Positioning data cleaning method and system based on data analysis

Abstract

The invention relates to the technical field of data processing, in particular to a positioning data cleaning method and system based on data analysis. The method comprises the following steps: acquiring position data of a vehicle at each moment, wherein the position data comprises longitude and latitude values; detecting the position data of each moment by using a saliency detection CA algorithm to obtain a saliency value of each moment, including: determining longitude and latitude special values and longitude and latitude trueness of a target moment; obtaining the trueness of equally dividing the position data into a plurality of data segments under each scale according to the length of each preset scale; determining a reference data segment and a preliminary saliency value, and further obtaining a saliency value; and performing interpolation processing according to the size of the significance value to complete positioning data cleaning based on data analysis. According to the method, indexes such as longitude and latitude special values and trueness are calculated, a more reliable basis is provided for preliminary significance value calculation, interference is effectively reduced, and the accuracy of invalid data judgment is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a positioning data cleaning method and system based on data analysis. Background Art

[0002] With the development of big data technology, especially the popularization of mobile Internet and Internet of Things, positioning data has become a core data type in many applications. As one of the positioning data, position data may contain noise, errors, missing and other problems due to various reasons. These problems may be caused by the instability of device hardware, sensor errors, loss in data transmission, etc. If these data errors are not cleaned, they will lead to distortion of downstream analysis, decision-making and prediction, and even affect important applications such as public safety and traffic scheduling. By cleaning the location data, the accuracy and completeness of the data can be improved, thereby providing more reliable information for relevant decisions. An existing method for invalid location data detection is to use the significant detection CA algorithm, which has the advantages of strong real-time performance and strong robustness. It can quickly and accurately identify and filter invalid data in the process of large-scale location data processing, and significantly improve the reliability and accuracy of the positioning system.

[0003] The patent application document with publication number CN114296063A discloses a collaborative positioning and ranging method based on CSMA / CA, including the following steps: S1: the sending station detects the state of the wireless channel and uses the backoff algorithm to choose whether to send the entire data frame; S2: when the sending station uses the backoff timer to repeat and send the data frame to wait for confirmation; S3: when the backoff timer of a sending station counts down to 0, use the RTS / CTS data frame to detect whether the receiving station has received the data frame; S4: the sending station repeats step S1 and continues to execute the next frame.

[0004] However, the above patent application document is not aimed at cleaning positioning data, and does not solve the problem that when using the significance detection CA algorithm to analyze whether the location data at a moment is invalid data, it will be judged by calculating the size of its significance value. Due to the existence of potential invalid data, when calculating the preliminary significance value of a data point at a scale, if there is invalid data in other data segments at the same scale, the data authenticity of the data segment to which the data point belongs is reduced, thereby affecting the accuracy of the preliminary significance value of the data point at one scale and the significance value at multiple scales, resulting in errors in the judgment of invalid data. Summary of the invention

[0005] In order to solve the problem that when a saliency detection CA algorithm determines whether the position data at each moment is invalid data, it determines by calculating the size of its saliency value, and due to the existence of potential invalid data, the authenticity of the data segment is reduced, thereby affecting the accuracy of the preliminary saliency value of the data point at one scale and the saliency value at multiple scales, resulting in errors in the judgment of invalid data, the present invention provides a positioning data cleaning method and system based on data analysis.

[0006] In a first aspect, the present invention provides a positioning data cleaning method based on data analysis, which adopts the following technical solution: A positioning data cleaning method based on data analysis, comprising: obtaining the position data of a vehicle at each time, wherein the position data includes longitude and latitude values; using saliency detection The algorithm detects the location data at each moment and obtains the significance value at each moment; interpolation processing is performed according to the size of the significance value to complete the positioning data cleaning based on data analysis; In the process of detection by the algorithm, the position data is equally divided into multiple data segments at each scale according to the length of each preset scale, and the preliminary significance value of the target moment at each scale is calculated. According to the preliminary significance value, the significance value of the target moment is determined, including: at the target scale in each scale, according to the longitude and latitude values ​​of the target moment and several previous moments, and the extreme values ​​of the longitude and latitude values ​​at each moment, the longitude and latitude special values ​​of the target moment are determined; according to the longitude and latitude special values, and the longitude and latitude values ​​of the target moment and several previous moments, the longitude and latitude authenticity of the target moment is determined; the sum of the longitude and latitude authenticity is used as the position authenticity of the target moment; for a data segment that does not contain the target moment, the sum of the position authenticity of each moment in the data segment is used as the authenticity of the data segment; according to the size of the authenticity, a reference data segment is determined; the average of the sum of the longitude special value and the latitude special value at each moment in each data segment is recorded as the characteristic value of each data segment; according to the characteristic values ​​of the data segment to which the target moment belongs and the reference data segment, the preliminary significance value of the target moment at the target scale is determined.

