Flying dust concentration data acquisition method and system for pavement construction

Abstract

The invention relates to the technical field of data processing, in particular to a raised dust concentration data acquisition method and system for pavement construction, and the method comprises the steps: analyzing the fluctuation of the raised dust concentration in each time sequence segment, and analyzing the stability index of environmental data in each time sequence segment to obtain the influence index of the environmental data; analyzing the relative change rate and change trend of the dust raising concentration in each time sequence section to obtain the final abnormal degree of the dust raising state in each time sequence section; and taking the final abnormal degree as a data weight of the flying dust concentration in the corresponding time sequence segment to obtain a weighted flying dust concentration. According to the method, different weights are given to different dust concentration data, so that the influence of noise data is reduced, the importance of unstable data when abnormity occurs in the construction process is improved, and the accuracy of the collected dust concentration data is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method and system for collecting dust concentration data in road construction. Background Art

[0002] The dust concentration in the highway construction area is the basis for the dynamic regulation of water demand for the automatic sprinkler. Predictive regulation is usually achieved by installing sensors to monitor the temperature, humidity, wind speed and other meteorological parameters in real time. Sudden weather changes, temperature fluctuations or sharp changes in wind speed in the construction area will cause fluctuations in sensor monitoring data. These environmental changes cause data fluctuations, which are important reference data for dynamic regulation of water demand.

[0003] However, the monitoring data of sensors installed in and around the construction area will inevitably be affected by construction activities, and may also be affected by the measurement errors of the sensors themselves, resulting in abnormal noise data fluctuations in the monitoring data. When the data fluctuations caused by environmental changes are not accurately distinguished from the noise data fluctuations, the accuracy of dust concentration data collection will be affected. Summary of the invention

[0004] In order to solve the above technical problems, the purpose of the present invention is to provide a method and system for collecting dust concentration data in road construction.

[0005] According to a first aspect of an embodiment of the present application, a method for collecting dust concentration data for road construction is provided, and the technical solution adopted specifically includes: Collecting environmental data and dust concentration in a road construction area, and dividing the environmental data and dust concentration into multiple time series segments; Analyze the volatility of the dust concentration in each time segment to obtain the apparent abnormality of the dust state in each time segment; Analyze the stability index of the environmental data in each time series segment, and combine the apparent abnormality degree to obtain the impact index of the environmental data; Analyze the relative change rate of the dust concentration in each time segment, combine the impact index of the environmental data and the apparent abnormality level, and obtain the comprehensive abnormality level of the dust state in each time segment; Analyze the change trend of the dust concentration in each time segment, and combine the comprehensive abnormality level to obtain the final abnormality level of the dust state in each time segment; The final abnormality degree is used as the data weight of the dust concentration in the corresponding time segment to obtain the weighted dust concentration.

[0006] In some embodiments of the present invention, the environmental data and the dust concentration are divided into a plurality of time series segments, including: Draw a dust concentration curve based on the time series data sequence composed of the dust concentration; Detecting peak points and valley points based on the dust concentration curve; Set a screening threshold, and select peak points and valley points that are smaller than the screening threshold as segmentation points; The environmental data and the dust concentration are divided into a plurality of time series segments, with the time at which the segmentation point is located as the segmentation time.

[0007] In some embodiments of the present invention, analyzing the volatility of the dust concentration in each time segment to obtain the apparent abnormality of the dust state in each time segment includes: Obtaining the severity of dust in each time segment according to the maximum value of the dust concentration in each time segment; The fluctuation range of the dust concentration in each time segment is obtained, and the apparent abnormality of the dust state in each time segment is obtained by combining the dust severity of each time segment and the duration of each time segment.

[0008] In some embodiments of the present invention, the fluctuation range is the difference between the maximum value and the minimum value of the dust concentration in the time sequence period.

[0009] In some embodiments of the present invention, analyzing the stability index of the environmental data in each time segment includes: The correlation between the mean and the standard deviation of the environmental data in each time series segment is analyzed to obtain a stability index of the environmental data in each time series segment.

