenly distributed to each area and improve the ventilation effect. 5. As one of the benchmarks for evaluating ventilation performance, the reference distance helps to achieve standardization and comparability of evaluation results.

[0025] It should be further explained that the analysis of the ventilation performance evaluation of each monitoring section requires the construction of a ventilation performance evaluation index for each monitoring section. The specific analysis method is as follows: extract the wind speed response adjustment amount and wind speed response adjustment time of each monitoring section, which are recorded as , , and then analyze and obtain the wind speed response adjustment evaluation index of each monitoring section in Indicates the number of the monitoring section. , Indicates the number of monitoring segments.

[0026] It should be noted that the wind speed response adjustment amount of each monitoring section refers to the absolute value of the difference between the wind speed after adjustment and the wind speed before adjustment corresponding to the ventilation performance test of each monitoring section.

[0027] It should be noted that the wind speed response adjustment time of each monitoring section refers to the wind speed adjustment time corresponding to the ventilation performance test of each monitoring section.

[0028] Extract the duct outlet area and reference distance of each monitoring section and record them as , , and then use the formula The ventilation performance correction coefficient of each monitoring section is obtained by analysis. ,in They represent the influence weights corresponding to the preset reference distance and the air duct outlet area, respectively. Indicates the preset reference duct outlet area, Indicates the preset reference distance.

[0029] It should be noted that the reference distance of each monitoring section refers to the distance from the center point of the air duct outlet corresponding to each monitoring section to the nearest air supply equipment.

[0030] It should be noted that the influence weights corresponding to the reference distance and the duct outlet area are set based on the following: 1. The choice of reference distance should be able to represent the performance of the ventilation system at different distances. A closer reference distance may more directly reflect the ventilation effect of the system in the near distance, while a farther reference distance can evaluate the impact of the system on a larger area. Therefore, the weight setting of the reference distance should take into account its representativeness and contribution to the overall ventilation effect. 2. The size of the duct outlet area directly affects the distribution and diffusion range of the airflow. A larger outlet area can provide a larger airflow channel, which is conducive to the uniform distribution and diffusion of air; while a smaller outlet area may lead to uneven airflow distribution and affect the ventilation effect. Therefore, the weight setting of the duct outlet area should fully consider its impact on the airflow distribution.

[0031] For example, .

[0032] Extract wind speed in each monitoring section , using the formula

[0033] The ventilation performance evaluation index of each monitoring section was obtained by analysis. ,in Indicates the preset reference wind speed. Indicates the permissible difference between the preset wind speed and the reference wind speed.

[0034] It should be further explained that the specific method of analyzing and obtaining the wind speed response adjustment evaluation index of each monitoring section is as follows: extract the wind speed response adjustment value of each monitoring section and wind speed response adjustment time .

[0035] Using the formula The wind speed response adjustment evaluation index of each monitoring section is obtained by analysis ,in Indicates the preset rated ventilation control efficiency.

[0036] It should be noted that the rated ventilation adjustment efficiency can be obtained from the manual of the ventilation equipment.

[0037] It needs to be further explained that the specific method of judging whether there is an abnormality in the ventilation facilities of each monitoring section is as follows: extract the ventilation performance evaluation index of each monitoring section, and then perform mean calculation to obtain the average ventilation performance evaluation index of the target corridor, and then compare the ventilation performance evaluation index of each monitoring section with the average ventilation performance evaluation index of the target corridor to obtain the deviation of the ventilation performance evaluation index of each monitoring section.

[0038] It should be supplemented that the specific steps for obtaining the deviation of the ventilation performance evaluation index of each monitoring section are as follows: the ventilation performance evaluation index of each monitoring section is subtracted from the average ventilation performance evaluation index of the target corridor to obtain the deviation of the wind performance evaluation index of each monitoring section, and then the absolute value of the wind performance evaluation index deviation of each monitoring section is ratio-calculated with the average ventilation performance evaluation index to obtain the deviation of the ventilation performance evaluation index of each monitoring section.

[0039] The ventilation performance evaluation index of each monitoring section is compared with the preset ventilation performance evaluation index threshold. At the same time, the ventilation performance evaluation index deviation of each monitoring section is compared with the preset ventilation performance evaluation index deviation threshold, so as to obtain the result whether there is any abnormality in the ventilation facilities of each monitoring section.

