Method for judging setting rationality of monitoring equipment

Abstract

According to the method for judging the setting rationality of the monitoring equipment, the working state of the monitoring equipment can be monitored in real time, the monitoring equipment with unreasonable setting can be automatically screened out, the screening working efficiency is high, manual participation is not needed, and the probability of traffic problems caused by unreasonable setting of the monitoring equipment is effectively reduced. According to the method, on the basis of vehicle abnormal driving behavior data collected by all monitoring devices in a to-be-judged area, the monthly data collection quantity data change trend of each device and the monthly average collection quantity data change trend of all devices in the area are obtained through calculation, a suspected monitoring device is found out according to preset screening conditions, and the suspected monitoring device is used for judging the abnormal driving behavior of a vehicle. And then the data change trend of the monthly traffic flow of the suspected monitoring equipment is confirmed, and the monitoring equipment with unreasonable arrangement is confirmed.

Description

Technical Field

[0001] The invention relates to the technical field of intelligent traffic control, and in particular to a method for judging the rationality of setting of monitoring equipment. Background Art

[0002] At present, the number of road monitoring equipment in the country is increasing year by year, and the number of traffic accidents is also increasing. Reasonable monitoring equipment settings can significantly reduce accidents and improve traffic order. However, unreasonable, non-compliant, and unnecessary traffic monitoring equipment will affect actual traffic operations. Although traffic monitoring equipment will be scientifically evaluated and optimized before installation to select the most suitable location for actual installation, because urban construction changes frequently, road conditions will also change accordingly, and the rationality of monitoring equipment also needs to be re-evaluated.

[0003] In actual applications, all data on abnormal driving behaviors of vehicles monitored by monitoring equipment in each city will be stored in real time in a table, which is called the monitoring result record table, and the dedicated electronic monitoring equipment at each intersection will also store the traffic flow data on this road in the daily traffic flow table. Due to the continuous increase in the number of vehicles in the city, the data in the monitoring result record table and the daily traffic flow table are very large, especially the daily traffic flow table needs to be stored in a distributed database specifically for big data.

[0004] In actual work, it is found that the rationality of monitoring equipment does not change suddenly, but is a process of change. At present, traffic control departments in various places lack effective supervision means and cannot judge the rationality of monitoring equipment settings based on the changing trends of these massive data in real time. At the same time, because most of the monitoring equipment affected by urban construction does not have obvious regularity, it is difficult to find problems in advance. In many cases, they are remedied after the fact, which brings great inconvenience to actual production and life. Summary of the invention

[0005] In order to solve the problem in the prior art that changes in the setting environment of the monitoring equipment lead to changes in the rationality of the setting of the monitoring equipment, which makes it difficult to discover the problem in advance, the present invention provides a method for judging the rationality of the setting of the monitoring equipment, which can monitor the working status of the monitoring equipment in real time, and automatically screen out monitoring equipment with unreasonable settings. The screening work is efficient and does not require manual participation, which effectively reduces the probability of traffic problems caused by unreasonable settings of the monitoring equipment.

[0006] The technical solution of the present invention is as follows: a method for judging the rationality of monitoring equipment settings, characterized in that it includes the following steps:

[0007] S1: Determine the area to be evaluated that needs to be analyzed and monitored, and find all monitoring devices set up in the area to be evaluated based on the monitoring equipment filing information, which are recorded as: equipment to be evaluated;

[0008] Set the data collection time period for judgment; the data collection time period is greater than 1 month, in months;

[0009] S2: In the historical data of the monitoring result record table saved by the system, for each device to be judged, the data collection amount of each month in the judgment data collection period is counted from the previous month forward; based on the monthly data collection amount of each device, the monthly average collection amount of all devices is calculated;

[0010] S3: Calculate data change trend;

[0011] The data change trend includes: the monthly data change trend of each device to be judged within the data collection period; and the average data change trend of all devices to be judged is calculated and analyzed based on the average data collection volume per month;

[0012] S4: All devices to be judged are screened according to the preset screening conditions, and the devices to be judged that meet all the conditions are suspected monitoring devices with unreasonable settings, and the rest of the devices are normal devices;

[0013] The screening conditions include:

[0014] Condition 1: The trend of the monthly data collection volume is increasing;

