A full-range heat preservation monitoring system for asphalt concrete transport vehicles
By deploying a multi-dimensional temperature sensor array on asphalt concrete transport vehicles and constructing a 3D temperature field model, the power of the insulation unit can be dynamically adjusted. This solves the problems of single monitoring dimensions and insufficient data accuracy in traditional technologies, achieving efficient insulation management and ensuring the stability of asphalt concrete temperature during transportation.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SHENZHEN LONGSHENG ENG CONSTR CO LTD
- Filing Date
- 2026-05-08
- Publication Date
- 2026-07-28
AI Technical Summary
Traditional asphalt concrete transport insulation testing technology has a single monitoring dimension, which cannot reflect the temperature differences in areas such as the tank sidewall. The data accuracy is insufficient, the sensors are easily affected by environmental interference, and there is a lack of data correction mechanism. This leads to the lag in insulation strategy and the inability of fixed power units to be dynamically adjusted, causing temperature fluctuations and quality problems.
Temperature sensor arrays are deployed on the front of the transport vehicle, the top of the tank, the side walls and the bottom to build a comprehensive temperature monitoring network. Multi-dimensional data is collected in real time. Through outlier correction, 3D temperature field model construction and heat loss path prediction, the power of the insulation unit is dynamically adjusted to achieve multi-dimensional and dynamic heat management.
It improves insulation efficiency, ensures data accuracy, dynamically adjusts insulation strategies, prevents sudden temperature drops, and guarantees project quality.
Smart Images
Figure CN122468291A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of thermal insulation monitoring technology, specifically to an all-around thermal insulation monitoring system for asphalt concrete transport vehicles. Background Technology
[0002] As a core material in the construction of infrastructure such as roads and bridges, the construction quality of asphalt concrete is directly related to the durability and safety of the engineering structure. During transportation, asphalt concrete must always maintain a suitable temperature range to prevent hardening due to excessively low temperatures or performance degradation due to excessively high temperatures. However, the heat exchange between the tank of the transport vehicle and the external environment will lead to continuous heat loss. Especially in low temperature, strong wind, or long-distance transportation scenarios, the internal temperature fluctuation of the tank may exceed the allowable range, which will lead to quality problems such as segregation and insufficient compaction of asphalt concrete.
[0003] However, traditional asphalt concrete transportation insulation testing technology has some shortcomings. Its monitoring dimensions are limited. Traditional methods only place a small number of temperature sensors in local locations on the tank, which cannot reflect the temperature differences in areas such as the side walls and edges of the tank. This leads to fuzzy location of heat loss paths and insufficient data accuracy. When sensors are affected by environmental interference or their own malfunctions, they are prone to generating abnormal data. Furthermore, traditional technology lacks a data correction mechanism, which may adjust the insulation strategy based on erroneous data, causing aggravated temperature fluctuations. Moreover, the control strategy is lagging, and the fixed-power insulation unit cannot dynamically adjust its power according to the real-time heat loss rate, resulting in insufficient insulation. Summary of the Invention
[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a comprehensive thermal insulation monitoring system for asphalt concrete transport vehicles. This invention constructs a temperature monitoring network covering the entire tank space and the external environment by arranging temperature sensor arrays at the front of the transport vehicle, the top of the tank, the side walls, and the bottom. It can collect temperature data from various parts of the tank and the external environment in real time, transforming discrete data into a continuous 3D temperature field model. Temperature gradient analysis identifies the main paths of heat loss, and combined with the heat loss rate calculation formula, the heat transfer rate along the main paths is quantified. The power of the insulation unit is dynamically adjusted through the thermal insulation control decision formula. This multi-dimensional and dynamic heat management method improves insulation efficiency and provides reliable assurance for project quality.
[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: an all-around thermal insulation monitoring system for asphalt concrete transport vehicles, the specific steps of which are as follows:
[0006] Multi-dimensional temperature data acquisition: Temperature sensor arrays are arranged at different dimensional positions on the front of the transport vehicle and the tank to collect multi-dimensional temperature data in real time;
[0007] Temperature data preprocessing: The collected multi-dimensional temperature data is preprocessed, and outlier detection and correction are performed to remove abnormal data;
[0008] 3D temperature field model construction: Based on the preprocessed and corrected temperature data, the temperature values of the tank's spatial coordinates are calculated through multi-dimensional temperature field interpolation formulas, and the discrete temperature data is transformed into a continuous temperature field distribution to construct a 3D temperature field model.
