Water-stable paver operation data acquisition and segregation early warning system based on sensor

Abstract

The invention relates to the technical field of early warning, in particular to a water-stable paver operation data acquisition and segregation early warning system based on a sensor, which comprises a processor and a memory, the processor executes a computer program stored in the memory to realize the following steps: acquiring temperature vibration coupling strength and segregation confidence of a to-be-analyzed area at a current monitoring moment, a target area at the current monitoring moment and a continuous area sequence corresponding to the target area, obtaining a trend continuous characterization value of the target region according to the continuous region sequence, obtaining a segregation risk value of the target region according to the trend continuous characterization value of the target region, the segregation confidence coefficient, the area, the temperature vibration coupling strength and the total number of data in the target region, and obtaining the segregation risk value of the target region according to the segregation risk value. And carrying out segregation early warning on the paving process of the water-stable paver. And the segregation early warning accuracy and reliability of the water-stable paver during paving can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of early warning, and in particular to a water-stable paver operation data acquisition and segregation early warning system based on sensors. BACKGROUND

[0002] Since the segregation phenomenon in the water-stable paving process is closely related to the quality of the paved road, segregation can significantly reduce the compactness, strength and durability of the base, and thus affect the overall performance of the road surface, therefore, in order to ensure the quality of the road, it is crucial to collect the operation data of the water-stable paver and perform segregation early warning during the water-stable paving process. In addition, segregation refers to the phenomenon that the aggregate (coarse and fine aggregate) in the cement stabilized gravel mixture is separated due to external force or improper construction process, resulting in uneven particle size distribution, coarse and fine separation, which is specifically manifested as increased porosity in the coarse aggregate concentration area and insufficient filling of fine aggregate, forming local loose or strip distribution.

[0003] In the prior art, the segregation early warning is achieved by comparing the paving temperature data collected based on sensors with a preset temperature range, that is, the current segregation early warning is performed when the collected paving temperature data exceeds the preset temperature range. However, the segregation phenomenon in the water-stable paving process is a complex physical process, which is influenced by multiple factors, such as the speed of the paver, the vibration condition, the paving temperature change and other factors, which can reflect the segregation phenomenon. The method of performing segregation early warning according to only single-dimensional data information in the prior art cannot comprehensively and accurately reflect the real situation of segregation, thereby resulting in low accuracy and reliability of the segregation early warning. For example, the phenomenon that the paving temperature is not within the preset temperature range may be a normal phenomenon caused by different material cooling speeds due to the change of the paver speed, rather than real segregation, so the segregation early warning based on only the paving temperature is prone to false early warning, and therefore how to improve the accuracy and reliability of the segregation early warning becomes a problem to be solved. SUMMARY

[0004] In order to solve the above problems, the present application provides a water-stable paver operation data acquisition and segregation early warning system based on sensors, and the technical solution is as follows: One embodiment of the present application provides a water-stable paver operation data acquisition and segregation early warning system based on sensors, which comprises a processor and a memory, and the processor executes the computer program stored in the memory to realize the following steps: In the paving process, current paving temperature data, current vibration data, current paving speed data, and current temperature baseline threshold, current vibration baseline threshold and speed baseline threshold interval are obtained, and according to the current temperature baseline threshold and the current vibration baseline threshold, the abnormal amplitude of the current paving temperature data and the current vibration data is obtained, and according to the abnormal amplitude, the current abnormal data is obtained, the current abnormal data includes abnormal paving temperature data and abnormal vibration data; According to the number of current abnormal data in the preset neighborhood range of the position coordinates of the sensor collecting the current abnormal data, the to-be-analyzed region at the current monitoring time is obtained, according to the abnormal paving temperature data and the abnormal vibration data in the to-be-analyzed region at the current monitoring time, the temperature vibration coupling strength of the to-be-analyzed region at the current monitoring time is obtained, according to the different data proportion, the temperature vibration coupling strength, the current paving speed data and the speed baseline threshold interval in the to-be-analyzed region at the current monitoring time and the abnormal amplitude, the segregation confidence of the to-be-analyzed region at the current monitoring time is obtained. According to the intersection between each to-be-analyzed region at the current monitoring time and each to-be-analyzed region at the historical monitoring time, the target region at the current monitoring time and the corresponding continuous region sequence of the target region are obtained, the trend persistence representation value of the target region is obtained according to the continuous region sequence, and the segregation risk value of the target region is obtained according to the trend persistence representation value, the segregation confidence, the area and the temperature vibration coupling strength of the target region and the total number of data in the target region. According to the segregation risk value, the segregation warning of the paving process of the water-stable paver is carried out.

[0005] Beneficial effects: first, according to the number of current abnormal data in the preset neighborhood range of the position coordinates of the sensor collecting the current abnormal data, the current monitoring time is obtained. According to the abnormal paving temperature data and abnormal vibration data in the current analysis area at the monitoring time, the temperature vibration coupling strength of the current analysis area at the monitoring time is obtained, and the segregation confidence of the current analysis area at the monitoring time is obtained according to the different data proportion, temperature vibration coupling strength, current paving speed data and speed baseline threshold interval and abnormal amplitude in the current analysis area at the monitoring time. Then, according to the intersection between each analysis area at the current monitoring time and each analysis area at the historical monitoring time, the target area at the current monitoring time and the corresponding continuous area sequence of the target area are obtained, the trend continuous representation value of the target area is obtained according to the continuous area sequence, and the segregation risk value of the target area is obtained according to the trend continuous representation value, segregation confidence, area and temperature vibration coupling strength of the target area and the total number of data in the target area. Finally, according to the segregation risk value, the segregation of the paving process of the water stable paver is warned. And the segregation risk value obtained based on the trend continuous representation value, segregation confidence, area and temperature vibration coupling strength and other parameters can improve the accuracy and reliability of the warning when the segregation of the paving process of the water stable paver is warned. BRIEF DESCRIPTION OF DRAWINGS

[0006] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the drawings needed in the description of the embodiments or the prior art will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without creating any creative labor.

[0007] Figure 1 The flowchart of the present application is a water stable paver operation data acquisition and segregation warning method based on sensor. DETAILED DESCRIPTION

[0008] The technical solutions in the embodiments of the present application will be described in detail below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art belong to the scope of protection of the embodiments of the present application.

[0009] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present application belongs.

[0010] The embodiment provides a sensor-based water-stable paver operation data acquisition and segregation early warning system, comprising a processor and a memory, wherein the processor executes a computer program stored in the memory to realize a sensor-based water-stable paver operation data acquisition and segregation early warning method. Figure 1 As shown in the figure, the sensor-based water-stable paver operation data acquisition and segregation early warning method comprises the following steps: Step S001, in the paving process, current paving temperature data, current vibration data, current paving speed data, and current temperature baseline threshold, current vibration baseline threshold and speed baseline threshold interval are obtained, and according to the current temperature baseline threshold and the current vibration baseline threshold, the abnormal amplitude of the current paving temperature data and the current vibration data is obtained, and according to the abnormal amplitude, current abnormal data is obtained, wherein the current abnormal data comprises abnormal paving temperature data and abnormal vibration data.

[0011] The embodiment mainly obtains the segregation risk value by fusing and analyzing the data of three dimensions of paving temperature, vibration and paving speed, and performs segregation early warning on the paving process of the water-stable paver based on the segregation risk value, so as to improve the accuracy and reliability of the segregation early warning. In order to facilitate understanding, the embodiment only performs early warning on the segregation of any water-stable paver paving process, that is, the subsequent collected data are all collected by the sensors on the water-stable paver.