[0010] In some embodiments of the present invention, analyzing the stability index of the environmental data in each time series segment and combining the apparent abnormality degree to obtain the impact index of the environmental data includes: Analyzing the stability index of the environmental data in each of the time series segments; Analyzing the rate of change of the dust concentration in each time sequence segment according to the apparent abnormality degree corresponding to the adjacent time sequence segments; According to the stability index and in combination with the change rate, an impact index of the environmental data is obtained.

[0011] In some embodiments of the present invention, the relative change rate of the dust concentration in each time segment is analyzed, and the impact index of the environmental data and the apparent abnormality degree are combined to obtain the comprehensive abnormality degree of the dust state in each time segment, including: Based on the apparent abnormality degree, cluster analysis is performed on all the time series segments to obtain multiple clusters; Analyze the ratio between the change rate of dust concentration in each time segment and the change rate of dust concentration in other time segments in the cluster where it is located, and obtain the relative change rate of dust concentration in each time segment; According to the relative change rate and in combination with the impact index of the environmental data, a comprehensive impact index of the environmental data in each time series segment is obtained; According to the comprehensive impact index and in combination with the apparent abnormality degree, the comprehensive abnormality degree of the dust state in each time segment is obtained.

[0012] In some embodiments of the present invention, analyzing the change trend of the dust concentration in each time segment, combining the comprehensive abnormality degree, and obtaining the final abnormality degree of the dust state in each time segment, includes: Based on the maximum value of the dust concentration in each time sequence segment, the dust concentration in each time sequence segment is divided into a target sequence and a reference sequence; Performing straight line fitting on the target sequence and the reference sequence respectively to obtain corresponding fitting errors and fitting slopes; Analyze the difference in the discreteness of the target sequence and the reference sequence, and combine the fitting error and the fitting slope to obtain the change trend of the dust concentration in each time segment; According to the change trend and in combination with the comprehensive abnormality degree, the final abnormality degree of the dust state in each time sequence segment is obtained.

[0013] According to a second aspect of an embodiment of the present application, a dust concentration data collection system for road construction is provided, and the technical solution adopted is specifically as follows: the system includes: a memory and a processor, wherein: The memory is used to store program code; The processor is used to read the program code stored in the memory and execute the method for collecting dust concentration data for road construction as described in the first aspect of the embodiment of the present application.

[0014] In some embodiments of the present invention, the processor comprises: A data collection and processing module, used to collect environmental data and dust concentration in the road construction area, and divide the environmental data and dust concentration into multiple time segments; The dust state abnormality degree analysis module is used to analyze the volatility of the dust concentration in each time segment to obtain the apparent abnormality degree of the dust state in each time segment; and analyze the stability index of the environmental data in each time segment, and combine the apparent abnormality degree to obtain the influence index of the environmental data; and analyze the relative change rate of the dust concentration in each time segment, and combine the influence index of the environmental data and the apparent abnormality degree to obtain the comprehensive abnormality degree of the dust state in each time segment; and analyze the change trend of the dust concentration in each time segment, and combine the comprehensive abnormality degree to obtain the final abnormality degree of the dust state in each time segment; The data correction module is used to use the final abnormality degree as the data weight of the dust concentration in the corresponding time segment to obtain the weighted dust concentration.

[0015] Compared with the prior art, the method and system for collecting dust concentration data during road construction provided by the present invention have the following beneficial effects: 1. The present invention takes into account the different environmental characteristics in different time periods. Therefore, by dividing the environmental data and dust concentration into multiple time segments, and then taking the data in each time segment as the analysis object, the influence weight of the environmental data in each time segment on the dust concentration data is finally obtained, thereby improving the accuracy of the influence weight.

[0016] 2. The present invention obtains the specific manifestations of the external environmental impact in each time period, and further reflects the real performance of the external environment through the dust performance in adjacent time periods; according to the real performance, find out the time period with similar environmental impact characteristics, and each part may correspond to a specific environmental performance; analyze and obtain the relative impact of the environment in the time period to obtain a comprehensive evaluation index, so as to evaluate the comprehensive abnormal degree of the environmental impact on the dust concentration, thereby improving the reliability of the environmental impact on the dust concentration; based on the fact that the impact of noise on the dust concentration data is generally short-term, by analyzing the changing trend of the dust concentration in each time segment, combined with the comprehensive abnormal degree of the impact on the dust concentration, the final abnormal degree of the dust state in each time segment is obtained, thereby improving the reliability of the abnormal degree of the dust concentration.

