Intelligent monitoring system for plain concrete pile construction based on Beidou positioning system

Abstract

The invention discloses a plain concrete pile construction intelligent monitoring system based on a Beidou positioning system, and relates to the technical field of data processing, the Beidou positioning data of each plain concrete pile is acquired, and the Beidou data qualification coefficient is calculated; acquiring sensor data and calculating a sensor data qualification coefficient; whether the monitoring data of the corresponding plain concrete piles are qualified or not is judged in combination with a corresponding preset qualified threshold value; when the monitoring data of the plain concrete piles are qualified, the sequence of uploading the monitoring data of all the plain concrete piles to the server is determined according to the Beidou data qualification coefficient, the sensor data qualification coefficient and the construction time of each plain concrete pile, and the accuracy of the monitoring data of plain concrete pile construction can be self-checked; and the monitoring data with qualified accuracy can be sequentially transmitted to the server, so that the problems of data conflict and overburden of the system possibly caused when all the data are simultaneously uploaded to the server can be effectively avoided, and the reliability and decision efficiency of the whole construction monitoring system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly to an intelligent monitoring system for the construction of plain concrete piles based on the Beidou positioning system. Background Art

[0002] The monitoring of the construction of concrete piles based on the Beidou positioning system refers to the use of Beidou satellite positioning technology to monitor and track the position of the piles at the construction site in real time, ensuring the positioning accuracy and construction progress of the piles during the construction process. By combining the Beidou positioning system with other sensor data (such as pressure, temperature, humidity, etc.), the system can provide a comprehensive feedback on the construction status, identify potential risks in real time, optimize the construction plan, and improve the construction quality and safety. This monitoring system can not only transmit the position information in real time, but also, through data analysis and warning mechanisms, detect abnormalities in the construction in advance, help the construction personnel adjust the construction strategy in time, and avoid construction problems caused by positioning errors or environmental changes.

[0003] The above-mentioned monitoring data is generally divided into two types of data, namely Beidou positioning data and sensor data. Generally, after obtaining these two types of monitoring data, the system will transmit the data to the server for centralized processing and storage in real time; the server, according to the preset algorithms and analysis models, conducts position accuracy verification and construction progress analysis on the Beidou positioning data, and at the same time combines the sensor data to monitor environmental parameters, such as the trend analysis of changes in temperature, humidity, pressure, etc.; through data fusion and intelligent analysis, the server can evaluate the status of the construction site in real time, generate detailed reports or warning information, and feedback them to the construction personnel or the project management team in time to ensure the construction quality and safety; at the same time, the data will also be archived for later review, analysis, and optimization of the construction process.

[0004] However, when the existing monitoring data of the construction of plain concrete piles based on the Beidou positioning system is uploaded to the server, it is often assumed that the obtained monitoring data is accurate, lacking an accuracy check on the monitoring data. In addition, uploading all the monitoring data of the plain concrete piles to the server at the same time may cause data upload conflicts, resulting in a decrease in positioning accuracy or data loss. Moreover, if there are errors in the monitoring data and they are still uploaded to the server, it will further increase the storage and processing burden of the server, and at the same time will further cause the server to make wrong decisions based on the incorrect monitoring data, affecting the reliability of the entire construction monitoring system. Summary of the Invention

[0005] The object of the present invention is to solve the above-mentioned problems and provide an intelligent monitoring system for the construction of plain concrete piles based on the Beidou positioning system.

[0006] The present invention provides an intelligent monitoring system for the construction of plain concrete piles based on the Beidou positioning system. The system includes:

[0007] Beidou data module: Obtain the Beidou positioning data corresponding to each plain concrete pile, calculate the accuracy anomaly coefficient and the positioning instability coefficient according to the Beidou positioning data, and calculate the Beidou data qualification coefficient according to the accuracy anomaly coefficient and the positioning instability coefficient;

[0008] Sensor data module: Obtain the sensor data corresponding to each plain concrete pile, calculate the data synchronization coefficient and the data fluctuation intensity coefficient according to the sensor data; and calculate the sensor data qualification coefficient according to the data synchronization coefficient and the data fluctuation intensity coefficient;

[0009] Comparison and judgment module: Judge whether the monitoring data of the corresponding plain concrete pile is qualified according to the Beidou data qualification coefficient, the sensor data qualification coefficient and the corresponding preset qualification threshold;

[0010] Monitoring data transmission module: When the monitoring data of the plain concrete pile is qualified, according to the Beidou data qualification coefficient and the sensor data qualification coefficient of each plain concrete pile, and obtain the construction time of the corresponding plain concrete pile, determine the order of uploading the monitoring data of all plain concrete piles to the server.

[0011] Optionally, calculating the accuracy anomaly coefficient according to the Beidou positioning data includes:

[0012] For each plain concrete pile, obtain the Beidou positioning data within a preset time period multiple times, extract the corresponding horizontal accuracy and vertical accuracy from each Beidou positioning data, and sort them in chronological order to obtain the accuracy sequence P; P = {(w 1 , y 1 ), (w 2 , y 2 ).....(w n , y n )}, where, w n represents the horizontal accuracy of the nth Beidou positioning data; y n represents the vertical accuracy of the nth Beidou positioning data; n represents the total number of times of positioning the Beidou positioning data within the preset time period, and n is a positive integer;

[0013] Compare the horizontal accuracy and vertical accuracy of each Beidou positioning data in the accuracy sequence with the preset horizontal accuracy threshold and the preset vertical accuracy threshold respectively. If the horizontal accuracy is not less than the preset horizontal accuracy threshold or the vertical accuracy is not less than the preset vertical accuracy threshold, then record the Beidou positioning data of this time as low-accuracy positioning;

[0014] Calculate the accuracy anomaly coefficient according to the total number of low-accuracy positions in the accuracy sequence P and the total number of positions in the accuracy sequence P. The calculation formula is: In the formula, PK is the accuracy anomaly coefficient, and v represents the total number of low-accuracy positions in the accuracy sequence P.