[0012] Based on the above description, it can be known that the subsequent embodiment needs to perform segregation early warning on the paving process of the water-stable paver based on the segregation risk value, and the paving temperature, vibration and paving speed are data support for obtaining the segregation risk value. Therefore, the embodiment needs to obtain the paving temperature data, vibration data and paving speed data of the water-stable paver at each monitoring time in the paving process, and the specific obtaining process of the paving temperature data, vibration data and paving speed data at each monitoring time is as follows: First, the sensor configuration of the water stable paver is performed, that is, temperature measuring points are arranged at equal intervals along the paving width direction at a distance of 0.5 meters from the front section of the paver screed, and an infrared non-contact temperature sensor is arranged at each temperature measuring point for collecting the paving temperature of the material. If the material paved by the water stable paver in the embodiment is cement stabilized gravel, the paving temperature of the material is the paving temperature of the cement stabilized gravel. A vibration sensor is arranged at each key vibration point, such as the screed vibration shaft, the tamper and the conveyor belt support, for collecting the vibration during paving. The vibration sensor is a three-axis accelerometer. A speed sensor is arranged on the water stable paver for collecting the speed of the water stable paver during paving. Then, data is collected by different sensors on the water stable paver to obtain the data collected by different sensors at different monitoring times. The data collected by the vibration sensor at the monitoring time is recorded as the to-be-processed vibration data at the corresponding monitoring time. The data collected by the sensor for collecting the paving temperature of the material at the monitoring time is recorded as the to-be-processed paving temperature data at the corresponding monitoring time. The data collected by the speed sensor at the monitoring time is recorded as the to-be-processed paving speed data at the corresponding monitoring time. Then, the to-be-processed paving temperature data, the to-be-processed vibration data and the to-be-processed paving speed data are preprocessed. The preprocessed data is recorded as the paving temperature data, the vibration data and the paving speed data at the corresponding monitoring time, respectively. The paving temperature data, the vibration data and the paving speed data at the current monitoring time are recorded as the current paving temperature data, the current vibration data and the current paving speed data, respectively. The number of the current paving temperature data and the current vibration data is greater than 1, that is, the current paving temperature data, the current vibration data and the current paving speed data are the results obtained by preprocessing the data collected by the sensors on the water stable paver at the current monitoring time. Moreover, each sensor for collecting the paving temperature of the material on the water stable paver can obtain a current paving temperature data at the current monitoring time, the speed sensor on the water stable paver can obtain a current paving speed data at the current monitoring time, and each vibration sensor on the water stable paver can obtain a current vibration data at the current monitoring time. If a current paving temperature data is the result obtained by preprocessing the data collected by a temperature sensor for collecting the paving temperature of the material on the water stable paver at the current monitoring time, the sensor for collecting the current paving temperature data is the temperature sensor.

[0013] The pretreatment includes but is not limited to data filtering, data cleaning, and de-dimensioning operations, and the de-dimensioning operation does not change the size of the data value, and in the embodiment, the sensors are configured to be synchronously collected; in addition, the position coordinate points of each sensor on the water-stable paver are obtained in real time through the GPS positioning system on the water-stable paver, and in the embodiment, the coordinates in the position coordinate points of the sensors are coordinates in the geodetic coordinate system, and as other real-time methods, the coordinates in the position coordinate points of the sensors can also be considered as coordinates in the world coordinate system, and in the embodiment, the coordinate values in the position coordinate points of the sensors change with the operation of the water-stable paver, or in other words, the position coordinates of the sensors in the embodiment are not coordinates relative to the water-stable paver.

[0014] The segregation phenomenon is a complex physical process, which is affected by multiple factors, such as the speed of the paver, the vibration condition, the change of paving temperature, etc., and when the data of the speed of the paver, the vibration condition, and the change of paving temperature deviate from the normal value, the segregation phenomenon may occur, so after obtaining the current paving temperature data, the current vibration data, and the current paving speed data, it is necessary to analyze and obtain the abnormal data in the current paving temperature data, the current vibration data, and the current paving speed data, and then based on the abnormal data, the segregation confidence is obtained, but since in the construction process, the specific working conditions in the actual construction are different in different regions and different seasons, if the abnormal data is still determined based on the fixed threshold value, it will lead to low credibility of the obtained abnormal data, therefore, in order to improve the credibility of the identification of the abnormal data, in the embodiment, the threshold value for identifying the abnormal data will be obtained in real time based on the actual working condition, that is, the current temperature baseline threshold value, the current vibration baseline threshold value, and the speed baseline threshold value interval, and the speed baseline threshold value interval does not change with the change of the working condition, so the specific obtaining process of the current temperature baseline threshold value, the current vibration baseline threshold value, and the current speed baseline threshold value interval is as follows: The current temperature baseline threshold acquisition process: due to heat exchange in the environment, the temperature changes over time, so the temperature baseline threshold should also change in real time, so the current temperature baseline threshold is determined by considering the environmental temperature and heat exchange and other factors; first, the temperature of the material paved by the water stable paver in the ideal environment is obtained, and it is recorded as the first influence value at the current monitoring time, and the temperature of the material paved by the water stable paver in the ideal environment is generally an interval, and in order to ensure that the material has good workability and compactness, the median value of the interval is usually taken as the temperature of the material paved by the water stable paver in the ideal environment, for example, if the paving temperature interval of cement stabilized gravel in the ideal environment is 140 to 150 degrees Celsius, in order to ensure that the material has good workability and compactness, the median value of 140 to 150 degrees Celsius, that is, 145 degrees Celsius, represents the temperature of the cement stabilized gravel in the ideal environment, and this value may vary slightly due to material ratio and environmental region. Then the temperature sensor for collecting the environmental temperature is used to collect the environmental temperature data at the current monitoring time, and the environmental temperature data at the current monitoring time is the environmental temperature at the location of the water stable paver at the current monitoring time, the absolute value of the difference between the environmental temperature data at the current monitoring time and the preset reference environmental temperature is calculated, and then multiplied by the preset heat exchange coefficient, and recorded as the second influence value at the current monitoring time, that is, due to the direct heat exchange between the environment and the material, the temperature will change, and the radiation heat exchange is approximately linear in the normal temperature range, so in order to facilitate calculation, the above is simplified by using a linear model to obtain the second influence value, the preset reference environmental temperature and the preset heat exchange coefficient in the embodiment are both empirical values, for example, the preset reference environmental temperature is 25 degrees Celsius, and the preset heat exchange coefficient is 0.8. Then the time interval from the production completion of the paving material used by the water stable paver at the current monitoring time to the current monitoring time is obtained, the unit of the time interval is minute, the production completion of the paving material usually refers to the state that the cement stabilized gravel (water stable) pavement base material reaches the state of being transported to the paving site after being proportioned and stirred at the mixing station, the product of the time interval from the production completion of the paving material used at the current monitoring time to the current monitoring time and the preset temperature change rate is calculated, and used as the third influence value at the current monitoring time, that is, the material temperature will gradually decrease with the passage of construction time, so the third influence value is obtained based on the time interval from the production completion of the paving material used at the current monitoring time to the current monitoring time and the preset temperature change rate, and the preset temperature change rate is an empirical value, for example, 0.1 degrees Celsius per minute can be taken as the preset temperature change rate; finally, the sum of the first influence value, the second influence value and the third influence value at the current monitoring time is calculated, and used as the current temperature baseline threshold.

[0015] The acquisition process of the current vibration baseline threshold value: a time period formed by the current monitoring time and a preset historical monitoring time period before the current monitoring time is obtained, and is recorded as a to-be-analyzed time period. The mean and standard deviation of all vibration data at all monitoring times in the to-be-analyzed time period are recorded as a current vibration mean and a current vibration standard deviation, respectively. The sum of the current vibration mean and the current vibration standard deviation multiplied by a preset multiple is taken as the current vibration baseline threshold value. The current vibration baseline threshold value is determined in a statistical manner, and in this embodiment, about 85% of normal vibration data is selected, so that the result obtained by adding 1.5 times the current vibration standard deviation to the current vibration mean is the current vibration baseline threshold value. In specific applications, the implementer needs to set the preset historical monitoring time period before the current monitoring time according to the actual situation. In this embodiment, 60 seconds before the current monitoring time is taken as the preset historical monitoring time period before the current monitoring time.