[0040] It should be noted that the setting basis of the ventilation performance evaluation index threshold and the ventilation performance evaluation index deviation threshold is: 1. The performance requirements of the ventilation system are also an important basis for setting the evaluation index threshold. Different ventilation systems may have different performance requirements, such as air volume, wind speed distribution, air quality, etc. These requirements will directly affect the threshold setting of the evaluation index. 2. The setting of the ventilation performance evaluation index deviation threshold must first consider the stability and reliability requirements of the system. Deviation is one of the important indicators to measure the performance fluctuation of the ventilation system. The setting of its threshold should ensure that the system can operate stably within a certain range to avoid affecting the overall performance of the system due to excessive performance fluctuations.

[0041] Exemplarily, the ventilation performance evaluation index threshold is , the ventilation performance evaluation index deviation threshold is .

[0042] It should be added that the specific judgment process for obtaining the result of whether there is any abnormality in the ventilation facilities of each monitoring section is as follows: if the ventilation performance evaluation index of a certain monitoring section is greater than or equal to the ventilation performance evaluation index threshold and the ventilation performance evaluation index deviation is less than the ventilation performance evaluation index deviation threshold, it is judged that there is no abnormality in the ventilation facilities of the monitoring section; otherwise, it is judged that there is an abnormality in the ventilation facilities of the monitoring section.

[0043] S3. Abnormal monitoring section identification: Collect the comprehensive sensor equipment operation data of each monitoring section and analyze the abnormal operation of the comprehensive sensor equipment in each monitoring section. Figure 2 As shown, the ventilation performance evaluation of each monitoring section is combined to determine whether each monitoring section is in an abnormal state, and the monitoring section determined to be in an abnormal state is recorded as an abnormal monitoring section.

[0044] For further explanation, see Figure 3 As shown, the comprehensive sensor device is composed of a temperature sensor, a humidity sensor, a hydrogen sulfide concentration sensor, a methane concentration sensor and an oxygen concentration sensor. The temperature sensor is used to collect the temperature of each monitoring section, the humidity sensor is used to collect the humidity of each monitoring section, the hydrogen sulfide concentration sensor is used to collect the hydrogen sulfide concentration of each monitoring section, the methane concentration sensor is used to collect the methane concentration of each monitoring section, and the oxygen concentration sensor is used to collect the oxygen concentration of each monitoring section.

[0045] It should be noted that the reasons for selecting temperature sensors, humidity sensors, hydrogen sulfide concentration sensors, methane concentration sensors and oxygen concentration sensors as the target tunnel comprehensive sensor equipment are as follows: 1. Temperature is an important factor affecting the normal operation of various equipment and pipelines in the tunnel. Too high or too low temperature may damage the equipment and pipelines, and even cause safety accidents. Humidity also has an important impact on the operation of equipment and pipelines in the tunnel. High humidity environment easily causes equipment to get damp and corrode, reducing the service life; while low humidity may cause problems such as static electricity. 2. Hydrogen sulfide (H2S) is a toxic and harmful gas that is extremely harmful to the human body and the environment. Methane (CH4) is the main component of natural gas and biogas, and is also a flammable and explosive gas. Oxygen is a gas necessary for human breathing. In closed or semi-closed spaces such as tunnels, due to poor ventilation or excessive oxygen consumption, the oxygen concentration may drop, posing a threat to human health.

[0046] It should be further explained that the analysis of the abnormal operation of the comprehensive sensor equipment in each monitoring section requires the construction of the abnormal operation index of the comprehensive sensor equipment in each monitoring section. The specific analysis method is as follows: extract the temperature, humidity, hydrogen sulfide concentration, methane concentration and oxygen concentration of each monitoring section collected by the comprehensive sensor equipment in each monitoring section, and record them as , , , , .

[0047] Use frequency detection equipment to obtain the vibration frequency of the integrated sensor equipment in each monitoring section , use magnetic field strength detection equipment to obtain the magnetic field strength of each monitoring section , and then analyze the comprehensive sensor equipment detection deviation coefficient of each monitoring section .

[0048] Using the formula Analyze and obtain the comprehensive sensor equipment operation abnormality index of each monitoring section ,in Indicates the preset reference temperature. Indicates the preset reference humidity. Indicates the preset hydrogen sulfide concentration threshold. Indicates the preset methane concentration threshold, Indicates the preset reference oxygen concentration. Indicates the allowable difference between the preset temperature and the reference temperature. Indicates the allowable difference between the preset humidity and the reference humidity. Indicates the permissible difference between the preset oxygen concentration and the reference oxygen concentration. They respectively represent the influence weight factors corresponding to the preset hydrogen sulfide concentration and methane concentration.

