CN116363863A - Traffic data anomaly detection method and device and traffic operation and maintenance system - Google Patents
Traffic data anomaly detection method and device and traffic operation and maintenance system Download PDFInfo
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Abstract
The application relates to a traffic data anomaly detection method, a device and a traffic operation and maintenance system, wherein the method comprises the following steps: acquiring traffic data to be detected from a traffic management service system, and extracting characteristic information of the traffic data to be detected; determining whether the traffic data to be detected is abnormal or not based on the characteristic information; if the traffic data to be detected is abnormal, determining that the associated equipment of the traffic data to be detected is abnormal, and detecting the working state of the traffic equipment in an indirect mode, thereby solving the problem that the abnormal working state of the traffic data associated equipment cannot be detected in the related technology, and the associated equipment failure cannot be found timely.
Description
Technical Field
The present disclosure relates to the field of intelligent transportation, and in particular, to a traffic data anomaly detection method, a traffic data anomaly detection device, and a traffic operation and maintenance system.
Background
Urban traffic flow is increasing and traffic management demands for traffic data are increasingly complex and diverse. For example, traffic data with larger data volume such as passing data has higher timeliness requirement; for traffic violation data, the accuracy and the integrity of the traffic violation data are required to be high. These demands have increased the demands for traffic monitoring equipment such as bayonets, electric alarms, cameras, detectors, annunciators, etc. in urban roads, and for routine operation maintenance monitoring of traffic detection equipment. However, in conventional traffic equipment monitoring, hardware fault conditions of traffic monitoring and detecting equipment are generally required to be monitored through direct access of hardware, and when a traffic monitoring system is complex and equipment or platforms with multiple protocols are involved, working states of the equipment or the platforms cannot be monitored through a direct access mode, so that fault troubleshooting and positioning of associated equipment cannot be performed, and faults of the associated equipment cannot be found timely.
Aiming at the problem that the abnormal working state of traffic associated equipment cannot be detected in the related technology, so that the failure of the associated equipment cannot be found in time, no effective solution is proposed at present.
Disclosure of Invention
The embodiment provides a traffic data anomaly detection method, a traffic data anomaly detection device and a traffic operation and maintenance system, which are used for solving the problem that the abnormal working state of traffic associated equipment cannot be detected in the related technology, so that the failure of the associated equipment cannot be found in time.
In a first aspect, in this embodiment, there is provided a traffic data anomaly detection method, including:
acquiring traffic data to be detected from a traffic management service system, and extracting characteristic information of the traffic data to be detected;
determining whether the traffic data to be detected is abnormal or not based on the characteristic information;
if the traffic data to be detected is abnormal, determining that the associated equipment of the traffic data to be detected is abnormal.
In some embodiments, the characteristic information includes at least one of a continuity characteristic value, a data amount characteristic value, a timeliness characteristic value, an integrity characteristic information, and a validity characteristic value, and the determining, based on the characteristic information, whether the traffic data to be detected includes an anomaly;
And determining whether the traffic data to be detected is abnormal or not based on the characteristic information and the corresponding detection index.
In some embodiments, the detection index corresponding to the continuity characteristic value is a continuity index, and the continuity characteristic value is determined based on time intervals of reporting data of different periods of traffic data to be detected; the determining whether the traffic data to be detected is abnormal based on the characteristic information and the corresponding detection index comprises:
acquiring a continuity index, wherein the continuity index is determined based on time intervals of reporting data of different time periods of historical traffic data;
and if the continuity characteristic value does not accord with the continuity index of the corresponding time period, determining that the traffic data to be detected is abnormal.
In some embodiments, the obtaining a continuity indicator, where the continuity indicator is determined based on a time interval of reporting of different time periods of historical traffic data includes:
determining a historical reporting date which is the same as the reporting date Zhou Xuri in the historical traffic data based on the reporting date of the traffic data to be detected;
acquiring corresponding historical traffic data based on the historical reporting date;
The continuity indicator is determined based on the historical traffic data.
In some embodiments, the detection index corresponding to the data quantity characteristic value is a data quantity index, and the data quantity characteristic value is determined based on the data quantity reported by the traffic data to be detected in different time periods; the determining whether the traffic data to be detected is abnormal based on the characteristic information and the corresponding detection index comprises:
acquiring a data quantity index, wherein the data quantity index is determined based on the data quantity reported by historical traffic data in different time periods;
and if the data quantity characteristic value does not accord with the data quantity index of the corresponding time period, determining that the traffic data to be detected is abnormal.
In some embodiments, the detection index corresponding to the timeliness characteristic value is a timeliness index, and the timeliness characteristic value is determined based on a difference value between the generation time and the warehousing time of traffic data to be detected; the determining whether the traffic data to be detected is abnormal based on the characteristic information and the corresponding detection index comprises:
determining the timeliness index based on a preset time delay threshold;
if the timeliness characteristic value does not accord with the timeliness index, determining that the traffic data to be detected is abnormal.
In some embodiments, the detection index corresponding to the integrity feature information is an integrity index; the determining whether the traffic data to be detected is abnormal based on the characteristic information and the corresponding detection index comprises:
determining the integrity index based on a predetermined traffic data type;
and if the integrity characteristic information does not accord with the integrity index, determining that the traffic data to be detected is abnormal.
In some embodiments, the detection index corresponding to the validity feature value is a validity index, and the validity feature value is determined based on a valid data proportion of traffic data to be detected; the determining whether the traffic data to be detected is abnormal based on the characteristic information and the corresponding detection index comprises:
determining the effectiveness index based on a preset effectiveness threshold;
and if the validity characteristic value does not accord with the validity index, determining that the traffic data to be detected is abnormal.
In a second aspect, in the present embodiment, there is provided a traffic data abnormality detection apparatus including:
the acquisition module is used for acquiring traffic data to be detected from the traffic management service system and extracting characteristic information of the traffic data to be detected;
The first determining module is used for determining whether the traffic data to be detected is abnormal or not based on the characteristic information;
and the second determining module is used for determining that the associated equipment of the traffic data to be detected is abnormal if the traffic data to be detected is abnormal.
