A threshold determination method for odometry wheel positioning

Abstract

The application discloses a threshold determination method for odometer wheel positioning, comprising: acquiring positioning measurement values of each odometer wheel at least once; judging the consistency of the positioning measurement values of each odometer wheel, and establishing a confidence distance measure between the positioning measurement values of each odometer wheel according to the judgment result; calculating corresponding excess mean values by using each confidence distance measure and a preset first threshold set, and drawing an excess mean function curve according to the first threshold set and the excess mean values to determine a second threshold set; determining a threshold value by using the second threshold set based on a preset iteration step; and wherein the first threshold set comprises a plurality of first thresholds set in equal difference. The application can objectively determine the threshold value, and make the pipeline internal deformation and corrosion leakage positioning more scientific and accurate.

Description

Technical Field

[0001] This invention belongs to the field of pipeline leakage detection technology and relates to a threshold determination method for odometer wheel positioning. Background Technology

[0002] Pipeline detectors utilize the pipeline medium to drive the detector within the pipeline, detecting and recording damage such as deformation and corrosion in real time, and accurately locating the defects. A mileage wheel is the primary method for determining the location of defects within the pipe. It is mounted on the periphery of the detection device's overall structure, in contact with the pipe wall, and rotates as the detection device moves. When the detection device reaches the location of a defect within the pipe, the recognition signal from the defect detection sensor changes. The mileage recorded by the mileage wheel at that moment is then extracted to locate the defect within the pipe.

[0003] In the odometer wheel positioning method, the odometer wheel and encoder are coaxially connected. Each rotation of the odometer wheel outputs a fixed number of pulses. By recording the pulse count and the circumference of the odometer wheel through the encoder, positional information such as the travel distance and speed of the internal detector in the pipeline can be obtained. To improve the accuracy of odometer wheel positioning and reduce the influence of random factors, multiple odometer wheels are typically installed evenly along the circumference of the detector for positioning. Ideally, the rotation distance of the odometer wheel is the travel distance of the internal detector in the pipeline. Due to the limitations of contact positioning, odometer wheel positioning results often contain various error factors, and the positioning error accumulates as the detection distance increases, often resulting in significant errors in the positioning data of long-distance oil and gas pipelines. Odometer wheel positioning error factors mainly include systematic errors and random errors. Systematic errors include errors caused by the inconsistency between the actual and nominal diameters of the odometer wheel, errors caused by diameter differences between individual odometer wheels, and errors caused by center offset of the detection device. Random errors include initial position errors, errors caused by impurities deposited on the inner wall of the pipeline, errors caused by odometer wheel slippage or jamming, and errors caused by excessive clearance between the odometer wheel and its arm. According to the positioning mechanism of the mileage wheel, its positioning error accumulates continuously as the detection device moves forward, and the positioning results of internal defects in long-distance oil and gas pipelines often contain large errors.

[0004] Currently, threshold determination methods mainly fall into several categories, including percentile methods, maximum entropy methods, Otsu's method, and extreme value theory. Percentile methods are statistical measures used to analyze the dispersion of data and can be applied to threshold selection in various fields. Image segmentation typically uses maximum entropy and Otsu's method for threshold selection. Extreme value theory methods for threshold selection include the excess mean function curve method, Hill plot method, and kurtosis method, which have been extensively studied in areas such as catastrophic losses and extreme weather. Other threshold selection methods utilize indicators designed specifically for the research content to determine the threshold. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide a threshold determination method for mileage wheel positioning, which can objectively determine the threshold and make the positioning of internal pipeline deformation, corrosion and leakage more scientific and accurate.

[0006] To achieve the above objectives, the present invention is implemented using the following technical solution:

[0007] This invention provides a threshold determination method for odometer wheel positioning, wherein multiple odometer wheels are installed on the outer periphery of the detector, and each odometer wheel is assigned a corresponding number;

[0008] The threshold determination method includes the following steps:

[0009] At least once, obtain the positioning measurement values ​​for each mileage wheel;

[0010] Determine the consistency of the positioning measurements of each mileage wheel, and establish a confidence distance measure between the positioning measurements of each mileage wheel based on the determination results;

[0011] The excess mean is calculated using various confidence distance measures and a preset first threshold set, and the excess mean function curve is plotted based on the first threshold set and the excess mean to determine the second threshold set.

