Solar-heated anti-frost heaving drainage roadbed in seasonal frozen area and construction method thereof

Abstract

The invention relates to the field of new-generation information technology and civil engineering, in particular to a frost heaving prevention technology of a roadbed in a seasonal frozen area, and particularly relates to a solar heating frost heaving prevention drainage roadbed in the seasonal frozen area and a construction method of the solar heating frost heaving prevention drainage roadbed. The method comprises the steps that environment sunlight data are obtained based on a solar heat collection device, heat energy is stored through an efficient heat storage material, and a heat energy output sequence is generated; acquiring soil temperature, humidity and frost heaving stress data from an environment sensor, predicting a frost heaving trend in combination with historical frost heaving characteristics, and generating regulation and control parameters; the heat energy output sequence and the frost heaving regulation and control parameters are input into a dynamic temperature control and drainage linkage module, heat distribution and optimization adjustment of a drainage path are carried out, and a linkage control strategy is generated; and performing feedback analysis on the operation condition of each module based on the data, and optimizing the control parameters of the dynamic temperature control and drainage linkage module. The temperature and humidity of the roadbed can be accurately adjusted, the frost heaving phenomenon is prevented, and roadbed drainage is optimized.

Description

Technical Field

[0001] The present invention relates to the fields of new generation information technology and civil engineering, and in particular to a solar-heated seasonally frozen zone anti-freezing heave drainage roadbed and a construction method thereof. Background Art

[0002] In cold regions and seasonally frozen areas, the freezing and thawing process of the soil can cause frost heave on the roadbed, resulting in deformation, cracks or settlement on the roadbed surface, which seriously affects the stability and service life of the road. The frost heave phenomenon is usually caused by the expansion pressure formed by the freezing of water in the soil at low temperatures. This process not only affects the structural stability of the roadbed, but also increases the maintenance cost of the road. Therefore, how to effectively prevent frost heave and ensure the stability of the roadbed structure has become a major technical problem in road construction in cold regions.

[0003] Traditional anti-frost heave methods mainly rely on the use of heating systems, drainage systems or modified soil materials, but these methods often have problems such as high energy consumption, complex systems, and high maintenance costs. Especially in seasonally frozen areas, due to frequent freeze-thaw cycles, traditional anti-frost heave technologies are difficult to meet the needs of long-term stable operation, and most technologies fail to take into account changes in ambient temperature and humidity and the linkage regulation of heat and moisture.

[0004] In recent years, with the development of renewable energy technology, solar heating systems have been introduced into roadbed heating and anti-freezing heave control, becoming a green and low-carbon solution. However, how to effectively combine solar heating systems with temperature control, drainage and other systems, and make real-time adjustments according to environmental changes, remains a technical challenge.

[0005] Therefore, the core issue of the present invention is how to accurately adjust the temperature and humidity of the roadbed in the seasonally frozen area by combining the solar heating system, dynamic temperature control and drainage linkage control, and environmental monitoring and prediction technology to effectively prevent frost heave and optimize roadbed drainage, thereby improving the stability and service life of the roadbed. Summary of the invention

[0006] The present invention provides a solar-heated frost-heaving and drainage-proof roadbed in seasonally frozen areas and a construction method thereof, so as to solve the problem of how to accurately adjust the temperature and humidity of the roadbed in seasonally frozen areas by combining a solar heating system, dynamic temperature control and drainage linkage control, and environmental monitoring and prediction technology, so as to effectively prevent frost heave and optimize roadbed drainage, thereby improving the stability and service life of the roadbed.

[0007] In order to solve the above technical problems, the present invention provides a solar-heated seasonally frozen area anti-freezing drainage roadbed and a construction method thereof, comprising: The environmental sunshine data is obtained based on the solar thermal collector, and the thermal energy is stored through the high-efficiency heat storage material to generate a thermal energy output sequence; Obtain soil temperature, moisture and frost heave stress data from environmental sensors, and predict frost heave trends based on historical frost heave characteristics to generate control parameters; The heat energy output sequence and the frost heave control parameters are input into a dynamic temperature control and drainage linkage module to dynamically adjust the heat distribution and drainage path to generate a linkage control strategy; Based on the heat energy output sequence, the frost heave regulation parameters and the linkage control strategy, feedback analysis is performed on the operating data of each module, and the control parameters of the dynamic temperature control and drainage linkage module are optimized.

[0008] Furthermore, before the step of obtaining the ambient sunshine data, the method further includes: An ambient light sensor is provided to collect sunlight radiation intensity data in real time and transmit the data to the solar thermal collector for heat energy capture.