[0017] 3. The present invention obtains weighted dust concentration by taking the final abnormality degree as the data weight of dust concentration in the corresponding time segment, completes the dust concentration data collection of road construction, and can effectively improve the accuracy of dust concentration data collection. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 A basic flow chart of a method for collecting dust concentration data during road construction provided by an embodiment of the present invention; Figure 2 A schematic diagram of a dust concentration data collection system for road construction provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0020] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the dust concentration data collection method and system for road construction proposed by the present invention, its specific implementation method, structure, characteristics and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present invention belongs. Terms such as "comprises", "comprising" or any other variants thereof are intended to cover non-exclusive inclusion, so that a circuit structure, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such article or device. In the absence of further restrictions, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the article or device including the element.

[0022] The following specifically describes a method and system for collecting dust concentration data during road construction provided by the present invention in conjunction with the accompanying drawings.

[0023] See also Figure 1 , which shows the basic process of a method for collecting dust concentration data for road construction provided by an embodiment of the present invention.

[0024] like Figure 1 As shown, an embodiment of the present invention provides a method for collecting dust concentration data during road construction, comprising: S100: Collect environmental data and dust concentration in the road construction area, and divide the environmental data and dust concentration into multiple time series segments.

[0025] First, high-precision temperature and humidity sensors, wind speed monitors, and dust concentration detectors are installed in the road construction area to collect environmental data and dust concentration in real time. Environmental data includes temperature, humidity, and wind speed. The collection frequency of all these parameters is set to record every 10 seconds to ensure the real-time and accuracy of the data. It should be noted that the water volume adjustment of the automatic sprinkler system is mainly based on these environmental parameters. Specifically, when the temperature in the construction area rises or the wind speed increases, more dust may be caused. At this time, the system needs to be adjusted to increase the water flow; when the humidity rises or the wind speed decreases, the dust phenomenon will usually decrease, and the system needs to reduce the water flow accordingly.

[0026] Then, the environmental data and dust concentration are divided into multiple time series segments, further including: drawing a dust concentration curve for the time series data sequence composed of dust concentration; using a peak detection algorithm based on the dust concentration curve to detect the peak points and valley points of the dust concentration; setting a screening threshold, wherein the screening value can be set to the environmental dust index value under the construction environment standard, and screening the peak points and valley points less than the screening threshold as split points; taking the time of the split point as the split time, dividing the environmental data and dust concentration into multiple time series segments.

[0027] S200: Analyze the volatility of dust concentration in each time segment to obtain the apparent abnormality of the dust state in each time segment.

[0028] Environmental data may change significantly when encountering dramatic weather changes, strong winds, or a sudden increase in human activities. For example, sudden high temperatures or strong winds may temporarily change the microclimate conditions in a specific area, triggering a series of chain reactions such as increased dust concentrations, which manifests as irregular fluctuations in environmental data obtained through monitoring. These changes not only affect the stability of nature, but may also affect the normal operations in the construction area. Fluctuation data is crucial for accurately assessing the degree of environmental abnormality.

[0029] Therefore, the actual volatility of dust concentration caused by various reasons is first analyzed. In the embodiment of the present invention, the apparent abnormality of the dust state in each time segment is obtained by analyzing the volatility of dust concentration in each time segment. Further including: According to the maximum value of dust concentration in each time segment, the severity of dust in each time segment is obtained. The difference between the maximum dust concentration in a time segment and the mean of the maximum dust concentration in all time segments is obtained. The severity of dust in each time series. The calculation formula for the severity of dust in each time segment is: In the formula, Indicates The severity of dust in each time series; Indicates The maximum value of all dust concentrations in a time series; It represents the average of the maximum dust concentration corresponding to all time series.

[0030] The greater the severity of dust, the The dust is serious during the corresponding time of each time segment. The greater and more drastic the change in dust concentration within the time segment, and the longer the time segment is, the more abnormal the dust state in the time segment is. The construction area needs to make major changes in the environmental pattern and alleviate the impact of human activities.

