CN116824867A - Multi-source highway facility data signal optimization collection processing method - Google Patents
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Abstract
The invention relates to the technical field of data processing, and provides a multisource highway facility data signal optimization collection processing method, which comprises the following steps: collecting a plurality of vehicle flow data, noise data and temperature data; acquiring a first time deviation degree of each vehicle flow data and a second time deviation degree of each noise data according to the actual sampling time and the theoretical sampling time; obtaining the difference degree of each vehicle flow data according to the vehicle flow data and the noise data at adjacent sampling moments; acquiring the abnormality degree of each vehicle flow data according to the difference degree, the first time deviation degree, the second time deviation degree and the temperature data; and screening abnormal traffic flow data according to the abnormal degree of the traffic flow data, and reacquiring the abnormal traffic flow data to realize the optimized collection and processing of the multi-source highway facility data signals. The invention aims to solve the problem of inaccurate detection results caused by temperature and noise influence in the road traffic flow data detection process.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a multisource highway facility data signal optimization collection processing method.
Background
In the collection processing scene of multisource highway facility data, the collection vehicle flow data often uses vehicle detector to monitor the vehicle flow, and current common vehicle detector includes induction coil detector, ultrasonic wave detector, microwave radar detector, infrared detector and sound detector, and various vehicle detectors all detect vehicle data based on the detection principle of self directly perceivedly, therefore carry out the vehicle flow monitoring in-process through the vehicle detector, can't get rid of external factor interference to the influence of detection and testing result.
In the existing method, in the process of monitoring traffic flow data by adopting an ultrasonic vehicle detector, traffic jam conditions in high-temperature weather can cause limited measuring distance of the detector due to high-temperature influence, so that the working range of a sensor is influenced, and the traffic flow data cannot be accurately monitored; meanwhile, noise existing in the environment also can interfere with the accuracy of the ultrasonic vehicle detector, so that the traffic flow data which possibly exceeds the standard cannot be accurately monitored, and more serious traffic jams are caused, and therefore, the acquisition of the data signals of the multi-source highway facilities needs to be optimized, and the interference of external factors on the data acquisition is avoided.
Disclosure of Invention
The invention provides a multisource highway facility data signal optimization collection processing method, which aims to solve the problem of inaccurate detection results caused by temperature and noise influence in the existing highway traffic flow data detection process, and adopts the following specific technical scheme:
one embodiment of the invention provides a multi-source highway facility data signal optimization collection processing method, which comprises the following steps:
collecting a plurality of vehicle flow data and corresponding noise data and temperature data;
acquiring a first time deviation degree of each vehicle flow data and a second time deviation degree of each noise data according to the actual sampling time and the theoretical sampling time;
obtaining the difference degree of each vehicle flow data according to the vehicle flow data and the noise data at adjacent sampling moments; acquiring the abnormality degree of each vehicle flow data according to the difference degree, the first time deviation degree, the second time deviation degree and the temperature data;
and screening abnormal traffic flow data according to the abnormal degree of the traffic flow data, and reacquiring the abnormal traffic flow data to realize the optimized collection and processing of the multi-source highway facility data signals.
Further, the method for collecting a plurality of vehicle flow data, corresponding noise data and temperature data comprises the following specific steps:
acquiring the passing number of vehicles under one road section per minute through an ultrasonic vehicle detector to obtain the vehicle flow data per minute; and under the same data acquisition time interval, acquiring noise data corresponding to each vehicle flow data through a noise monitor, and acquiring temperature data corresponding to each vehicle flow data through a high-precision thermometer.
Further, the specific method for obtaining the first time deviation degree of each vehicle flow data and the second time deviation degree of each noise data includes:
taking any one piece of traffic flow data as target traffic flow data, marking the sampling time corresponding to the stipulated sampling time of the target traffic flow data as the theoretical sampling time of the target traffic flow data, and marking the actual sampling time of the target traffic flow data as the actual sampling time of the target traffic flow data; acquiring theoretical sampling time and actual sampling time of each vehicle flow data;
obtaining theoretical sampling time and actual sampling time of each noise data according to the specified sampling time and the actual acquisition time of the noise monitor;
and obtaining a first time deviation degree of each vehicle flow data and a second time deviation degree of each noise data according to the theoretical sampling time and the actual sampling time.