[0049] For example, .

[0050] It should be further explained that the specific method of analyzing the detection deviation coefficient of the comprehensive sensor equipment in each monitoring section is as follows: extract the vibration frequency of the comprehensive sensor equipment in each monitoring section and magnetic field strength .

[0051] Using the formula The comprehensive sensor equipment detection deviation coefficient of each monitoring section is obtained by analysis ,in Indicates the preset vibration frequency threshold. Indicates the preset magnetic field strength threshold.

[0052] It should be noted that the reasons for selecting vibration frequency and magnetic field strength as the factors affecting the detection deviation coefficient of the comprehensive sensor equipment in each monitoring section are: 1. Vibration frequency is one of the important factors affecting the accuracy and stability of the sensor. Different sensors have different response ranges and sensitivities to vibration frequency. When the vibration frequency exceeds the design range of the sensor, it may cause deviation or distortion of the measurement data. 2. Magnetic field strength is another important factor affecting sensor performance. Many sensors contain magnetic elements or rely on magnetic fields to work, so changes in magnetic field strength will directly affect the measurement accuracy of the sensor.

[0053] It should be noted that when analyzing the abnormal state of each monitoring section of the target tunnel, the present invention analyzes the environmental factors of the sensor data collected in each monitoring section, and then analyzes the abnormal operation of the comprehensive sensor equipment. This analysis method can eliminate or weaken the influence of environmental factors, thereby improving the accuracy of the sensor data. Monitoring and correcting the abnormal operation of the sensor equipment itself can eliminate erroneous data caused by equipment failure or instability, and ensure that the data based on the analysis is true and reliable. Based on accurate data and correction considering environmental factors, the abnormal state of each monitoring section of the tunnel can be judged more accurately, reducing the false alarm and missed alarm rate caused by inaccurate data.

[0054] It needs to be further explained that the specific method for determining whether each monitoring section is in an abnormal state is as follows: extracting the comprehensive sensor equipment operation abnormality index of each monitoring section and the ventilation performance evaluation index of each monitoring section, and then analyzing to obtain the corrected comprehensive sensor equipment operation abnormality index corresponding to each monitoring section.

[0055] It should be added that the specific steps of the analysis to obtain the corrected comprehensive sensor equipment operation abnormality index corresponding to each monitoring section are: the comprehensive sensor equipment operation abnormality index of each monitoring section and the ventilation performance evaluation index of each monitoring section are multiplied to obtain the correction amount of the comprehensive sensor equipment operation abnormality index of each monitoring section, and then the correction amount is calculated by summing it with the corresponding comprehensive sensor equipment operation abnormality index of each monitoring section to obtain the corrected comprehensive sensor equipment operation abnormality index corresponding to each monitoring section.

[0056] The corrected comprehensive sensor equipment operation abnormality index corresponding to each monitoring section is compared with the pre-set comprehensive sensor equipment operation abnormality index threshold. If the corrected comprehensive sensor equipment operation abnormality index of a monitoring section is greater than the comprehensive sensor equipment operation abnormality index threshold, it is judged that the monitoring section is in an abnormal state; otherwise, it is judged that the monitoring section does not have an abnormal state.

[0057] It should be noted that the present invention corrects the abnormal operation of the comprehensive sensor equipment in each monitoring section based on the ventilation performance evaluation of each monitoring section when judging the existence of abnormal conditions, and then judges the existence of abnormal conditions. This analysis method introduces ventilation performance evaluation to more comprehensively understand the actual operating environment of the monitoring section. The quality of ventilation performance directly affects the distribution of parameters such as gas concentration and temperature in the corridor, and thus affects the accuracy of sensor data. Combining ventilation performance evaluation for abnormal judgment can more accurately identify abnormal conditions caused by environmental factors and reduce misjudgments.

[0058] It should be noted that the setting basis of the threshold of the abnormal index of the operation of the comprehensive sensor equipment is as follows: 1. By analyzing the historical operation data of the sensor equipment, its fluctuation range and change trend under normal conditions can be understood. These data provide an important reference for setting the threshold of the abnormal index. 2. By analyzing the failure cases that have occurred in the past, the data characteristics when the failure occurs can be understood. These characteristics help to identify potential abnormal patterns and provide guidance for setting the threshold of the abnormal index.