[0031] Therefore, by obtaining the fluctuation range of dust concentration in each time segment, combined with the dust severity of each time segment and the duration of each time segment, the apparent abnormality of the dust state in each time segment is obtained, where the fluctuation range is the difference between the maximum and minimum dust concentrations in the time segment. The calculation formula for the apparent abnormal degree of dust state in a time series is: In the formula, Indicates The apparent abnormality of dust status in a time series; Indicates The severity of dust in each time series; Indicates The duration of each time segment; Indicates The fluctuation range of dust concentration in a time series, that is, The difference between the maximum and minimum dust concentrations in a time series; represents the linear normalization function.

[0032] Dust severity during the time series The larger the value, the more serious the dust is, and the greater the apparent abnormality of the dust state in the time series; the length of the time series The larger the value, the longer the time segment lasts, and the greater the apparent abnormality of the dust state in the time segment; the fluctuation range of dust concentration in the time segment The larger the value, the greater the variation of dust concentration in the time period, the greater the instability and volatility of dust concentration, and the greater the apparent abnormality of dust state in the time period.

[0033] Environmental changes and construction activities will have a certain impact on the change of dust concentration. Therefore, when considering the dust concentration state, it is also necessary to consider whether the construction has a significant impact on the dust state, which is conducive to reflecting the true state of dust. Specifically: First, obtain the specific manifestation of the external environmental impact of each time segment, and further reflect the true performance of the external environment through the dust performance of adjacent time segments. According to the true performance, find time segments with similar environmental impact characteristics, and each part may correspond to a specific environmental performance. Analyze and obtain the relative impact of the environment of each time segment, and obtain a comprehensive evaluation index to evaluate the comprehensive abnormality of the environment on the dust concentration time series. Specifically include step S300 and step S400.

[0034] S300: Analyze the stability index of the environmental data in each time series segment, and combine the apparent abnormality degree to obtain the impact index of the environmental data.

[0035] First, analyze the stability index of the environmental data in each time segment, that is, analyze whether there are drastic changes in the environment in each time segment. Further including: analyzing the correlation between the mean and standard deviation of the environmental data in each time segment, obtaining a stability performance of the environmental data in each time segment, and obtaining the stability index of the environmental data in each time segment. The specific implementation method is: The stability index calculation formula for constructing temperature data is: In the formula, Indicates Stability index of temperature data within a time series segment; Indicates The mean value of temperature data in a time series segment; Indicates The variance of temperature data within a time series segment; represents the linear normalization function.

[0036] The larger the The mean and variance of the temperature data in this time series are large, which indicates that the ambient temperature changes greatly in this time series, and this change is unlikely to be caused by noise, but more likely to be caused by actual environmental changes. Therefore, the temperature data in this time series is considered to be more reliable.

[0037] The stability index calculation formula for constructing humidity data is: In the formula, Indicates Stability index of humidity data within a time series; Indicates The mean value of humidity data in a time series; Indicates The variance of humidity data in each time series; represents the linear normalization function.

[0038] The larger the The variance of the humidity data in a time series is larger than its mean, which means that the humidity fluctuates greatly in this time series, and this change is not caused by noise, but more likely caused by actual environmental changes. Therefore, the humidity data in this time series is considered to be more reliable.

[0039] The calculation formula for the stability index of wind speed data is: In the formula, Indicates Stability index of wind speed data within a time series; Indicates The mean value of wind speed data in a time series; Indicates The variance of wind speed data within a time series; represents the linear normalization function.

[0040] The larger the The mean and variance of the wind speed data in this time series are large, which indicates that the wind speed changes greatly in this time series, and this change is unlikely to be caused by noise, but more likely to be caused by actual environmental changes. Therefore, the wind speed data in this time series is considered to be more reliable.

[0041] Then, the stability index of the environmental data in each time series is combined with the apparent abnormality to obtain the impact index of the environmental data. Further analysis includes: First, according to the apparent abnormality corresponding to the adjacent time segments, the change rate of dust concentration in each time segment is analyzed. The time segment and The difference in apparent abnormality corresponding to the time series segment, and the calculation of the The time segment and The difference in apparent abnormality corresponding to the time series segment is then used as the ratio of the two differences to obtain the first The rate of change of dust concentration in each time series. The calculation formula for the change rate of dust concentration in each time series is: In the formula, Indicates The rate of change of dust concentration within a time series; Indicates The apparent abnormality of dust status in a time series; Indicates The apparent abnormality of dust status in a time series; Indicates The apparent abnormality of dust status in a time series; This is to prevent the denominator from being 0.