[0015] Optionally, calculating the positioning instability coefficient based on the Beidou positioning data includes:

[0016] For each plain concrete pile, obtain the Beidou positioning data within a preset time period multiple times, extract the corresponding longitude, latitude, and altitude from the Beidou positioning data each time, and sort them in chronological order to obtain a positioning coordinate sequence Q; Q = {(z 1 , d 1 , u 1 ), (z 2 , d 2 , u 2 ).....(z n , d n , u n )}, where z n represents the longitude of the nth Beidou positioning data; d n represents the latitude of the nth Beidou positioning data, and u n represents the altitude of the nth Beidou positioning data; n represents the total number of times of positioning by the Beidou positioning data within the preset time period, and n is a positive integer;

[0017] Calculate the mean value ze of longitude, the mean value de of latitude, and the mean value ue of altitude in the positioning coordinate sequence;

[0018] Calculate the longitude fluctuation value zf, and the calculation formula is: In the formula, z i represents the longitude of the ith Beidou positioning data; calculate the latitude fluctuation value df, and the calculation formula is: In the formula, d i represents the latitude of the ith Beidou positioning data; calculate the altitude fluctuation value uf, and the calculation formula is: In the formula, u i represents the altitude of the ith Beidou positioning data;

[0019] Calculate the positioning instability coefficient, and the calculation formula is: In the formula, RG is the positioning instability coefficient.

[0020] Optionally, calculating the Beidou data qualification coefficient based on the accuracy anomaly coefficient and the positioning instability coefficient includes:

[0021]

[0022] In the formula, GHK is the Beidou data qualification coefficient, PK and RG are the accuracy anomaly coefficient and the positioning instability coefficient respectively, a1 and a2 are the preset proportionality coefficients of the accuracy anomaly coefficient and the positioning instability coefficient respectively, and both a1 and a2 are greater than 0.

[0023] Optionally, calculating the data synchronization coefficient based on the sensor data includes:

[0024] For each plain concrete pile, obtain the timestamps of the data collected by each sensor within a preset time period to obtain the data collection timestamp sequence of the corresponding sensor;

[0025] Calculate the mean deviation between the timestamps of each pair of sensors based on the data collection timestamp sequences of each pair of sensors, and calculate the data synchronization value SER between the timestamps of this pair of sensors. The calculation formula is: In the formula, ΔT c is the mean deviation between the timestamps of this pair of sensors, T max is the maximum deviation threshold between the timestamps of this pair of sensors preset; c is the sequence number of the order of the data collection times of the plain concrete pile, and k represents the total number of data collections of the plain concrete pile;

[0026] Calculate the data synchronization coefficient between all sensors based on the data synchronization values between the timestamps of each pair of sensors. The calculation formula is: In the formula, BN is the data synchronization coefficient, M is the total number of pairs of sensors, and j is the sequence number of the order of the pairs of sensors.

[0027] Optionally, calculating the data fluctuation intensity coefficient based on the sensor data includes:

[0028] For each plain concrete pile, obtain the collected data of each sensor within a preset time period to obtain the collected data sequence of the corresponding sensor;

[0029] For each sensor, calculate the standard deviation of the corresponding collected data sequence as the fluctuation intensity value of the corresponding sensor;

[0030] Calculate the mean value of the fluctuation intensity values of all sensors to obtain the data fluctuation intensity coefficient.

[0031] Optionally, calculating the sensor data qualification coefficient based on the data synchronization coefficient and the data fluctuation intensity coefficient includes:

[0032]

[0033] In the formula, TYU is the sensor data qualification coefficient, BN and BR are the data synchronization coefficient and the data fluctuation intensity coefficient respectively, and b1 and b2 are the preset proportionality coefficients of the data synchronization coefficient and the data fluctuation intensity coefficient respectively, and both b1 and b2 are greater than 0.

[0034] Optionally, judging whether the monitoring data of the corresponding plain concrete pile is qualified according to the Beidou data qualification coefficient, the sensor data qualification coefficient and the corresponding preset qualification threshold includes:

[0035] Compare the Beidou data qualification coefficient of each plain concrete pile with the preset Beidou data qualification coefficient threshold, and compare the sensor data qualification coefficient with the preset sensor data qualification coefficient threshold;

[0036] If the Beidou data qualification coefficient of the plain concrete pile is not less than the preset Beidou data qualification coefficient threshold, and the sensor data qualification coefficient is not less than the preset sensor data qualification coefficient threshold, then the monitoring data of the corresponding plain concrete pile is qualified; otherwise, the monitoring data of the corresponding plain concrete pile is unqualified, and the monitoring data of the corresponding plain concrete pile is recollected until the monitoring data is qualified.

[0037] Optionally, according to the Beidou data qualification coefficient and sensor data qualification coefficient of each plain concrete pile, and obtaining the construction time of the corresponding plain concrete pile, determining the order of uploading the monitoring data of all plain concrete piles to the server includes:

[0038] Obtain the total planned construction time and the actual constructed time of each plain concrete pile, divide the actual constructed time by the total planned construction time to obtain the construction progress value;

[0039] Obtain the priority transfer coefficient of the monitoring data of the plain concrete pile according to the Beidou data qualification coefficient, sensor data qualification coefficient and construction progress value, and the calculation formula is:

[0040]

[0041] In the formula, Hxg is the priority transfer coefficient, GHK, TYU, and Bt are the Beidou data qualification coefficient, sensor data qualification coefficient and construction progress value respectively, f1, f2, and f3 are the preset proportional coefficients of GHK, TYU, and Bt respectively, and f1, f2, and f3 are all greater than 0;

[0042] Transfer the monitoring data of the corresponding plain concrete pile to the server in descending order of the priority transfer coefficient. The beneficial effects of the present invention:

[0043] The present invention provides an intelligent monitoring system for the construction of plain concrete piles based on the Beidou positioning system. For each plain concrete pile, the Beidou positioning data corresponding to it is obtained to calculate the Beidou data qualification coefficient; the sensor data corresponding to it is obtained to calculate the sensor data qualification coefficient; and whether the monitoring data of the corresponding plain concrete pile is qualified is judged according to the Beidou data qualification coefficient, the sensor data qualification coefficient and the corresponding preset qualification threshold. When the monitoring data of the plain concrete pile is qualified, according to the Beidou data qualification coefficient and the sensor data qualification coefficient of each plain concrete pile, and by obtaining the construction time of the corresponding plain concrete pile, the sequence of uploading all the monitoring data of the plain concrete piles to the server is determined. In this way, the accuracy of the monitoring data of the construction of the plain concrete piles can be self-checked, and the monitoring data of the plain concrete piles with qualified accuracy can be sequentially transmitted to the server, which can effectively avoid the problems of data conflict and overloading of the system that may occur when all data is uploaded to the server at the same time, ensure the timeliness and accuracy of the data, reduce the wrong decisions caused by incorrect data, and thus improve the reliability and decision-making efficiency of the entire construction monitoring system. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The present invention will be further described below with reference to the accompanying drawings.

[0045] Figure 1 It is a framework diagram of an intelligent monitoring system for the construction of plain concrete piles based on the Beidou positioning system. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0046] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0047] Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0048] The embodiments of the present invention provide an intelligent monitoring system for the construction of plain concrete piles based on the Beidou positioning system. Refer to Figure 1 , Figure 1 which is a framework diagram of the intelligent monitoring system for the construction of plain concrete piles based on the Beidou positioning system provided by the embodiments of the present invention. The system includes:

[0049] Beidou data module: obtaining the Beidou positioning data corresponding to each plain concrete pile, calculating the accuracy anomaly coefficient and the positioning instability coefficient according to the Beidou positioning data, and calculating the Beidou data qualification coefficient according to the accuracy anomaly coefficient and the positioning instability coefficient;

[0050] Sensor data module: Obtain the sensor data corresponding to each plain concrete pile, calculate the data synchronization coefficient and the data fluctuation intensity coefficient according to the sensor data; and calculate the sensor data qualification coefficient according to the data synchronization coefficient and the data fluctuation intensity coefficient;

[0051] Comparison and judgment module: Judge whether the monitoring data of the corresponding plain concrete pile is qualified according to the Beidou data qualification coefficient, the sensor data qualification coefficient and the corresponding preset qualification threshold;

[0052] Monitoring data transmission module: When the monitoring data of the plain concrete pile is qualified, according to the Beidou data qualification coefficient and the sensor data qualification coefficient of each plain concrete pile, and obtain the construction time of the corresponding plain concrete pile, determine the order of uploading the monitoring data of all plain concrete piles to the server.

[0053] Based on the intelligent monitoring system for plain concrete pile construction provided by the embodiment of the present invention, it can self-check the accuracy of the monitoring data of plain concrete pile construction, and can transmit the qualified plain concrete pile monitoring data to the server in sequence, which can effectively avoid the data conflict and the problem of overloaded system burden that may occur when all data are uploaded to the server at the same time, ensure the timeliness and accuracy of the data, reduce the wrong decisions caused by wrong data, and thus improve the reliability and decision-making efficiency of the entire construction monitoring system.

[0054] In one embodiment, calculating the accuracy anomaly coefficient according to the Beidou positioning data includes:

[0055] For each plain concrete pile, obtain the Beidou positioning data within a preset time period multiple times, extract the corresponding horizontal accuracy and vertical accuracy from each Beidou positioning data, and sort them in chronological order to obtain the accuracy sequence P; P = {(w 1 , y 1 ), (w 2 , y 2 ).....(w n , y n )}, where w n represents the horizontal accuracy of the nth Beidou positioning data; y n represents the vertical accuracy of the nth Beidou positioning data; n represents the total number of times of positioning of the Beidou positioning data within the preset time period, and n is a positive integer;

[0056] Compare the horizontal accuracy and vertical accuracy of each Beidou positioning data in the accuracy sequence with the preset horizontal accuracy threshold and the preset vertical accuracy threshold respectively. If the horizontal accuracy is not less than the preset horizontal accuracy threshold or the vertical accuracy is not less than the preset vertical accuracy threshold, then record the Beidou positioning data of this time as low-accuracy positioning;

[0057] Calculate the accuracy anomaly coefficient based on the total number of low-accuracy positioning in the accuracy sequence P and the total number of positioning in the accuracy sequence P. The calculation formula is as follows: In the formula, PK is the accuracy anomaly coefficient, and v represents the total number of low-accuracy positioning in the accuracy sequence P.

[0058] It should be noted that each plain concrete pile is set by professionals according to the actual situation, and specific details are not limited and elaborated here. In addition, the Beidou positioning data of each plain concrete pile can be obtained through the Beidou positioning module installed inside the plain concrete pile. The Beidou positioning module provides high-precision Beidou positioning data, including longitude (the east-west position of the positioning point, usually in degrees (°)), latitude (the north-south position of the positioning point, also in degrees (°)), altitude (the height of the positioning point relative to the sea level), horizontal accuracy (the horizontal component of the positioning accuracy, usually a numerical value, the smaller the value, the higher the accuracy and the smaller the accuracy error), vertical accuracy (representing the vertical component of the positioning accuracy, also a numerical value, and this value is smaller when the accuracy is higher), and timestamp (the acquisition time of the positioning data, usually in UTC time format), and extract the corresponding horizontal accuracy and vertical accuracy from the Beidou positioning data.

[0059] It should be noted that both the preset horizontal accuracy threshold and the preset vertical accuracy threshold are set by professionals according to the actual situation, usually determined by engineering specifications or construction standards. These thresholds set the minimum requirements for accuracy. If the horizontal accuracy or vertical accuracy of a certain positioning is lower than the preset threshold, it is considered that the accuracy of this positioning is unqualified, which may affect the accuracy and safety of construction.

