CN117629131A - Precision evaluation method and system for optical fiber shape sensing - Google Patents

Abstract

The invention provides a precision evaluation method and a system for optical fiber shape sensing, comprising the steps of reconstructing a sensor and obtaining a reconstructed sensor curve; dividing a curve of the sensor into a plurality of sensing sections, and respectively inspecting error distribution of the sensing sections; acquiring error parameters for evaluating the overall performance of the sensor; error parameters of sensors of different specifications are normalized to the same dimension to laterally compare reconstruction effects of the sensors of different specifications. Compared with the prior art, the method and the device can find the precision change trend of each segment in the local error investigation, give the segment weighted total error with investigation bias to the sensing segment in the global error investigation, comprehensively evaluate the optical fiber shape sensing precision according to the global and local reconstruction curve error information, realize the transverse evaluation of the shape reconstruction effect of the sensors with different specifications, and assist researchers in balancing the precision improvement brought by the sensing points and the cost improvement of the sensors so as to meet the investigation requirements of differentiation under different scenes.

Description

Accuracy evaluation method and system for optical fiber shape sensing

Technical field

The present invention relates to the technical field of optical fiber shape sensing, and specifically to an accuracy evaluation method and system for optical fiber shape sensing.

Background technique

Optical fiber shape sensing technology is an emerging technology in the field of shape sensing in recent years. Based on the advantages of optical fiber, such as small size, insulation, high voltage resistance, high temperature resistance, corrosion resistance, and strong biological adaptability, optical fiber shape sensing technology is widely used in civilian applications. Ideal solution in areas such as facilities, precision machinery and aerospace engineering as well as biomedical and medical applications. Fiber optic shape sensing technology can be seen as an organic combination of fiber optic strain measurement technology, fiber optic sensor configuration design and advanced three-dimensional reconstruction algorithms. Optical fiber strain measurement technology measures the strain response of the optical fiber sensor integrated inside the object to be measured under deformation, and substitutes the demodulated strain information into the three-dimensional shape reconstruction algorithm update iteration to restore the spatial coordinate information of the entire sensor, thereby obtaining The spatial attitude of the object to be measured. For fiber optic shape sensing technology, shape sensing accuracy is a key issue whether it can be applied in practical engineering.

However, the accuracy evaluation methods used for optical fiber shape sensing in the existing technology have shortcomings, such as:

Existing accuracy evaluation methods lack detailed evaluation of the location of interest and sometimes cannot meet engineering needs. Moreover, due to differences in sensor specifications proposed by various researchers, horizontal comparison between sensors can only be completed through a certain parameter, which will introduce Serious evaluation bias. At the same time, the existing accuracy evaluation method is not suitable for the data-driven optical fiber shape sensing method proposed by Sefati S et al., IEEESensors Journal (2020), 21(3):3066-3076.

Khan F et al., Sensors and Actuators A:Physical(2021),317:112442 used an accuracy evaluation method based on terminal error. In the research of three-dimensional reconstruction algorithm, due to the step-by-step reconstruction algorithm based on optical fiber shape sensing, The point recursive update method will bring about the accumulation of reconstruction errors. Usually the end of the reconstruction curve is the maximum point of error accumulation. Researchers evaluate the sensor by examining the deviation between the end point of the reconstruction curve and the end point of the original curve. Accuracy, this evaluation method has the following problems that need to be solved:

(1) This accuracy evaluation method based on end error ignores the spatial information in the longer distance from the reconstruction starting point to the end, and in some cases, the end error of the reconstruction curve is not the reconstruction curve error. At the maximum point, in a restrictive environment, the error in the middle section of the reconstruction curve may be greater than the reconstruction accuracy requirement, causing subsequent reconstruction calculations to be invalid.

