A high-precision laser tilt angle integrated measurement method and system

Abstract

This application discloses a high-precision laser tilt angle integrated measurement method and system, comprising the following steps: S1: periodically collecting measurement data, with the observation processing dataset storing historical measurement data; calculating the observation mean data and the mean error data; S2: if the mean error data is less than or equal to a preset mean error threshold, then the observation mean data is reported to the cloud platform; otherwise, more measurement data is collected; S3: using a gross error criterion to judge and filter the measurement data; S4: recalculating the mean error data; if the mean error data is still greater than the mean error threshold, using a preset trend change point detection method to analyze the changing trend of several of the measurement data; if a changing trend exists, more measurement data is collected and the process returns to step S3; S5: training a data filtering model, which receives instructions from the cloud platform to judge and filter the measurement data. This application has the effect of solving the displacement error caused by angle deviation and improving measurement accuracy.

Description

Technical Field

[0001] This application relates to the field of laser ranging technology, and in particular to a high-precision laser tilt angle integrated measurement method and system. Background Technology

[0002] Currently, laser rangefinders used in construction monitoring can be broadly categorized into handheld laser rangefinders and automated laser rangefinders. Handheld laser rangefinders offer higher measurement accuracy and are widely used in the construction industry, but they cannot automatically collect and report data. Automated laser rangefinders are primarily used in high-formwork monitoring systems. While they can automatically collect data, their accuracy still needs improvement.

[0003] Automated laser rangefinders suffer from poor measurement accuracy, thus leaving room for improvement. Summary of the Invention

[0004] To improve the measurement accuracy of automated laser rangefinders, this application provides a high-precision laser tilt angle integrated measurement method and system.

[0005] The first objective of this invention is achieved by the following technical solution:

[0006] A high-precision laser tilt angle integrated measurement method, the high-precision laser tilt angle integrated measurement method includes the following steps:

[0007] S1: Periodically collect measurement data and input the measurement data into a preset observation processing dataset, which stores historical measurement data; calculate the observation mean data and standard error data based on the historical measurement data;

[0008] S2: If the mean error data is not greater than the preset mean error threshold, the observed mean data is reported to the cloud platform; if the mean error data is greater than the mean error threshold, additional measurement data is collected.

[0009] S3: Use a preset gross error criterion to judge and filter the added measurement data. If the added measurement data is determined to be gross error data, delete the added measurement data from the observation processing dataset. If the measurement data in the observation processing dataset is less than the quantity threshold after deletion, continue to add measurement data.

[0010] S4: Recalculate the mean error data. If the mean error data is not greater than the mean error threshold, report the observed mean data to the cloud platform. If the mean error data is still greater than the mean error threshold, first use a preset trend change point detection method to analyze the changing trend of several measured data. If a changing trend exists, increase the collection of measured data and return to step S3. If no changing trend exists, then use a normality transformation method to transform the measured data.

[0011] S5: Based on a preset data filter, a data filtering model is trained using the observation processing dataset. The data filtering model receives instructions from the cloud platform to judge and filter the measurement data.

[0012] By adopting the above technical solution, this application proposes a data analysis filtering algorithm from the perspective of data error analysis, which improves the effectiveness of automated monitoring data and can determine the state of the monitored object, thus improving the measurement accuracy of automated laser rangefinders. Specifically, data errors are divided into systematic errors, gross errors, and random errors. Systematic errors originate from tool errors, adjustment errors, habitual errors, conditional errors, and methodological errors. Gross errors originate from the use of defective measuring instruments, sudden changes in indication caused by external vibrations, electromagnetic interference, etc. Random errors originate from the influence of the measuring device, the measuring environment, or the measuring personnel. The error analysis process is divided into the following steps: First, the observation processing dataset stores measurement data from three collections and a preset mean error threshold according to the requirements of the "Standard for Measurement of Building Deformation". If the mean error data is less than or equal to the preset mean error threshold, it meets the requirements of the "Standard for Measurement of Building Deformation". Next, the mean observation data and the mean error data are calculated. The mean error data is compared with the mean error threshold. If the mean error data is less than or equal to the preset mean error threshold, the mean observation data is reported to the cloud platform. If the mean error data is greater than the mean error threshold, additional measurement data is collected, and gross error criteria are used to delete gross error data. Delete invalid data, repeat the calculation of the mean square error data and compare them. If the mean square error data is still greater than the mean square error threshold, combine the trend change point detection method to analyze the changing trend of several measurement data; that is, identify the changing state of the measured object. At this time, there are two states: the first state is discrete, fluctuating within a certain range, then the normality transformation method is used to transform the measurement data so that the measurement data conforms to the normal distribution law; the second state has a clear changing trend, then more measurement data are collected and step S3 is repeated, and the data filter is calculated by using historical measurement data in the observation dataset to obtain the applicable measurement scenario. According to the filtering model, the measurement data collected in the three times are then judged and filtered, and outliers are processed. This application improves the filtering accuracy by adding data filters, so that the filtering scheme can adapt to more measurement data fluctuations. Furthermore, the model fusion method is used to process the measurement data in step S1 in three ways, and the collected measurement data is processed to form an observation processing dataset. The pre-trained data filter is trained to update the weight parameters of the data filter in real time, so that the data filter can better adapt to the data scenario and improve the filtering accuracy of the data filter. This is beneficial to improving the measurement accuracy of the automated laser rangefinder.

[0013] In a preferred embodiment of this application: the preset data filters include observation data filters and time series model filters, and step S5 includes:

[0014] S51: The observation data filter is constructed based on a recurrent neural network. The observation data filter is trained based on the historical measurement data and the added measurement data to obtain the observation data model of the observation data filter. Then, the observation data filter judges and filters the measurement data to obtain the final measurement data. If the measurement data is determined to be invalid data, the invalid data is deleted from the observation processing dataset.

[0015] S52: The time series model filter is trained based on the measurement data and the observation mean data obtained in step S2 to obtain a time series model; then the time series model filter judges and filters the measurement data. If the measurement data is determined to be invalid data, the invalid data is deleted from the observation processing dataset.

[0016] By adopting the above technical solution, in step S51, to further confirm whether the measurement data is close to the true value, an observation data filter is constructed based on a recurrent neural network (RNN) to obtain the final measurement data. Therefore, in order to build a suitable RNN network model, an observation data model is trained using the observation processing dataset. Then, the observation data model is used to judge and filter the three timed measurement data, and to continue or end the observation according to the indication of the observation data filter. In step S52, to further optimize the detection of abnormal measurement data and effectively avoid filtering out abnormal measurement structures and false alarms, a time series model filter is trained with the observation mean data in step S2 to obtain a time series model. The time series model is used to judge and filter the three timed measurement data, and to continue or end the observation according to the indication of the time series model filter. The fusion of the two filters, the observation data filter and the time series model filter, improves the filtering accuracy of the filter.

[0017] In a preferred embodiment, this application includes a stored number of data collections and a preset maximum data collection threshold. In step S4, the analysis of the changing trends of several measurement data points using a preset trend abrupt change point detection method specifically involves:

[0018] S41: If the trend change is less than the preset trend change threshold, then determine the current number of collections. If the number of collections is less than the maximum number of collections threshold, then increase the collection of measurement data and recalculate the observation mean data and the mean error data. If the number of collections is equal to the maximum number of collections threshold, then calculate the maximum and minimum measurement data in the current observation dataset and delete them. Then, recalculate the observation mean data and the mean error data.

[0019] S42: If the trend of change is greater than or equal to the preset trend change threshold, increase the frequency of collecting measurement data until the trend of change is less than the preset trend change threshold.