[0015] Condition 2: The difference between the change trend of the monthly data collection volume and the change trend of the monthly average collection volume of all devices is greater than the preset abnormal threshold;

[0016] S5: confirming the geographical location of the suspected monitoring device, and finding the corresponding vehicle passing data in the daily vehicle flow table stored in the system according to the geographical location;

[0017] The geographical location includes: a road code and a road section code;

[0018] S6: extracting the monthly traffic flow of the suspected monitoring device from the daily traffic flow table within the period of the judgment data collection starting from the previous month;

[0019] Calculate the data change trend corresponding to the monthly vehicle flow of the suspected monitoring device;

[0020] S7: Determine whether the data change trend of the monthly vehicle flow rate is increasing or decreasing;

[0021] If it is a decrease, the suspected monitoring device is set as an unreasonable device;

[0022] Otherwise, end this judgment.

[0023] It is further characterized by:

[0024] It also includes the following steps:

[0025] S8: Set monitoring interval time period INTE;

[0026] Every INTE time period, steps S1 to S7 are executed cyclically to judge the rationality of all monitoring devices in the area to be judged;

[0027] The method for calculating the data change trend comprises the following steps:

[0028] a1: For a set of data (x1,y1),(x2,y2)...(x n ,y n ), the least square method is used to simulate the trend curve and the equation is obtained:

[0029] y = a + bx;

[0030] Among them, x takes the value of x 1, x2...x i ...x n , y takes the value of y 1, y2...y i ....y n , 1≤i≤n;

[0031] n is the total number of months included in the data collection period; x i Indicates the actual month corresponding to the i-th month, y i . represents the trend data of the i-th month;

[0032] a2: Solve constants a and b to minimize the sum of squares of deviations between the actual value and the simulated value;

[0033] make is the minimum,

[0034] a3: According to the extreme value principle get:

[0035]

[0036] a4: Solve the simultaneous equations to find a and b:

[0037]

[0038] The trend data includes: the monthly data collection volume change trend of a single device, the monthly average collection volume change trend of all devices, or the monthly traffic volume change trend of a single device;

[0039] The calculation method of the screening condition includes:

[0040] Condition 1: The trend of the monthly data collection volume is increasing;

[0041] After simulating the trend curve using the least squares method, let the trend of the monthly data collection volume be b i ;

[0042] b i >0, indicating that the trend of data collection volume is increasing;

[0043] Condition 2: The difference between the change trend of the monthly data collection volume and the change trend of the monthly average collection volume of all devices is greater than the preset abnormal threshold;

[0044] Assume that the average data change trend is b j , the abnormal threshold is c, then the calculation method of condition 2 is:

[0045] b i -b j >c;

[0046] The data collection period is set to be greater than or equal to 12 months.

[0047] The present application provides a method for judging the rationality of monitoring equipment settings. Based on the data of abnormal driving behaviors of vehicles collected by all monitoring equipment in the area to be judged, the monthly data collection volume data change trend of each device and the monthly average collection volume data change trend of all devices in the area are calculated, and the suspected monitoring equipment is found according to the preset screening conditions, and then the data change trend of the monthly traffic volume of the suspected monitoring equipment is confirmed to confirm the monitoring equipment with unreasonable settings; the method does not need to add new hardware equipment, and judges the data collection volume and traffic volume of the road where the monitoring equipment is located based on the data already collected by the existing monitoring equipment, comprehensively considers the two factors of the data collection volume reported by the monitoring equipment and the traffic volume of the road section, and timely and quickly screens out the monitoring equipment with unreasonable settings from a large number of monitoring equipment, so as to achieve an effective evaluation of the rationality of the settings of the monitoring equipment; in the present application, based on the least squares method to simulate the change trend curve, not only can the data trend be accurately expressed, but also the calculation process is simple and the operation efficiency is high, which is particularly suitable for the simulation and calculation of massive data of road monitoring equipment, has low requirements on hardware performance, reduces the cost of system operation, and is suitable for the regular and regular execution of the method for continuous and automatic judgment of the rationality of monitoring equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 A flowchart of an example of a method for judging the rationality of monitoring equipment settings provided in this application;

[0049] Figure 2 This is an example of a curve showing the monthly data change trend of device 1;

