Road block stone roadbed data management method and system based on temperature field distribution

Abstract

The invention discloses a road block stone roadbed data management method and system based on temperature field distribution, and relates to the technical field of data processing.The method comprises the steps that a data collection direction is obtained, data collection points are distributed in the data collection direction, and a current settlement data sequence is obtained; acquiring a historical temperature data sequence, acquiring a temperature field characteristic value of each data acquisition point, sequentially arranging the temperature field characteristic values of the data acquisition points according to the position sequence of the data acquisition points in the data acquisition direction, and forming a temperature field characteristic sequence in the data acquisition direction; judging whether adjacent data acquisition points in the data acquisition direction are continuous or not according to the temperature field feature sequence; and if the adjacent data acquisition points in the data acquisition direction are not continuous, performing abnormal marking on the current settlement data sequence of the adjacent data acquisition points, and outputting the current settlement data sequence with abnormal marks. The method has the advantages of recognition continuity, double correction and dynamic marking.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, in particular to a highway block stone roadbed data management method and system based on temperature field distribution. BACKGROUND

[0002] In the settlement monitoring of highway block stone roadbed (especially in permafrost regions), due to factors such as extreme temperature fluctuations, freeze-thaw cycles, and non-uniformity of roadbed materials, the settlement data collected by sensors often have local anomalies. Such anomalies may be caused by physical real deformation (such as local frost heaving and thawing, non-continuous settlement caused by block stone loosening), or by equipment failure or environmental interference (such as sensor temperature drift, connection line breakage, and transient electromagnetic interference). The existing methods only rely on the settlement data itself for judgment, and cannot effectively distinguish between the above two types of anomalies.

[0003] Specifically, analyzing the change pattern of settlement time series data (such as mutation value filtering) cannot verify whether the anomaly is caused by the destruction of roadbed structure continuity from a physical mechanism; at the same time, temperature field data is not used, and the distribution of roadbed temperature field is essentially a direct manifestation of the continuity of thermodynamics. The temperature field characteristics of adjacent measuring points should show spatial gradualness, but the existing system does not use the spatial continuity of temperature field as a criterion for the reliability of settlement data; and when a single sensor outputs false settlement data due to its own failure (such as temperature drift), the system may misjudge as local roadbed failure, resulting in unnecessary maintenance cost, on the contrary, the real roadbed discontinuous deformation will be ignored, which will cause structural safety hazards. SUMMARY

[0004] In order to solve the technical problem that the credibility of settlement data cannot be identified, the real roadbed deformation and equipment abnormal data cannot be accurately distinguished, and the accuracy and maintenance efficiency of highway block stone roadbed monitoring are reduced, the present application provides a highway block stone roadbed data management method and system based on temperature field distribution.

[0005] The application discloses a highway block stone roadbed data management method based on a temperature field distribution, which comprises the following steps: obtaining a data collection direction located in a highway block stone roadbed, and uniformly distributing a plurality of data collection points in the data collection direction; obtaining a current settlement data sequence of each data collection point in the data collection direction in a current monitoring period; obtaining a historical temperature data sequence of each data collection point in the data collection direction in a monitoring period before the current monitoring period, and obtaining a temperature field characteristic value of each data collection point according to the historical temperature data sequence of each data collection point; sequentially arranging the temperature field characteristic values of each data collection point according to the position sequence of each data collection point in the data collection direction, and forming a temperature field characteristic sequence of the data collection direction; judging whether each adjacent data collection point in the data collection direction is continuous according to the temperature field characteristic sequence; if the adjacent data collection points in the data collection direction are not continuous, marking the current settlement data sequence of the adjacent data collection points as abnormal, and outputting the current settlement data sequence with the abnormal mark.

[0006] Optionally, the temperature field characteristic value of each data collection point is obtained according to the historical temperature data sequence of each data collection point, which comprises the following steps: obtaining a correction ratio according to the historical temperature data sequence; obtaining a historical average temperature according to the historical temperature data sequence, and obtaining the temperature field characteristic value according to the correction ratio and the historical average temperature.

[0007] Optionally, the correction ratio is obtained according to the historical temperature data sequence, which comprises the following steps: obtaining a temperature difference between adjacent temperature data in the historical temperature data sequence; accumulating all the temperature differences in the historical temperature data sequence to obtain a total change amount, and obtaining the correction ratio according to the total change amount and a preset standard change amount.

[0008] Optionally, the historical average temperature is obtained according to the historical temperature data sequence, which comprises the following steps: adding all the temperature data in the historical temperature data sequence to obtain a total calculation amount, and obtaining the number of temperature data in the historical temperature data sequence, and obtaining the historical average temperature according to the total calculation amount and the number of temperature data.

[0009] Optionally, whether each adjacent data collection point in the data collection direction is continuous is judged according to the temperature field characteristic sequence, which comprises the following steps: obtaining a change rate of adjacent temperature field characteristic values in the temperature field characteristic sequence, and obtaining a preset allowable range; if the change rate is not in the preset allowable range, obtaining the adjacent temperature field characteristic value corresponding to the change rate, and identifying the adjacent data collection point corresponding to the adjacent temperature field characteristic value as discontinuous; if the change rate is in the preset allowable range, obtaining the adjacent temperature field characteristic value corresponding to the change rate, and identifying the adjacent data collection point corresponding to the adjacent temperature field characteristic value as continuous.