[0020] By adopting the above technical solution, if the trend of change is less than the preset trend change threshold, the measurement data state of the corresponding measured object is discrete, fluctuating within a certain range. In this case, a normality transformation method is needed to transform the discrete measurement data. If the trend of change is greater than or equal to the preset trend change threshold, the corresponding measured object exhibits a significant trend of change, requiring additional data collection and reversal step S3. Normalization transformation is a data transformation method for statistical modeling, enabling linear regression models to satisfy linearity, normality, independence, and variance without losing information. Using normalization transformation to transform error data is more conducive to fitting linear models and analyzing the correlation of characteristics among several measurement data points. If the recalculated mean error data still does not meet the requirements of the "Standard for Measurement of Building Deformation," the total number of historical measurement data in the current observation processing dataset is obtained and determined. For example, is the total number of historical measurement data in the current observation not less than four? If the requirements are not met, i.e., the mean error data is still greater than the mean error threshold, then measurement data collection continues until the total number of historical measurement data in the observation and processing dataset meets the requirements. That is, the measurement data does not meet the gross error judgment criteria and the total number meets the requirements. Then, the trend change point detection method is used to analyze the changing trends of several measurement data. The time series model sorts several measurement data according to time order to obtain the changing trends of the measurement data. The trend change point detection method is suitable for long-period trend testing and change point detection. It is used when continuous response variables do not meet the normal distribution. This method can be used to analyze time series with unstable central trends. Based on the rank of the data, it is suitable for analyzing continuous growth or decline trends. Therefore, the changing trends include growth trends and decline trends. The trend change point detection method corrects some abnormal measurement data of change points. It is suitable for filtering invalid data caused by random errors, which helps to improve the accuracy of measurement data of the laser tilt angle integrated measurement system.

[0021] In a preferred embodiment of this application: In step S3, the added measurement data is judged and filtered using a preset gross error criterion. If the added measurement data is determined to be gross error data, the added measurement data is deleted from the observation processing dataset. Specifically:

[0022] S31: Calculate the residual data of each measurement data to obtain several residual data, and then compare the several residual data to obtain gross error suspicion values;

[0023] S32: Recalculate the observed mean data and standard error data;

[0024] S33: Obtain the test coefficient based on the preset significance data. If the gross error suspected value is greater than or equal to the test system, delete the measurement data corresponding to the gross error suspected value and return to step S31; until the gross error suspected values ​​corresponding to several measurement data are all less than the test coefficient.

[0025] By adopting the above technical solution, the high-precision laser tilt measurement method of this application can be applied to laser ranging data or tilt measurement data. The same measurement method can be used to process the laser ranging data or tilt measurement data respectively. The gross error judgment criterion is used to judge the gross error of the measurement data and filter out the measurement data with errors that significantly exceed the expected conditions. If the measured laser ranging data or tilt measurement data meets the gross error judgment criterion, the currently collected measurement data will be filtered out. When the measurement data fluctuates within a certain range but has no upward or downward trend, it is unreliable to remove the gross error measurement data. The gross error judgment criterion is not only applicable to the case of a small amount of data, but also applicable to the case where the observed object is in an unstable state or has systematic errors, so as to facilitate the trend and fluctuation study of time series measurement data obtained from multiple observations.

[0026] The second objective of this application is achieved by the following technical solution:

[0027] A high-precision laser tilt angle integrated measurement system, based on the high-precision laser tilt angle integrated measurement method described above, includes: a power supply module, a main control module for storing and processing the measurement data, a laser measurement module for acquiring the measurement data, and a communication module for communicating between the high-precision laser tilt angle integrated measurement system and a cloud platform; the main control module processes the measurement data using the high-precision laser tilt angle integrated measurement method described above; the power supply module is sequentially coupled to the main control module, the laser measurement module, and the communication module; the first communication terminal of the main control module is communicatively connected to the laser measurement module, the second communication terminal of the main control module is communicatively connected to the first communication terminal of the communication module, and the second communication terminal of the communication module is communicatively connected to the cloud platform.

[0028] By adopting the above technical solution, the power supply module is used to supply power to the main control module, the laser measurement module, and the communication module; the main control module is equipped with an observation processing dataset to store measurement data, and has the function of receiving measurement data and performing calculations on the measurement data, such as comparing the measurement data with error thresholds, trend change thresholds, or filtering and deleting invalid data and gross error data; the laser measurement module is used to collect measurement data of the horizontal distance, slope distance, and angle changes of the measured object, and is used to transmit the measurement data to the main control module for processing. The main control module realizes data interaction and communication with the laser measurement module through the first communication terminal, and realizes communication interaction with the communication module through the second communication terminal. The main control module, in conjunction with the communication module, realizes the reporting of measurement data and the output of early warning signals with the cloud platform. At the same time, the cloud configuration parameters input on the system front-end page are transmitted to the cloud platform and the main control module through the communication module.

[0029] In a preferred embodiment of this application: the laser measurement module includes a laser ranging submodule and an tilt measurement submodule. The power supply terminal of the laser measurement module is coupled to the power supply module, and the communication terminal of the laser ranging submodule is coupled to the first communication terminal of the main control module. The power supply terminal of the tilt measurement submodule is coupled to the power supply module, and the communication terminal of the tilt measurement submodule is coupled to the first communication terminal of the main control module. The laser ranging submodule has a sleep mode and a normal working mode, and the tilt measurement submodule has a normal working mode. When the tilt measurement data collected by the tilt measurement submodule is less than a preset angle change threshold, the laser ranging submodule is in sleep mode. When the tilt measurement data collected by the tilt measurement submodule is greater than the preset angle change threshold, the tilt measurement submodule sends a wake-up message to the laser ranging submodule to switch to normal working mode.

[0030] By adopting the above technical solution, the deformation of the measured object is often accompanied by a change in the detection angle. The measurement system uses a laser ranging submodule to measure the horizontal and oblique distances of the displacement of the measured object via laser ranging. Furthermore, the measurement system uses an inclination measurement submodule to measure the angle changes of the measured object. The laser ranging submodule has two operating modes: a sleep mode and a normal operating mode. In the former, the laser ranging submodule performs timed acquisition of laser ranging data. The laser ranging submodule and the power supply module are connected via a short-circuit power supply connection. This short-circuit power supply connection includes two power supply states: one is that when the system reaches the data reporting time, the power supply module provides normal power to the main control module, laser ranging submodule, and communication module; after completing data acquisition and reporting, the power is turned off and the system enters sleep mode. The tilt measurement submodule is always in normal working condition to monitor tilt data in real time. When the tilt measurement data detected by the measurement end of the tilt measurement submodule exceeds a preset threshold, the tilt measurement submodule wakes up the power supply module through the communication terminal to collect and report measurement data from the main control module, laser ranging submodule, and communication module. After the work is completed, the power is turned off, and the main control module, laser ranging submodule, and communication module enter sleep mode again. The tilt measurement submodule and the power supply module have a long-term power supply link. The long-term power supply link means that the power supply module provides power to the tilt measurement submodule for long-term local measurement data monitoring. The measured data is monitored locally but not reported. When the tilt data exceeds the preset threshold, the measured data is reported through the communication terminal, and other modules are woken up to start working. Therefore, the measurement system of this application adopts a low-power design combined with the function of triggering the reporting of measurement data to the cloud platform, which effectively ensures the validity of the measurement data, can ensure the long-term operation of the measurement system, and the long-term monitoring of tilt measurement data is conducive to improving the measurement accuracy of the measurement system.

[0031] In a preferred embodiment of this application: the main control module includes a main control chip, and the laser measurement module includes a laser ranging chip and an inclination measurement chip; the general control terminal of the laser ranging chip is coupled to the first control terminal of the main control chip, the power supply terminal of the laser ranging chip is coupled to the power supply module, and the communication terminal of the laser ranging chip is coupled to the ranging communication terminal of the main control chip; the general control terminal of the inclination measurement chip is coupled to the second control terminal of the main control chip, the power supply terminal of the inclination measurement chip is coupled to the power supply module, and the communication terminal of the inclination measurement chip is coupled to the inclination communication terminal of the main control chip.

[0032] By adopting the above technical solution, the main control chip is used to store measurement data and process, judge, and filter the measurement data according to the high-precision laser tilt angle integrated measurement method described above. The laser ranging chip is powered by a power supply terminal and uses laser measurement to measure the horizontal and slant displacements of the object under deformation. The measurement data of the horizontal and slant displacements are transmitted to the ranging communication terminal of the main control chip through a communication terminal. The general control terminal of the tilt angle measurement chip wakes up the main control module to power on and start working when the tilt angle data exceeds a preset threshold. The main control module controls the main control module through multiple control terminals. The laser ranging chip is activated to begin operation; the tilt measurement chip uses laser measurement to measure the angle change of the deformed object and transmits the angle change measurement data to the main control chip via a communication terminal. The tilt measurement chip is also equipped with an angle change threshold. When the collected angle measurement data exceeds the threshold, an alarm signal is triggered and uploaded to the cloud platform. The tilt communication terminal of the main control chip receives the measurement data transmitted from the tilt measurement chip's communication terminal, communicates with the cloud platform to report data, and performs calculations based on the measurement data against error thresholds, angle change thresholds, and quantity thresholds.