[0042] The larger the value, the more likely it is that a sudden environmental event or construction activity occurred during the time series, such as construction exacerbating dust performance.

[0043] Then, based on the stability index and the change rate, the impact index of the environmental data is obtained. The specific implementation method is as follows: The calculation formula for the influence index of constructed temperature data is: In the formula, Indicates The impact index of temperature data in a time series segment; Indicates The rate of change of dust concentration in each time series; Indicates The stability index of temperature data within a time series.

[0044] The larger the value is, the greater the impact index of temperature data is, indicating that a sudden environmental event or construction activity has occurred in the time series; The larger the value is, the greater the change in ambient temperature is during this time period, and this change is less likely to be caused by noise, but more likely to be caused by actual environmental changes, indicating that the impact index of temperature data is greater.

[0045] Similarly, the calculation formula for constructing the impact index of humidity data is: In the formula, Indicates The impact index of humidity data in a time series; Indicates The rate of change of dust concentration in each time series; Indicates The stability index of humidity data within a time series.

[0046] The larger the value is, the greater the impact index of humidity data is, indicating that a sudden environmental event or construction activity has occurred in the time series; The larger the value is, the greater the change in ambient humidity is during this time series, and this change is less likely to be caused by noise, but more likely to be caused by actual environmental changes, indicating that the impact index of humidity data is greater.

[0047] Similarly, the calculation formula for constructing the influence index of wind speed data is: In the formula, Indicates The influence index of wind speed data in a time series; Indicates The rate of change of dust concentration in each time series; Indicates The stability index of wind speed data within a time series.

[0048] The larger the value is, the greater the impact index of wind speed data is, indicating that a sudden environmental event or construction activity has occurred in the time series; The larger the value is, the greater the change in wind speed is during this time series, and this change is less likely to be caused by noise, but more likely to be caused by actual environmental changes, indicating that the impact index of wind speed data is greater.

[0049] S400: Analyze the relative change rate of dust concentration in each time segment, combine the impact index and the apparent abnormality level, and obtain the comprehensive abnormality level of the dust state in each time segment.

[0050] Analyze the relative change rate of dust concentration in each time segment, combine the impact index and apparent abnormality, and obtain the comprehensive abnormality of dust status in each time segment. Further analysis includes: First, based on the apparent abnormality degree, all time series segments are clustered and analyzed to obtain multiple clusters.

[0051] Then, the ratio of the change rate of dust concentration in each time segment to the change rate of dust concentration in other time segments in the cluster where it is located is analyzed to obtain the relative change rate of dust concentration in each time segment; The calculation formula for the relative change rate of dust concentration in a time series is: In the formula, Indicates The time segment is relative to the The relative change rate of dust concentration in other time series within a cluster; Indicates The rate of change of dust concentration within a time series; Indicates The time segment belongs to The other The rate of change of dust concentration in each time series; Indicates The time segment belongs to The number of time series segments within a cluster; This is to prevent the denominator from being 0.

[0052] The larger the value, the more The more significant the negative impact of construction activities on the environment in a time sequence, the more likely it is that special attention should be paid and corresponding mitigation measures should be taken.

[0053] According to the relative change rate and the impact index, the comprehensive impact index of the environmental data in each time series is obtained; the specific implementation method is as follows: The calculation formula for the comprehensive impact index of temperature data is: In the formula, Indicates Comprehensive impact index of temperature data in a time series; Indicates The impact index of temperature data in a time series segment; Indicates The time segment is relative to the The relative change rate of dust concentration in other time series within the cluster.

[0054] A larger value may indicate a greater risk of potential impact of ambient temperature on construction activities during this time period.

[0055] The calculation formula for the comprehensive impact index of humidity data is: In the formula, Indicates The comprehensive impact index of humidity data in a time series; Indicates The impact index of humidity data in a time series; Indicates The time segment is relative to the The relative change rate of dust concentration in other time series within the cluster.

[0056] A larger value may indicate a greater risk of potential impact of ambient humidity on construction activities during this time period.