[0012] Based on the preset iterative steps, the threshold is determined using the second threshold set;

[0013] The first threshold set includes multiple first thresholds set in an arithmetic progression.

[0014] Furthermore, the positioning measurements of each mileage wheel are independent of each other.

[0015] Furthermore, the step of determining the consistency of the positioning measurements of each mileage wheel and establishing a confidence distance measure between the positioning measurements of each mileage wheel based on the determination result includes:

[0016] The probability density of each wheel at each mileage is calculated using positioning measurements;

[0017] Calculate the confidence distance between any two wheel alignment measurements using each probability density function;

[0018] Based on a pre-defined judgment strategy, confidence distance analysis is used to determine whether there is consistency between the two corresponding wheel positioning measurements.

[0019] If two odometer wheel positioning measurements are consistent, the confidence distance measure between the two odometer wheel positioning measurements is determined by the following formula:

[0020] d′ ajz =d ajz

[0021] In the formula, d′ ajz d is the confidence distance measure between the positioning measurements of the mileage wheels numbered j and z obtained in the a-th iteration. ajz The confidence distance between the positioning measurement value of wheel j (obtained in the a-th time) and the positioning measurement value of wheel z (obtained in the z-th time).

[0022] Otherwise, the confidence distance measure between the two odometer wheel positioning measurements is determined by the following formula:

[0023]

[0024] In the formula, d ajz Let be the confidence distance between the positioning measurement value of the mileage wheel numbered z obtained in the a-th acquisition and the positioning measurement value of the mileage wheel numbered j.

[0025] Furthermore, the calculation of the probability density of each mileage wheel using positioning measurements includes the following formula:

[0026]

[0027] In the formula, P() is the probability density function, x is the distance between the pipeline defect point and the odometer wheel, and l aj σ represents the positioning measurement value of the mileage wheel numbered j obtained in the a-th measurement. j The measurement accuracy is given by the mileage wheel numbered j.

[0028] Furthermore, the calculation of the confidence distance between any two mileage wheel positioning measurements using each probability density includes the following formula:

[0029]

[0030]

[0031] In the formula, l az The value is the positioning measurement of the mileage wheel with the number z, obtained in the a-th iteration.

[0032] Furthermore, the judgment strategy includes:

[0033] If the confidence distance between the positioning measurements of any two odometer wheels satisfies d ajz =d azj If the two odometer wheel positioning measurements are consistent, then they are consistent; otherwise, they are not consistent.

[0034] Furthermore, the calculation of the corresponding excess mean using each confidence distance measure and a preset first threshold set includes:

[0035] Write each confidence distance measure into the confidence distance measure set;

[0036] The excess mean of the confidence distance measure set and each first threshold is calculated using the following formula:

[0037]

[0038] In the formula, e() is the excess mean function, N is the number of elements in the distance measure set, and X is the value of X. i Let μ be an element in the distance measure set, and let μ be the first threshold.

[0039] Further, the step of plotting the excess mean function curve based on the first threshold set and the excess mean to determine the second threshold set includes:

[0040] Using the first threshold set as the independent variable and the excess mean as the dependent variable, plot the excess mean function curve;

[0041] Each independent variable corresponding to a linear curve segment in the excess mean function curve is designated as a second threshold, and each second threshold is written into the second threshold set.

[0042] Furthermore, the determination of the threshold using the second threshold set based on the preset iterative steps includes:

[0043] Repeat the following iterative steps until the second kurtosis is greater than a preset kurtosis threshold, then stop the iteration and determine the second threshold with the largest absolute value in the set of second thresholds as the threshold:

[0044] The second mean is calculated using the second threshold set, as follows:

[0045]

[0046] In the formula, The second mean, μ1, μ2, ..., μ n All are second thresholds, and n is the number of second thresholds in the set of second thresholds.

[0047] The second kurtosis is calculated using the second threshold set, as follows:

[0048]

[0049] In the formula, k n It is the second peak.

[0050] Determine the size of the second kurtosis: If the second kurtosis is less than or equal to a preset kurtosis threshold, then remove the second threshold with the largest absolute value of the difference between the second threshold and the second mean from the second threshold set to update the second threshold set;

[0051] Furthermore, the kurtosis threshold is 50000.