[0010] Optionally, the rate of change of the adjacent temperature field characteristic values in the temperature field characteristic sequence is obtained by dividing the absolute value of the difference between the adjacent temperature field characteristic values by the former one of the adjacent temperature field characteristic values, and taking the result as the rate of change of the adjacent temperature field characteristic values.

[0011] Optionally, the preset allowable range is obtained by analyzing historical rate of change data of the temperature field characteristic sequence in a continuous state based on historical monitoring data in the data collection direction of the highway block stone subgrade, and obtaining a rate of change allowable boundary reflecting spatial continuity as the preset allowable range according to the historical rate of change data.

[0012] Also provided is a highway block stone subgrade data management system based on temperature field distribution, which comprises: a data acquisition module for acquiring data collection directions (there can be multiple, each of which is processed according to the following steps) in a highway block stone subgrade, and multiple data collection points are uniformly distributed in each data collection direction, and current settlement data sequences of each data collection point in the data collection direction in a current monitoring period are acquired; a data processing module for acquiring historical temperature data sequences of each data collection point in a previous monitoring period before the current monitoring period, and acquiring temperature field characteristic values of each data collection point according to the historical temperature data sequences of each data collection point, and arranging the temperature field characteristic values of each data collection point in order according to the positions of each data collection point in the data collection direction to form a temperature field characteristic sequence of the data collection direction; a data judgment module for judging whether each adjacent data collection point in the data collection direction is continuous according to the temperature field characteristic sequence; and a data management module for marking the current settlement data sequence of the adjacent data collection point as abnormal when the adjacent data collection points in the data collection direction are not continuous, and outputting the current settlement data sequence with the abnormal mark.

[0013] Optionally, the data processing module is further configured to: obtain a correction ratio according to the historical temperature data sequence; obtain a historical average temperature according to the historical temperature data sequence, and obtain the temperature field characteristic value according to the correction ratio and the historical average temperature.

[0014] Optionally, the data processing module is further configured to: obtain a temperature difference between adjacent temperature data in the historical temperature data sequence; accumulate all the temperature differences in the historical temperature data sequence to obtain a total change amount, and obtain the correction ratio according to the total change amount and a preset standard change amount.

[0015] The beneficial effects of the present application are embodied in: In the whole highway block stone subgrade data management method based on temperature field distribution, firstly, by analyzing the temperature field characteristic sequence distributed along the line direction constructed by historical temperature data, the physical discontinuity (such as thermal resistance mutation area formed by block stone loosening or data jump point caused by sensor failure) between adjacent measuring points can be accurately identified, so as to distinguish whether the settlement anomaly is caused by structure damage or equipment failure; secondly, based on the double correction mechanism (i.e. the correction proportion generated by combining the cumulative intensity of temperature fluctuation and the long-term reference temperature) of historical temperature sequence, the noise influence of short-time environmental interference on temperature field characteristic value is effectively removed, so that the characteristic sequence truly reflects the thermal conduction continuity law of subgrade material; finally, the dynamic comparison framework of temperature characteristic value change rate of spatial adjacent points is established, the non-physical jump interval is locked through the preset boundary range, and then the settlement data of high-risk area is marked with directional anomaly-the marking serves as an intelligent early warning signal, which not only compresses the artificial verification range (engineers only need to verify the marked points), but also avoids the invalid maintenance caused by misjudging equipment anomaly as subgrade damage (such as blind excavation repair), prevents the safety hazards caused by missing the real structure instability (such as collapse caused by local thawing settlement), and finally improves the monitoring accuracy and optimizes the life cycle maintenance cost under complex working conditions in permafrost area. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the drawings needed in the specific embodiments or the prior art description will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, each element or part is not necessarily drawn according to the actual proportion.

[0017] Figure 1 Part of the flowchart of the highway block stone subgrade data management method based on temperature field distribution of the present application in one embodiment; Figure 2 Another part of the flowchart of the highway block stone subgrade data management method based on temperature field distribution of the present application in one embodiment; Figure 3 The step schematic diagram of the highway block stone subgrade data management method based on temperature field distribution of the present application; Figure 4 Part of the step schematic diagram of S2 in the highway block stone subgrade data management method based on temperature field distribution of the present application; Figure 5 Part of the step schematic diagram of S3 in the highway block stone subgrade data management method based on temperature field distribution of the present application. DETAILED DESCRIPTION

[0018] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the following will be combined with the accompanying drawings of the embodiments of the present application to make a clear and complete description of the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the accompanying drawings can be arranged and designed in various different configurations.

[0019] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative labor are within the scope of protection of the present application.