[0009] Furthermore, the step of obtaining soil temperature, humidity and frost heave stress data from the environmental sensor specifically includes: Temperature sensors, humidity sensors and frost heave stress sensors are installed to collect the soil temperature, humidity and frost heave stress data in real time, and transmit the data to the data processing module.

[0010] Furthermore, the historical frost heave characteristic data includes soil freezing temperature, freezing duration and frost heave stress data, and the frost heave trend is predicted in combination with the historical frost heave characteristic data to generate the control parameters.

[0011] Furthermore, the step of inputting the heat energy output sequence and the frost heave control parameters into the dynamic temperature control and drainage linkage module specifically includes: Based on the heat energy output sequence and frost heave control parameters, the heat distribution weight of each roadbed area is calculated through an algorithm model to obtain a heat distribution sequence, and the drainage intensity is adjusted according to soil moisture and frost heave stress to generate a drainage path adjustment sequence.

[0012] Furthermore, the dynamic temperature control and drainage linkage module adjusts the temperature and humidity of the roadbed in real time according to the heat distribution sequence and the drainage path adjustment sequence to prevent the occurrence of frost heave.

[0013] Furthermore, the step of optimizing the control parameters of the dynamic temperature control and drainage linkage module based on the feedback analysis result specifically includes: The regulation process of the heat distribution and drainage path is monitored in real time, deviations and performance bottlenecks in system operation are identified through feedback analysis, and control parameters are adjusted according to the analysis results.

[0014] Furthermore, the optimized control parameters include heat energy release intensity, drainage intensity and adjustment coefficients of control parameters to generate optimized parameter configuration for the next cycle.

[0015] Furthermore, the generated feedback analysis results are further used to generate a construction quality analysis report as a basis for construction scheduling and adjustment.

[0016] Furthermore, a solar-heated seasonally frozen area anti-freezing and drainage roadbed system comprises: The data acquisition module 10 is used to obtain data related to the roadbed in the seasonally frozen area from the field environment, and the data is sent to the data processing module in real time through wireless transmission technology; A data processing module 20 is used to pre-process the collected data. The processed data will be standardized and stored in a central database; The environmental monitoring and prediction module 30 is used to analyze the environmental data collected in real time, and predict the frost heave trend in combination with the historical frost heave characteristics, and generate a frost heave control parameter sequence. The prediction results provide a basis for dynamic temperature control and drainage regulation; The dynamic temperature control and drainage linkage module 40 is used to receive the heat output sequence and frost heave control parameters, and optimize the heat distribution and drainage path; The system feedback and optimization module 50 is used to monitor the operation status of the dynamic temperature control and drainage linkage module in real time, analyze the deviations and bottlenecks in operation, adjust the temperature control system and drainage path based on the difference between the actual roadbed frost heave and the predicted results, and generate the optimization parameters for the next cycle; The decision support and report generation module 60 provides construction scheduling suggestions and adjustment plans and generates a construction quality analysis report based on real-time data, frost heave prediction results, and feedback from control strategy optimization; User interaction and visualization module 70, which provides an intuitive user interface to display the system's prediction results, adjustment parameters and analysis reports, helping users to quickly understand the system's operating status and improve decision-making efficiency; The system monitoring and self-adaptation module 80 is used to continuously monitor various operating data and performance of the system and adjust system parameters through self-adaptation.

[0017] The key innovative features of the present invention include: (1) Linkage between solar heating and dynamic temperature control: Ambient sunlight data is obtained through solar thermal collectors and combined with high-efficiency heat storage materials to achieve efficient storage and intelligent regulation of thermal energy, thereby reducing energy consumption.

[0018] (2) Environmental monitoring and prediction: Real-time soil temperature, moisture, and frost heave stress data are collected, and the frost heave trend is predicted based on historical frost heave characteristics to generate precise control parameters, providing a basis for precise control of roadbed temperature and moisture.

[0019] (3) Dynamic temperature control and drainage linkage control: Through the dynamic temperature control and drainage linkage module, the heat distribution and drainage path are optimized and adjusted to ensure the stability of the roadbed during the frost heave season and optimize the operation effect of the system.

[0020] The following are its main beneficial effects: The present invention combines solar heating, dynamic temperature control and drainage linkage control, environmental monitoring and prediction technology to accurately adjust the temperature and humidity of the roadbed in the seasonal freezing zone, solving the technical problems of low energy efficiency, complex system and lack of dynamic adaptability of traditional anti-frost heave methods. Compared with traditional single heating or drainage technology, the present invention can adjust the temperature and humidity of the roadbed in real time by optimizing the linkage control of heat energy distribution and drainage path, effectively preventing the occurrence of frost heave, and ensuring the stability and safety of the roadbed in the seasonal freezing zone environment.

