Optical fiber vibration sensor operation performance monitoring system based on artificial intelligence

Abstract

The invention relates to the technical field of sensor monitoring, in particular to an optical fiber vibration sensor operation performance monitoring system based on artificial intelligence, which comprises a performance monitoring center, a database, an operation performance analysis unit, a shutdown monitoring unit, an operation hindering unit, a historical factor analysis unit and a management response unit, performance monitoring analysis is carried out from the two angles of operation and shutdown of the target sensor, that is, the operation performance of the target sensor is analyzed in a multi-angle analysis mode, the credibility of an analysis result is improved, meanwhile, fusion evaluation analysis is carried out in combination with physical interference, non-physical interference and historical comprehensive interference, and the reliability of the analysis result is improved. According to the method, the comprehensiveness of data analysis can be increased in the process of analyzing the operation performance of the target sensor, so that the operation performance of the target sensor can be visually known according to the output result, the target sensor can be managed and adjusted, and meanwhile, the monitoring management efficiency of the target sensor is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of sensor monitoring, and particularly to an operation performance monitoring system for an optical fiber vibration sensor based on artificial intelligence. Background Art

[0002] With the development of technology to date, in the fields of industry, aerospace, scientific research, etc., the demand for vibration measurement is becoming more and more extensive and the requirements are getting higher and higher. In the industrial field, vibration measurement can study the vibration conditions of mechanical parts and check for faults in mechanical equipment, such as bearing wear, eccentricity, looseness, etc.; in the construction field, vibration measurement can be used to monitor the vibration and deformation conditions of building structures; the methods for measuring vibration include contact measurement methods and non-contact measurement methods.

[0003] However, in the existing operation performance monitoring technology of optical fiber vibration sensors, there are problems of single data acquisition and single analysis angle, which in turn lead to large deviations in the operation performance monitoring results of optical fiber vibration sensors, low credibility of the operation performance monitoring results, and inability to analyze from two points of the operation and shutdown of the optical fiber vibration sensor, thus making it impossible to rationally manage the optical fiber vibration sensor based on the information feedback situation.

[0004] In view of the above technical defects, a solution is proposed now. Summary of the Invention

[0005] The purpose of the present invention is to provide an operation performance monitoring system for an optical fiber vibration sensor based on artificial intelligence to solve the above-mentioned technical defects. The present invention conducts performance monitoring and analysis from two angles of the operation and shutdown of the target sensor, that is, analyzes the operation performance of the target sensor through multi-angle analysis, which helps to improve the credibility of the analysis results. At the same time, combined with physical interference, non-physical interference and historical comprehensive interference for fusion evaluation and analysis, it helps to increase the comprehensiveness of data analysis during the analysis of the operation performance of the target sensor, and then intuitively understand the operation performance of the target sensor based on the output results, which helps to improve the monitoring and management efficiency of the target sensor.

[0006] The purpose of the present invention can be achieved through the following technical solutions: An operation performance monitoring system for an optical fiber vibration sensor based on artificial intelligence, including a performance monitoring center, a database, an operation performance analysis unit, a shutdown monitoring unit, an operation hindrance unit, a historical factor analysis unit and a management response unit; The performance monitoring center is used to retrieve the working condition evaluation information and shutdown information of the target sensor from the database, and send the working condition evaluation information and shutdown information to the operation performance analysis unit and the shutdown monitoring unit respectively.

[0007] The operation performance analysis unit is used to obtain and analyze the operation performance evaluation coefficient of the received working condition evaluation information, perform discrimination processing on the obtained operation performance evaluation coefficient, and obtain a normal signal or an alarm signal.

[0008] The shutdown monitoring unit is used to perform shutdown performance evaluation feedback analysis on the received shutdown information, perform discrimination processing on the obtained shutdown characteristic performance coefficient, and obtain a shutdown stability signal or a shutdown risk signal.