Further, the method for obtaining the first time deviation degree of each vehicle flow data and the second time deviation degree of each noise data includes the following specific steps:
wherein ,/>Indicate->Actual sampling instant of the individual traffic flow data, +.>Indicate->Theoretical sampling moment of the individual traffic flow data, +.>Indicate->Order value of the acquisitions corresponding to the individual traffic data,/-for each of the plurality of traffic data>Indicate->Actual sampling instant of the individual traffic flow data, +.>Indicate->Theoretical sampling moment of the individual traffic flow data, +.>Representing absolute value;
acquiring a first time deviation degree of each vehicle flow data;
and acquiring a second time deviation degree of each piece of noise data according to the theoretical sampling moment and the actual sampling moment of each piece of noise data.
Further, the specific method for obtaining the second time deviation degree of each noise data includes:
and calculating the time deviation degree of the noise data according to the theoretical sampling time and the actual sampling time of the noise data, and recording the obtained result as a second time deviation degree of each noise data.
Further, the method for obtaining the difference degree of each vehicle flow data comprises the following specific steps:
acquiring the variation degree of each vehicle flow data according to the vehicle flow data and the noise data at adjacent sampling moments; first, the
Degree of difference in individual traffic flow data
The calculation method of (1) is as follows:
wherein ,/>Indicate->Degree of variation of individual traffic data, +.>Indicate->Order value of the acquisitions corresponding to the individual traffic data,/-for each of the plurality of traffic data>Indicate->Degree of variation of individual traffic data, +.>Representing absolute value;
the degree of difference of each traffic flow data is acquired, and the degree of difference of the first traffic flow data is set to 0.
Further, the specific method for obtaining the variation degree of each traffic flow data comprises the following steps:
wherein ,/>Indicate->Data value of individual traffic data, +.>Indicate->Data value of individual traffic data, +.>Indicate->Data value of individual noise data,/->Indicate->The data value of the individual noise data,to avoid a super parameter with a denominator of 0;
the degree of change of each of the traffic data except the first traffic data is acquired.
Further, the specific obtaining method of the abnormality degree of each traffic flow data comprises the following steps:
taking any one of the vehicle flow data as target vehicle flow data, and recording the product of the difference degree of the target vehicle flow data, the first time deviation degree, the second time deviation degree corresponding to noise data and the data value corresponding to temperature data as an abnormal coefficient of the target vehicle flow data; obtaining an abnormal coefficient of each vehicle flow data;
and obtaining the abnormality degree of each vehicle flow data according to the abnormality coefficient.
Further, the method for obtaining the abnormality degree of each vehicle flow data according to the abnormality coefficient includes the following specific steps:
and (3) carrying out linear normalization on the abnormal coefficients of all the traffic flow data, and recording the obtained result as the abnormal degree of each traffic flow data.
Further, the method for screening and reacquiring abnormal traffic flow data according to the abnormal degree of the traffic flow data comprises the following specific steps:
if the abnormality degree of any one of the traffic flow data is greater than an abnormality threshold, the traffic flow data acquisition process is abnormal, and the traffic flow data corresponding to the specified sampling moment is acquired through other vehicle detectors positioned on the same road section as the vehicle detector with the abnormal traffic flow data acquisition; if the abnormality degree of the traffic flow data is smaller than or equal to an abnormality threshold value, the traffic flow data acquisition process is normal and is reserved as normal traffic flow data.