[0021] In addition, the present invention uses solar energy as the main source of heat energy, which reduces the energy consumption of traditional electric heating systems, reduces maintenance costs, and reduces manual intervention through intelligent control systems, thereby improving construction efficiency. The system continuously optimizes the control strategy through feedback analysis, making the anti-freeze heave effect more accurate and continuous, significantly extending the service life of the roadbed and reducing the subsequent maintenance costs. The overall system not only improves energy utilization efficiency, but also optimizes the anti-freeze heave performance of the roadbed, effectively improving the sustainability and economy of the roadbed project in seasonally frozen areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A schematic diagram of a solar-heated seasonally frozen zone anti-freezing drainage roadbed and a construction method thereof provided in an embodiment of the present application; Figure 2 A structural block diagram of a solar-heated seasonally frozen zone anti-frost heave drainage roadbed and a construction method thereof provided in an embodiment of the present application. DETAILED DESCRIPTION

[0023] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of the present application; the terms used in the specification of the application herein are only for the purpose of describing specific embodiments and are not intended to limit the present application; the terms "including" and "having" and any variations thereof in the specification and claims of the present application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of the present application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0024] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0025] Example 1: Reference Figure 1 , is a schematic flow chart of a solar-heated seasonally frozen zone anti-freezing drainage roadbed and a construction method thereof provided by an embodiment of the present invention, the process may at least include steps S100-S400: S100, obtaining environmental sunshine data based on a solar thermal collector, and storing thermal energy through a high-efficiency heat storage material to generate a thermal energy output sequence; S200, obtaining soil temperature, humidity and frost heave stress data from environmental sensors, and predicting the frost heave trend in combination with historical frost heave characteristics to generate control parameters; S300, inputting the heat energy output sequence and frost heave control parameters into a dynamic temperature control and drainage linkage module, dynamically adjusting heat distribution and drainage paths, and generating a linkage control strategy; S400: Based on the heat output sequence, frost heave regulation parameters and linkage control strategy, feedback analysis is performed on the operation data of each module, and control parameters of the dynamic temperature control and drainage linkage module are optimized.

[0026] Step S100 at least includes steps S110-S130: S110, obtaining sunlight radiation intensity data of the solar thermal collector, and transmitting the data to the high-efficiency heat storage material for heat energy capture.

[0027] Specifically, the ambient light sensor is used to collect the solar radiation intensity in the area where the solar thermal collector is located. (t), where (t) represents the instantaneous sunshine intensity at time t (unit: ). The data acquisition period is Δ , the cycle range is determined by the response capability of the collector.

[0028] Calculate the total solar input power by the formula (t):

[0029] in, is the effective collecting area of ​​the solar thermal collector (unit: ), The efficiency of solar collectors depends on the material properties.

[0030] The power (t) The data is transmitted in real time to the heat capture module of the high-efficiency heat storage material. The heat storage material is Converts input heat into stored energy :

[0031] in, is the cumulative heat storage (unit: J), and The collection start and end time.

[0032] S120, performing layered storage and heat conversion on the captured thermal energy to generate a heat distribution sequence.

[0033] Furthermore, the heat Layered storage is performed according to the heat demand of different soil layers, and the layered heat storage is calculated using zoned heat storage technology :

[0034] in, is the thermal energy stored in layer i (unit: J). is the stratification coefficient, satisfying , dynamically adjusted according to soil layer location and frost heave risk assessment.

[0035] After the stratified storage is completed, the heat is further converted into heat flux density (i,t), used as input for dynamic temperature control module:

[0036] in, is the action area of ​​the target soil layer (unit: ), is the heat flux release period (unit: s).

[0037] Generated heat distribution sequence ={ (1,t), (2,t),…, (n, t)} will be used as the input of the next module. S130, based on the heat distribution sequence, output a stable heat energy supply sequence for operation input of the dynamic temperature control module.

[0038] Based on the hierarchical heat distribution sequence , calculate the final heat energy supply sequence through the balanced heat flow release algorithm (t):

[0039] in, (t) represents the total heat energy output at time t (unit: W / ). Release the heat flow weight for each layer to meet , whose value is dynamically adjusted by the control parameters of the frost heave prediction module.

[0040] Said (t) will be transmitted to the dynamic temperature control module and directly participate in the heat regulation of subsequent modules.

