An artificial intelligence-based optical fiber vibration sensor operation performance monitoring system

Abstract

The present invention relates to the field of sensor monitoring technology, and in particular to an artificial intelligence-based optical fiber vibration sensor operation performance monitoring system, comprising a performance monitoring center, a database, an operation performance analysis unit, a shutdown monitoring unit, an operation obstruction unit, a historical factor analysis unit, and a management response unit; the present invention performs performance monitoring and analysis from two perspectives: operation and shutdown of a target sensor, that is, the operation performance of the target sensor is analyzed through multi-angle analysis, which helps to improve the credibility of the analysis results, and at the same time combines physical interference, non-physical interference, and historical comprehensive interference for fusion evaluation and analysis, which helps to increase the comprehensiveness of data analysis in the process of analyzing the operation performance of the target sensor, and then intuitively understands the operation performance of the target sensor based on the output results, helps to manage and adjust the target sensor, and at the same time improves the monitoring and management efficiency of the target sensor.

Description

Technical Field

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

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

[0003] However, in the existing operation performance monitoring technology of optical fiber vibration sensors, there are problems with single data collection and single analysis angle, which leads to large deviations in the operation performance monitoring results of optical fiber vibration sensors, and at the same time makes the reliability of the operation performance monitoring results low. It is also impossible to analyze from the two points of operation and shutdown of the optical fiber vibration sensor, and thus it is impossible to rationally manage the optical fiber vibration sensor based on information feedback.

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

[0005] The purpose of the present invention is to provide an artificial intelligence-based optical fiber vibration sensor operation performance monitoring system to solve the technical defects mentioned above. The present invention performs performance monitoring and analysis from the two perspectives of operation and shutdown of the target sensor, that is, the target sensor operation performance is analyzed through multi-angle analysis, which helps to improve the credibility of the analysis results. At the same time, it combines physical interference, non-physical interference and historical comprehensive interference for fusion evaluation and analysis, which helps to increase the comprehensiveness of data analysis in the process of target sensor operation performance analysis, 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 object of the present invention can be achieved by the following technical solutions: an artificial intelligence-based optical fiber vibration sensor operation performance monitoring system, comprising 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;

[0007] 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.

[0008] 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.

[0009] 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.

[0010] The operation obstruction 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.

[0011] 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 the preset operation interference factor Gm.

[0012] Preferably, the process of obtaining and analyzing the operating performance evaluation coefficient is as follows:

[0013] 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 the sensitivity evaluation coefficient and the stability evaluation coefficient.

[0014] 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 judgment processing on the operating performance evaluation coefficient to obtain a normal signal or an alarm signal.

[0015] Preferably, the analysis process of the stability evaluation coefficient is as follows: the time threshold is divided into i sub-time periods, where i is a natural number greater than zero, and 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.

[0016] 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 average value, construct a set A of the time period sensitivity average values, and set the discrete coefficient of set A as the sensitivity evaluation coefficient.

[0017] Preferably, the shutdown performance evaluation feedback analysis process is as follows:

[0018] The duration between the target sensor shutdown moment and the preset monitoring moment within the time threshold is obtained and set as the analysis duration. The shutdown information of the target sensor within the analysis duration is obtained. The shutdown information includes the shutdown performance value and the characteristic defect index.

[0019] 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 discriminated and processed to obtain a shutdown stability signal or a shutdown risk signal.

[0020] Preferably, the analysis process of the shutdown performance value is as follows: obtaining the duration between the shutdown moment of the target sensor and the moment when the signal attenuates to zero within the analysis time, and setting it as the shutdown performance value;

[0021] A signal attenuation characteristic curve of the target sensor within the analysis time is obtained, and a standard signal attenuation characteristic curve of the target sensor is obtained at the same time. 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.

[0022] Preferably, the actual interference evaluation coefficient acquisition and analysis process is as follows:

[0023] The operation interference information of the working area where the target sensor is located within the time threshold is obtained, and the operation interference information includes an interference characteristic coefficient and a physical interference index.