[0009] The hindering operation unit is used to retrieve the operation interference information of the target sensor from the database, and at the same time perform actual interference evaluation coefficient acquisition and analysis on the operation interference information to obtain the actual interference evaluation coefficient.

[0010] The historical factor analysis unit is used to collect risk interference information of the target sensor, and at the same time perform historical hindering operation division analysis on the risk interference information to obtain a preset operation interference factor Gm.

[0011] Preferably, the operation performance evaluation coefficient acquisition and analysis process is as follows: The operating time period of the optical fiber vibration sensor is collected and set as the time threshold, the optical fiber vibration sensor is set as the target sensor, and the working condition evaluation information of the target sensor within the time threshold is obtained. The working condition evaluation information includes a sensitivity evaluation coefficient and a stability evaluation coefficient.

[0012] Retrieve the preset operating interference factor Gm of the current target sensor, set the value obtained by multiplying the sensitivity evaluation coefficient, the stability evaluation coefficient and the corresponding values ​​of the preset operating interference factor Gm of the current target sensor as the operating performance evaluation coefficient, and perform discrimination processing on the operating performance evaluation coefficient to obtain a normal signal or an alarm signal.

[0013] Preferably, the analysis process of the stability evaluation coefficient is as follows: divide the time threshold into i sub-time periods, where i is a natural number greater than zero, obtain the infrared characteristic image of the target sensor in the sub-time period, obtain the area corresponding to the temperature value exceeding the preset temperature value threshold from the infrared characteristic image, and set it as the red temperature area, and then obtain the area of ​​the same area corresponding to the red temperature area in each sub-time period, and set it as the stability evaluation coefficient.

[0014] The process of obtaining the sensitivity evaluation coefficient is as follows: obtain the sensitivity characteristic curve of the target sensor in the sub-time period, obtain the average of the maximum peak value and the minimum trough value from the sensitivity characteristic curve, and set it as the time period sensitivity mean, construct a set A of the time period sensitivity means, and set the discrete coefficient of set A as the sensitivity evaluation coefficient.

[0015] Preferably, the shutdown performance evaluation feedback analysis process is as follows: Obtain the duration between the shutdown moment of the target sensor within the time threshold and the preset monitoring moment, and set it as the analysis duration. Obtain the shutdown information of the target sensor within the analysis duration, where the shutdown information includes the shutdown performance value and the characteristic defect index.

[0016] Multiply the shutdown performance value, the characteristic defect index, and the corresponding value of the preset operation interference factor Gm of the current target sensor to obtain the shutdown characteristic performance coefficient, and perform discrimination processing on the shutdown characteristic performance coefficient to obtain a shutdown stable signal or a shutdown risk signal.

[0017] Preferably, the analysis process of the shutdown performance value is as follows: Obtain the duration between the shutdown moment of the target sensor within the analysis duration and the moment when the signal decays to zero, and set it as the shutdown performance value; Obtain the signal attenuation characteristic curve of the target sensor within the analysis duration, and at the same time obtain the standard signal attenuation characteristic curve of the target sensor. Obtain the difference value between the signal attenuation characteristic curve and the standard signal attenuation characteristic curve, and set the difference value between the signal attenuation characteristic curve and the standard signal attenuation characteristic curve as the characteristic defect index.

[0018] Preferably, the analysis process for obtaining the on-site interference evaluation coefficient is as follows: Obtain the operation interference information of the working area where the target sensor is located within the time threshold, and the operation interference information includes the interference characteristic coefficient and the physical interference index.

[0019] The interference characteristic coefficient represents the product value obtained by multiplying the maximum value of the frequency corresponding to the environmental interference information value exceeding the preset threshold in the working area where the target sensor is located and the superposition duration after data normalization processing. The environmental interference information includes temperature, humidity, and electromagnetic interference. The superposition duration represents the total sum of the coincidence durations corresponding to the environmental interference information values exceeding the preset threshold.

