Intelligent protection method and system for pre-pressing process of highway construction support

Abstract

The application is suitable for the field of tunnel construction, and provides an intelligent protection method and system for a highway construction support pre-pressing process, which comprises the following steps: collecting real-time data of multiple source sensors; calculating and generating corresponding quantitative values of branches based on the real-time data of the multiple source sensors; establishing time series of strain quantitative values and inclination quantitative values; extracting cross-correlation functions of the strain quantitative values and the inclination quantitative values under different scales based on wavelet transform and the time series of the strain quantitative values and the inclination quantitative values; constructing a dynamic relationship quantitative index of the strain quantitative values and the inclination quantitative values based on the cross-correlation functions of the strain quantitative values and the inclination quantitative values; generating a comprehensive protection early warning index based on the dynamic relationship quantitative index and displacement quantitative values and pressure quantitative values in the corresponding quantitative values of branches, and triggering and sending an adjustment instruction based on the identification of the comprehensive protection early warning index, so that intelligent and accurate monitoring and protection of the highway construction support pre-pressing process are realized.

Description

Technical Field

[0001] This invention belongs to the field of tunnel construction, and in particular relates to an intelligent protection method and system for the prestressing process of highway construction supports. Background Technology

[0002] In the field of highway construction, construction scaffolds, as key structures supporting various construction loads, directly affect the safety of the entire project. Scaffold preloading, as an indispensable step in ensuring scaffold stability, is of paramount importance. However, traditional scaffold preloading methods have revealed many problems that urgently need to be addressed in practical operation.

[0003] From a monitoring perspective, manual monitoring dominates. During pre-stressing, staff must frequently travel between various monitoring points on the construction site, using basic tools such as levels and pressure gauges to collect data. This process is not only labor-intensive but also extremely inefficient. Taking a medium-sized highway bridge construction section as an example, a complete round of manual monitoring often takes several hours, which is insufficient to meet the needs of real-time monitoring of the support structure's status. Furthermore, manual readings are easily affected by subjective factors such as fatigue and observation angle deviations, leading to a significant reduction in data accuracy. When capturing support deformation data, minute deformations may be overlooked, and pressure change data is difficult to obtain with the required precision.

[0004] In conclusion, it is urgent to develop an intelligent, efficient, and economical method for protecting highway construction supports during the prestressing process. This is of great significance for improving construction safety, accelerating project progress, and reducing construction costs. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent protection method and system for the prestressing process of highway construction supports, aiming to solve the problems mentioned in the background art.

[0006] The present invention is implemented as follows: On one hand, an intelligent protection method for the prestressing process of highway construction supports is provided, the method comprising:

[0007] Real-time data is collected from multiple sources of sensors, including displacement sensors, pressure sensors, strain gauge sensors, and tilt sensors.

[0008] Based on real-time data from multiple sensor sources, calculate and generate the quantization values ​​corresponding to the branches;

[0009] Establish time series of strain quantization values ​​and tilt angle quantization values;

[0010] Based on wavelet transform and time series of strain quantization and tilt quantization values, the cross-correlation function of strain quantization and tilt quantization values ​​at different scales is extracted;

[0011] Based on the cross-correlation function between strain quantification value and tilt angle quantification value, a dynamic relationship quantification index for strain-tilt dynamics between strain quantification value and tilt angle quantification value is constructed.

[0012] Based on the dynamic relationship quantification index and the displacement and pressure quantification values ​​in the corresponding branch values, a comprehensive protection early warning index is generated, and an adjustment command is triggered and sent based on the identification of the comprehensive protection early warning index.

[0013] As a further aspect of the present invention, the calculation and generation of the quantization value corresponding to the branch based on real-time data from multiple sources specifically includes:

[0014] Obtain the real-time measurement value S from the displacement sensor;

[0015] Calculate the displacement branch quantization value based on the displacement sensor accuracy value X1.

[0016] Obtain the real-time measured value P from the pressure sensor;

[0017] Calculate the pressure branch quantization value based on the pressure sensor accuracy value Y1.