In a third aspect, in this embodiment, there is provided a traffic operation and maintenance system, including: the access device accesses the traffic management service system through the data access service and acquires the traffic data to be detected, the traffic data abnormality detection device for detecting the abnormality of the traffic data to be detected, and the alarm device for alarming based on the detection result.
Compared with the related art, the traffic data anomaly detection method provided in the embodiment obtains the traffic data to be detected from the traffic management service system, extracts the characteristic information of the traffic data to be detected, obtains various traffic data generated by each traffic device under the condition that the traffic data is not directly connected with the traffic device, and extracts the characteristic information for judging whether the data meets the requirement; determining whether traffic data to be detected is abnormal or not based on the characteristic information, and taking the characteristic information as a basis for judging whether the traffic data meets the requirement or not; if the traffic data to be detected is abnormal, determining that the associated equipment of the traffic data to be detected is abnormal, judging whether the corresponding associated equipment is abnormal according to whether the characteristic information meets the requirement, and detecting the working state of the traffic equipment in an indirect mode, thereby solving the problem that the abnormal working state of the traffic associated equipment cannot be detected in the related technology, and the associated equipment failure cannot be found timely.
The details of one or more embodiments of the application are set forth in the accompanying drawings and the description below to provide a more thorough understanding of the other features, objects, and advantages of the application.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute an undue limitation to the application. In the drawings:
FIG. 1 is a schematic view of an application environment of a traffic data anomaly detection method according to some embodiments of the present application;
FIG. 2 is a flow chart of a traffic data anomaly detection method of some embodiments of the present application;
FIG. 3 is a flow chart of determining traffic data anomalies based on continuity metrics, according to some embodiments of the present application;
FIG. 4 is a flow chart of acquiring a continuity indicator according to some embodiments of the present application;
FIG. 5 is a schematic diagram of partitioning time periods based on data volume according to some embodiments of the present application;
FIG. 6 is a flow chart of determining traffic data anomalies based on data volume indicators, according to some embodiments of the present application;
FIG. 7 is a flow chart of determining traffic data anomalies based on timeliness indicators, according to some embodiments of the present application;
FIG. 8 is a flow chart of determining traffic data anomalies based on integrity metrics, according to some embodiments of the present application;
FIG. 9 is a flow chart of determining traffic data anomalies based on validity indicators, according to some embodiments of the present application;
FIG. 10 is a flow chart of a traffic data anomaly detection method of some preferred embodiments of the present application;
fig. 11 is a block diagram of a traffic data anomaly detection device according to some embodiments of the present application.
Detailed Description
For a clearer understanding of the objects, technical solutions and advantages of the present application, the present application is described and illustrated below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
Unless defined otherwise, technical or scientific terms used herein shall have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terms "a," "an," "the," "these," and the like in this application are not intended to be limiting in number, but rather are singular or plural. The terms "comprising," "including," "having," and any variations thereof, as used in the present application, are intended to cover a non-exclusive inclusion; for example, a process, method, and system, article, or apparatus that comprises a list of steps or modules (units) is not limited to the list of steps or modules (units), but may include other steps or modules (units) not listed or inherent to such process, method, article, or apparatus. The terms "connected," "coupled," and the like in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. Reference to "a plurality" in this application means two or more. "and/or" describes an association relationship of an association object, meaning that there may be three relationships, e.g., "a and/or B" may mean: a exists alone, A and B exist together, and B exists alone. Typically, the character "/" indicates that the associated object is an "or" relationship. The terms "first," "second," "third," and the like, as referred to in this application, merely distinguish similar objects and do not represent a particular ordering of objects.
The traffic data anomaly detection method provided by the embodiment of the application can be applied to an application environment shown in fig. 1. The computing device 104 may be configured to implement the traffic data anomaly detection method according to the embodiment of the present application. The computing device 104 accesses the traffic management system 102 through the data access service to obtain traffic data stored in the traffic management system 102. The computing device 104 may be, but is not limited to being, a server, workstation, personal computer, smart phone, tablet, etc. Traffic management system 102 may be implemented, but is not limited to, as a stand-alone server or as a cluster of servers. It will be appreciated by those of ordinary skill in the art that the configuration shown in fig. 1 is merely illustrative and is not intended to limit the above-described construction of application environments. For example, traffic system 102 and computing device 104 may also include more or fewer components than shown in fig. 1, or have a different configuration than shown in fig. 1. Traffic data for traffic management system 102 may be stored on a local server, or may be placed on a cloud or other network server.
The control unit of the computing device 104 may include one or more processors and memory for storing data, where the processors may include, but are not limited to, a processing device such as a microprocessor MCU or programmable logic device FPGA. The control unit may further include a transmission device for communication functions and an input-output device, may communicate with a remote server through a network, and may perform data processing and storage through the remote server.
In this embodiment, a traffic data anomaly detection method is provided, and fig. 2 is a flowchart of a traffic data anomaly detection method according to some embodiments of the present application, as shown in fig. 2, where the flowchart includes the following steps:
step S201, obtaining traffic data to be detected from the traffic management service system, and extracting characteristic information of the traffic data to be detected.
Traffic data to be detected may include, but is not limited to, data generated by front-end devices such as bayonets, electronic police, cameras, detectors, etc., such as passing data, offending data, etc.; and data obtained by analysis based on data generated by the front-end equipment, such as intelligent secondary analysis data; audit result data, device data, organization data, etc. for further analysis or statistics of such data may also be included. The device data refers to the number information of the device itself, and is used for uniquely identifying the device corresponding to the traffic data. The organization data may be organization information of the device, such as a traffic police team, etc., for determining the installation location of the device. The audit result data may be the result of a manual audit of the illegal data or the like.