(2) This accuracy evaluation method based on terminal error is proposed based on the error accumulation caused by the point-by-point recursive update method in the fiber-optic shape sensing reconstruction algorithm in the research of three-dimensional reconstruction algorithm. For Sefati This evaluation method will fail based on the data-driven optical fiber shape sensing method proposed by S et al., IEEE Sensors Journal (2020), 21(3):3066-3076.

S et al., International journal of computer assisted radiology and surgery (2019), 14(12):2137-2145, used an average error evaluation method and a maximum error evaluation method that consider global information. The basic idea of this method is relatively simple. , that is, calculate the deviation value of each point of the reconstruction curve and average it according to the number of reconstruction points to obtain the average deviation, and find the maximum deviation point among all reconstructed data points, and evaluate the shape transmission as a whole through the average deviation and the maximum deviation. Sense accuracy; use the overall average deviation and maximum deviation value of the reconstructed curve as the basis for accuracy evaluation. This evaluation method has the following problems that need to be solved:

(1) The average error used in this accuracy evaluation method divides the global error equally over the entire length of the sensor, resulting in the inability to accurately examine the distribution of local error information. When extreme situations occur, such as when the error distribution of the reconstructed curve is centrally symmetrical, the average errors calculated by the two are the same, resulting in distortion of the accuracy evaluation.

(2) Although the maximum error used by this accuracy evaluation method has obtained the largest error point in the entire space, the error distribution of the entire sensor space has not been studied in depth. When there are multiple intervals with large losses, there will be a large number of Spatial information is ignored.

Contents of the invention

In order to overcome the shortcomings of the existing technology, the present invention provides an accuracy evaluation method and system for optical fiber shape sensing. The specific technical solutions are as follows:

Accuracy assessment methods for fiber optic shape sensing, including:

Reconstruct the sensor and obtain the reconstructed curve of the sensor;

Divide the curve of the sensor into multiple sensing segments, and examine the error distribution of the sensing segments respectively;

Obtain error parameters for evaluating the overall performance of the sensor;

The error parameters of sensors of different specifications are normalized to the same dimension to horizontally compare the reconstruction effects of the sensors of different specifications.

In a specific embodiment, the "lateral comparison of the reconstruction effects of the sensors with different specifications" specifically includes:

Obtain multiple sensors of different specifications, and calculate the average sensing length of the sensors of different specifications respectively;

Compare the average sensing length of each sensor, find the maximum value of the average sensing length, normalize the remaining average sensing lengths except the maximum value, and calculate the remaining The ratio between the average sensing length and the maximum value of the average sensing length to obtain a series of normalized proportional coefficients;

The normalized proportional coefficient is multiplied by the corresponding weighted error to obtain the normalized weighted error, and the normalized weighted error is analyzed and compared.

In a specific embodiment, the "respectively examining the error distribution of the sensing segments" specifically includes:

Based on the number of sensing points in each sensing segment, the average error of the sensing segment is calculated, and the average errors of the sensing segments are analyzed and compared one by one to obtain the error of each part of the sensor distributed.

In a specific embodiment, the "obtaining error parameters for evaluating the overall performance of the sensor" specifically includes:

Weights are set for different sensing segments according to the error distribution and reconstruction effect, and then the average errors of the sensing segments are weighted and summed to obtain error parameters for evaluating the overall performance of the sensor.

In a specific embodiment, the calculation method of "respectively examining the error distribution of the sensing segments" specifically includes:

in, Represents the error distribution of the sensing segment,/> Indicates the number of sensing points within the sensing segment,/> Represents the three-dimensional coordinates of the end of the reconstructed curve of the sensing segment,/> represents the three-dimensional coordinates of the end of the reference curve of the sensing segment, and i represents the i-th sensing segment after segmentation.

In a specific embodiment, the calculation method of "obtaining error parameters for evaluating the overall performance of the sensor" specifically includes:

Among them, e _weighti represents the error parameter for evaluating the overall performance of the sensor, W ₁ represents the weight value of the sensing segment error set according to the use environment, and n represents the number of segmented segments of the sensor.