[0020] It should be noted that: similar reference numerals and letters represent similar items in the following drawings, therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. In addition, the terms "first", "second", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0021] As shown in Figure 1 , Figure 2 and Figure 3 , a highway block stone roadbed data management method based on temperature field distribution is provided, in one embodiment, the method comprises: S1, acquiring a data acquisition direction located in a highway block stone roadbed, and a plurality of data acquisition points are uniformly distributed in the data acquisition direction, and acquiring a current settlement data sequence of each data acquisition point in the data acquisition direction in a current monitoring period; S2, acquiring a historical temperature data sequence of each data acquisition point in the data acquisition direction in a monitoring period before the current monitoring period, and acquiring a temperature field characteristic value of each data acquisition point according to the historical temperature data sequence of each data acquisition point, and arranging the temperature field characteristic values of each data acquisition point in order according to the position sequence of each data acquisition point in the data acquisition direction and forming a temperature field characteristic sequence of the data acquisition direction; S3, judging whether each adjacent data acquisition point in the data acquisition direction is continuous according to the temperature field characteristic sequence; S4, if the adjacent data acquisition points in the data acquisition direction are not continuous, the current settlement data sequence of the adjacent data acquisition points is marked as abnormal, and the current settlement data sequence with the abnormal mark is outputted.

[0022] In the present embodiment, it is noted that in S1, a set of raw data representing the vertical deformation of the embankment in the current monitoring period is obtained in a specific spatial direction. Specifically, it is necessary to first determine the "data collection direction" to be analyzed, which generally refers to a straight or approximately straight monitoring path in a specific longitudinal direction (such as the driving direction) or transverse direction (such as the width direction of the embankment) along the embankment. On this path, a plurality of data collection points (i.e. sensor positions) are pre-arranged, which must satisfy the key condition of uniform distribution, i.e. the spatial interval between points is consistent. On this basis, for each pre-set data collection point in the data collection direction, all the settlement data recorded by the sensor in the current complete monitoring period (e.g. the past 24 hours, one week or a defined period of freeze-thaw stage) are collected. These settlement data are time-ordered measurement values, which together form an independent "current settlement data sequence" for each measurement point. This sequence reflects the vertical height change of the embankment at that specific location in the recent period due to factors such as temperature change, freeze-thaw cycle, vehicle load or self-deformation, and is the original basis for subsequent judgment of embankment behavior.

[0023] Further, for example, assume that in the direction of the central axis of a highway embankment passing through a permafrost region (which is a typical data collection direction), the engineer has evenly arranged 10 settlement monitoring points at a fixed interval (such as every 10 meters). The current time is at the end of a 7-day monitoring period. When performing S1, the "central axis direction" is determined as the spatial axis to be analyzed from the structure of the monitoring network. Then, for each of the 10 points arranged continuously along this axis, all the settlement measurement values collected by the settlement sensor automatically, continuously or at a set frequency (such as once an hour) in the past 7 days of the "current monitoring period" are extracted. In this way, each measurement point obtains a set of data arranged in chronological order, i.e. the settlement change curve of the measurement point in the last 7 days. The 10 independent time series data (one sequence for each measurement point) together form the settlement data set of the data collection direction in the current monitoring period, providing basic data for subsequent analysis of its reliability and continuity in combination with the temperature field.

[0024] In S2, historical temperature data is used to construct spatial characteristic indicators reflecting the thermodynamic continuity of the roadbed. Specifically, for each measuring point along the data acquisition direction, all historical temperature values ​​continuously recorded by temperature sensors within the previous full cycle (e.g., the previous week) are extracted, forming a historical temperature data sequence arranged chronologically. Based on this sequence, two key intermediate variables need to be calculated: first, a correction ratio is generated by analyzing the cumulative intensity of temperature fluctuations between adjacent moments within the sequence (this ratio reflects the sensitivity of the temperature field at that point to short-term environmental disturbances); second, the historical average temperature of the entire historical sequence is calculated (reflecting the long-term baseline level of the temperature at that point). Finally, the temperature field characteristic value is generated by fusing the above correction ratio and the historical average temperature according to specific rules. This value retains the spatial distribution trend of temperature while weakening the influence of random temperature fluctuations, making it the core indicator characterizing the thermodynamic state of that point. After generating the characteristic values ​​for all measuring points, they are strictly arranged according to their spatial position along the roadbed direction (e.g., mileage order), forming a characteristic sequence reflecting the gradual change law of the temperature field along the entire data acquisition direction.

[0025] To further illustrate, suppose 21 monitoring points are evenly spaced at 50-meter intervals along the longitudinal axis of a 1-kilometer-long riprap roadbed. If the analysis needs to capture settlement data for week N, step S2 will retrieve all historical temperature records for each monitoring point over the full 7 days of week (N-1) (e.g., one data point per hour, totaling 168 temperature values). Taking the midpoint of the axis (monitoring point 11) as an example: first, data is extracted from its historical temperature sequence—if this point is located in a region of direct sunlight, its temperature sequence may experience large temperature fluctuations due to frequent day-night cycles, resulting in a larger calculated correction ratio (reflecting strong temperature sensitivity); simultaneously, the average of all temperature values ​​for that week is calculated as the historical average temperature (reflecting the overall temperature level). Next, the correction ratio and the average temperature are correlated according to specific rules to generate the temperature field characteristic value for this monitoring point (this value integrates stability and baseline temperature information). After repeating this process to process all measuring points along the axis, the 21 characteristic values ​​are arranged sequentially according to the station number order from 0km+000m to 0km+1000m, thus forming the temperature field characteristic sequence of the longitudinal axis. This sequence essentially reveals the continuous and gradual change law of the roadbed thermodynamic state along the spatial path, laying the foundation for subsequent identification of abnormal discontinuities.