Different types of traffic data may have different content. For example, the drive-through data may include license plate number, snapshot time; the illegal data can comprise the type of illegal event and an illegal snapshot picture; the intelligent secondary analysis data may include snap shots, intelligent recognition results, etc. The characteristic information is determined according to the type of the traffic data and the detection requirement of the user on the type of the traffic data. For example, for passing data, the characteristic information may be the number of passing data generated in a certain period of time in the same gate to determine whether the gate works normally; the time interval between the generation time and the warehouse-in time of the passing data can also be used for determining whether the associated equipment on the transmission path from the bayonet to the traffic management system database transmits the data in time.
The characteristic information can be directly obtained from traffic data to be detected of the traffic management service system, and can also be obtained by calculation or statistics according to the traffic data to be detected.
Step S202, based on the feature information, it is determined whether the traffic data to be detected is abnormal.
The different characteristic information reflects the real state of the traffic data to be detected in the links of generation, transmission, processing, use and the like, so that the traffic data can be used for judging whether the traffic data is abnormal or not. For different types of detection requirements, the characteristic information can be judged by combining corresponding detection indexes so as to determine whether traffic data to be detected is abnormal or not.
Step S203, if the traffic data to be detected is abnormal, determining that the associated equipment of the traffic data to be detected is abnormal.
The association device is a traffic device for generating, transmitting, processing and using traffic data to be detected, and has a close relation on traffic with the traffic data to be detected. If traffic data is determined to be abnormal, traffic devices associated with traffic links of the traffic data may also be abnormal. For example, if the number of traffic data generated by a certain gate is abnormally reduced within a certain period of time, it may be suspected that a snapshot module or a data storage module of the gate device fails; if the number of the passing data generated by the gate is normal, but the interval between the generation time and the warehousing time of the passing data is abnormally increased, whether the transmission equipment from the gate to the database is faulty or whether the transmission network is interrupted or not can be suspected.
Through the steps S201 to S203, by acquiring traffic data to be detected from the traffic management service system, extracting characteristic information of the traffic data to be detected, acquiring various traffic data generated by each traffic device without directly connecting with the traffic device, and extracting characteristic information for judging whether the data meets the requirement; determining whether traffic data to be detected is abnormal or not based on the characteristic information, and taking the characteristic information as a basis for judging whether the traffic data meets the requirement or not; if the traffic data to be detected is abnormal, determining that the associated equipment of the traffic data to be detected is abnormal, judging whether the corresponding associated equipment is abnormal according to whether the characteristic information meets the requirement, and detecting the working state of the traffic equipment in an indirect mode, thereby solving the problem that the abnormal working state of the traffic associated equipment cannot be detected in the related technology, and the associated equipment failure cannot be found timely.
In some embodiments, the feature information includes at least one of a continuity feature value, a data amount feature value, a timeliness feature value, an integrity feature information, and a validity feature value, and the method for determining whether traffic data to be detected is abnormal based on the feature information includes:
And determining whether the traffic data to be detected is abnormal or not based on the characteristic information and the corresponding detection index.
The continuity detection can be used for but not limited to traffic data continuously reported by passing vehicles, illegal data and the like. The continuity detection can monitor the equipment channels corresponding to the traffic equipment in real time to determine whether the equipment channels have the phenomenon that data are not reported for a long time, such as the problem that the equipment channels cannot be put in storage in time due to queue accumulation. The continuity characteristic value may be a reporting time interval of traffic data, which may be obtained based on a difference in reporting time of each piece of data. The time interval may be counted based on a combination of various conditions of different data types, different device numbers, different dates, different time periods, etc. Specifically, statistics may be performed according to reporting time intervals corresponding to the passing data of the same bayonet device in different periods in a day, or statistics may be performed according to average reporting time intervals of the passing data of a plurality of bayonet devices in the same period.
The data volume detection can be used for but not limited to traffic data with larger data volume in a single day, such as vehicle passing, illegal data and the like. The data volume detection may perform regular or irregular data volume statistics for device channels corresponding to multiple traffic devices, so as to determine whether the transmission channel has abnormal data volume, for example, abnormal data volume caused by network interruption and other faults. The characteristic value of the data quantity can be the data quantity of a certain device channel in a preset time range, or the difference value, the rising or falling ratio and the like obtained by comparing the data quantity with the data quantity in a historical event range.
The timeliness detection can be used for traffic data with higher requirements on timeliness, such as passing data. The timeliness detection can monitor the equipment channels corresponding to the plurality of traffic equipment in real time to determine whether the equipment channels have the problem of data delay reporting. The timeliness characteristic value can be a real-time monitoring value of the difference value of the data production time and the warehouse-in time of the equipment channel, or can be an average value counted in a segmented mode according to the time period or the data quantity.
Integrity detection may be used for, but is not limited to, traffic data such as illegal data that has a high integrity requirement. Integrity detection may be monitored in real-time for each device channel to determine whether traffic data is complete. The integrity characteristic information can comprise information such as license plate numbers, snapshot time, event types, whether pictures are normally checked, and the like. For example, if traffic data sent by a channel of a device has a problem that a key field is missing or a picture cannot be opened normally, the traffic data of the channel is considered to be incomplete, and traffic devices for acquiring the traffic data may have anomalies.
The validity detection can be used for traffic data with high validity requirements, such as vehicle passing data, illegal data and the like, but is not limited to the traffic data. Validity refers to the validity of the traffic data for event analysis or identification. For example, in practical application, the traffic front-end equipment captures a traffic violation scene, obtains a captured picture and sends the captured picture to the traffic management service system; and the traffic management service system carries out manual auditing on the snapshot pictures in the illegal data and records auditing results, and the validity of the data is determined according to the auditing results. Possible reasons for invalidation of data include: abnormal snapshot function of the equipment, low quality of the snapshot image, abnormal data transmission and the like. The validity feature value may be the amount of valid data or the ratio of valid data sent by the device channel.