[0018] Obtain the real-time measured value Q from the strain gauge sensor;

[0019] Calculate the strain branch quantization value based on the strain gauge sensor accuracy value Z1.

[0020] Obtain the real-time measurement value M from the tilt sensor;

[0021] Calculate the tilt angle branch quantization value based on the tilt sensor accuracy value θ1.

[0022] As a further aspect of the present invention, establishing the time series of strain quantization values ​​and tilt angle quantization values ​​specifically includes:

[0023] Based on a preset fixed time interval, the strain branch quantization value E q and tilt angle branch quantization value W q Perform sampling;

[0024] Based on the strain-branched quantization value E q and tilt angle branch quantization value W q The sampled data is used to generate time series of strain quantization values ​​and tilt angle quantization values.

[0025] In the formula, n = 1, 2...N, where n represents the index identifier of the time series and N represents the total number of samples of the time series.

[0026] As a further aspect of the present invention, the extraction of cross-correlation functions of strain quantization values ​​and tilt quantization values ​​at different scales based on wavelet transform and time series of strain quantization values ​​and tilt angle quantization values ​​specifically includes:

[0027] Based on wavelet transform, time series Perform multi-scale decomposition;

[0028] Generating time series Detail coefficients and approximation coefficients at different scales;

[0029] Calculate the strain bifurcation quantization value E at different scales q and tilt angle branch quantization value W q The cross-correlation function;

[0030] The calculation process of the cross-correlation function is as follows:

[0031]

[0032] In the formula, m represents the time delay parameter;

[0033] When m = 0, calculate the correlation between Eq(n) and Wq(n) at the same time. When m > 0, calculate the correlation between Eq(n) and Wq(n+m) lagged by m time intervals.

[0034] As a further aspect of the present invention, the quantitative index of the dynamic relationship specifically includes:

[0035]

[0036] In the formula, j = 1, 2... J, where j represents the index identifier of the scale. Let ω represent the cross-correlation function at the j-th scale. j For the corresponding scale weights, and

[0037] As a further aspect of the present invention, the step of generating a comprehensive protection early warning index based on the dynamic relationship quantification index and the displacement quantification value and pressure quantification value in the corresponding branch quantification value, and triggering and sending an adjustment command based on the identification of the comprehensive protection early warning index, specifically includes:

[0038] Define the displacement branch quantization value weight α and the pressure branch quantization value weight β;

[0039] Calculate and generate a comprehensive protection and early warning index C. The calculation process for the comprehensive protection and early warning index C is as follows:

[0040] C=α·S q +β·P q +ω j ·Q;

[0041] When C > 0.7, a Level 1 warning is triggered, which sends a command to turn on the yellow warning light and a command to turn on the buzzer.

[0042] When C > 0.85, a level 2 warning is triggered, which sends a red warning light activation command and a voice broadcast activation command.

[0043] When C > 0.95, a level 3 warning is triggered, and the level 3 warning sends a command to start the fixed-point support servo hydraulic press.

[0044] As a further aspect of the present invention, another option is an intelligent protection system for the prestressing process of highway construction supports, the system comprising:

[0045] The data acquisition module is used to acquire real-time data from multiple sensor sources.

[0046] The multi-source sensors include: displacement sensors, pressure sensors, strain gauge sensors, and tilt sensors;

[0047] The calculation and generation module is used to calculate and generate the quantization values ​​corresponding to the branches based on real-time data from multiple sources of sensors.

[0048] The time series module is used to create time series of strain quantization values ​​and tilt angle quantization values;

[0049] The cross-correlation function module is used to extract cross-correlation functions of strain quantization values ​​and tilt quantization values ​​at different scales based on wavelet transform and time series of strain quantization values ​​and tilt quantization values;

[0050] The Strain-Inclination Dynamic Relationship Quantification Index Module is used to construct a strain-inclination dynamic relationship quantification index based on the cross-correlation function of strain quantification value and inclination angle quantification value;

[0051] The generation module is used to generate comprehensive protection and early warning indicators based on the dynamic relationship quantitative index of stress tilt and the displacement and pressure quantitative values ​​in the corresponding quantitative values ​​of branches;

[0052] The triggering and sending module is used to trigger and send adjustment instructions based on the identification of comprehensive protection and early warning indicators.