[0026] In S3, the spatial variation of the temperature field is used to verify the true state of the roadbed's physical continuity. Specifically, it is necessary to analyze the temperature field characteristic sequence generated in the previous step along the data acquisition direction—this sequence reflects the orderly distribution of the thermodynamic state of the measuring points continuously arranged along a specific path of the roadbed (such as the longitudinal axis). By calculating the rate of change between the temperature field characteristic values ​​corresponding to adjacent measuring points (i.e., two data acquisition points in close proximity) one by one, and comparing this rate of change with a preset allowable range: if the rate of change between adjacent points exceeds the allowable range, it indicates that the temperature field distribution in the area where the two points are located does not conform to the gradual change law that a thermodynamically continuous medium should have, and the physical continuity is disrupted; conversely, if it is within a reasonable range, it indicates that the heat conduction state of this section of the roadbed is continuous. This judgment process essentially uses the spatial continuity characteristics of the temperature field (because the continuity of the roadbed material structure is a necessary condition for the continuity of heat transfer) to indirectly verify the integrity of the roadbed's physical structure, providing a physical basis for the subsequent judgment of the anomaly nature of settlement data.

[0027] Furthermore, to illustrate with an example, consider the longitudinal axis of an east-west oriented boulders roadbed in a permafrost region. Its temperature field characteristic sequence shows that from point K to point K+1 (10-meter interval), the characteristic value smoothly transitions from the value representing the "low-temperature stable zone" to the value representing the "medium-temperature transition zone," and the calculated rate of change is within the preset allowable range. Therefore, this interval is determined to be physically continuous. However, between points M and M+1 (also at 10-meter intervals), the characteristic value suddenly jumps from the value representing the "uniform cooling zone" to the value representing the "abnormally high-temperature zone," with the rate of change far exceeding the allowable boundary. At this point, this interval is determined to be physically discontinuous. This abrupt change often originates from localized loosening of boulders creating voids that lead to abnormal thermal resistance (actual roadbed damage), or from sensor malfunction causing data anomalies. Regardless of the cause, the settlement data for this area is marked to avoid misjudging roadbed fractures as normal settlement or mistaking equipment malfunctions for structural failure. It should also be noted that this marking does not directly indicate that the settlement data is invalid, but rather warns that the settlement data here may be distorted due to structural damage (such as the interruption of heat transfer caused by loose rocks) or equipment failure. Further verification by other means is required to avoid misjudging real roadbed collapse as sensor temperature drift or misdiagnosing sensor disconnection as local melting settlement.

[0028] In S4, based on the continuity assessment results, settlement data with reliability risks are marked with warnings to provide decision support for precise maintenance. When step S3 determines that there is a physical discontinuity between a set of adjacent data acquisition points in the data acquisition direction (such as point P2 and point P3), the current settlement data sequence corresponding to those adjacent points is immediately marked as abnormal. This marking does not directly delete or correct the data, but rather reminds the analyst through specific visual indicators (such as color highlighting, bolding of data frames, or additional warning symbols): the settlement data recently acquired at these two points may lose reliability due to structural damage (such as a sudden change in settlement caused by the detachment of the boulder layer between P2 and P3) or equipment failure (such as temperature field distortion caused by a damaged temperature sensor at point P3). By marking only the settlement data associated with physically discontinuous areas, the ability to spatially pinpoint the scope of risk is achieved.

[0029] Furthermore, for example, suppose that on the longitudinal monitoring axis of a frozen soil subgrade, step S3 detects that the rate of change of the temperature field characteristic value between two adjacent measuring points, "K5+040" and "K5+050," exceeds the limit (e.g., the abrupt change rate reaches 3 times the normal value), determining that the interval is physically discontinuous. Step S4 then marks the entire settlement data sequence of these two points for the current monitoring period (e.g., the latest week) as anomalies (e.g., by adding identifiers). When engineers review the monitoring report, they can directly locate the marked high-risk section—if the settlement curve at this location also shows drastic fluctuations (e.g., a sudden drop of 10 cm in a single day), combined with the marking, it can be preliminarily inferred that there is actual structural instability (requiring emergency reinforcement); conversely, if the settlement curve is stable but marked, it indicates a possible sensor malfunction (requiring equipment repair). This directional marking significantly reduces the scope of manual verification, preventing overreaction to abnormal equipment data (avoiding accidental triggering of excavation and maintenance) and timely detection of potential subgrade fractures (eliminating the risk of missed detection).

[0030] In summary, the entire method for managing highway block subgrade data based on temperature field distribution firstly utilizes the spatially gradual nature of the subgrade's thermodynamic state. By analyzing historical temperature data to construct a temperature field characteristic sequence distributed along the route, it can accurately identify physical discontinuities between adjacent measuring points (such as thermal resistance abrupt changes caused by loose blocks or data jumps due to sensor malfunctions), thereby distinguishing whether settlement anomalies are caused by structural damage or equipment failure. Secondly, based on a dual correction mechanism of historical temperature sequences (i.e., combining the correction ratio generated by the cumulative intensity of temperature fluctuations with the long-term reference temperature), it effectively eliminates the noise influence of short-term environmental disturbances on the temperature field characteristic values, allowing the characteristic sequence to truly reflect... The study investigates the continuity of thermal conductivity in roadbed materials. Finally, it establishes a dynamic comparison framework for the rate of change of temperature characteristic values ​​at spatially adjacent points. By locking non-physical jump intervals within a preset boundary range, it implements directional anomaly marking for settlement data in high-risk areas. This marking serves as an intelligent early warning signal, which not only reduces the scope of manual verification (engineers only need to focus on verifying the marked points), but also avoids ineffective maintenance caused by misjudging equipment anomalies as roadbed damage (such as blind excavation and repair) in existing methods. At the same time, it prevents the failure to detect safety hazards caused by actual structural instability (such as collapse caused by failure to address local thaw settlement in a timely manner). Ultimately, it achieves improved monitoring accuracy and optimized life-cycle maintenance costs under complex working conditions in permafrost regions.