[0057] The calculation formula for constructing the comprehensive impact index of wind speed data is: In the formula, Indicates Comprehensive impact index of wind speed data in a time series; Indicates The influence index of wind speed data in a time series; Indicates The time segment is relative to the The relative change rate of dust concentration in other time series within the cluster.

[0058] A larger value may indicate that the risk of potential impact of ambient wind speed on construction activities is greater during this time period.

[0059] Finally, based on the comprehensive impact index and the apparent abnormality, the comprehensive abnormality of the dust state in each time series is obtained. The calculation formula for the comprehensive abnormal degree of dust status in a time series is: In the formula, Indicates The comprehensive abnormal degree of dust status in a time series; Indicates The apparent abnormality of dust status in a time series; Indicates Comprehensive impact index of temperature data in a time series; Indicates The comprehensive impact index of humidity data in a time series; Indicates Comprehensive impact index of wind speed data in a time series; represents the linear normalization function.

[0060] S500: Analyze the change trend of dust concentration in each time segment, and combine the comprehensive abnormality degree to obtain the final abnormality degree of the dust state in each time segment.

[0061] When the construction area encounters drastic weather changes, strong winds, or sudden human activities, wind speed and dust concentration tend to increase immediately, and then decrease and gradually stabilize as these external influences ease. However, the time series of dust concentration may cause short-term fluctuations in the data due to noise, thereby reducing the credibility of the data in the analysis.

[0062] Therefore, by analyzing the changing trend of dust concentration in each time segment and combining the comprehensive abnormality degree, the final abnormality degree of dust state in each time segment is obtained. Further analysis includes: First, based on the maximum value of dust concentration in each time series segment, the dust concentration in each time series segment is divided into a target sequence and a reference sequence. STL decomposition (Seasonal and Trend decomposition using Loess) is performed on each time series segment to obtain the trend item.

[0063] In the trend item, the sequence consisting of the maximum value in the trend item and all the data before the maximum value is recorded as the target sequence, and the sequence consisting of the maximum value in the trend item and all the data after the maximum value is recorded as the reference sequence. If there are multiple maximum values, the first maximum value that appears is taken. The degree of abnormality of dust concentration can be analyzed by comparing the target sequence and the reference sequence. For example, if the standard deviation of the target sequence is significantly higher than that of the reference sequence, this may indicate that the dust concentration fluctuated greatly during the period before the maximum value, resulting in a higher degree of abnormality. In addition, the dust concentration in the reference sequence has a large decline rate, indicating that the dust concentration drops rapidly after reaching the peak, which may be related to sudden environmental changes or human intervention.

[0064] Then, the target sequence and the reference sequence are fitted with straight lines to obtain the corresponding fitting error and fitting slope.

[0065] Then, the difference in the degree of discreteness between the target sequence and the reference sequence is analyzed, and the changing trend of dust concentration in each time series is obtained by combining the fitting error and fitting slope.

[0066] Finally, according to the change trend and the comprehensive abnormality degree, the final abnormality degree of dust state in each time series is obtained. The calculation formula for the final abnormal degree of dust status in a time series is: In the formula, Indicates The final abnormal degree of dust status in a time series; Indicates The comprehensive abnormal degree of dust status in a time series; Indicates The normalized value of the sum of the fitting errors of the target sequence and the reference sequence in a time series segment; Indicates The standard deviation of dust concentration data in the target sequence within a time series; Indicates The standard deviation of dust concentration data in the reference sequence within a time series; Indicates The fitting slope of the dust concentration data in the reference sequence within a time series segment.

[0067] It represents the absolute difference between the standard deviation of the target sequence and the reference sequence. The larger the difference, the greater the difference between the two sequences, that is, the greater the difference between the rising and falling trends, and the higher the degree of abnormality. The larger it is, the faster the dust concentration drops, that is, the period is a sudden drop caused by drastic changes in the external environment, reflecting a stronger degree of abnormality. It indicates the size of the fitting slope considering only the reference sequence; The degree of abnormality is weighted and corrected. The smaller the fitting error, the more reliable the data.

[0068] S600: The final abnormality degree is used as the data weight of the dust concentration in the corresponding time series segment to obtain the weighted dust concentration.