[0007] The beneficial effects are as follows: by combining the saliency detection CA algorithm with multi-scale analysis, not only can the accuracy of the data be improved, but also invalid data can be effectively filtered out through saliency value judgment and interpolation processing at each scale, thereby reducing the possibility of misjudgment; in the saliency detection process, a comparative analysis of the position data at the target moment and several previous moments is adopted, which reduces the influence of invalid data on the overall authenticity of the data segment, thereby avoiding the positioning error caused by invalid data; by analyzing the position data at multiple scales, it is possible to capture potential valid information in the data at different scales, and effectively isolate inaccurate or abnormal values, thereby enhancing the adaptability of the method to data changes; by calculating the special values ​​of the longitude and latitude values ​​of the target moment and its adjacent moments, combined with the change of their authenticity, the characteristic value of the data segment is further determined, and finally the comprehensive evaluation ability of the moment and the data segment is improved; by judging the saliency value according to the characteristic values ​​of the data segment and the target moment, reliable data can be screened out more accurately, making the final positioning result more accurate, thereby improving the stability and reliability of the positioning system in practical applications.

[0008] Furthermore, the longitude and latitude special values ​​respectively satisfy: , ; In the formula, and Separately for the moment The longitude special value and latitude special value of and Separately for the moment The longitude and latitude values ​​of and Separately for the moment The average of the longitude and latitude values ​​at several previous moments, and are the maximum values ​​of longitude and latitude at each moment, and are the minimum values ​​of longitude and latitude at each moment, is a hyperparameter, is the standard normalization function, is the minimum function, is the absolute value symbol.

[0009] The beneficial effects are: through the precise calculation of special values ​​of longitude and latitude, the changes in location data can be more accurately reflected, thereby improving the accuracy of data cleaning; considering the mean of historical data, reducing the interference of instantaneous errors and improving the representativeness of target data; normalization and standardization operations eliminate errors caused by differences in data ranges, ensuring that different data can be compared fairly; through the calculation of the difference between the maximum and minimum values, extreme data can be effectively identified and processed to reduce its negative impact on the results.

[0010] Furthermore, the latitude and longitude authenticity respectively satisfy: , ; In the formula, and Separately for the moment The true longitude and latitude of and Separately for the moment The longitude special value and latitude special value of and Separately for the moment The variance of the longitude and latitude values ​​at several previous moments, and Separately for the moment The variance of the longitude and latitude values ​​at several previous moments, and Separately for the moment The difference in longitude and latitude from the previous moment.

[0011] The beneficial effects are as follows: by calculating the authenticity of longitude and latitude, the authenticity of positioning data at each moment can be accurately evaluated to ensure that the data is more reliable; based on the comparison of longitude and latitude variance, the credibility of the data can be dynamically adjusted, data with large fluctuations can be identified, and data processing can be optimized; the introduction of differences between moments can capture the trend of position changes, which helps to identify and adjust mutation data to avoid its impact on the results; a comprehensive evaluation is carried out by combining special values, variances, and differences to improve the depth and accuracy of data cleaning.

[0012] Furthermore, determining the reference data segment according to the authenticity includes: in response to the authenticity being greater than a preset reference threshold, identifying the data segment as a reference data segment.

[0013] Furthermore, the preliminary significance value satisfies: ; In the formula, For the moment In scale The initial significance value of For the moment In scale The number of reference data segments under For the moment In scale The characteristic value of the data segment below, For the moment In scale The next The characteristic value of the reference data segment, is the absolute value symbol.

[0014] The beneficial effects are as follows: by calculating the preliminary significance value, it is possible to effectively evaluate the significant changes in the data at different scales at the target moment, helping to identify key changes or anomalies in the data; by comparing with multiple reference data segments, the accuracy and robustness of the significance evaluation are improved, and the deviation of a single reference data is avoided; by averaging the differences, the influence of a single outlier on the significance calculation is effectively reduced, ensuring that the evaluation results are more stable and reliable.