[0033] In a preferred embodiment of this application: the communication module includes a communication chip, a SIM card socket submodule, and a prompting submodule. The power supply terminal of the communication chip is coupled to the communication power supply terminal of the power supply module. The SIM card control terminal of the communication chip is coupled to the control terminal of the SIM card socket submodule. The startup mode terminal of the communication chip is coupled to the battery operation mode terminal of the prompting submodule. The network indication terminal of the communication chip is coupled to the network indication terminal of the prompting submodule. The ground terminal of the communication chip is grounded. The prompting submodule is used to output an audible and visual prompt signal when the tilt angle measurement data exceeds a preset angle change threshold.

[0034] By adopting the above technical solution, the communication chip, combined with the SIM card slot submodule, realizes the function of communication connection with the cloud platform and the main control module. After the communication chip transmits the original measurement data to the cloud platform, it receives the instruction signals from the cloud platform to interact with the main control module. The prompt submodule is used to output an audible and visual prompt signal when the tilt angle measurement data exceeds the preset angle change threshold to prompt the user that the angle change of the measured object is obvious at this time.

[0035] The third objective of this invention is achieved by the following technical solution:

[0036] A high-precision laser tilt angle integrated measuring device includes a housing and an adsorption plate. The adsorption plate is fixedly installed in the housing. The high-precision laser tilt angle integrated measuring system described above is installed inside the housing. A measuring hole is provided on the side wall of the housing.

[0037] By adopting the above technical solution, the object being measured is generally a metal structure made of steel plates, such as steel sheet piles, high formwork equipment, and steel walers. The measuring device can be attached to the object being measured by an adsorption plate to achieve the purpose of quickly installing a high-precision laser tilt angle integrated measuring device. The housing protects the high-precision laser tilt angle integrated measuring system, effectively preventing it from being damaged by foreign objects during the measurement process. Furthermore, the laser measuring module in the high-precision laser tilt angle integrated measuring system performs laser measurement through the measuring hole, which is conducive to achieving the purpose of laser tilt angle integrated measurement.

[0038] The fourth objective of this invention is achieved by the following technical solution:

[0039] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the aforementioned high-precision laser tilt angle integrated measurement method.

[0040] In summary, this application includes at least one of the following beneficial technical effects:

[0041] 1. The data error analysis process is divided into the following steps: First, the observation processing dataset centrally stores the observation data from three collections and a preset mean error threshold according to the requirements of the "Standard for Measurement of Building Deformation". If the mean error data is less than or equal to the preset mean error threshold, it meets the requirements of the "Standard for Measurement of Building Deformation". Next, the mean observation data and mean error data are calculated. The mean error data is compared with the error threshold. If the mean error data is less than or equal to the preset mean error threshold, the mean observation data is reported to the cloud platform. If the mean error data is greater than the mean error threshold, additional measurement data is collected, and the gross error criterion and normality are used. The data is processed and analyzed using transformation and trend change detection methods, and outliers are handled. This application improves the filtering accuracy by adding filters, making the filtering scheme adaptable to more situations of measurement data fluctuations. Furthermore, a model fusion method is used to process the measurement data in step S1 in three ways, forming an observation processing dataset. The pre-trained data filter is then trained to update the weight parameters of the data filter in real time, making the data filter more adaptable to the data scenario and improving the filtering accuracy. This, in turn, helps to improve the measurement accuracy of the automated laser rangefinder.

[0042] 2. The laser ranging submodule and the power supply module are connected via a short-term power supply link. This short-term link includes two power supply states: First, when the system reaches the data reporting time, the power supply module provides normal power to the main control module, laser ranging submodule, and communication module. After completing data acquisition and reporting, the power is turned off and the system enters a sleep state. Second, when the tilt measurement submodule detects tilt data exceeding a preset threshold, it wakes up the power supply module to collect and report measurement data from the main control module, laser ranging submodule, and communication module. After completing the work, the power is turned off again and the system enters a sleep state. The tilt measurement submodule and the power supply module are also connected via a long-term power supply link. This long-term link means that the power supply module provides continuous power to the tilt measurement submodule for local measurement data listening. Locally, the measured data is not reported. When the tilt data exceeds a preset threshold, the measured data is reported, and other modules are woken up to power the submodule. Therefore, the measurement system of this application adopts an innovative low-power design combined with the function of triggering and reporting measurement data to the cloud platform, effectively ensuring the validity of the measurement data and guaranteeing long-term operation of the measurement system. Furthermore, the long-term listening for tilt measurement data is beneficial to the measurement accuracy of the system.

[0043] 3. The power supply module provides power to the main control module, laser measurement module, and communication module. The main control module is equipped with an observation processing dataset to store valid measurement data. It also has the functions of receiving measurement data and performing calculations on the data, such as comparing the measurement data with error thresholds and angle change thresholds, or filtering and deleting invalid and gross error data. The main control module interacts and communicates with the laser measurement module through the first communication terminal and with the communication module through the second communication terminal. The main control module, in conjunction with the communication module, reports measurement data and outputs early warning signals to the cloud platform. Simultaneously, it transmits cloud configuration parameters entered on the system's front-end page to the cloud platform and the main control module through the communication module. Attached Figure Description

[0044] Figure 1 This is a flowchart of a high-precision laser tilt angle integrated measurement method according to one embodiment of this application;

[0045] Figure 2 This is an overall structural diagram of a high-precision laser tilt angle integrated measurement system according to one embodiment of this application;

[0046] Figure 3 This is a circuit diagram of the power supply module in a high-precision laser tilt angle integrated measurement system according to an embodiment of this application;

[0047] Figure 4 This is a circuit diagram of the main control module in a high-precision laser tilt angle integrated measurement system according to one embodiment of this application.

[0048] Figure 5This is a circuit diagram of the laser measurement module in a high-precision laser tilt angle integrated measurement system according to one embodiment of this application.

[0049] Figure 6 This is a circuit diagram of the communication module in a high-precision laser tilt angle integrated measurement system according to one embodiment of this application.

[0050] Figure 7 This is a schematic diagram of the installation structure of a high-precision laser tilt angle integrated measuring device in one embodiment of this application.

[0051] Explanation of reference numerals in the attached figures:

[0052] 1. Power supply module; 11. Battery voltage regulator submodule; 12. Battery protection submodule; 13. Power management submodule; 14. Driver submodule; 2. Main control module; 21. Voltage conversion submodule; 22. Capacitor oscillation submodule; 23. Filtering submodule; 3. Laser measurement module; 31. Laser ranging submodule; 32. Tilt angle measurement submodule; 4. Communication module; 41. SIM card slot submodule; 42. Prompt submodule; 5. Adsorption plate; 6. Measurement hole. Detailed Implementation

[0053] The present application will be further described in detail below with reference to the accompanying drawings.

[0054] In one embodiment, such as Figure 1 As shown, this application discloses a high-precision laser tilt angle integrated measurement method, which specifically includes the following steps:

[0055] S1: Collect measurement data periodically and input the measurement data into a preset observation processing dataset. The observation processing dataset stores historical measurement data. Calculate the observation mean data and mean error data based on the historical measurement data.

[0056] In this embodiment, the automated measurement and value acquisition process involves measuring three or more times and taking the average value as the observation mean data. The mean error is a numerical standard for measuring the accuracy of observation, also known as the standard deviation or root mean square error.

[0057] Specifically, in engineering surveying of objects such as high-support formwork equipment and steel sheet piles, accuracy is evaluated using the mean error data S. Based on the principle of average value, the mean data is observed. With the mean error data S.

[0058]

[0059] Furthermore, data errors are categorized into systematic errors, gross errors, and random errors. Systematic errors originate from tool errors, adjustment errors, habitual errors, conditional errors, and methodological errors. Gross errors arise from the use of defective measuring instruments, sudden changes in indication caused by external vibrations or electromagnetic interference, etc. Random errors are caused by the influence of the measuring device, the measuring environment, or the measuring personnel. The data error analysis process involves: firstly, setting the observation processing dataset to centrally store measurement data from three acquisitions and a preset mean error threshold according to the requirements of the "Standard for Measurement of Building Deformation". If the mean error data S is less than or equal to the preset mean error threshold, it meets the requirements of the "Standard for Measurement of Building Deformation".

[0060] S2: If the mean error data is not greater than the preset mean error threshold, the observed mean data will be reported to the cloud platform; if the mean error data is greater than the mean error threshold, additional measurement data will be collected.