[0060] It should be noted that the accuracy anomaly coefficient of each plain concrete pile refers to the degree to which the horizontal accuracy of the positioning is not less than the preset horizontal accuracy threshold and the vertical accuracy is not less than the preset vertical accuracy threshold during Beidou positioning. If the accuracy anomaly coefficient is larger, it means that the degree of unqualified Beidou positioning data of the plain concrete pile is larger, the monitoring data of the plain concrete pile is more unqualified, and it is less necessary to upload it to the server. Because the larger the accuracy anomaly coefficient, it means that there are more low-accuracy positionings in the Beidou positioning data of the plain concrete pile, and both the horizontal accuracy and the vertical accuracy do not meet the preset accuracy requirements. This indicates that there are relatively large errors in the positioning information of this pile body, which may lead to an increase in position errors during construction, thereby affecting the construction progress, quality, and safety. Therefore, when the accuracy anomaly coefficient is relatively large, it indicates that the monitoring data of this plain concrete pile is unreliable. Uploading these data may cause the server to receive inaccurate position information, thereby affecting the judgment and decision-making of the overall construction monitoring system. In addition, uploading low-accuracy data will increase the processing burden on the server, waste system resources, and may even lead to data conflicts and losses. Therefore, these unqualified data should be avoided from being uploaded to the server to avoid unnecessary risks and misjudgments.

[0061] In one implementation, the advantage of analyzing the precision anomaly coefficient of the plain concrete pile for judging the accuracy of the monitoring data of the plain concrete pile is as follows: through the precision anomaly coefficient, the precision status of the Beidou positioning data of each plain concrete pile can be quantified, helping the system to timely identify which piles have large errors in the positioning data. When the precision anomaly coefficient is relatively high, it means that the reliability of the positioning data is relatively poor, which helps to avoid the transmission of incorrect position information to the server, thereby preventing incorrect decisions, construction problems and safety hazards caused by inaccurate data. At the same time, the precision anomaly coefficient can also serve as a screening mechanism to help the system screen out low-precision data, reduce the transmission of invalid information, reduce the processing pressure of the server, and improve the monitoring precision and efficiency of the overall system.

[0062] In one embodiment, calculating the positioning instability coefficient based on the Beidou positioning data includes:

[0063] For each plain concrete pile, obtain the Beidou positioning data within a preset time period multiple times, extract the corresponding longitude, latitude, and altitude from the Beidou positioning data each time, and sort them in chronological order to obtain the positioning coordinate sequence Q; Q = {(z 1 , d 1 , u 1 ), (z 2 , d 2 , u 2 ).....(z n , d n , u n )}, where z n represents the longitude of the nth Beidou positioning data; d n represents the latitude of the nth Beidou positioning data, and u n represents the altitude of the nth Beidou positioning data; n represents the total number of times of positioning of the Beidou positioning data within the preset time period, and n is a positive integer;

[0064] Calculate the mean value ze of longitude, the mean value de of latitude, and the mean value ue of altitude in the positioning coordinate sequence;

[0065] Calculate the longitude fluctuation value zf, and the calculation formula is: In the formula, z i represents the longitude of the ith Beidou positioning data; calculate the latitude fluctuation value df, and the calculation formula is: In the formula, d i represents the latitude of the ith Beidou positioning data; calculate the altitude fluctuation value uf, and the calculation formula is: In the formula, u i represents the altitude of the ith Beidou positioning data;

[0066] Calculate the positioning instability coefficient, and the calculation formula is as follows: In the formula, RG is the positioning instability coefficient.

[0067] It should be noted that the positioning instability coefficient refers to the degree of fluctuation of the positioning longitude, latitude, and altitude during Beidou positioning. If the positioning instability coefficient is larger, it means that the Beidou positioning data of the plain concrete pile is more unqualified, the monitoring data of the plain concrete pile is more unqualified, and it is less necessary to upload it to the server. Because the larger the positioning instability coefficient, the greater the fluctuation of the positioning longitude, latitude, and altitude, indicating poor stability of the positioning data and possible large positioning errors. If the instability coefficient is relatively high, it means that the Beidou positioning data of the plain concrete pile is unreliable, which may lead to deviation of the pile body position, thereby affecting the construction accuracy, progress, and safety. At this time, uploading these unstable data to the server may lead to incorrect construction judgments, decision-making mistakes, and increase the processing burden of the system. To ensure the accuracy and effectiveness of the construction monitoring system, these unqualified monitoring data should be avoided from being uploaded to prevent inaccurate data from affecting the overall construction process.

[0068] In one implementation manner, the benefit of analyzing the positioning instability coefficient of the plain concrete pile for judging the accuracy of the monitoring data of the plain concrete pile is as follows: By calculating the positioning instability coefficient, the degree of fluctuation of the Beidou positioning data of each plain concrete pile within a time period can be effectively measured, and those positioning data with large fluctuations and instability can be identified. If the positioning instability coefficient is relatively high, it means that the positioning data of the pile body has a large uncertainty in space and may have large errors. If such data is uploaded to the server, it will have a serious impact on the construction progress, quality, and safety judgment. Through this coefficient, the system can timely detect unstable positioning data and take measures to avoid its upload, ensuring that the uploaded data is more reliable, thereby improving the accuracy of construction monitoring and the effectiveness of decision-making, optimizing the use of resources, and reducing the risks brought by inaccurate information.

[0069] In one embodiment, calculating the Beidou data qualification coefficient according to the precision anomaly coefficient and the positioning instability coefficient includes:

[0070]

[0071] In the formula, GHK is the Beidou data qualification coefficient, PK and RG are the precision anomaly coefficient and the positioning instability coefficient respectively, a1 and a2 are the preset proportional coefficients of the precision anomaly coefficient and the positioning instability coefficient respectively, and both a1 and a2 are greater than 0;

[0072] It should be noted that before calculating the qualified coefficient of Beidou data, it is necessary to remove the unit and normalize the precision anomaly coefficient and the positioning instability coefficient. Common normalization methods include Min-Max normalization, Z-Score standardization, etc. a1 and a2 are set according to the actual situation. For example, the expert weighting method is adopted, that is, experts in related fields are invited to determine the preset proportional coefficients of various indicators through professional opinion surveys and comprehensive evaluations; generally, the sum of a1 and a2 is 1, and the specific values are determined according to the actual situation, without limitation and elaboration.