[0053] As a further aspect of the present invention, the cross-correlation function module specifically includes:

[0054] Decomposition units, used for wavelet transform-based decomposition of time series Perform multi-scale decomposition;

[0055] Generation unit, used to generate time series Detail coefficients and approximation coefficients at different scales;

[0056] The computational unit is used to calculate the strain bifurcation quantization value E at different scales. q and tilt angle branch quantization value W q The cross-correlation function.

[0057] This invention provides an intelligent protection method and system for the preloading process of highway construction supports. This method and system achieve intelligent and precise monitoring and protection of the preloading process of highway construction supports. Multi-source sensors collect data comprehensively, significantly improving the comprehensiveness and accuracy of monitoring. Through the construction of dynamic stress-tilt relationship quantification indicators and comprehensive protection early warning indicators, potential risks to the supports are keenly detected, and adjustment commands are triggered in a timely manner, effectively preventing accidents and significantly improving construction safety and efficiency. Attached Figure Description

[0058] Figure 1 This is the main flowchart of an intelligent protection method for the prestressing process of highway construction supports.

[0059] Figure 2 This is a flowchart illustrating how a smart protection method for the preloading process of highway construction supports calculates and generates quantized values ​​corresponding to branches based on real-time data from multi-source sensors.

[0060] Figure 3 This is a flowchart of the time series for establishing strain quantification and tilt angle quantification values ​​in an intelligent protection method for the prestressing process of highway construction supports.

[0061] Figure 4 This is a flowchart illustrating the extraction of cross-correlation functions of strain quantization and tilt angle quantization values ​​at different scales based on wavelet transform and time series analysis of strain quantization and tilt angle quantization values ​​in an intelligent protection method for the preloading process of highway construction supports.

[0062] Figure 5 This is a flowchart illustrating a smart protection method for the prestressing process of highway construction supports. It generates a comprehensive protection early warning index based on the dynamic relationship quantitative index of stress and the displacement and pressure quantitative values ​​in the corresponding branch quantitative values. Based on the identification of the comprehensive protection early warning index, it triggers and sends adjustment instructions.

[0063] Figure 6 This is a main structural diagram of an intelligent protection system for the prestressing process of highway construction supports.

[0064] Figure 7 This is a structural block diagram of the cross-correlation function module in an intelligent protection system for the preloading process of highway construction supports. Detailed Implementation

[0065] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0066] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0067] The present invention provides an intelligent protection method and system for the prestressing process of highway construction supports, which solves the technical problems in the background art.

[0068] like Figure 1 The diagram shown is a main flowchart of an intelligent protection method for the preloading process of highway construction supports, according to an embodiment of the present invention. The intelligent protection method for the preloading process of highway construction supports includes:

[0069] Step S100: Collect real-time data from multiple sensors; the multiple sensors include: displacement sensor, pressure sensor, strain gauge sensor and tilt sensor;

[0070] Step S200: Calculate and generate the quantization value corresponding to the branch based on real-time data from multiple sources of sensors;

[0071] Step S300: Establish time series of strain quantization values ​​and tilt angle quantization values;

[0072] Step S400: Based on wavelet transform and the time series of strain quantization and tilt angle quantization values, extract the cross-correlation function of strain quantization and tilt angle quantization values ​​at different scales;

[0073] Step S500: Based on the cross-correlation function of strain quantification value and tilt angle quantification value, construct a dynamic relationship quantification index of strain quantification value and tilt angle quantification value;

[0074] Step S600: Based on the dynamic relationship quantification index and the displacement and pressure quantification values ​​in the corresponding branch quantification values, generate a comprehensive protection early warning index, and trigger and send adjustment instructions based on the identification of the comprehensive protection early warning index.