[0061] In this embodiment, when determining whether the mean error data S conforms to the "Standard for Measurement of Building Deformation", a mean error threshold is preset according to the standard requirements of the "Standard for Measurement of Building Deformation". Then, the set mean error threshold is compared with the calculated mean error data S.

[0062] Specifically, when the mean error data S is not greater than the mean error threshold, it meets the measurement requirements. Based on the assumption that random error (random error = true value - measured value) follows a normal distribution, the observed mean of multiple measurement data... This is the final measured value; when the mean error data S is greater than the mean error threshold, the number of measurements is increased.

[0063] Furthermore, when increasing the number of observations, the maximum number of data collections shall not exceed 9.

[0064] S3: Use a preset gross error criterion to judge and filter the added measurement data. If the added measurement data is judged to be gross error data, delete the added measurement data from the observation processing dataset. If the measurement data in the observation processing dataset is less than the quantity threshold after deletion, continue to add measurement data.

[0065] In this embodiment, the gross error judgment criterion is the Lomnaofski criterion, and the number threshold is 4.

[0066] Specifically, when the mean error data S is greater than the mean error threshold, the Lomnaofski criterion is first used to preprocess the measurement data to remove gross errors. However, during the removal of gross errors, it is necessary to ensure that there are no fewer than 4 remaining measurement data. Otherwise, the number of measurement acquisitions will be increased.

[0067] Furthermore, the quantity threshold can be determined according to actual needs, such as 4 or more, which is beneficial for users to adjust the total number of judgments for the gross error judgment criterion according to actual needs.

[0068] Specifically, such as Figure 1 As shown, in step S3, a preset gross error criterion is used to filter the added measurement data. If the added measurement data is determined to be gross error data, it is deleted from the observation processing dataset. Specifically, this includes:

[0069] S31: Calculate the residual data of each measurement data to obtain several residual data, and then compare the several residual data to obtain gross error suspected values;

[0070] In this embodiment, since the amount of data is too small (n<9), it is necessary to use the Romanovsky criterion (t-distribution test criterion) for processing small amounts of data to verify the calculation and identify gross errors according to the actual error distribution range of the t-distribution.

[0071] Specifically, calculate the residual data for each measurement. Where i = 1, 2, ..., n. First, using three times the mean error data S calculated in step S1 as a reference, the residual data v corresponding to each measurement data is... i Compare and identify potential gross errors. Set the absolute value of the residual data in the measurement data that is greater than 3S as a potential gross error value (x). d If all values ​​are less than 3S, then a value is randomly selected as the gross error suspicion value x. d The gross error suspected value x d Pre-selection.

[0072] S32: Recalculate the observed mean data and standard error data.

[0073] In this embodiment, the observed mean data are recalculated using the following formula. With the mean error data S′.

[0074]

[0075] S33: Obtain the test coefficient based on the preset significance data. If the gross error suspected value is greater than or equal to the test system, delete the measurement data corresponding to the gross error suspected value and return to step S31; until the gross error suspected values ​​corresponding to several measurement data are all less than the test coefficient.

[0076] In this embodiment, a significance value α of 0.05 is selected, along with the test coefficient K(n,α) of the t-distribution that conforms to the Romanovsky criterion (t-distribution test criterion). If Then x dIf the error is gross, it should be removed; otherwise, it should be retained. After removal, return to step S31 to find new gross error suspect values ​​for judgment, until all measurement data are less than K(n,α)S′.

[0077] S4: Recalculate the mean error data. If the mean error data is not greater than the mean error threshold, report the observed mean data to the cloud platform. If the mean error data is still greater than the mean error threshold, first use a preset trend change point detection method to analyze the changing trend of several measured data. If a changing trend exists, increase the collection of measured data and return to step S3. If no changing trend exists, then use a data conversion method that conforms to the normal distribution law to convert the measured data.

[0078] In this embodiment, the normality transformation method is the Box-Cox transformation. The Box-Cox transformation is used when continuous response variables do not conform to a normal distribution. After the measurement data that does not conform to a normal distribution undergoes the Box-Cox transformation, the unobservable measurement error and the correlation of the predictor variable can be reduced to a certain extent. The trend change point detection method is the Mann-Kendall monotonic trend test. The Mann-Kendall drive test is a non-parametric statistical test method. Its advantage is that it does not require the measurement data in the dataset to follow a certain distribution, nor is it affected by a few outlier measurements.

[0079] Specifically, the trend change point detection method is used to analyze the changing trend of several measurement data. That is, when identifying the changing state of the measured object, there are two states:

[0080] The first state is discrete, fluctuating within a certain range. In this case, a normality transformation method is used to transform the measurement data so that it conforms to a normal distribution. If several measurement data points fluctuate within a certain range but without a clear upward or downward trend, the number of observations is increased according to the number of data collections, returning to step S3, or one or two sets of measurement data with their maximum and minimum values ​​are removed, and the mean value of the remaining measurement data is calculated. The mean error data S is then compared with the mean error threshold for a new judgment. If the second state shows a clear trend of change, more measurement data is collected and step S3 is repeated.

[0081] The formula for the BOX-COX transformation method is as follows, where λ is a transformation coefficient to be determined, and the optimal value is estimated using the maximum likelihood method; x is the measured data. (λ) The converted measurement data, where c is a constant used to satisfy the requirement x + c > 0, L (λ) For the transformation coefficients, This is the transformed observation mean data.

[0082]

[0083]

[0084]

[0085] Furthermore, it is necessary to first select the range of values ​​for λ and the step size, such as λ∈(-2,2) with a step size of 0.01. Following the above formula, the optimal λ is obtained, such that L... (λ) Maximize, then use the corresponding optimal λ, according to x (λ) The calculation formula is used to perform a normal transformation to obtain the transformed observed mean data.

[0086] Furthermore, this includes the stored number of data collections and a preset maximum data collection threshold; in step S4, such as... Figure 1 As shown, the changing trends of several measurement data are analyzed using a preset trend abrupt change point detection method. Specifically:

[0087] S41: If the trend change is less than the preset trend change threshold, then determine the current number of collections. If the number of collections is less than the maximum number of collections threshold, then add more measurement data and recalculate the observation mean data and the mean error data. If the number of collections is equal to the maximum number of collections threshold, then calculate the maximum and minimum measurement data in the current observation dataset and delete them. Then recalculate the observation mean data and the mean error data.

[0088] S42: If the trend of change is greater than or equal to the preset trend change threshold, increase the frequency of collecting measurement data until the trend of change is less than the preset trend change threshold.

[0089] In this embodiment, if the trend of change is less than a preset trend change threshold, the measurement data of the corresponding object is discrete, showing fluctuations within a range; if the trend of change is greater than or equal to the preset trend change threshold, the measurement data of the corresponding object shows a significant lateral trend change; normalization transformation is a data transformation method for statistical modeling, which allows the linear regression model to retain information while satisfying linearity, normality, independence, and variance; using the Box-Cox transformation to transform the error data is more conducive to fitting the linear model and analyzing the correlation of features of several measurement data; if the recalculated mean error data S still does not conform to the "Building Deformation Measurement" standard... As required by the "Quantity Standard", the total number of historical measurement data in the current observation processing dataset is obtained and judged. For example, is the total number of historical measurement data in the current observation not less than 4? If the total number of historical measurement data does not meet the requirements, that is, the mean error data S is still greater than the mean error threshold, then the measurement data is collected again until the total number of historical measurement data in the observation processing dataset meets the requirements. That is, the measurement data does not meet the gross error judgment criteria and the total number meets the requirements. Then, the trend change point detection method is used to analyze the changing trend of several measurement data. The time series model sorts the several measurement data in chronological order to obtain the changing trend of the measurement data.

[0090] Specifically, in step S42, if the measurement data shows an upward or downward trend, the frequency of collecting the measurement data is increased, such as from once every 8 minutes to once every 5 minutes, until the trend of the measurement data gradually flattens out.

[0091] In this embodiment, the Mann-Kendall test is used to perform trend analysis on the time series observation data in order to obtain the final measurement value.