The beneficial effects of the invention are as follows: noise data and temperature data are collected simultaneously in the process of acquiring traffic flow data, the advance or the hysteresis generated by faults of a sensor in the process of acquiring the data is quantified by calculating the time deviation degree, meanwhile, the difference degree is quantified by combining the slow change characteristics of the data, the abnormal degree is obtained by combining the time deviation degree and the temperature data, the abnormal traffic flow data is screened and re-acquired, the optimized collection processing of the multi-source highway facility data signals is realized, the time deviation influence caused by the faults of the sensor is eliminated, and the environmental factors of high temperature and high noise are considered, so that the accuracy and the effectiveness of the traffic flow data acquisition are improved.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only some embodiments of the invention, and that other drawings can be obtained according to these drawings without inventive faculty for a person skilled in the art.
FIG. 1 is a schematic flow chart of a method for optimized collection and processing of data signals of a multi-source highway facility according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of traffic flow data collected in accordance with one embodiment of the present invention;
FIG. 3 is a schematic view showing the degree of abnormality of the calculated traffic flow data according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, a flowchart of a multi-source highway facility data signal optimizing and collecting processing method according to an embodiment of the present invention is shown, and the method includes the following steps:
step S001, collecting a plurality of vehicle flow data and corresponding noise data and temperature data.
The aim of the embodiment is to perform optimization processing on the data signal acquisition of the multi-source highway facility, so that the aim of optimization collection processing is fulfilled, and the accuracy of the data signal is ensured; the vehicle flow data is collected by the ultrasonic vehicle detector, and the ultrasonic sensor is interfered by temperature and noise in the environment, so that the accuracy of the vehicle flow data can be affected, and the vehicle flow data, the temperature data and the noise data need to be acquired at first.
Specifically, in this embodiment, every 500 minutes is taken as a monitoring process, the time interval of data acquisition is 1 minute, noise data is acquired through a noise monitor, and temperature data is acquired through a high-precision thermometer; for the traffic flow data, the number of vehicles passing through a road section per minute is monitored and acquired by an ultrasonic vehicle detector, so that the traffic flow data per minute is obtained, the traffic flow data shown in fig. 2 is obtained, the ultrasonic vehicle detector acquires the traffic flow data as a known technology, and the embodiment is not repeated; processing and describing the vehicle flow data, the temperature data and the noise data acquired in any monitoring process; since the vehicle flow rate data and the noise data are the same at a predetermined sampling time and the predetermined sampling time corresponds to the temperature data, the vehicle flow rate data, the noise data, and the temperature data are in one-to-one correspondence with each other.
Thus, a plurality of traffic flow data, temperature data and noise data are obtained.
Step S002, according to the actual sampling time and the theoretical sampling time, obtaining a first time deviation degree of each vehicle flow data and a second time deviation degree of each noise data.
In the process of acquiring the traffic flow data through the ultrasonic vehicle detector, the specified sampling time interval is 1 minute, however, due to the influence of the ambient temperature, the detector may be failed to generate a clamping phenomenon, the actual sampling time interval exceeds 1 minute, the subsequent sampling is continued for one minute, the hysteresis of the traffic flow data exists in the subsequent sampling, the influence of the hysteresis is increased when the next clamping section appears, and therefore, the first time deviation degree of each traffic flow data needs to be acquired, and the first time deviation degree is ensured to only show the hysteresis influence caused by the clamping; when the blocking phenomenon exists and the phenomenon of advanced measurement occurs, and further the traffic flow data generates an advanced property, the influence of the hysteresis and the advanced property is required to be eliminated by the first time deviation degree, and only the hysteresis or the advanced property under the corresponding acquisition process is reflected; in the same way, in the process of acquiring noise data, the noise monitor also can generate the phenomenon of blocking or measuring in advance, so that a second time deviation degree of the noise data is required to be acquired at the same time.