[0073] It can be seen from the above calculation expressions that the larger the precision anomaly coefficient and the positioning instability coefficient are, the smaller the qualified coefficient of Beidou data is, indicating that the Beidou positioning data of the corresponding plain concrete pile is less accurate during construction; on the contrary, the smaller the precision anomaly coefficient and the positioning instability coefficient are, the larger the qualified coefficient of Beidou data is, indicating that the Beidou positioning data of the corresponding plain concrete pile is more accurate during construction.

[0074] In one embodiment, calculating the data synchronization coefficient according to the sensor data includes:

[0075] For each plain concrete pile, obtain the timestamps of each sensor's collected data within a preset time period to obtain the data collection timestamp sequence of the corresponding sensor, and mark each timestamp in the data collection timestamp sequence as T pc , T pc represents the timestamp of the c-th collection of plain concrete pile data by the p-th type of sensor, where c is the order number of the plain concrete pile data collection times, and p ∈ [1, l], c ∈ [1, k], l represents the total number of sensor types, and k represents the total number of plain concrete pile data collections;

[0076] Calculate the deviation mean between the timestamps of each pair of sensors according to the data collection timestamp sequences of each pair of sensors, and calculate the data synchronization value SER between the timestamps of this pair of sensors. The calculation formula is: In the formula, ΔT c is the deviation mean between the timestamps of this pair of sensors, and T max is the maximum deviation threshold between the timestamps of this pair of sensors preset;

[0077] Calculate the data synchronization coefficient between all sensors according to the data synchronization values between the timestamps of each pair of sensors. The calculation formula is: In the formula, BN is the data synchronization coefficient, M is the total number of sensor pairs, and j is the order number of the sensor pairs.

[0078] It should be noted that during the construction process of the plain concrete pile, multiple sensors are usually installed to monitor various states of the pile body to ensure construction quality and safety; and one sensor of each type is installed to monitor various states of the pile body; common sensor types include: displacement sensor: used to monitor the horizontal and vertical displacements of the pile body to ensure the stability of the pile body during construction; pressure sensor: used to measure the pressure changes of the soil or concrete to help judge whether the pile body is uniformly stressed and whether there is a risk of excessive compression; temperature sensor: monitors the temperature changes of the concrete during construction to avoid concrete cracking caused by excessive temperature differences; humidity sensor: measures the humidity of the soil or concrete to ensure that the concrete hardens at an appropriate humidity and monitors the humidity changes in the construction environment; vibration sensor: used to detect whether there are abnormal vibrations during construction to avoid the pile body being interfered by the outside world or improper operation of construction equipment; strain sensor: monitors the strain changes of the pile body under the action of force to warn of possible structural problems; these sensors will regularly collect data within a preset time period and transmit it through the sensor data acquisition system. The monitoring data of the plain concrete pile usually includes real-time values related to the pile body position, stress, temperature, humidity, displacement, etc., reflecting various environmental factors during the construction process and the health status of the pile body. Through these data, it is possible to judge in real time whether there are potential risks during the construction process, and then take corresponding adjustment measures to ensure the construction progress and quality. At the same time, indicators such as the data synchronization coefficient and the precision anomaly coefficient help to ensure the reliability and accuracy of the data, avoiding construction decision-making errors caused by data errors or asynchronization.

[0079] It should be noted that within the preset time period, the number of data collected by each sensor type is usually the same, because the system usually synchronizes different types of sensors according to the same time interval or sampling frequency. For example, all sensors (such as temperature, pressure, displacement, etc.) may collect data at intervals of one second, one minute, or other fixed time intervals to ensure that the monitoring data of various sensors are recorded on the same time scale. This synchronous sampling method ensures that the amount of data between sensors is consistent, facilitating subsequent comparison and analysis of data from different sensors, reducing errors that may be caused by data inconsistency, and improving the data integrity and reliability of the monitoring system.

[0080] It should be noted that the maximum deviation threshold between the time stamps of the pair of sensors preset is set by professionals according to the actual situation, which is determined according to the actual situation and will not be limited and elaborated.

[0081] It should be noted that the data synchronization coefficient refers to the synchronization status of different sensors of each plain concrete pile when collecting data. The smaller the data synchronization coefficient, the greater the degree of unqualified sensor data of the plain concrete pile, the more unqualified the monitoring data of the plain concrete pile, and the less necessary it is to upload to the server. Because poor data synchronization means that there are large deviations in the time of the data collected by the sensors, which may lead to inaccurate or inconsistent analysis results. For example, if sensors such as temperature, pressure, and displacement cannot accurately collect data within the same time window, it may miss key environmental changes or construction progress, thus affecting the judgment of the construction status by the entire monitoring system. Therefore, when the data synchronization coefficient is low, the data uploaded to the server may not effectively reflect the actual construction status, and may even mislead subsequent decisions and risk assessments, resulting in unnecessary resource waste or safety hazards.

[0082] In one implementation, the benefits of analyzing the data synchronization coefficient of the plain concrete pile for judging the accuracy of the monitoring data of the plain concrete pile are as follows: By calculating the data synchronization coefficient, it is possible to objectively evaluate whether the data collection among different sensors is coordinated and consistent, and ensure that various sensors provide accurate and consistent monitoring data within the same time window. If the data synchronization coefficient is high, it indicates that the sensor data can accurately reflect the construction status at the same moment, which helps to improve the overall accuracy and reliability of the monitoring data, thus providing a more credible basis for decision-making during the construction process. On the contrary, if the data synchronization coefficient is low, it may indicate that there is a time deviation in the sensors, resulting in data distortion, thus affecting the evaluation of the construction status and the early warning of potential risks, and increasing the difficulty of construction management. Through this analysis, unqualified sensor data can be identified in a timely manner, avoiding uploading unreliable data to the server, and improving the overall efficiency and safety of the system.

[0083] In one embodiment, calculating the data fluctuation intensity coefficient according to the sensor data includes:

[0084] For each plain concrete pile, obtain the collected data of each sensor within a preset time period to obtain the collected data sequence of the corresponding sensor;

[0085] For each sensor, calculate the standard deviation of the corresponding collected data sequence as the fluctuation intensity value of the corresponding sensor;

[0086] Calculate the mean value of the fluctuation intensity values of all sensors to obtain the data fluctuation intensity coefficient.

