Surveying instrument equipment quality monitoring method and system suitable for land reclamation

Abstract

The invention discloses a surveying instrument equipment quality monitoring method and system suitable for land reclamation, and relates to the technical field of data quality monitoring, and the method comprises the steps: setting a reference value, calculating an error residual error, respectively calculating a skewness value and a kurtosis value through employing a statistic method, and carrying out the normal distribution classification of surveying and mapping data; and constructing a Levy log-likelihood objective function and calculating a Levy error value based on non-compliance normal distribution, constructing a natural log-likelihood objective function and calculating a natural error value based on compliance normal distribution, and setting a monitoring threshold value to carry out quality monitoring on the surveying and mapping data. According to the method, the statistical magnitude and the Jarque-Bra test are introduced, the log-likelihood functions are respectively constructed according to the distribution types, the adaptability and the stability of the surveying and mapping equipment in a complex operation scene are improved, and the precision and the adaptability of quality monitoring are improved through automatic analysis and fault judgment of the error set.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data quality monitoring, and in particular to a surveying and mapping instrument equipment quality monitoring method and system suitable for land consolidation. BACKGROUND

[0002] With the continuous advancement of land consolidation projects in rural infrastructure construction and farmland protection in China, the role of surveying and mapping technology in high-precision topographic data acquisition, plot boundary confirmation and construction process supervision is increasingly prominent. To adapt to the surveying and mapping needs in complex terrain and multiple scenarios, various high-precision surveying and mapping equipment such as GNSS receivers, inertial measurement units, and laser radar sensors have been widely used. These devices can record spatial position, attitude, distance and other data at different frequencies and multiple dimensions, improving surveying and mapping efficiency and accuracy. As surveying and mapping operations gradually move towards automation and intelligentization, the stability, accuracy retention and data consistency of equipment performance are increasingly concerned.

[0003] The current surveying and mapping instrument quality monitoring technology still has deficiencies. The existing method generally assumes that the surveying and mapping error obeys normal distribution, and does not fully consider the non-normal error characteristics such as skewness, peak and long tail phenomenon generated by multi-source heterogeneous sensors in non-ideal environment, thereby leading to inaccurate error monitoring and difficult timely discovery of equipment hidden dangers. SUMMARY

[0004] In view of the above existing problems, the present application is proposed.

[0005] Therefore, the present application provides a surveying and mapping instrument equipment quality monitoring method and system suitable for land consolidation, which solves the problem that the existing method generally assumes that the surveying and mapping error obeys normal distribution, and does not fully consider the non-normal error characteristics such as skewness, peak and long tail phenomenon generated by multi-source heterogeneous sensors in non-ideal environment, thereby leading to inaccurate error monitoring and difficult timely discovery of equipment hidden dangers.

[0006] To solve the above technical problems, the present application provides the following technical solutions: In a first aspect, the present application provides a surveying and mapping instrument equipment quality monitoring method suitable for land consolidation, which includes the following steps: Collecting surveying and mapping data using intelligent sensors, preprocessing the collected surveying and mapping data, setting a reference value, calculating error residuals, calculating skewness and kurtosis values using statistical methods, and classifying the surveying and mapping data according to normal distribution using statistical distribution method; Based on not obeying normal distribution, constructing a Levy logarithmic likelihood objective function to calculate Levy error values, based on obeying normal distribution, constructing a natural logarithmic likelihood objective function to calculate natural error values, and setting a monitoring threshold to monitor the quality of the surveying and mapping data.

[0007] As a preferred scheme of the land consolidation surveying instrument equipment quality monitoring method, the method comprises the following steps: The surveying data of the surveying instrument equipment is collected by using the intelligent sensor and preprocessed. The intelligent sensor comprises a GNSS, an inertial measurement unit, and a laser radar sensor. The surveying data comprises spatial coordinates, angular velocities, accelerations, and point cloud distance data.

[0008] As a preferred scheme of the land consolidation surveying instrument equipment quality monitoring method, the method comprises the following steps: The surveying data is time-synchronized by using a time network protocol, abnormal data is identified and deleted by using an IQR method, missing data is filled by using a linear interpolation method, the surveying data after filling is denoised by using a Gaussian filter, and the surveying data after denoising is normalized.