[0069] The final abnormal degree is used as the data weight of the dust concentration in the corresponding time series segment to obtain the weight of each dust concentration in each time series segment, that is, the weighted dust concentration. The weighted dust concentration corresponding to each dust concentration in the dust concentration time series sequence is obtained.

[0070] Subsequently, implementers can also use the weighted ARIMA model to predict the dust concentration time series and obtain the predicted data. The predicted data of dust concentration is input into the PID controller, and a control instruction for the current spray flow is output; the control instruction for the current spray flow is used to control the spray flow at the next moment of the current moment. Similarly, the control instruction for the spray flow at each moment is obtained to complete the dynamic control of the spray flow of the construction site spray system.

[0071] Based on the same inventive concept as the above method, this embodiment also provides a dust concentration data collection system for road construction.

[0072] Figure 2 A schematic diagram of a dust concentration data collection system for road construction provided by an embodiment of the present invention is shown in FIG. Figure 2As shown, the system includes: a memory 10 and a processor 20, wherein: the memory 10 is used to store program codes; the processor 20 is used to read the program codes stored in the memory, and execute to collect environmental data and dust concentration of the road construction area, and divide the environmental data and dust concentration into multiple time segments; analyze the volatility of the dust concentration in each time segment to obtain the apparent abnormality of the dust state in each time segment; analyze the stability index of the environmental data in each time segment, and combine the apparent abnormality to obtain the influence index of the environmental data; analyze the relative change rate of the dust concentration in each time segment, and combine the influence index and the apparent abnormality to obtain the comprehensive abnormality of the dust state in each time segment; analyze the change trend of the dust concentration in each time segment, and combine the comprehensive abnormality to obtain the final abnormality of the dust state in each time segment; use the final abnormality as the data weight of the dust concentration in the corresponding time segment to obtain the weighted dust concentration.

[0073] Furthermore, the processor 20 includes a data collection and processing module 21, a dust state abnormality degree analysis module 22 and a data correction module 23. Among them: The data collection and processing module 21 is used to collect environmental data and dust concentration in the road construction area, and divide the environmental data and dust concentration into multiple time series segments; The dust state abnormality degree analysis module 22 is used to analyze the volatility of dust concentration in each time segment to obtain the apparent abnormality degree of the dust state in each time segment; and analyze the stability index of environmental data in each time segment, combined with the apparent abnormality degree, to obtain the influence index of environmental data; and analyze the relative change rate of dust concentration in each time segment, combined with the influence index and the apparent abnormality degree, to obtain the comprehensive abnormality degree of the dust state in each time segment; and analyze the change trend of dust concentration in each time segment, combined with the comprehensive abnormality degree, to obtain the final abnormality degree of the dust state in each time segment; The data correction module 23 is used to use the final abnormality degree as the data weight of the dust concentration in the corresponding time series section to obtain the weighted dust concentration.

[0074] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0075] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A method for collecting dust concentration data during road construction, characterized in that: The method comprises: Collecting environmental data and dust concentration in a road construction area, and dividing the environmental data and dust concentration into multiple time series segments; Analyze the volatility of the dust concentration in each time segment to obtain the apparent abnormality of the dust state in each time segment; Analyze the stability index of the environmental data in each time series segment, and combine the apparent abnormality degree to obtain the impact index of the environmental data; Analyze the relative change rate of the dust concentration in each time segment, combine the impact index of the environmental data and the apparent abnormality level, and obtain the comprehensive abnormality level of the dust state in each time segment; Analyze the change trend of the dust concentration in each time segment, and combine the comprehensive abnormality level to obtain the final abnormality level of the dust state in each time segment; The final abnormality degree is used as the data weight of the dust concentration in the corresponding time segment to obtain the weighted dust concentration.

2. The method for collecting dust concentration data during road construction according to claim 1, characterized in that: Dividing the environmental data and the dust concentration into multiple time series segments, including: Draw a dust concentration curve based on the time series data sequence composed of the dust concentration; Detecting peak points and valley points based on the dust concentration curve; Set a screening threshold, and select peak points and valley points that are smaller than the screening threshold as segmentation points; The environmental data and the dust concentration are divided into a plurality of time series segments, with the time at which the segmentation point is located as the segmentation time.