[0024] The interference characteristic coefficient represents the product of the maximum value of the frequency corresponding to the value of the environmental interference information in the working area of ​​the target sensor exceeding the preset threshold and the superposition duration after data normalization processing. The environmental interference information includes temperature, humidity, and electromagnetic interference. The superposition duration represents the sum of the overlapping durations corresponding to the value of the environmental interference information exceeding the preset threshold.

[0025] 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 interference source is less than a preset threshold, and the interference sources include inverter power supply equipment and frequency converter.

[0026] The interference characteristic coefficient and the actual interference index are compared and analyzed with the preset interference characteristic coefficient threshold and the preset actual interference index threshold, and the number of interference characteristic coefficients and actual interference indices that are greater than or equal to the preset interference characteristic coefficient threshold and the preset actual interference index threshold is set as the actual interference evaluation coefficient.

[0027] Preferably, the historical blockage operation partition analysis process is as follows:

[0028] The risk interference information of the target sensor within the time threshold is obtained, and the risk interference information includes parameter offset value and working condition performance value.

[0029] The parameter offset value indicates the duration of time during which the corresponding value of the operating parameter of the target sensor exceeds the preset threshold value during the historical operation process. The operating parameters include operating voltage and operating power.

[0030] The proportion of the number of times the startup response time exceeds the preset startup response time threshold in the total number of historical startup times of the target sensor is obtained, and the product obtained by multiplying the proportion by the historical output delay times of the target sensor after data normalization is set as the working condition performance value.

[0031] 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.

[0032] Preferably, the actual interference evaluation coefficient and the historical evaluation coefficient are subjected to discriminant analysis:

[0033] 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.

[0034] The beneficial effects of the present invention are as follows:

[0035] (1) The present invention performs performance monitoring and analysis from two perspectives: operation and shutdown of the target sensor. That is, the target sensor's operating performance is analyzed through multi-angle analysis, which helps to improve the credibility of the analysis results. At the same time, it combines physical interference, non-physical interference and historical comprehensive interference for fusion evaluation and analysis, which helps to increase the comprehensiveness of data analysis in the process of analyzing the target sensor's operating performance, and then intuitively understand the target sensor's operating performance based on the output results.

[0036] (2) The present invention obtains and analyzes the operating performance evaluation coefficient from the operating angle of the target sensor to determine whether the operating performance of the target sensor is normal, so as to perform reasonable and targeted management of the target sensor to improve the operating safety of the target sensor and improve the stability of the operating performance of the target sensor. Based on the information progressive method, the shutdown performance evaluation feedback analysis is further performed 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 operating performance monitoring results of the target sensor based on the output results, thereby facilitating the management and adjustment of the target sensor and improving the monitoring and management efficiency of the target sensor. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The present invention will be further described below with reference to the accompanying drawings;

[0038] Figure 1 It is a flow chart of the system of the present invention;

[0039] Figure 2 It is a reference diagram for local analysis of the present invention. DETAILED DESCRIPTION

[0040] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0041] Example 1:

[0042] See also Figures 1 to 2 As shown, the present invention is an artificial intelligence-based optical fiber vibration sensor operation performance monitoring system, including 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 database is connected to the performance monitoring center and the obstruction operation unit in a one-way communication manner, the performance monitoring center is connected to the operation performance analysis unit and the shutdown monitoring unit in a one-way communication manner, the obstruction operation unit is connected to the historical factor analysis unit in a one-way communication manner, the historical factor analysis unit is connected to the performance monitoring center in a one-way communication manner, the operation performance analysis unit is connected to the shutdown monitoring unit and the management response unit in a one-way communication manner, and the shutdown monitoring unit is connected to the management response unit in a one-way communication manner.

[0043] 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.

[0044] The operation performance analysis unit is used to obtain and analyze the operation performance evaluation coefficients of the received working condition evaluation information, so as to carry out reasonable and targeted management of the target sensor, thereby improving the operation safety of the target sensor and improving the stability of the target sensor's operation performance. The specific operation performance evaluation coefficient acquisition and analysis process is as follows:

[0045] 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 the sensitivity evaluation coefficient and the stability evaluation coefficient.