[0092] Specifically, the first step is to calculate x. j -x k Where j>k, j,k=1,2,…,n; then the Mann-Kendall statistic M is calculated using the following formula:

[0093]

[0094]

[0095] Next, a significance level α of 0.05 is chosen. The null hypothesis H0 is no trend (i.e., fluctuation), and the alternative hypothesis HA is an upward trend. The Mann-Kendall test provides corresponding nonparametric trend probability distribution tables for different significance levels: if M is positive, and the calculated probability value of M in the trend probability distribution table with a significance level α of 0.05 is less than the prior-specified significance level α, then H0 is rejected and HA is accepted. Similarly, to test the alternative hypothesis HA (no trend H0) and the alternative hypothesis (downward trend), if M is negative, and the probability value corresponding to the absolute value of M in the trend probability distribution table with a significance level α of 0.05 is less than the prior-specified α, then H0 is rejected and HA is accepted. If a two-sided test is needed to detect an upward or downward trend, the probability level corresponding to the absolute value of M is doubled. If the doubling value is less than the prior α level, then H0 is rejected.

[0096] S5: Based on a preset data filter, a data filtering model is trained by combining the observation and processing dataset. The data filtering model receives instructions from the cloud platform to judge and filter the measurement data.

[0097] In this embodiment, the preset data filters include observation data filters and time series model filters, and the observation data filters are RNN data filters.

[0098] Specifically, the data filter calculates a data filtering model applicable to the measurement scenario using historical measurement data from the observation dataset, and then performs judgment filtering on the measurement data collected from the three times, processing outliers. This application improves the filtering accuracy by adding a data filter, enabling the filtering scheme to adapt to more fluctuations in measurement data.

[0099] Furthermore, the data filter can be used to process horizontal and slant distance measurement data in laser ranging or to process angle measurement data.

[0100] In one embodiment, such as Figure 1 As shown, in step S5, a data filtering model is trained based on a preset data filter and the observation processing dataset. The data filtering model receives instructions from the cloud platform to filter the measurement data, specifically including:

[0101] S51: The observation data filter is constructed based on a recurrent neural network. The observation data filter is trained based on the historical measurement data and the added measurement data to obtain the observation data model of the observation data filter. Then, the observation data filter judges and filters the measurement data to obtain the final measurement data. If the measurement data is determined to be invalid data, the invalid data is deleted from the observation processing dataset.

[0102] Specifically, by setting appropriate hyperparameters, an observation data filter based on an RNN network is built to handle observation data of different lengths. Two types of filters are trained using observation processing datasets containing error data with multiple scenarios, enabling the data filters to handle systematic errors, gross errors, random errors, and errors that occur in combination. The effectiveness of the observation data filters is verified through training. The dataset labels (output data) are one-dimensional vectors with two elements: the first element is 0 or 1, indicating whether the measurement value is finalized (0 for incomplete, 1 for finalized); the second element is the current optimal measurement value.

[0103] S52: The time series model filter is trained based on the measurement data and the observation mean data obtained in step S2 to obtain a time series model; then the time series model filter judges and filters the measurement data. If the measurement data is determined to be invalid data, the invalid data is deleted from the observation processing dataset.

[0104] Specifically, appropriate hyperparameters are set to build a time series model filter based on a BP network; the final measurement values ​​obtained from the previous observations (i.e., the observation mean data obtained in step S2) are used to create a time series model dataset to predict the later measurement values ​​for outlier detection; the effectiveness of the time series model filter is verified through testing, and the dataset data (input data) consists of the first three final measurement values, the interval time, and the construction progress of the previous observations.

[0105] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0106] In one embodiment, a high-precision laser tilt angle integrated measurement system is provided, which corresponds to the high-precision laser tilt angle integrated measurement method described in the above embodiments.

[0107] like Figure 2 As shown, a high-precision laser tilt angle integrated measurement system includes a power supply module 1, a main control module 2, a laser measurement module 3, and a communication module 4. Detailed descriptions of each functional module are as follows:

[0108] The power supply module 1 is sequentially coupled to the main control module 2, the laser measurement module 3, and the communication module 4.

[0109] Optional, such as Figure 3As shown, the power supply module 1 includes a battery (not shown), a battery voltage regulator submodule 11, a battery protection submodule 12, a power management submodule 13, and a drive submodule 14. The battery voltage regulator submodule 11 includes a battery voltage regulator chip U3, capacitors C29, C30, and C31. The battery voltage regulator chip U3 is a voltage regulator XC6206. The third pin VIN of the battery voltage regulator chip U3 is the input terminal VIN, and the second pin of the battery voltage regulator chip U3 is the output terminal VOUT. The first pin GND of the battery voltage regulator chip U3 is grounded. The second pin VOUT of the battery voltage regulator chip U3 is connected in series with capacitor C30 and then grounded. Capacitor C31 is connected in parallel with capacitor C30. The third pin VIN of the battery voltage regulator chip U3 is connected to the positive terminal of the battery. The third pin VIN of the battery voltage regulator chip U3 is connected in series with capacitor C29 and then grounded. The second pin VOUT of the battery voltage regulator chip U3 is the output terminal VOUT, and the second pin VOUT of the battery voltage regulator chip U3 stably outputs a voltage of 3.3V. After processing by the voltage regulator XC6206, when the input voltage is too high, it is adjusted to output a smaller voltage, thereby playing a protective role for the circuit.

[0110] like Figure 3As shown, the battery protection submodule 12 includes a battery protection chip U12, a terminal block P10, a terminal block P11, resistors R54 and R55, a capacitor C48, and a resettable fuse L4. The first pin V- of the battery protection chip U12 is connected in series with resistor R54 and then grounded. The second pin S1 of the battery protection chip U12 is grounded. The third pin S2 of the battery protection chip U12 is coupled to the second pin of terminal block P10. The fourth pin D of the battery protection chip U12 is an undefined pin. The fifth pin VDD of the battery protection chip U12 is connected in series with capacitor C48 and then coupled to the second pin of terminal block P10. The fifth pin VDD of the battery protection chip U12 is connected in series with resistor R55 and resettable fuse L4 and then coupled to the first pin of terminal block P10. The positive terminal of the battery is coupled to the connection point of resistor R55 and resettable fuse L4, meaning one end of resettable fuse L4 is coupled to the terminal block. The first pin of terminal P10 and the other end of the resettable fuse L4 are coupled to the positive terminal of the battery; the sixth pin VSS of the battery protection chip U12 is connected to the second pin of the busbar terminal P10; the first pin of the busbar terminal P11 is coupled to the first pin of the busbar terminal P10, and the second pin of the busbar terminal P11 is coupled to the second pin of the busbar terminal P10; the third pin VIN of the aforementioned voltage regulator XC6206 is connected to one end of the resettable fuse L4, so that when the current in the circuit is greater than the threshold current value of the resettable fuse L4, the resistance value of the resettable fuse will become very large; the maximum current of the resettable fuse L4 is 2A, and the maximum voltage of the resettable fuse L4 is 6V; when an abnormally large current passes through, the temperature of the resettable fuse L4 rises rapidly and then expands and breaks, causing the conductive path to break, resulting in a sharp increase in impedance, a decrease in the current passing through, and the circuit is as if it is disconnected, so as to achieve the purpose of protecting the circuit.

[0111] like Figure 3 As shown, the battery protection submodule 12 also includes resistors R56 and R58, which are connected in series. The other end of resistor R56 is connected to the positive terminal of the battery, and the other end of resistor R58 is grounded. The connection node of resistors R56 and R58 is the battery presence detection node.

[0112] like Figure 3As shown, the power management submodule 13 includes a battery management chip U6, a Schottky diode D11, an inductor L2, capacitors C55, C71, and C72, and resistors R41, R42, and R45. The battery management chip U6 is model AP2008TCER-ADJ. The first pin SW of the battery management chip U6 is connected in series with the Schottky diode D11 and then connected to the positive terminal of the 12V power supply. One end of capacitors C71 and C72 is connected to the positive terminal of the 2V power supply, and the other end of capacitors C71 and C72 is grounded. Capacitors C71 and C72 are connected in parallel, thus serving a filtering function. The second pin (GND) of the battery management chip U6 is grounded; the third pin (FB) of the battery management chip U6 is connected in series with resistor R41 and then to the positive terminal of the 12V power supply; the third pin (FB) of the battery management chip U6 is connected in series with resistor R42 and then to grounded; the fourth pin (SHDN) of the battery management chip U6 is connected in series with resistor R45 and inductor L2 and then to the anode of Schottky diode D11; the fifth pin (VIN) of the battery management chip U6 is connected in series with capacitor C55 and then to grounded; the fifth pin (VIN) of the battery management chip U6 is connected to the negative terminal of the 12V power supply; the sixth pin (NC) of the battery management chip U6 is an undefined pin.

