Passive optical fiber sensing multi-parameter remote data analysis system

Abstract

This invention relates to the field of fiber optic sensing technology, specifically disclosing a passive fiber optic sensing multi-parameter remote data analysis system. It performs continuous temporal analysis on lost and retransmitted data periods, filters out retransmission time-series analysis periods, and performs temporal disorder superposition analysis on the retransmitted data within these periods. The system determines the degree of temporal disorder superposition, and when there is a high degree of temporal disorder superposition, it performs time-series adjustment operations on the disordered retransmitted data. This restores data continuity, ensuring that each parameter has a corresponding record, providing a complete data foundation for subsequent analysis. Furthermore, temporal disorder disrupts the temporal correspondence between these parameters, leading to inaccurate parameter correlations. Restoring the correct temporal order of the parameter data ensures that the analyzed parameter relationships conform to reality, improving the accuracy of data analysis.

Description

Technical Field

[0001] This invention relates to the field of fiber optic sensing technology, and specifically to a passive fiber optic sensing multi-parameter remote data analysis system. Background Technology

[0002] In the field of oil and gas pipeline monitoring, passive fiber optic sensing technology has become a highly promising and valuable monitoring method due to its advantages such as high sensitivity, resistance to electromagnetic interference, and distributed measurement capabilities. Through passive fiber optic sensing systems, multi-parameter information along the oil and gas pipeline, such as temperature, pressure, and strain, can be acquired in real time. This data plays a crucial role in timely detection of potential safety hazards and ensuring the safe and stable operation of the pipeline.

[0003] Data loss poses a significant challenge to the monitoring and analysis of oil and gas pipelines. When data is lost, it not only leads to incomplete monitoring data, preventing analysts from fully and accurately grasping the actual operating status of the pipeline at various moments, but it also disrupts the inherent temporal correlations between multiple parameters. During the operation of oil and gas pipelines, various parameters are closely interconnected physically; for example, temperature changes may affect pressure distribution, and stress alterations may be related to pipeline deformation. Disordered data makes the analysis of these parameter correlations inaccurate, hindering the timely detection of potential problems and risks in pipeline operation and severely impacting the assessment and prediction of pipeline health status.

[0004] Furthermore, even if the data retransmission mechanism is triggered, the retransmitted data may still suffer from overlapping time-series errors, meaning it may not arrive at the receiving end in the original chronological order. Highly time-series-disordered retransmissions further exacerbate data incompleteness and inaccuracy, making subsequent data analysis more difficult and potentially leading to erroneous decisions, posing a serious threat to the safe operation of oil and gas pipelines.

[0005] Therefore, the present invention provides a passive fiber optic sensing multi-parameter remote data analysis system. Summary of the Invention

[0006] The purpose of this invention is to provide a passive fiber optic sensing multi-parameter remote data analysis system to solve the aforementioned background problems.

[0007] The objective of this invention can be achieved through the following technical solutions:

[0008] A passive fiber optic sensing multi-parameter remote data analysis system includes:

[0009] Data loss analysis module: During the monitoring of oil and gas pipelines using passive fiber optic sensors, data loss analysis is performed on the data received during each data transmission monitoring period to identify periods of data loss and periods of data integrity.

[0010] Loss and retransmission judgment module: Analyze and compare the lost data during the data loss period with the received data during the data integrity period to assess whether the data retransmission mechanism is triggered. If data retransmission is triggered, the loss and retransmission period is extracted.

[0011] Retransmission aliasing assessment module: Performs continuous analysis of the lost retransmission period in the time dimension, filters out the retransmission time sequence analysis period, performs time sequence aliasing and superposition analysis on the data retransmission within the retransmission time sequence analysis period, and judges the degree of time sequence aliasing and superposition of the retransmitted data.

[0012] Timing adjustment module: When there is a high degree of timing disorder in the retransmitted data, the module analyzes the priority of data loss during the data loss period and performs timing adjustment operations on the retransmitted data with timing disorder.

[0013] As a further aspect of the present invention, the process for filtering data loss periods and data integrity periods is as follows:

[0014] Set a data transmission monitoring cycle and divide it into several equal data monitoring periods;

[0015] Within each data monitoring period, the frame sequence number of each sensor data from the sending port and the frame sequence number of the sensor data from the receiving port are obtained respectively, and used as the sensor sending frame sequence number and the sensor receiving frame sequence number.