[0020] The physical interference index represents the number of interference sources in the working area where the target sensor is located whose minimum straight-line distance from the target sensor is less than the preset threshold. The interference sources include inverter power supply equipment and frequency converters.

[0021] Compare and analyze the interference characteristic coefficient and the physical interference index with the preset interference characteristic coefficient threshold and the preset physical interference index threshold, and set the number of the interference characteristic coefficient and the physical interference index that are greater than or equal to the preset interference characteristic coefficient threshold and the preset physical interference index threshold as the on-site interference evaluation coefficient.

[0022] Preferably, the analysis process for historical hindrance operation division is as follows: Obtain the risk interference information of the target sensor within the time threshold, and the risk interference information includes the parameter offset value and the working condition performance value.

[0023] The parameter offset value represents the duration during which the corresponding value of the operating parameter exceeds the preset threshold in the historical operation of the target sensor. The operating parameters include operating voltage and operating power.

[0024] Obtain the proportion value of the number of times when the start response duration exceeds the preset start response duration threshold in the total number of historical starts of the target sensor, and set the product value obtained by multiplying the proportion value by the value obtained by normalizing the historical output delay times of the target sensor as the operating condition performance value.

[0025] Label the parameter offset value and the operating condition performance value as CP and GB respectively, and substitute the parameter offset value CP and the operating condition performance value GB into the formula to obtain the historical evaluation coefficient.

[0026] Preferably, perform discriminant analysis on the live interference evaluation coefficient and the historical evaluation coefficient: If the live interference evaluation coefficient = 0 and the historical evaluation coefficient is less than the preset historical evaluation coefficient threshold, it is judged as a conventional interference; if the live interference evaluation coefficient ≠ 0 and the historical evaluation coefficient is less than the preset historical evaluation coefficient threshold, it is judged as a high-level interference, and obtain the preset operating interference factors Gm corresponding to the conventional interference and the high-level interference, where m ∈ {1, 2} and 0 < G1 < G2.

[0027] The beneficial effects of the present invention are as follows: (1) The present invention performs performance monitoring and analysis from two perspectives of the operation and shutdown of the target sensor, that is, analyzes the operation performance of the target sensor through multi-angle analysis, which helps to improve the credibility of the analysis results. At the same time, combining physical interference, non-physical interference and historical comprehensive interference for fusion evaluation and analysis helps to increase the comprehensiveness of data analysis during the analysis of the operation performance of the target sensor, and thus intuitively understand the operation performance of the target sensor based on the output results.

[0028] (2) The present invention obtains and analyzes the operation performance evaluation coefficient from the operation angle of the target sensor to judge whether the operation performance of the target sensor is normal, so as to manage the target sensor reasonably and pertinently to improve the operation safety of the target sensor, and at the same time improve the stability of the operation performance of the target sensor. And based on the information progression method, further perform shutdown performance evaluation and feedback analysis from the shutdown angle of the target sensor to understand whether the shutdown performance of the target sensor is normal, so as to intuitively feedback the operation performance monitoring results based on the output results, and thus help to manage and adjust the target sensor, which helps to improve the monitoring and management efficiency of the target sensor. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The following further describes the present invention with reference to the drawings; Figure 1 It is the system flow block diagram of the present invention; Figure 2 It is the local analysis reference diagram of the present invention. Specific embodiments

[0030] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0031] Embodiment 1: Please refer to Figures 1 to 2 As shown, the present invention is an operation performance monitoring system for an optical fiber vibration sensor based on artificial intelligence, including a performance monitoring center, a database, an operation performance analysis unit, a shutdown monitoring unit, an operation hindrance unit, a historical factor analysis unit, and a management response unit. The database is in one-way communication connection with both the performance monitoring center and the operation hindrance unit. The performance monitoring center is in one-way communication connection with both the operation performance analysis unit and the shutdown monitoring unit. The operation hindrance unit is in one-way communication connection with the historical factor analysis unit. The historical factor analysis unit is in one-way communication connection with the performance monitoring center. The operation performance analysis unit is in one-way communication connection with both the shutdown monitoring unit and the management response unit. The shutdown monitoring unit is in one-way communication connection with the management response unit.

