Method and system for evaluating confidence degree of spatial position of underground optical cable pipeline measurement point

Abstract

The invention discloses a confidence evaluation method and system for the spatial position of an underground optical cable pipeline measurement point. The method comprises the following steps: sampling the position of an underground optical cable pipeline according to a set distance interval through a measurement instrument; the underground optical cable pipeline point positions obtained through sampling are judged through a pipe point position coarse screening model, and obvious error data collected under the abnormal condition are removed; a curve fitting model is used for fitting all underground optical cable pipeline point positions judged and processed by the pipe point position coarse screening model, and a fitting curve is obtained; calculating the confidence of all underground optical cable pipeline point locations according to the fitting curve through a pipe point location confidence evaluation model; and taking points by using the pipeline bidirectional fitting model to perform pipeline attitude simulation so as to perform rechecking. The method can quickly judge the confidence of the measurement position of the optical cable pipeline, and ensures the accuracy and credibility of the position information of the underground optical cable pipeline detection point.

Description

Technical Field

[0001] The present invention relates to the technical field of big data mining and geographic information processing, and in particular to a confidence evaluation method and system for the spatial position of underground optical cable pipeline measurement points. Background Art

[0002] With the continuous advancement of urbanization, the scale of urban underground pipeline networks continues to expand. Various pipelines such as water supply pipelines, gas pipelines, power cables, etc. crisscross to form a complex underground infrastructure network. The protection and avoidance of pipelines in urban construction and excavation processes, maintenance and repair of underground pipelines, and troubleshooting all rely on the measurement and control of the precise location of pipelines, which places high demands on the acquisition of the precise location of pipelines.

[0003] There are currently some methods for detecting the location of underground pipelines, but without ground excavation, whether there is a large deviation between the measured location of the pipeline and its actual location is still a difficult problem to solve. Various pipeline positioning systems have some problems, such as the low accuracy of the guidance instrument, the underground pipeline detector is susceptible to electromagnetic interference and is difficult to adapt to various detection situations, and the inertial navigation detector has error accumulation, etc., which makes it difficult to obtain reliable data on the precise location of the pipeline.

[0004] Therefore, obtaining reliable data on the precise location of underground pipelines and achieving a clear and reliable description of the pipeline location are technical issues that urgently need to be solved in the field of underground pipeline detection and positioning. At the same time, the bending restrictions of optical cables and the characteristics of the wrapping materials make the trend and curvature of underground optical cable pipelines have a certain regularity, making it possible to evaluate the confidence of the measured position of optical cable pipelines through models, which helps to improve the credibility of the precise location data of underground optical cable pipelines. Summary of the invention

[0005] The purpose of the present invention is to provide a confidence evaluation method and system for the spatial position of underground optical cable pipeline measurement points, so as to quickly judge the confidence of the measured position of the optical cable pipeline at low cost and solve the problem of insufficient credibility of the measured precise position in the existing detection system.

[0006] To achieve the above purpose, the technical solution provided by the present invention is:

[0007] The first aspect of the present application provides a confidence evaluation method for the spatial position of a measurement point of an underground optical cable pipeline, comprising the following steps:

[0008] The measurement starts from one optical cable well as the starting point, the underground optical cable pipeline position is sampled by the measuring instrument at the set distance interval, and the measurement ends with another optical cable well as the end point to obtain the underground optical cable pipeline point position;

[0009] The location of underground optical cable pipelines obtained by sampling is determined through the pipeline location coarse screening model, and the obviously erroneous data collected under abnormal conditions are eliminated;

[0010] The curve fitting model is used to fit all underground optical cable pipeline points after the pipeline point position rough screening model determines and processes, and a fitting curve is obtained;

[0011] The confidence level of all underground optical cable pipeline points is calculated based on the fitting curve using the pipeline point location confidence evaluation model;

[0012] The pipeline bidirectional fitting model is used to select points from the starting point and the ending point of the underground optical cable pipeline in turn to simulate the pipeline posture, and to verify whether each underground optical cable pipeline point meets the actual pipeline posture requirements.