The characteristic information may be combined with a corresponding detection index to determine whether the traffic data is abnormal. The detection index can be a threshold value set according to requirements, for example, a timeliness threshold value is preset, and when the difference value between the data production time and the warehousing time in the equipment channel monitored in real time is greater than the timeliness threshold value, the traffic data is judged to be abnormal; the detection index can be a threshold value obtained according to historical data statistics, for example, the continuity threshold value is obtained according to the historical data statistics, and when the data reporting time interval in the equipment channel is monitored to be greater than the continuity threshold value in real time, the traffic data is judged to be abnormal; the detection index may also be a predetermined detection standard, for example, the predetermined integrity detection standard includes license plate number information, integrity of snapshot time information, normal opening of a snapshot picture, and the like, and when the real-time monitoring that the traffic data does not meet the integrity detection standard, it is determined that the traffic data is abnormal.
According to the traffic data abnormality detection method, whether the traffic data to be detected is abnormal or not is determined based on the characteristic information and the corresponding detection indexes, an explicit determination mode is provided for abnormality determination of the traffic data, namely, the detection indexes are determined according to the requirements or the history related data, whether the traffic data is abnormal or not is determined according to comparison or comparison results of the characteristic information and the corresponding detection indexes, and therefore whether the associated equipment is abnormal or not is determined, and the detection mode of detection of the working state of the traffic data associated equipment is expanded.
In some embodiments, the detection index corresponding to the continuity characteristic value is a continuity index, and the continuity characteristic value is determined based on time intervals of reporting data of different periods of traffic data to be detected. FIG. 3 is a flow chart of determining traffic data anomalies based on continuity metrics, as shown in FIG. 3, according to some embodiments of the present application, including the steps of:
step S301, a continuity indicator is acquired, where the continuity indicator is determined based on time intervals of reporting data of different periods of historical traffic data.
In this embodiment, for a traffic device corresponding to traffic data to be detected, historical traffic data of the traffic device may be obtained and time division may be performed, where the time division may be divided into Gao Fengqi and peaked periods according to how much data is, or may be divided according to time, for example, a day may be divided into 24 time periods in units of hours. And obtaining an average value of time intervals of data reporting in each time period, and determining a continuity index according to the average value, wherein the continuity index is smaller than or equal to 1 minute if the average value is 1 minute. The continuity index [ mu-nσ, mu+nσ ] can also be determined according to the mean value mu and the standard deviation sigma of the time interval, and n can be set according to requirements.
Step S302, if the continuity characteristic value does not accord with the continuity index of the corresponding period, determining that the traffic data to be detected is abnormal.
And determining whether the continuity characteristic value accords with the threshold value or the threshold value interval of the continuity index.
Through the steps S301 to S302, the acquisition sources and the bases of the continuity indexes are determined by acquiring the continuity indexes based on the time intervals of reporting the data of different time periods of the historical traffic data; whether the continuity characteristic value accords with the threshold value or the threshold value interval of the continuity index is determined, whether the traffic data and the corresponding associated equipment are abnormal is determined through the continuity characteristic value, the detection mode of detecting the working state of the traffic data associated equipment is expanded, and the detection efficiency is improved.
In some of these embodiments, fig. 4 is a flowchart of acquiring a continuity indicator according to some embodiments of the present application, as shown in fig. 4, the flowchart including the steps of:
in step S401, based on the reporting date of the traffic data to be detected, the historical reporting date which is the same as the reporting date Zhou Xuri in the historical traffic data is determined.
In practical applications, there may be a large difference in the reporting time continuity of traffic data at different dates and different periods. In order to determine the continuity indicator more accurately, the reporting date factor may be included in the determination process of the continuity indicator, so as to determine whether the traffic data is abnormal more accurately. The reporting date can be obtained from the traffic data to be detected.
Zhou Xuri is the same as the reporting date and the historical reporting date, which are the same day of the week. For example, in the case where the reporting date is monday, the historical reporting dates corresponding to all mondays in the historical traffic data of the device may be obtained.
Step S402, based on the historical reporting date, corresponding historical traffic data is obtained.
Step S403, determining a continuity indicator based on the historical traffic data.
And determining a continuity index according to the reporting time interval of the historical traffic data.
Through the steps S401 to S403, the corresponding Zhou Xuri is determined by acquiring the reporting date of the traffic data to be detected; by determining the historical reporting date which is the same as the reporting date Zhou Xuri in the historical traffic data based on the reporting date, selecting the corresponding historical reporting date so as to avoid the difference of data reporting interval time caused by Zhou Xuri difference; acquiring corresponding historical traffic data based on the historical reporting date, and acquiring a data source of a continuity index; by determining the continuity index based on the historical traffic data, pertinence and accuracy of the continuity index are improved.
Further, in the above step S403, the continuity index of the different periods may also be determined by the period division of Gao Fengqi and the flat period, respectively. Gao Fengqi and flat periods here are divided by the time distribution trend of the data. The method comprises the following steps:
Step S11, dividing the reporting date into a plurality of micro time periods based on a preset time interval;
for example, 24 hours of a day may be divided into a plurality of micro-periods with 15 minutes as one sampling period.
Step S12, determining the data quantity X corresponding to the micro-period based on the data reporting time of the historical traffic data i ；
Step S13, generating a data quantity array based on the time sequence of the micro time periods and the corresponding data quantity;
the data quantity sequence may be expressed as { X } 1 ，X 2 ，...，X i ，...，X n }，1≤i≤n。
Step S14, carrying out ordered clustering segmentation on the data quantity sequence to obtain at least two data segments;
the Fisher ordered sample clustering method or other ordered clustering segmentation methods can be used for carrying out ordered clustering segmentation on the data quantity array to obtain k data segments, wherein k is more than or equal to 2. Ordered cluster segmentation can achieve data classification without disturbing the order of data arrangement. The data arranged in time order is not disturbed in this embodiment.
Step S15, determining corresponding time periods based on micro-time periods corresponding to data amounts in at least two data segments;
fig. 5 is a schematic diagram of dividing time periods based on data volume according to some embodiments of the present application, as shown in fig. 5, according to the reported data volume of different time periods, dividing 24 hours of a day into 6 time periods, and determining a continuity indicator corresponding to each time period based on the average value of the data reporting time interval of each time period.
Step S16, determining the continuity index of the time period based on the reporting time interval of the historical traffic data corresponding to the time period.