[0032] The performance monitoring center is used to retrieve the working condition evaluation information and shutdown information of the target sensor from the database, and send the working condition evaluation information and shutdown information to the operation performance analysis unit and the shutdown monitoring unit respectively.

[0033] The operation performance analysis unit is used to perform analysis on obtaining the operation performance evaluation coefficient for the received working condition evaluation information, so as to perform reasonable and targeted management on the target sensor, improve the operation safety of the target sensor, and at the same time improve the stability of the operation performance of the target sensor. The specific process of obtaining and analyzing the operation performance evaluation coefficient is as follows: Collect the operation period of the optical fiber vibration sensor and set it as the time threshold. Set the optical fiber vibration sensor as the target sensor, and obtain the working condition evaluation information of the target sensor within the time threshold. The working condition evaluation information includes a sensitivity evaluation coefficient and a stability evaluation coefficient.

[0034] In the embodiment of the present invention, the analysis process of the stability evaluation coefficient is as follows: Divide the time threshold into \(i\) sub - time periods, where \(i\) is a natural number greater than zero. Obtain the infrared feature image of the target sensor within the sub - time period. From the infrared feature image, obtain the area corresponding to the region where the temperature value exceeds the preset temperature value threshold, and set it as the red - temperature region. Then, obtain the area of the same region corresponding to the red - temperature region within each sub - time period, and set it as the stability evaluation coefficient. It should be noted that the stability evaluation coefficient is an influencing parameter reflecting the actual operating performance of the target sensor. The larger the value of the stability evaluation coefficient, the greater the risk of abnormal operating performance of the target sensor.

[0035] In the embodiment of the present invention, the process of obtaining the sensitivity evaluation coefficient is as follows: Obtain the sensitivity characteristic curve of the target sensor within the sub - time period. Obtain the mean value of the maximum peak value and the minimum valley value from the sensitivity characteristic curve, and set it as the time - period sensitivity mean. Construct a set \(A\) of the time - period sensitivity means, and set the coefficient of variation of the set \(A\) as the sensitivity evaluation coefficient. It should be noted that the larger the value of the sensitivity evaluation coefficient, the greater the risk of abnormal sensitivity fluctuation of the target sensor.

[0036] Retrieve the preset operating interference factor \(G_m\) of the current target sensor. Set the value obtained by multiplying the corresponding values of the sensitivity evaluation coefficient, the stability evaluation coefficient, and the preset operating interference factor \(G_m\) of the current target sensor as the operating performance evaluation coefficient, and perform a discrimination process on the operating performance evaluation coefficient: If the operating performance evaluation coefficient is less than the preset operating performance evaluation coefficient threshold, a normal signal is generated; If the operating performance evaluation coefficient is greater than or equal to the preset operating performance evaluation coefficient threshold, an alarm signal is generated. Send the normal signal or the alarm signal to the management response unit. After receiving the normal signal or the alarm signal, the management response unit immediately performs the preset warning operation corresponding to the normal signal or the alarm signal, so as to manage the target sensor reasonably and pertinently, improve the operating safety of the target sensor, and at the same time improve the stability of the operating performance of the target sensor.

[0037] The shutdown monitoring unit is used to perform a shutdown performance evaluation feedback analysis on the received shutdown information, so as to analyze the performance risk situation of the target sensor from the perspective of shutdown, and improve the stability of the operating performance of the target sensor. The specific process of the shutdown performance evaluation feedback analysis is as follows: Obtain the duration between the shutdown moment and the preset monitoring moment of the target sensor within the time threshold, and set it as the analysis duration. Obtain the shutdown information of the target sensor within the analysis duration. The shutdown information includes the shutdown performance value and the characteristic defect index; In the embodiment of the present invention, the analysis process of the shutdown performance value is as follows: Obtain the duration between the shutdown moment of the target sensor and the moment when the signal decays to zero within the analysis duration, and set it as the shutdown performance value. It should be noted that the larger the value of the shutdown performance value, the greater the failure risk of the target sensor.