[0016] The acquisition process of the speed baseline threshold value interval: standard paving speed data is obtained, and an interval formed by plus and minus a preset value of the standard paving speed data is recorded as a speed baseline threshold value interval. The preset value needs to be set by the implementer according to the actual situation. In this embodiment, the preset value can be set to 15, so that the speed baseline threshold value interval is , and s is the standard paving speed data. If the standard paving speed during the working process of the water-stable paver is 1 meter to 3 meters per minute, the median value, that is, 1.5 meters, is taken as the standard paving speed.

[0017] Therefore, the current temperature baseline threshold value, the current vibration baseline threshold value, and the speed baseline threshold value interval can be obtained by the above calculation. After obtaining the current temperature baseline threshold value, the current vibration baseline threshold value, and the speed baseline threshold value interval, the abnormal amplitude of the current paving temperature data and the abnormal amplitude of the current vibration data are obtained based on the current temperature baseline threshold value and the current vibration baseline threshold value. The absolute value of the difference between any current paving temperature data and the current temperature baseline threshold value is the abnormal amplitude of the current paving temperature data, and the difference between any current vibration data and the current vibration baseline threshold value is the abnormal amplitude of the current vibration data. According to the abnormal amplitude of the current paving temperature data and the abnormal amplitude of the current vibration data, the abnormal paving temperature data and the abnormal vibration data at the current monitoring time are obtained. The abnormal paving temperature data and the abnormal vibration data at the current monitoring time are both current abnormal data.

[0018] The specific acquisition process of the abnormal paving temperature data and the abnormal vibration data at the current monitoring moment is: for any current paving temperature data, it is judged whether the abnormal amplitude of the current paving temperature data is greater than a preset first abnormal amplitude threshold, if yes, the current paving temperature data is recorded as an abnormal paving temperature data at the current monitoring moment, for any current vibration data, it is judged whether the abnormal amplitude of the current vibration data is greater than a preset second abnormal amplitude threshold, if yes, the current vibration data is recorded as an abnormal vibration data at the current monitoring moment, and the implementer can set the preset first abnormal amplitude threshold according to the engineering practice experience, for example, based on the engineering practice experience, it is known that when the temperature deviation exceeds 5 degrees Celsius, the compaction quality will be significantly affected, therefore, the preset first abnormal amplitude threshold can be set to 5 degrees Celsius; since when the current vibration data exceeds the current vibration baseline threshold, it can be indicated that the current vibration data deviates from the normal vibration range, therefore, the preset second abnormal amplitude threshold is set to 0 in the embodiment, that is, when the current vibration data is greater than the current vibration baseline threshold, the corresponding current vibration data is the abnormal vibration data. In addition, since the speed is a global quantity, it needs to be processed separately and does not participate in the subsequent multi-sensor simultaneous analysis, that is, the current paving speed data and the speed baseline threshold interval are mainly used for the calculation of the mechanical segregation matching degree in the subsequent process.

[0019] Therefore, the current abnormal data at the current monitoring moment or all the abnormal paving temperature data and all the abnormal vibration data at the current monitoring moment can be obtained through the above process.

[0020] In step S002, the current abnormal data in the preset neighborhood range of the position coordinates of the sensor collecting the current abnormal data is obtained to obtain a to-be-analyzed region at the current monitoring moment, the abnormal paving temperature data and the abnormal vibration data in the to-be-analyzed region at the current monitoring moment are obtained to obtain the temperature vibration coupling strength of the to-be-analyzed region at the current monitoring moment, and the segregation confidence of the to-be-analyzed region at the current monitoring moment is obtained according to the different data proportion, the temperature vibration coupling strength, the current paving speed data and the speed baseline threshold interval, and the abnormal amplitude in the to-be-analyzed region at the current monitoring moment.

[0021] Since the generation of the segregation phenomenon has regionality, that is, when the number of abnormal data in a certain region is relatively dense, the necessity of analysis is higher, if the single abnormal data is analyzed, the calculation difficulty is large, and the necessity is not strong, so the embodiment needs to carry out multi-sensor simultaneous analysis first, that is, the simultaneous analysis of multiple current abnormal data is carried out, and the to-be-analyzed region at the current monitoring time and the temperature vibration coupling strength of the to-be-analyzed region are obtained. The temperature vibration coupling strength can reflect the matching of different types of segregation, which is an important parameter for obtaining the segregation confidence in the subsequent. Based on the above description, the embodiment needs to carry out simultaneous analysis on multiple current abnormal data to obtain the to-be-analyzed region at the current monitoring time, that is, the embodiment needs to obtain the to-be-analyzed region at the current monitoring time according to the number of current abnormal data in the preset neighborhood range of the position coordinates of the sensor collecting the current abnormal data, and the specific process of obtaining the to-be-analyzed region at the current monitoring time is: First, the position coordinate point of the sensor collecting each current abnormal data at the current monitoring time is recorded as the sensor position point corresponding to the current abnormal data, that is, the coordinate value in the sensor position point of the current abnormal data is composed of the position coordinate of the sensor collecting the corresponding current abnormal data at the current monitoring time. Then, each current abnormal data is traversed to obtain all to-be-processed regions corresponding to the current monitoring time, that is, for any current abnormal data r, the number of current abnormal data in the preset first neighborhood range of the sensor position point of the current abnormal data r is obtained, and it is judged whether the number of current abnormal data in the preset first neighborhood range of the sensor position point of the current abnormal data r is greater than or equal to the preset data number threshold. If it is greater, the preset first neighborhood range of the sensor position point of the current abnormal data r is taken as one to-be-processed region corresponding to the current monitoring time. And in specific application, the implementer can set the preset first neighborhood range and the preset data number threshold according to the actual situation such as the distance of the sensor time, for example, the preset first neighborhood range can be set to a range with a radius of 1 meter, and the preset data number threshold can be set to 2. Then, the regions with intersection in all to-be-processed regions corresponding to the current monitoring time are merged, and all regions obtained after merging are recorded as to-be-analyzed regions at the current monitoring time. There is no intersection between the to-be-analyzed regions at the current monitoring time. For example, if the to-be-processed regions corresponding to the current monitoring time are region 1, region 2, region 3 and region 4, there is intersection between region 1 and region 2, there is intersection between region 3 and region 4, and there is no intersection between region 1 and region 2 and region 3 and region 4. Then, the region obtained after merging region 1 and region 2 is recorded as a to-be-analyzed region, and the region obtained after merging region 3 and region 4 is also recorded as a to-be-analyzed region.