[0075] In this embodiment, a multi-source sensor real-time data acquisition network is first established using four types of sensors: displacement, pressure, strain gauges, and tilt angle sensors. Displacement sensors acquire linear deformation data of the support structure in real time with millimeter-level precision; pressure sensors accurately obtain the pressure borne by the support; strain gauge sensors deeply measure the strain of the support structure, intuitively reflecting the stress state; and tilt angle sensors comprehensively monitor the tilt angle changes of various parts of the support, providing comprehensive and accurate data support for subsequent analysis from multiple dimensions. After data acquisition, the real-time data from the multi-source sensors is processed to calculate and generate corresponding quantized values ​​for each branch, transforming various physical quantities into a unified and analyzable quantized form. At set time intervals, strain quantized values ​​and tilt angle quantized values ​​are accurately extracted from these quantized values ​​to construct a time series, presenting their changes over time. Wavelet transform technology is used to conduct in-depth analysis of this time series, extracting the cross-correlation function of strain quantized values ​​and tilt angle quantized values ​​at different scales, and exploring the correlation patterns between the two at different frequency characteristics. Based on this cross-correlation function, a dynamic relationship quantification index for strain-tilt is constructed to quantify the complex dynamic relationship between the two. Finally, this quantitative indicator is combined with the displacement and pressure values ​​to generate a comprehensive protection and early warning indicator. Once this indicator exceeds the preset range, the system immediately identifies it, triggers an early warning, and sends an adjustment command, enabling real-time control and timely adjustment of the construction support status.

[0076] like Figure 2 As shown, in a preferred embodiment of the present invention, the step of calculating and generating the quantization value corresponding to the branch based on real-time data from multiple sensors specifically includes:

[0077] Step S201: Obtain the real-time measurement value S from the displacement sensor;

[0078] Step S202: Calculate the displacement branch quantization value based on the displacement sensor accuracy value X1.

[0079] Step S203: Obtain the real-time measurement value P from the pressure sensor;

[0080] Step S204: Calculate the pressure branch quantization value based on the pressure sensor accuracy value Y1.

[0081] Step S205: Obtain the real-time measurement value Q from the strain gauge sensor;

[0082] Step S206: Calculate the strain branch quantization value based on the strain gauge sensor accuracy value Z1.

[0083] Step S207: Obtain the real-time measurement value M from the tilt sensor;

[0084] Step S208: Calculate the tilt angle branch quantization value based on the tilt sensor accuracy value θ1.

[0085] In this embodiment, the pressure sensor focuses on acquiring pressure data borne by the support structure, which helps in understanding the load condition of the support. The strain gauge sensor meticulously measures the strain of the support structure, reflecting the stress state of the support at a microscopic level, which is crucial for detecting potential structural hazards. The tilt sensor monitors changes in the tilt angle of various parts of the support, enabling control over the spatial attitude of the support. For the displacement sensor, after acquiring its real-time measurement value, the displacement branch quantization value is calculated based on its accuracy value, converting the raw displacement data into a dimensionless form that is easy to analyze. Similarly, for the pressure sensor, strain gauge sensor, and tilt sensor, real-time measurement values ​​are acquired, and the pressure branch quantization value is calculated based on their accuracy values, achieving standardization of the pressure data. Real-time measurement values ​​are acquired separately, and strain branch quantization values ​​and tilt branch quantization values ​​are calculated based on their respective accuracy values.

[0086] like Figure 3 As shown, in a preferred embodiment of the present invention, establishing the time series of strain quantization values ​​and tilt angle quantization values ​​specifically includes:

[0087] Step S301: Based on a preset fixed time interval, quantize the strain branch value E. q and tilt angle branch quantization value W q Perform sampling;

[0088] Step S302: Based on the strain branch quantization value E q and tilt angle branch quantization value W q The sampled data is used to generate time series of strain quantization values ​​and tilt angle quantization values.