[0041] The correlation between the previous and next steps: ①The correlation between S110 and S120: (t) and It is the input source for the S120 thermal stratification storage, ensuring that the thermal storage process is dynamically adjusted based on the actual solar energy input.

[0042] ②The correlation between S120 and S130: It is the core input of S130, generating a stable heat supply sequence after dynamic adjustment of layered heat distribution (t), ensuring the input accuracy of subsequent modules.

[0043] ③Connection between S130 and S200: The output of (t) is used as the dynamic temperature control input of the frost heave state prediction (S200), which directly affects the accuracy of the frost heave trend analysis.

[0044] Step S200 at least includes steps S210-S230: S210, obtaining real-time soil temperature, humidity and frost heave stress data from environmental sensors, and performing basic data integration.

[0045] First, the soil temperature is obtained from the deployed environmental sensors (t), humidity (t) and frost heave stress (t), the specific collection point number is i (i=1,2,…,n), and the time is t.

[0046] Record the data as: (t)={ (t), (t), (t)},i=1,2,…,n in, (t) represents the soil temperature at the ith sampling point (unit: °C). (t): represents the soil moisture at the ith collection point (unit: percentage). (t): represents the frost heave stress at the ith collection point (unit: kPa).

[0047] Combined with the heat supply sequence output by S130: The heat supply sequence obtained from the S130 module (t) As supplementary input data, calculate the actual frost heave trend response of each collection point (t): (t)= (t)− · (t) in, is the sensitivity coefficient of frost heave stress to thermal energy response, which is obtained by experimental calibration. (t) represents the frost heave stress state after thermal energy regulation.

[0048] Furthermore, the sensor collects data (t) and response data (t) is integrated to obtain the comprehensive basic data matrix M(t): M(t)=

[0049] S220, combining the integrated data with historical frost heave characteristics to perform frost heave dynamic trend analysis to obtain a trend prediction sequence.

[0050] First, call the historical frost heave characteristics stored in the database (i,t), contains the multi-year frost heave trend characteristics of different collection points, including the soil frost heave start temperature , Freeze duration , maximum frost heave stress .

[0051] Furthermore, the dynamic trend fitting of M(t) data is performed, and the frost heave trend sequence is calculated by combining historical data. (t): (t)=α·( (t)− )+γ· (t)−δ· (t) Among them, α is the temperature sensitivity coefficient, γ is the humidity sensitivity coefficient, and δ is the feedback coefficient of frost heave stress to thermal energy adjustment.

[0052] Furthermore, the frost heave trend parameters of all the collected points are processed into time series to obtain the frost heave trend prediction sequence F(t)={ , ,…, } and stored for subsequent control parameter generation.

[0053] S230: Generate frost heave control parameters for different locations based on the trend prediction sequence and current sensor data.

[0054] First, based on the frost heave trend prediction sequence F(t) and real-time data M(t), the heat energy demand adjustment of each collection point is calculated. (i,t): (i,t)=κ· (t)+λ· (t) Among them, κ is the trend control coefficient and λ is the frost heave stress weight factor.

[0055] Furthermore, based on humidity data (t) and frost heave stress data (t), calculate the drainage demand at different locations (i,t): (i,t)=μ· (t)−ν· (t) Among them, μ is the humidity influence coefficient. ν is the negative feedback coefficient of stress on drainage regulation.

[0056] Furthermore, the comprehensive control parameters are generated: comprehensive heat energy demand and drainage needs , generate the control parameter matrix C(t): C(t)=

[0057] The matrix C(t) will be used as input for subsequent modules.

[0058] Description of the connection between the previous and next steps: The correlation between S210 and S220: M(t) is the parameter for calculating the frost heave trend in S220 (t) direct input, integrating real-time sensor data and thermal energy response data to ensure the accuracy of trend prediction.

[0059] The correlation between S220 and S230: (t) is the core basis for calculating the S230 control parameter C(t), ensuring that the control scheme is based on accurate frost heave trend prediction.

[0060] Association with the previous and next modules: The control parameter C(t) output by S230 will be transmitted to the dynamic temperature control and drainage linkage module (S300) as the core input for subsequent heat energy distribution and drainage path optimization.

[0061] Step S300 at least includes steps S310-S330: S310, inputting the heat energy output sequence and frost heave control parameters into the dynamic temperature control module, and performing heat distribution calculation for different roadbed areas.

[0062] First, input the heat energy sequence and frost heave control parameters: receive the heat energy output sequence from module S130 (t), and the frost heave control parameter matrix C(t) generated by module S230: C(t)=

[0063] in, is the heat energy demand of the ith roadbed area (unit: ). is the drainage demand of the ith roadbed area (unit: L / s).