[0022] After obtaining the to-be-analyzed region at the current monitoring moment, the current abnormal data in the to-be-analyzed region at the current monitoring moment is obtained, that is, the abnormal paving temperature data and the abnormal vibration data in the to-be-analyzed region at the current monitoring moment are obtained, and only the current abnormal data exists in the to-be-analyzed region at the current monitoring moment without other data. For example, for the abnormal paving temperature data v1, the abnormal paving temperature data v2 and the abnormal vibration data v3 at the current monitoring moment, the position coordinate points of the sensors collecting the abnormal paving temperature data v1, the abnormal paving temperature data v2 and the abnormal vibration data v3 at the current monitoring moment are all located in the to-be-analyzed region A at the current monitoring moment, so the abnormal paving temperature data v1 and the abnormal paving temperature data v2 are the abnormal paving temperature data in the to-be-analyzed region A, and the abnormal vibration data v3 is the abnormal vibration data in the to-be-analyzed region A. Then, according to the abnormal paving temperature data and the abnormal vibration data in the to-be-analyzed region at the current monitoring moment, the temperature vibration coupling strength of the to-be-analyzed region at the current monitoring moment is obtained. The temperature vibration coupling strength is a key parameter for obtaining the segregation confidence. Therefore, the specific acquisition process of the temperature vibration coupling strength of the to-be-analyzed region at the current monitoring moment is as follows: The comprehensive coupling strength of each abnormal paving temperature data in any to-be-analyzed region A at the current monitoring moment is obtained, and the mean value of the comprehensive coupling strength of all abnormal paving temperature data in the to-be-analyzed region A is taken as the temperature vibration coupling strength of the to-be-analyzed region A. The specific acquisition process of the comprehensive coupling strength of any abnormal paving temperature data a in the to-be-analyzed region A is as follows: First, in the region A to be analyzed, all abnormal vibration data located in the preset second neighborhood range of the sensor position point of the abnormal paving temperature data a and belonging to the region A to be analyzed are obtained and recorded as the associated abnormal vibration data corresponding to the abnormal paving temperature data a, that is, the associated abnormal vibration data corresponding to the abnormal paving temperature data a belongs to the region A to be analyzed, and in specific applications, the implementer needs to set the preset second neighborhood range according to the actual situation such as engineering experience, for example, the range with a radius of 2 meters can be taken as the preset second neighborhood range; then, according to the Gaussian kernel function between the sensor position point of the abnormal paving temperature data a and the sensor position point of each associated abnormal vibration data corresponding to the abnormal paving temperature data a, the absolute value of the abnormal amplitude of the abnormal paving temperature data a and the absolute value of the abnormal amplitude of each associated abnormal vibration data corresponding to the abnormal paving temperature data a, the coupling strength between the abnormal paving temperature data a and each associated abnormal vibration data corresponding to the abnormal paving temperature data a is obtained, and in this embodiment, the result of multiplying the Gaussian kernel function between the sensor position point of the abnormal paving temperature data a and the sensor position point of the bth associated abnormal vibration data corresponding to the abnormal paving temperature data a, the absolute value of the abnormal amplitude of the abnormal paving temperature data a and the absolute value of the abnormal amplitude of the bth associated abnormal vibration data is recorded as the coupling strength between the abnormal paving temperature data a and the bth associated abnormal vibration data, then the specific expression for calculating and obtaining the coupling strength between the abnormal paving temperature data a and the bth associated abnormal vibration data is: wherein, is the coupling strength between the abnormal paving temperature data a and the bth associated abnormal vibration data; is the Gaussian kernel function between the sensor position point of the abnormal paving temperature data a and the sensor position point of the bth associated abnormal vibration data corresponding to the abnormal paving temperature data a, which is used to represent the influence of spatial distance on the coupling strength, and exp() is an exponential function with constant e as the base, is the position distance between the sensor position point of the abnormal paving temperature data a and the sensor position point of the bth associated abnormal vibration data corresponding to the abnormal paving temperature data a, which can be calculated by the Euclidean distance, is the spatial correlation length, which is generally calibrated by experiment, for example, 0.5 meters, is the abnormal amplitude of the abnormal paving temperature data a, is the abnormal amplitude of the bth associated abnormal vibration data. The closer the position distance between the sensor position point of the abnormal paving temperature data a and the sensor position point of the bth associated abnormal vibration data, the stronger the coupling effect, should be larger; the larger, the more serious the segregation, and the greater the contribution to the coupling strength, that is, The greater the temperature-vibration coupling strength is, the more obvious the vibration anomaly is, the greater the contribution to the coupling strength is, that is The greater the temperature-vibration coupling strength is, the more obvious the vibration anomaly is, the greater the contribution to the coupling strength is, that is The greater the temperature-vibration coupling strength is, the more obvious the vibration anomaly is, the greater the contribution to the coupling strength is, that is The greater the temperature-vibration coupling strength is, the more obvious the vibration anomaly is, the greater the contribution to the coupling strength is, that is The greater the temperature-vibration coupling strength is, the more obvious the vibration anomaly is, the greater the contribution to the coupling strength is, that is

[0023] Since the segregation is mainly divided into temperature segregation, particle size segregation and mechanical segregation, among them, the temperature segregation is mainly manifested as abnormal paving temperature, secondarily manifested as normal vibration or slightly abnormal vibration, and the temperature-vibration coupling is usually low; the particle size segregation is mainly manifested as vibration anomaly, secondarily manifested as possible abnormal paving temperature, and the temperature-vibration coupling strength is usually high; the mechanical segregation is mainly manifested as abnormal paving speed, secondarily manifested as possible abnormal vibration and paving temperature, and its spatial distribution is related to the machine running direction; based on the above analysis, it can be known that the embodiment is based on the temperature-vibration coupling strength of each to-be-analyzed region at the current monitoring moment obtained above, and then the matching degrees between the to-be-analyzed region and different types of segregation at the current monitoring moment are obtained according to different data proportions in the to-be-analyzed region, the current paving speed data, the speed baseline threshold interval and the abnormal amplitude and the like parameters, and the segregation confidence of the to-be-analyzed region at the current monitoring moment is finally obtained based on the matching degrees between the to-be-analyzed region and different types of segregation at the current monitoring moment obtained; based on the above description, it can be known that the embodiment will first obtain the temperature segregation matching degree, the particle size segregation matching degree and the mechanical segregation matching degree corresponding to the to-be-analyzed region at the current monitoring moment according to the temperature-vibration coupling strength of the to-be-analyzed region at the current monitoring moment, the proportion of abnormal paving temperature data and the proportion of abnormal vibration data in the to-be-analyzed region at the current monitoring moment, the abnormal amplitude of the abnormal paving temperature data and the abnormal vibration data in the to-be-analyzed region at the current monitoring moment, the current paving speed data and the speed baseline threshold interval, and then the maximum matching degree is selected from the temperature segregation matching degree, the particle size segregation matching degree and the mechanical segregation matching degree corresponding to any to-be-analyzed region A at the current monitoring moment as the segregation confidence of the to-be-analyzed region A, and the greater the segregation confidence is, the greater the probability or risk of segregation appears.

[0024] And in the embodiment, the specific process of obtaining the temperature segregation matching degree, the particle size segregation matching degree and the mechanical segregation matching degree corresponding to the to-be-analyzed region at the current monitoring moment is as follows: for the to-be-analyzed region A at the current monitoring moment, Firstly, the weighted sum of , , is obtained and taken as the temperature segregation matching degree corresponding to the to-be-analyzed region A, and the weighted sum of , , is obtained and taken as the particle size segregation matching degree corresponding to the to-be-analyzed region A.

[0025] The specific calculation expression of the temperature segregation matching degree corresponding to the to-be-analyzed area A is as follows: wherein, the temperature segregation matching degree corresponding to the to-be-analyzed area A, , , is a first proportion value, is a second proportion value, the first proportion value is a ratio of the number of abnormal paving temperature data in the to-be-analyzed area A to the total number of data in the to-be-analyzed area A, the second proportion value is a ratio of the abnormal vibration data in the to-be-analyzed area A to the total number of data in the to-be-analyzed area A, min() is a minimum value function, and the minimum value function is used to make the value interval 0 to 1, is a mean value of the absolute values of the abnormal amplitudes of all the abnormal paving temperature data in the to-be-analyzed area A, is a mean value of the absolute values of the abnormal amplitudes of all the abnormal vibration data in the to-be-analyzed area A, z is a preset second temperature abnormal threshold value, is a preset second vibration abnormal threshold value, is a temperature-vibration coupling strength of the to-be-analyzed area A, c0 is a preset coupling strength reference threshold value, W1, W2 and W3 are respectively a first weight value, a second weight value and a third weight value; the first term is mainly determined by the proportions of different types, which reflects the dominant type of the abnormality, the second term is mainly determined by the abnormal amplitude, which reflects the severity of the abnormality, and the third term is determined by the temperature-vibration coupling strength, which reflects the correlation between the abnormalities, since the proportions of different types and the abnormal amplitude provide a large amount of information, the weight values of the first term and the second term should be larger, for example, in the embodiment, W1, W2 and W3 can be respectively set to 0.35, 0.35 and 0.3; the preset second temperature abnormal threshold value and the preset second vibration abnormal threshold value are empirical values determined based on engineering experience, that is, since a temperature deviation of more than 8 degrees Celsius usually indicates a serious segregation, the preset second temperature abnormal threshold value can be set to 8 in the embodiment, a vibration deviation of more than 0.1g usually indicates a significant abnormality, so the preset second vibration abnormal threshold value is set to 0.1g, the vibration data in the embodiment is collected by a three-axis accelerometer, and the unit of the vibration data collected by the three-axis accelerometer is usually meter per second squared, and the commonly used unit of gravity acceleration is g (about 9.8 m / s²); the c0 preset coupling strength reference threshold value can also be set based on engineering experience, for example, greater than 0.3, the vibration is likely to be the main reason for the temperature abnormality, so c0 is set to 0.3 in the embodiment.