In a specific embodiment, the calculation formula for "respectively calculating the average sensing length of the sensors of different specifications" specifically includes:

Where, L _avgi represents the average sensing length of the sensors with different specifications, _Li represents the length of the sensors with different specifications, and n _i represents the number of sensing points of the sensors with different specifications.

In a specific embodiment, the calculation formula of the "normalized proportional coefficient" specifically includes:

Wherein, K _im represents the normalized proportion coefficient, L _avgm represents the maximum value of the average sensing length of the sensor, and L _avgi represents the remaining average sensing lengths of the sensor excluding the maximum value.

In a specific embodiment, the calculation formula of the "normalized weighted error" specifically includes:

e _normi = k _im · e _weighti ;

Among them, e _normi represents the normalized weighted error, K _im represents the normalized proportion coefficient, and e _weighti represents the error parameter for evaluating the overall performance of the sensor.

In a specific embodiment, an accuracy evaluation system for optical fiber shape sensing is also provided, including:

A reconstruction unit, used to reconstruct the sensor and obtain the reconstructed curve of the sensor;

An investigation unit, used to divide the curve of the sensor into multiple sensing segments, and examine the error distribution of the sensing segments respectively;

An acquisition unit, used to acquire error parameters for evaluating the overall performance of the sensor;

A normalization unit is used to normalize the error parameters of sensors of different specifications to the same dimension to horizontally compare the reconstruction effects of the sensors of different specifications.

Compared with the existing technology, the present invention has the following beneficial effects:

The accuracy evaluation method and system for optical fiber shape sensing provided by the present invention can see the accuracy change trend of each segment in the local error inspection, and provide an analysis bias for the sensing segment in the global error inspection. Segment-weighted total error can comprehensively evaluate the accuracy of fiber shape sensing by integrating global and local reconstruction curve error information. It can achieve horizontal evaluation of the shape reconstruction effect of sensors of different specifications, and can assist researchers in balancing the accuracy brought by sensing points. Improve the cost of sensors and reduce evaluation bias to meet differentiated inspection needs in different scenarios, avoid losing spatial location information, and enhance universality.

In order to make the above-mentioned objects, features and advantages of the present invention more obvious and understandable, preferred embodiments are given below and described in detail with reference to the accompanying drawings.

Description of drawings

In order to explain the technical solutions of the embodiments of the present invention more clearly, the drawings required to be used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and therefore do not It should be regarded as a limitation of the scope. For those of ordinary skill in the art, other relevant drawings can be obtained based on these drawings without exerting creative efforts.

Figure 1 is an evaluation parameter calculation flow chart of the accuracy evaluation method for optical fiber shape sensing in the embodiment;

Figure 2 is a flow chart of the method for normalizing the length of the sensing segment in the embodiment;

Figure 3 is a schematic diagram of sensors with different specifications in the embodiment;

4 is a schematic diagram of an accuracy evaluation system for optical fiber shape sensing in an embodiment.

Detailed ways

Various embodiments of the invention are described more fully below. The invention may have various embodiments, and modifications and changes may be made therein. It is to be understood, however, that there is no intention to limit the various embodiments of the invention to the particular embodiments disclosed herein, but that the invention is to be construed to cover all embodiments falling within the spirit and scope of the invention. All adjustments, equivalents and/or alternatives.

Hereinafter, the terms "comprises" or "may include" which may be used in various embodiments of the present invention indicate the presence of disclosed functions, operations, or elements and do not limit the presence of one or more functions, operations, or elements. Increase. Furthermore, as used in various embodiments of the present invention, the terms "including," "having," and their cognates are only intended to represent specific features, numbers, steps, operations, elements, components, or combinations of the foregoing. and should not be understood as first excluding the presence of one or more other features, numbers, steps, operations, elements, components or combinations of the foregoing or adding one or more features, numbers, steps, operations, elements, components or the possibility of a combination of the foregoing.