[0016] If all sensor transmit frame sequence numbers correspond to and coincide with all sensor receive frame sequence numbers in sequence, then it is marked as a data complete period.

[0017] If the sequence numbers of all transmitted sensor frames do not correspond sequentially with the sequence numbers of all received sensor frames, then this period is marked as a data loss period.

[0018] As a further aspect of the present invention, the process of analyzing and comparing the lost data during the data loss period with the received data during the data integrity period is as follows:

[0019] During the period of data loss, the frame sequence number corresponding to the lost data is obtained from the sending port and used as the lost frame sequence number.

[0020] Select the data integrity period that is adjacent to the data loss period but later in time as the target analysis period;

[0021] Obtain all frame sequence numbers of the sensor data received from the receiving port within the target analysis period, and sort them according to the order of reception time to obtain the target received frame sequence number sequence.

[0022] The sequence number of the lost frame within the data loss period that does not overlap with the sequence number of the target received frame is taken as the target received frame sequence number.

[0023] The sequence number of the lost frame that coincides with the sequence number of the target received frame within the data loss period is used as the sequence number of the lost retransmission frame.

[0024] As a further aspect of the present invention, the analysis process for triggering the data retransmission mechanism is as follows:

[0025] The proportion of all lost and retransmitted frame sequence numbers to the total number of lost frame sequence numbers within the data loss period is used as the loss and retransmission ratio.

[0026] Extract the transmission timestamp of the sending port corresponding to each lost retransmission frame sequence number within the data loss period, and obtain the duration between the transmission timestamp of the receiving port within the target analysis period, as the lost retransmission duration;

[0027] The retransmission interval for a single frame number is calculated by comparing the retransmission duration corresponding to the lost retransmission frame number with the duration of the data monitoring period.

[0028] The retransmission interval values ​​of the single frame sequence numbers corresponding to all lost and retransmitted frame sequence numbers are summed and averaged to obtain the sequence number retransmission interval analysis value.

[0029] As a further aspect of the present invention, after determining whether the data retransmission mechanism has been triggered, the process for determining the data loss retransmission period is as follows:

[0030] The retransmission trigger analysis value is obtained by summing the loss retransmission ratio value with the sequence number retransmission interval analysis value.

[0031] If the retransmission trigger analysis value is greater than or equal to the retransmission trigger analysis threshold, the data retransmission mechanism is triggered, and the target analysis period is taken as the lost retransmission period.

[0032] As a further aspect of the present invention, the process for obtaining the retransmission timing analysis period is as follows:

[0033] Extract all lost and retransmitted periods within the data transmission monitoring period, and obtain the start and end times of each lost and retransmitted period;

[0034] If the end time of a lost retransmission period earlier in the time dimension coincides with the start time of a lost retransmission period later in the time dimension, then the adjacent lost retransmission periods will be merged and used as the retransmission time series analysis period.

[0035] As a further aspect of the present invention, the process of performing time sequence disorder superposition analysis on data retransmissions within the retransmission time sequence analysis period is as follows:

[0036] During the retransmission timing analysis period, all received frame sequence numbers are obtained and used as timing analysis frame sequence numbers. The timing analysis frame sequence is then constructed according to the time sequence before and after reception.

[0037] Extract the lost retransmission frame sequence number and the target received frame sequence number from the time sequence analysis frame sequence respectively;

[0038] Obtain the lost and retransmitted frame sequence numbers between adjacent target received frame sequence numbers, and count the number of lost and retransmitted frame sequence numbers, and the proportion of the number of lost and retransmitted frame sequence numbers to the total number of received frame sequence numbers in the time sequence analysis frame sequence, as the unit retransmission interleaving ratio.

[0039] The sequence numbers of adjacent target received frames with lost retransmission frame numbers are combined to form a lost retransmission interleaving group, and a disorder superposition analysis curve is constructed based on the ratio of the number of lost retransmission interleaving groups to the number of unit retransmission interleavings.