[0009] Heat loss path and rate prediction: Based on the constructed 3D temperature field model, the main heat loss path is determined. According to the three-dimensional coordinates of the tank space, quantified uniformly distributed spatial sampling points are selected in the tank interior and wall area with a fixed grid spacing. Then, the heat transfer rate in the area is calculated using the heat loss rate calculation formula.
[0010] Control of the insulation unit: Based on the main paths and rates of heat loss, an insulation control strategy is formulated through the insulation control decision formula, and instructions are sent to the insulation unit.
[0011] Furthermore, in the multi-dimensional temperature data acquisition, temperature sensors are arranged at different dimensional positions on the top of the truck cab and the top, side walls, and bottom of the tank to form a temperature sensor array. This array collects multi-dimensional temperature data of the external environment and various parts of the tank in real time. Specifically, 1-2 temperature sensors are arranged on the top of the truck cab, with a sampling frequency of 1-3 seconds per scan, to collect the external ambient temperature; 1-2 temperature sensors are arranged at the center of the top of the tank, with a sampling frequency of 1-3 seconds per scan, to collect the temperature data of the central area of the top of the tank; 2-4 temperature sensors are arranged at the middle position of the side walls of the tank, with a sampling frequency of 1-3 seconds per scan, to collect the temperature data of the middle part of the side walls of the tank; and 1-2 temperature sensors are arranged at the center of the bottom of the tank, with a sampling frequency of 1-3 seconds per scan, to collect the temperature data of the central area of the bottom of the tank.
[0012] Furthermore, in the temperature data preprocessing, the collected multi-dimensional temperature data is subjected to format unification and smoothing processing, and outlier detection and correction are performed using the temperature data outlier correction formula to remove outlier data.
[0013] Furthermore, in the temperature data preprocessing, outliers are detected and corrected in the collected multi-dimensional temperature data using a temperature data outlier correction formula to remove abnormal data. The temperature data outlier correction formula is as follows: ,in, After outlier correction, the first The temperature value corresponding to each temperature sensor. For the first Raw temperature data collected by a temperature sensor. For the current number Temperature data collected by other temperature sensors around this temperature sensor. This is the outlier correction factor, set based on historical data.
[0014] Furthermore, in the construction of the 3D temperature field model, based on the preprocessed and corrected temperature data, the spatial coordinates of the tank are calculated using a multi-dimensional temperature field interpolation formula. Temperature value at This transforms discrete temperature data into a continuous temperature field distribution, constructing a 3D temperature field model of the temperature distribution and changing trends inside the tank.
[0015] Furthermore, in the construction of the 3D temperature field model, the spatial coordinates of the tank are calculated using a multi-dimensional temperature field interpolation formula. Temperature value at Its multidimensional temperature field interpolation formula is: ,in, Tank spatial coordinates Temperature value at that location, After outlier correction, the first The temperature value corresponding to each temperature sensor. The number of sensors around this coordinate. For the first The weight of each sensor is calculated using the following formula: ,in, Spatial coordinates To the The distance between the sensors This is a correction factor for the thermal conductivity of the tank material, calibrated based on the actual metal material used in the tank and the thermal conductivity of the insulation layer material, with a value ranging from 0.85 to 1.15. The spatial orientation correction coefficient is determined based on the relative orientation of the sensor and the target spatial coordinates. The value is 1.05-1.2 for the tank wall area and 0.9-1.0 for the tank interior area.
[0016] Furthermore, in the prediction of heat loss paths and rates, the step of determining the main heat loss paths based on the constructed 3D temperature field model is as follows: In the 3D temperature field model, according to the three-dimensional coordinates of the tank space, uniformly distributed spatial sampling points are selected in the tank interior and wall areas with a cubic grid spacing of 5cm×5cm×5cm. The sampling points fully cover the entire tank volume and all areas of the tank wall. The total number of sampling points is determined according to the actual tank volume, and the temperature value of each sampling point is obtained. The temperature difference between each sampling point and its adjacent sampling points is calculated to obtain the temperature change between adjacent sampling points. Then, based on the spatial distance between adjacent sampling points and the temperature change, the temperature gradient value around each sampling point is calculated. And set a temperature gradient threshold. , temperature gradient value The area where the sampling point is located is determined to be a region with a large temperature gradient in the temperature field;
[0017] Then, the heat transfer rate along the main heat loss path is calculated using the heat loss rate calculation formula. .