In various embodiments of the invention, the expression "or" or "at least one of A or/and B" includes any and all combinations of words listed simultaneously. For example, the expression "A or B" or "at least one of A or/and B" may include A, may include B, or may include both A and B.

Expressions (such as “first”, “second”, etc.) used in various embodiments of the present invention may modify various constituent elements in various embodiments, but may not limit the corresponding constituent elements. For example, the above statements do not limit the order and/or importance of the elements described. The above expressions are only for the purpose of distinguishing one element from other elements. For example, a first user device and a second user device indicate different user devices, although both are user devices. For example, a first element could be termed a second element, and likewise a second element could be termed a first element, without departing from the scope of various embodiments of the invention.

It should be noted that in the present invention, unless otherwise clearly stated and defined, terms such as "installation", "connection" and "fixing" should be understood in a broad sense. For example, it can be a fixed connection or a detachable connection. Or integrally connected; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be an internal connection between two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

In the present invention, those of ordinary skill in the art need to understand that the terms indicating the orientation or positional relationship in the text are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating Or it is implied that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be construed as a limitation of the present invention.

The terminology used in the various embodiments of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the various embodiments of the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which various embodiments of this invention belong. Said terms (such as terms defined in commonly used dictionaries) will be interpreted to have the same meaning as the contextual meaning in the relevant technical field and will not be interpreted as having an idealized meaning or an overly formal meaning, Unless expressly limited in various embodiments of the invention.

Example

As shown in Figures 1-3, this embodiment provides an accuracy evaluation method for optical fiber shape sensing, including:

Reconstruct the sensor and obtain the reconstructed sensor curve; divide the sensor curve into multiple sensing segments, and examine the error distribution of the sensing segments respectively; obtain error parameters to evaluate the overall performance of the sensor; divide sensors of different specifications into The error parameters are normalized to the same dimension to horizontally compare the reconstruction effects of sensors with different specifications.

This embodiment can solve the problems of lost spatial position information and poor universality of existing accuracy evaluation methods. It can achieve horizontal comparison of the sensing performance of sensors with different specifications through the normalized sensing length of the present invention, and can assist researchers to balance sensing. The accuracy improvement brought by the sensing point and the cost of the sensor increase. Preferably, this embodiment can see the accuracy change trend of each segment in the local error inspection, and provides the segmented weighted total error with an inspection bias for the sensing segment in the global error inspection, and can integrate global and local errors. The reconstruction curve error information evaluates the accuracy of optical fiber shape sensing, which can realize the horizontal evaluation of the shape reconstruction effect of sensors of different specifications. It can help researchers balance the accuracy improvement brought by sensing points and the cost increase of sensors, and reduce evaluation bias. To meet differentiated inspection needs in different scenarios, avoid losing spatial location information, and enhance universality.

Optionally, this embodiment can synthesize global and local reconstructed curve error information, and can also set different weights for different segments to achieve flexible inspection emphasis; later, a normalized sensing can be developed on this basis. The segment length method normalizes the error parameters of sensors of different specifications to the same dimension to achieve horizontal comparison of the reconstruction effects of sensors of different specifications. This embodiment has the potential to assist researchers in finding a suitable balance point between the accuracy improvement brought by sensing points and the increase in sensor cost, so as to meet differentiated inspection needs in different scenarios.

In order to achieve horizontal comparison of the reconstruction effects of sensors of different specifications, a method of normalizing the length of the sensing segment is further developed in this method, which normalizes the error parameters of sensors of different specifications to the same dimension. Its implementation flow chart as shown in picture 2.