[0040] As a further aspect of the present invention, the process for determining the degree of temporal disorder superposition is as follows:

[0041] The difference between the Y-axis coordinates of adjacent coordinate points on the disordered superposition analysis curve is used to obtain the amplitude of the interlacing quantity.

[0042] Extract the adjacent coordinate points of the interlacing quantity amplitude with a positive sign, and use the local curve between the adjacent points as the interlacing superposition sub-curve;

[0043] After summing the lengths of each interlacing and superimposed sub-curve, the ratio of this summation to the disordered superimposed analysis curve is calculated to obtain the interlacing and superimposed degree value.

[0044] The summation and average of the unit retransmission interleaving ratios corresponding to each lost retransmission interleaving group are used to obtain the interleaving superposition quantity analysis value.

[0045] The sum of the interlacing and superposition quantity analysis value and the interlacing and superposition degree value is used to obtain the disordered superposition analysis value;

[0046] If the value of the disorder superposition analysis is greater than the disorder superposition analysis threshold, it is determined to be a highly disordered temporal superposition signal.

[0047] As a further aspect of the present invention, the process of analyzing the priority of data loss during different time periods is as follows:

[0048] Within each data loss period, the difference between the number of times when all sensor transmitted frame sequence numbers and all sensor received frame sequence numbers do not correspond and overlap sequentially is taken as the number of frame sequence numbers lost.

[0049] The ratio of the number of lost frame sequence numbers corresponding to each data loss period to the number of frame sequence numbers sent by the sensor is calculated to obtain the frame sequence number loss ratio for each period.

[0050] The ratio of the number of lost frames in each data loss period is compared, and the lost frame numbers in the data loss period are prioritized and adjusted in descending order to obtain multiple loss sorting periods.

[0051] As a further aspect of the present invention, the process of performing timing adjustment on retransmitted data with timing errors is as follows:

[0052] Arbitrarily extract the sequence number of lost frames within the lost frame sorting period, compare it with the sequence number of lost retransmission frames within the retransmission timing analysis period, and take the sequence number of lost retransmission frames that coincides with the sequence number of lost frames within the lost frame sorting period as the sequence number of lost frames rearranged.

[0053] All lost rearranged frame sequence numbers are sorted sequentially according to their corresponding reception time points within the lost sorting period, and then added to the frames within the lost sorting period.

[0054] The beneficial effects of this invention are as follows:

[0055] 1. In the process of monitoring oil and gas pipelines using passive optical fiber sensing, this invention performs data loss analysis on the data received during each data transmission monitoring period, filters out data loss periods, analyzes the lost data within each data loss period, and assesses whether a data retransmission mechanism is triggered. If data retransmission is triggered, the lost retransmission period is extracted. This helps to understand the type and quantity of lost data in detail, identify the contribution of lost data to data analysis after loss, and reorganize the lost data during the lost retransmission period to provide data support for subsequent data analysis.

[0056] 2. This invention performs continuous temporal analysis on lost and retransmitted data, filters out retransmission time-series analysis periods, and performs temporal disorder superposition analysis on the retransmissions within these periods. It determines the degree of temporal disorder superposition in the retransmitted data. When there is a high degree of temporal disorder superposition, a time-series adjustment operation is performed on the disordered retransmitted data to restore data continuity. This ensures that each parameter data point has a corresponding record, providing a complete data foundation for subsequent analysis. Furthermore, temporal disorder disrupts the temporal correspondence between these parameters, leading to inaccurate parameter correlations obtained from the analysis. Restoring the correct temporal order between parameter data ensures that the parameter relationships obtained from the analysis conform to the actual situation, thus improving the accuracy of data analysis. Attached Figure Description

[0057] The invention will now be further described with reference to the accompanying drawings.

[0058] Figure 1 This is a functional block diagram of a passive fiber optic sensing multi-parameter remote data analysis system according to the present invention.