[0009] As a preferred scheme of the land consolidation surveying instrument equipment quality monitoring method, the method comprises the following steps: The mean value of the surveying data is calculated, a reference value is set, the error residual is calculated by using a difference method, and the skewness value and the kurtosis value are calculated by using a statistical quantity method. The test statistic is calculated by using a Jarque-Bera test, the significance level is calculated by using a cumulative distribution function, the judgment threshold is set by using a statistical distribution method, the significance level is compared with the judgment threshold, if the significance level is less than the judgment threshold, the null hypothesis is rejected, it is determined that the surveying data does not obey the normal distribution, otherwise, the null hypothesis is accepted, and it is determined that the surveying data obeys the normal distribution.

[0010] As a preferred scheme of the land consolidation surveying instrument equipment quality monitoring method, the method comprises the following steps: Based on the non-normal distribution, the position parameter is set by using a minimum value offset method, the Levy logarithmic likelihood objective function is constructed, the numerical solution is obtained by using a Newton method, the final parameter value is obtained, the final scale parameter and the position parameter are included, the Levy error model is constructed, and the Levy error value is calculated.

[0011] As a preferred scheme of the land consolidation surveying instrument equipment quality monitoring method, the method comprises the following steps: Based on obeying normal distribution, the maximum likelihood estimation method is used to estimate the parameters of normal distribution, a natural logarithm likelihood objective function is constructed, a gradient descent is used for numerical solution, final normal distribution parameters are obtained, a natural error model is constructed, and a natural error value is calculated.

[0012] As a preferred scheme of the land consolidation surveying and mapping instrument equipment quality monitoring method, the monitoring threshold is set to monitor the quality of the surveying and mapping data, which comprises: The Levy error value and the natural error value are combined to generate an error set The monitoring threshold is set using the empirical rule, the Levy error value and the natural error value of the error set are compared with the monitoring threshold, the Levy error value and the natural error value greater than the monitoring threshold are determined as faults, and the quality inspector is notified to repair, otherwise, it is determined as normal and the monitoring continues.

[0013] In a second aspect, the application provides a land consolidation surveying and mapping instrument equipment quality monitoring system, comprising: The collection and classification module is used to collect surveying and mapping data using intelligent sensors, pre-process the collected surveying and mapping data, set a reference value, calculate error residuals, calculate skewness and kurtosis values using statistical methods, and set a judgment threshold using statistical distribution method to classify the surveying and mapping data according to normal distribution. The target monitoring module is used to construct a Levy log-likelihood objective function based on not obeying normal distribution, calculate a Levy error value, construct a natural log-likelihood objective function based on obeying normal distribution, calculate a natural error value, and set a monitoring threshold to monitor the quality of the surveying and mapping data.

[0014] In a third aspect, the application provides a computer device comprising a memory and a processor, the memory storing a computer program, wherein the computer program is executed by the processor to implement any step of the land consolidation surveying and mapping instrument equipment quality monitoring method according to the first aspect of the application.

[0015] In a fourth aspect, the application provides a computer readable storage medium storing a computer program, wherein the computer program is executed by the processor to implement any step of the land consolidation surveying and mapping instrument equipment quality monitoring method according to the first aspect of the application.

[0016] The application has the following advantages: the application introduces statistical quantities and Jarque-Bera test, constructs log-likelihood functions according to distribution types, improves the adaptability and stability of surveying and mapping equipment in complex operation scenarios, and improves the accuracy and adaptability of quality monitoring through automatic analysis and fault determination of the error set. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort on the basis of these drawings.

[0018] Figure 1 Flow chart of the method for monitoring the quality of surveying and mapping equipment suitable for land consolidation in embodiment 1.

[0019] Figure 2 Schematic diagram of the system for monitoring the quality of surveying and mapping equipment suitable for land consolidation in embodiment 1. DETAILED DESCRIPTION

[0020] In order to make the above-mentioned purposes, features and advantages of the present application more apparent and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the drawings.

[0021] In the following description, many specific details are set forth in order to provide a thorough understanding of the present application. However, the present application can be practiced in other manners different from those described herein, and those skilled in the art can make similar extensions without departing from the scope of the present application. Therefore, the present application is not limited to the specific embodiments disclosed below.

[0022] Secondly, the "one embodiment" or "embodiment" referred to herein means that the specific features, structures or characteristics can be included in at least one implementation of the present application. "In one embodiment" appearing in different places in the specification does not mean the same embodiment, nor is it an independent or alternative embodiment that excludes other embodiments.