[0046] In the embodiment of the present invention, the analysis process of the stability evaluation coefficient is as follows:

[0047] The time threshold is divided into i sub-time periods, where i is a natural number greater than zero. The infrared characteristic image of the target sensor in the sub-time period is obtained, and 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. 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. It should be noted that the stability evaluation coefficient is an influencing parameter that reflects 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.

[0048] In the embodiment of the present invention, the process of obtaining the sensitivity evaluation coefficient is as follows:

[0049] 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 average value, construct a set A of the time period sensitivity average values, and set the dispersion coefficient of 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 sensitivity fluctuation abnormality of the target sensor.

[0050] Retrieve the preset operation interference factor Gm of the current target sensor, multiply the sensitivity evaluation coefficient, the stability evaluation coefficient, and the corresponding value of the preset operation interference factor Gm of the current target sensor, and set the value obtained as the operation performance evaluation coefficient, and perform discrimination processing on the operation performance evaluation coefficient:

[0051] If the operation performance evaluation coefficient is less than the preset operation performance evaluation coefficient threshold and a normal signal is generated;

[0052] If the operation performance evaluation coefficient is greater than or equal to the preset operation performance evaluation coefficient threshold, an alarm signal is generated, and the normal signal or alarm signal is sent 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 carry out reasonable and targeted management of the target sensor, thereby improving the operation safety of the target sensor and improving the stability of the operation performance of the target sensor.

[0053] The shutdown monitoring unit is used to perform shutdown performance evaluation feedback analysis on the received shutdown information, so as to analyze the performance risk of the target sensor from the perspective of shutdown and improve the stability of the target sensor's operating performance. The specific shutdown performance evaluation feedback analysis process is as follows:

[0054] The duration between the target sensor's shutdown time and the preset monitoring time within the time threshold is obtained and set as the analysis duration. The shutdown information of the target sensor within the analysis duration is obtained, and the shutdown information includes the shutdown performance value and the characteristic defect index;

[0055] In an embodiment of the present invention, 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. It should be noted that the larger the numerical value of the shutdown performance value, the greater the failure risk of the target sensor.

[0056] In an embodiment of the present invention, 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. The difference value between the signal attenuation characteristic curve and the standard signal attenuation characteristic curve is set as a 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.

[0057] 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:

[0058] If the shutdown characteristic performance coefficient is less than the preset shutdown characteristic performance coefficient threshold, a shutdown stability signal is generated;

[0059] If the shutdown characteristic performance coefficient is greater than or equal to the preset shutdown characteristic performance coefficient threshold, a shutdown risk signal is generated, and the shutdown stability signal or shutdown risk signal is sent to the management response unit. After receiving the shutdown stability signal or shutdown risk signal, the management response unit immediately performs the preset warning operation corresponding to the shutdown stability signal or shutdown risk signal, so as to carry out reasonable and targeted management of the target sensor to improve the stability of the target sensor's operating performance.

[0060] Example 2:

[0061] The obstruction operation unit is used to retrieve the operation interference information of the target sensor from the database, and at the same time obtain and analyze the actual interference evaluation coefficient of the operation interference information, so as to analyze it from the perspectives of physical interference and non-physical interference, and understand the comprehensive operation interference degree of physical interference and non-physical interference on the target sensor. The specific process of obtaining and analyzing the actual interference evaluation coefficient is as follows:

[0062] The operation interference information of the working area where the target sensor is located within the time threshold is obtained, and the operation interference information includes an interference characteristic coefficient and a physical interference index.

[0063] In an embodiment of the present invention, the interference characteristic coefficient represents the product of the maximum value of the frequency corresponding to the preset threshold value when the corresponding value of the environmental interference information in the working area of ​​the target sensor exceeds the preset threshold value and the superposition time after data normalization processing. The environmental interference information includes temperature, humidity, electromagnetic interference, etc. The superposition time represents the sum of the overlapping time values ​​corresponding to the environmental interference information exceeding the preset threshold value. 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.

[0064] In an embodiment of the present invention, the physical interference index indicates the number of interference sources in the working area of ​​the target sensor where the minimum straight-line distance between the target sensor and the interference source is less than a preset threshold. The interference sources include inverter power supply equipment, frequency converter, etc. It should be noted that the operating performance risk of the target sensor is analyzed from the perspective of physical interference 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.