[0059] Figure 2 This is a flowchart of the steps of a passive fiber optic sensing multi-parameter remote data analysis system in this invention;

[0060] Figure 3 This is a flowchart of the judgment process within a passive fiber optic sensing multi-parameter remote data analysis system of the present invention. Detailed Implementation

[0061] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0062] Example 1

[0063] When passive fiber optic sensors are used for fiber optic monitoring of oil and gas pipelines, the pipelines often traverse mountainous areas, deserts, and uninhabited regions without wired network coverage. This leads to packet loss during wireless transmission at unattended nodes in the field. Furthermore, random packet loss during wireless transmission triggers retransmission mechanisms, causing data timing disorder. When two consecutive wireless transmissions result in packet loss, multiple sensor nodes or a single node transmitting multiple parameters simultaneously cause retransmitted data packets to compete for bandwidth with newly generated normal data packets. Ultimately, this results in a cumulative effect when two consecutive wireless transmissions of packet loss occur during retransmission. Therefore, if... Figure 1 - Figure 3 As shown, this embodiment of the invention provides a passive fiber optic sensing multi-parameter remote data analysis system, comprising:

[0064] Data loss analysis module: During the monitoring of oil and gas pipelines using passive fiber optic sensors, the module performs data loss analysis on the data received during each data transmission monitoring period and identifies the periods in which data was lost.

[0065] In some embodiments, a data transmission monitoring cycle is set and equally divided into several data monitoring periods, wherein the duration of each data monitoring period is equal.

[0066] Within each data monitoring period, the frame sequence number of each sensor data from the sending port and the frame sequence number of the sensor data from the receiving port are obtained respectively, and used as the sensor sending frame sequence number and the sensor receiving frame sequence number.

[0067] Compare all sensor transmission frame sequence numbers with all sensor reception frame sequence numbers. If all sensor transmission frame sequence numbers and all sensor reception frame sequence numbers correspond and overlap sequentially, it means that the total amount of sensor data transmitted and received within the analyzed time period is consistent, and no data loss has occurred. Then, it is marked as a data complete time period.

[0068] If the sequence numbers of all transmitted sensor frames do not correspond sequentially with the sequence numbers of all received sensor frames, it indicates that the total amount of sensor data transmitted and received within the analyzed time period is inconsistent, resulting in data loss, which is then marked as the data loss period.

[0069] Data loss and retransmission judgment module: Analyze the lost data within each data loss period, assess whether the data retransmission mechanism is triggered, and if data retransmission is triggered, extract the data loss and retransmission period.

[0070] In some embodiments, during the data loss period, the frame sequence number corresponding to the lost data is obtained from the sending port and used as the lost frame sequence number;

[0071] For example, a data integrity period that is adjacent to the data loss period but later in time than the data loss period is selected as the target analysis period;

[0072] Obtain all frame sequence numbers of the sensor data received from the receiving port within the target analysis period, and sort them according to the order of reception time to obtain the target received frame sequence number sequence.

[0073] The sequence number of the lost frame within the data loss period that does not overlap with the sequence number of the target received frame is taken as the target received frame sequence number.

[0074] The sequence number of the lost frame that coincides with the sequence number of the target received frame within the data loss period is taken as the sequence number of the lost retransmission frame.

[0075] The proportion of all lost and retransmitted frame sequence numbers to the total number of lost frame sequence numbers within the data loss period is used as the loss and retransmission ratio.

[0076] Extract the transmission timestamp of the sending port corresponding to each lost retransmission frame sequence number within the data loss period, and obtain the duration between the transmission timestamp of the receiving port within the target analysis period, as the lost retransmission duration;

[0077] The retransmission interval for a single frame number is calculated by comparing the retransmission duration corresponding to the lost retransmission frame number with the duration of the data monitoring period.

[0078] The retransmission interval values ​​of the single frame sequence number corresponding to all lost retransmission frame sequence numbers are summed and averaged to obtain the sequence number retransmission interval analysis value.

[0079] The retransmission trigger analysis value is obtained by summing the loss retransmission ratio value with the sequence number retransmission interval analysis value.

[0080] It is understandable that the retransmission trigger analysis value represents two key aspects related to data retransmission. On the one hand, the retransmission ratio reflects the proportion of successfully retransmitted data frames in the total number of lost data frames during the target analysis period. On the other hand, the sequence retransmission interval analysis value reflects the average retransmission time efficiency of the lost retransmission frames. Specifically, if the retransmission trigger analysis value is larger, it indicates that the proportion of retransmissions in the lost data is higher during the target analysis period, and the retransmission interval of each lost data is more in line with the data retransmission duration. If the retransmission trigger analysis value is smaller, the proportion of retransmissions in the lost data is lower, and the retransmission interval of each lost data is less in line with the data retransmission duration.