[0113] like Figure 3 As shown, the driving submodule 14 includes a first driving submodule and a second driving submodule. The first driving submodule 14 includes a MOSFET Q13, a transistor Q14, resistors R33, R35, and R36. The source of the MOSFET Q13 is coupled to the negative terminal of the battery, and the drain of the MOSFET Q13 is coupled to the fifth pin VIN of the battery management chip U6. The gate of the MOSFET Q13 is coupled to the collector of the transistor Q14. The gate and source of the MOSFET Q13 are connected in series with resistor R33. Resistor R33 provides bias voltage for the MOSFET Q13 and also acts as a discharge resistor to protect the gate and source of the MOSFET Q13. The base of the transistor Q14 is connected in series with resistor R35 and then coupled to the main control module 2. The emitter of the transistor Q14 is grounded, and the base and emitter of the transistor Q14 are connected in series with resistor R36. The connection between the MOSFET Q13 and the transistor Q14 serves to fix the voltage level.

[0114] like Figure 3As shown, the second driving submodule 14 includes a MOSFET Q15, a transistor Q16, resistors R38, R39, and R40. The source of MOSFET Q15 is coupled to the negative terminal of the battery, and the drain of MOSFET Q15 is coupled to the power supply terminal of the laser measurement module 3. Resistor R38 is connected in series between the gate and source of MOSFET Q15. Resistor R38 serves two purposes: providing a bias voltage for MOSFET Q15 and acting as a discharge resistor. The gate of MOSFET Q15 is coupled to the collector of transistor Q16, and the emitter of transistor Q16 is grounded. Resistor R39 is connected in series between the collector and emitter of transistor Q16. The base of MOSFET Q15 is coupled to the main control module 2 after being connected in series with resistor R40. The connection between MOSFET Q15 and transistor Q16 serves to fix the voltage level.

[0115] like Figure 3 As shown, the power supply module 1 also includes MOSFET Q8 and MOSFET Q12. The gate of MOSFET Q8 is coupled to the main control module 2, the drain of MOSFET Q8 is coupled to the communication module 4, and the source of MOSFET Q8 is coupled to the second pin VOUT of the battery voltage regulator chip U3. A resistor R63 is connected in series between the gate and the source of MOSFET Q8. The gate of MOSFET Q12 is coupled to the main control module 2, the drain of MOSFET Q12 is coupled to the communication module 4, and the source of MOSFET Q12 is coupled to the second pin VOUT of the battery voltage regulator chip U3. A resistor R67 is connected in series between the gate and the source of MOSFET Q12.

[0116] like Figure 3 and Figure 4As shown, the first communication terminal of the main control module 2 is connected to the laser measurement module 3, and the second communication terminal of the main control module 2 is connected to the first communication terminal of the communication module 4. The main control module 2 includes a voltage conversion submodule 21, a capacitor oscillation submodule 22, a main control chip U11, a socket P2, and a female connector P3. The main control chip U11 is an STM32F103RCT6, and the socket P2 is a 6-pin connector. The main control chip U11 includes two parts, U11A and U11B, in the circuit diagram. The voltage conversion submodule 21 includes an inductor L6, a capacitor C58, and a capacitor C59. One end of the inductor L6 is coupled to the second pin VOUT of the battery voltage regulator chip U3, and the other end of the inductor L6 is connected in series with the capacitor C59 and then grounded. The capacitors C58 and C59 are connected in parallel. The inductor L6, capacitors C58 and C59 are used to provide the main control chip U11 with a 3.3V operating voltage. The voltage conversion submodule 21 is also coupled to a filter submodule 23, which includes capacitors C16, C17, C18, C19, C20, and C21. One end of capacitor C21 is coupled to the connection node of inductor L6 and capacitor C59, and the other end of capacitor C21 is grounded. Capacitors C16, C17, C18, C19, and C21 are connected in parallel, as are C20 and C21. The filter submodule 23 provides filtering for the main control module 2, which can effectively prevent interference from high-frequency signals and improve the overall circuit reliability.

[0117] like Figure 4 As shown, the capacitor oscillation submodule 22 includes a crystal X1, a capacitor C24, and a capacitor C25. One end of the crystal X1 is coupled to the third pin PC14-OSC32_IN of the main control chip U11, and the other end of the crystal X1 is coupled to the fourth pin PC14-OSC32_OUT of the main control chip U11. One end of the capacitor C24 is coupled to the third pin PC14-OSC32_IN of the main control chip U11, and the other end of the capacitor C24 is grounded. One end of the capacitor C25 is coupled to the fourth pin PC14-OSC32_OUT of the main control chip U11, and the other end of the capacitor C25 is grounded. The crystal X1, capacitor C24, and capacitor C25 form a three-point capacitor oscillation circuit, which is easy to start and convenient to adjust the frequency, thus facilitating the main control module 2 to adjust the frequency of the acquired measurement data.

[0118] like Figure 4As shown, pins VBAT (1st), VDDA (13th), VDD_4 (19th), VDD_1 (32nd), VDD_2 (48th), and VDD_3 (64th) of the main control chip U11 are all coupled to the connection node of inductor L6 and capacitor C59; pins VSSA (12th), VSS_4 (18th), VSS_1 (31st), VSS_2 (47th), and VSS_3 (63rd) of the main control chip U11 are grounded; pin PC14-OSC32_IN of the main control chip U11 is coupled to pin PD0-OS of the main control chip U11. C_IN, the fourth pin PC14-OSC32_OUT of the main control chip U11 is coupled to the sixth pin PD1-OSC_OUT of the main control chip U11; the seventh pin NRST of the main control chip U11 is coupled to an RC oscillation circuit, which includes a resistor R18 and a capacitor C80; the resistor R18 and the capacitor C80 are connected in series, the other end of the resistor R18 is coupled to the power supply, and the other end of the capacitor C80 is grounded; the seventh pin NRST of the main control chip U11 is coupled to the connection node of the resistor R18 and the capacitor C80; the twenty-eighth pin PB2 of the main control chip U11 is connected in series with the resistor R22 and then grounded.

[0119] like Figure 4 As shown, pin 10 PC2 of the main control chip U11 is coupled to the gate of MOSFET Q8 in power supply module 1, pin 9 PC1 of the main control chip U11 is coupled to the gate of MOSFET Q12 in power supply module 1, pin 11 PC3 of the main control chip U11 is coupled to the base of transistor Q16 after being connected in series with resistor R40, and pin 24 PC4 of the main control chip U11 is coupled to the base of transistor Q14 after being connected in series with resistor R35. Thus, power supply module 1 provides operating power to the main control chip U11.

[0120] like Figure 4 As shown, the first pin of socket P2 is coupled to the connection node of capacitor L6 and capacitor C59; the second pin of socket P2 is coupled to the 49th pin PA14 of the main control chip U11; the third pin of socket P2 is coupled to the 46th pin PA13 of the main control chip U11; the fourth pin of socket P2 is grounded; the fifth pin of socket P2 is coupled to the 60th pin BOOT0 of the main control chip U11; and the sixth pin of socket P2 is coupled to the 7th pin NRST of the main control chip U11. The first pin of the female connector P3 is coupled to the 51st pin PC10 of the main control chip U11 after being connected in series with resistor R28; the second pin of the female connector P3 is coupled to the 52nd pin PC11 of the main control chip U11 after being connected in series with resistor R29; and the third pin of the female connector P3 is grounded.

[0121] like Figure 3-5As shown, the laser measurement module 3 includes a laser ranging submodule 31 for collecting and measuring horizontal distance and slope distance data, and an inclination measurement submodule 32 for collecting and measuring angle data. The power supply terminal of the laser ranging submodule 31 is coupled to the power supply module 1, and the communication terminal of the laser ranging submodule 31 is coupled to the first communication terminal of the main control module 2. The power supply terminal of the inclination measurement submodule 32 is coupled to the power supply module 1, and the communication terminal of the inclination measurement submodule 32 is coupled to the first communication terminal of the main control module 2. The working modes of the laser ranging submodule 31 include a sleep mode and a normal working mode, and the inclination measurement submodule 32 includes a normal working mode. When the inclination measurement data collected by the inclination measurement submodule 32 is less than the preset angle change threshold, the laser ranging submodule 31 is in sleep mode. When the inclination measurement data collected by the inclination measurement submodule 32 is greater than the preset angle change threshold, the inclination measurement submodule 32 sends a wake-up message to the laser ranging submodule 31 to switch to normal working mode.