[0087] It should be noted that the data fluctuation intensity coefficient refers to the degree of fluctuation of the data collected by each sensor of the plain concrete pile. If the data fluctuation intensity coefficient is smaller, it means that the degree of unqualified sensor data of the plain concrete pile is greater, and the monitoring data of the plain concrete pile is more unqualified, and it is less necessary to upload it to the server. Because the data fluctuation intensity coefficient reflects the stability of the data collected by each sensor of the plain concrete pile. If the coefficient is smaller, it means that the fluctuation range of the sensor-collected data is lower and tends to be consistent, and there may be problems such as data anomalies or collection errors, such as sensor malfunctions and data being interfered by the outside world. At this time, the accuracy and reliability of the monitoring data cannot be guaranteed, and uploading it to the server may lead to deviations in the system analysis results and cannot correctly reflect the actual state of the plain concrete pile. Therefore, this type of data does not need to be uploaded to avoid affecting subsequent decision-making and construction quality assessment.

[0088] In one implementation, the benefits of analyzing the data fluctuation intensity coefficient of the plain concrete pile for judging the accuracy of the monitoring data of the plain concrete pile are as follows: Analyzing the data fluctuation intensity coefficient of the plain concrete pile helps to quantify the stability of the data collected by the sensor, so as to judge the accuracy of the monitoring data of the plain concrete pile. By calculating the fluctuation intensity value of the data collected by each sensor and comprehensively obtaining the data fluctuation intensity coefficient, abnormal situations with too large or too small data fluctuations can be quickly screened out. For example, too large fluctuations may indicate that the sensor data is affected by the external environment or equipment aging problems, while too small fluctuations may mean sensor malfunctions or data collection delays. In addition, this coefficient can provide an intuitive reference index for the construction monitoring system, help to detect and correct data anomalies in a timely manner, ensure that the data uploaded to the server has high reliability and consistency, and thus improve the quality of the overall monitoring data and the accuracy of decision-making.

[0089] In one embodiment, calculating the sensor data qualification coefficient according to the data synchronization coefficient and the data fluctuation intensity coefficient includes:

[0090]

[0091] In the formula, TYU is the sensor data qualification coefficient, BN and BR are the data synchronization coefficient and the data fluctuation intensity coefficient respectively, b1 and b2 are the preset proportionality coefficients of the data synchronization coefficient and the data fluctuation intensity coefficient respectively, and both b1 and b2 are greater than 0;

[0092] It should be noted that before calculating the qualified coefficient of sensor data, it is necessary to remove the units and normalize the data synchronization coefficient and the data fluctuation intensity coefficient. Common normalization methods include Min-Max normalization, Z-Score standardization, etc. a1 and a2 are set according to the actual situation. For example, the expert weighting method is adopted, that is, experts in the relevant field are invited to determine the preset proportional coefficients of each index through professional opinion surveys and comprehensive evaluations; generally, the sum of b1 and b2 is 1, and the specific values are determined according to the actual situation, without further limitation or elaboration.

[0093] As can be seen from the above calculation expressions, the larger the data synchronization coefficient and the smaller the data fluctuation intensity coefficient, the larger the qualified coefficient of sensor data, indicating that the sensor data of the corresponding plain concrete pile is more accurate during construction; conversely, the smaller the data synchronization coefficient and the larger the data fluctuation intensity coefficient, the smaller the qualified coefficient of sensor data, indicating that the sensor data of the corresponding plain concrete pile is less accurate during construction.

[0094] In one embodiment, determining whether the monitoring data of the corresponding plain concrete pile is qualified according to the Beidou data qualified coefficient, the sensor data qualified coefficient, and the corresponding preset qualified threshold includes:

[0095] Compare the Beidou data qualified coefficient of each plain concrete pile with the preset Beidou data qualified coefficient threshold, and compare the sensor data qualified coefficient with the preset sensor data qualified coefficient threshold.

[0096] If the Beidou data qualified coefficient of the plain concrete pile is not less than the preset Beidou data qualified coefficient threshold, and the sensor data qualified coefficient is not less than the preset sensor data qualified coefficient threshold, then the monitoring data of the corresponding plain concrete pile is qualified; otherwise, the monitoring data of the corresponding plain concrete pile is unqualified, and the monitoring data of the corresponding plain concrete pile is re-collected until the monitoring data is qualified.

[0097] It should be noted that the preset Beidou data qualified coefficient threshold and the preset sensor data qualified coefficient threshold are set by professionals according to the actual situation, without further detailed elaboration and limitation.

[0098] In one implementation manner, by comprehensively comparing the Beidou data qualified coefficient and the sensor data qualified coefficient with their preset thresholds, it can be ensured that the monitoring data of the plain concrete pile meets the qualified standards in terms of positioning and sensor performance. This dual verification mechanism can effectively filter out abnormal data caused by data fluctuations, time synchronization deviations, or positioning accuracy problems, ensuring that the data uploaded to the server is more accurate and reliable. At the same time, through the real-time re-collection and inspection of unqualified data, potential monitoring problems can be discovered and corrected in a timely manner, improving the monitoring quality of the plain concrete pile and ensuring the scientificity and accuracy of subsequent analysis and project management.

[0099] In one embodiment, according to the Beidou data qualification coefficient and the sensor data qualification coefficient of each plain concrete pile, and obtaining the construction time corresponding to the plain concrete pile, determining the order of uploading the monitoring data of all plain concrete piles to the server includes:

[0100] Obtain the total planned construction time and the actual constructed time of each plain concrete pile, divide the actual constructed time by the total planned construction time to obtain the construction progress value;

[0101] Obtain the priority transfer coefficient of the monitoring data of the plain concrete pile according to the Beidou data qualification coefficient, the sensor data qualification coefficient and the construction progress value. The calculation formula is:

[0102]

[0103] In the formula, Hxg is the priority transfer coefficient, GHK, TYU, and Bt are the Beidou data qualification coefficient, the sensor data qualification coefficient and the construction progress value respectively, f1, f2, and f3 are the preset proportional coefficients of GHK, TYU, and Bt respectively, and f1, f2, and f3 are all greater than 0;

[0104] Transfer the monitoring data of the corresponding plain concrete pile to the server in turn according to the order from large to small of the priority transfer coefficient.