[0081] If the retransmission trigger analysis value is greater than or equal to the retransmission trigger analysis threshold, the data retransmission mechanism is triggered. This indicates that the proportion of retransmissions of lost data is relatively high within the target analysis period, and the retransmission time interval of each lost data is more in line with the data retransmission duration. The target analysis period is then taken as the lost data retransmission period.

[0082] If the retransmission trigger analysis value is less than the retransmission trigger analysis threshold, and the data retransmission mechanism is not triggered, it indicates that the proportion of retransmissions among the lost data is low, and the retransmission time interval of each lost data does not conform to the data retransmission duration. Therefore, the target analysis period is taken as the non-loss retransmission period.

[0083] The specific solution of this invention is as follows: During the monitoring of oil and gas pipelines using passive optical fiber sensing, data loss analysis is performed on the data received during each data transmission monitoring period. Data loss periods are screened out, and the lost data within each data loss period is analyzed to assess whether a data retransmission mechanism is triggered. If data retransmission is triggered, the lost retransmission period is extracted. This helps to understand the type and quantity of lost data in detail, identify the contribution of lost data to data analysis after loss, and reorganize the lost data during the lost retransmission period to provide data support for subsequent data analysis.

[0084] Example 2

[0085] like Figure 1 - Figure 3 As shown, this embodiment of the invention provides a passive fiber optic sensing multi-parameter remote data analysis system, which further includes:

[0086] Retransmission aliasing assessment module: Performs continuous analysis of the lost retransmission period in the time dimension, filters out the retransmission time sequence analysis period, performs time sequence aliasing and superposition analysis on the data retransmission within the retransmission time sequence analysis period, and judges the degree of time sequence aliasing and superposition of the retransmitted data.

[0087] In some embodiments, the analysis process for adjacent lost retransmission periods is as follows:

[0088] Extract all lost and retransmitted periods within the data transmission monitoring period, and obtain the start and end times of each lost and retransmitted period;

[0089] If the end time of the earlier loss and retransmission period does not coincide with the start time of the later loss and retransmission period, it indicates that the two are non-continuous loss and retransmission periods in the time dimension.

[0090] If the end time of the earlier loss and retransmission period in the time dimension coincides with the start time of the later loss and retransmission period in the time dimension, it means that the two are consecutive loss and retransmission periods in the time dimension, that is, adjacent loss and retransmission periods.

[0091] Adjacent lost retransmission periods are merged into a single retransmission time sequence analysis period.

[0092] During the retransmission timing analysis period, all received frame sequence numbers are obtained and used as timing analysis frame sequence numbers. The timing analysis frame sequence is then constructed according to the time sequence before and after reception.

[0093] Extract the lost retransmission frame sequence number and the target received frame sequence number from the time sequence analysis frame sequence respectively;

[0094] Obtain the lost and retransmitted frame sequence numbers between adjacent target received frame sequence numbers, and count the number of lost and retransmitted frame sequence numbers, and the proportion of the number of lost and retransmitted frame sequence numbers to the total number of received frame sequence numbers in the time sequence analysis frame sequence, as the unit retransmission interleaving ratio.

[0095] The sequence numbers of adjacent target received frames with lost retransmission frame sequence numbers are combined to form a lost retransmission interleaving group.

[0096] It should be noted that each lost retransmission interleaving group corresponds to a unit retransmission interleaving ratio;

[0097] Using the X-axis as the lost retransmission interleaving group and the Y-axis as the unit retransmission interleaving ratio, a disorder superposition analysis curve is constructed.

[0098] It is understandable that the lost retransmission interleaving group on the X-axis is sorted on the X-axis based on the sequence number of the target received frame that is earlier in the time dimension within the lost retransmission interleaving group.

[0099] The difference between the Y-axis coordinates of adjacent coordinate points on the disordered superposition analysis curve is used to obtain the amplitude of the interlacing quantity.