[0050] Figure 3 This is an example of a curve showing the monthly data change trend of device 2;

[0051] Figure 4 An example of a curve showing the average data change trend of device 1 and device 2;

[0052] Figure 5 This is an example of a curve that simulates the flow rate change trend. DETAILED DESCRIPTION

[0053] Generally speaking, each monitoring device in the city is managed by a designated traffic control department. Once an unreasonable monitoring device appears, the management department of the monitoring device needs to take countermeasures. Therefore, when this method is implemented specifically, it will be promoted according to the management department. The management department is the provincial, municipal, and district-level administrative region. Because reasonable monitoring equipment is set up, after long-term operation, the number of accidents on the road section where it is located will show a downward trend, while the traffic flow will show an upward trend. Therefore, this application judges the rationality of the monitoring equipment based on these two perspectives.

[0054] The basic data used for calculation in this application are two types of data reported by monitoring equipment to the management department. The first type is: the abnormal driving behavior record data of monitored vehicles reported by all equipment, and the second type is: the traffic flow data of the road section to be judged, which will only be reported by certain specified types of monitoring equipment on the road section in the area to be judged. In the following content of this application, "data collection volume" refers to the "abnormal driving behavior collection volume data" collected by the monitoring equipment, and "traffic flow" refers to the "road section traffic flow".

[0055] The present application includes a method for judging the rationality of monitoring equipment settings, which includes the following steps.

[0056] S1: Determine the area to be evaluated that needs to be analyzed and monitored, find all monitoring devices installed in the area to be evaluated based on the monitoring equipment filing information, and record them as: equipment to be evaluated; set the data collection time period for judgment.

[0057] Reference Figure 1 , determine the area to be analyzed and monitored, that is, determine the monitoring equipment management department to be analyzed. Based on the monitoring equipment filing information, determine the equipment within the jurisdiction of the target management department. The specific method is:

[0058] The monitoring equipment registration information form should include field information such as equipment number, management department, road code, section code, location, etc. If any field is empty, it may be a mobile monitoring device and should be removed. If it is a provincial management department, the first two digits of the management department code in the monitoring equipment registration information form should be matched; for municipal management departments, the first four digits of the management department code should be matched; for district management departments, the first six digits of the management department code should be matched.

[0059] The data collection period is greater than 1 month, in months. In practical applications, the data collection period is set according to the characteristics of urban construction. In order to ensure the validity of the fitted trend curve, the data collection period is usually set to be greater than or equal to 12 months.

[0060] In this embodiment, the area to be evaluated is a district-level administrative area in a prefecture-level city in Jiangsu Province, and the data collection period is set to 12 months.

[0061] In specific implementation, if the target management department is a district-level administrative area, the first 6 digits of the management department should be used to match in the monitoring equipment filing table, and the mobile equipment with empty road codes should be removed, resulting in 129 sets of equipment in use. For the convenience of statistics, a statistical intermediate table is established for these 129 sets of equipment. The table fields are equipment number, management department, road code, section code, location, activation time, violation 1, violation 2, violation 3... violation 12, flow 1, flow 2... flow 12. The equipment number, management department, road code, section code, and location are obtained from the equipment filing table.

[0062] S2: In the historical data of the monitoring result record table saved by the system, for each device to be judged, the data collection volume for each month during the judgment data collection period is counted from the previous month forward; based on the monthly data collection volume of each device, the monthly average collection volume of all devices is calculated; based on the monthly data collection volume of each device, the monthly average collection volume of all devices is calculated.

[0063] The monitoring data fields included in the monitoring result record table are: abnormal driving behavior occurrence time, equipment number, license plate type, license plate number and other field information. Usually, in the monitoring result record table, the time of occurrence of abnormal driving behavior formally recorded in the first item of each device collection is determined according to the monitoring device number and recorded as the device activation time. If the activation time of a monitoring device to the calculation time is less than the data collection time period, because the amount of data is not enough to accurately express the status of the monitoring device, it should be removed from the device to be judged.