[0065] The interference characteristic coefficient and the actual interference index are compared and analyzed with the preset interference characteristic coefficient threshold and the preset actual interference index threshold, and the number of interference characteristic coefficients and actual interference indices that are greater than or equal to the preset interference characteristic coefficient threshold and the preset actual interference index threshold is set as the actual interference evaluation coefficient. It should be noted that the actual interference evaluation coefficient is an influencing parameter that reflects the operating performance of the target sensor.

[0066] The historical factor analysis unit is used to collect risk interference information of target sensors and perform historical obstruction operation classification analysis on the risk interference information. That is, it analyzes the impact of historical factors on current operation performance from a comprehensive perspective to provide data support for subsequent analysis. The specific historical obstruction operation classification analysis process is as follows:

[0067] The risk interference information of the target sensor within the time threshold is obtained, and the risk interference information includes parameter offset value and working condition performance value.

[0068] In an embodiment of the present invention, the parameter offset value indicates the duration of time during which the corresponding value of the operating parameter of the target sensor exceeds the preset threshold value during the historical operation process. The operating parameters include operating voltage, operating power, etc. It should be noted that the larger the value of the parameter offset value, the deeper the damage caused by the legacy problem of the target sensor.

[0069] In an embodiment of the present invention, the proportion of the number of times the startup response time exceeds a preset startup response time threshold in the total number of historical startup times of the target sensor is obtained, and the product obtained by multiplying the proportion by the number of historical output delays of the target sensor after data normalization is set as the operating condition performance value. It should be noted that the operating risk of the target sensor is understood from the perspective of historical operating performance. The larger the numerical value of the operating condition performance value, the greater the risk of abnormal operating performance of the target sensor.

[0070] The parameter offset value and the working condition performance value are labeled CP and GB respectively. Substitute the parameter offset value CP and the working condition performance value GB into the formula The historical evaluation coefficient is obtained, where a1 and a2 are the preset weight factor coefficients of the parameter offset value and the operating condition performance value, respectively, and a3 is the preset error correction factor coefficient. a1, a2, and a3 are all greater than zero. It should be noted that a comprehensive evaluation of historical impacts is performed from the perspective of data fusion in order to analyze the impact of historical factors on current operating performance from a comprehensive perspective.

[0071] Perform discriminant analysis on the actual interference evaluation coefficient and the historical evaluation coefficient:

[0072] If the actual interference evaluation coefficient = 0, and the historical evaluation coefficient is less than the preset historical evaluation coefficient threshold, it is determined to be regular interference;

[0073] 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 high-level interference, where the interference impacts corresponding to conventional interference and high-level interference increase in turn, and the preset operating interference factors Gm corresponding to conventional interference and high-level interference are obtained, m∈{1, 2}, that is, when m=1, the preset operating interference factor G1 represents conventional interference, and when m=2, the preset operating interference factor G2 represents high-level interference, 0<G1<G2, and the preset operating interference factor Gm is sent to the performance monitoring center.

[0074] In summary, the present invention performs performance monitoring and analysis from the two perspectives of operation and shutdown of the target sensor, that is, the target sensor operation performance is analyzed through multi-angle analysis, which helps to improve the credibility of the analysis results. At the same time, it combines physical interference, non-physical interference and historical comprehensive interference for fusion evaluation and analysis, which helps to increase the comprehensiveness of data analysis in the process of target sensor operation performance analysis, and then intuitively understand the operation performance of the target sensor based on the output results. The operation performance evaluation coefficient is obtained and analyzed from the operation perspective of the target sensor to determine whether the target sensor operation performance is normal, so as to perform reasonable and targeted management of the target sensor, so as to improve the operation safety of the target sensor and improve the stability of the target sensor operation performance. Based on the information progressive method, the shutdown performance evaluation feedback analysis is further performed from the shutdown perspective of the target sensor to understand whether the shutdown performance of the target sensor is normal, so as to intuitively feedback the target sensor operation performance monitoring results based on the output results, which helps to manage and adjust the target sensor and improve the monitoring and management efficiency of the target sensor.