[0059] Exemplarily, the threshold value of the comprehensive sensor equipment operation abnormality index is .

[0060] S4. Abnormal direction analysis: Identify the specific abnormal direction of the abnormal monitoring section.

[0061] It needs to be further explained that the specific analysis method for identifying the specific abnormal direction of the abnormal monitoring section is as follows: extract the temperature, humidity, hydrogen sulfide concentration, methane concentration and oxygen concentration of each abnormal monitoring section, and compare them with the pre-set reference temperature, reference humidity, hydrogen sulfide concentration threshold, methane concentration threshold and reference oxygen concentration to obtain the temperature deviation, humidity deviation, hydrogen sulfide concentration deviation, methane concentration deviation and oxygen concentration deviation of each abnormal monitoring section, and then compare them with the pre-set temperature deviation threshold, humidity deviation threshold, hydrogen sulfide concentration deviation threshold, methane concentration deviation threshold and oxygen concentration deviation threshold respectively to obtain the specific abnormal direction of each abnormal monitoring section.

[0062] It should be added that the specific analysis steps for obtaining the temperature deviation of each abnormal monitoring section are: the temperature of each abnormal monitoring section is difference calculated with the reference temperature to obtain the temperature deviation of each abnormal monitoring section, and then the absolute value of the temperature deviation of each abnormal monitoring section is ratio calculated with the pre-set reference temperature to obtain the temperature deviation of each abnormal monitoring section.

[0063] It should be noted that the analysis steps of the humidity deviation, hydrogen sulfide concentration deviation, methane concentration deviation and oxygen concentration deviation of each abnormal monitoring section refer to the specific analysis steps of the temperature deviation of each abnormal monitoring section.

[0064] It should be added that the specific method of obtaining the specific abnormal direction of each abnormal monitoring section is: if the temperature deviation of an abnormal monitoring section is greater than a preset temperature deviation threshold, the specific abnormal direction of the abnormal monitoring section is judged to be temperature abnormality; if the humidity deviation of an abnormal monitoring section is greater than a preset humidity deviation threshold, the specific abnormal direction of the abnormal monitoring section is judged to be humidity abnormality; if the hydrogen sulfide concentration deviation of an abnormal monitoring section is greater than a preset hydrogen sulfide concentration deviation threshold, the specific abnormal direction of the abnormal monitoring section is judged to be hydrogen sulfide concentration abnormality; if the methane concentration deviation of an abnormal monitoring section is greater than a preset methane concentration deviation threshold, the specific abnormal direction of the abnormal monitoring section is judged to be methane concentration abnormality; if the oxygen concentration deviation of an abnormal monitoring section is greater than a preset oxygen concentration deviation threshold, the specific abnormal direction of the abnormal monitoring section is judged to be oxygen concentration abnormality.

[0065] It should be noted that the specific abnormality indication of the abnormal monitoring section may be one or more of abnormal temperature, abnormal humidity, abnormal hydrogen sulfide concentration, abnormal methane concentration and abnormal oxygen concentration.

[0066] S5. Abnormal state control: perform abnormal state control on abnormal sections.

[0067] It needs to be further explained that the specific analysis method for controlling the abnormal state of the abnormal section is as follows: by adjusting the air duct outlet area of ​​the abnormal monitoring section, the wind speed of each abnormal monitoring section is controlled until the corrected comprehensive sensor equipment operation abnormality index of the corresponding abnormal monitoring section is less than the comprehensive sensor equipment operation abnormality index threshold, and the air duct outlet area of ​​the abnormal monitoring section is restored to the reference air duct outlet area.

[0068] It should be noted that the present invention can quickly determine the specific location and possible causes of the abnormality by performing abnormal directional analysis and automatic control of abnormal conditions on the abnormal monitoring section, and by conducting in-depth abnormal directional analysis on the abnormal monitoring section. This precise positioning helps operation and maintenance personnel to quickly understand the root cause of the problem and avoid blind investigation and waste of resources. The present invention introduces an automatic control mechanism that can automatically take corresponding control measures after detecting an abnormal state. This automated processing method not only reduces the need for manual intervention and reduces labor costs, but also improves response speed and processing efficiency.