[0026] Since The larger and The smaller, The larger the value, the more it conforms to the characteristics of abnormal paving temperature and normal or slightly abnormal vibration in temperature segregation. The larger the value, the greater the match between the analyzed region A and the temperature separation, or in other words... The larger; In The larger and The smaller, The larger the value, the more it conforms to the characteristics of abnormal paving temperature and normal or slightly abnormal vibration in temperature segregation. The larger the value, the greater the match between the analyzed region A and the temperature separation, or in other words... The larger; In The smaller, The larger the value, the more it conforms to the characteristic of lower temperature vibration coupling strength in temperature segregation. The larger the value, the greater the match between the analyzed region A and the temperature separation, or in other words... The larger it is, the more we can know The larger, The larger and When it is larger, The larger, and A larger value indicates a higher degree of matching between the analyzed region A and temperature segregation, and vice versa. The smaller the value, the lower the match between the analyzed region A and the temperature segregation.

[0027] The specific formula for calculating the particle size segregation matching degree corresponding to the region A to be analyzed is as follows: in, The particle size separation matching degree corresponds to region A to be analyzed. , ,and The formula is primarily based on particle size segregation characteristics; The larger, The larger and When it is larger, The larger, and A larger value indicates a higher degree of matching between the analyzed region A and the particle size distribution, and vice versa. The smaller the size, the lower the match between the analyzed region A and the particle size separation. Formula Logic AND The logic is similar, so it will not be described in detail.

[0028] Next, it is determined whether the current paving speed data is within the speed baseline threshold range. If not, a constant of 1 is used as the first index value; otherwise, 0 is used as the first index value. Then, the absolute value of the difference between the temperature vibration coupling strength of the area to be analyzed (A) and the preset coupling strength reference threshold is obtained and negatively correlated, and this result is used as the second index value. Finally, the weighted sum of the first and second index values ​​is used as the mechanical segregation matching degree corresponding to the area to be analyzed (A). The specific calculation expression for the mechanical segregation matching degree corresponding to the area to be analyzed (A) is as follows: in, To determine the mechanical segregation matching degree corresponding to region A to be analyzed, F1 is the first index value, W4 is the fourth weight value, and W5 is the fifth weight value. Since velocity provides a significant amount of information, W4 is required to be greater than W5. For example, W4 can be set to 0.6 and W5 to 0.4. In actual engineering... The rounding range is usually between 0 and 1. The purpose of subtracting 1 from the result of comparing it with 3 is to make the second index value... It falls approximately within the range of 0.7 to 1, thus ensuring that the matching accuracy does not drop to zero due to extreme deviations, preserving the baseline value, and conforming to the deviations in engineering. Smaller values ​​indicate a higher degree of matching. Furthermore, in mechanical segregation, it is usually not caused by a single factor, but rather by the combined effects of velocity, vibration, and temperature anomalies. If velocity anomalies are accompanied by vibration-temperature coupling anomalies, the confidence level of mechanical segregation is higher. Additionally, velocity instability alters the material's residence time under the paver, affecting heat dissipation and temperature distribution. As a correlation indicator of vibration and temperature anomalies, it can capture this combined effect, improving the reliability of diagnosis. Furthermore, by combining... This can filter out some accidental situations, such as when constructing on curves or slopes, the speed may be adjusted normally, but if there is no coupling abnormality, the risk of mechanical segregation is low.

[0029] Therefore, this embodiment can obtain the separation confidence level of each region to be analyzed at the current monitoring time through the above process.

[0030] Step S003: Based on the intersection between each region to be analyzed at the current monitoring time and each region to be analyzed at the historical monitoring time, obtain the target region at the current monitoring time and the continuous region sequence corresponding to the target region. Obtain the trend continuity characterization value of the target region based on the continuous region sequence. Obtain the separation risk value of the target region based on the trend continuity characterization value of the target region, the separation confidence level, the area and temperature vibration coupling strength, and the total number of data in the target region.

[0031] Since the segregation is a process, the early warning by combining only the parameter values at a single moment will affect the accuracy of the early warning, and in order to further ensure the accuracy of the subsequent early warning, the trend analysis will be performed next, that is, whether the current monitoring moment meets the continuous condition is analyzed, if it meets, the trend continuous representation value is analyzed and obtained, that is, in the embodiment, the target region at the current monitoring moment and the corresponding continuous region sequence of the target region are obtained according to the intersection between the current monitoring moment and the historical monitoring moment of each region to be analyzed, and the trend continuous representation value of the target region at the current monitoring moment is obtained according to the continuous region sequence. The trend continuous representation value is an important parameter for determining the segregation risk value subsequently, and the specific process of obtaining the target region at the current monitoring moment and the corresponding continuous region sequence of the target region according to the intersection between the current monitoring moment and the historical monitoring moment of each region to be analyzed is as follows: For any to-be-analyzed region A at the current monitoring moment, first, obtain each to-be-analyzed region at each monitoring moment before the current monitoring moment, and the obtaining method of the to-be-analyzed region at any monitoring moment is the same as that of the to-be-analyzed region at the current monitoring moment, and the current monitoring moment is recorded as the tth monitoring moment; then, it is judged whether the to-be-analyzed region at the (t-1) th monitoring moment intersects with the to-be-analyzed region A, if yes, the to-be-analyzed region at the (t-1) th monitoring moment that intersects with the to-be-analyzed region A is recorded as the 1st historical region corresponding to the to-be-analyzed region A, that is, if the to-be-analyzed region n at the (t-1) th monitoring moment intersects with the to-be-analyzed region A, then the to-be-analyzed region n is the 1st historical region corresponding to the to-be-analyzed region A, it is continuously judged whether the to-be-analyzed region at the (t-2) th monitoring moment intersects with the 1st historical region, if yes, the to-be-analyzed region at the (t-2) th monitoring moment that intersects with the 1st historical region is recorded as the 2nd historical region corresponding to the to-be-analyzed region A, and so on, and the traversal is continued forward until all to-be-analyzed regions at a certain monitoring moment do not intersect with the historical region, and the sequence of all historical regions corresponding to the to-be-analyzed region A and the to-be-analyzed region A is recorded as the region sequence corresponding to the to-be-analyzed region A, the 1st region in the region sequence corresponding to the to-be-analyzed region A is the to-be-analyzed region A, and the (d+1) th region in the region sequence corresponding to the to-be-analyzed region A is the dth historical region corresponding to the to-be-analyzed region A, that is, the 2nd region in the region sequence corresponding to the to-be-analyzed region A is the 1st historical region corresponding to the to-be-analyzed region A; then, the time interval between the monitoring moments corresponding to the first and last regions in the region sequence corresponding to the to-be-analyzed region A is obtained, and is recorded as the duration of the region sequence corresponding to the to-be-analyzed region A, then it is judged whether the duration of the region sequence corresponding to the to-be-analyzed region A is greater than a preset duration threshold, if yes, it indicates that the to-be-analyzed region A is likely to have a risk of segregation, and it is necessary to calculate the segregation risk value, therefore, if the duration of the region sequence corresponding to the to-be-analyzed region A is greater than the preset duration threshold, the to-be-analyzed region A is recorded as a target region, and the region sequence of the to-be-analyzed region A is taken as the duration region sequence corresponding to the target region, and each region in the duration region sequence is recorded as a duration region, that is, if a target region is the to-be-analyzed region A, the duration region sequence corresponding to the target region is the region sequence corresponding to the to-be-analyzed region A. And in specific application, the implementer needs to set the preset duration threshold according to the actual situation, for example, the preset duration threshold can be set to 10 seconds in this embodiment.