[0023] Embodiment 1, refer to Figure 1 , the first embodiment of the present application, the embodiment provides a kind of surveying and mapping equipment quality monitoring method suitable for land consolidation, comprising the following steps: S1, collect surveying and mapping data and pre-process, set reference value, calculate error residual, calculate skewness and kurtosis using statistical method respectively, set judgment threshold using statistical distribution method, and classify the normal distribution of surveying and mapping data; Specifically, the surveying and mapping data is collected using an intelligent sensor, and the collected surveying and mapping data is pre-processed, including: The surveying and mapping data of the surveying and mapping equipment is collected using an intelligent sensor and pre-processed; The intelligent sensor includes GNSS, an inertial measurement unit and a laser radar sensor; The surveying and mapping data includes spatial coordinates, angular velocity, acceleration and point cloud distance data; The preprocessing includes time synchronization of surveying data using time network protocol, identification and deletion of abnormal data using IQR method, filling of missing data using linear interpolation method, denoising of the filled surveying data using Gaussian filter, and normalization of the denoised surveying data.

[0024] The time network protocol is used to unify the time stamp of multi-source data, solve the problem of internal clock difference of different devices, ensure the comparability of data in the same space-time framework, use the IQR method to identify and eliminate outliers, avoid distortion of extreme values on subsequent mean, variance and other statistical quantities, use linear interpolation method to fill the data during sensor signal loss or interruption, restore continuity, use Gaussian filter to smooth the completed data, effectively suppress random noise introduced by sensor high-frequency jitter, unify the dimension and scale of various data, and enhance the comparability and fusion of multi-index.

[0025] Further, a statistical distribution method is used to set a judgment threshold to classify the surveying data into a normal distribution, including: The mean of the surveying data is calculated, a reference value is set, and the error residual is calculated using the difference method, with the formula being: , Wherein is the error residual at time t, is the actual measurement value of the device at time t, is the reference value at time t; Based on the error residual, the skewness and kurtosis are calculated using the statistical method, with the formula being: , , Wherein and are the skewness and kurtosis of the error residual, is the i-th residual value, indicating the error residual, and n is the number of surveying data, is the mean of the error residual, is the standard deviation of the error residual; The Jarque-Bera test is used to calculate the test statistic, with the formula being: , Wherein JB is the test statistic; The cumulative distribution function is used to calculate the significance level, with the formula being: , Wherein P is the significance level, is the cumulative distribution function of the chi-square distribution with 2 degrees of freedom; The judgment threshold is set using a statistical distribution method, a significance level is compared with the judgment threshold, if the significance level is less than the judgment threshold, the null hypothesis is rejected, it is determined that the surveying and mapping data does not obey a normal distribution, otherwise the null hypothesis is accepted, and it is determined that the surveying and mapping data obeys a normal distribution.

[0026] Through the double mechanism of statistical quantity and normality test, the distribution attribution recognition of equipment error is realized, a more reasonable error modeling method can be selected accordingly, compared with the traditional method based on standard deviation, the abnormal characteristics of asymmetric distribution and long-tail distribution can be recognized, and the method is more suitable for equipment quality monitoring in complex terrain or dynamic construction scene.

[0027] S2, based on not obeying a normal distribution, a Levy logarithmic likelihood objective function is constructed, a Levy error value is calculated, based on obeying a normal distribution, a natural logarithmic likelihood objective function is constructed, a natural error value is calculated, and a monitoring threshold is set to monitor the quality of the surveying and mapping data; Specifically, based on not obeying a normal distribution, a Levy logarithmic likelihood objective function is constructed, and a Levy error value is calculated, including: Based on not obeying a normal distribution, a position parameter is set using a minimum value offset method, a Levy logarithmic likelihood objective function is constructed, a Newton method is used for numerical solution, and final parameter values including a final scale parameter and a final position parameter are obtained, and the formula is: , Wherein is a Levy logarithmic likelihood function, is a scale parameter, and Q is a position parameter; A Levy error model is constructed, and a Levy error value is calculated, and the formula is: , Wherein is a Levy error value, and are final parameter values including a final scale parameter and a final position parameter.