[0064] Furthermore, according to the frost heave control parameters , calculate the heat distribution weight : =

[0065] in, Represents the heat distribution ratio of the ith roadbed area, satisfying .

[0066] Furthermore, combined with and , calculate the heat energy allocated to each roadbed area : = ·

[0067] in, is the actual thermal energy allocated to the i-th region (unit: ).

[0068] S320: Based on the heat distribution calculation result, optimize the drainage path design, dynamically adjust the drainage intensity, and generate a path adjustment sequence.

[0069] First, receive the heat distribution sequence calculated in S310 and drainage requirements in the frost heave control matrix .

[0070] Combined with soil moisture and drainage needs , calculate the drainage path optimization coefficient for each roadbed area : =

[0071] in, Represents the drainage optimization weight of the i-th area.

[0072] according to and current drainage intensity, dynamically adjusting the drainage volume of each path : = ·

[0073] in, is the drainage intensity of the i-th roadbed area after optimization (unit: L / s).

[0074] S330: Combining the heat distribution calculation with the path adjustment sequence, generating an integrated linkage control strategy for real-time regulation of the anti-freezing heave state of the roadbed.

[0075] First, the heat distribution sequence generated by S310 is synthesized and the drainage path adjustment sequence generated by S320 , forming a regional linkage control matrix: (t)=

[0076] Further, using (t) and frost heave trend prediction series (t), calculate the comprehensive control strategy U(i,t) for each area: U(i,t)=α· +β· −γ· (t) Among them, α, β, and γ are the weights of heat, drainage, and trend influence in the control strategy. U(i,t) is the comprehensive control intensity of the i-th region (unit: dimensionless).

[0077] Furthermore, the comprehensive control strategy U(i,t) of all areas is output to the real-time control system for dynamically adjusting the heat release and drainage paths.

[0078] Correlation between previous and next steps: Correlation between S310 and S320: Heat distribution sequence (i, t) is the dynamic drainage intensity in drainage path optimization The input parameters of (i, t) ensure the linkage between heat and drainage design.

[0079] Relevance of S320 and S330: Drainage Path Adjustment Sequence (i,t) and heat distribution The combination of (i, t) forms a regional linkage control matrix (t), providing support for comprehensive control strategy calculations.

[0080] Connection with previous and next modules: The linkage control strategy U(i,t) directly enters the feedback optimization module S400 as output to dynamically optimize the system control parameters.

[0081] Step S400 at least includes steps S410-S430: S410, obtaining the operating data of the heat output sequence, frost heave regulation parameters and linkage control strategy, and performing multi-dimensional integration.

[0082] First, obtain the heat supply sequence output by S130 (t), the frost heave control parameter matrix C(t) generated by S230, and the comprehensive linkage control strategy U(i,t) generated by S330: C(t)= U(i,t)=α· +β (i,t)−γ· (t)

[0083] in, is the heat energy demand of the ith area; Drainage requirements for area iii; (i, t) is the heat distribution; (i, t) is the drainage path adjustment sequence.

[0084] Furthermore, the above data are combined with real-time sensor monitoring data (such as soil temperature ,humidity (t), frost heave stress Integration, building a comprehensive data matrix : =

[0085] Furthermore, for the matrix Serialize by time dimension to generate time series , providing basic data for subsequent feedback analysis.

[0086] S420: Perform feedback analysis on the integrated data to identify deviations and performance bottlenecks in operation.

[0087] First, according to the actual soil status monitored ( (t), (t), (t)) and predict frost heave trends (t), calculate the deviation between the actual and target ΔF(i,t): ΔF(i,t)= (t)− (t) in, (t) is the predicted frost heave trend; (t) is the actual frost heave trend obtained by monitoring.

[0088] Furthermore, based on U(i,t) and ΔF(i,t), the performance of the linkage control strategy in heat regulation and drainage optimization is evaluated:

[0089] Among them, E(i,t) represents the effect of the linkage control strategy in the i-th area.

[0090] Furthermore, we analyze the distribution of E(i,t) to locate areas with low control effect. , identify the influencing factors (such as insufficient heat distribution or low drainage intensity).

[0091] S430. Optimize key control parameters of the dynamic temperature control and drainage linkage module based on the feedback analysis results, and generate optimized parameter configuration for the next cycle.

[0092] First, based on the feedback analysis results of S420, adjust the core parameters of the dynamic temperature control module and the drainage linkage module: heat distribution weight Adjustment: = +η·ΔF(i,t) Where η is the adjustment coefficient. Drainage path optimization weight Adjustment: = −ζ·ΔF(i,t) where ζ is the adjustment coefficient.