[0105] It should be noted that before calculating the priority transfer coefficient, it is necessary to remove the units and perform normalization processing on the Beidou data qualification coefficient, the sensor data qualification coefficient and the construction progress value. Common normalization processing methods include Min-Max normalization, Z-Score standardization, etc. f1, f2, and f3 are set according to the actual situation. For example, the expert weighting method is adopted, that is, relevant experts in the field are invited to determine the preset proportional coefficients of each index through professional opinion surveys and comprehensive evaluations; generally, the sum of f1, f2, and f3 is 1, and the specific values are determined according to the actual situation, without limitation and elaboration.

[0106] It should be noted that the total planned construction time of each plain concrete pile can be obtained through the construction plan management system or the construction plan preset in the project schedule, usually formulated in advance by the construction party according to the project design requirements and construction plan; the actual constructed time can be obtained through the operation records of on-site construction equipment (such as the working time log of the pile driver), the timestamp records of the construction monitoring system, or the construction daily report manually entered. These data are usually uploaded and stored in real time through the sensors of on-site equipment or the construction site management system.

[0107] It should be noted that when the Beidou data qualification coefficient of the plain concrete pile, the sensor data qualification coefficient, and the construction progress value are larger, the priority of transmitting the monitoring data of the corresponding concrete pile to the server is higher. Because when the Beidou data qualification coefficient of the plain concrete pile is larger, it means that its positioning accuracy and stability are higher, and it can more accurately reflect the position changes and related dynamics during the construction process; the larger the sensor data qualification coefficient, the more synchronous the data collected by the sensor and the smaller the fluctuation range, and the stronger the reliability and accuracy of the data; the larger the construction progress value, it indicates that the construction of the plain concrete pile is approaching completion and is in a critical stage, and its data needs to be processed preferentially to timely judge the construction quality and safety. Therefore, uploading these high-priority monitoring data to the server first can ensure that the data at key nodes are processed in time, thereby supporting real-time construction monitoring and decision-making, and avoiding affecting the overall construction progress and quality management due to data delay.

[0108] In one implementation manner, according to the order from large to small of the priority transfer coefficient, the monitoring data of the corresponding plain concrete pile are sequentially transmitted to the server, which can effectively avoid the problems of data conflict and overloading of the system burden that may occur when all data are uploaded to the server at the same time. Prioritizing the upload of high-quality positioning data and sensor data can ensure that the system processes the most accurate and stable data, thereby improving the positioning accuracy and reducing the risks caused by data errors. In addition, through this ordered upload method, the system can reasonably allocate server resources, avoid overloading of storage and processing, ensure the timeliness and accuracy of data, reduce wrong decisions caused by wrong data, and thus improve the reliability and decision-making efficiency of the entire construction monitoring system.

[0109] The above has described an embodiment of the present invention in detail, but the content described is only a preferred embodiment of the present invention and cannot be used to artificially limit the scope of implementation of the present invention. All equivalent changes and improvements made according to the scope of the present invention application should still fall within the scope covered by the patent of the present invention.

Claims

1. The intelligent monitoring system for the construction of plain concrete piles based on the Beidou positioning system is characterized by: The system comprises: Beidou data module: obtain the Beidou positioning data corresponding to each plain concrete pile, and calculate the accuracy anomaly coefficient and positioning instability coefficient based on the Beidou positioning data, and calculate the Beidou data qualification coefficient based on the accuracy anomaly coefficient and positioning instability coefficient; Sensor data module: obtains the sensor data corresponding to each plain concrete pile, and calculates the data synchronization coefficient and the data fluctuation intensity coefficient according to the sensor data; and calculates the sensor data qualification coefficient according to the data synchronization coefficient and the data fluctuation intensity coefficient; Comparison and judgment module: judge whether the monitoring data of the corresponding plain mixed soil pile is qualified according to the Beidou data qualification coefficient, the sensor data qualification coefficient and the corresponding preset qualification threshold; Monitoring data transmission module: When the monitoring data of the plain concrete piles is qualified, the order in which the monitoring data of all the plain concrete piles are uploaded to the server is determined based on the Beidou data qualification coefficient and sensor data qualification coefficient of each plain concrete pile and the construction time of the corresponding plain concrete pile.

2. According to the Beidou positioning system-based intelligent monitoring system for plain concrete pile construction according to claim 1, it is characterized in that: The accuracy anomaly coefficient calculated based on Beidou positioning data includes: For each plain concrete pile, the Beidou positioning data within the preset time period is obtained multiple times, and the corresponding horizontal accuracy and vertical accuracy are extracted from each Beidou positioning data, and the accuracy sequence P is obtained by sorting them based on the time sequence; P = {(w1, y1), (w2, y2).....(w n ,y n )}, where w n Indicates the horizontal accuracy of the nth Beidou positioning data; y n Indicates the vertical accuracy of the nth Beidou positioning data; n represents the total number of Beidou positioning data positioning within a preset time period, and n is a positive integer; The horizontal accuracy and vertical accuracy of each Beidou positioning data in the accuracy sequence are compared with the preset horizontal accuracy threshold and the preset vertical accuracy threshold respectively. If the horizontal accuracy is not less than the preset horizontal accuracy threshold or the vertical accuracy is not less than the preset vertical accuracy threshold, the Beidou positioning data of this time is recorded as low-precision positioning; The accuracy anomaly coefficient is calculated based on the total number of low-precision positioning in the accuracy sequence P and the total number of positioning in the accuracy sequence P. The calculation formula is: Where PK is the accuracy anomaly coefficient, and v represents the total number of low-precision positioning in the accuracy sequence P.