[0013] To optimize the above technical solutions, the specific measures taken also include:

[0014] The curve fitting model performs data mining learning and training on the three-dimensional information of the real underground optical cable pipeline, including pipeline direction, pipeline posture and pipeline curvature, according to large-scale real underground optical cable pipeline feature point data combined with characterization parameters, and is generated through simulation data correction.

[0015] The curve fitting equation of the curve fitting model is:

[0016]

[0017] Where f(λ) represents the curve fitting equation, T ex is the characteristic parameter of pipeline attributes, w i is the pipeline sampling point P i The weight coefficient of is the pipeline sampling point P i The three-dimensional space coordinates of i ,y i Respectively represent the longitude and latitude coordinates of the underground optical cable pipeline point, z i Indicates the elevation coordinates of underground optical cable pipeline points, M i，p (λ) is the reference curve.

[0018] The formula of the pipe point location confidence evaluation model is:

[0019] C(i)=f(d i )*t d +w i t p -f(e i )*t e

[0020] Where C(i) is the pipeline sampling point P i The confidence level, f(di ) is the pipeline sampling point P i Deviation factor from the fitted curve, w i is the pipeline sampling point P i The weight coefficient, f(e i ) is the pipeline sampling point P i The residual, t d ,t p ,t e are the weights of different confidence factors.

[0021] The pipeline bidirectional fitting model regards the confidence weights of several underground optical cable pipeline points near the starting point and the ending point as highly reliable based on the characteristics of strong detection signals and few interference items at the starting point and the ending point at both ends of the pipeline, and approaches from both ends to the middle to verify whether the confidence of each underground optical cable pipeline point is greater than the threshold.

[0022] The second aspect of the present application provides a confidence evaluation system for the spatial position of underground optical cable pipeline measurement points, comprising:

[0023] The measuring module is used to start the measurement from one optical cable well as the starting point, sample the position of the underground optical cable pipeline at a set distance interval through a measuring instrument, and end the measurement with another optical cable well as the end point to obtain the position of the underground optical cable pipeline;

[0024] The coarse screening module is used to determine the underground optical cable pipeline points obtained by sampling through the pipe point location coarse screening model, and eliminate the obviously erroneous data collected under abnormal conditions;

[0025] A fitting module is used to fit all underground optical cable pipeline points after the pipeline point position rough screening model determines the processing using a curve fitting model to obtain a fitting curve;

[0026] A confidence evaluation module is used to calculate the confidence of all underground optical cable pipeline points according to the fitting curve through the pipeline point location confidence evaluation model;

[0027] The review module is used to use the pipeline bidirectional fitting model to select points from the starting point and the end point of the underground optical cable pipeline in sequence to simulate the pipeline posture, and review whether each underground optical cable pipeline point meets the actual pipeline posture requirements.

[0028] The third aspect of the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the confidence evaluation method for the spatial position of the underground optical cable pipeline measurement point as described above is implemented.

[0029] A fourth aspect of the present application provides a computer-readable storage medium storing a computer program, wherein the computer program enables a computer to execute the confidence evaluation method for the spatial position of a measurement point of an underground optical cable pipeline as described above.

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] The present invention combines the underground optical cable pipeline characteristics with the confidence evaluation through data mining technology, thereby ensuring the accuracy and reliability of the underground optical cable pipeline detection point location information.

[0032] The present invention constrains the output result through a two-way approximation fitting method, thereby increasing the reliability of the result.

[0033] The present invention mostly uses existing equipment, has low cost, can give results quickly, and is easy to promote. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 : A schematic diagram of a confidence evaluation method for the spatial position of an underground optical cable pipeline measurement point of the present invention.