[0028] Through the "minimum value offset" method, that is, the minimum error of the sample is translated as a reference value, the robustness and adaptability to extreme values of the model can be enhanced, the Newton method is used to solve the extreme value of the above nonlinear logarithmic likelihood function, and accurate distribution parameters can be obtained. Levy distribution is suitable for sharp peak long tail error modeling, and makes up for the shortcomings of traditional normal model in extreme error scene.

[0029] Further, based on obeying a normal distribution, a natural logarithmic likelihood objective function is constructed, a natural error value is calculated, including: Based on the normal distribution, the maximum likelihood estimation method is used to estimate the parameters of the normal distribution, the natural logarithm likelihood objective function is constructed, the gradient descent is used for numerical solution, and the final normal distribution parameters are obtained, the formula is: , Among them is the natural logarithm likelihood function, and are the parameters of the normal distribution, representing the variance and mean of the error residual; The natural error model is constructed, and the natural error value is calculated, the formula is: , Among them is the natural error value, and are the final normal distribution parameters, representing the variance and mean of the final error residual.

[0030] Through the position-scale double parameter modeling, the joint effect of sudden measurement deviation and system drift can be quantitatively evaluated, the Newton method is adopted, which converges quickly and has high precision, is suitable for real-time operation in embedded surveying and mapping equipment, the likelihood function based on normal distribution has analytical solution and smooth gradient, which is suitable for efficient estimation, and the error value is simple, which is suitable for fast hardware deployment.

[0031] Further, the monitoring threshold is set to monitor the quality of the surveying and mapping data, including: The Levy error value and the natural error value are combined to generate an error set The monitoring threshold is set using the empirical rule, the Levy error value and the natural error value of the error set are compared with the monitoring threshold, the Levy error value and the natural error value greater than the monitoring threshold are judged as faults, and the quality inspector is notified to repair, otherwise it is judged as normal and continues to be monitored.

[0032] The error value and the threshold value comparison does not require complex operation, which is suitable for edge computing scene, the double error model improves the coverage of abnormal identification, supports customizing threshold strategy according to different construction areas and equipment types, compared with the traditional single error model, the scheme considers the precision and sensitivity, and effectively avoids the "equipment misjudgment normal" or "normal misjudgment fault".

[0033] Embodiment 2, refer to Figure 2 The second embodiment of the application is a surveying and mapping instrument equipment quality monitoring system suitable for land consolidation, comprising: The collection and classification module is used for collecting surveying and mapping data using intelligent sensors, preprocessing the collected surveying and mapping data, setting a reference value, calculating error residuals, calculating skewness and kurtosis values using statistical methods, setting a judgment threshold using statistical distribution method, and classifying the surveying and mapping data according to normal distribution; The target monitoring module is configured to construct a Levy logarithmic likelihood target function based on non-compliance with a normal distribution, calculate a Levy error value, construct a natural logarithmic likelihood target function based on compliance with a normal distribution, calculate a natural error value, and set a monitoring threshold to monitor the quality of the surveying and mapping data.

[0034] The embodiment also provides a computer device suitable for the land consolidation surveying and mapping instrument quality monitoring method, which comprises a memory and a processor.

[0035] The computer device can be a terminal, and the computer device comprises a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is configured to perform wired or wireless communication with external terminals. The wireless communication can be achieved through WIFI, an operator network, NFC (Near Field Communication) or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer overlaid on the display screen, or a key, trackball or touchpad arranged on the shell of the computer device, or an external keyboard, touchpad or mouse.

[0036] The embodiment also provides a storage medium having a computer program stored thereon, and the program is executed by a processor to implement the land consolidation surveying and mapping instrument quality monitoring method. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as a static random access memory (SRAM), an electrically erasable programmable read-only memory (EEPROM), an erasable programmable read-only memory (EPROM), a programmable read-only memory (PROM), a read-only memory (ROM), a magnetic storage device, a flash memory, a magnetic disk or an optical disk.

[0037] It should be noted that the above examples are only used to illustrate the technical solutions of the present application but not to limit the present application. Although the present application is described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalently replaced, without departing from the spirit and scope of the technical solutions of the present application, which should be covered in the scope of the claims of the present application.

Claims

1. A method for monitoring the quality of surveying and mapping equipment suitable for land consolidation, characterized by: The steps include: Use smart sensors to collect surveying and mapping data, pre-process the collected surveying and mapping data, set benchmark values, calculate error residuals, use statistical methods to calculate skewness and kurtosis values, use statistical distribution methods to set judgment thresholds, and classify surveying and mapping data into normal distribution; Based on the non-normal distribution, the Levy log-likelihood objective function is constructed and the Levy error value is calculated. Based on the normal distribution, the natural log-likelihood objective function is constructed and the natural error value is calculated. The monitoring threshold is set to monitor the quality of the surveying and mapping data.