[0064] In the specific implementation, in the data history of abnormal driving behavior of vehicles in the monitoring result record table, statistics are collected for each monitoring device for 12 consecutive months starting from the previous month. The detailed steps are:

[0065] c1: Associate the data in the monitoring result record table with the device number;

[0066] c2: Based on the abnormal driving behavior occurrence time field in each piece of data, the abnormal driving behavior data submitted by each monitoring device is counted for 12 consecutive months starting from the previous month, including the previous month;

[0067] c3: If the amount of monitoring data collected in a certain month is 0, the device may be deactivated, and the amount of abnormal driving behavior collected for another month should be counted, and so on, until a complete 12 months of abnormal driving behavior collection data is available.

[0068] This method judges the rationality of the monitoring equipment based on the recent historical data within the specified data collection time period. It does not analyze data anomalies caused by sudden or accidental events, but judges the rationality of the monitoring equipment based on long-term historical data within a time period to ensure accurate judgment results. Moreover, whether the initial setting of the monitoring equipment is unreasonable or the subsequent rationality changes caused by urban construction and other reasons can be presented by the historical data in the recent period, ensuring that this method is more practical.

[0069] In this embodiment, in this application embodiment, the collected data of abnormal driving behaviors of vehicles for 12 months are sequentially filled into the fields of collected data 1, collected data 2, ..., collected data 12 of the statistical intermediate table, so as to obtain the following Table 1.

[0070] Table 1: Example of monthly data collection volume

[0071] serial number 1 2 3 4 5 6 7 8 9 10 11 12 Device 1 878 887 880 867 824 522 1176 1053 1085 1046 1290 1266 Device 2 649 718 639 619 651 654 594 236 766 364 552 633 average 269 255 239 218 207 202 227 228 238 224 191 173

[0072] In Table 1, the numbering of the first row is set as follows: the collected months are sorted in chronological order, and the 12th number corresponds to the data of the previous month. The data corresponding to the "average" in the third row is the monthly average collection volume of all the devices to be judged.

[0073] S3: Calculate data change trend;

[0074] The data change trend includes: the monthly data change trend of each device to be judged within the data collection period; and the average data change trend of all devices to be judged is calculated and analyzed based on the average data collection volume each month.

[0075] The calculation method of the data change trend in this application includes the following steps:

[0076] a1: For a set of data (x1,y1),(x2,y2)...(x n ,y n ), the least square method is used to simulate the trend curve and the equation is obtained:

[0077] y=a+bx; so that the straight line simulates the changing trend of the amount of monitoring data submitted by the monitoring device as much as possible, where a and b are constants to be determined;

[0078] Among them, x takes the value of x1, x2...x i ...x n , y takes the value of y 1, y2...y i ....y n , 1≤i≤n;

[0079] n is the total number of months included in the data collection period. In this embodiment, n is 12;

[0080] x i Indicates the actual month corresponding to the i-th month, and the specific value of i corresponds to the number in the numbered row in Table 1;

[0081] y i . represents the trend data of the i-th month, corresponding to the data under each number;

[0082] a2: Solve constants a and b to minimize the sum of squares of deviations between the actual value and the simulated value;

[0083] make is the minimum,

[0084] a3: According to the extreme value principle get:

[0085]

[0086] a4: Solve the simultaneous equations to find a and b:

[0087]

[0088] a and b are the constants to be determined, where b reflects the magnitude of the changing trend.

[0089] The trend data solved by the above method in this application include: monthly data change trend, average data change trend or monthly traffic flow change trend. The trend changes of the three types of data can be calculated based on the methods of steps a1 to a4.

[0090] In this embodiment, based on the data in Table 1, the curve reference of the monthly data change trend and the average data change trend is obtained according to the above method. Figure 2 , Figure 3 and Figure 4 , the specific equation is:

[0091] Device 1: y = 40.168x + 720.08;

[0092] Device 2: y = -14.563x + 684.24;

[0093] Average: y = -5.3914x + 257.76.

[0094] S4: All devices to be judged are screened according to the preset screening conditions. The devices to be judged that meet all the conditions are suspected monitoring devices with unreasonable settings, and the rest of the devices are normal devices;

[0095] Filters include:

[0096] Condition 1: The trend of the monthly data collection volume of a single device is increasing;

[0097] Condition 2: The difference between the monthly data collection volume change trend of a single device and the monthly average data collection volume change trend of all devices is greater than the preset abnormal threshold.