[0089] In the formula, n = 1, 2...N, where n represents the index identifier of the time series and N represents the total number of samples in the time series;

[0090] In this embodiment, the data collected by multiple sensors, including displacement, pressure, strain gauges, and tilt angle sensors, are processed at preset fixed time intervals to obtain strain branch quantization values ​​and tilt angle branch quantization values, respectively. The setting of this fixed time interval must comprehensively consider factors such as the deformation rate of the support and data processing capabilities to ensure effective capture of the support's state changes during pre-compression. If the support deforms slowly during pre-compression, the time interval can be appropriately increased; if the deformation is rapid, the time interval needs to be shortened to ensure the timeliness and accuracy of the data. Subsequently, based on the determined time intervals, the strain branch quantization values ​​and tilt angle branch quantization values ​​are sampled, with each sample recording the strain and tilt angle quantization at that moment. The sampling process is performed according to the preset time intervals to ensure the isochronism and continuity of the time series. Finally, a time series of strain and tilt angle quantization values ​​is generated based on the sampled data. In the time series, an index n is used to mark each sampling point, n = 1, 2...N, thus clearly reflecting the quantization information at different times. This provides a data foundation for subsequent use of wavelet transform and other techniques to extract the characteristics of strain and tilt angle at different frequency components, analyze their correlation and time-delay relationships.

[0091] like Figure 4 As shown, in a preferred embodiment of the present invention, the extraction of the cross-correlation function of strain quantization values ​​and tilt quantization values ​​at different scales based on wavelet transform and time series of strain quantization values ​​and tilt quantization values ​​specifically includes:

[0092] Step S401: Based on wavelet transform, process the time series... Perform multi-scale decomposition;

[0093] Step S402: Generate time series Detail coefficients and approximation coefficients at different scales;

[0094] Step S403: Calculate the strain bifurcation quantization value E at different scales q and tilt angle branch quantization value W q The cross-correlation function;

[0095] The calculation process of the cross-correlation function is as follows:

[0096]

[0097] In the formula, m represents the time delay parameter;

[0098] When m = 0, calculate the correlation between Eq(n) and Wq(n) at the same time. When m > 0, calculate the correlation between Eq(n) and Wq(n+m) lagged by m time intervals.

[0099] It should be understood that, firstly, wavelet transform is used to perform multi-scale decomposition on the time series of strain and tilt angle quantization values ​​constructed earlier. Wavelet transform can decompose the original time series signal into different frequency sub-bands. After multi-scale decomposition, detail coefficients and approximation coefficients of the time series at different scales are generated. The detail coefficients reflect the high-frequency components of the signal, corresponding to the rapid changes and subtle local state characteristics of the stent during pre-compression; the approximation coefficients reflect the low-frequency components of the signal, capturing the overall, slowly changing state trend of the stent. Based on these coefficients at different scales, the cross-correlation function of the strain branch quantization values ​​and the tilt angle branch quantization values ​​is further calculated. The cross-correlation function can measure the correlation and time lag relationship between the two at different scales, helping to determine the intrinsic relationship between strain and tilt angle changes.

[0100] In a preferred embodiment of the present invention, the quantitative index of the dynamic relationship specifically includes:

[0101]

[0102] In the formula, j = 1, 2... J, where j represents the index identifier of the scale. Let ω represent the cross-correlation function at the j-th scale. j For the corresponding scale weights, and

[0103] like Figure 5 As shown, in another preferred embodiment of the present invention, the step of generating a comprehensive protection early warning index based on the displacement quantification value and pressure quantification value in the dynamic relationship quantification index and the corresponding branch quantification value, and triggering and sending an adjustment command based on the identification of the comprehensive protection early warning index specifically includes:

[0104] Step S601: Define the displacement branch quantization value weight α and the pressure branch quantization value weight β;

[0105] Step S602: Calculate and generate the comprehensive protection early warning index C. The calculation process of the comprehensive protection early warning index C is as follows:

[0106] C=α·S q +β·P q +ω j ·Q;

[0107] Step S603: When C > 0.7, a Level 1 warning is triggered, and the Level 1 warning sends a command to turn on the yellow warning light and a command to turn on the buzzer;

[0108] Step S604: When C > 0.85, a level 2 warning is triggered, and the level 2 warning sends a red warning light activation command and a voice broadcast activation command;

[0109] Step S605: When C > 0.95, a level 3 early warning is triggered, and the level 3 early warning sends a command to start the fixed-point support servo hydraulic press;