3. According to claim 2, the intelligent monitoring system for the construction of plain concrete piles based on the Beidou positioning system is characterized in that: Calculating the positioning instability coefficient based on Beidou positioning data includes: For each plain concrete pile, the Beidou positioning data within the preset time period is obtained multiple times, and the corresponding longitude, latitude and altitude are extracted from each Beidou positioning data, and the positioning coordinate sequence Q is obtained by sorting them based on the time sequence; Q = {(z1, d1, u1), (z2, d2, u2).....(z n , d n ,u n )}, where z n Indicates the longitude of the nth Beidou positioning data; d n Indicates the latitude of the nth Beidou positioning data, u n Indicates the height of the nth Beidou positioning data; n represents the total number of Beidou positioning data positioning within a preset time period, and n is a positive integer; Calculate the mean longitude ze, the mean latitude de and the mean altitude ue in the positioning coordinate sequence; Calculate the longitude fluctuation value zf, the calculation formula is: In the formula, z i Indicates the longitude of the i-th Beidou positioning data; calculate the latitude fluctuation value df, the calculation formula is: Where, d i Indicates the latitude of the i-th Beidou positioning data; calculate the height fluctuation value uf, the calculation formula is: In the formula, u i Indicates the height of the i-th Beidou positioning data; Calculate the positioning instability coefficient, the calculation formula is: Where RG is the positioning instability coefficient.

4. According to the Beidou positioning system-based simple concrete pile construction intelligent monitoring system of claim 3, it is characterized in that: The Beidou data qualification coefficient is calculated based on the accuracy anomaly coefficient and positioning instability coefficient, including: Where GHK is the Beidou data qualification coefficient, PK and RG are the accuracy anomaly coefficient and positioning instability coefficient respectively, a1 and a2 are the preset proportional coefficients of the accuracy anomaly coefficient and positioning instability coefficient respectively, and a1 and a2 are both greater than 0.

5. The intelligent monitoring system for the construction of plain-mixed soil piles based on the Beidou positioning system according to claim 1 is characterized in that: Calculating the data synchronization coefficient based on sensor data includes: For each plain soil pile, obtain the timestamp of each sensor collecting data within a preset time period to obtain a data collection timestamp sequence of the corresponding sensor; The deviation mean between each pair of sensor timestamps is calculated based on the data acquisition timestamp sequence of each pair of sensors, and the data synchronization value SER between the timestamps of the pair of sensors is calculated. The calculation formula is: In the formula, ΔT c is the mean deviation between the timestamps of the pair of sensors, T max is the preset maximum deviation threshold between the timestamps of the pair of sensors; c is the sequence number of the plain concrete pile data collection times, and k represents the total number of plain concrete pile data collection times; The data synchronization coefficient between all sensors is calculated based on the data synchronization value between each pair of sensor timestamps. The calculation formula is: Where BN is the data synchronization coefficient, M is the total number of sensor pairs, and j is the order number of the sensor pairs.

6. The intelligent monitoring system for the construction of plain-mixed soil piles based on the Beidou positioning system according to claim 5 is characterized in that: Calculating the data fluctuation intensity coefficient based on sensor data includes: For each plain soil pile, the collected data of each sensor within a preset time period is obtained to obtain a collection data sequence of the corresponding sensor; For each sensor, the standard deviation of the corresponding collected data sequence is calculated as the fluctuation intensity value of the corresponding sensor; Calculate the mean of the fluctuation intensity values ​​of all sensors to obtain the data fluctuation intensity coefficient.

7. The intelligent monitoring system for the construction of plain-mixed soil piles based on the Beidou positioning system according to claim 6 is characterized in that: The calculation of sensor data qualification coefficient based on data synchronization coefficient and data fluctuation intensity coefficient includes: Where TYU is the sensor data qualification coefficient, BN and BR are the data synchronization coefficient and the data fluctuation intensity coefficient respectively, b1 and b2 are the preset proportional coefficients of the data synchronization coefficient and the data fluctuation intensity coefficient respectively, and b1 and b2 are both greater than 0.

8. The intelligent monitoring system for the construction of plain-mixed soil piles based on the Beidou positioning system according to claim 1 is characterized in that: Judging whether the corresponding plain concrete pile monitoring data is qualified according to the Beidou data qualification coefficient, the sensor data qualification coefficient and the corresponding preset qualification threshold value includes: Compare the Beidou data qualification coefficient of each plain concrete pile with the preset Beidou data qualification coefficient threshold, and compare the sensor data qualification coefficient with the preset sensor data qualification coefficient threshold; If the Beidou data qualification coefficient of the plain concrete pile is not less than the preset Beidou data qualification coefficient threshold, and the sensor data qualification coefficient is not less than the preset sensor data qualification coefficient threshold, then the corresponding monitoring data of the plain concrete pile is qualified; if not, the corresponding monitoring data of the plain concrete pile is unqualified, then the corresponding monitoring data of the plain concrete pile is recollected until the monitoring data is qualified.

9. The intelligent monitoring system for the construction of plain-mixed soil piles based on the Beidou positioning system according to claim 1 is characterized in that: According to the Beidou data qualification coefficient and sensor data qualification coefficient of each plain concrete pile, and the construction time of the corresponding plain concrete pile, the order of uploading the monitoring data of all plain concrete piles to the server is determined, including: Obtain the planned total construction time and the actual construction time of each plain concrete pile, and divide the actual construction time by the planned total construction time to obtain the construction progress value; The priority transmission coefficient of the monitoring data of the plain concrete pile is obtained according to the Beidou data qualification coefficient, the sensor data qualification coefficient and the construction progress value. The calculation formula is: Where Hxg is the priority transfer coefficient, GHK, TYU, and Bt are the Beidou data qualification coefficient, sensor data qualification coefficient, and construction progress value, respectively; f1, f2, and f3 are the preset proportional coefficients of GHK, TYU, and Bt, respectively, and f1, f2, and f3 are all greater than 0; The monitoring data of the corresponding plain mixed soil piles are transmitted to the server in sequence according to the priority transmission coefficient from large to small.

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