[0100] Extract adjacent coordinate points whose symbols are positive interlacing magnitude, and use the local curves between adjacent points as interlacing superposition sub-curves;

[0101] After summing the lengths of each interlacing and superimposed sub-curve, the ratio of this summation to the disordered superimposed analysis curve is calculated to obtain the interlacing and superimposed degree value.

[0102] The summation and average of the unit retransmission interleaving ratios corresponding to each lost retransmission interleaving group are used to obtain the interleaving superposition quantity analysis value.

[0103] The sum of the interlacing and superposition quantity analysis value and the interlacing and superposition degree value is used to obtain the disordered superposition analysis value;

[0104] It is understandable that the meaning of the disorder overlay analysis value is: an indicator that measures the degree of disorder overlay of data retransmission timing within adjacent loss and retransmission periods. On the one hand, the overlay quantity analysis value reflects the average degree of overlay of lost retransmission frames between normal received frames within the analysis period after merging adjacent loss and retransmission periods. On the other hand, the overlay degree value reflects the degree of dispersion of the overlay of lost retransmission frames in the normal received frame sequence. Specifically, if the disorder overlay analysis value is larger, it indicates that the frequency of overlay of lost retransmission frames is higher and the degree of overlay is more concentrated. If the disorder overlay analysis value is smaller, it indicates that the frequency of overlay of lost retransmission frames is lower and the degree of overlay is more dispersed.

[0105] If the disorder superposition analysis value is greater than the disorder superposition analysis threshold, it indicates that the frequency of lost and retransmitted frames is high and the degree of superposition is relatively concentrated, showing a highly disordered superposition signal.

[0106] If the disorder superposition analysis value is less than or equal to the disorder superposition analysis threshold, it indicates that the frequency of lost and retransmitted frames is low and the degree of superposition is relatively dispersed, showing a low-degree timing disorder superposition signal.

[0107] Timing adjustment module: When there is a high degree of timing disorder in the retransmitted data, the loss priority of the data loss period is analyzed, and then timing adjustment operation is performed on the retransmitted data with timing disorder.

[0108] In some embodiments, within each data loss period, the difference between the number of times when all sensor transmitted frame sequence numbers and all sensor received frame sequence numbers do not correspond and overlap sequentially is obtained as the number of frame sequence numbers lost.

[0109] The ratio of the number of lost frame sequence numbers corresponding to each data loss period to the number of frame sequence numbers sent by the sensor is calculated to obtain the frame sequence number loss ratio for each period.

[0110] The ratio of the number of lost frames in each data loss period is compared, and the lost frame numbers in the data loss period are prioritized and adjusted in descending order to obtain multiple loss sorting periods.

[0111] For example, the data loss period corresponding to the largest ratio of frame order loss in a given time period is selected as the optimal data loss sorting period;

[0112] Extract the sequence number of lost frames within the optimal loss sorting period, compare it with the sequence number of lost retransmission frames within the retransmission timing analysis period, and take the sequence number of lost retransmission frames that coincides with the sequence number of lost frames within the optimal loss sorting period as the optimal loss rearrangement frame sequence number.

[0113] All optimal lost rearranged frame numbers are sorted sequentially according to their corresponding reception time points within the optimal lost sorting period, and then added to the optimal lost sorting period.

[0114] For example, the data loss period corresponding to the minimum frame order loss ratio is selected as the latest data loss sorting period;

[0115] Extract the sequence number of lost frames within the latest loss sorting period and compare it with the sequence number of lost retransmission frames within the retransmission timing analysis period. The sequence number of lost retransmission frames that overlaps with the sequence number of lost frames within the latest loss sorting period is taken as the latest lost rearrangement frame sequence number.

[0116] All the latest lost rearranged frame sequence numbers are sorted in the time dimension according to the corresponding reception time points within the latest lost sorting period, and then added to the latest lost sorting period;

[0117] The lost sequence numbers are sorted according to the optimal and latest lost sorting time periods, and the remaining lost sorting time periods are sorted.