[0122] like Figure 5 As shown, the laser measurement module 3 includes a laser ranging chip and an inclination measurement chip; the general control terminal of the laser ranging chip is coupled to the first control terminal of the main control chip, the power supply terminal of the laser ranging chip is coupled to the power supply module 1, and the communication terminal of the laser ranging chip is coupled to the ranging communication terminal of the main control chip; the general control terminal of the inclination measurement chip is coupled to the second control terminal of the main control chip, the power supply terminal of the inclination measurement chip is coupled to the power supply module 1, and the communication terminal of the inclination measurement chip is coupled to the inclination communication terminal of the main control chip.

[0123] like Figure 3-5 As shown, the power supply terminal of the laser ranging submodule 31 is the VCC pin of the laser ranging chip, which is coupled to the power supply VCC. The GND pin of the laser ranging chip is grounded. The USART2_TX and USART2_RX pins of the laser ranging chip are the communication terminals of the laser ranging submodule 31.

[0124] like Figure 4 and Figure 5 As shown, the GPIO pins of the laser ranging chip are general-purpose control terminals. The GPIO pins of the laser ranging chip are coupled to the fourteenth pin PA0-WKUP of the main control chip U11. The USART3_TX pin of the laser ranging chip is coupled to the sixteenth pin PA2 of the main control chip U11. The USART3_RX pin of the laser ranging chip is coupled to the seventeenth pin PA3 of the main control chip U11.

[0125] like Figure 4 and Figure 5As shown, the power supply terminal of the tilt measurement submodule 32 is the VCC pin of the tilt measurement chip, which is coupled to the power supply VCC. The GND pin of the tilt measurement chip is grounded. The USART3_TX and USART3_RX pins of the tilt measurement chip are the communication terminals of the tilt measurement submodule 32. The USART3_TX pin of the tilt measurement chip is coupled to the 29th pin PB10 of the main control chip U11, and the USART3_RX pin of the tilt measurement chip is coupled to the 30th pin PB11 of the main control chip U11. The 14th pin PA0-WKUP of the main control chip U11 is the second control terminal.

[0126] like Figure 6 As shown, the communication module 4 includes a communication chip U1, a SIM card socket submodule 41, and a prompt submodule 42. The communication chip U1 in the circuit diagram includes two parts, U1A and U1B. The power supply terminal of the communication chip U1 is coupled to the communication power supply terminal of the power supply module 1, which is the drain of the MOSFET Q8. The SIM card control terminal of the communication chip U1 is coupled to the control terminal of the SIM card socket submodule 41. The startup mode terminal of the communication chip U1 is coupled to the battery operation mode terminal of the prompt submodule 42. The network indicator terminal of the communication chip U1 is coupled to the network indicator terminal of the prompt submodule 42. The ground terminal of the communication chip U1 is grounded. The prompt submodule 42 is used to output an audible and visual prompt signal when the tilt angle measurement data exceeds a preset angle change threshold. Figure 3 and Figure 6As shown, pins 1-58 of communication chip U1 (GND, G ... Communication chip U1 establishes a communication connection with main control module 2. Pin 20 (RI) of communication chip U1 is coupled to pin 39 (PC8) of main control chip U11. Pins 42 (VBAT) and 43 (VBAT) of communication chip U1 are both coupled to the drain of MOSFET Q8 in power supply module 1. Pin 35 (RF-ANT) of communication chip U1 is connected in series with resistor R1 and then coupled to antenna mount J1. Resistor R1 is also coupled to capacitors C1 and C2. One end of capacitor C1 is coupled to one end of resistor R1, and the other end of capacitor C1 is grounded. One end of capacitor C2 is coupled to the first pin of antenna mount J1, and the other end of capacitor C2 is grounded. Capacitors C1 and C2 provide filtering. Resistor R1 is coupled to the first pin of antenna mount J1, and the second and third pins of antenna mount J1 are grounded. Antenna mount J1 is a J-PEX RF mount, which enhances the communication connection between communication module 4 and the cloud platform.

[0127] like Figure 3 and Figure 6As shown, the SIM card socket submodule 41 includes a CARD1 chip, an ESD suppressor E1, resistors R1, R2, and R3, and capacitors C3, C4, C5, and C6. The CARD1 chip is an SMN-305, and the ESD suppressor E1 is an RLST236A054LV. The first, second, third, and fourth pins of the CARD1 chip are grounded. The VCC pin of the CARD1 chip is coupled to the fourteenth pin, USIM_VDD, of the communication chip U1. The RST pin of the CARD1 chip is connected in series with resistor R4 and then coupled to the twelfth pin, USIM_RST, of the communication chip U1. The CLK pin of the CARD1 chip is connected in series with resistor R5 and then coupled to... Pin 13 of communication chip U1 (USIM_CLK) is connected to the GND pin of chip CARD1, which is then grounded via series with resistor R2. Pin 11 of main control chip U1 (USIM_DATA) is connected to the I / O pin of chip CARD1 via series with resistor R6. One end of capacitor C3 is connected to the VCC pin of chip CARD1, and the other end is grounded. One end of capacitor C4 is connected to the I / O pin of chip CARD1, and the other end is grounded. One end of capacitor C5 is connected to the RST pin of chip CARD1, and the other end is grounded. One end of capacitor C6 is connected to the CLK pin of chip CARD1, and the other end is grounded. Capacitors C3, C4, C5, and C6 serve as filters.

[0128] like Figure 6 As shown, the first input terminal of ESD suppressor E1 is coupled to the CLK pin of chip CARD1, the second input terminal of ESD suppressor E1 is coupled to the I / O pin of chip CARD1, and the third input terminal of ESD suppressor E1 is grounded; the first output terminal of ESD suppressor E1 is coupled to the VCC pin of chip CARD1, the second output terminal of ESD suppressor E1 is coupled to the RST pin of chip CARD1, and the third output terminal of ESD suppressor E1 is an undefined pin; ESD suppressor E1 protects each input and output pin from ESD and high surge phenomena.

[0129] like Figure 6As shown, the prompt submodule 42 includes a light-emitting diode LED1, a transistor Q1, a resistor R11, and a resistor R12. The anode of LED1 is connected in series with the resistor R11 and then coupled to the drain of the MOSFET Q8 in the power supply module 1. The cathode of LED1 is coupled to the collector of the transistor Q1. The emitter of the transistor Q1 is grounded, and the base of the transistor Q1 is connected in series with the resistor R12 and then coupled to the sixteenth pin NEILIGHT of the communication chip U1. The prompt submodule 42 is used to flash when the tilt angle measurement submodule 32 is woken up, thereby prompting the user that the communication module 4 is communicating with the cloud platform and reporting measurement data.

[0130] For specific limitations regarding the high-precision laser tilt measurement system, please refer to the limitations of the high-precision laser tilt measurement method mentioned above, which will not be repeated here. Each module in the high-precision laser tilt measurement system can be implemented entirely or partially through software, hardware, or a combination thereof. Each module can be embedded in the processor of the computer device in hardware form or independent of it, or it can be stored in the memory of the computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0131] In one embodiment, such as Figure 6 As shown, a high-precision laser tilt angle integrated measuring device is provided, including a housing and an adsorption plate 5. The side wall of the housing is also provided with a prism screw hole 51, which facilitates the operator to fix the high-precision laser tilt angle integrated measuring device on the device under test by means of threaded connection. The adsorption plate 5 is fixedly installed on the housing, and a mudguard 52 is provided on the top of the housing. The mudguard 52 effectively reduces the contamination caused by mud splashing from the object under test during the use of the high-precision laser tilt angle integrated measuring device. The aforementioned high-precision laser tilt angle integrated measuring system is installed inside the housing, and a measuring hole 6 is provided on the side wall of the housing.

[0132] In this embodiment, the adsorption plate 5 is a strong magnetic plate. In actual applications, the object being measured is generally a metal structure made of steel plates, such as sheet piles, high formwork equipment, and steel walers. The measuring device can be attached to the object being measured by the adsorption plate 5 to achieve the purpose of quickly installing the high-precision laser tilt angle integrated measuring device. The housing protects the high-precision laser tilt angle integrated measuring system, effectively preventing it from being damaged by foreign objects during the measurement process. Furthermore, the laser measuring module 3 in the high-precision laser tilt angle integrated measuring system performs laser measurement through the measuring hole 6, which is beneficial to achieving the purpose of laser tilt angle integrated measurement.