Specifically, for any traffic flow data, the theoretical sampling time corresponding to the specified sampling time interval is known, and in the process of acquiring the traffic flow data, the actual sampling time corresponding to the traffic flow data can be acquired from the ultrasonic vehicle detector, then the following is the followingFirst time deviation of the individual traffic data +.>The calculation method of (1) is as follows:
wherein ,/>Indicate->Actual sampling instant of the individual traffic flow data, +.>Indicate->Theoretical sampling moment of the individual traffic flow data, +.>Indicate->Actual sampling instant of the individual traffic flow data, +.>Represent the firstOrder value of the acquisitions corresponding to the individual traffic data,/-for each of the plurality of traffic data>Indicate->Theoretical sampling moment of the individual traffic flow data, +.>Representing absolute value; the time difference value obtained by subtracting the theoretical sampling time from the actual sampling time, and removing all the time difference values before the traffic flow data at the same time, so that the time deviation degree is finally obtained, and the advance or the hysteresis is the same under the influence of the subsequent abnormal degree, so that the first time deviation degree is obtained through an absolute value; the first time deviation degree of each vehicle flow data is obtained according to the method, and specifically, the first time deviation degree of the first vehicle flow data is the absolute value of the difference between the actual sampling time and the theoretical sampling time.
Further, the theoretical sampling time and the actual sampling time of each piece of noise data are obtained, the time deviation degree is calculated for the noise data according to the theoretical sampling time and the actual sampling time, the obtained result is recorded as the second time deviation degree of each piece of noise data, and specifically, the second time deviation degree of the first piece of noise data is the absolute value of the difference between the actual sampling time and the theoretical sampling time.
Thus, the first time deviation degree of each vehicle flow data and the second time deviation degree of each noise data are obtained, and the units of the two time deviation degrees are seconds.
Step S003, according to the traffic flow data and the noise data at adjacent sampling moments, the difference degree of each traffic flow data is obtained; and acquiring the abnormality degree of each vehicle flow data according to the difference degree, the first time deviation degree, the second time deviation degree and the temperature data.
It should be noted that, the change of the traffic flow data at adjacent sampling moments is slower, if the difference between the traffic flow data is too large, the traffic flow data is more likely to increase or decrease suddenly due to the abnormal situation, so the change degree can be quantified according to the difference of the traffic flow data, and the abnormal degree can be obtained through the change degree; meanwhile, the change of the noise data has an influence on the traffic flow data, the noise change is related to the traffic flow change, the traffic flow is increased, the noise is increased, the traffic flow is reduced, and the noise is reduced, so that when the traffic flow change is larger, and the noise change is smaller, the abnormality is likely to occur; meanwhile, the change degree is compared with the mean value of the previous change degree, the change degree in the same period of time is similar, and the larger the difference of the comparison results is, the greater the possibility of abnormality is, and the larger the difference degree is.
Specifically, for the firstIndividual traffic data, the degree of difference of the traffic data +.>The calculation method of (1) is as follows:
wherein ,/>Indicate->Data value of individual traffic data, +.>Indicate->Data value of individual traffic data, +.>Indicate->Noise data, i.e.)>Data value of noise data corresponding to individual traffic data,/->Indicate->Data value of individual noise data,/->Indicate->The collected sequence value corresponding to the individual traffic flow data; />Indicate->Data value of individual traffic data, +.>Indicate->Data value of individual traffic data, +.>Indicate->Personal noiseData value of acoustic data->Indicate->Data value of individual noise data,/->To avoid super parameters with denominator 0, this embodiment c uses +.>To make a description of->Representing absolute value; />Indicate->The variation degree of the individual traffic flow data, the variation degree is quantified by the difference between the variation degree and the mean value of the previous variation degree, and the larger the difference between the variation degree and the mean value of the variation degree is, the larger the difference degree is; the degree of difference of each traffic flow data is obtained according to the above method, wherein the degree of difference of the first traffic flow data is set to 0.
Further, for any one of the traffic flow data, recording the product of the difference degree of the traffic flow data, the first time deviation degree, the second time deviation degree corresponding to the noise data and the data value corresponding to the temperature data as an anomaly coefficient of the traffic flow data; the degree of difference and the degree of time deviation are larger and can reflect the occurrence of abnormality of the traffic flow data, and the higher the temperature is, the greater the possibility of abnormality is, so that the abnormality coefficient is represented by the product; obtaining the abnormal coefficient of each vehicle flow data according to the method, and carrying out linear normalization on all the abnormal coefficients, wherein the obtained result is recorded as the abnormal degree of each vehicle flow data.