[0032] An example of obtaining the region sequence corresponding to the to-be-analyzed region A and the duration of the region sequence corresponding to the to-be-analyzed region A is as follows: if the to-be-analyzed region u1 at the t-1th monitoring moment has an intersection with the to-be-analyzed region A, the to-be-analyzed region u2 at the t-2th monitoring moment has an intersection with the to-be-analyzed region u1, the to-be-analyzed region u3 at the t-3th monitoring moment has an intersection with the to-be-analyzed region u2, and all to-be-analyzed regions at the t-4th monitoring moment do not have an intersection with the to-be-analyzed region u3, then the region sequence corresponding to the to-be-analyzed region A is {to-be-analyzed region A, to-be-analyzed region u1, to-be-analyzed region u2, to-be-analyzed region u3}, and the duration of the region sequence corresponding to the to-be-analyzed region A is the time interval between the time corresponding to the tth monitoring moment and the time corresponding to the t-3th monitoring moment, the to-be-analyzed region A is the to-be-analyzed region at the current monitoring moment, and the current monitoring moment is recorded as the tth monitoring moment.

[0033] In this embodiment, the specific process of obtaining the trend duration representation value of the target region at the current monitoring moment according to the duration region sequence is as follows: for any target region B: First, the result of normalizing the duration of the duration region sequence corresponding to the target region B is recorded as the time duration representation value corresponding to the target region B; and the time duration representation value Z1 corresponding to the target region B is , is the duration of the duration region sequence corresponding to the target region B, and the unit is second, and T0 is a preset duration, according to engineering experience, an abnormality exceeding 60 seconds needs to be handled urgently, so T0 is set to 60 seconds in this embodiment.

[0034] Then in the continuous region sequence corresponding to the target region B, every interval of preset number of regions is marked, and the marked regions are recorded as the marked regions of the target region B, and a new sequence formed by the marked regions of the target region B is recorded as a continuous region sub-sequence of the target region B, the region position relationship in the continuous region sub-sequence corresponds to the region position relationship in the continuous region sequence corresponding to the target region B, for example, the a-th marked region in the continuous region sub-sequence is the a-th marked region in the continuous region sequence corresponding to the target region B; in specific application, the implementer needs to set the value of the preset number according to the actual situation, for example, the preset number can be set to 5; the acquisition process of the continuous region sub-sequence of the target region B: if the preset number is 5 and the number of regions in the continuous region sequence corresponding to the target region B is 11, then the first region in the continuous region sequence corresponding to the target region B is marked first, then the first+5 region in the continuous region sequence corresponding to the target region B is marked, and then the first+5+5 region in the continuous region sequence corresponding to the target region B is marked, then the continuous region sub-sequence is composed of the first region, the sixth region and the eleventh region in the continuous region sequence corresponding to the target region B; the function of taking the minimum value in the embodiment is to realize normalization and saturation effect.

[0035] Then according to the area of each region in the continuous region sub-sequence of the target region B and the abnormal amplitude of the abnormal paving temperature data in each region, the region area change characteristic value corresponding to the target region B and the abnormal amplitude change characteristic value are obtained, and the specific acquisition process of the region area change characteristic value corresponding to the target region B and the abnormal amplitude change characteristic value is as follows: an area change rate sequence corresponding to the continuous region sub-sequence is obtained, the mean value of the area change rate sequence is recorded as the target area change rate corresponding to the target region B, and the result of the normalization processing of the target area change rate is recorded as the region area change characteristic value corresponding to the target region B, the k-th area change rate in the area change rate sequence , is the absolute value of the difference between the area of the k-th region and the area of the k+1-th region in the continuous region sub-sequence, is the time interval between the monitoring time corresponding to the k-th region and the monitoring time corresponding to the k+1-th region, if a region is a region at a certain monitoring time, then the monitoring time corresponding to the region is the monitoring time; an abnormal amplitude change rate sequence corresponding to the continuous region sub-sequence is obtained, the mean value of the abnormal amplitude change rate sequence is recorded as the target abnormal amplitude change rate corresponding to the target region B, and the result of the normalization processing of the target abnormal amplitude change rate is recorded as the abnormal amplitude change characteristic value corresponding to the target region B, the h-th abnormal amplitude change rate in the abnormal amplitude change rate sequence is , the absolute value of the difference between the mean value of the abnormal amplitude of all abnormal paving temperature data in the hth region in the continuous regional subsequence and the mean value of the abnormal amplitude of all abnormal paving temperature data in the (h+1) th region, is the time interval between the monitoring time corresponding to the hth region and the monitoring time corresponding to the (h+1) th region, if any region is a region at a certain time, then the time of collecting all data in the region is the time. Finally, the weighted sum of the time duration representation value, the regional area change representation value and the abnormal amplitude change representation value corresponding to the target region B is taken as the trend duration representation value of the target region B. The regional area change representation value Z2 corresponding to the target region B is , is the target area change rate corresponding to the target region B, is the maximum value of the target area change rate or is the maximum value of the target area change rates corresponding to all target regions at all monitoring times, which is used to normalize , and the abnormal amplitude change representation value Z3 corresponding to the target region B is , is the target abnormal amplitude change rate corresponding to the target region B, and R0 is the maximum value of the target abnormal amplitude change rate or is the maximum value of the target abnormal amplitude change rates corresponding to all target regions at all monitoring times, which is used to normalize .

[0036] The calculation expression of the trend duration representation value of the target region B is: wherein QB is the trend duration representation value of the target region B, v1, v2 and v3 are respectively the first weight factor, the second weight factor and the third weight factor, Z1 is the time duration representation value corresponding to the target region B, Z2 is the regional area change representation value corresponding to the target region B, and Z3 is the abnormal amplitude change representation value corresponding to the target region B; since the duration length can better reflect the segregation situation, the value of v1 is required to be larger in this embodiment, for example, v1, v2 and v3 can be respectively set to 0.4, 0.3 and 0.3; and the greater Z1, Z2 and Z3 are, the faster the abnormality deteriorates, and the greater the segregation risk is, that is, the greater QB is, the greater the segregation risk is, and vice versa.

[0037] After the trend persistence representation value of the target region at the current monitoring moment is obtained, the segregation risk value of the target region at the current monitoring moment is obtained according to the trend persistence representation value of the target region at the current monitoring moment, the segregation confidence, the area and the temperature vibration coupling strength of the target region at the current monitoring moment, and the total number of data in the target region at the previous monitoring moment. The specific process of obtaining the segregation risk value of the target region at the current monitoring moment is that, for the target region B, the value obtained by multiplying the trend persistence representation value of the target region B, the segregation confidence, the result of positively correlating mapping of the total number of data in the target region B, the result of positively correlating mapping of the area of the target region B, and the result of positively correlating mapping of the temperature vibration coupling strength of the target region B is taken as the segregation risk value of the target region B.