[0133] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0134] S1: Collect measurement data periodically, input the measurement data into a preset observation processing dataset, which stores historical measurement data; calculate the observation mean data and standard error data based on the historical measurement data;

[0135] S2: If the mean error data is not greater than the preset mean error threshold, the observed mean data will be reported to the cloud platform; if the mean error data is greater than the mean error threshold, additional measurement data will be collected.

[0136] S3: Use a preset gross error criterion to judge and filter the added measurement data. If the added measurement data is determined to be gross error data, delete the added measurement data from the observation processing dataset. If the measurement data in the observation processing dataset is less than the quantity threshold after deletion, continue to add measurement data.

[0137] S4: Recalculate the mean error data. If the mean error data is not greater than the mean error threshold, report the observed mean data to the cloud platform. If the mean error data is still greater than the mean error threshold, first use a preset analysis method to determine the changing trend of several measured data. If a changing trend exists, increase the collection of measured data and return to step S3. If no changing trend exists, then perform data transformation on the measured data using the normality transformation method.

[0138] S5: Based on a preset data filter, a data filtering model is trained by combining the observation processing dataset. The data filtering model receives instructions from the cloud platform to judge and filter the measurement data.

[0139] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0140] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0141] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A high-precision laser tilt angle integrated measurement method, characterized in that: The high-precision laser tilt angle integrated measurement method includes the following steps: S1: Periodically collect measurement data and input the measurement data into a preset observation processing dataset, which stores historical measurement data; calculate the observation mean data and standard error data based on the historical measurement data; S2: If the mean error data is not greater than the preset mean error threshold, the observed mean data is reported to the cloud platform; if the mean error data is greater than the mean error threshold, additional measurement data is collected. S3: Use a preset gross error criterion to judge and filter the added measurement data. If the added measurement data is determined to be gross error data, delete the added measurement data from the observation processing dataset. If the measurement data in the observation processing dataset is less than the quantity threshold after deletion, continue to add measurement data. S4: Recalculate the mean error data. If the mean error data is not greater than the mean error threshold, report the observed mean data to the cloud platform. If the mean error data is still greater than the mean error threshold, first use a preset trend change point detection method to analyze the changing trend of several measured data. If a changing trend exists, increase the collection of measured data and return to step S3. If no changing trend exists, then use a normality transformation method to transform the measured data. S5: Based on a preset data filter, a data filtering model is trained using the observation processing dataset. The data filtering model receives instructions from the cloud platform to judge and filter the measurement data. The preset data filters include observation data filters and time series model filters. Step S5 includes: S51: The observation data filter is constructed based on a recurrent neural network. The observation data filter is trained based on the historical measurement data and the added measurement data to obtain the observation data model of the observation data filter. Then, the observation data filter judges and filters the measurement data to obtain the final measurement data. If the measurement data is determined to be invalid data, the invalid data is deleted from the observation processing dataset. S52: The time series model filter is trained based on the measurement data and the observation mean data obtained in step S2 to obtain a time series model; then the time series model filter judges and filters the measurement data. If the measurement data is determined to be invalid data, the invalid data is deleted from the observation processing dataset.

2. The high-precision laser tilt angle integrated measurement method according to claim 1, characterized in that: Including the stored number of data collections and a preset maximum data collection threshold, in step S4, the analysis of the changing trends of several measured data points using a preset trend abrupt change point detection method specifically involves: S41: If the trend of change is less than the preset trend change threshold, then determine the current number of collections. If the number of collections is less than the maximum number of collections threshold, then increase the collection of measurement data and recalculate the observation mean data and the mean error data. If the number of collections equals the maximum number of collections threshold, then the maximum and minimum measurement data in the current observation dataset are calculated and deleted, and then the observation mean data and mean error data are recalculated. S42: If the trend of change is greater than or equal to the preset trend change threshold, increase the frequency of collecting measurement data until the trend of change is less than the preset trend change threshold.

3. The high-precision laser tilt angle integrated measurement method according to claim 1, characterized in that: In step S3, the added measurement data is judged and filtered using a preset gross error criterion. If the added measurement data is determined to be gross error data, it is deleted from the observation processing dataset. Specifically: S31: Calculate the residual data of each measurement data to obtain several residual data, and then compare the several residual data to obtain gross error suspicion values; S32: Recalculate the observed mean data and standard error data; S33: Obtain the test coefficient based on the preset significance data. If the gross error suspected value is greater than or equal to the test system, delete the measurement data corresponding to the gross error suspected value and return to step S31; until the gross error suspected values ​​corresponding to several measurement data are all less than the test coefficient.

4. A high-precision laser tilt angle integrated measurement system, used to implement the high-precision laser tilt angle integrated measurement method according to any one of claims 1-3, characterized in that, include: The system comprises a power supply module (1), a main control module (2) for storing and processing the measurement data, a laser measurement module (3) for acquiring the measurement data, and a communication module (4) for communicating between the high-precision laser tilt angle integrated measurement system and the cloud platform. The main control module (2) processes the measurement data using a high-precision laser tilt angle integrated measurement method as described in any one of claims 1-3. The power supply module (1) is sequentially coupled to the main control module (2), the laser measurement module (3), and the communication module (4). The first communication terminal of the main control module (2) is communicatively connected to the laser measurement module (3), the second communication terminal of the main control module (2) is communicatively connected to the first communication terminal of the communication module (4), and the second communication terminal of the communication module (4) is communicatively connected to the cloud platform.

5. The high-precision laser tilt angle integrated measurement system according to claim 4, characterized in that, The laser measurement module (3) includes a laser ranging submodule (31) for collecting and measuring horizontal distance and slant distance data, and an inclination measurement submodule (32) for collecting and measuring angle data. The power supply terminal of the laser ranging submodule (31) is coupled to the power supply module (1), and the communication terminal of the laser ranging submodule (31) is coupled to the first communication terminal of the main control module (2). The power supply terminal of the inclination measurement submodule (32) is coupled to the power supply module (1), and the communication terminal of the inclination measurement submodule (32) is coupled to the first communication terminal of the main control module (2). Coupled; the working modes of the laser ranging submodule (31) include a sleep mode and a normal working mode, and the tilt measurement submodule (32) includes a normal working mode; when the tilt measurement data collected by the tilt measurement submodule (32) is less than the preset angle change threshold, the laser ranging submodule (31) is in a sleep mode; when the tilt measurement data collected by the tilt measurement submodule (32) is greater than the preset angle change threshold, the tilt measurement submodule (32) sends a wake-up working message to the laser ranging submodule (31) to switch to the normal working mode.

6. The high-precision laser tilt angle integrated measurement system according to claim 4, characterized in that, The main control module (2) includes a main control chip, and the laser measurement module (3) includes a laser ranging chip and an inclination measurement chip. The general control terminal of the laser ranging chip is coupled to the first control terminal of the main control chip, the power supply terminal of the laser ranging chip is coupled to the power supply module (1), and the communication terminal of the laser ranging chip is coupled to the ranging communication terminal of the main control chip. The general control terminal of the inclination measurement chip is coupled to the second control terminal of the main control chip, the power supply terminal of the inclination measurement chip is coupled to the power supply module (1), and the communication terminal of the inclination measurement chip is coupled to the inclination communication terminal of the main control chip.

7. The high-precision laser tilt angle integrated measurement system according to claim 4, characterized in that, The communication module (4) includes a communication chip, a SIM card socket submodule (41), and a prompt submodule (42). The power supply terminal of the communication chip is coupled to the communication power supply terminal of the power supply module (1). The SIM card control terminal of the communication chip is coupled to the control terminal of the SIM card socket submodule (41). The startup mode terminal of the communication chip is coupled to the battery working mode terminal of the prompt submodule (42). The network indicator terminal of the communication chip is coupled to the network indicator terminal of the prompt submodule (42). The ground terminal of the communication chip is grounded. The prompt submodule (42) is used to output an audio-visual prompt signal when the tilt angle measurement data exceeds a preset angle change threshold.

8. A high-precision laser tilt angle integrated measuring device, characterized in that, The system includes a housing and an adsorption plate (5), the adsorption plate (5) being fixedly installed in the housing, and a high-precision laser tilt angle integrated measurement system as described in any one of claims 4-7 being installed inside the housing, with a measurement hole (6) provided on the side wall of the housing.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the high-precision laser tilt angle integrated measurement method as described in any one of claims 1 to 3.

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