[0118] The specific solution in this embodiment is as follows: A continuous analysis is performed on the lost retransmission period over time to filter out the retransmission time-series analysis period. Time-series disorder superposition analysis is then performed on the retransmission data within the time-series analysis period to determine the degree of time-series disorder superposition. When there is a high degree of time-series disorder superposition in the retransmission data, a time-series adjustment operation is performed on the disordered retransmission data to restore data continuity and ensure that the parameter data at each moment has a corresponding record, providing a complete data foundation for subsequent analysis. Furthermore, time-series disorder can disrupt the temporal correspondence between these parameters, leading to inaccurate parameter correlations obtained from the analysis. Restoring the correct temporal order between parameter data ensures that the parameter relationships obtained from the analysis conform to the actual situation, thereby improving the accuracy of data analysis.

[0119] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the present invention should still fall within the scope of the present invention.

Claims

1. A passive optical fiber sensing multi-parameter remote data analysis system, characterized in that: Includes the following modules: Data loss analysis module: During the monitoring of oil and gas pipelines using passive fiber optic sensors, data loss analysis is performed on the data received during each data transmission monitoring period to identify periods of data loss and periods of data integrity. Loss and retransmission judgment module: Analyze and compare the lost data during the data loss period with the received data during the data integrity period to assess whether the data retransmission mechanism is triggered. If data retransmission is triggered, the loss and retransmission period is extracted. Retransmission aliasing assessment module: Performs continuous analysis of the lost retransmission period in the time dimension, filters out the retransmission time sequence analysis period, performs time sequence aliasing and superposition analysis on the data retransmission within the retransmission time sequence analysis period, and judges the degree of time sequence aliasing and superposition of the retransmitted data. Timing adjustment module: When there is a high degree of timing disorder in the retransmitted data, the module analyzes the priority of data loss during the data loss period and performs timing adjustment operations on the retransmitted data with timing disorder.

2. The passive optical fiber sensing multi-parameter remote data analysis system according to claim 1, wherein: The process for filtering data loss periods and data integrity periods is as follows: Set a data transmission monitoring cycle and divide it into several equal data monitoring periods; Within each data monitoring period, the frame sequence number of each sensor data from the sending port and the frame sequence number of the sensor data from the receiving port are obtained respectively, and used as the sensor sending frame sequence number and the sensor receiving frame sequence number. If all sensor transmit frame sequence numbers correspond to and coincide with all sensor receive frame sequence numbers in sequence, then it is marked as a data complete period. If the sequence numbers of all transmitted sensor frames do not correspond sequentially with the sequence numbers of all received sensor frames, then this period is marked as a data loss period.

3. The passive optical fiber sensing multi-parameter remote data analysis system according to claim 1, wherein: The process of analyzing and comparing the lost data during the data loss period with the received data during the data integrity period is as follows: During the period of data loss, the frame sequence number corresponding to the lost data is obtained from the sending port and used as the lost frame sequence number. Select the data integrity period that is adjacent to the data loss period but later in time as the target analysis period; Obtain all frame sequence numbers of the sensor data received from the receiving port within the target analysis period, and sort them according to the order of reception time to obtain the target received frame sequence number sequence. The sequence number of the lost frame within the data loss period that does not overlap with the sequence number of the target received frame is taken as the target received frame sequence number. The sequence number of the lost frame that coincides with the sequence number of the target received frame within the data loss period is used as the sequence number of the lost retransmission frame.

4. The passive fiber optic sensing multi-parameter remote data analysis system according to claim 3, characterized in that: The analysis process for triggering the data retransmission mechanism is as follows: The proportion of all lost and retransmitted frame sequence numbers to the total number of lost frame sequence numbers within the data loss period is used as the loss and retransmission ratio. Extract the transmission timestamp of the sending port corresponding to each lost retransmission frame sequence number within the data loss period, and obtain the duration between the transmission timestamp of the receiving port within the target analysis period, as the lost retransmission duration; The retransmission interval for a single frame number is calculated by comparing the retransmission duration corresponding to the lost retransmission frame number with the duration of the data monitoring period. The retransmission interval values ​​of the single frame sequence numbers corresponding to all lost and retransmitted frame sequence numbers are summed and averaged to obtain the sequence number retransmission interval analysis value.