[0031] like Figure 4 As shown, in one embodiment, S2, obtaining the temperature field characteristic values ​​of each data acquisition point based on the historical temperature data sequence of each data acquisition point includes: S21. Obtain the correction ratio based on the historical temperature data sequence; S22. Obtain the historical average temperature based on the historical temperature data sequence, and obtain the temperature field characteristic value based on the correction ratio and the historical average temperature.

[0032] In this embodiment, it should be noted that in step S21, under the complex temperature change environment of permafrost regions, this step aims to quantify the response intensity of data collection points to short-term temperature disturbances. The specific operations include: first, extracting historical temperature data sequences from the measuring points (e.g., temperature records for a specific location over 168 consecutive hours); analyzing the temperature difference changes (e.g., diurnal temperature difference) between each pair of adjacent monitoring values ​​in the sequence; and summing all single-step temperature differences to obtain the total change; subsequently, comparing this actual total change with a preset standard change (a threshold set based on a typical local permafrost thermal stability model) to generate a correction ratio. This ratio essentially characterizes the sensitivity of the temperature field at this location to transient environmental disturbances (e.g., intense sunlight, cold waves); if the location is on a sunny slope, frequent large temperature fluctuations will result in a high correction ratio; a stable shady slope will have a lower ratio.

[0033] For example, on the longitudinal monitoring axis, the mountain top location may be calculated with a correction ratio of 0.8 (high sensitivity) due to strong sunlight and strong winds, while the valley location may only have a correction ratio of 0.2 (low sensitivity), reflecting spatial heterogeneity.

[0034] In step S22, noise-resistant eigenvalues ​​are generated by integrating long-term temperature benchmarks. This is achieved by focusing on constructing temperature field eigenvalues ​​that possess both stability and physical significance, relying on the synergy of two factors: first, the historical average temperature (e.g., the thermodynamic benchmark level of a location within a continuous monitoring period) is obtained by calculating the summation and quantitative averaging of all monitoring values ​​from the historical temperature sequence; second, the correction ratio derived in step S21. The key operation lies in applying the correction ratio to the historical average temperature—a high correction ratio (high perturbation sensitivity) moderately reduces the weight of the average temperature, weakening the impact of transient disturbances; a low correction ratio fully preserves the representativeness of the benchmark temperature. The final output eigenvalues ​​are anchored to the long-term thermal state of the location while dynamically correcting and suppressing short-term noise, making them reliable indicators of spatial continuity.

[0035] For example, if the historical average temperature of a certain point is negative, its high correction ratio of 0.8 will drive the characteristic value to be more conservative and closer to the stable value (such as -10℃ rather than the drastically fluctuating measured average of -8℃), thereby avoiding false alarms in subsequent continuity criteria.

[0036] In one implementation, obtaining the correction ratio based on the historical temperature data sequence in step S21 includes: S211. Obtain the temperature difference between adjacent temperature data in the historical temperature data sequence; S212. Accumulate all temperature differences in the historical temperature data series to obtain the total change, and obtain the correction ratio based on the total change and the preset standard change.

[0037] In this embodiment, it should be noted that in S211, the analysis of the time-varying characteristics of temperature data is specifically implemented in two layers: First, for the entire historical temperature data sequence of a specific measuring point (such as a continuous 168-hour record), the actual temperature difference between all adjacent time points (i.e., the temperature rise or fall of a certain point per hour within a continuous period) needs to be extracted one by one, and all single-step temperature differences (including positive and negative temperature differences, reflecting the instantaneous change of temperature trend) are calculated; Second, key aggregation is performed on these scattered temperature difference data - the absolute values ​​of all single-step temperature differences in the sequence (i.e., ignoring the direction of temperature rise and fall, focusing on the intensity of change) are completely accumulated to generate a total change index (this value essentially integrates all temperature fluctuation energy of the measuring point in the entire historical monitoring period, reflecting the comprehensive response intensity of the point to external thermal disturbances).

[0038] In S212, the correlation between temperature fluctuation intensity and regional thermodynamic background is established. First, the preset standard change value is called (this value is derived from the empirical model of frozen soil engineering, combined with the preset environmental benchmark value of parameters such as roadbed orientation, altitude, and seasonal temperature change law, such as using different standards for sunny slopes and shady slopes); then, the actual total change value obtained in S211 is compared with the standard change value corresponding to the point (if the actual total change value significantly exceeds the standard value, it indicates that the temperature at this point is highly susceptible to environmental disturbances; otherwise, it indicates good thermal stability), and finally, the correction ratio is output (for example, if the total change value of a windward slope point reaches 1.5 times the standard value due to frequent strong cold air invasions, the correction ratio is 1.5; while the total change value of the sheltered valley bottom point is only 0.7 times the standard value, the correction ratio is 0.7).