[0052] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0053] This invention analyzes the positioning measurements of each mileage wheel from multiple angles to determine the confidence distance measure, thereby improving the reliability of plotting the excess mean function curve. This invention constructs a second threshold set including multiple second thresholds by using linear curve segments in the excess mean function curve. This not only provides a data foundation for accurately determining the thresholds in subsequent steps but also eliminates unreasonable data, reduces the number of iterative calculations and judgments, and improves analysis efficiency. The thresholds of this invention are more objective, enabling more scientific and accurate location of internal pipeline deformation and corrosion leaks. Attached Figure Description

[0054] Figure 1 This is a flowchart of an embodiment of the threshold determination method for mileage wheel positioning according to the present invention;

[0055] Figure 2 This is the excess mean function curve of Embodiment 3 of the present invention;

[0056] Figure 3 This is the excess mean function curve of Embodiment 4 of the present invention. Detailed Implementation

[0057] The present invention will now be described in detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, illustrating only the basic structure of the invention, and therefore only show the components relevant to the invention.

[0058] Example 1

[0059] During the detection and recording of pipeline damage such as deformation and corrosion, and in the accurate positioning process, setting a threshold can be used to remove large error data from the positioning measurements of the odometer wheel, thereby improving the accuracy of odometer wheel positioning. Traditional methods rely on human experience to manually determine the threshold, but manually determined thresholds are subjective. Therefore, this embodiment provides a threshold determination method for odometer wheel positioning.

[0060] refer to Figure 1 The threshold determination method for odometer wheel positioning in this embodiment includes the following steps:

[0061] S1: Obtain the positioning measurement value of each mileage wheel at least once. The raw data acquisition in this embodiment is simple and readily available.

[0062] In the application, the positioning measurements of each mileage wheel are independent of each other.

[0063] S2: Determine the consistency of the positioning measurements of each mileage wheel, and establish a confidence distance measure between the positioning measurements of each mileage wheel based on the determination result.

[0064] S3: Calculate the corresponding excess mean using various confidence distance measures and a preset first threshold set, and plot the excess mean function curve based on the first threshold set and the excess mean to determine the second threshold set. This not only provides a data foundation for accurately determining the thresholds in subsequent steps but also eliminates unreasonable data.

[0065] In the application, the first threshold set includes multiple first thresholds with equal arithmetic progressions.

[0066] S4: Determine the threshold using the second threshold set based on the preset iteration steps.

[0067] This invention analyzes the positioning measurements of each mileage wheel from multiple angles to determine the confidence distance measure, thereby improving the reliability of plotting the excess mean function curve. This invention constructs a second threshold set including multiple second thresholds by using linear curve segments in the excess mean function curve. This not only provides a data foundation for accurately determining the thresholds in subsequent steps but also eliminates unreasonable data, reduces the number of iterative calculations and judgments, and improves analysis efficiency. The thresholds of this invention are more objective, enabling more scientific and accurate location of internal pipeline deformation and corrosion leaks.

[0068] Example 2

[0069] Based on Example 1, this example details the calculation method of the confidence distance measure, the determination method of the second threshold set, and the determination method of the threshold.

[0070] (I) Calculation of Confidence Distance Measure

[0071] Determining the consistency of positioning measurements at each mileage wheel, and establishing a confidence distance measure between the positioning measurements based on the determination results, includes:

[0072] S21 calculates the probability density of each mileage wheel using positioning measurements, as shown in the following formula:

[0073]

[0074] In the formula, P() is the probability density function, x is the distance between the pipeline defect point and the odometer wheel, and l aj σ represents the positioning measurement value of the mileage wheel numbered j obtained in the a-th measurement.j The measurement accuracy is given for the mileage wheel numbered j, with the unit being meters in the application.

[0075] S22 calculates the confidence distance between any two wheel alignment measurements using each probability density function, as shown in the following formula:

[0076]

[0077]

[0078] In the formula, l az The value is the positioning measurement of the mileage wheel with the number z, obtained in the a-th iteration.

[0079] S23 uses a preset judgment strategy to analyze whether there is consistency between the two corresponding mileage wheel positioning measurements using confidence distance analysis.

[0080] If the confidence distance between the positioning measurements of any two odometer wheels satisfies d ajz =d azj If the two mileage wheel positioning measurements are consistent, that is, the weights and measurement accuracies of the two mileage wheel positioning measurements are equal; otherwise, the two mileage wheel positioning measurements are inconsistent, that is, the weights and measurement accuracies of the two mileage wheel positioning measurements are not equal.