5. The passive fiber optic sensing multi-parameter remote data analysis system according to claim 4, characterized in that: After determining whether the data retransmission mechanism has been triggered, the process for determining the data loss retransmission period is as follows: The retransmission trigger analysis value is obtained by summing the loss retransmission ratio value with the sequence number retransmission interval analysis value. If the retransmission trigger analysis value is greater than or equal to the retransmission trigger analysis threshold, the data retransmission mechanism is triggered, and the target analysis period is taken as the lost retransmission period.

6. The passive fiber optic sensing multi-parameter remote data analysis system according to claim 5, characterized in that: The process of obtaining the retransmission timing analysis period is as follows: Extract all lost and retransmitted periods within the data transmission monitoring period, and obtain the start and end times of each lost and retransmitted period; If the end time of a lost retransmission period earlier in the time dimension coincides with the start time of a lost retransmission period later in the time dimension, then the adjacent lost retransmission periods will be merged and used as the retransmission time series analysis period.

7. The passive fiber optic sensing multi-parameter remote data analysis system according to claim 6, characterized in that: The process of performing time sequence disorder superposition analysis on data retransmissions within the retransmission time sequence analysis period is as follows: During the retransmission timing analysis period, all received frame sequence numbers are obtained and used as timing analysis frame sequence numbers. The timing analysis frame sequence is then constructed according to the time sequence before and after reception. Extract the lost retransmission frame sequence number and the target received frame sequence number from the time sequence analysis frame sequence respectively; Obtain the lost and retransmitted frame sequence numbers between adjacent target received frame sequence numbers, and count the number of lost and retransmitted frame sequence numbers, and the proportion of the number of lost and retransmitted frame sequence numbers to the total number of received frame sequence numbers in the time sequence analysis frame sequence, as the unit retransmission interleaving ratio. The sequence numbers of adjacent target received frames with lost retransmission frame numbers are combined to form a lost retransmission interleaving group, and a disorder superposition analysis curve is constructed based on the ratio of the number of lost retransmission interleaving groups to the number of unit retransmission interleavings.

8. The passive fiber optic sensing multi-parameter remote data analysis system according to claim 7, characterized in that: The process for determining the degree of temporal disorder superposition is as follows: The difference between the Y-axis coordinates of adjacent coordinate points on the disordered superposition analysis curve is used to obtain the amplitude of the interlacing quantity. Extract the adjacent coordinate points of the interlacing quantity amplitude with a positive sign, and use the local curve between the adjacent points as the interlacing superposition sub-curve; After summing the lengths of each interlacing and superimposed sub-curve, the ratio of this summation to the disordered superimposed analysis curve is calculated to obtain the interlacing and superimposed degree value. The summation and average of the unit retransmission interleaving ratios corresponding to each lost retransmission interleaving group are used to obtain the interleaving superposition quantity analysis value. The sum of the interlacing and superposition quantity analysis value and the interlacing and superposition degree value is used to obtain the disordered superposition analysis value; If the value of the disorder superposition analysis is greater than the disorder superposition analysis threshold, it is determined to be a highly disordered temporal superposition signal.

9. The passive fiber optic sensing multi-parameter remote data analysis system according to claim 1, characterized in that: The process of analyzing the priority of data loss based on the time period is as follows: Within each data loss period, the difference between the number of times when all sensor transmitted frame sequence numbers and all sensor received frame sequence numbers do not correspond and overlap sequentially is taken as the number of frame sequence numbers lost. The ratio of the number of lost frame sequence numbers corresponding to each data loss period to the number of frame sequence numbers sent by the sensor is calculated to obtain the frame sequence number loss ratio for each period. The ratio of the number of lost frames in each data loss period is compared, and the lost frame numbers in the data loss period are prioritized and adjusted in descending order to obtain multiple loss sorting periods.

10. A passive fiber optic sensing multi-parameter remote data analysis system according to claim 9, characterized in that: The process of timing adjustment for retransmitted data with out-of-sequence timing is as follows: Arbitrarily extract the sequence number of lost frames within the lost frame sorting period, compare it with the sequence number of lost retransmission frames within the retransmission timing analysis period, and take the sequence number of lost retransmission frames that coincides with the sequence number of lost frames within the lost frame sorting period as the sequence number of lost frames rearranged. All lost rearranged frame sequence numbers are sorted sequentially according to their corresponding reception time points within the lost sorting period, and then added to the frames within the lost sorting period.

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