[0039] In one implementation, obtaining the historical average temperature based on the historical temperature data sequence in step S22 includes: The total computational cost is obtained by summing all temperature data in the historical temperature data sequence and obtaining the number of temperature data in the historical temperature data sequence. The historical average temperature is obtained based on the total computational cost and the number of temperature data.

[0040] In this embodiment, it should be noted that the calculation of historical average temperature is essentially a quantification of the overall temperature level of a certain measuring point throughout the entire historical monitoring period. First, the temperature monitoring values ​​of all time points in the historical temperature data sequence of the measuring point are fully accumulated to obtain a sum representing the total temperature. At the same time, the number of all valid temperature data points actually existing in the sequence (i.e., the number of record points participating in the accumulation) is counted. Finally, the sum is divided by the total number of data points to output the average index that reflects the overall thermal state level of the point in the corresponding time period.

[0041] like Figure 5 As shown, in one embodiment, S3, determining whether adjacent data acquisition points are continuous based on the temperature field characteristic sequence includes: S31. Obtain the rate of change of adjacent temperature field feature values ​​in the temperature field feature sequence, and obtain the preset allowable range; S32. If the rate of change is not within the preset allowable range, then obtain the adjacent temperature field characteristic value corresponding to the rate of change, and identify the adjacent data acquisition point corresponding to the adjacent temperature field characteristic value as discontinuous. S33. If the rate of change is within the preset allowable range, then obtain the adjacent temperature field characteristic value corresponding to the rate of change, and identify the adjacent data acquisition points corresponding to the adjacent temperature field characteristic value as continuous.

[0042] In this embodiment, it should be noted that in S31, the physical integrity of the roadbed is verified through spatial gradient analysis. First, the rate of change of adjacent point characteristic values ​​is calculated for the generated temperature field characteristic sequence of the data acquisition direction (e.g., a feature value queue sorted by mileage along the longitudinal axis): the characteristic values ​​of each pair of adjacent points (e.g., point A and point B) are extracted from the sequence. Using the previous point's characteristic value as a benchmark, the absolute offset ratio of the subsequent point relative to the previous point is calculated (reflecting the spatial abrupt change intensity of the temperature field state between the two points). Simultaneously, a dynamically preset allowable range boundary is invoked (this boundary is set based on statistical analysis of a large amount of historical normal road section temperature field change data, covering the gradual change law of heat conduction in typical materials in permafrost regions). This step outputs both the measured rate of change and the allowable range data for each pair of adjacent points, forming the quantitative basis for continuity determination.

[0043] In S32, spatial discontinuities corresponding to abrupt changes in the temperature field are identified: the rate of change of each pair of adjacent points calculated in S31 is strictly compared with the allowable range. If the rate of change of a pair of points (such as point M and point M+1) significantly exceeds the upper limit of the allowable range (e.g., a feature value jump caused by the loosening of boulders on a sunny slope), it is immediately determined that there is a physical discontinuity in the roadbed section corresponding to these two points (possibly caused by structural fracture or sensor failure). At this time, the two points are automatically associated with the settlement dataset to generate a high-risk label. For example, in a river valley section, the rate of change of points P7 (foot of the mountain) and P8 (slope) exceeds the limit by 3 times due to the thermal anomaly channel formed by meltwater infiltration, triggering a discontinuity alarm.

[0044] In S33, reliability verification is performed on continuous sections. For roadbed sections conforming to thermodynamic gradual change laws, credibility certification is conducted: when the rate of change between adjacent points is within a preset allowable range (e.g., a smooth transition in characteristic values ​​between point X and point X+1), the section is determined to be physically continuous (indicating that heat transfer is not affected by structural damage or equipment failure). The corresponding settlement data is marked as physically reliable. For example, in the section from K3+100 to K3+150 along the longitudinal axis, the temperature field characteristic values ​​transition from the "low temperature zone" to the "normal temperature zone" at an allowable rate of change. The settlement data for this section can be directly used for roadbed stability analysis without additional verification.

[0045] In one embodiment, obtaining the rate of change of adjacent temperature field feature values ​​in the temperature field feature sequence in S31 includes: S311. Divide the absolute value of the difference between adjacent temperature field feature values ​​in the temperature field feature sequence by the previous temperature field feature value in the adjacent temperature field feature value, and use the calculation result as the rate of change of adjacent temperature field feature values.

[0046] In this embodiment, it should be noted that in S311, the standardized characterization of the thermal state jump intensity of adjacent measuring points is achieved. Specifically, the following steps are taken: Feature values ​​of each pair of adjacent measuring points (e.g., measuring point J and measuring point J+1 along the route) in the temperature field feature sequence are extracted in spatial order. First, the absolute difference between the two feature values ​​is calculated (reflecting the numerical gap in the thermal field between spatially adjacent points). Then, this difference is relativized with the feature value of the previous measuring point in the sequence—that is, based on the feature value of the previous point, the relative offset of the feature value of the next point is obtained (essentially eliminating the influence of the base temperature level on gradient evaluation). It is important to note that the previous temperature field feature value is not zero. If the previous temperature field feature value is zero, the lowest temperature data in the historical temperature data sequence corresponding to that feature value is removed, and the temperature field feature value is recalculated to ensure that the temperature field feature value is not zero. Finally, the standardized rate of change is output, making the abrupt change intensity comparable across different temperature zones (e.g., a 5℃ jump in a -20℃ environment has different physical meanings than a 5℃ jump in a -10℃ environment).