[0038] In the embodiment of the present invention, obtain the signal attenuation characteristic curve of the target sensor within the analysis duration, and at the same time obtain the standard signal attenuation characteristic curve of the target sensor. Obtain the difference value between the signal attenuation characteristic curve and the standard signal attenuation characteristic curve, and set the difference value between the signal attenuation characteristic curve and the standard signal attenuation characteristic curve as the characteristic defect index. It should be noted that the larger the value of the characteristic defect index, the greater the failure risk of the target sensor.

[0039] Set the value obtained by multiplying the corresponding values of the shutdown performance value, the characteristic defect index, and the preset operation interference factor Gm of the current target sensor as the shutdown characteristic performance coefficient, and perform discrimination processing on the shutdown characteristic performance coefficient: If the shutdown characteristic performance coefficient is less than the preset shutdown characteristic performance coefficient threshold, generate a shutdown stable signal; If the shutdown characteristic performance coefficient is greater than or equal to the preset shutdown characteristic performance coefficient threshold, generate a shutdown risk signal, and send the shutdown stable signal or the shutdown risk signal to the management response unit. After receiving the shutdown stable signal or the shutdown risk signal, the management response unit immediately performs the preset warning operation corresponding to the shutdown stable signal or the shutdown risk signal, so as to perform reasonable and targeted management on the target sensor to improve the stability of the operation performance of the target sensor.

[0040] Embodiment 2: The operation hindrance unit is used to retrieve the operation interference information of the target sensor from the database, and at the same time perform analysis on obtaining the on-site interference evaluation coefficient for the operation interference information, so as to perform analysis from two perspectives of physical interference and non-physical interference, in order to understand the comprehensive operation interference degree of physical interference and non-physical on the target sensor. The specific process of obtaining and analyzing the on-site interference evaluation coefficient is as follows: Obtain the operation interference information of the working area where the target sensor is located within the time threshold, and the operation interference information includes the interference characteristic coefficient and the physical interference index.

[0041] In the embodiments of the present invention, the interference characteristic coefficient represents the product value obtained by multiplying the maximum value in the frequency corresponding to the environmental interference information value exceeding the preset threshold in the working area where the target sensor is located and the superposition duration after data normalization processing. The environmental interference information includes temperature, humidity, electromagnetic interference, etc. The superposition duration represents the total sum of the coincidence durations corresponding to the environmental interference information values exceeding the preset threshold. It should be noted that the larger the value of the interference characteristic coefficient, the greater the risk of abnormal operation of the target sensor.

[0042] In the embodiments of the present invention, the physical interference index represents the number of interference sources in the working area where the target sensor is located whose minimum straight-line distance from the target sensor is less than the preset threshold. The interference sources include inverter power supply equipment, frequency converters, etc. It should be noted that from the perspective of physical interference, the operating performance risk of the target sensor is analyzed to improve the accuracy of the analysis results. The larger the value of the physical interference index, the greater the operating performance risk of the target sensor.

[0043] The interference characteristic coefficient and the physical interference index are compared and analyzed with the preset interference characteristic coefficient threshold and the preset physical interference index threshold. The number of the interference characteristic coefficient and the physical interference index that are greater than or equal to the preset interference characteristic coefficient threshold and the preset physical interference index threshold is set as the live interference evaluation coefficient. It should be noted that the live interference evaluation coefficient is an influence parameter reflecting the operating performance of the target sensor.

[0044] The historical factor analysis unit is used to collect the risk interference information of the target sensor and simultaneously conduct a historical obstacle operation division analysis on the risk interference information, that is, analyze the influence of historical factors on the current operating performance from a comprehensive perspective to provide data support for subsequent analysis. The specific historical obstacle operation division analysis process is as follows: Obtain the risk interference information of the target sensor within the time threshold. The risk interference information includes the parameter deviation value and the working condition performance value.