[0015] Furthermore, the significance value satisfies: ; In the formula, For the moment The significance value of is the number of scales, For the moment In scale The initial significance value of .

[0016] The beneficial effects are as follows: by averaging the preliminary significance values ​​at multiple scales, the significance of the target moment can be evaluated more comprehensively, avoiding the deviation of a single scale; by smoothing the significance values ​​at each scale, the influence of data noise is reduced, ensuring more accurate results; through normalization and averaging operations, the sensitivity to subtle changes is improved, thereby enhancing detection capabilities.

[0017] Furthermore, the interpolation processing according to the size of the significance value includes: in response to the significance value being greater than a preset invalid threshold, determining that the position data at the target moment is invalid data, otherwise, the position data at the target moment is real data; for all invalid data, selecting multiple real data immediately before the invalid data, assigning the average of the longitude and latitude values ​​of the multiple real data to the invalid data, and completing the interpolation processing.

[0018] In a second aspect, the present invention provides a positioning data cleaning system based on data analysis, which adopts the following technical solutions: A positioning data cleaning system based on data analysis includes: a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned positioning data cleaning method based on data analysis is implemented.

[0019] By adopting the above technical solution, the above-mentioned positioning data cleaning method based on data analysis is generated into a computer program and stored in a memory so as to be loaded and executed by a processor, thereby making a terminal device based on the memory and the processor for easy use.

[0020] The present invention has the following technical effects: Traditional significance detection CA algorithms are susceptible to interference from potential invalid data, resulting in reduced authenticity of data segments, affecting the accuracy of significance values, and causing errors in invalid data judgment. The present invention refines the evaluation of data reliability by calculating indicators such as special values ​​of longitude and latitude and authenticity, and only retains data segments with higher authenticity as reference data segments to avoid error propagation, ensuring that the preliminary significance values ​​calculated at each scale of the position data at the target moment are more accurate, providing a more reliable basis for the calculation of preliminary significance values, effectively reducing interference, and improving the accuracy of invalid data judgment; interpolation processing is performed based on more accurate significance values, and accurate significance values ​​can more clearly identify invalid data, making interpolation more reasonable, and the cleaned data after filling invalid data is more in line with the actual driving trajectory of the vehicle; multi-scale analysis is used when calculating the preliminary significance value, and the position data is equally divided into multiple data segments according to different scales, which can comprehensively consider the data from local and global perspectives, comprehensively and accurately evaluate the significance of the target moment, and provide rich information for invalid data judgment; the present invention effectively removes invalid data, improves the quality of positioning data, provides a reliable basis for applications such as vehicle positioning and trajectory analysis, enhances the practicality of the system, and helps related industries improve operation and management efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] By reading the detailed description below with reference to the accompanying drawings, the above and other purposes, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-restrictive manner, and the same or corresponding numbers are the same or corresponding parts.

[0022] Figure 1 The present invention is a flowchart of a method for cleaning positioning data based on data analysis in an embodiment of the present invention.

[0023] Figure 2 It is a method flow chart of step S2 in a positioning data cleaning method based on data analysis in an embodiment of the present invention. DETAILED DESCRIPTION

[0024] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0025] It should be understood that when the terms "first", "second", etc. are used in the claims, descriptions, and drawings of the present invention, they are only used to distinguish different objects, rather than to describe a specific order. The terms "include" and "comprise" used in the description and claims of the present invention indicate the presence of the described features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their collections.

[0026] The embodiment of the present invention discloses a positioning data cleaning method based on data analysis, referring to Figure 1 , comprising steps S1 to S3: S1: Obtaining the location data of the vehicle at each time, wherein the location data includes longitude and latitude values.

[0027] GPS is used to collect the vehicle's location data at each moment, that is, the longitude and latitude coordinate data. Since the longitude data and latitude data corresponding to each moment need to be analyzed separately later, it is necessary to use an analog-to-digital conversion device to convert the longitude data and latitude data to obtain the longitude and latitude values ​​at each moment.

[0028] Implementers can set the collection frequency according to specific implementation conditions, for example, 1s / time.

[0029] S2: Using saliency detection The algorithm detects the position data at each moment and obtains the significance value at each moment.

[0030] It should be noted that the core purpose of the present invention is to use the saliency detection CA algorithm to detect invalid data in the location data, and then interpolate all invalid data. During the process, when calculating the saliency value of the location data at each moment at a scale, only the reference data segments with higher authenticity are retained. This is mainly based on the analysis of the numerical characteristics of the location data at each moment, thereby achieving the cleaning of the location data.