[0098] Specifically, the calculation method of the screening conditions includes:

[0099] Condition 1: The trend of the monthly data collection volume of a single device is increasing;

[0100] After simulating the trend curve using the least squares method, let the trend of the monthly data collection volume be b i ;

[0101] b i >0, indicating that the trend of monthly data collection volume is increasing;

[0102] Condition 2: The difference between the monthly data collection trend of a single device and the monthly average data collection trend of all devices is greater than the preset abnormal threshold;

[0103] Assume that the average data change trend is b j , the abnormal threshold is c, then the calculation method of condition 2 is:

[0104] b i -b j >c.

[0105] In this embodiment, the monthly average data collection volume change trend of all devices is -5.3914, the preset threshold c=20, and the monthly data collection volume change trend of device 1 is 40.168, which exceeds the threshold and is a suspected monitoring device with unreasonable settings. Device 2 is a normal device.

[0106] S5: confirming the geographic location of the suspected monitoring device, and finding the corresponding vehicle passing data in the daily vehicle flow table saved by the system according to the geographic location;

[0107] Geographic location includes: road code and section code;

[0108] S6: From the daily traffic flow table, extract the monthly traffic flow of the suspected monitoring equipment during the period of data collection for judgment starting from the previous month.

[0109] Similar to the method of extracting data from the monitoring result record table, the method of extracting monthly traffic flow of a road section includes the following steps:

[0110] The daily traffic flow table includes: statistical date, road code, section code, traffic volume and other field information.

[0111] d1: Correlate the road code and section code in the suspected equipment registration information table with the road code and section code in the daily traffic flow table.

[0112] d2: Based on the statistical date field, the traffic volume at each monitoring device location is counted for 12 consecutive months starting from the previous month;

[0113] d3: If the traffic volume in a certain month is 0, the traffic volume of the previous month should be counted, and so on, until there is a complete 12-month traffic flow data.

[0114] S7: Calculate the monthly traffic flow rate change trend according to the monthly traffic flow rate, and determine whether the monthly traffic flow rate data change trend is increasing or decreasing;

[0115] If it is a decrease, the suspected monitoring device is set as an unreasonable device;

[0116] Otherwise, end this judgment.

[0117] That is: based on the least squares method, find out the traffic flow change curve y=a+bx, and the equipment with b<0 is the equipment with unreasonable settings.

[0118] In this embodiment, the traffic flow is matched in the daily traffic flow table according to the road code 60029 and the section code 1177 of device 1, and the traffic flow for 12 consecutive months is filled into the traffic 1, traffic 2, traffic 3...traffic 12 fields in the statistical intermediate table. The data is shown in Table 2 below.

[0119] Table 2: Monthly traffic flow table

[0120] serial number 1 2 3 4 5 6 7 8 9 10 11 12 Device 1 537119 511502 517539 498400 549634 477342 288848 526468 530056 429410 420214 390007

[0121] In Table 2, the numbering of the first row is set as follows: the collected months are sorted in chronological order, and the 12th number corresponds to the data of the previous month.

[0122] In this embodiment, according to the traffic flow data in Table 2, the traffic flow change curve of the suspected device 1 can be obtained. Figure 5 It can be seen that the traffic volume of device 1 is on a downward trend. Combined with the above-mentioned upward trend of violations, it can be seen that the setting of device 1 is unreasonable.

[0123] S8: Set monitoring interval time period INTE;

[0124] Every INTE time period, steps S1 to S7 are executed cyclically to achieve continuous rationality judgment on all monitoring devices in the area to be judged.

[0125] The monitoring interval time period INTE is set according to the frequency of changes in the city's urban construction. In this embodiment, the monitoring interval time period INTE is set to 2 months.

[0126] After using the technical solution of the present application, local traffic management departments can obtain traffic data such as the number of abnormal driving behaviors collected by all monitoring devices in their jurisdiction, abnormal driving behaviors or traffic flow trends in the location, which is convenient for analysis and judgment. Through the above data, the rationality of the setting of the monitoring equipment can be effectively evaluated, and the two factors of the traffic flow of the road section and the data collection volume of abnormal driving behaviors reported by the monitoring equipment can be comprehensively considered to timely and quickly screen out the unreasonable monitoring equipment from a large number of monitoring equipment. Based on the technical solution of the present application, there is no need to add new hardware equipment. The data of the existing road-mounted vehicle checkpoint equipment and special monitoring equipment can be used as the data source of the present application. Through computer programming, the process is simple, the operation is fast, and the efficiency is high. The technical solution of the present application can not only actively identify monitoring equipment with unreasonable settings, but also provide strong support for the evaluation of the law enforcement effect of existing monitoring equipment.