[0110] First, the weights of the displacement branch quantification value and the pressure branch quantification value are defined. The weight setting needs to comprehensively consider factors such as the structural characteristics of the support and the construction conditions to accurately reflect the degree of influence of each factor on the support status. Based on the dynamic relationship quantification index of stress-tilt, displacement quantification value, pressure quantification value and corresponding weights, a comprehensive protection early warning index is generated. This index comprehensively reflects the status of the support during the pre-stressing process. A three-level early warning mechanism is set according to the numerical range of the comprehensive protection early warning index. When C > 0.7, a level one early warning is triggered, and a yellow warning light and buzzer are activated to gently remind on-site personnel that the support status has changed and needs attention. If C > 0.85, a level two early warning is triggered, and a red warning light and voice broadcast are activated to more clearly inform personnel that the support status may have potential risks and further investigation is required. When C > 0.95, a level three early warning is triggered, and the pre-installed fixed-point support servo hydraulic press is immediately started to automatically adjust the support, control the support status in a timely manner, and prevent dangerous situations from occurring.

[0111] like Figure 6 As shown, in another preferred embodiment of the present invention, a smart protection system for the preloading process of highway construction supports is provided, the system comprising:

[0112] The acquisition module 100 is used to acquire real-time data from multiple sensor sources;

[0113] The multi-source sensors include: displacement sensors, pressure sensors, strain gauge sensors, and tilt sensors;

[0114] The calculation and generation module 200 is used to calculate and generate the quantization value corresponding to the branch based on real-time data from multiple sources of sensors;

[0115] Time series module 300 is used to establish time series of strain quantization values ​​and tilt angle quantization values;

[0116] The cross-correlation function module 400 is used to extract cross-correlation functions of strain quantization values ​​and tilt quantization values ​​at different scales based on wavelet transform and time series of strain quantization values ​​and tilt quantization values;

[0117] The Strain-Inclination Dynamic Relationship Quantification Index Module 500 is used to construct a strain-inclination dynamic relationship quantification index based on the cross-correlation function of strain quantification value and inclination angle quantification value;

[0118] The generation module 600 is used to generate a comprehensive protection and early warning index based on the dynamic relationship quantitative index of stress tilt and the displacement and pressure quantitative values ​​in the corresponding quantitative values ​​of branches.

[0119] The triggering and sending module 700 is used to trigger and send adjustment instructions based on the identification of comprehensive protection and early warning indicators.

[0120] In this embodiment, during application, the acquisition module 100 acquires real-time data from multiple sources of sensors. Based on this data, the calculation and generation module 200 calculates and generates quantized values ​​corresponding to branches. The time series module 300 establishes time series of strain quantized values ​​and tilt angle quantized values. Based on wavelet transform and the time series of strain quantized values ​​and tilt angle quantized values, the cross-correlation function module 400 extracts cross-correlation functions of strain quantized values ​​and tilt angle quantized values ​​at different scales. Based on the cross-correlation functions of strain quantized values ​​and tilt angle quantized values, the strain-tilt dynamic relationship quantification index module 500 constructs a strain-tilt dynamic relationship quantification index of strain quantized values ​​and tilt angle quantized values. Based on the strain-tilt dynamic relationship quantification index and the displacement and pressure quantized values ​​in the branch-corresponding quantized values, the generation module 600 generates a comprehensive protection and early warning index. Based on the identification of the comprehensive protection and early warning index, the triggering and sending module 700 triggers and sends adjustment commands.

[0121] like Figure 7 As shown, in another preferred embodiment of the present invention, the cross-correlation function module 400 specifically includes:

[0122] Decomposition unit 401 is used for wavelet transform-based decomposition of time series. Perform multi-scale decomposition;

[0123] Generation unit 402 is used to generate time series. Detail coefficients and approximation coefficients at different scales;

[0124] Calculation unit 403 is used to calculate the strain bifurcation quantization value E at different scales. q and tilt angle branch quantization value W q The cross-correlation function.