[0018] Furthermore, in the prediction of heat loss paths and rates, the heat transfer rate along the main loss paths is calculated using a heat loss rate calculation formula, which is: ,in, Location of the tank The rate of heat loss at that location, The thermal conductivity coefficient of the tank material is determined by setting the tank material. For position Temperature gradient value at that location, The convective heat transfer coefficient is set based on the operating environment of the same transport vehicles in historical data. The external ambient temperature.
[0019] Furthermore, the control of the heat preservation unit is based on the main paths and rates of heat loss. The tank position is calculated using the thermal insulation control decision formula. The power of the insulation unit is controlled, and an insulation control strategy is formulated. The insulation control strategy is as follows: when Regional heat loss rate ≥6 At that time, according to the calculated tank position The power of the insulation unit at one location sends a command to the corresponding insulation unit to adjust the temperature.
[0020] Furthermore, in the control of the insulation unit, the tank position is calculated using an insulation control decision formula. The power of the insulation unit is determined by the following insulation control decision formula: ,in, Location of the tank The power of the insulation unit, This is the power regulation coefficient, set based on historical data. Location of the tank The rate of heat loss at that location.
[0021] Compared with existing technologies, this all-around thermal insulation monitoring system for asphalt concrete transport vehicles has the following advantages:
[0022] I. This invention constructs a temperature monitoring network covering the entire tank space and external environment by arranging temperature sensor arrays at the front of the transport vehicle, the top, side walls, and bottom of the tank. This network can collect temperature data from various parts of the tank and the external environment in real time, transforming discrete data into a continuous 3D temperature field model. The interpolation calculation of this 3D temperature field model integrates the thermal conductivity characteristics of the tank material and its spatial orientation features. The sampling points are selected using quantified grid spacing and quantity standards, making the temperature field simulation more closely resemble the real-world temperature distribution. Temperature gradient analysis identifies the main paths of heat loss, and combined with the heat loss rate calculation formula, the rate of heat transfer along these main paths is quantified. The power of the insulation unit is dynamically adjusted through the insulation control decision formula. This multi-dimensional and dynamic heat management method improves insulation efficiency and provides reliable assurance for project quality.
[0023] Second, this invention preprocesses the collected multi-dimensional temperature data and corrects the data using an outlier correction formula to ensure data accuracy and avoid data distortion caused by sensor failure or environmental interference. Based on the corrected data, the heat loss rate of each area can be dynamically calculated, and a heat preservation control strategy can be formulated through a heat preservation control decision formula. When the heat loss rate of a certain area exceeds the threshold, the power of the heat preservation unit is quickly adjusted to prevent a sudden drop in temperature.
[0024] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description
[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.
[0026] Figure 1 A flowchart of an all-around thermal insulation monitoring system for asphalt concrete transport vehicles;
[0027] Figure 2 This is a framework diagram of an all-around thermal insulation monitoring system for asphalt concrete transport vehicles. Detailed Implementation
[0028] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.
[0029] Example 1:
[0030] Multi-dimensional temperature data acquisition: In the scenario of short-distance transportation of asphalt concrete for urban road construction, two temperature sensors are installed on the top of the truck cab, with a sampling frequency of 2 seconds per measurement, to collect the ambient temperature in real time; two temperature sensors are installed at the center of the top of the tank, with a sampling frequency of 2 seconds per measurement, to collect the temperature of the asphalt concrete in the central area of the top of the tank in real time, monitoring temperature changes in the top area; four temperature sensors are evenly distributed at the midpoint of the sidewalls along the height of the tank, with a sampling frequency of 2 seconds per measurement, to collect temperature data in the center of the front, back, left, and right sides of the tank sidewalls, comprehensively understanding the temperature distribution of the sidewalls; two temperature sensors are installed at the center of the bottom of the tank, with a sampling frequency of 2 seconds per measurement, to collect temperature data in the central area of the bottom of the tank, and the tank volume is 15m³. 3 The tank material is carbon steel, the calibration correction factor for the thermal conductivity of the tank material is 0.95, the spatial orientation correction factor for the tank wall area is 1.1, and the spatial orientation correction factor for the internal area of the tank is 0.95.