[0035] Figure 2 : Flow chart of the confidence evaluation method for the spatial position of underground optical cable pipeline measurement points of the present invention.

[0036] Figure 3 : A structural diagram of the confidence evaluation system for the spatial position of underground optical cable pipeline measurement points of the present invention. DETAILED DESCRIPTION

[0037] The above contents of the present invention are further described in detail below in the form of embodiments, but this should not be understood as the scope of the above subject matter of the present invention being limited to the following embodiments, and all technologies realized based on the above contents of the present invention belong to the scope of the present invention.

[0038] Some of the terms in the present invention are explained as follows:

[0039] Curve fitting: Curve fitting refers to finding a curve or function to approximate the relationship between a known set of data points.

[0040] Confidence level: In statistics, confidence level refers to the probability of accuracy of an estimate of a parameter. It is usually used to indicate the reliability of data.

[0041] The pipe points in the following embodiments are abbreviations for pipeline sampling points.

[0042] The present application provides a confidence evaluation method for the spatial position of underground optical cable pipeline measurement points, such as Figure 1 As shown:

[0043] The measurement starts from one optical cable well as the starting point, the underground optical cable pipeline position is sampled by the measuring instrument at the set distance interval, and the measurement ends with another optical cable well as the end point to obtain the underground optical cable pipeline point position;

[0044] The location of underground optical cable pipelines obtained by sampling is determined through the pipeline location coarse screening model, and the obviously erroneous data collected under abnormal conditions are eliminated;

[0045] The curve fitting model is used to fit all underground optical cable pipeline points after the pipeline point position rough screening model determines and processes, and a fitting curve is obtained;

[0046] The confidence level of all underground optical cable pipeline points is calculated based on the fitting curve using the pipeline point location confidence evaluation model;

[0047] The pipeline bidirectional fitting model is used to select points from the starting point and the ending point of the underground optical cable pipeline in turn to simulate the pipeline posture, and to verify whether each underground optical cable pipeline point meets the actual pipeline posture requirements.

[0048] like Figure 2 As shown, the specific steps include:

[0049] S1: The pipeline detection system starts measuring from a fiber optic cable well as the starting point, samples the underground fiber optic cable pipeline position at a fixed approximate distance interval (e.g., 10 meters) through the measuring instrument, and ends the measurement with another fiber optic cable well as the end point; each sampling obtains the latitude and longitude coordinates and elevation of the current underground fiber optic cable pipeline point, which is expressed as the pipeline sampling point P i (x i ,y i , z i ), which is the underground optical cable pipeline point; receiving pipeline sampling point P i And push it to the pipe point location coarse screening model M1, judge the pipe point, eliminate the obviously erroneous location data collected under abnormal conditions, and save the pipe point information without abnormalities in the pipe point original sampling table of the database.

[0050] S2: The curve fitting model M2 obtains all the pipe point data sets of the current pipeline from the pipe point original sampling table and fits the current pipe point data set; wherein the curve fitting equation is:

[0051]

[0052] In the above formula, f(λ) represents the curve fitting equation, T ex is the characteristic parameter of pipeline attributes, w i is the pipeline sampling point P i The weight coefficient of is the pipeline sampling point Pi The three-dimensional space coordinates, M i，p (λ) is the reference curve.

[0053] S3: Pipe point location confidence evaluation model M3 calculates the confidence of all current pipe points according to the fitting curve. The formula is:

[0054] C(i)=f(d i )*t d +w i t p -f(e i )*t e

[0055] Where C(i) is the pipe point P i The confidence level, f(d i ) is the pipe point P i Deviation factor from the fitted curve, w i is the pipeline sampling point P i The weight coefficient, f(e i ) is the pipeline sampling point P i The residual, t d ,t p ,t e are the weights of different confidence factors.

[0056] S4: Save the newly added pipeline sampling points whose confidence C(i) exceeds the threshold into the pipeline point confidence evaluation table, and update the confidence of the existing pipeline sampling points.