[0125] In this embodiment, the time series is decomposed by unit 401 based on wavelet transform. Multi-scale decomposition is performed, and unit 402 generates time series. The detail coefficients and approximation coefficients at different scales are used, and the strain bifurcation quantization value E is calculated by computational unit 403 at different scales. q and tilt angle branch quantization value W q The cross-correlation function.

[0126] The above embodiments of the present invention provide an intelligent protection method for the preloading process of highway construction supports, and an intelligent protection system for the preloading process of highway construction supports. Firstly, a multi-source sensor real-time data acquisition network is established using four types of sensors: displacement, pressure, strain gauges, and tilt angle sensors. Displacement sensors acquire linear deformation data of the support in real time with millimeter-level precision; pressure sensors accurately obtain the pressure borne by the support; strain gauge sensors deeply measure the strain of the support structure, intuitively reflecting the stress state; and tilt angle sensors comprehensively monitor the changes in the tilt angle of various parts of the support, providing comprehensive and accurate data support for subsequent analysis from multiple dimensions. After data acquisition, the real-time data from the multi-source sensors is processed to calculate and generate corresponding quantized values ​​for each branch, transforming various physical quantity data into a unified and analyzable quantized form. At set time intervals, strain quantized values ​​and tilt angle quantized values ​​are accurately extracted from these quantized values ​​to construct a time series, presenting the changing trends of both over time. Wavelet transform technology is used to conduct in-depth analysis of this time series, extracting the cross-correlation functions of strain quantized values ​​and tilt angle quantized values ​​at different scales, and exploring the correlation patterns between the two at different frequency characteristics. Based on this cross-correlation function, a quantitative index for the dynamic relationship between stress and tilt is constructed to quantify the complex dynamic connection between the two. Finally, this quantitative index is combined with the quantitative values ​​of displacement and pressure to generate a comprehensive protection and early warning index. Once this index exceeds the preset range, the system immediately identifies it, triggers an early warning, and sends an adjustment command, realizing real-time control and timely adjustment of the construction support status. This method and system realize intelligent and precise monitoring and protection of the preloading process of highway construction supports. Multi-source sensors collect data from all directions, significantly improving the comprehensiveness and accuracy of monitoring. Through the constructed quantitative index for the dynamic relationship between stress and tilt and the comprehensive protection and early warning index, potential risks of the supports are keenly captured, adjustment commands are triggered in a timely manner, accidents are effectively prevented, and construction safety and efficiency are significantly improved.

[0127] In order for the above methods and systems to operate smoothly, the system may include more or fewer components than those described above, or combine certain components, or different components, in addition to the various modules mentioned above. For example, it may include input / output devices, network access devices, buses, processors, and memory.

[0128] The processor can be a central processing unit, or other general-purpose processors, digital signal processors, application-specific integrated circuits (ASICs), off-the-shelf programmable gate arrays (OPGs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. This processor is the control center of the system, connecting various parts via various interfaces and lines.

[0129] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0130] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