[0069] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they shall all fall within the protection scope of the present invention.

Claims

1. The method for intelligent operation and maintenance of pipe gallery using abnormal sensors in real time based on AI technology is characterized by: include: S1. Monitoring section division: The target corridor is divided into several monitoring sections based on equal interval lengths, and the midpoint of each monitoring section is recorded as a monitoring point; S2. Ventilation performance evaluation: Conduct ventilation performance tests on each monitoring section, obtain ventilation performance evaluation data of each monitoring section, including wind speed, wind speed response adjustment amount, wind speed response adjustment time, air duct outlet area and reference distance, analyze the ventilation performance evaluation of each monitoring section, and then determine whether there is any abnormality in the ventilation facilities of each monitoring section; S3. Identification of abnormal monitoring sections: Collecting the operation data of the comprehensive sensor equipment of each monitoring section, analyzing the abnormal operation of the comprehensive sensor equipment of each monitoring section, and then combining the ventilation performance evaluation of each monitoring section to determine whether each monitoring section is in an abnormal state, and recording the monitoring section judged to be in an abnormal state as an abnormal monitoring section; S4. Abnormal direction analysis: Identify the specific abnormal direction of the abnormal monitoring section; S5. Abnormal state control: abnormal state control of abnormal sections; The analysis of the ventilation performance evaluation of each monitoring section requires the construction of a ventilation performance evaluation index for each monitoring section, and the specific analysis method is as follows: The wind speed response adjustment amount and wind speed response adjustment time of each monitoring section are extracted and recorded as , , and then analyze and obtain the wind speed response adjustment evaluation index of each monitoring section ,in Indicates the number of the monitoring section. , Indicates the number of monitoring sections; Extract the duct outlet area and reference distance of each monitoring section and record them as , , and then use the formula The ventilation performance correction coefficient of each monitoring section is obtained by analysis ,in They represent the influence weights corresponding to the preset reference distance and the air duct outlet area, respectively. Indicates the preset reference duct outlet area, Indicates the preset reference distance; Extract wind speed in each monitoring section , using the formula The ventilation performance evaluation index of each monitoring section was obtained by analysis. ,in Indicates the preset reference wind speed. Indicates the permissible difference between the preset wind speed and the reference wind speed; The analysis of the abnormal operation of the comprehensive sensor equipment in each monitoring section requires the construction of the abnormal operation index of the comprehensive sensor equipment in each monitoring section. The specific analysis method is as follows: The temperature, humidity, hydrogen sulfide concentration, methane concentration and oxygen concentration of each monitoring section collected by the comprehensive sensor equipment of each monitoring section are extracted and recorded as , , , , ; Use frequency detection equipment to obtain the vibration frequency of the integrated sensor equipment in each monitoring section , use magnetic field strength detection equipment to obtain the magnetic field strength of each monitoring section , and then analyze the comprehensive sensor equipment detection deviation coefficient of each monitoring section ; Using the formula Analyze and obtain the comprehensive sensor equipment operation abnormality index of each monitoring section ,in Indicates the preset reference temperature. Indicates the preset reference humidity. Indicates the preset hydrogen sulfide concentration threshold. Indicates the preset methane concentration threshold, Indicates the preset reference oxygen concentration, Indicates the allowable difference between the preset temperature and the reference temperature. Indicates the allowable difference between the preset humidity and the reference humidity. Indicates the permissible difference between the preset oxygen concentration and the reference oxygen concentration. They represent the influence weight factors corresponding to the preset hydrogen sulfide concentration and methane concentration respectively; The specific method of analyzing the comprehensive sensor equipment detection deviation coefficient of each monitoring section is as follows: Extract the comprehensive sensor equipment vibration frequency of each monitoring section and magnetic field strength ; Using the formula The comprehensive sensor equipment detection deviation coefficient of each monitoring section is obtained by analysis ,in Indicates the preset vibration frequency threshold. Indicates the preset magnetic field strength threshold; The specific method of determining whether each monitoring section is in an abnormal state is as follows: Extract the comprehensive sensor equipment operation abnormality index of each monitoring section and the ventilation performance evaluation index of each monitoring section, and then analyze and obtain the modified comprehensive sensor equipment operation abnormality index corresponding to each monitoring section; The specific steps of analyzing and obtaining the corrected comprehensive sensor equipment operation abnormality index corresponding to each monitoring section are as follows: the comprehensive sensor equipment operation abnormality index of each monitoring section and the ventilation performance evaluation index of each monitoring section are multiplied to obtain the correction amount of the comprehensive sensor equipment operation abnormality index of each monitoring section, and then the correction amount of the comprehensive sensor equipment operation abnormality index of each monitoring section is summed with the corresponding comprehensive sensor equipment operation abnormality index of each monitoring section to obtain the corrected comprehensive sensor equipment operation abnormality index corresponding to each monitoring section; The corrected comprehensive sensor equipment operation abnormality index corresponding to each monitoring section is compared with the pre-set comprehensive sensor equipment operation abnormality index threshold. If the corrected comprehensive sensor equipment operation abnormality index of a monitoring section is greater than the comprehensive sensor equipment operation abnormality index threshold, it is judged that the monitoring section is in an abnormal state; otherwise, it is judged that the monitoring section does not have an abnormal state.