[0057] S5: Starting from the second pipeline sampling point, repeat the S2-S4 process until the current pipeline detection is completed.

[0058] S6: After the current pipeline detection is completed, the pipe point position data in the pipe point confidence evaluation table is pushed to the pipeline bidirectional fitting model M4. The pipeline bidirectional fitting model M4 takes points from the starting position and the ending position in sequence to simulate the pipeline posture and verify whether each pipe point position meets the actual pipeline posture requirements.

[0059] S7: After all the reviews are completed, all the pipe point data are pushed into the pipeline database, and the pipe point location data of this pipeline is used as a training sample to perform model iteration.

[0060] In order to realize the above technical solution, the content is specifically described:

[0061] In step S1, the pipeline detection system includes but is not limited to various detection devices and their integrated systems that can obtain accurate pipeline location data.

[0062] In step S1, the position of the pipeline point is a three-dimensional spatial coordinate composed of three components: longitude, latitude, and elevation. The coordinate system is in accordance with the coordinate system adopted by the pipeline detection system or other agreed coordinate systems, such as WGS84, CGCS2000, etc. The pipeline point information includes the pipeline number to which the pipeline point belongs, the pipeline point serial number, the material and diameter of the pipeline to which the pipeline point belongs, etc.

[0063] In step S1, each pipeline sampling point is represented by P i , where i is the serial number of the current point relative to the entire pipeline, which is input by the detection system: the sampling interval is i, that is, when the detection is sampled every ten meters, the starting point i=0, the first point i=10, the second point i=20, and so on; if the subsequent detection requires encrypted sampling of the points of the same pipeline, and the sampling is changed to every five meters, only the points that were not collected in the last sampling are collected, so the first point i=5, the second point i=15, and so on.

[0064] In step S2, the curve fitting model M2 is based on large-scale real underground optical cable pipeline feature point data, combined with characterization parameters, to perform data mining learning and training on the three-dimensional information such as pipeline direction, pipeline posture, and pipeline curvature of the real h pipeline, and generates model M2 through simulation data correction.

[0065] In some embodiments, in step S2, T in the formula ex It is calculated based on pipeline properties such as pipeline diameter, material, construction method, and direction, and is an inherent characteristic parameter of the pipeline.

[0066] In some embodiments, in step S2, the method for generating the curve fitting model M2 comprises the following steps:

[0067] (I) Dataset preparation and model generation: Using the real data of existing underground optical cable pipelines in the database as samples, the training data set of the model is generated through data cleaning and processing, which includes the following steps:

[0068] 1. Obtain the data of each sampling point of the underground optical cable pipeline, including the three-dimensional spatial coordinates (x i ,y i , z i ), pipeline burial depth h i , the distance between the pipeline sampling point and the starting and ending pipe wells (d1 i , d2 i ), the characterization parameter T of the current pipeline ex And other key data.

[0069] 2. The location data of the underground optical cable pipeline sampling points P i (x i ,y i , z i,h i ), and then normalized to get P i (x i ,y i , z i ,h i `), where x i ,y i , is the latitude and longitude, z i is the elevation, h i The buried depth of pipeline.

[0070] 3. Sampling point P of underground optical cable pipeline i Distance d from the pipe wells on both sides i Perform minimization to obtain P i (x i ,y i , z i ,h i `,d i `).

[0071] 4. Sampling point P of underground optical cable pipeline i Additional characterization parameter T ex , and get P i (x i ,y i , z i ,h i `,d i `, T ex ).

[0072] 5. Determine the core equation form of curve fitting, including the equation describing the fitting curve and the calculation method of weight coefficients; design data mining learning algorithm.

[0073] 6. Perform model training based on the pre-designed data mining learning algorithm and training data set to obtain the benchmark curve

[0074] M i，p (λ), the weight coefficient w for each pipeline sampling point i wait.