[0045] In the embodiments of the present invention, the parameter deviation value represents the duration corresponding to the operating parameter value exceeding the preset threshold during the historical operation of the target sensor. The operating parameters include operating voltage, operating power, etc. It should be noted that the larger the value of the parameter deviation value, the deeper the damage degree caused by the remaining problems of the target sensor.

[0046] In the embodiments of the present invention, obtain the ratio of the number of times the startup response duration exceeds the preset startup response duration threshold in the total number of historical startups of the target sensor, and set the product value obtained by multiplying the ratio value by the historical output delay times of the target sensor after data normalization processing as the working condition performance value. It should be noted that from the perspective of historical operation performance, understand the operating risk situation of the target sensor. The larger the value of the working condition performance value, the greater the risk of abnormal operation performance of the target sensor.

[0047] Label the parameter offset value and the operating condition performance value as CP and GB respectively, and substitute the parameter offset value CP and the operating condition performance value GB into the formula to obtain the historical evaluation coefficient, where a1 and a2 are the preset weight factor coefficients of the parameter offset value and the operating condition performance value respectively, a3 is the preset error correction factor coefficient, and a1, a2, and a3 are all greater than zero. It should be noted that the historical influence is comprehensively evaluated from the perspective of data fusion to analyze the influence of historical factors on the current operating performance from a comprehensive perspective.

[0048] Conduct discriminant analysis on the live interference evaluation coefficient and the historical evaluation coefficient: If the live interference evaluation coefficient = 0 and the historical evaluation coefficient is less than the preset historical evaluation coefficient threshold, it is judged as a normal interference; If the live interference evaluation coefficient ≠ 0 and the historical evaluation coefficient is less than the preset historical evaluation coefficient threshold, it is judged as a high-level interference. Among them, the interference effects corresponding to normal interference and high-level interference increase in sequence. Obtain the preset operating interference factor Gm corresponding to normal interference and high-level interference, m ∈ {1, 2}, that is, when m = 1, the preset operating interference factor G1 represents normal interference, and when m = 2, the preset operating interference factor G2 represents high-level interference, 0 < G1 < G2. Send the preset operating interference factor Gm to the performance monitoring center.

[0049] In summary, the present invention conducts performance monitoring and analysis from the two perspectives of the operation and shutdown of the target sensor, that is, analyzes the operating performance of the target sensor through multi-angle analysis, which helps to improve the credibility of the analysis results. At the same time, combining physical interference, non-physical interference, and historical comprehensive interference for fusion evaluation and analysis helps to increase the comprehensiveness of data analysis during the analysis of the operating performance of the target sensor, and then intuitively understand the operating performance of the target sensor based on the output results. Obtain and analyze the operating performance evaluation coefficient from the operating perspective of the target sensor to judge whether the operating performance of the target sensor is normal, so as to manage the target sensor reasonably and pertinently to improve the operating safety of the target sensor and at the same time improve the stability of the operating performance of the target sensor. And further conduct shutdown performance evaluation and feedback analysis from the shutdown perspective of the target sensor based on the information progression method to understand whether the shutdown performance of the target sensor is normal, so as to intuitively feedback the operating performance monitoring results of the target sensor based on the output result situation, which helps to manage and adjust the target sensor and improve the monitoring and management efficiency of the target sensor.

[0050] The setting of the threshold value is for the convenience of comparison. Regarding the size of the threshold value, it depends on the amount of sample data and the base quantity set by those skilled in the art for each group of sample data; as long as it does not affect the proportional relationship between the parameter and the quantified value.

[0051] The size of the coefficient is a specific value obtained by quantifying each parameter for subsequent comparison. Regarding the size of the coefficient, it depends on the amount of sample data and the corresponding operation coefficient initially set by those skilled in the art for each group of sample data; as long as it does not affect the proportional relationship between the parameter and the quantified value.