[0031] Temperature data preprocessing: First, the temperature data collected by various sensors is format-unified, converting the different formats output by different sensors into a unified format. Next, smoothing is performed on the unified temperature data to eliminate small fluctuations caused by instantaneous sensor data changes, resulting in stable temperature data. Finally, outlier detection and correction are performed using a temperature data outlier correction formula to remove abnormal data. The temperature data outlier correction formula is as follows: ,in, After outlier correction, the first The temperature value corresponding to each temperature sensor. For the first Raw temperature data collected by a temperature sensor. For the current number Temperature data collected by other temperature sensors around this temperature sensor. This is the outlier correction factor, set based on historical data.
[0032] 3D Temperature Field Model Construction: Based on preprocessed and corrected temperature data, the spatial coordinates of the tank are calculated using multi-dimensional temperature field interpolation formulas. The temperature value at that location, and its multidimensional temperature field interpolation formula is: ,in, Tank spatial coordinates Temperature value at that location, After outlier correction, the first The temperature value corresponding to each temperature sensor. The number of sensors around this coordinate. For the first The weight of each sensor is calculated using the following formula: ,in, Spatial coordinates To the The distance between the sensors This is a correction factor for the thermal conductivity of the tank material, calibrated based on the actual metal material used in the tank and the thermal conductivity of the insulation layer material, with a value ranging from 0.85 to 1.15. The spatial orientation correction coefficient is determined based on the relative orientation of the sensor and the target spatial coordinates, with values ranging from 1.05 to 1.2 for the tank wall area and 0.9 to 1.0 for the tank interior area. Discrete temperature data are transformed into a continuous temperature field distribution, constructing a 3D temperature field model that reflects the temperature distribution and variation trend of the asphalt concrete inside the tank, such as... Figure 1 As shown.
[0033] Heat loss path and rate prediction: Based on the constructed 3D temperature field model, the main heat loss path is determined. In the 3D temperature field model, since the tank volume is 15m³, 3 Spatial sampling points were selected with a uniform grid spacing of 5cm×5cm×5cm, with a total of 12,000 sampling points, fully covering the entire volume of the tank and all areas of the tank wall. The temperature value of each sampling point was obtained. The temperature difference between each sampling point and its adjacent sampling points is calculated to obtain the temperature change between adjacent sampling points. Then, based on the spatial distance between adjacent sampling points and the temperature change, the temperature gradient value around each sampling point is calculated. A temperature gradient threshold was set based on the short-distance transportation conditions in this scenario. , temperature gradient value The sampling point was located in an area with a large temperature gradient in the temperature field, which is the main path for heat loss. Monitoring revealed that the rear sidewall and bottom edge of the tank were the main heat loss paths in this scenario. The heat transfer rate along the main loss paths was then calculated using the heat loss rate calculation formula. The formula for calculating its heat loss rate is: ,in, Location of the tank The rate of heat loss at that location, The thermal conductivity coefficient of the tank material is determined by setting the tank material. For position Temperature gradient value at that location, The convective heat transfer coefficient is set based on the operating environment of the same transport vehicles in historical data. The external ambient temperature is used; calculations show that the heat loss rate in the rear region of the tank sidewall under this scenario is 4-7 W / m. 2 Between 5-8 W / m in the bottom edge area 2 between.
[0034] The control of the insulation unit is based on the main heat loss paths, the rear sidewall and bottom edge areas of the tank, and the heat loss rate in each area. The corresponding position of the tank is calculated using the heat preservation and control decision formula. The power of the insulation unit is determined by the following insulation control decision formula: ,in, Location of the tank The power of the insulation unit, This is the power regulation coefficient, set based on historical data. Location of the tank The heat loss rate at the location; and based on the calculated tank position To determine the power of the insulation unit, an insulation control strategy is formulated: when the heat loss rate in the (x, y, z) region is monitored... ≥6 At that time, according to the calculated power of the corresponding insulation unit, a command is sent to the insulation unit in that area to adjust the temperature; in this scenario, the heat loss rate in a certain area of the rear sidewall of the tank is ≥6W / m. 2 The heat loss rate in most areas of the bottom edge is ≥6W / m 2 The insulation units in these two areas receive instructions and start heating according to the calculated power to maintain the temperature of the asphalt concrete within a reasonable range.