Through the steps S11-S16, a time interval dividing basis is obtained by counting the data quantity of the historical traffic data corresponding to the reporting date; by dividing different time periods according to the data quantity, the difference of data continuity caused by the different time periods is avoided, and the accuracy of the continuity index is influenced; by determining the continuity index of the time period based on the reporting time interval of the historical traffic data corresponding to the time period, the accuracy of judging whether the traffic data and the related traffic equipment are abnormal is improved.
In some embodiments, the detection index corresponding to the data quantity characteristic value is a data quantity index, and the data quantity characteristic value is determined based on the data quantity reported by the traffic data to be detected in different periods. FIG. 6 is a flow chart of determining traffic data anomalies based on data volume indicators, as shown in FIG. 6, according to some embodiments of the present application, including the steps of:
in step S601, a data amount index is obtained, where the data amount index is determined based on the data amounts reported in different periods of the historical traffic data.
In this embodiment, for the traffic device corresponding to the traffic data to be detected, the historical traffic data of the traffic device in the same Zhou Xuri may be obtained, or further time division may be performed, and the data amount of the corresponding historical traffic data in the same Zhou Xuri and the same time period may be obtained through statistics. And calculating a data quantity index according to the average value of the data quantity, wherein the index can be a threshold value or a threshold value interval.
Step S602, if the characteristic value of the data quantity does not accord with the data quantity index of the corresponding time period, determining that the traffic data to be detected is abnormal.
And determining whether the characteristic value of the data quantity accords with the threshold value or the threshold value interval of the data quantity index.
Through the steps S601-S602, the data quantity indexes are obtained, the data quantity indexes are determined based on the data quantity reported by the historical traffic data in different time periods, and the obtaining sources and the basis of the data quantity indexes are defined; and if the characteristic value of the data quantity does not accord with the data quantity index of the corresponding time period, determining that the traffic data to be detected is abnormal, expanding the detection mode of detecting the working state of the traffic data association equipment, and improving the detection efficiency.
In some embodiments, the detection index corresponding to the timeliness characteristic value is a timeliness index, and the timeliness characteristic value is determined based on a difference value between the generation time and the warehousing time of the traffic data to be detected. FIG. 7 is a flow chart of determining traffic data anomalies based on timeliness indicators, as shown in FIG. 7, according to some embodiments of the present application, the flow comprising the steps of:
step S701, determining a timeliness index based on a preset delay threshold.
In practical application, a delay threshold can be set according to the requirement, and the delay threshold is used as a timeliness index. The timeliness characteristic value may be equal to a difference between the generation time and the warehouse entry time of the traffic data.
Step S702, if the timeliness characteristic value does not accord with the timeliness index, determining that the traffic data to be detected is abnormal.
When the timeliness characteristic value is larger than the timeliness index, determining that the traffic data to be detected is abnormal; or when the timeliness characteristic value is a negative value, the front-end equipment is not subjected to time correction, and the traffic data can be determined to be abnormal, and the channel alarm is triggered to remind the equipment of time correction.
Through the steps S701 to S702, the timeliness index is determined based on a preset delay threshold, and the acquisition source and basis of the timeliness index are clarified; if the timeliness characteristic value does not accord with the timeliness index, the abnormal traffic data to be detected is determined, the detection mode of detecting the working state of the traffic data association equipment is expanded, and the detection efficiency is improved.
In some embodiments, the detection indicator corresponding to the integrity feature information is an integrity indicator. FIG. 8 is a flow chart of determining traffic data anomalies based on integrity metrics, as shown in FIG. 8, according to some embodiments of the present application, the flow including the steps of:
Step S801, an integrity index is determined based on a predetermined traffic data type.
In practical application, the integrity index can be defined according to the requirements, such as whether the information of traffic data types, such as license plate numbers, snapshot time, event types, whether the pictures are normally checked, is complete or not. When any one of the information is missing or unavailable, the integrity feature information is determined to be not in accordance with the integrity index.
Step S802, if the integrity characteristic information does not accord with the integrity index, determining that the traffic data to be detected is abnormal.
Through the steps S801 to S802, the integrity index is determined based on the predetermined traffic data type, and the acquisition source and the basis of the integrity index are determined; if the integrity characteristic information does not accord with the integrity index, the abnormal condition of the traffic data to be detected is determined, the detection mode of the working state detection of the traffic data association equipment is expanded, and the detection efficiency is improved.
In some of these embodiments, the detection index corresponding to the validity feature value is a validity index, and the validity feature value is determined based on a valid data proportion of the traffic data to be detected. FIG. 9 is a flow chart of determining traffic data anomalies based on effectiveness metrics, as shown in FIG. 9, according to some embodiments of the present application, including the steps of:
Step S901, determining a validity index based on a preset validity threshold.
In practical application, the validity threshold can be set according to the requirement, and the validity threshold is used as a validity index. The validity characteristic value may be equal to a ratio of a valid data amount to a total data amount in the channel traffic data. For example, detecting total amount of illegal data of each channel and data amount passing manual auditing, and calculating to obtain effective illegal data proportion; for example, the total quantity of the passing data of each channel and the intelligent secondary analysis success data quantity are detected, and the analysis success data proportion is calculated.
Step S902, if the validity characteristic value does not accord with the validity index, determining that the traffic data to be detected is abnormal.
And when the validity characteristic value is smaller than the validity index, determining that the traffic data to be detected is abnormal.
Through the steps S901 to S902, the availability index is determined based on the preset availability threshold, so that the acquisition source and basis of the availability index are clarified; and if the validity characteristic value does not accord with the validity index, determining that the traffic data to be detected is abnormal, expanding the detection mode of detecting the working state of the traffic data association equipment, and improving the detection efficiency.
The present embodiment is described and illustrated below by way of preferred embodiments.