[0052] The above formulas are all obtained by collecting a large amount of data for software simulation and selecting a formula close to the true value. The coefficients in the formulas are set by those skilled in the art according to the actual situation. As mentioned above, this is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, making equivalent substitutions or changes according to the technical solution and inventive concept of the present invention, shall be covered by the protection scope of the present invention.

Claims

1. An artificial intelligence-based optical fiber vibration sensor operation performance monitoring system, characterized in that: It includes a performance monitoring center, a database, an operation performance analysis unit, a shutdown monitoring unit, an obstruction operation unit, a historical factor analysis unit, and a management response unit; The performance monitoring center is used to retrieve the working condition evaluation information and shutdown information of the target sensor from the database, and send the working condition evaluation information and shutdown information to the operation performance analysis unit and the shutdown monitoring unit respectively; The operation performance analysis unit is used to obtain and analyze the operation performance evaluation coefficient of the received working condition evaluation information, and to perform discrimination processing on the obtained operation performance evaluation coefficient to obtain a normal signal or an alarm signal; The shutdown monitoring unit is used to perform shutdown performance evaluation feedback analysis on the received shutdown information, perform discrimination processing on the obtained shutdown characteristic performance coefficient, and obtain a shutdown stability signal or a shutdown risk signal; The hindering operation unit is used to retrieve the operation interference information of the target sensor from the database, and at the same time, perform real-time interference evaluation coefficient acquisition and analysis on the operation interference information to obtain the real-time interference evaluation coefficient; The historical factor analysis unit is used to collect risk interference information of the target sensor, and at the same time perform historical hindering operation division analysis on the risk interference information to obtain a preset operation interference factor Gm.

2. According to the artificial intelligence-based optical fiber vibration sensor operation performance monitoring system of claim 1, it is characterized in that: The process of obtaining and analyzing the operating performance evaluation coefficient is as follows: The operating time period of the optical fiber vibration sensor is collected and set as a time threshold, the optical fiber vibration sensor is set as a target sensor, and the working condition evaluation information of the target sensor within the time threshold is obtained, the working condition evaluation information includes a sensitivity evaluation coefficient and a stability evaluation coefficient; Retrieve the preset operating interference factor Gm of the current target sensor, set the value obtained by multiplying the sensitivity evaluation coefficient, the stability evaluation coefficient and the corresponding values ​​of the preset operating interference factor Gm of the current target sensor as the operating performance evaluation coefficient, and perform discrimination processing on the operating performance evaluation coefficient to obtain a normal signal or an alarm signal.

3. According to the artificial intelligence-based optical fiber vibration sensor operation performance monitoring system of claim 2, it is characterized in that: The analysis process of the stability evaluation coefficient is as follows: the time threshold is divided into i sub-time periods, i is a natural number greater than zero, the infrared characteristic image of the target sensor in the sub-time period is obtained, the area corresponding to the temperature value exceeding the preset temperature value threshold is obtained from the infrared characteristic image, and it is set as the red temperature area, and then the area corresponding to the same area of ​​the red temperature area in each sub-time period is obtained, and it is set as the stability evaluation coefficient; The process of obtaining the sensitivity evaluation coefficient is as follows: obtain the sensitivity characteristic curve of the target sensor in the sub-time period, obtain the average of the maximum peak value and the minimum trough value from the sensitivity characteristic curve, and set it as the time period sensitivity mean, construct a set A of the time period sensitivity means, and set the discrete coefficient of set A as the sensitivity evaluation coefficient.

4. According to the artificial intelligence-based optical fiber vibration sensor operation performance monitoring system of claim 1, it is characterized in that: The shutdown performance evaluation feedback analysis process is as follows: The duration between the shutdown time of the target sensor within the time threshold and the preset monitoring time is obtained, and it is set as the analysis time, and the shutdown information of the target sensor within the analysis time is obtained, and the shutdown information includes the shutdown performance value and the characteristic defect index; The value obtained by multiplying the shutdown performance value, the characteristic defect index and the corresponding value of the preset operation interference factor Gm of the current target sensor is set as the shutdown characteristic performance coefficient, and the shutdown characteristic performance coefficient is judged and processed to obtain a shutdown stability signal or a shutdown risk signal.