[0035] In summary, in the short-distance transportation scenario of asphalt concrete for urban road maintenance, multi-dimensional temperature data collection comprehensively covers all key parts of the truck cab and tank, capturing real-time temperature dynamics. After data preprocessing to ensure data reliability, a 3D temperature field model is constructed by integrating tank material and spatial orientation characteristics. Spatial sampling points are selected according to quantitative standards, and the main heat loss paths are accurately located and the heat loss rate is calculated based on the 3D temperature field model. Finally, the operation of the insulation unit is achieved through insulation control strategies, ensuring that the asphalt concrete meets the construction temperature requirements when it arrives at the maintenance site, thus guaranteeing the quality and efficiency of urban road maintenance operations.
[0036] Example 2:
[0037] Multi-dimensional temperature data acquisition: In the long-distance transportation scenario of asphalt concrete for highway construction, one temperature sensor is installed on the top of the truck cab, with a sampling frequency of 1 second / time to collect the external ambient temperature in real time; one temperature sensor is installed at the center of the top of the tank, with a sampling frequency of 1 second / time to collect the temperature of the central area of the top of the tank in real time; four temperature sensors are evenly distributed at the middle of the sidewalls along the height of the tank, with a sampling frequency of 1 second / time to collect the temperature data of the center of the front, back, left, and right sides of the sidewalls of the tank; one temperature sensor is installed at the center of the bottom of the tank, with a sampling frequency of 1 second / time to collect the temperature of the central area of the bottom of the tank. The tank material is stainless steel, and the calibration correction coefficient for the thermal conductivity of the tank material is 1.1, the spatial orientation correction coefficient for the tank wall area is 1.2, and the spatial orientation correction coefficient for the internal area of the tank is 1.0.
[0038] Temperature data preprocessing: The temperature data collected by each sensor is format-unified by converting all sensor outputs to the same format to eliminate format differences. Then, smoothing is performed to obtain stable temperature data. Finally, outlier detection and correction are performed using a temperature data outlier correction formula to remove abnormal data. The temperature data outlier correction formula is as follows: .
[0039] 3D Temperature Field Model Construction: Based on preprocessed and corrected temperature data, the spatial coordinates of the tank are calculated using multi-dimensional temperature field interpolation formulas. The temperature value at that location, and its multidimensional temperature field interpolation formula is: ,in, For the first The weight of each sensor is calculated using the following formula: Discrete data is transformed into a continuous temperature field distribution, and a 3D temperature field model that reflects the temperature changes inside the tank in real time is constructed.
[0040] Heat loss path and rate prediction: Determining the main heat loss path based on a 3D temperature field model: The tank has a volume of 25m³. 3 Spatial sampling points were selected with a uniform grid spacing of 5cm×5cm×5cm, with a total of 25,000 sampling points, fully covering the entire volume of the tank and all areas of the tank wall. Due to the harsh environment of this scenario, the sampling interval needed to be reduced to more accurately capture temperature changes; the temperature value of each sampling point was obtained. The temperature difference between each sampling point and its adjacent sampling points is calculated to obtain the temperature change. Then, based on the spatial distance between adjacent sampling points and the temperature change, the temperature gradient value around each sampling point is calculated. A temperature gradient threshold was set based on the long-distance transportation conditions in this scenario. , temperature gradient value The sampling points were identified as the main heat loss paths. Monitoring revealed that the windward side of the tank wall, the top edge of the tank, and the bottom were the primary heat loss paths in this scenario. The heat transfer rate was then calculated using the heat loss rate calculation formula. The formula for calculating its heat loss rate is: Calculations show that the heat loss rate in the windward area of the side wall is 9-15 W / m. 2 Between 7-12 W / m in the top edge area 2 Between 8-13 W / m at the bottom 2 between.