[0047] In one implementation, obtaining the preset allowed range in S31 includes: S312. Based on historical monitoring data in the data acquisition direction of the roadbed with boulders, analyze the historical rate of change of the temperature field characteristic sequence under continuous conditions. S313. Obtain the allowable boundary of the rate of change to reflect spatial continuity based on historical rate of change data and use it as the preset allowable range.

[0048] In this implementation, it should be noted that in S312, a baseline for the gradual change pattern of the temperature field under the healthy condition of the roadbed is established. All temperature field characteristic sequences recorded in the historical database for this data acquisition direction during periods without structural damage (such as monitoring data from the past three years of freeze-thaw stability) are retrieved, and the change rates of all adjacent points are extracted to form a historical change rate dataset. The typical distribution characteristics of this dataset are analyzed using professional algorithms (such as the 3σ principle based on normal distribution or the percentile method): firstly, the concentration of the change rate data is statistically analyzed (e.g., the median reflects the most common gradual change intensity); secondly, its discrete range is quantified (e.g., the interquartile range reflects the normal fluctuation range). For example, historical data for a certain shady slope section shows that 95% of the change rate is concentrated between 1% and 5%, and this range is the physical boundary prototype of the gradual change pattern.

[0049] In S313, dynamic continuous boundary engineering adaptation is implemented. Statistical patterns are transformed into actionable engineering criteria. Based on the historical rate of change distribution analysis results of S312, a dual-threshold allowable range is automatically generated (e.g., 0.8%~6.2% for sunny slopes and 0.5%~4.5% for shady slopes). The lower limit must cover the gentlest gradual change conditions (e.g., thermal inertia conduction in dense rock areas), and the upper limit must encompass the strongest reasonable gradual changes (e.g., normal thermal resistance differences in lithological transition zones). This range must meet two principles: first, boundary integrity (it must cover typical intervals of historical normal rate of change to avoid misjudging reasonable gradual changes as abnormal); second, spatial specificity (differentiated thresholds are set for different roadbed orientations and elevation sections). For example, the upper limit of the boundary for monitoring axes crossing ridges needs to be particularly relaxed (e.g., up to 8%) to accommodate strong thermal gradients between sunny and shady sides.

[0050] A data management system for highway boulders subgrade based on temperature field distribution is also provided. The system includes: The data acquisition module is used to acquire data collection directions (there can be multiple directions, and each data collection direction can be processed according to the following steps) located in the roadbed of the highway. Multiple data collection points are evenly distributed in the data collection directions, and the current settlement data sequence of each data collection point in the current monitoring period is acquired. The data processing module is used to acquire the historical temperature data sequence of each data acquisition point in the previous monitoring cycle in the current monitoring cycle, and to acquire the temperature field characteristic value of each data acquisition point based on the historical temperature data sequence of each data acquisition point. The temperature field characteristic values ​​of each data acquisition point are arranged in the order of their positions in the data acquisition direction to form the temperature field characteristic sequence of the data acquisition direction. The data judgment module is used to determine whether there is continuity between adjacent data acquisition points in the data acquisition direction based on the temperature field characteristic sequence. The data management module is used to mark the current settlement data sequence of adjacent data acquisition points as anomalies when there is a discontinuity between adjacent data acquisition points in the data acquisition direction, and output the current settlement data sequence with the anomaly mark.

[0051] In one implementation, the data processing module is further configured to: obtain a correction ratio based on a historical temperature data sequence; obtain a historical average temperature based on the historical temperature data sequence; and obtain temperature field characteristic values ​​based on the correction ratio and the historical average temperature.

[0052] In one embodiment, the data processing module is further configured to: obtain the temperature difference between adjacent temperature data in the historical temperature data sequence; accumulate all temperature differences in the historical temperature data sequence to obtain the total change, and obtain a correction ratio based on the total change and a preset standard change.

[0053] In this embodiment, it should be noted that the specific method of performing the operation of the above-mentioned highway boulders subgrade data management system based on temperature field distribution has been described in detail in the embodiments of the highway boulders subgrade data management method based on temperature field distribution, and will not be elaborated here.

[0054] The preferred embodiments of this disclosure have been described in detail above with reference to the accompanying drawings. However, this disclosure is not limited to the specific details of the above embodiments. Within the scope of the technical concept of this disclosure, various simple modifications can be made to the technical solutions of this disclosure, and these simple modifications all fall within the protection scope of this disclosure.

[0055] It should also be noted that the various specific technical features described in the above embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, this disclosure will not describe the various possible combinations separately.

[0056] Furthermore, various different embodiments of this disclosure can be combined in any way, as long as they do not violate the spirit of this disclosure, they should also be regarded as the content disclosed in this disclosure.