[0075] (II) Model application

[0076] Model M2 is updated regularly based on new data to optimize and improve model performance.

[0077] In some implementations, in step S3, the management point location confidence evaluation model M3 calculates the confidence of all input points in the current situation each time.

[0078] In some embodiments, in step S4, the confidence of the existing pipeline sampling points in the pipeline point confidence evaluation table is updated and the newly added pipeline sampling points with confidence greater than the threshold are stored in the pipeline point confidence evaluation table, and corresponding prompts are given for situations where the new confidence of the existing pipeline sampling points is lower than the threshold and the confidence of the newly added pipeline sampling points is lower than the threshold.

[0079] In some embodiments, in step S6, the pipeline bidirectional fitting model M4 regards the confidence weights of several pipeline sampling points near the start and end pipe wells as highly reliable based on the characteristics of strong detection signals and few interference items at both ends of the pipeline (i.e., near the start pipe well and the end pipe well), and approaches from both ends to the middle to verify whether the confidence of each pipe point position is greater than a threshold.

[0080] In some embodiments, corresponding prompts are given to the pipe points with problems in the review.

[0081] In some implementations, in step S7, the verified pipeline sampling point data set is used as training data for the curve fitting model M2, and the model is iteratively trained to further improve the accuracy and reliability of the model.

[0082] In some embodiments, in step S2, M i，p (λ) is the pipeline benchmark curve, which is iterated with the model.

[0083] The present invention is further described in detail below in conjunction with specific embodiments:

[0084] Step S1: The pipeline detection system and the detection personnel start from the starting point of the underground optical cable pipeline L, that is, the pipeline well of the optical cable pipeline, and sample the entire pipeline at a fixed detection distance interval of 10 meters. The first point is represented as P0 (x0, y0, z0), which is the latitude, longitude and elevation of the starting pipeline well of this pipeline by convention. The coordinate system adopts the WGS84 coordinate system according to the pipeline detection system; thereafter, each time a point is sampled, the pipeline point number increases according to the detection distance interval. In this example, sampling is performed every 10 meters, which is represented as the second pipeline sampling point P 10 , the third pipeline sampling point P 20 , and so on; starting from the second point, each sampling point P i, and push it to the coarse screening model M1 of the pipe point position. The coarse screening model M1 of the pipe point position receives the pushed pipe point position information and eliminates the abnormal position data collected due to improper operation, equipment failure, electromagnetic interference, GPS signal drift, etc. The specific method is: ① If one or more data of the longitude, latitude and elevation of the detected pipe point position is missing, it will be eliminated; ② The residual of the detected pipe point position is checked. If one or more data residuals of the longitude, latitude and elevation exceed the limit, it will be eliminated; for the pipe points that do not pass the coarse screening model M1 test, the system prompt is triggered so that the detection personnel can re-detect this pipe point; for the pipe points that pass the coarse screening model M1 test, their pipe point position information is pushed to the original sampling table of the pipe point in the database for storage.

[0085] Step S2: The curve fitting model M2 obtains all the pipe point data sets of the current pipeline from the pipe point original sampling table, represented as P0, P 10 , P 20 , P 30 , …P i At the same time, the material, diameter, construction method and other information of the current pipeline are obtained from the pipeline attribute table, and the information is calculated and fitted within the model to obtain the posture of the pipeline with the highest confidence in the three-dimensional space.

[0086] Step S3: The pipe point position confidence evaluation model M3 calculates the confidence of all current pipe points in sequence according to the fitting curve.

[0087] Step S4: Add the newly added pipeline sampling points whose confidence is greater than the threshold to the pipeline point confidence evaluation table, and update the confidence of the existing pipeline sampling points in the table; if the confidence is less than the threshold, it is regarded as unreliable data, triggering a system prompt.