[0038] The specific calculation expression for obtaining the segregation risk value of the target region B is: Wherein, RB is the segregation risk value of the target region B, CB is the segregation confidence of the target region B, QB is the trend persistence representation value of the target region B, NB is the total number of data in the target region B, ln() is the logarithmic function with constant e as the base, AB is the area of the target region B, the temperature vibration coupling strength of the target region B; CB indicates that the greater the necessity of taking targeted measures if the system determines that the current anomaly belongs to a certain segregation type, so the segregation risk value should be proportional to CB, QB indicates the development trend of the anomaly, and the greater the trend persistence representation value indicates that the anomaly is rapidly deteriorating, the influence of the abnormal scale, but the logarithmic growth is used because the risk is not linearly increased but gradually saturated when the number of abnormal points increases, the logarithmic function is used to map NB to avoid the problem that the term is too large when the number of abnormal points is too large, and the constant 1 is added to prevent the term from being 0; AB indicates the influence range of the anomaly, and the greater the area, the greater the influence range and the higher the risk, and AB is multiplied by 0.1 and then added by 1 to make the area term linearly increase with AB and make the contribution of the area term to the risk value relatively moderate, and 0.1 is a scaling factor to avoid excessive influence of the area, the greater the temperature vibration coupling strength, the more complex the segregation caused by vibration may be, and the higher the risk is, and the temperature vibration coupling strength is multiplied by 0.5 to increase the influence of the temperature vibration coupling strength on the segregation risk value, and 0.5 is a weight factor. The greater the RB is, and the greater the RB is, which indicates that the risk of segregation is greater.

[0039] Therefore, the embodiment can obtain the segregation risk value of the target region at the current monitoring moment.

[0040] ​Step S004, according to the segregation risk value, a segregation early warning is performed on the paving process of the water stable paver.

[0041] After obtaining the segregation risk value, the embodiment performs a segregation early warning on the paving process of the water stable paver based on the segregation risk value of the target region at the current monitoring moment, specifically: For any target region at the current monitoring moment, the risk level of the target region is obtained according to the segregation risk value of the target region, and if the risk level of the target region is a medium risk level or a high risk level, a segregation early warning is immediately performed, and corresponding measures are taken, such as adjusting the heating temperature of the screed, checking the uniformity of material supply or focusing on monitoring the temperature change of the abnormal region, adjusting the vibration parameters, checking the material gradation, monitoring the vibration anomaly and material separation, optimizing the speed, assisting in checking the mechanical state, monitoring the speed stability and paving uniformity. In addition, if the segregation risk value is less than a preset first threshold, it is determined to be a low risk level, if the segregation risk value is greater than or equal to the preset first threshold and less than a preset second threshold, it is determined to be a medium risk level, and if the segregation risk value is greater than or equal to the preset second threshold, it is determined to be a high risk level, that is, if the segregation risk value of a target region is greater than the preset second threshold, the risk level of the target region is a high risk level. In specific applications, the implementer needs to set the preset first threshold and the preset second threshold according to the actual situation, for example, the preset first threshold and the preset second threshold can be set to 5 and 15 respectively.

[0042] At this point, the embodiment completes the water stable paver operation data acquisition and segregation early warning, and the segregation risk value obtained based on the multi-dimensional parameters can accurately and reliably perform a segregation early warning. It should be noted that all parameters participating in the formula addition and multiplication in the embodiment are subjected to a dimensionless processing.

[0043] To sum up, in the embodiment, first, the number of current abnormal data in the preset neighborhood range of the position coordinates of the sensor collecting the current abnormal data is obtained to obtain the to-be-analyzed area at the current monitoring moment, the temperature vibration coupling strength of the to-be-analyzed area at the current monitoring moment is obtained according to the abnormal paving temperature data and the abnormal vibration data in the to-be-analyzed area at the current monitoring moment, the segregation confidence of the to-be-analyzed area at the current monitoring moment is obtained according to the different data proportion, the temperature vibration coupling strength, the current paving speed data, the speed baseline threshold interval and the abnormal amplitude in the to-be-analyzed area at the current monitoring moment; then, the target area at the current monitoring moment and the corresponding continuous area sequence of the target area are obtained according to the intersection between each to-be-analyzed area at the current monitoring moment and each to-be-analyzed area at the historical monitoring moment, the trend continuous representation value of the target area is obtained according to the continuous area sequence, and the segregation risk value of the target area is obtained according to the trend continuous representation value, the segregation confidence, the area and the temperature vibration coupling strength of the target area and the total number of data in the target area; finally, the segregation early warning of the paving process of the water stable paver is performed according to the segregation risk value. The segregation risk value obtained based on the trend continuous representation value, the segregation confidence, the area and the temperature vibration coupling strength and the like can improve the accuracy and reliability of the early warning when the segregation early warning of the paving process of the water stable paver is performed.

[0044] The above-described embodiments are only used to illustrate the technical solutions of the present application, but not limit the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those skilled in the art that the technical solutions recorded in the foregoing embodiments can be modified, or some technical features can be replaced by equivalent ones; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A sensor-based data acquisition and segregation early warning system for water-stabilized paver operation, comprising a processor and a memory, characterized in that, The processor executes the computer program stored in the memory to perform the following steps: During the paving process, the current paving temperature data, current vibration data, current paving speed data, and current temperature baseline threshold, current vibration baseline threshold, and speed baseline threshold range are acquired. Based on the current temperature baseline threshold and current vibration baseline threshold, the abnormal amplitude of the current paving temperature data and current vibration data is obtained. Based on the abnormal amplitude, the current abnormal data is obtained, which includes abnormal paving temperature data and abnormal vibration data. Based on the number of current abnormal data within a preset neighborhood of the sensor's location coordinates that collects current abnormal data, the area to be analyzed at the current monitoring time is obtained. Based on the abnormal paving temperature data and abnormal vibration data in the area to be analyzed at the current monitoring time, the temperature-vibration coupling intensity of the area to be analyzed at the current monitoring time is obtained. Based on the proportion of different data, temperature-vibration coupling intensity, current paving speed data, speed baseline threshold range, and abnormal amplitude in the area to be analyzed at the current monitoring time, the segregation confidence level of the area to be analyzed at the current monitoring time is obtained. Based on the intersection of each region to be analyzed at the current monitoring time and each region to be analyzed at the historical monitoring time, the target region at the current monitoring time and the corresponding continuous region sequence are obtained. The trend continuity characterization value of the target region is obtained based on the continuous region sequence. Based on the trend continuity characterization value of the target region, the segregation confidence, the area and temperature vibration coupling strength, and the total number of data in the target region, the segregation risk value of the target region is obtained. Based on the segregation risk value, a segregation warning is issued for the paving process of the water-stabilized paver.

2. The sensor-based water-stabilized paver operation data acquisition and segregation early warning system as described in claim 1, characterized in that, The methods for obtaining the current temperature baseline threshold, the current vibration baseline threshold, and the velocity baseline threshold range include: The temperature of the paving material under ideal conditions is recorded as the first influence value at the current monitoring time. The absolute value of the difference between the ambient temperature data at the current monitoring time and the preset reference ambient temperature is multiplied by the preset heat exchange coefficient and recorded as the second influence value at the current monitoring time. The product of the time interval from the completion of production of the paving material used at the current monitoring time to the current monitoring time and the preset temperature change rate is used as the third influence value at the current monitoring time. The sum of the first influence value, the second influence value and the third influence value is used as the current temperature baseline threshold. The mean and standard deviation of all vibration data collected during the time period formed by the current monitoring time and the preset historical monitoring time period before the current monitoring time are recorded as the current vibration mean and the current vibration standard deviation. The sum of the current vibration mean and the current vibration standard deviation by a preset multiple is used as the current vibration baseline threshold. The interval formed by the preset values ​​of plus or minus a certain percentage of the standard paving speed data is denoted as the speed baseline threshold interval.

3. The sensor-based water-stabilized paver operation data acquisition and segregation early warning system as described in claim 1, characterized in that, The absolute value of the difference between the current paving temperature data and the current temperature baseline threshold is the abnormal amplitude of the current paving temperature data. The result of subtracting the current vibration baseline threshold from the current vibration data is the abnormal amplitude of the current vibration data. The abnormal amplitude of the current abnormal data is greater than the corresponding threshold.