5. The optical fiber vibration sensor operation performance monitoring system based on artificial intelligence according to claim 4 is characterized in that: The analysis process of the shutdown performance value is as follows: the duration between the shutdown moment of the target sensor and the moment when the signal attenuates to zero within the analysis time is obtained, and it is set as the shutdown performance value; A signal attenuation characteristic curve of the target sensor within the analysis time is obtained, and at the same time, a standard signal attenuation characteristic curve of the target sensor is obtained, and a difference value between the signal attenuation characteristic curve and the standard signal attenuation characteristic curve is obtained, and the difference value between the signal attenuation characteristic curve and the standard signal attenuation characteristic curve is set as a characteristic defect index.

6. The optical fiber vibration sensor operation performance monitoring system based on artificial intelligence according to claim 1 is characterized in that: The actual interference evaluation coefficient acquisition and analysis process is as follows: Obtaining operation interference information of a working area where a target sensor is located within a time threshold, the operation interference information including an interference characteristic coefficient and a physical interference index; The interference characteristic coefficient represents the product value obtained by multiplying the maximum value of the frequency corresponding to the preset threshold value and the superposition time length after data normalization processing when the corresponding value of the environmental interference information in the working area of ​​the target sensor exceeds the preset threshold value. The environmental interference information includes temperature, humidity, and electromagnetic interference. The superposition time length represents the sum of the overlap time lengths corresponding to the corresponding value of the environmental interference information exceeding the preset threshold value; The physical interference index indicates the number of interference sources in the working area where the target sensor is located whose minimum straight-line distance between the target sensor and the target sensor is less than a preset threshold, and the interference sources include inverter power supply equipment and frequency converter; The interference characteristic coefficient and the actual interference index are compared and analyzed with the preset interference characteristic coefficient threshold and the preset physical interference index threshold, and the number of interference characteristic coefficients and physical interference indices that are greater than or equal to the preset interference characteristic coefficient threshold and the preset physical interference index threshold is set as the actual interference evaluation coefficient.

7. The optical fiber vibration sensor operation performance monitoring system based on artificial intelligence according to claim 6 is characterized in that: The historical blocking operation partition analysis process is as follows: Obtain risk interference information of the target sensor within the time threshold, the risk interference information including parameter offset value and working condition performance value; The parameter offset value indicates the time duration corresponding to the value of the operating parameter exceeding the preset threshold value during the historical operation of the target sensor, and the operating parameters include operating voltage and operating power; The proportion of the number of times the start-up response time exceeds the preset start-up response time threshold in the total number of historical starts of the target sensor is obtained, and the product value obtained by multiplying the proportion value by the number of historical output delays of the target sensor after data normalization is set as the working condition performance value; The parameter offset value and the operating condition performance value are labeled CP and GB respectively, and the parameter offset value CP and the operating condition performance value GB are substituted into the formula to obtain the historical evaluation coefficient.

8. The optical fiber vibration sensor operation performance monitoring system based on artificial intelligence according to claim 7 is characterized in that: Conduct discriminant analysis on the actual interference evaluation coefficient and the historical evaluation coefficient: If the actual interference evaluation coefficient = 0 and the historical evaluation coefficient is less than the preset historical evaluation coefficient threshold, it is judged as conventional interference; if the actual interference evaluation coefficient = 0 is not satisfied, and the historical evaluation coefficient is less than the preset historical evaluation coefficient threshold, it is judged as advanced interference, and the preset operating interference factors Gm corresponding to conventional interference and advanced interference are obtained, m∈{1, 2}, 0<G1<G2.

Images