[0093] Furthermore, according to the optimized and , generate the optimization parameter matrix for the next cycle : =

[0094] Will The output is sent to the dynamic temperature control module (S300) as the input parameter of the next cycle, which is used to adjust the heat distribution and drainage path in real time.

[0095] Correlation between previous and next steps: Correlation between S410 and S420: Data integration matrix (t) Provides a multi-dimensional basis for feedback analysis, ensuring the accuracy of deviation analysis and performance evaluation.

[0096] The correlation between S420 and S430: The linkage control strategy effect evaluation E(i, t) and the deviation analysis result ΔF(i, t) are the core basis for optimizing control parameters.

[0097] Connection with previous and next modules: Generated optimization parameter configuration It is the input of the dynamic temperature control module (S300) and directly affects the control scheme of the next cycle.

[0098] The key innovative features of the present invention include: (1) Linkage between solar heating and dynamic temperature control: Ambient sunlight data is obtained through solar thermal collectors and combined with high-efficiency heat storage materials to achieve efficient storage and intelligent regulation of thermal energy, thereby reducing energy consumption.

[0099] (2) Environmental monitoring and prediction: Real-time soil temperature, moisture, and frost heave stress data are collected, and the frost heave trend is predicted based on historical frost heave characteristics to generate precise control parameters, providing a basis for precise control of roadbed temperature and moisture.

[0100] (3) Dynamic temperature control and drainage linkage control: Through the dynamic temperature control and drainage linkage module, the heat distribution and drainage path are optimized and adjusted to ensure the stability of the roadbed during the frost heave season and optimize the operation effect of the system.

[0101] The following are its main beneficial effects: The present invention combines solar heating, dynamic temperature control and drainage linkage control, environmental monitoring and prediction technology to accurately adjust the temperature and humidity of the roadbed in the seasonal freezing zone, solving the technical problems of low energy efficiency, complex system and lack of dynamic adaptability of traditional anti-frost heave methods. Compared with traditional single heating or drainage technology, the present invention can adjust the temperature and humidity of the roadbed in real time by optimizing the linkage control of heat energy distribution and drainage path, effectively preventing the occurrence of frost heave, and ensuring the stability and safety of the roadbed in the seasonal freezing zone environment.

[0102] In addition, the present invention uses solar energy as the main source of heat energy, which reduces the energy consumption of traditional electric heating systems, reduces maintenance costs, and reduces manual intervention through intelligent control systems, thereby improving construction efficiency. The system continuously optimizes the control strategy through feedback analysis, making the anti-freeze heave effect more accurate and continuous, significantly extending the service life of the roadbed and reducing the subsequent maintenance costs. The overall system not only improves energy utilization efficiency, but also optimizes the anti-freeze heave performance of the roadbed, effectively improving the sustainability and economy of the roadbed project in seasonally frozen areas.

[0103] Embodiment 2: Figure 2 The structural block diagram of a solar-heated seasonally frozen zone anti-freezing drainage roadbed and its construction method according to an embodiment of the present invention is shown. Figure 2 As shown, the structure may include: The data acquisition module 10 is used to obtain various data related to the roadbed in the seasonally frozen area from the on-site environment, including solar radiation intensity, ambient temperature and humidity, soil temperature, humidity, frost heave stress and other sensor data. Through the intelligent sensors set at the construction site, various key parameters such as ground temperature, humidity, soil frost heave stress, and on-site solar radiation intensity data are collected in real time. The collected data will be sent to the data processing module in real time through wireless transmission technology.

[0104] The data processing module 20 is used to pre-process the collected data, including data denoising, outlier removal, missing value filling and other processing operations to ensure the accuracy and consistency of the data. The processed data will be standardized and stored in the central database to provide a reliable data basis for subsequent analysis. The key task of this module is to ensure the quality of the data so that subsequent analysis and control are more accurate.

[0105] The environmental monitoring and prediction module 30 predicts the frost heave trend by analyzing the real-time collected environmental data (such as temperature, humidity, frost heave stress, etc.) and combining the historical frost heave characteristics. Based on the environmental data and historical frost heave data, this module predicts the frost heave development trend in the future through an algorithm model and generates a frost heave control parameter sequence. These prediction results provide a basis for dynamic temperature control and drainage regulation, ensuring that the anti-frost heave measures of the roadbed are adjusted in time during the actual construction process.