Claims

1. A method for judging the rationality of monitoring equipment settings, characterized in that: It includes the following steps: S1: Determine the area to be evaluated that needs to be analyzed and monitored, and find all monitoring devices set up in the area to be evaluated based on the monitoring equipment filing information, which are recorded as: equipment to be evaluated; Set the data collection time period for judgment; the data collection time period is greater than 1 month, in months; S2: In the historical data of the monitoring result record table saved by the system, for each device to be judged, the amount of data collected each month in the judgment data collection period is counted from the previous month forward; Based on the monthly data collection volume of each device, calculate the monthly average collection volume of all devices; S3: Calculate data change trend; The data change trend includes: the monthly data change trend of each device to be judged within the data collection period; and the average data change trend of all devices to be judged is calculated and analyzed based on the average data collection volume per month; S4: All devices to be judged are screened according to the preset screening conditions, and the devices to be judged that meet all the conditions are suspected monitoring devices with unreasonable settings, and the rest of the devices are normal devices; The screening conditions include: Condition 1: The trend of the monthly data collection volume is increasing; Condition 2: The difference between the change trend of the monthly data collection volume and the change trend of the monthly average collection volume of all devices is greater than the preset abnormal threshold; S5: confirming the geographical location of the suspected monitoring device, and finding the corresponding vehicle passing data in the daily vehicle flow table stored in the system according to the geographical location; The geographical location includes: a road code and a road section code; S6: extracting the monthly traffic flow of the suspected monitoring device from the daily traffic flow table within the period of the judgment data collection starting from the previous month; Calculate the data change trend corresponding to the monthly vehicle flow of the suspected monitoring device; S7: Determine whether the data change trend of the monthly vehicle flow rate is increasing or decreasing; If it is a decrease, the suspected monitoring device is set as an unreasonable device; Otherwise, end this judgment.

2. A method for judging the rationality of monitoring equipment settings according to claim 1, characterized in that: It also includes the following steps: S8: Set the monitoring interval time period INTE; Every INTE time period, steps S1 to S7 are executed cyclically to make rationality judgments on all monitoring devices in the area to be judged.

3. A method for judging the rationality of monitoring equipment settings according to claim 1, characterized in that: The method for calculating the data change trend comprises the following steps: a1: For a set of data (x1,y1),(x2,y2)...(x n ,y n ), the least square method is used to simulate the trend curve and the equation is obtained: y = a + bx; Among them, x takes the value of x 1, x2...x i ...x n , y takes the value of y 1, y2...y i ....y n , 1≤i≤n; n is the total number of months included in the data collection period; x i Indicates the actual month corresponding to the i-th month, y i . represents the trend data of the i-th month; a2: Solve constants a and b to minimize the sum of squares of deviations between the actual value and the simulated value; make is the minimum, a3: According to the extreme value principle get: a4: Solve the simultaneous equations to find a and b:

4. A method for judging the rationality of monitoring equipment settings according to claim 3, characterized in that: The trend data includes: a monthly data collection volume change trend of a single device, a monthly average collection volume change trend of all devices, or a monthly traffic flow change trend of a single device.

5. A method for judging the rationality of monitoring equipment settings according to claim 1, characterized in that: The calculation method of the screening conditions includes: Condition 1: the trend of the monthly data collection volume is increasing; After simulating the trend curve using the least squares method, let the trend of the monthly data collection volume be b i ; b i >0, indicating that the trend of data collection volume is increasing; Condition 2: The difference between the change trend of the monthly data collection volume and the change trend of the monthly average collection volume of all devices is greater than the preset abnormal threshold; Assume that the average data change trend is b j , the abnormal threshold is c, then the calculation method of condition 2 is: b i -b j >c。 6. A method for judging the rationality of monitoring equipment settings according to claim 1, characterized in that: The data collection period is set to be greater than or equal to 12 months.

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