Fig. 10 is a flowchart of the traffic data abnormality detection method of the present preferred embodiment. As shown in fig. 10, the flow includes the steps of:
step S1001, obtaining traffic data to be detected from the traffic management service system, where the traffic data is not limited to traffic data, illegal data, intelligent secondary analysis data, audit results, and equipment/organization data, and the obtained form is not limited to forms such as service platform docking, database docking, etc.;
step S1002, acquiring the same date as the traffic data Zhou Xuri to be detected in the historical traffic data;
step S1003, obtaining historical traffic data corresponding to the date;
step S1004, dividing the date into a plurality of time periods by a Fisher ordered sample clustering method based on the data amount corresponding to each time period in the historical traffic data;
step S1005, obtaining a mean value mu 1 and a standard deviation sigma 1 of a data reporting time interval corresponding to each period, and determining that a continuity index corresponding to the period is [ mu 1-3 sigma 1, mu 1+3 sigma 1];
step S1006, according to the time interval dividing mode, reporting time intervals of the traffic data to be detected in each time interval are counted and used as continuity characteristic values;
Step S1007, determining that the traffic data to be detected is abnormal in the case that the continuity characteristic value does not fall into the range of the continuity index [ mu 1-3 sigma 1, mu 1+3 sigma 1] of the corresponding period;
step S1008, counting a data quantity mean value mu 2 and a standard deviation sigma 2 corresponding to the same date as the to-be-detected traffic data reporting date Zhou Xuri in the historical traffic data, and determining that the data quantity index is [ mu 2-3 sigma 2, mu 2+3 sigma 2];
step S1009, counting the data quantity of the reporting date of the traffic data to be detected as a data quantity characteristic value;
step S1010, determining that the traffic data to be detected is abnormal under the condition that the characteristic value of the data quantity does not fall into the range of the data quantity indexes [ mu 2-3 sigma 2, mu 2+3 sigma 2];
step S1011, determining that the timeliness index of the traffic data is a preset time delay threshold;
step S1012, counting the difference value between the data generation time and the warehouse-in time interval of the traffic data to be detected as a timeliness characteristic value;
step S1013, determining that the traffic data to be detected is abnormal under the condition that the difference value is larger than the delay threshold value;
step S1014, determining whether the integrity index of the traffic data comprises the integrity of any one of the four information including license plate number, snapshot time, event type and picture;
Step S1015, detecting the four items of information of the traffic data to be detected;
step S1016, determining that the traffic data to be detected is abnormal under the condition that any one of the four pieces of information is incomplete;
step S1017, determining the effectiveness index of the traffic data as a preset effectiveness threshold;
step S1018, counting the ratio of the effective data amount and the total data amount of the traffic data to be detected as an effective characteristic value;
step S1019, determining that the traffic data to be detected is abnormal under the condition that the ratio is smaller than the validity threshold;
step S1020, determining that the associated device of the traffic data to be detected has an abnormality according to any one of the determination results of steps S1007, S1010, S1013, S1016, and S1019.
Through the steps S1001 to S1020, traffic data to be detected is obtained by interfacing with traffic management system data, and a data source for determining abnormal data is obtained without directly connecting with traffic equipment; the historical traffic data which is the same as the traffic data to be detected Zhou Xuri is obtained, the time interval is divided for statistics, the corresponding continuity index is obtained, the continuity characteristic value of the traffic data to be detected is correspondingly obtained according to the time interval division, and the historical traffic data and the continuity characteristic value of the traffic data to be detected are correspondingly compared, so that the accuracy of abnormal data judgment is improved; the characteristic value is detected based on preset timeliness, integrity and effectiveness indexes, so that the abnormal data judging mode is expanded; the working state of the traffic equipment is detected in an indirect mode, so that the problem that the abnormal working state of the traffic associated equipment cannot be detected in the related technology, and the associated equipment failure cannot be found in time is solved.
It should be noted that the steps illustrated in the above-described flow or flow diagrams of the figures may be performed in a computer system, such as a set of computer-executable instructions, and that, although a logical order is illustrated in the flow diagrams, in some cases, the steps illustrated or described may be performed in an order other than that illustrated herein.
In some embodiments, the present application further provides a traffic data anomaly detection device, which is used to implement the foregoing embodiments and preferred embodiments, and is not described in detail. The terms "module," "unit," "sub-unit," and the like as used below may refer to a combination of software and/or hardware that performs a predetermined function.
In some embodiments, fig. 11 is a block diagram of the traffic data abnormality detection apparatus of the present embodiment, as shown in fig. 11, including:
the obtaining module 1101 is configured to obtain traffic data to be detected from the traffic management service system, and extract feature information of the traffic data to be detected;
a first determining module 1102, configured to determine, based on the feature information, whether traffic data to be detected is abnormal;
the second determining module 1103 is configured to determine that an abnormality exists in the associated device of the traffic data to be detected if the traffic data to be detected has an abnormality.
In the traffic data anomaly detection device in this embodiment, the obtaining module 1101 obtains traffic data to be detected from the traffic management service system, extracts characteristic information of the traffic data to be detected, obtains various traffic data generated by each traffic device without directly connecting with the traffic device, and extracts characteristic information for determining whether the data meets the requirement; determining whether the traffic data to be detected is abnormal or not based on the feature information through the first determining module 1102, and taking the feature information as a basis for judging whether the traffic data meets the requirement or not; if the traffic data to be detected is abnormal, the second determining module 1103 determines that the associated device of the traffic data to be detected is abnormal, whether the corresponding associated device is abnormal or not is determined by whether the characteristic information meets the requirement, and the working state of the traffic device is detected in an indirect mode, so that the problem that the abnormal working state of the traffic associated device cannot be detected in the related technology, and the failure of the associated device cannot be found in time is solved.
In some embodiments, the feature information includes at least one of a continuity feature value, a data amount feature value, a timeliness feature value, an integrity feature information, and a validity feature value, and the first determining module includes a determining submodule, where the determining submodule is configured to determine whether traffic data to be detected is abnormal based on the feature information and a corresponding detection index.