[0031] In using significant During the detection process of the algorithm, according to the preset length of each scale (for example, the first scale is 21 data points, the upper limit of the extended scale is 5, and 5 data points are selected on both sides of the previous scale for extension each time), the position data is equally divided into multiple data segments at each scale (wherein, the data segment to which the target moment belongs is based on the target moment as the center point of the data segment. If the data points on either side are insufficient when the target moment is used as the center point, the data points on the other side can be used to make up for it), the preliminary significance value of the target moment at each time in each scale is calculated, and the significance value of the target moment is determined according to the preliminary significance value; refer to Figure 2, step S2 includes steps S201 to S206, which are specifically as follows: S201: Under a target scale in each scale, determine a special longitude and latitude value at the target time according to the longitude and latitude values ​​at the target time and at several previous times, and extreme values ​​of the longitude and latitude values ​​at each time.

[0032] Implementers can set the number of previous moments according to specific implementation situations, for example, 50. If the position data before the sampling moment is less than 50, the first 1-minute position data collected will be used as a reference for analysis and will not be used for actual significance value calculation.

[0033] It should be noted that in this step, the longitude and latitude values ​​at each moment will be analyzed to obtain the special longitude and latitude values ​​at each moment; the quantification of this indicator is for the subsequent analysis of the authenticity of the location data. Since the invalid data in the location data is usually special and extreme in terms of numerical expression, when analyzing this indicator, the smaller the difference between the longitude and latitude values ​​corresponding to each moment and the maximum / minimum value of the longitude and latitude values ​​in the historical location data of the vehicle (at each moment), the more special the longitude and latitude data at the target moment is, and the larger its special value is. However, since there may be an extreme longitude that the vehicle has traveled to in the recent period of time that it has never reached before, it is necessary to analyze the difference between the longitude and latitude values ​​at the target moment and the longitude and latitude values ​​at the adjacent moments. The larger the difference, the more special the longitude and latitude values ​​at the target moment are.

[0034] Specifically, the longitude and latitude special values ​​satisfy: , ; In the formula, and Separately for the moment The longitude special value and latitude special value of and Separately for the moment The longitude and latitude values ​​of and Separately for the moment The average of the longitude and latitude values ​​at several previous moments, and are the maximum values ​​of longitude and latitude at each moment, and are the minimum values ​​of longitude and latitude at each moment, is a hyperparameter, is the standard normalization function, is the minimum function, is the absolute value symbol.

[0035] Implementers can set hyperparameters according to specific implementation conditions, for example, 0.000001. The existence of hyperparameters is to prevent When the value of is 0, the calculation result is meaningless.

[0036] in, Quantify the moment The difference between the longitude value of the vehicle and the extreme value of the longitude value of the vehicle's historical position data (at each time). The smaller the value, the smaller the difference, which can indicate the time The more special the longitude value of , the greater its special value; Indicates the time The longitude value of the time is the difference between the longitude values ​​of the time before it. The larger the value, the faster the time. The more special the longitude value of a is in the longitude values ​​of several previous moments, the more it can also explain the moment The smaller the difference between the longitude value of the vehicle and the extreme value of the longitude value in the historical location data, the greater the credibility. The larger the longitude special value, the larger it will be; the same is true for the latitude special value, which will not be repeated here.

[0037] S202: Determine the authenticity of the longitude and longitude at the target time according to the special longitude and longitude values ​​and the longitude and longitude values ​​at the target time and several previous times.

[0038] It should be noted that in this step, the change characteristics of the position data at each moment and the position data at several previous moments will be analyzed, and the special values ​​of the position data at each moment will be optimized to obtain the authenticity of the position data at each moment; in specific analysis, if the longitude and latitude values ​​at the target moment contribute more to the variance of the longitude and latitude values ​​at several previous moments, it can be explained that the possibility that it is interfered with and belongs to invalid data is greater, and the authenticity is lower; however, during vehicle driving, there will be regular behaviors such as turning and U-turning. If such behaviors exist at the target moment, the longitude and latitude values ​​at the target moment will contribute more to the variance value of the longitude and latitude values ​​at several previous moments. Therefore, in this step, it is necessary to continue to analyze the difference in the position data between the target moment and its immediately previous moment. If the difference between the longitude and latitude values ​​between the target moment and its immediately previous moment is greater, it can be explained that the longitude and latitude values ​​at the target moment are interfered with and belong to invalid data. The possibility is greater, and the authenticity is lower.