[0075] The threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by technicians in this field for each set of sample data; as long as it does not affect the proportional relationship between the parameter and the quantized value.

[0076] The size of the coefficient is to quantify each parameter to obtain a specific numerical value, which is convenient for subsequent comparison. The size of the coefficient depends on the amount of sample data and the preliminary setting of the corresponding operating coefficient for each set of sample data by technical personnel in this field; as long as it does not affect the proportional relationship between the parameter and the quantized value.

[0077] The above formulas are obtained by collecting a large amount of data and performing software simulation, and a formula close to the actual value is selected. The coefficients in the formula are set by those skilled in the art according to actual conditions. The above is only a preferred specific implementation method of the present invention, but the protection scope of the present invention is not limited to this. Any technician familiar with this technical field, within the technical scope disclosed by the present invention, can make equivalent replacements or changes based on the technical solution and inventive concept of the present invention, which should 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, perform discrimination processing on the obtained operation performance evaluation coefficient, and 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 obstruction 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 perform historical hindering operation classification analysis on the risk interference information to obtain the preset operation interference factor Gm. 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 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 operation interference factor Gm of the current target sensor, multiply the sensitivity evaluation coefficient, the stability evaluation coefficient, and the corresponding value of the preset operation interference factor Gm of the current target sensor, and set the value obtained as the operation performance evaluation coefficient, and perform discrimination processing on the operation performance evaluation coefficient to obtain a normal signal or an alarm signal; The shutdown performance evaluation feedback analysis process is as follows: The duration between the target sensor's shutdown time and the preset monitoring time within the time threshold is obtained and set as the analysis duration. The shutdown information of the target sensor within the analysis duration 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 discriminated and processed to obtain a shutdown stability signal or a shutdown risk signal; The process of obtaining and analyzing the actual interference evaluation coefficient is as follows: Obtaining operational interference information of a working area of ​​a target sensor within a time threshold, the operational interference information including an interference characteristic coefficient and a physical interference index; The interference characteristic coefficient represents the product of the maximum value of the frequency corresponding to the value of the environmental interference information corresponding to the working area of ​​the target sensor exceeding the preset threshold and the superposition duration after data normalization processing. The environmental interference information includes temperature, humidity, and electromagnetic interference. The superposition duration represents the sum of the overlapping durations corresponding to the value of the environmental interference information exceeding the preset threshold. The physical interference index indicates the number of interference sources in the working area of ​​the target sensor where the minimum straight-line distance between the target sensor and the interference source is less than a preset threshold. The interference sources include inverter power supply equipment and frequency converters. Compare and analyze the interference characteristic coefficient and the actual interference index with the preset interference characteristic coefficient threshold and the preset actual interference index threshold, and set the number of the interference characteristic coefficients and the actual interference indexes that are greater than or equal to the preset interference characteristic coefficient threshold and the preset actual interference index threshold as the actual interference evaluation coefficient; The analysis process of the shutdown performance value is as follows: obtaining the duration between the shutdown moment of the target sensor and the moment when the signal attenuates to zero within the analysis time, and setting it as the shutdown performance value; A signal attenuation characteristic curve of the target sensor within the analysis time is obtained, and a standard signal attenuation characteristic curve of the target sensor is obtained at the same time. 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.

2. The optical fiber vibration sensor operation performance monitoring system based on artificial intelligence according to claim 1 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, where 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 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 average value, construct a set A of the time period sensitivity average values, and set the discrete coefficient of set A as the sensitivity evaluation coefficient.

3. The optical fiber vibration sensor operation performance monitoring system based on artificial intelligence according to claim 1 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 includes parameter offset value and working condition performance value; The parameter offset value indicates the duration of time during which the corresponding value of the operating parameter of the target sensor exceeds the preset threshold value during the historical operation process, and the operating parameters include operating voltage and operating power; Obtaining the percentage of the number of times the startup response time exceeds a preset startup response time threshold in the total number of historical startups of the target sensor, and multiplying the percentage by the number of historical output delays of the target sensor after data normalization to obtain the product value 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.

4. 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 and the historical evaluation coefficient are subjected to discriminant analysis: 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.

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