According to the traffic data abnormality detection device in the embodiment, whether the traffic data to be detected is abnormal is determined by the determination submodule based on the characteristic information and the corresponding detection indexes, an explicit determination mode is provided for abnormality determination of the traffic data, namely, the detection indexes are determined according to the requirements or the historical related data, whether the traffic data is abnormal is determined according to comparison or comparison results of the characteristic information and the corresponding detection indexes, so that whether the associated equipment is abnormal is determined, and the detection mode of detection of the working state of the traffic data associated equipment is expanded.
In some embodiments, the detection index corresponding to the continuity characteristic value is a continuity index, and the continuity characteristic value is determined based on time intervals of reporting data of different periods of traffic data to be detected; the determining submodule comprises a first obtaining unit and a first determining unit, wherein the first obtaining unit is used for obtaining a continuity index, the continuity index is determined based on time intervals of reporting of data of different time periods of historical traffic data, and the first determining unit is used for determining that the traffic data to be detected is abnormal if the continuity characteristic value does not accord with the continuity index of the corresponding time period.
According to the traffic data anomaly detection device, the first acquisition unit acquires the continuity index, the continuity index is determined based on the time intervals of reporting the data of different time periods of the historical traffic data, and the acquisition source and the basis of the continuity index are defined; and determining whether the continuity characteristic value accords with the threshold value or the threshold value interval of the continuity index through the first determining unit, and determining whether the traffic data and the corresponding associated equipment are abnormal through the continuity characteristic value, so that the detection mode of detecting the working state of the traffic data associated equipment is expanded, and the detection efficiency is improved.
In some embodiments, the first obtaining unit includes a first determining subunit, an obtaining subunit and a second determining subunit, where the first determining subunit is configured to determine, based on a reporting date of traffic data to be detected, a historical reporting date that is the same as the reporting date Zhou Xuri in the historical traffic data, the obtaining subunit is configured to obtain, based on the historical reporting date, the corresponding historical traffic data, and the second determining subunit is configured to determine, based on the historical traffic data, a continuity indicator.
The traffic data anomaly detection device in the embodiment obtains the reporting date of the traffic data to be detected through the first determination subunit, and determines corresponding Zhou Xuri; by determining the historical reporting date which is the same as the reporting date Zhou Xuri in the historical traffic data based on the reporting date, selecting the corresponding historical reporting date so as to avoid the difference of data reporting interval time caused by Zhou Xuri difference; acquiring corresponding historical traffic data based on the historical reporting date by an acquisition subunit, and acquiring a data source of a continuity index; and the second determining subunit determines the continuity index based on the historical traffic data, so that the pertinence and the accuracy of the continuity index are improved.
In some embodiments, the detection index corresponding to the data quantity characteristic value is a data quantity index, and the data quantity characteristic value is determined based on the data quantity reported by the traffic data to be detected in different periods. The determining submodule comprises a second obtaining unit and a second determining unit, wherein the second obtaining unit is used for obtaining data quantity indexes, the data quantity indexes are determined based on the data quantity reported by the historical traffic data in different time periods, and the second determining unit is used for determining that the traffic data to be detected is abnormal if the data quantity characteristic value does not accord with the data quantity indexes in the corresponding time periods.
According to the traffic data anomaly detection device, the second acquisition unit acquires the data quantity index, the data quantity index is determined based on the data quantity reported by the historical traffic data in different time periods, and the acquisition source and the basis of the data quantity index are defined; if the characteristic value of the data quantity does not accord with the data quantity index of the corresponding time period, the second determining unit determines that the traffic data to be detected is abnormal, so that the detection mode of detecting the working state of the traffic data association equipment is expanded, and the detection efficiency is improved.
In some embodiments, the detection index corresponding to the timeliness characteristic value is a timeliness index, and the timeliness characteristic value is determined based on a difference value between the generation time and the warehousing time of the traffic data to be detected. The determining submodule comprises a third obtaining unit and a third determining unit, the third obtaining unit is used for determining timeliness indexes based on a preset time delay threshold, and the third determining unit is used for determining that the traffic data to be detected are abnormal if the timeliness characteristic value does not accord with the timeliness indexes.
According to the traffic data anomaly detection device, the third acquisition unit is used for determining timeliness indexes based on a preset time delay threshold value, so that acquisition sources and bases of the timeliness indexes are defined; if the timeliness characteristic value does not accord with the timeliness index, the third determining unit determines that the traffic data to be detected is abnormal, so that the detection mode of detecting the working state of the traffic data association equipment is expanded, and the detection efficiency is improved.
In some embodiments, the detection indicator corresponding to the integrity feature information is an integrity indicator. The determining submodule comprises a fourth obtaining unit and a fourth determining unit, wherein the fourth obtaining unit is used for determining an integrity index based on a predetermined traffic data type, and the fourth determining unit is used for determining that the traffic data to be detected is abnormal if the integrity characteristic information does not accord with the integrity index.
In the traffic data anomaly detection device in the embodiment, the fourth acquisition unit determines the integrity index based on the predetermined traffic data type, and the acquisition source and basis of the integrity index are defined; if the integrity characteristic information does not accord with the integrity index, the fourth determining unit determines that the traffic data to be detected is abnormal, so that the detection mode of detecting the working state of the traffic data association equipment is expanded, and the detection efficiency is improved.
In some embodiments, the determining submodule includes a fifth obtaining unit and a fifth determining unit, the fifth obtaining unit is configured to determine a validity index based on a preset validity threshold, and the fifth determining unit is configured to determine that the traffic data to be detected is abnormal if the validity feature value does not conform to the validity index.
According to the traffic data anomaly detection device, the fifth acquisition unit is used for determining the effectiveness index based on the preset effectiveness threshold value, so that the acquisition source and the basis of the effectiveness index are clear; and if the validity characteristic value does not accord with the validity index, the fifth determining unit determines that the traffic data to be detected is abnormal, so that the detection mode of detecting the working state of the traffic data association equipment is expanded, and the detection efficiency is improved.
In some embodiments, the present application also provides a traffic operation and maintenance system, comprising: the traffic data anomaly detection device is used for detecting the anomaly of the traffic data to be detected, and the alarm device is used for alarming based on the detection result.