[0081] If two odometer wheel positioning measurements are consistent, the confidence distance measure between the two odometer wheel positioning measurements is determined by the following formula:

[0082] d′ ajz =d ajz

[0083] In the formula, d′ ajz d is the confidence distance measure between the positioning measurements of the mileage wheels numbered j and z obtained in the a-th iteration. ajz The confidence distance between the positioning measurement value of wheel j (obtained in the a-th time) and the positioning measurement value of wheel z (obtained in the z-th time).

[0084] Otherwise, the confidence distance measure between the two odometer wheel positioning measurements is determined by the following formula:

[0085]

[0086] In the formula, d azj Let be the confidence distance between the positioning measurement value of the mileage wheel numbered z obtained in the a-th acquisition and the positioning measurement value of the mileage wheel numbered j.

[0087] In application, the confidence distance measure reflects the degree of consistency and integration between the positioning measurements of two corresponding odometer wheels. If the confidence distance measure is small, the deviation between the positioning measurements of the two odometer wheels is small, the mutual support is large, and the integration is high; conversely, the deviation between the positioning measurements of the two odometer wheels is large, the mutual support is small, and the integration is low.

[0088] (II) Determination of the Second Threshold Set

[0089] S31 calculates the corresponding excess mean using each confidence distance measure and a preset first threshold set, as follows:

[0090] Write each confidence distance measure into the confidence distance measure set;

[0091] The excess mean of the confidence distance measure set and each first threshold is calculated using the following formula:

[0092]

[0093] In the formula, e() is the excess mean function, N is the number of elements in the distance measure set, and X is the value of X. i Let μ be an element in the distance measure set, and let μ be the first threshold.

[0094] S32 plots the excess mean function curve based on the first threshold set and the excess mean to determine the second threshold set, as follows:

[0095] Using the first threshold set as the independent variable and the excess mean as the dependent variable, plot the excess mean function curve. In application, the excess mean function curve includes the observation point, and the curve segment extending from the observation point tends to be linear.

[0096] Traditional methods only record the independent variable corresponding to the observation point as the second threshold. However, in reality, the starting point of a curve segment exhibiting linear characteristics cannot be determined by visual inspection. Therefore, this embodiment does not fixate on a single independent variable, but instead records the independent variables corresponding to the curve segments in the excess mean function curve that tend to be linear as the second threshold, and writes each second threshold into a second threshold set, providing a data basis for accurately determining the threshold in subsequent steps.

[0097] (III) Threshold Determination

[0098] This embodiment determines the threshold using a second threshold set based on preset iterative steps.

[0099] Repeat the following iterative steps until the second kurtosis is greater than a preset kurtosis threshold, then stop the iteration and determine the second threshold with the largest absolute value in the set of second thresholds as the threshold:

[0100] S41 calculates the second mean using the second threshold set, as follows:

[0101]

[0102] In the formula, The second mean, μ1, μ2, ..., μ n All are second thresholds, and n is the number of second thresholds in the set of second thresholds.

[0103] S42 calculates the second kurtosis using the second threshold set, as follows:

[0104]

[0105] In the formula, k n It is the second peak.

[0106] S3 determines the size of the second kurtosis: if the second kurtosis is less than or equal to the preset kurtosis threshold, then the second threshold with the largest absolute value of the difference between the second threshold set and the second mean is removed to update the second threshold set;

[0107] In this application, the kurtosis threshold is 50,000.

[0108] Example 3

[0109] In 2016, Wang Zegen and Tan Jing published a paper titled "An Improved Algorithm for Consistency Verification of Pipeline Internal Defect Location" in Volume 33, Issue 4 of the *Journal of Surveying and Mapping Science and Technology*. The paper describes an experiment where a pipeline defect was artificially created at a location 25m along a section of pipeline. A total of 30 experiments were conducted, each using five odometer wheels for synchronous location. These five wheels were evenly distributed around the pipeline internal defect detector. Furthermore, confidence distance measures were calculated for the two sets of defect location values ​​in Table 1, and confidence distance measure matrices D1 and D2 were constructed.