Thus, the degree of abnormality of each traffic flow data is acquired as shown in fig. 3.
And S004, screening the abnormal traffic flow data according to the abnormal degree of the traffic flow data, and realizing the optimized collection and processing of the multi-source highway facility data signals.
After the abnormal degree of each vehicle flow data is obtained, an abnormal threshold value is preset, the abnormal threshold value of the embodiment is described by 0.8, if the abnormal degree of the vehicle flow data is larger than the abnormal threshold value, the abnormal condition is indicated in the vehicle flow data collecting process, and the vehicle flow data of the secondary vehicle flow data corresponding to the specified sampling moment is required to be obtained through other vehicle detectors of the road section; the optimal collection and processing of the traffic flow data through the multi-source highway facilities are realized by re-acquiring all abnormal traffic flow data in the monitoring process; if the abnormality degree of the traffic flow data is smaller than or equal to the abnormality threshold value, the traffic flow data acquisition process is normal and is reserved as normal traffic flow data.
Further, if no abnormal traffic flow data occurs in the primary monitoring process, the corresponding vehicle detector works normally; if abnormal traffic flow data exists in one monitoring process, timely overhaul is needed for the vehicle detectors, the traffic flow data of the road section is acquired through other vehicle detectors in the subsequent monitoring process, and it is noted that the same road section is usually provided with a plurality of vehicle detectors, and one stopping operation for overhaul is considered to not influence the acquisition of the traffic flow data in the embodiment.
Therefore, the optimized collection processing of the traffic flow data through the multi-source highway facilities is completed, so that the traffic flow data for monitoring is more accurate and reliable.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.
Claims (10)
1. A method for optimized collection and processing of data signals of a multi-source highway facility, which is characterized by comprising the following steps:
collecting a plurality of vehicle flow data and corresponding noise data and temperature data;
acquiring a first time deviation degree of each vehicle flow data and a second time deviation degree of each noise data according to the actual sampling time and the theoretical sampling time;
obtaining the difference degree of each vehicle flow data according to the vehicle flow data and the noise data at adjacent sampling moments; acquiring the abnormality degree of each vehicle flow data according to the difference degree, the first time deviation degree, the second time deviation degree and the temperature data;
and screening abnormal traffic flow data according to the abnormal degree of the traffic flow data, and reacquiring the abnormal traffic flow data to realize the optimized collection and processing of the multi-source highway facility data signals.
2. The method for optimized collection and processing of data signals of multi-source highway facilities according to claim 1, wherein the steps of collecting a plurality of traffic flow data and corresponding noise data and temperature data comprise the following specific steps:
acquiring the passing number of vehicles under one road section per minute through an ultrasonic vehicle detector to obtain the vehicle flow data per minute; and under the same data acquisition time interval, acquiring noise data corresponding to each vehicle flow data through a noise monitor, and acquiring temperature data corresponding to each vehicle flow data through a high-precision thermometer.
3. The method for optimized collection and processing of data signals of multi-source highway facilities according to claim 1, wherein the specific obtaining method comprises the following steps:
taking any one piece of traffic flow data as target traffic flow data, marking the sampling time corresponding to the stipulated sampling time of the target traffic flow data as the theoretical sampling time of the target traffic flow data, and marking the actual sampling time of the target traffic flow data as the actual sampling time of the target traffic flow data; acquiring theoretical sampling time and actual sampling time of each vehicle flow data;
obtaining theoretical sampling time and actual sampling time of each noise data according to the specified sampling time and the actual acquisition time of the noise monitor;
and obtaining a first time deviation degree of each vehicle flow data and a second time deviation degree of each noise data according to the theoretical sampling time and the actual sampling time.