3. The method for collecting dust concentration data during road construction according to claim 1, characterized in that: Analyze the volatility of the dust concentration in each time segment to obtain the apparent abnormality of the dust state in each time segment, including: Obtaining the severity of dust in each time segment according to the maximum value of the dust concentration in each time segment; The fluctuation range of the dust concentration in each time segment is obtained, and the apparent abnormality of the dust state in each time segment is obtained by combining the dust severity of each time segment and the duration of each time segment.

4. The method for collecting dust concentration data during road construction according to claim 3, characterized in that: The fluctuation range is the difference between the maximum value and the minimum value of the dust concentration in the time sequence period.

5. The method for collecting dust concentration data during road construction according to claim 1, characterized in that: Analyzing the stability index of the environmental data in each time series segment, including: The correlation between the mean and the standard deviation of the environmental data in each time series segment is analyzed to obtain a stability index of the environmental data in each time series segment.

6. The method for collecting dust concentration data during road construction according to claim 5, characterized in that: Analyzing the stability index of the environmental data in each time series segment and combining the apparent abnormality degree to obtain the impact index of the environmental data includes: Analyzing the stability index of the environmental data in each of the time series segments; Analyzing the rate of change of the dust concentration in each time sequence segment according to the apparent abnormality degree corresponding to the adjacent time sequence segments; According to the stability index and in combination with the change rate, an impact index of the environmental data is obtained.

7. The method for collecting dust concentration data during road construction according to claim 1, characterized in that: Analyze the relative change rate of the dust concentration in each time segment, combine the impact index of the environmental data and the apparent abnormality, and obtain the comprehensive abnormality of the dust state in each time segment, including: Based on the apparent abnormality degree, cluster analysis is performed on all the time series segments to obtain multiple clusters; Analyze the ratio between the change rate of dust concentration in each time segment and the change rate of dust concentration in other time segments in the cluster where it is located, and obtain the relative change rate of dust concentration in each time segment; According to the relative change rate and in combination with the impact index of the environmental data, a comprehensive impact index of the environmental data in each time series segment is obtained; According to the comprehensive impact index and in combination with the apparent abnormality degree, the comprehensive abnormality degree of the dust state in each time segment is obtained.

8. The method for collecting dust concentration data during road construction according to claim 1, characterized in that: Analyze the change trend of the dust concentration in each time segment, and combine the comprehensive abnormality level to obtain the final abnormality level of the dust state in each time segment, including: Based on the maximum value of the dust concentration in each time sequence segment, the dust concentration in each time sequence segment is divided into a target sequence and a reference sequence; Performing straight line fitting on the target sequence and the reference sequence respectively to obtain corresponding fitting errors and fitting slopes; Analyze the difference in the discreteness of the target sequence and the reference sequence, and combine the fitting error and the fitting slope to obtain the change trend of the dust concentration in each time segment; According to the change trend and in combination with the comprehensive abnormality degree, the final abnormality degree of the dust state in each time sequence segment is obtained.

9. A dust concentration data collection system for road construction, characterized in that: The system comprises: a memory and a processor, wherein: The memory is used to store program code; The processor is used to read the program code stored in the memory and execute the dust concentration data collection method for road construction according to any one of claims 1 to 8.

10. The dust concentration data collection system for road construction according to claim 9, characterized in that: The processor comprises: A data collection and processing module, used to collect environmental data and dust concentration in the road construction area, and divide the environmental data and dust concentration into multiple time segments; The dust state abnormality degree analysis module is used to analyze the volatility of the dust concentration in each time segment to obtain the apparent abnormality degree of the dust state in each time segment; and analyze the stability index of the environmental data in each time segment, and combine the apparent abnormality degree to obtain the influence index of the environmental data; and analyze the relative change rate of the dust concentration in each time segment, and combine the influence index of the environmental data and the apparent abnormality degree to obtain the comprehensive abnormality degree of the dust state in each time segment; and analyze the change trend of the dust concentration in each time segment, and combine the comprehensive abnormality degree to obtain the final abnormality degree of the dust state in each time segment; The data correction module is used to use the final abnormality degree as the data weight of the dust concentration in the corresponding time segment to obtain the weighted dust concentration.

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