[0039] Specifically, the latitude and longitude authenticity respectively satisfy: , ; In the formula, and Separately for the moment The true longitude and latitude of and Separately for the moment The longitude special value and latitude special value of and Separately for the moment The variance of the longitude and latitude values ​​at several previous moments, and Separately for the moment The variance of the longitude and latitude values ​​at several previous moments, and Separately for the moment The difference in longitude and latitude from the previous moment.

[0040] in, The bigger, the more time The larger the special value of longitude is, the greater the possibility that it is interfered with and is invalid data, and the lower its authenticity will be; Indicates the time The contribution of the longitude value to the variance of the longitude value at several previous moments. The larger the value, the greater the contribution, which can explain the moment The greater the possibility that the longitude value is interfered with and is invalid data, the lower the authenticity of the corresponding longitude; The bigger it is, the more it can describe the moment The more the longitude value contributes to the variance between it and the longitude values ​​at several previous moments, the greater the credibility is, which means that the longitude value at the target moment is more likely to be interfered with and is invalid data, and the lower its longitude authenticity is; the same is true for latitude authenticity, which will not be elaborated here.

[0041] S203: taking the sum of the longitude and latitude authenticity as the position authenticity at the target moment; for a data segment that does not include the target moment, taking the sum of the position authenticity at each moment in the data segment as the authenticity of the data segment.

[0042] It should be noted that after obtaining the location authenticity at the target moment, in this step, the authenticity of all data segments when calculating the significance value of the location data at a scale is calculated based on the indicator.

[0043] S204: Determine a reference data segment according to the degree of authenticity.

[0044] It should be noted that after obtaining the authenticity of the data segments, it is necessary to eliminate the data segments with lower authenticity and only retain the data segments with higher authenticity as reference data segments.

[0045] Specifically, determining the reference data segment according to the degree of authenticity includes: In response to the authenticity being greater than a preset reference threshold, it indicates that the possibility that the data in the data segment is interfered with is smaller, the authenticity of the data segment is higher, and the data segment is identified as a reference data segment.

[0046] Implementers can set the reference threshold according to specific implementation circumstances, for example, 0.6.

[0047] S205: Record the average of the sum of the longitude special value and the latitude special value at each time in each data segment as the characteristic value of each data segment; determine the preliminary significance value of the target moment at the target scale according to the characteristic values ​​of the data segment to which the target moment belongs and the reference data segment.

[0048] It should be noted that after the data segments are screened, reference data segments with a high degree of authenticity are obtained. Subsequently, based on the reference data segments, a preliminary significance value of the position data at a scale at the target moment is calculated.

[0049] Specifically, the preliminary significance value satisfies: ; In the formula, For the moment In scale The initial significance value of For the moment In scale The number of reference data segments under For the moment In scale The characteristic value of the data segment below, For the moment In scale The next The characteristic value of the reference data segment, is the absolute value symbol.

[0050] S206: Determine the saliency value of the target moment according to the preliminary saliency value.

[0051] It should be noted that, through the above analysis steps, the preliminary significance value of the target moment location data at one scale is obtained. Now, the optimized significance detection CA algorithm is used to detect the target moment location data, and the average of the preliminary significance values ​​of the target moment location data at all scales is calculated as its significance value.

[0052] Specifically, the significance value satisfies: ; In the formula, For the moment The significance value of is the number of scales, For the moment In scale The initial significance value of .

[0053] S3: performing interpolation processing according to the magnitude of the significance value, and completing positioning data cleaning based on data analysis.

[0054] Specifically, the interpolation processing according to the magnitude of the significance value includes: In response to the significance value being greater than a preset invalid threshold, determining that the position data at the target time is invalid data, otherwise, the position data at the target time is true data; For all invalid data, multiple real data immediately before the invalid data are selected, and the average of the longitude and latitude values ​​of the multiple real data is assigned to the invalid data to complete the interpolation process.

[0055] The implementer may set the invalid threshold and the number of real data immediately preceding the invalid threshold according to the specific implementation situation. For example, the invalid threshold is 0.75 and the number of real data is 5.

[0056] An embodiment of the present invention further discloses a positioning data cleaning system based on data analysis, including a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a positioning data cleaning method based on data analysis according to the present invention is implemented.

[0057] The above system also includes other components well known to those skilled in the art, such as a communication bus and a communication interface, and their configuration and functions are known in the art, so they will not be described in detail here.