4. A method for optimizing collection and processing of data signals of a multi-source highway facility according to claim 3, wherein the obtaining the first time deviation of each traffic flow data and the second time deviation of each noise data comprises the following specific steps:
wherein ,indicate->Actual sampling instant of the individual traffic flow data, +.>Indicate->Theoretical sampling moment of the individual traffic flow data, +.>Indicate->Order value of the acquisitions corresponding to the individual traffic data,/-for each of the plurality of traffic data>Indicate->Actual sampling instant of the individual traffic flow data, +.>Indicate->Theoretical sampling moment of the individual traffic flow data, +.>Representing absolute value;
acquiring a first time deviation degree of each vehicle flow data;
and acquiring a second time deviation degree of each piece of noise data according to the theoretical sampling moment and the actual sampling moment of each piece of noise data.
5. The method for optimized collection and processing of data signals of multi-source highway facilities according to claim 4, wherein the second time deviation degree of each noise data is obtained by the following steps:
and calculating the time deviation degree of the noise data according to the theoretical sampling time and the actual sampling time of the noise data, and recording the obtained result as a second time deviation degree of each noise data.
6. The method for optimizing and collecting and processing the data signals of the multi-source highway facility according to claim 1, wherein the step of obtaining the difference degree of each traffic flow data comprises the following specific steps:
acquiring the variation degree of each vehicle flow data according to the vehicle flow data and the noise data at adjacent sampling moments; first, theDegree of difference in individual traffic data +.>The calculation method of (1) is as follows:
wherein ,indicate->Degree of variation of individual traffic data, +.>Indicate->Order value of the acquisitions corresponding to the individual traffic data,/-for each of the plurality of traffic data>Indicate->Degree of variation of individual traffic data, +.>Representing absolute value;
the degree of difference of each traffic flow data is acquired, and the degree of difference of the first traffic flow data is set to 0.
7. The method for optimized collection and processing of data signals of multi-source highway facilities according to claim 6, wherein the variation degree of each traffic flow data is obtained by the following specific method:
wherein ,indicate->Data value of individual traffic data, +.>Indicate->Data value of individual traffic data, +.>Indicate->Data value of individual noise data,/->Indicate->Data value of individual noise data,/->To avoid a super parameter with a denominator of 0;
the degree of change of each of the traffic data except the first traffic data is acquired.
8. The method for optimized collection and processing of data signals of multi-source highway facilities according to claim 1, wherein the degree of abnormality of each traffic flow data is specifically obtained by:
taking any one of the vehicle flow data as target vehicle flow data, and recording the product of the difference degree of the target vehicle flow data, the first time deviation degree, the second time deviation degree corresponding to noise data and the data value corresponding to temperature data as an abnormal coefficient of the target vehicle flow data; obtaining an abnormal coefficient of each vehicle flow data;
and obtaining the abnormality degree of each vehicle flow data according to the abnormality coefficient.
9. The method for optimized collection and processing of data signals of multi-source highway facilities according to claim 8, wherein the obtaining the abnormality degree of each traffic flow data according to the abnormality coefficient comprises the following specific steps:
and (3) carrying out linear normalization on the abnormal coefficients of all the traffic flow data, and recording the obtained result as the abnormal degree of each traffic flow data.
10. The method for optimized collection and processing of data signals of multi-source highway facilities according to claim 1, wherein the method for screening abnormal traffic data and reacquiring the abnormal traffic data according to the abnormal level of the traffic data comprises the following specific steps:
if the abnormality degree of any one of the traffic flow data is greater than an abnormality threshold, the traffic flow data acquisition process is abnormal, and the traffic flow data corresponding to the specified sampling moment is acquired through other vehicle detectors positioned on the same road section as the vehicle detector with the abnormal traffic flow data acquisition; if the abnormality degree of the traffic flow data is smaller than or equal to an abnormality threshold value, the traffic flow data acquisition process is normal and is reserved as normal traffic flow data.
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