[0088] Step S5: Repeat the above steps until the current underground optical cable pipeline detection point reaches the end pipe well and the detection is finished. After the current pipeline detection is finished, the pipeline point position data stored in the pipeline point confidence evaluation table is pushed to the pipeline bidirectional fitting model M4. The pipeline bidirectional fitting model M4 takes points from the starting position and the ending position in sequence. In this example, the starting detection pipe point of the pipeline is P0, and the ending detection pipe point is P 1440 , then the first time (P0, P 10 , P 20 , P 1420 , P 1430 , P 1420 ) a total of six points are input into the model to simulate the entire pipeline posture, recheck whether the position of each pipe point meets the requirements of the actual pipeline posture, and analyze the confidence of the above six points; the second time (P0, P 10 , P 20 , P 30 , P 1410 , P1420 , P 1430 , P 1420 ), a total of eight points are input into the model to simulate the entire pipeline posture and verify P 30 , P 1410 Whether it meets the requirements of the real pipeline posture, analyze P 30 , P 1410 The confidence level; the third time (P0, P 10 , P 20 , P 30 , P 40 , P 1400 , P 1410 , P 1420 , P 1430 , P 1420 ), a total of ten points are input into the model to simulate and review the entire pipeline posture, and review P 40 , P 1400 Whether it meets the requirements of the real pipeline posture, analyze P 40 , P 1400 Confidence level... and so on, until each point is fitted and verified.

[0089] Step S6: After all the reviews are completed, all the pipe point data are pushed into the pipeline database, and the pipe point location data of this pipeline is added to the training sample to perform iterative model updates.

[0090] In another embodiment, the present invention proposes a confidence evaluation system for the spatial position of underground optical cable pipeline measurement points, such as Figure 3 As shown, including:

[0091] The measuring module is used to start the measurement from one optical cable well as the starting point, sample the position of the underground optical cable pipeline at a set distance interval through a measuring instrument, and end the measurement with another optical cable well as the end point to obtain the position of the underground optical cable pipeline;

[0092] The coarse screening module is used to determine the underground optical cable pipeline points obtained by sampling through the pipe point location coarse screening model, and eliminate the obviously erroneous data collected under abnormal conditions;

[0093] A fitting module is used to fit all underground optical cable pipeline points after the pipeline point position rough screening model determines the processing using a curve fitting model to obtain a fitting curve;

[0094] A confidence evaluation module is used to calculate the confidence of all underground optical cable pipeline points according to the fitting curve through the pipeline point location confidence evaluation model;

[0095] The review module is used to use the pipeline bidirectional fitting model to select points from the starting point and the end point of the underground optical cable pipeline in sequence to simulate the pipeline posture, and review whether each underground optical cable pipeline point meets the actual pipeline posture requirements.

[0096] In another embodiment of the present invention, an electronic device is proposed, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the confidence evaluation method for the spatial position of the underground optical cable pipeline measurement point as described above is implemented.

[0097] In another embodiment, the present invention provides a computer-readable storage medium storing a computer program, wherein the computer program enables a computer to execute the confidence evaluation method for the spatial position of a measurement point of an underground optical cable pipeline as described above.

[0098] In the embodiments disclosed in the present application, the computer storage medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. The computer storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the above. More specific examples of computer storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above.

[0099] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Any technician familiar with the profession, without departing from the scope of the technical solution of the present invention, according to the technical essence of the present invention, any simple modification, equivalent replacement and improvement made to the above embodiment still falls within the protection scope of the technical solution of the present invention.