[0041] Insulation unit control: based on the main heat loss pathways and heat loss rates in each area ,like Figure 2 As shown, the power of the insulation unit at the corresponding location is calculated using the insulation control decision formula, which is: Based on the calculated tank position... Power control strategy for insulation units: When the heat loss rate in region (x, y, z) is monitored... ≥6 At that time, instructions are sent to the corresponding insulation unit according to the calculated power to adjust the temperature. In this scenario, the heat loss rate of almost all areas along the main loss path is ≥6W / m². Therefore, the insulation units on the windward side of the tank wall, the top edge, and the bottom perimeter are all started to heat according to the calculated power to ensure that the temperature of the asphalt concrete is always kept within the reasonable range required for construction during long-distance transportation.
[0042] In summary, in the long-distance transportation scenario of asphalt concrete for new highway construction projects, given the characteristics of long transportation distances and complex environments, high-frequency collection of multi-dimensional temperature data is used to cope with environmental fluctuations. After preprocessing to eliminate data errors, a 3D temperature field model is constructed by integrating the thermal conductivity characteristics of the tank material and its spatial orientation features. Spatial sampling points are selected according to quantified grid spacing and quantity standards to understand the temperature change patterns of the tank. Based on the 3D temperature field model, the main heat loss paths, such as the windward side, are determined and the heat loss rate is calculated. Then, the heating power of the corresponding insulation unit is calculated using the insulation control formula to achieve targeted heating and ensure that the asphalt concrete maintains a suitable temperature throughout the entire process, thus providing a guarantee for the smooth progress of new highway construction projects.
[0043] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.
Claims
1. A full-scope thermal monitoring system for an asphalt concrete transport vehicle, characterized by, The specific steps of this method are as follows: Multi-dimensional temperature data acquisition: Temperature sensor arrays are arranged at different dimensional positions on the front of the transport vehicle and the tank to collect multi-dimensional temperature data in real time; Temperature data preprocessing: The collected multi-dimensional temperature data is preprocessed, and outlier detection and correction are performed to remove abnormal data; 3D temperature field model construction: Based on the preprocessed and corrected temperature data, the temperature values of the tank's spatial coordinates are calculated through multi-dimensional temperature field interpolation formulas, and the discrete temperature data is transformed into a continuous temperature field distribution to construct a 3D temperature field model. Heat loss path and rate prediction: Based on the constructed 3D temperature field model, the main heat loss path is determined. According to the three-dimensional coordinates of the tank space, quantified uniformly distributed spatial sampling points are selected in the tank interior and wall area with a fixed grid spacing. Then, the heat transfer rate in the area is calculated using the heat loss rate calculation formula. Control of the insulation unit: Based on the main paths and rates of heat loss, an insulation control strategy is formulated through the insulation control decision formula, and instructions are sent to the insulation unit.
2. The all-terrain monitoring system for an asphalt concrete transport vehicle of claim 1, wherein, In the multi-dimensional temperature data acquisition, temperature sensors are arranged at different dimensional positions on the top of the truck cab and the top, side walls, and bottom of the tank, forming a temperature sensor array. This array collects multi-dimensional temperature data of the external environment and various parts of the tank in real time. Specifically, 1-2 temperature sensors are arranged on the top of the truck cab, sampling at a frequency of 1-3 seconds per scan, to collect the external ambient temperature; 1-2 temperature sensors are arranged at the center of the top of the tank, sampling at a frequency of 1-3 seconds per scan, to collect temperature data of the central area of the top of the tank; 2-4 temperature sensors are arranged at the middle of the side walls along the height direction of the tank, sampling at a frequency of 1-3 seconds per scan, to collect temperature data of the middle part of the side walls; and 1-2 temperature sensors are arranged at the center of the bottom of the tank, sampling at a frequency of 1-3 seconds per scan, to collect temperature data of the central area of the bottom of the tank.
3. The all-weather monitoring system for asphalt concrete transport vehicles according to claim 1, characterized in that, In the temperature data preprocessing, the collected multi-dimensional temperature data is processed for format unification and smoothing. At the same time, outlier detection and correction are performed using the temperature data outlier correction formula to remove outlier data.