[0106] The dynamic temperature control and drainage linkage module 40 inputs the heat energy output sequence and frost heave control parameters obtained from the data processing module into the system to optimize the heat distribution and drainage path. Based on real-time temperature and humidity data, this module intelligently adjusts the working state of the solar heating system and optimizes the strength and path of the roadbed drainage system. By optimizing the joint regulation of heat release and water removal, this module can effectively prevent the occurrence of soil frost heave and improve the stability of the roadbed in the construction area.

[0107] The system feedback and optimization module 50 monitors the working status of the dynamic temperature control and drainage linkage module in real time and analyzes the deviations and bottlenecks in the system operation. Based on the difference between the actual roadbed frost heave situation and the predicted results, the module adjusts the temperature control system and drainage path in real time. The feedback analysis results will be used to generate the optimization parameters for the next cycle, and continuously optimize the control strategies of each module to adapt to changes in different environments and working conditions, ensuring the efficient operation of anti-frost heave measures.

[0108] The decision support and report generation module 60 provides detailed construction scheduling suggestions and adjustment plans based on real-time data, frost heave prediction results, and feedback from control strategy optimization, and generates a construction quality analysis report. This module helps construction management personnel understand the anti-frost heave status of the roadbed during construction, and adjusts the construction plan based on the analysis report to ensure that the project quality meets the design requirements.

[0109] The user interaction and visualization module 70 provides an intuitive user interface to display the system's prediction results, adjustment parameters and analysis reports. Through the graphical operation interface, construction personnel and management personnel can quickly understand the system's operating status, simulate the roadbed anti-freezing effect under different environmental conditions, and make decisions based on the system's suggestions. This module helps users improve construction efficiency and decision-making efficiency through interactive operations.

[0110] The system monitoring and adaptive module 80 continuously monitors the system's operating data and performance to ensure that each module operates stably and efficiently during actual operation. Through real-time data feedback, the module can adaptively adjust system parameters, such as adjusting the working intensity of the solar heating device according to changes in soil temperature, or adjusting the working mode of the drainage system according to humidity and frost heave stress. This module ensures that the entire system remains in optimal condition in the changing seasonal freezing zone environment.

[0111] The solar-heated seasonally frozen zone anti-frost heave drainage roadbed system provided by the present invention realizes precise anti-frost heave control of the seasonally frozen zone roadbed by integrating multiple technical means such as solar heating, environmental data collection, dynamic temperature control and drainage linkage. The system effectively prevents the damage of frost heave to the roadbed structure through real-time monitoring, data analysis, dynamic optimization and other functions, and improves the stability and service life of the roadbed. In addition, the system can adaptively adjust various control parameters according to changes in the on-site environment, thereby reducing energy consumption, reducing manual intervention, and improving construction efficiency and safety. It has the following beneficial effects: 1. Accurately prevent frost heave: Through modules such as environmental monitoring and prediction, dynamic temperature control and drainage linkage, the roadbed temperature and moisture status can be accurately controlled to effectively prevent frost heave.

[0112] 2. Improve construction efficiency: The system has a high degree of automation, which reduces manual intervention. Construction personnel can quickly understand the construction status through the visual interface and improve decision-making efficiency.

[0113] 3. Energy saving and environmental protection: Using solar energy as the main source of heat energy reduces the energy consumption of traditional heating systems and has no pollution emissions.

[0114] 4. Adaptive adjustment: The system can adjust control parameters in real time to ensure stable operation under different climatic conditions and adapt to the complex seasonal freezing environment.

[0115] 5. Extend the service life of the roadbed: Through effective anti-freeze heave and drainage management, the service life of the roadbed is extended and the subsequent maintenance costs are reduced.

[0116] The invention has broad application prospects and is particularly suitable for infrastructure construction such as roads, railways, and airport runways in cold regions, and can provide an economical, green, and intelligent solution for transportation construction in cold regions.

[0117] Obviously, the embodiments described above are only some embodiments of the present application, rather than all embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application is described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific implementation methods, or to perform equivalent replacement of some of the technical features therein. Any equivalent structure made using the contents of the specification and drawings of this application, directly or indirectly used in other related technical fields, is similarly within the scope of patent protection of this application.

Claims

1. A solar-heated seasonally frozen zone anti-freezing drainage roadbed and its construction method, characterized in that: The steps include: Acquire environmental sunshine data based on solar thermal collectors, store thermal energy through efficient heat storage materials, and generate thermal energy output sequences; Obtain soil temperature, moisture and frost heave stress data from environmental sensors, and predict frost heave trends based on historical frost heave characteristics to generate control parameters; The heat energy output sequence and the frost heave control parameters are input into a dynamic temperature control and drainage linkage module to dynamically adjust the heat distribution and drainage path to generate a linkage control strategy; Based on the heat energy output sequence, the frost heave regulation parameters and the linkage control strategy, feedback analysis is performed on the operating data of each module, and the control parameters of the dynamic temperature control and drainage linkage module are optimized.