[0057] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A method for managing highway block subgrade data based on temperature field distribution, characterized in that, include: The data acquisition direction located in the roadbed of the highway is obtained, and multiple data acquisition points are evenly distributed in the data acquisition direction. The current settlement data sequence of each data acquisition point in the current monitoring period is obtained. The historical temperature data sequence of each data acquisition point in the data acquisition direction within the previous monitoring cycle is obtained, and the temperature field characteristic value of each data acquisition point is obtained based on the historical temperature data sequence of each data acquisition point. The temperature field characteristic values ​​of each data acquisition point are arranged in order of their position in the data acquisition direction to form a temperature field characteristic sequence in the data acquisition direction. Determine whether adjacent data acquisition points are continuous based on the temperature field characteristic sequence; If adjacent data acquisition points are not continuous in the data acquisition direction, the current settlement data sequence of the adjacent data acquisition points is marked as an anomaly, and the current settlement data sequence with the anomaly mark is output.

2. The method for managing highway block subgrade data based on temperature field distribution according to claim 1, characterized in that, The step of obtaining the temperature field feature values ​​of each data acquisition point based on the historical temperature data sequence of each data acquisition point includes: The correction ratio is obtained based on historical temperature data sequences; Historical average temperature is obtained from historical temperature data series, and temperature field characteristic values ​​are obtained based on correction ratio and historical average temperature.

3. The method for managing highway block subgrade data based on temperature field distribution according to claim 22, characterized in that, The step of obtaining the correction ratio based on historical temperature data sequences includes: Obtain the temperature difference between adjacent temperature data in a historical temperature data sequence; Accumulate all temperature differences in the historical temperature data series to obtain the total change, and obtain the correction ratio based on the total change and the preset standard change.

4. The method for managing highway block subgrade data based on temperature field distribution according to claim 2, characterized in that, The step of obtaining the historical average temperature based on the historical temperature data sequence includes: The total computational cost is obtained by summing all temperature data in the historical temperature data sequence and obtaining the number of temperature data in the historical temperature data sequence. The historical average temperature is obtained based on the total computational cost and the number of temperature data.

5. The method for managing highway block subgrade data based on temperature field distribution according to claim 1, characterized in that, The step of determining whether adjacent data acquisition points are continuous based on the temperature field characteristic sequence includes: Obtain the rate of change of adjacent temperature field feature values ​​in the temperature field feature sequence, and obtain a preset allowable range; If the rate of change is not within the preset allowable range, the adjacent temperature field characteristic value corresponding to the rate of change is obtained, and the adjacent data acquisition points corresponding to the adjacent temperature field characteristic value are identified as discontinuous. If the rate of change is within a preset allowable range, the adjacent temperature field feature value corresponding to the rate of change is obtained, and the adjacent data acquisition points corresponding to the adjacent temperature field feature value are identified as continuous.

6. The method for managing highway block subgrade data based on temperature field distribution according to claim 5, characterized in that, The rate of change of adjacent temperature field feature values ​​in the acquired temperature field feature sequence includes: The absolute value of the difference between adjacent temperature field feature values ​​in the temperature field feature sequence is divided by the previous temperature field feature value in the adjacent temperature field feature value, and the calculation result is used as the rate of change of adjacent temperature field feature values.

7. The method for managing highway block subgrade data based on temperature field distribution according to claim 5, characterized in that, The acquisition of the preset allowed range includes: Based on historical monitoring data in the data acquisition direction of the roadbed with boulders, the historical rate of change of temperature field characteristic sequence under continuous state is analyzed. The allowable boundary of the rate of change, which reflects spatial continuity, is obtained based on historical rate of change data and used as a preset allowable range.

8. A data management system for highway boulders subgrade based on temperature field distribution, characterized in that, The system includes: The data acquisition module is used to acquire data collection directions (there can be multiple directions, and each data collection direction can be processed according to the following steps) located in the roadbed of the highway. Multiple data collection points are evenly distributed in the data collection directions, and the current settlement data sequence of each data collection point in the current monitoring period is acquired. The data processing module is used to acquire the historical temperature data sequence of each data acquisition point in the previous monitoring cycle in the current monitoring cycle, and to acquire the temperature field characteristic value of each data acquisition point based on the historical temperature data sequence of each data acquisition point. The temperature field characteristic values ​​of each data acquisition point are arranged in the order of their positions in the data acquisition direction to form the temperature field characteristic sequence of the data acquisition direction. The data judgment module is used to determine whether there is continuity between adjacent data acquisition points in the data acquisition direction based on the temperature field characteristic sequence. The data management module is used to mark the current settlement data sequence of adjacent data acquisition points as anomalies when there is a discontinuity between adjacent data acquisition points in the data acquisition direction, and output the current settlement data sequence with the anomaly mark.

9. The highway boulders subgrade data management system based on temperature field distribution according to claim 8, characterized in that, The data processing module is also used for: The correction ratio is obtained based on historical temperature data sequences; Historical average temperature is obtained from historical temperature data series, and temperature field characteristic values ​​are obtained based on correction ratio and historical average temperature.

10. The highway boulders subgrade data management system based on temperature field distribution according to claim 9, characterized in that, The data processing module is also used for: Obtain the temperature difference between adjacent temperature data in a historical temperature data sequence; Accumulate all temperature differences in the historical temperature data series to obtain the total change, and obtain the correction ratio based on the total change and the preset standard change.

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