[0131] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A smart protection method for the prestressing process of highway construction supports, characterized in that, The method includes: Real-time data is collected from multiple sources of sensors, including displacement sensors, pressure sensors, strain gauge sensors, and tilt sensors. Based on real-time data from multiple sensor sources, calculate and generate the quantization values ​​corresponding to the branches; Establish time series of strain quantization values ​​and tilt angle quantization values; Based on wavelet transform and time series of strain quantization and tilt quantization values, the cross-correlation function of strain quantization and tilt quantization values ​​at different scales is extracted; Based on the cross-correlation function between strain quantification value and tilt angle quantification value, a dynamic relationship quantification index for strain-tilt dynamics between strain quantification value and tilt angle quantification value is constructed. Based on the dynamic relationship quantification index and the displacement and pressure quantification values ​​in the corresponding branch values, a comprehensive protection early warning index is generated, and an adjustment command is triggered and sent based on the identification of the comprehensive protection early warning index. The calculation and generation of the quantization value corresponding to the branch based on real-time data from multiple sensors specifically includes: Obtain real-time measurement values ​​from displacement sensors ; Based on displacement sensor accuracy value Calculate the displacement branch quantization value ; Obtain real-time measurement values ​​from pressure sensors ; Based on pressure sensor accuracy Calculate the pressure branch quantization value ; Obtain real-time measurement values ​​from strain gauge sensors ; Based on strain gauge sensor accuracy Calculate strain branch quantization value ; Obtain real-time measurement values ​​from tilt sensor ; Based on tilt sensor accuracy value Calculate the tilt angle branch quantization value ; Establishing time series of strain quantization values ​​and tilt angle quantization values ​​specifically includes: Based on a preset fixed time interval, the strain branch quantization value and tilt branch quantization value Perform sampling; Based on strain-branched quantization values and tilt branch quantization value The sampled data is used to generate time series of strain quantization values ​​and tilt angle quantization values. ; In the formula, , An index identifier representing a time series. This represents the total number of samples in the time series. The extraction of cross-correlation functions of strain quantization values ​​and tilt quantization values ​​at different scales based on wavelet transform and time series of strain quantization values ​​and tilt quantization values ​​specifically includes: Based on wavelet transform, time series Perform multi-scale decomposition; Generating time series Detail coefficients and approximation coefficients at different scales; Calculate strain bifurcation quantization values ​​at different scales and tilt branch quantization value The cross-correlation function; The calculation process of the cross-correlation function is as follows: ； In the formula, Indicates the time delay parameter; when At the same time, calculate the same moment. and The correlation, when At that time, calculate and lag time intervals The correlation; The specific quantitative indicators of the dynamic relationship between the inclination and the tendency include: ； In the formula, , Index identifier indicating scale, Indicates the first Cross-correlation function at each scale For the corresponding scale weights, and ; The process of generating a comprehensive protection early warning index based on the dynamic relationship quantification index and the displacement and pressure quantification values ​​in the corresponding branch quantification values, and triggering and sending adjustment instructions based on the identification of the comprehensive protection early warning index, specifically includes: Define the weight of the displacement branch quantization value and pressure branch quantization value weight ; Calculate and generate comprehensive protection and early warning indicators The comprehensive protection and early warning indicators The calculation process is as follows: ； when When this occurs, a Level 1 warning is triggered, which sends a command to turn on the yellow warning light and a command to turn on the buzzer. when When this occurs, a Level 2 warning is triggered, which sends a red warning light activation command and a voice broadcast activation command. when When this occurs, a Level 3 warning is triggered, and the Level 3 warning sends a command to start the fixed-point support servo hydraulic press.

2. An intelligent protection system for the prestressing process of highway construction supports, characterized in that, The intelligent protection method for the preloading process of highway construction supports as described in claim 1, wherein the system comprises: The data acquisition module is used to acquire real-time data from multiple sensor sources. The multi-source sensors include: displacement sensors, pressure sensors, strain gauge sensors, and tilt sensors; The calculation and generation module is used to calculate and generate the quantization values ​​corresponding to the branches based on real-time data from multiple sources of sensors. The time series module is used to create time series of strain quantization values ​​and tilt angle quantization values; The cross-correlation function module is used to extract cross-correlation functions of strain quantization values ​​and tilt quantization values ​​at different scales based on wavelet transform and time series of strain quantization values ​​and tilt quantization values; The Strain-Inclination Dynamic Relationship Quantification Index Module is used to construct a strain-inclination dynamic relationship quantification index based on the cross-correlation function of strain quantification value and inclination angle quantification value; The generation module is used to generate comprehensive protection and early warning indicators based on the dynamic relationship quantitative index of stress tilt and the displacement and pressure quantitative values ​​in the corresponding quantitative values ​​of branches; The triggering and sending module is used to trigger and send adjustment instructions based on the identification of comprehensive protection and early warning indicators.

3. The intelligent protection system for the prestressing process of highway construction supports according to claim 2, characterized in that, The cross-correlation function module specifically includes: Decomposition units, used for wavelet transform-based decomposition of time series Perform multi-scale decomposition; Generation unit, used to generate time series Detail coefficients and approximation coefficients at different scales; The computational unit is used to calculate strain bifurcation quantization values ​​at different scales. and tilt branch quantization value The cross-correlation function.

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