2. The solar-heated seasonally frozen area anti-freezing drainage roadbed and construction method thereof according to claim 1, characterized in that: Before the step of obtaining the ambient sunshine data, the method further includes: An ambient light sensor is provided to collect sunlight radiation intensity data in real time and transmit the data to the solar thermal collector for heat energy capture.

3. The solar-heated seasonally frozen area anti-freezing drainage roadbed and construction method thereof according to claim 1, characterized in that: The step of obtaining soil temperature, humidity and frost heave stress data from the environmental sensor specifically includes: Temperature sensors, humidity sensors and frost heave stress sensors are installed to collect the soil temperature, humidity and frost heave stress data in real time, and transmit the data to the data processing module.

4. The solar-heated seasonally frozen area anti-freezing drainage roadbed and construction method thereof according to claim 3, characterized in that: The historical frost heave characteristic data include soil freezing temperature, freezing duration and frost heave stress data, and the frost heave trend is predicted in combination with the historical frost heave characteristic data to generate the control parameters.

5. The solar-heated seasonally frozen area anti-freezing drainage roadbed and construction method thereof according to claim 1, characterized in that: The step of inputting the heat energy output sequence and the frost heave control parameters into the dynamic temperature control and drainage linkage module specifically includes: Based on the heat energy output sequence and frost heave control parameters, the heat distribution weight of each roadbed area is calculated through an algorithm model to obtain a heat distribution sequence, and the drainage intensity is adjusted according to soil moisture and frost heave stress to generate a drainage path adjustment sequence.

6. The solar-heated seasonally frozen area anti-freezing drainage roadbed and construction method thereof according to claim 5, characterized in that: The dynamic temperature control and drainage linkage module adjusts the temperature and humidity of the roadbed in real time according to the heat distribution sequence and the drainage path adjustment sequence to prevent the occurrence of frost heave.

7. The solar-heated seasonally frozen area anti-freezing drainage roadbed and construction method thereof according to claim 1, characterized in that: The step of optimizing the control parameters of the dynamic temperature control and drainage linkage module based on the feedback analysis results specifically includes: The regulation process of the heat distribution and drainage path is monitored in real time, deviations and performance bottlenecks in system operation are identified through feedback analysis, and control parameters are adjusted according to the analysis results.

8. The solar-heated seasonally frozen area anti-freezing drainage roadbed and construction method thereof according to claim 7, characterized in that: The optimized control parameters include heat energy release intensity, drainage intensity and adjustment coefficients of control parameters, generating optimized parameter configuration for the next cycle.

9. The solar-heated seasonally frozen area anti-freezing drainage roadbed and construction method thereof according to claim 1, characterized in that: The generated feedback analysis results are further used to generate a construction quality analysis report as a basis for construction scheduling and adjustment.

10. A solar-heated frost-heaving and drainage roadbed system for seasonally frozen areas, characterized in that: include: The data acquisition module 10 is used to obtain data related to the roadbed in the seasonally frozen area from the field environment, and the data is sent to the data processing module in real time through wireless transmission technology; A data processing module 20 is used to pre-process the collected data. The processed data will be standardized and stored in a central database; The environmental monitoring and prediction module 30 is used to analyze the environmental data collected in real time, and predict the frost heave trend in combination with the historical frost heave characteristics, and generate a frost heave control parameter sequence. The prediction results provide a basis for dynamic temperature control and drainage regulation; The dynamic temperature control and drainage linkage module 40 is used to receive the heat output sequence and frost heave control parameters to optimize the heat distribution and drainage path; The system feedback and optimization module 50 is used to monitor the operation status of the dynamic temperature control and drainage linkage module in real time, analyze the deviations and bottlenecks in operation, adjust the temperature control system and drainage path based on the difference between the actual roadbed frost heave and the predicted results, and generate the optimization parameters for the next cycle; The decision support and report generation module 60 provides construction scheduling suggestions and adjustment plans and generates a construction quality analysis report based on real-time data, frost heave prediction results, and feedback from control strategy optimization; User interaction and visualization module 70, which provides an intuitive user interface to display the system's prediction results, adjustment parameters and analysis reports, helping users to quickly understand the system's operating status and improve decision-making efficiency; The system monitoring and self-adaptation module 80 is used to continuously monitor various operating data and performance of the system and adjust system parameters through self-adaptation.

Images