2. The AI-based abnormal sensor real-time data collection pipeline intelligent operation and maintenance method according to claim 1 is characterized by: The specific method of analyzing and obtaining the wind speed response adjustment evaluation index of each monitoring section is as follows: Extract the wind speed response adjustment value of each monitoring section and wind speed response adjustment time ; Using the formula The wind speed response adjustment evaluation index of each monitoring section is obtained by analysis ,in Indicates the preset rated ventilation control efficiency.

3. The AI-based abnormal sensor real-time data collection pipeline intelligent operation and maintenance method according to claim 1 is characterized by: The specific method of judging whether there is an abnormality in the ventilation facilities of each monitoring section is as follows: Extract the ventilation performance evaluation index of each monitoring section, and then perform mean calculation to obtain the average ventilation performance evaluation index of the target corridor, and then compare the ventilation performance evaluation index of each monitoring section with the average ventilation performance evaluation index of the target corridor to obtain the deviation of the ventilation performance evaluation index of each monitoring section; The ventilation performance evaluation index of each monitoring section is compared with the preset ventilation performance evaluation index threshold. At the same time, the ventilation performance evaluation index deviation of each monitoring section is compared with the preset ventilation performance evaluation index deviation threshold, so as to obtain the result whether there is any abnormality in the ventilation facilities of each monitoring section.

4. The AI-based abnormal sensor real-time data collection pipeline intelligent operation and maintenance method according to claim 1 is characterized by: The comprehensive sensor device is composed of a temperature sensor, a humidity sensor, a hydrogen sulfide concentration sensor, a methane concentration sensor and an oxygen concentration sensor; The temperature sensor is used to collect the temperature of each monitoring section, the humidity sensor is used to collect the humidity of each monitoring section, the hydrogen sulfide concentration sensor is used to collect the hydrogen sulfide concentration of each monitoring section, the methane concentration sensor is used to collect the methane concentration of each monitoring section, and the oxygen concentration sensor is used to collect the oxygen concentration of each monitoring section.

5. The AI-based abnormal sensor real-time data collection pipeline intelligent operation and maintenance method according to claim 1, characterized in that: The specific analysis method for identifying the specific abnormal direction of the abnormal monitoring section is as follows: The temperature, humidity, hydrogen sulfide concentration, methane concentration and oxygen concentration of each abnormal monitoring section are extracted, and compared with the preset reference temperature, reference humidity, hydrogen sulfide concentration threshold, methane concentration threshold and reference oxygen concentration to obtain the temperature deviation, humidity deviation, hydrogen sulfide concentration deviation, methane concentration deviation and oxygen concentration deviation of each abnormal monitoring section, and then compared with the preset temperature deviation threshold, humidity deviation threshold, hydrogen sulfide concentration deviation threshold, methane concentration deviation threshold and oxygen concentration deviation threshold respectively to obtain the specific abnormal direction of each abnormal monitoring section.

6. The AI-based abnormal sensor real-time data collection pipeline intelligent operation and maintenance method according to claim 1, characterized in that: The specific analysis method of performing abnormal state control on the abnormal section is as follows: By adjusting the air duct outlet area of ​​the abnormal monitoring section, the wind speed of each abnormal monitoring section is controlled until the corrected comprehensive sensor equipment operation abnormality index of the corresponding abnormal monitoring section is less than the comprehensive sensor equipment operation abnormality index threshold, and the air duct outlet area of ​​the abnormal monitoring section is restored to the reference air duct outlet area.

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