Claims

1. A confidence evaluation method for the spatial position of underground optical cable pipeline measurement points, characterized in that: The following steps are involved: The measurement starts from one optical cable well as the starting point, the underground optical cable pipeline position is sampled by the measuring instrument at the set distance interval, and the measurement ends with another optical cable well as the end point to obtain the underground optical cable pipeline point position; The location of underground optical cable pipelines obtained by sampling is determined through the pipeline location coarse screening model, and the obviously erroneous data collected under abnormal conditions are eliminated; The curve fitting model is used to fit all underground optical cable pipeline points after the pipeline point position rough screening model determines and processes, and a fitting curve is obtained; The confidence level of all underground optical cable pipeline points is calculated based on the fitting curve using the pipeline point location confidence evaluation model; The pipeline bidirectional fitting model is used to select points from the starting point and the ending point of the underground optical cable pipeline in turn to simulate the pipeline posture, and to verify whether each underground optical cable pipeline point meets the actual pipeline posture requirements.

2. The confidence evaluation method for the spatial position of underground optical cable pipeline measurement points according to claim 1 is characterized in that: The curve fitting model performs data mining learning and training on the three-dimensional information of the real underground optical cable pipeline, including pipeline direction, pipeline posture and pipeline curvature, according to large-scale real underground optical cable pipeline feature point data combined with characterization parameters, and is generated through simulation data correction.

3. The confidence evaluation method for the spatial position of underground optical cable pipeline measurement points according to claim 1 is characterized in that: The curve fitting equation of the curve fitting model is: Where f(λ) represents the curve fitting equation, T ex is the characteristic parameter of pipeline attributes, w i is the pipeline sampling point P i The weight coefficient of is the pipeline sampling point P i The three-dimensional space coordinates of i ,y i Respectively represent the longitude and latitude coordinates of the underground optical cable pipeline point, z i Indicates the elevation coordinates of underground optical cable pipeline points, M i，p (λ) is the reference curve.

4. The confidence evaluation method for the spatial position of underground optical cable pipeline measurement points according to claim 1 is characterized in that: The formula of the pipe point location confidence evaluation model is: C(i)=f(d i )*t d +w i t p -f(e i )*t e Where C(i) is the pipeline sampling point P i The confidence level, f(d i ) is the pipeline sampling point P i Deviation factor from the fitted curve, w i is the pipeline sampling point P i The weight coefficient, f(e i ) is the pipeline sampling point P i The residual, t d ,t p ,t e are the weights of different confidence factors.

5. The confidence evaluation method for the spatial position of underground optical cable pipeline measurement points according to claim 1 is characterized in that: The pipeline bidirectional fitting model regards the confidence weights of several underground optical cable pipeline points near the starting point and the ending point as highly reliable based on the characteristics of strong detection signals and few interference items at the starting point and the ending point at both ends of the pipeline, and approaches from both ends to the middle to verify whether the confidence of each underground optical cable pipeline point is greater than the threshold.

6. A confidence evaluation system for the spatial position of underground optical cable pipeline measurement points, characterized in that: include: The measuring module is used to start the measurement from one optical cable well as the starting point, sample the position of the underground optical cable pipeline at a set distance interval through a measuring instrument, and end the measurement with another optical cable well as the end point to obtain the position of the underground optical cable pipeline; The coarse screening module is used to determine the underground optical cable pipeline points obtained by sampling through the pipe point location coarse screening model, and eliminate the obviously erroneous data collected under abnormal conditions; A fitting module is used to fit all underground optical cable pipeline points after the pipeline point position rough screening model determines the processing using a curve fitting model to obtain a fitting curve; A confidence evaluation module is used to calculate the confidence of all underground optical cable pipeline points according to the fitting curve through the pipeline point location confidence evaluation model; The review module is used to use the pipeline bidirectional fitting model to select points from the starting point and the end point of the underground optical cable pipeline in sequence to simulate the pipeline posture, and review whether each underground optical cable pipeline point meets the actual pipeline posture requirements.

7. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the confidence evaluation method for the spatial position of a measurement point of an underground optical cable pipeline as described in any one of claims 1 to 5 is implemented.

8. A computer-readable storage medium storing a computer program, wherein the computer program enables a computer to execute the confidence evaluation method for the spatial position of a measurement point of an underground optical cable pipeline as described in any one of claims 1 to 5.

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