4. The method for acquiring the area to be analyzed at the current monitoring time in the sensor-based water-stabilized paver operation data acquisition and segregation early warning system as described in claim 1 includes: The location coordinates of the sensor that collects the current abnormal data at the current monitoring time are recorded as the sensor location point corresponding to the current abnormal data. For any current abnormal data, determine whether the number of current abnormal data within a preset first neighborhood of the sensor location point of the current abnormal data is greater than or equal to a preset data number threshold. If so, the preset first neighborhood of the sensor location point of the current abnormal data is taken as a processing area corresponding to the current monitoring time. Merge any overlapping regions among all regions to be processed at the current monitoring time, and record all regions obtained after merging as regions to be analyzed at the current monitoring time.

5. The sensor-based water-stabilized paver operation data acquisition and segregation early warning system as described in claim 4, characterized in that, The method for obtaining the temperature-vibration coupling intensity of the region to be analyzed at the current monitoring time includes: For any abnormal paving temperature data a in any area A to be analyzed at the current monitoring time: In the region A to be analyzed, abnormal vibration data within a preset second neighborhood of the sensor location point of the abnormal paving temperature data a are acquired and recorded as the associated abnormal vibration data corresponding to the abnormal paving temperature data a. The Gaussian kernel function between the sensor location point of the abnormal paving temperature data a and the sensor location point of the b-th associated abnormal vibration data corresponding to the abnormal paving temperature data a, the absolute value of the abnormal amplitude of the abnormal paving temperature data a, and the absolute value of the abnormal amplitude of the b-th associated abnormal vibration data are multiplied and recorded as the coupling strength between the abnormal paving temperature data a and the b-th associated abnormal vibration data. The average value of the coupling strength between the abnormal paving temperature data a and all associated abnormal vibration data corresponding to the abnormal paving temperature data a is recorded as the comprehensive coupling strength of the abnormal paving temperature data a. The average of the combined coupling strength of all abnormal paving temperature data in the region A to be analyzed is taken as the temperature vibration coupling strength of the region A to be analyzed.

6. The sensor-based water-stabilized paver operation data acquisition and segregation early warning system as described in claim 1, characterized in that, Methods for obtaining the segregation confidence level of the region to be analyzed at the current monitoring time include: For any region A to be analyzed at the current monitoring time: Will , , The weighted sum is used as the temperature segregation matching degree corresponding to the region A to be analyzed. , , The weighted sum is used as the particle size separation matching degree corresponding to the region A to be analyzed; As the first percentage value, The second proportion value is the ratio of the number of abnormal paving temperature data in the area to be analyzed to the total number of data in the area to be analyzed, and the second proportion value is the ratio of the number of abnormal vibration data in the area to be analyzed to the total number of data in the area to be analyzed. , min() is the function that finds the minimum value. The mean of the absolute values ​​of the abnormal amplitudes of all abnormal paving temperature data in the area to be analyzed, A. Let z be the mean of the absolute values ​​of the abnormal amplitudes of all abnormal vibration data in the region A to be analyzed, and z be a preset second temperature anomaly threshold. To preset the second vibration anomaly threshold; , , The temperature vibration coupling strength of the region A to be analyzed is given by c0, where c0 is a preset coupling strength reference threshold. Determine whether the current paving speed data is within the speed baseline threshold range. If it is not, use a constant 1 as the first index value; otherwise, use 0 as the first index value. Use the result of negative correlation mapping between the absolute value of the difference between the temperature vibration coupling intensity of the area to be analyzed A and the preset coupling intensity reference threshold as the second index value. Use the weighted sum of the first index value and the second index value as the mechanical segregation matching degree corresponding to the area to be analyzed A. The maximum value among the temperature segregation matching degree, particle size segregation matching degree, and mechanical segregation matching degree is selected as the segregation confidence degree of the region A to be analyzed.

7. The sensor-based water-stabilized paver operation data acquisition and segregation early warning system as described in claim 1, characterized in that, The method for obtaining the target area at the current monitoring time and the corresponding continuous region sequence includes: For any region A to be analyzed at the current monitoring time: Let the current monitoring time be denoted as the t-th monitoring time. Determine whether the region to be analyzed at the (t-1)-th monitoring time intersects with region A. If so, record the region to be analyzed that intersects with region A as the first historical region corresponding to region A. Continue determining whether the region to be analyzed at the (t-2)-th monitoring time intersects with the first historical region. If so, record the region to be analyzed that intersects with the first historical region as the second historical region corresponding to region A, and so on, until all regions to be analyzed at any given monitoring time no longer intersect with any historical region. Then, record all historical regions corresponding to region A. The sequence consisting of the historical region and the region to be analyzed A is denoted as the region sequence corresponding to the region to be analyzed A. The first region in the region sequence corresponding to the region to be analyzed A is the region to be analyzed A, and the (d+1)th region in the region sequence corresponding to the region to be analyzed A is the dth historical region corresponding to the region to be analyzed A. The time interval between the monitoring times corresponding to the first and last regions in the region sequence is denoted as the duration of the region sequence. It is determined whether the duration of the region sequence is greater than a preset duration threshold. If it is greater, the region to be analyzed A is denoted as the target region, and the region sequence of the region to be analyzed A is taken as the continuous region sequence corresponding to the target region.

8. The sensor-based water-stabilized paver operation data acquisition and segregation early warning system as described in claim 1, characterized in that, The method for obtaining the trend persistence characterization value of the target region includes: For any target region B: the normalized result of the time interval between the monitoring times corresponding to the first and last regions in the continuous region sequence corresponding to the target region B is recorded as the time duration characterization value; in the continuous region sequence corresponding to the target region B, a preset number of regions are marked once, and the marked regions are all recorded as the marked regions of the target region B, and the new sequence formed by the marked regions of the target region B is recorded as the continuous region subsequence; based on the area of ​​each region in the continuous region subsequence and the abnormal amplitude of the abnormal paving temperature data in each region, the region area change characterization value and the abnormal amplitude change characterization value are obtained, and the weighted sum of the time duration characterization value, the region area change characterization value, and the abnormal amplitude change characterization value is used as the trend duration characterization value of the target region B.

9. The sensor-based water-stabilized paver operation data acquisition and segregation early warning system as described in claim 8, characterized in that, The methods for obtaining the regional area change characterization value and the abnormal amplitude change characterization value include: Obtain the area change rate sequence corresponding to the persistent region subsequence, and record the normalized result of the mean of the area change rate sequence as the region area change characterization value. The k-th area change rate in the area change rate sequence is... , It is the absolute value of the area difference between the k-th region and the (k+1)-th region in the continuous region subsequence. The time interval between the monitoring time corresponding to the k-th region and the monitoring time corresponding to the (k+1)-th region is defined. The abnormal amplitude change rate sequence corresponding to the continuous region subsequence is obtained, and the normalized result of the mean of the abnormal amplitude change rate sequence is recorded as the abnormal amplitude change characterization value. The h-th abnormal amplitude change rate in the abnormal amplitude change rate sequence is defined as... , It is the absolute value of the difference between the mean of the abnormal amplitudes of all abnormal paving temperature data in the h-th region of the continuous region subsequence and the mean of the abnormal amplitudes of all abnormal paving temperature data in the (h+1)-th region. The time interval between the monitoring time corresponding to the h-th region and the monitoring time corresponding to the (h+1)-th region is given.

10. The sensor-based water-stabilized paver operation data acquisition and segregation early warning system as described in claim 1, characterized in that, The result of multiplying the trend persistence characterization value of the target region, the segregation confidence level, the total number of data in the target region, the area of ​​the target region, and the temperature vibration coupling strength of the target region are the segregation risk values ​​of the corresponding target region.

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