[0058] In the present invention, the aforementioned memory may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium may be any suitable magnetic storage medium or magneto-optical storage medium, or any other medium that can be used to store the required information and can be accessed by an application, module, or both. Any such computer storage medium may be part of the device or accessible or connectable to the device.

[0059] Although this specification has shown and described a number of embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will conceive of many modifications, changes and alternatives without departing from the ideas and spirit of the present invention. It should be understood that in the practice of the present invention, various alternatives to the embodiments of the present invention described herein may be employed.

[0060] The above are all preferred embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Therefore, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A positioning data cleaning method based on data analysis, characterized in that: include: Obtaining the location data of the vehicle at each time, wherein the location data includes longitude and latitude values; Using saliency detection The algorithm detects the location data at each moment to obtain the significance value at each moment; interpolation processing is performed according to the magnitude of the significance value to complete the positioning data cleaning based on data analysis; In using significant During the detection process of the algorithm, the position data is equally divided into multiple data segments at each scale according to the length of each preset scale, and the preliminary significance value of the target moment at each time is calculated. According to the preliminary significance value, the significance value of the target moment is determined, including: At the target scale in each scale, the longitude and latitude special values ​​of the target time are determined according to the longitude and latitude values ​​of the target time and several previous times, and the extreme values ​​of the longitude and latitude values ​​at each time; the longitude and latitude authenticity of the target time is determined according to the longitude and latitude special values, and the longitude and latitude values ​​of the target time and several previous times; the sum of the longitude and latitude authenticity is used as the position authenticity of the target time; for a data segment that does not include the target time, the sum of the position authenticity of each time in the data segment is used as the authenticity of the data segment; according to the size of the authenticity, a reference data segment is determined; the average of the sum of the longitude special value and the latitude special value at each time in each data segment is recorded as the characteristic value of each data segment; According to the characteristic values ​​of the data segment to which the target moment belongs and the reference data segment, the preliminary significance value of the target moment at the target scale is determined.

2. A positioning data cleaning method based on data analysis according to claim 1, characterized in that: The special values ​​of longitude and latitude satisfy: ， ； In the formula, and Separately for the moment The longitude special value and latitude special value of and Separately for the moment The longitude and latitude values ​​of and Separately for the moment The average of the longitude and latitude values ​​at several previous moments, and are the maximum values ​​of longitude and latitude at each moment, and are the minimum values ​​of longitude and latitude at each moment, is a hyperparameter, is the standard normalization function, is the minimum function, is the absolute value symbol.

3. The method for cleaning positioning data based on data analysis according to claim 1, characterized in that: The longitude and latitude truths satisfy: ， ； In the formula, and Separately for the moment The true longitude and latitude of and Separately for the moment The longitude special value and latitude special value of and Separately for the moment The variance of the longitude and latitude values ​​at several previous moments, and Separately for the moment The variance of the longitude and latitude values ​​at several previous moments, and Separately for the moment The difference in longitude and latitude from the previous moment.

4. The method for cleaning positioning data based on data analysis according to claim 1, characterized in that: The step of determining the reference data segment according to the degree of authenticity includes: In response to the authenticity being greater than a preset reference threshold, the data segment is identified as a reference data segment.

5. The method for cleaning positioning data based on data analysis according to claim 1, characterized in that: The preliminary significance value satisfies: ； In the formula, For the moment In scale The initial significance value of For the moment In scale The number of reference data segments under For the moment In scale The characteristic value of the data segment below, For the moment In scale The next The characteristic value of the reference data segment, is the absolute value symbol.

6. The method for cleaning positioning data based on data analysis according to claim 1, characterized in that: The significance value satisfies: ； In the formula, For the moment The significance value of is the number of scales, For the moment In scale The initial significance value of .

7. The method for cleaning positioning data based on data analysis according to claim 1, characterized in that: The interpolation processing is performed according to the magnitude of the significance value, comprising: In response to the significance value being greater than a preset invalid threshold, determining that the position data at the target time is invalid data, otherwise, the position data at the target time is true data; For all invalid data, multiple real data immediately before the invalid data are selected, and the average of the longitude and latitude values ​​of the multiple real data is assigned to the invalid data to complete the interpolation process.

8. A positioning data cleaning system based on data analysis, characterized in that: include: A processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a positioning data cleaning method based on data analysis according to any one of claims 1 to 7 is implemented.

Images