Specifically, in this embodiment, "lateral comparison of the reconstruction effects of sensors with different specifications" specifically includes:

Obtain multiple sensors of different specifications. Suppose there are k sensors of different specifications, with lengths L ₁ , L ₂ ,..., L _k , and n ₁ , n ₂ ,..., n _k sensing points respectively. Calculate the average sensing length of the sensors of these specifications respectively; compare the average sensing length of each sensor, find the maximum value L _avgm of the average sensing length, and normalize the other average sensing lengths Processing, calculate the ratio to the maximum average sensing length respectively, that is: normalize the remaining average sensing lengths excluding the maximum value, and calculate the remaining average sensing lengths and the maximum value of the average sensing length. to obtain a series of normalized proportional coefficients k _1m ,k _2m ,...,k _km ; multiply the normalized proportional coefficient by the corresponding weighted error to obtain the normalized weighted Error e _norm , by analyzing and comparing the normalized weighted error e _norm , the reconstruction effects of sensors of different specifications can be compared horizontally with emphasis.

In this embodiment, "respectively examining the error distribution of the sensing segments" specifically includes:

Based on the number of sensing points in each sensing segment, the average error of each sensing segment is calculated. By analyzing and comparing the average errors of several sensing segments of the reconstructed curve one by one, the accuracy of each part of the sensor can be known. Error distribution.

In this embodiment, "obtaining error parameters for evaluating the overall performance of the sensor" specifically includes:

Set weights for different sensing segments based on the approximate error distribution and reconstruction effect in the actual application scenario, and then weight and sum the average errors of the sensing segments to obtain an error parameter e _weight that evaluates the overall performance of the sensor.

Specifically, the evaluation parameter calculation flow chart of the accuracy evaluation method for optical fiber shape sensing is shown in Figure 1. This evaluation method first divides the reconstructed sensor curve into several sensing segments through the segmentation method, and A certain weight is set for each segment to adjust the importance of each position in the evaluation; then, the normalized error value is obtained by normalizing it according to the length of the sensor and the number of sensing points for comparison. The specific steps are described as follows:

(1) Divide the reconstructed curve into several sensing segments S ₁ , S ₂ ,..., S _j . The sum of the lengths of each sensing segment is the total length L of the sensor, and examine the errors of each sensing segment separately; (2) Based on the number of sensing points in each sensing segment, calculate the average error of each sensing segment. By analyzing and comparing the errors of several sensing segments of the reconstructed curve one by one, the error distribution of each part of the sensor can be known; (3) Set weights for different segments according to the approximate error distribution and reconstruction effect in the actual application scenario, and then weight and sum the average errors of each sensing segment to obtain a parameter e _weight that evaluates the overall performance of the sensor. .

In this embodiment, by segmenting the sensors and setting different weights respectively, the accuracy change trend of each segment can be seen in the local error investigation, and the segmentation biased toward the sensing segment can be given in the global error inspection. The total error is weighted to evaluate the fiber shape sensing accuracy by integrating global and local reconstructed curve error information. The sensing segment lengths of different sensors can be normalized on the basis of segmented weighted errors, thereby normalizing the error parameters of sensors of different specifications to the same dimension, and achieving horizontal evaluation of the shape reconstruction effects of sensors of different specifications.

In this embodiment, the calculation method of "respectively examining the error distribution of the sensing segments" specifically includes:

in, Represents the error distribution of the sensing segment,/> Indicates the number of sensing points in the sensing segment,/> Represents the three-dimensional coordinates of the end of the reconstruction curve of the sensing segment,/> Represents the three-dimensional coordinates of the end of the reference curve of the sensing segment, i represents the i-th sensing segment after segmentation.

In this embodiment, the calculation method of "obtaining error parameters for evaluating the overall performance of the sensor" specifically includes:

Among them, e _weighti represents the error parameter for evaluating the overall performance of the sensor, W ₁ represents the weight value of the sensing segment error set according to the use environment, and n represents the number of segmented segments of the sensor.

In this embodiment, the calculation formula for "respectively calculating the average sensing length of sensors with different specifications" specifically includes:

Among them, L _avgi represents the average sensing length of sensors with different specifications, _Li represents the length of sensors with different specifications, and n _i represents the number of sensing points of sensors with different specifications.