2. The method for monitoring the quality of surveying and mapping equipment suitable for land consolidation according to claim 1, characterized in that: The use of smart sensors to collect surveying and mapping data includes: Use smart sensors to collect and pre-process surveying data from surveying equipment; The smart sensors include GNSS, inertial measurement units and lidar sensors; The surveying and mapping data includes spatial coordinates, angular velocity, acceleration and point cloud distance data.

3. The method for monitoring the quality of a surveying and mapping instrument for land reclamation according to claim 2, wherein: The pre-processing of the collected surveying and mapping data includes: The surveying and mapping data were synchronized using the time network protocol, abnormal data were identified and deleted using the IQR method, missing data were filled using the linear interpolation method, the surveying and mapping data after filling were denoised using the Gaussian filter, and the denoised surveying and mapping data were normalized.

4. The method for monitoring the quality of a surveying and mapping instrument for land reclamation according to claim 3, wherein: The method of using the statistical distribution method to set the judgment threshold and classify the surveying and mapping data into normal distribution includes: Calculate the mean of the surveying and mapping data, set the benchmark value, use the difference method to calculate the error residual, and use the statistical method to calculate the skewness value and kurtosis value respectively; The Jarque–Bera test was used to calculate the test statistic, the cumulative distribution function was used to calculate the significance level, the statistical distribution method was used to set the judgment threshold, and the significance level was compared with the judgment threshold. If the significance level was less than the judgment threshold, the null hypothesis was rejected and it was determined that the surveying and mapping data did not obey the normal distribution. Otherwise, the null hypothesis was accepted and it was determined that the surveying and mapping data obeyed the normal distribution.

5. The method for monitoring the quality of surveying and mapping equipment suitable for land reclamation according to claim 4, characterized in that: The method of constructing a Levy log-likelihood objective function based on the non-normal distribution and calculating the Levy error value includes: Based on the non-normal distribution, the minimum shift method is used to set the location parameters, the Levy log-likelihood objective function is constructed, and the Newton method is used for numerical solution to obtain the final parameter values, including the final scale parameter and location parameter. The Levy error model is constructed and the Levy error value is calculated.

6. The method for monitoring the quality of surveying and mapping equipment suitable for land reclamation according to claim 5, characterized in that: The method constructs a natural logarithm likelihood objective function based on the normal distribution and calculates the natural error value, including: Based on the normal distribution, the maximum likelihood estimation method is used to estimate the normal distribution parameters, the natural logarithm likelihood objective function is constructed, and the gradient descent is used for numerical solution to obtain the final normal distribution parameters. The natural error model is constructed and the natural error value is calculated.

7. The method for monitoring the quality of a surveying instrument for land consolidation according to claim 6, wherein: The setting of monitoring thresholds to monitor the quality of surveying and mapping data includes: Merge the Levy error value and the natural error value to generate an error set Use empirical rules to set the monitoring threshold, extract the Levy error value and natural error value of the error set and compare them with the monitoring threshold. If the Levy error value and natural error value are greater than the monitoring threshold, it will be judged as a fault and the quality inspector will be notified to perform repairs. Otherwise, it will be judged as normal and monitoring will continue.

8. A system for monitoring the quality of surveying and mapping equipment for land consolidation, for implementing the method according to any one of claims 1 to 7, characterized in that: include: The collection and classification module is used to collect surveying and mapping data using intelligent sensors, pre-process the collected surveying and mapping data, set benchmark values, calculate error residuals, calculate skewness and kurtosis values ​​using statistical methods, set judgment thresholds using statistical distribution methods, and classify the surveying and mapping data into normal distributions; The target monitoring module is used to construct the Levy log-likelihood objective function based on non-normal distribution and calculate the Levy error value, and to construct the natural log-likelihood objective function based on normal distribution and calculate the natural error value, and to set the monitoring threshold to monitor the quality of surveying and mapping data.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for monitoring the quality of surveying and mapping equipment applicable to land reclamation according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for monitoring the quality of surveying and mapping equipment applicable to land reclamation according to any one of claims 1 to 7 are implemented.

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