[0110] Table 1. Location values ​​of internal defects in pipelines

[0111] Mileage wheel number First set of defect location values Second set of defect location values 1 25.348 23.463 2 25.158 25.448 3 25.067 25.097 4 23.223 24.688 5 24.806 25.243

[0112]

[0113]

[0114] Based on the confidence distance measurement matrix D1, this embodiment uses the threshold determination method described in Embodiment 1 or 2 to determine the threshold of the first group of defect location values ​​in Table 1.

[0115] First, write the confidence distance measure matrix D1 into the confidence distance measure set;

[0116] Next, a first threshold set is set, which includes five arithmetic first thresholds: 0.2, 0.4, 0.6, 0.8, and 1.

[0117] Then, the excess mean of the confidence distance metric set and each first threshold is calculated, as follows:

[0118]

[0119]

[0120]

[0121]

[0122]

[0123] Next, using the first threshold set as the independent variable and the excess mean as the dependent variable, the excess mean function curve is plotted. Through... Figure 2 It can be seen that, taking the point on the excess mean function curve corresponding to the independent variable 0.70 as the observation point, the curve segment extending from the observation point tends to be linear. Therefore, the independent variables corresponding to the curve segments on the excess mean function curve that tend to be linear include 0.70, 0.75, 0.80, 0.85 and 0.9. Therefore, 0.70, 0.75, 0.80, 0.85 and 0.9 are all recorded as the second threshold, and each second threshold is written into the second threshold set.

[0124] Finally, the iteration step is performed to determine the threshold, as follows:

[0125] Step 1: Calculate the second mean using the following formula.

[0126]

[0127] Step 2, calculate the second kurtosis k using the following formula. n :

[0128]

[0129] Step 3: Determine the size of the second kurtosis.

[0130] Since 23520 is less than 50000, 0.7 is removed from the second threshold set. The updated second threshold set includes 0.75, 0.8, 0.85, and 0.9. Therefore, return to steps 1-3 to update the second mean. Second kurtosis k n Until the second peak value is greater than 50,000.

[0131] The second threshold set at this time includes 0.75, 0.8 and 0.85. Therefore, the threshold for determining the first set of defect location values ​​is 0.85.

[0132] Example 4

[0133] Based on Example 3, this example uses the confidence distance measure matrix D2 to determine the threshold of the second group of defect location values ​​in Table 1.

[0134] First, write the confidence distance measure matrix D2 into the confidence distance measure set;

[0135] Next, a first threshold set is set, which includes five arithmetic first thresholds: 0.2, 0.4, 0.6, 0.8, and 1.

[0136] Then, the excess mean of the confidence distance metric set and each first threshold is calculated, as follows:

[0137]

[0138]

[0139]

[0140]

[0141]

[0142] Next, using the first threshold set as the independent variable and the excess mean as the dependent variable, the excess mean function curve is plotted. Through... Figure 3 It can be seen that, taking the point on the excess mean function curve corresponding to the independent variable of 0.70 as the observation point, the curve segment extending from the observation point tends to be linear. Therefore, the independent variables corresponding to the curve segments on the excess mean function curve that tend to be linear include 0.70, 0.75, 0.80, 0.85 and 0.9. Therefore, it is inferred that the threshold is around 0.85, so the first threshold set is reset.

[0143] In practical applications, 0.72, 0.77, 0.82, and 0.87 are reset to the first threshold of the first threshold set, and then the iteration step is entered to determine the threshold, as follows:

[0144] Step 1: Calculate the second mean using the following formula.

[0145]

[0146] Step 2, calculate the second kurtosis k using the following formula. n :

[0147]

[0148] Step 3: Determine the size of the second kurtosis.

[0149] Since 62440 is greater than 50000, the threshold for determining the second group of defect location values ​​is 0.87.

[0150] As can be seen from Examples 3 and 4, although the first threshold sets set by humans are different, the thresholds determined by the threshold determination method described in this application are very close. Therefore, the threshold determination method of this application is reliable.

[0151] In summary, this invention enables the threshold to be more objective, and makes the location of internal pipeline deformation, corrosion and leakage more scientific and accurate, avoiding the negative impact of inaccurate location results caused by improper threshold selection due to determination of threshold based on experience.