4. The all-around thermal insulation monitoring system for asphalt concrete transport vehicles according to claim 3, characterized in that, In the temperature data preprocessing, outliers are detected and corrected in the collected multi-dimensional temperature data using a temperature data outlier correction formula to remove outliers. The temperature data outlier correction formula is as follows: ,in, After outlier correction, the first The temperature value corresponding to each temperature sensor. For the first Raw temperature data collected by a temperature sensor. For the current number Temperature data collected by other temperature sensors around this temperature sensor. This is the outlier correction factor, set based on historical data.
5. The all-around thermal insulation monitoring system for asphalt concrete transport vehicles according to claim 1, characterized in that, In the construction of the 3D temperature field model, the spatial coordinates of the tank are calculated based on the preprocessed and corrected temperature data using a multi-dimensional temperature field interpolation formula. Temperature value at This transforms discrete temperature data into a continuous temperature field distribution, constructing a 3D temperature field model of the temperature distribution and changing trends inside the tank.
6. The all-around thermal insulation monitoring system for asphalt concrete transport vehicles according to claim 5, characterized in that, In the construction of the 3D temperature field model, the spatial coordinates of the tank are calculated using a multi-dimensional temperature field interpolation formula. Temperature value at Its multidimensional temperature field interpolation formula is: ,in, Tank spatial coordinates Temperature value at that location, After outlier correction, the first The temperature value corresponding to each temperature sensor. The number of sensors around this coordinate. For the first The weight of each sensor is calculated using the following formula: ,in, Spatial coordinates To the The distance between the sensors This is a correction factor for the thermal conductivity of the tank material, calibrated based on the actual metal material used in the tank and the thermal conductivity of the insulation layer material, with a value ranging from 0.85 to 1.
15. The spatial orientation correction coefficient is determined based on the relative orientation of the sensor and the target spatial coordinates. The value is 1.05-1.2 for the tank wall area and 0.9-1.0 for the tank interior area.
7. The all-around thermal insulation monitoring system for asphalt concrete transport vehicles according to claim 1, characterized in that, In the prediction of heat loss paths and rates, the steps for determining the main heat loss paths based on the constructed 3D temperature field model are as follows: In the 3D temperature field model, according to the three-dimensional coordinates of the tank space, uniformly distributed spatial sampling points are selected in the tank's interior and wall areas with a cubic grid spacing of 5cm×5cm×5cm. The sampling points fully cover the entire tank volume and all areas of the tank wall. The total number of sampling points is determined based on the actual tank volume. The temperature value of each sampling point is then obtained. The temperature difference between each sampling point and its adjacent sampling points is calculated to obtain the temperature change between adjacent sampling points. Then, based on the spatial distance between adjacent sampling points and the temperature change, the temperature gradient value around each sampling point is calculated. And set a temperature gradient threshold. , temperature gradient value The area where the sampling point is located is determined to be a region with a large temperature gradient in the temperature field; Then, the heat transfer rate along the main heat loss path is calculated using the heat loss rate calculation formula. .
8. The all-around thermal insulation monitoring system for asphalt concrete transport vehicles according to claim 7, characterized in that, In the prediction of heat loss paths and rates, the heat transfer rate along the main loss paths is calculated using a heat loss rate calculation formula, which is: ,in, Location of the tank The rate of heat loss at that location, The thermal conductivity coefficient of the tank material is determined by setting the tank material. For position Temperature gradient value at that location, The convective heat transfer coefficient is set based on the operating environment of the same transport vehicles in historical data. The external ambient temperature.
9. The all-around thermal insulation monitoring system for asphalt concrete transport vehicles according to claim 1, characterized in that, The control of the insulation unit is based on the main paths and rates of heat loss. The tank position is calculated using the thermal insulation control decision formula. The power of the insulation unit is controlled, and an insulation control strategy is formulated. The insulation control strategy is as follows: when Regional heat loss rate ≥6 At that time, according to the calculated tank position The power of the insulation unit at one location sends a command to the corresponding insulation unit to adjust the temperature.
10. The all-around thermal insulation monitoring system for asphalt concrete transport vehicles according to claim 9, characterized in that, In the control of the insulation unit, the tank position is calculated using the insulation control decision formula. The power of the insulation unit is determined by the following insulation control decision formula: ,in, Location of the tank The power of the insulation unit, This is the power regulation coefficient, set based on historical data. Location of the tank The rate of heat loss at that location.