In this embodiment, the calculation formula of "normalized proportional coefficient" specifically includes:

Among them, K _im represents the normalized proportion coefficient, L _avgm represents the maximum value of the average sensing length of the sensor, and L _avgi represents the remaining average sensing length in the sensor excluding the maximum value.

In this embodiment, the calculation formula of "normalized weighted error" specifically includes:

e _normi = k _im ·e _weighti ;

Among them, e _normi represents the normalized weighted error, K _im represents the normalized proportion coefficient, and e _weighti represents the error parameter for evaluating the overall performance of the sensor.

Optionally, the accuracy evaluation method used for optical fiber shape sensing in this implementation weights the sensor segmentally, and can evaluate the sensing accuracy based on the user's wishes for the sensing segments or interesting sensing segments that have a greater impact on the reconstruction accuracy. We will focus on the impact of the sensor to realize the evaluation of the sensor shape reconstruction effect that fully integrates spatial information.

Optionally, the accuracy evaluation method for optical fiber shape sensing in this embodiment can further achieve a horizontal comparison of the shape reconstruction performance of sensors with different specifications through a normalization method, which helps users balance the number of sensing points of the sensor. The accuracy improvement and cost increase brought by the improvement are an important reference information for the commercialization of optical fiber sensing technology.

As shown in Figure 4, in this embodiment, an accuracy evaluation system for optical fiber shape sensing is also provided, including:

The reconstruction unit is used to reconstruct the sensor and obtain the curve of the reconstructed sensor;

An investigation unit is used to divide the sensor curve into multiple sensing segments and examine the error distribution of the sensing segments respectively;

An acquisition unit is used to acquire error parameters for evaluating the overall performance of the sensor;

The normalization unit is used to normalize the error parameters of sensors of different specifications to the same dimension to horizontally compare the reconstruction effects of sensors of different specifications.

Compared with the existing technology, the accuracy evaluation method and system for optical fiber shape sensing provided by the present invention can see the accuracy change trend of each segment in the inspection of local errors, and provide an analysis of the transmission accuracy in the inspection of global errors. The segmented weighted total error of the sensing segment has an inspection bias, which can comprehensively evaluate the accuracy of fiber shape sensing by integrating global and local reconstruction curve error information. It can achieve lateral evaluation of the shape reconstruction effect of sensors of different specifications, and can assist researchers in balancing The accuracy improvement brought by sensing points and the cost of sensors reduce evaluation deviations to meet differentiated inspection needs in different scenarios, avoid losing spatial location information, and enhance universal applicability.

Those skilled in the art can understand that the accompanying drawing is only a schematic diagram of a preferred implementation scenario, and the modules or processes in the accompanying drawing are not necessarily necessary for implementing the present invention.

Those skilled in the art can understand that the modules in the devices in the implementation scenario can be distributed in the devices in the implementation scenario according to the description of the implementation scenario, or can be correspondingly changed and located in one or more devices different from the implementation scenario. The modules of the above implementation scenarios can be combined into one module or further split into multiple sub-modules.

The above serial numbers of the present invention are only for description and do not represent the advantages and disadvantages of the implementation scenarios.

What is disclosed above are only a few specific implementation scenarios of the present invention. However, the present invention is not limited thereto. Any changes that can be thought of by those skilled in the art should fall within the protection scope of the present invention.