[0152] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0153] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0154] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0155] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0156] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A threshold determination method for mile wheel positioning, characterized in that, The outer peripheral part of the detector is provided with a plurality of odometer wheels, each of which is provided with a corresponding number; The threshold determination method comprises the following steps: At least one positioning measurement value of each odometer wheel is obtained; Consistency of the positioning measurement values of each odometer wheel is judged, and a confidence distance measure between the positioning measurement values of each odometer wheel is established according to the judgment result; The corresponding excess mean is calculated by using each confidence distance measure and a preset first threshold set, and a excess mean function curve is drawn according to the first threshold set and the excess mean, so as to determine a second threshold set; The threshold is determined by using the second threshold set based on a preset iteration step; The first threshold set comprises a plurality of first thresholds set in equal difference; The threshold is determined by using the second threshold set based on a preset iteration step, which comprises: The following iteration step is executed in a loop until the second kurtosis is greater than a preset kurtosis threshold, the iteration step is stopped, and the second threshold with the largest absolute value in the second threshold set is determined as the threshold: The second mean is calculated by using the second threshold set, as follows: / n， In the formula, is a second mean value, are second threshold values, n is the number of second threshold values in the second threshold value set, The second kurtosis is calculated by using the second threshold set, as follows: ， In the formula, is the second kurtosis; The size of the second kurtosis is judged: if the second kurtosis is less than or equal to the preset kurtosis threshold, the second threshold with the largest absolute difference from the second mean in the second threshold set is removed, so as to update the second threshold set.

2. The threshold determination method for odometry wheel positioning according to claim 1, characterized in that, The positioning measurement values of each odometer wheel are independent of each other.

3. The threshold determination method for odometry wheel positioning of claim 1, wherein, The consistency of the positioning measurement values of each odometer wheel is judged, and a confidence distance measure between the positioning measurement values of each odometer wheel is established according to the judgment result, which comprises: The probability density of each odometer wheel is calculated by using the positioning measurement value; The confidence distance between the positioning measurement values of any two odometer wheels is calculated one by one by using each probability density; Whether the positioning measurement values of the corresponding two odometer wheels have consistency is analyzed based on a preset judgment strategy by using the confidence distance: If the positioning measurement values of the two odometer wheels have consistency, the confidence distance measure between the positioning measurement values of the two odometer wheels is determined by the following formula: ， wherein is a confidence distance measure between the a-th acquired positioning measurement of the odometer with index j and the positioning measurement of the odometer with index z, is a confidence distance of the a-th acquired positioning measurement of the odometer with index j to the positioning measurement of the odometer with index z. Otherwise, the confidence distance measure between the positioning measurement values of the two odometer wheels is determined by the following formula: ， wherein is the confidence distance of the positioning measurement of the odometer with number z for the a-th acquisition to the positioning measurement of the odometer with number j.

4. The threshold determination method for odometry wheel positioning according to claim 3, characterized in that, The probability density of each odometer wheel is calculated by using the positioning measurement value, which comprises the following formula: ， wherein is a probability density function, , is the positioning measurement value of the odometer numbered j acquired for the a-th time, is the measurement accuracy of the odometer numbered j.

5. The threshold determination method for odometry wheel positioning of claim 3, wherein, The confidence distance between the positioning measurement values of any two odometer wheels is calculated one by one by using each probability density, which comprises the following formula: ， ， In the formula, is the positioning measurement value of the odometer numbered z acquired for the a-th time.

6. The threshold determination method for odometry wheel positioning of claim 5, wherein, The judgment strategy comprises: If the confidence distance between positioning measurements of any two odometry wheels satisfies = then the two odometry positioning measurements are consistent, otherwise, the two odometry positioning measurements are not consistent.

7. The threshold determination method for odometry wheel positioning of claim 1, wherein, The corresponding excess mean is calculated by using each confidence distance measure and a preset first threshold set, which comprises: Each confidence distance measure is written into a confidence distance measure set; The excess mean of the confidence distance measure set and each first threshold is calculated by the following formula respectively: ， In the formula, N is the number of elements in the set of signal distance measures, is an element in the set of signal distance measures, is a first threshold value.

8. The threshold determination method for odometry wheel positioning of claim 1, wherein, The excess mean function curve is drawn with the first threshold set as the independent variable and the excess mean as the dependent variable, so as to determine the second threshold set. Each independent variable corresponding to the linear curve segment of the excess mean function curve is recorded as a second threshold, and each second threshold is written into the second threshold set. The kurtosis threshold is 50000.

9. The threshold determination method for odometry wheel positioning of claim 1, wherein, ​

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