The traffic operation and maintenance system in the embodiment accesses the traffic management service system through the access device, and obtains various traffic data generated by each traffic device under the condition that the traffic system is not directly connected with the traffic device; extracting characteristic information and corresponding historical data of data to be detected from traffic data through a traffic data abnormality detection device, determining whether the traffic data to be detected is abnormal or not based on the characteristic information, and further determining whether associated traffic equipment is abnormal or not; and generating a corresponding alarm signal through the alarm device to prompt maintenance of the associated traffic equipment determined to be abnormal.
In addition, in combination with the traffic data anomaly detection method provided in the above embodiment, a storage medium may be provided in the present embodiment. The storage medium has a computer program stored thereon; the computer program, when executed by a processor, implements any of the traffic data anomaly detection methods of the above embodiments.
It should be noted that, specific examples in this embodiment may refer to examples described in the foregoing embodiments and alternative implementations, and are not described in detail in this embodiment.
It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to be limiting. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present application, are within the scope of the present application in light of the embodiments provided herein.
It is evident that the drawings are only examples or embodiments of the present application, from which the present application can also be adapted to other similar situations by a person skilled in the art without the inventive effort. In addition, it should be appreciated that while the development effort might be complex and lengthy, it would nevertheless be a routine undertaking of design, fabrication, or manufacture for those of ordinary skill having the benefit of this disclosure, and thus should not be construed as an admission of insufficient detail.
The term "embodiment" in this application means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive. It will be clear or implicitly understood by those of ordinary skill in the art that the embodiments described in this application can be combined with other embodiments without conflict.
The above examples only represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the patent. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application shall be subject to the appended claims.
Claims (10)
1. A traffic data anomaly detection method, the method comprising:
acquiring traffic data to be detected from a traffic management service system, and extracting characteristic information of the traffic data to be detected;
determining whether the traffic data to be detected is abnormal or not based on the characteristic information;
if the traffic data to be detected is abnormal, determining that the associated equipment of the traffic data to be detected is abnormal.
2. The method of claim 1, wherein the characteristic information comprises at least one of a continuity characteristic value, a data amount characteristic value, a timeliness characteristic value, an integrity characteristic information, a validity characteristic value, and wherein the determining whether the traffic data to be detected is abnormal comprises based on the characteristic information;
and determining whether the traffic data to be detected is abnormal or not based on the characteristic information and the corresponding detection index.
3. The method according to claim 2, wherein the detection index corresponding to the continuity characteristic value is a continuity index, and the continuity characteristic value is determined based on time intervals of data reporting of different periods of traffic data to be detected; the determining whether the traffic data to be detected is abnormal based on the characteristic information and the corresponding detection index comprises:
Acquiring a continuity index, wherein the continuity index is determined based on time intervals of reporting data of different time periods of historical traffic data;
and if the continuity characteristic value does not accord with the continuity index of the corresponding time period, determining that the traffic data to be detected is abnormal.
4. The method of claim 3, wherein the obtaining a continuity indicator, the continuity indicator determined based on time intervals of different time period data reporting of the historical traffic data comprises:
determining a historical reporting date which is the same as the reporting date Zhou Xuri in the historical traffic data based on the reporting date of the traffic data to be detected;
acquiring corresponding historical traffic data based on the historical reporting date;
the continuity indicator is determined based on the historical traffic data.
5. The method according to claim 2, wherein the detection index corresponding to the characteristic data amount value is a characteristic data amount index, and the characteristic data amount value is determined based on the data amounts reported by traffic data to be detected in different time periods; the determining whether the traffic data to be detected is abnormal based on the characteristic information and the corresponding detection index comprises:
Acquiring a data quantity index, wherein the data quantity index is determined based on the data quantity reported by historical traffic data in different time periods;
and if the data quantity characteristic value does not accord with the data quantity index of the corresponding time period, determining that the traffic data to be detected is abnormal.
6. The method according to claim 2, wherein the detection index corresponding to the timeliness characteristic value is a timeliness index, and the timeliness characteristic value is determined based on a difference between a generation time and a warehouse-in time of traffic data to be detected; the determining whether the traffic data to be detected is abnormal based on the characteristic information and the corresponding detection index comprises:
determining the timeliness index based on a preset time delay threshold;
if the timeliness characteristic value does not accord with the timeliness index, determining that the traffic data to be detected is abnormal.
7. The method according to claim 2, wherein the detection index corresponding to the integrity feature information is an integrity index; the determining whether the traffic data to be detected is abnormal based on the characteristic information and the corresponding detection index comprises:
determining the integrity index based on a predetermined traffic data type;
And if the integrity characteristic information does not accord with the integrity index, determining that the traffic data to be detected is abnormal.
8. The method according to claim 2, wherein the detection index corresponding to the validity feature value is a validity index, and the validity feature value is determined based on a valid data proportion of traffic data to be detected; the determining whether the traffic data to be detected is abnormal based on the characteristic information and the corresponding detection index comprises:
determining the effectiveness index based on a preset effectiveness threshold;
and if the validity characteristic value does not accord with the validity index, determining that the traffic data to be detected is abnormal.
9. A traffic data abnormality detection device, characterized by comprising:
the acquisition module is used for acquiring traffic data to be detected from the traffic management service system and extracting characteristic information of the traffic data to be detected;
the first determining module is used for determining whether the traffic data to be detected is abnormal or not based on the characteristic information;
and the second determining module is used for determining that the associated equipment of the traffic data to be detected is abnormal if the traffic data to be detected is abnormal.
10. A traffic maintenance system, the traffic maintenance system comprising: an access device for accessing a traffic management service system through a data access service and obtaining traffic data to be detected, a traffic data anomaly detection device for anomaly detecting the traffic data to be detected, and an alarm device for alarming based on the detection result, as set forth in claim 9.
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| CN116561707B (en) * | 2023-07-11 | 2023-09-05 | 南京大全电气研究院有限公司 | Transformer fault checking and early warning method and system |
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