Claims (10)

1. Accuracy evaluation method for optical fiber shape sensing, characterized by including: Reconstruct the sensor and obtain the reconstructed curve of the sensor; Divide the curve of the sensor into multiple sensing segments, and examine the error distribution of the sensing segments respectively; Obtain error parameters for evaluating the overall performance of the sensor; The error parameters of sensors of different specifications are normalized to the same dimension to horizontally compare the reconstruction effects of the sensors of different specifications. 2. The accuracy evaluation method for optical fiber shape sensing according to claim 1, characterized in that the "lateral comparison of the reconstruction effects of the sensors of different specifications" specifically includes: Obtain multiple sensors of different specifications, and calculate the average sensing length of the sensors of different specifications respectively; Compare the average sensing length of each sensor, find the maximum value of the average sensing length, normalize the remaining average sensing lengths except the maximum value, and calculate the remaining The ratio between the average sensing length and the maximum value of the average sensing length to obtain a series of normalized proportional coefficients; The normalized proportional coefficient is multiplied by the corresponding weighted error to obtain the normalized weighted error, and the normalized weighted error is analyzed and compared. 3. The accuracy evaluation method for optical fiber shape sensing according to claim 1, wherein the "respectively examining the error distribution of the sensing segments" specifically includes: Based on the number of sensing points in each sensing segment, the average error of the sensing segment is calculated, and the average errors of the sensing segments are analyzed and compared one by one to obtain the error of each part of the sensor distributed. 4. The accuracy evaluation method for optical fiber shape sensing according to claim 1, characterized in that said "obtaining error parameters for evaluating the overall performance of the sensor" specifically includes: Weights are set for different sensing segments according to the error distribution and reconstruction effect, and then the average errors of the sensing segments are weighted and summed to obtain error parameters for evaluating the overall performance of the sensor. 5. The accuracy evaluation method for optical fiber shape sensing according to claim 1, characterized in that the calculation method of "respectively examining the error distribution of the sensing segments" specifically includes: in, Represents the error distribution of the sensing segment,/> Indicates the number of sensing points within the sensing segment,/> represents the three-dimensional coordinates of the end of the reconstruction curve of the sensing segment, r _i ^gt represents the three-dimensional coordinates of the end of the reference curve of the sensing segment, and i represents the i-th sensing segment after segmentation. 6. The accuracy evaluation method for optical fiber shape sensing according to claim 1, characterized in that the calculation method of "obtaining error parameters for evaluating the overall performance of the sensor" specifically includes: Among them, e _weighti represents the error parameter for evaluating the overall performance of the sensor, W ₁ represents the weight value of the sensing segment error set according to the use environment, and n represents the number of segmented segments of the sensor. 7. The accuracy evaluation method for optical fiber shape sensing according to claim 2, characterized in that the calculation formula of "respectively calculating the average sensing length of the sensors of different specifications" specifically includes: Where, L _avgi represents the average sensing length of the sensors with different specifications, _Li represents the length of the sensors with different specifications, and n _i represents the number of sensing points of the sensors with different specifications. 8. The accuracy evaluation method for optical fiber shape sensing according to claim 2, characterized in that the calculation formula of the "normalized proportional coefficient" specifically includes: Wherein, K _im represents the normalized proportion coefficient, L _avgm represents the maximum value of the average sensing length of the sensor, and L _avgi represents the remaining average sensing lengths of the sensor excluding the maximum value. 9. The accuracy evaluation method for optical fiber shape sensing according to claim 2, characterized in that the calculation formula of the "normalized weighted error" specifically includes: _enotmi ＝ _kim · _eweighti ; Among them, e _normi represents the normalized weighted error, K _im represents the normalized proportion coefficient, and e _weighti represents the error parameter for evaluating the overall performance of the sensor. 10. Accuracy evaluation system for optical fiber shape sensing, characterized by including: A reconstruction unit, used to reconstruct the sensor and obtain the reconstructed curve of the sensor; An investigation unit, used to divide the curve of the sensor into multiple sensing segments, and examine the error distribution of the sensing segments respectively; An acquisition unit, used to acquire error parameters for evaluating the overall performance of the sensor; A normalization unit is used to normalize the error parameters of sensors of different specifications to the same dimension to horizontally compare the reconstruction effects of the sensors of different specifications.