Method for automatically detecting stations during operation of public transport vehicle

Abstract

The invention discloses an automatic station detection method during bus operation, and relates to the technical field of intelligent traffic, and the method comprises the steps of S1, station data detection, S2, departure parameter adjustment, S3, data continuous collection, S4, data analysis and S5, feedback processing. A two-way sensing network is formed in combination with vehicle-mounted positioning information, so that the real-time performance and accuracy of dynamic scheduling of the public transportation system are improved; physical verification and identification are realized through periodic data acquisition and multi-source data fusion, the abnormal behavior early warning capability and a responsibility tracing mechanism are enhanced, a monitoring-analysis-optimization complete management closed loop is formed, and information synchronization and decision closed loop are realized through channels such as an electronic screen; an innovative solution is provided for digital and refined operation of urban public transportation, and the travel experience and safety guarantee of passengers are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent transportation, in particular to a bus station automatic detection method during bus operation. BACKGROUND

[0002] With the acceleration of urbanization, the demand for urban transportation is increasing, and the operation and management efficiency of the bus station as the core node of passenger flow distribution directly affects the travel experience of citizens and the efficiency of urban transportation. The traditional bus station detection relies on manual or fixed electronic fence, which has the problems of poor real-time performance and weak adaptability. With the continuous expansion of urban rail transit network and the popularity of diversified travel modes, the traditional bus system is facing increasing passenger diversion pressure, and its dominant position in the public transportation system is showing a gradual weakening trend. Therefore, in order to realize the intelligent management of bus stations and improve the operation efficiency and passenger experience, it is extremely necessary to conduct automatic detection of bus stations.

[0003] The prior art such as the invention application patent with the announcement number CN107742434A discloses a bus station automatic detection method during bus operation, which is aimed at complex bus line scenarios, and realizes automatic detection of the station and dynamic updating of the station sequence by presetting bidirectional station coordinates and attribute data, combining real-time position data, driving direction and wireless communication verification. The multi-dimensional data is fused to construct a geographic fence verification model, which effectively solves the misjudgment problem caused by trajectory intersection and guarantees the accuracy and real-time performance of station detection in multi-type line operation.

[0004] It is found that the above-mentioned scheme focuses on the one-way monitoring of the target station by the vehicle-mounted terminal in the prior art, and lacks the active sensing ability of the station end to the parked vehicle. The one-way information publishing mode causes passengers to be unable to obtain real-time dynamic information, reduces the efficiency of transfer scheduling, and reduces the attractiveness of bus travel. The actual bearing state of the station cannot be obtained, which makes it difficult to support real-time scheduling decision. There is a lack of physical verification mechanism of the station end, which cannot identify abnormal parking behavior of the vehicle. The neglect of the prior art in these aspects on the one hand limits the improvement of intelligent management of the bus system, which leads to the lack of real-time data support for dynamic scheduling decision, and restricts the fine operation of the bus system. On the other hand, the lack of physical verification mechanism leads to the inability to intervene in abnormal parking behavior in time, which causes safety hazards and service disputes, and affects the travel experience of passengers. SUMMARY

[0005] The purpose of the present application is to provide a bus station automatic detection method during bus operation, which solves the problems in the background art.

[0006] To solve the above technical problems, the application adopts the following technical solutions: The application provides a bus vehicle operation site automatic detection method, which comprises the following steps: site data detection, real-time detection of the flow data of the target site.

[0007] Departure parameter adjustment, obtaining the passing rate restriction factors of the bus vehicles at the target site, calculating the expected arrival time of the bus vehicles at the target site, adjusting the departure frequency of the bus vehicles at the target site, and adjusting and obtaining the corrected arrival time of the bus vehicles at the target site.

[0008] Continuous data collection, continuous collection of the flow data, bus vehicle stop data and facility monitoring data of the target site for one period.

[0009] Data analysis, determining the transfer schedule of the target site based on the flow data of the target site, determining the warning responsibility personnel of the target site based on the bus vehicle stop data of the target site, and evaluating the abnormal type of the target site based on the bus vehicle stop data and the facility monitoring data of the target site.

[0010] Feedback processing, displaying the corrected arrival time of the bus vehicles at the target site on the electronic display screen of the target site, and feeding back the abnormal type and the transfer schedule of the target site.

[0011] The application has the following advantages: 1. The application constructs a bidirectional perception system in site data detection, deploys site end flow data detection equipment, obtains environmental parameters such as passenger flow and vehicle stop state in real time, forms a closed-loop monitoring network in combination with vehicle positioning data, and improves the accuracy of bus vehicle running time prediction through multi-dimensional data fusion and intelligent algorithm optimization, thereby providing reliable data basis for dynamic scheduling.

[0012] 2. The application constructs a multi-dimensional evaluation system in data analysis, establishes an abnormal behavior physical verification mechanism by fusing stop data and facility monitoring data, can effectively identify abnormal stop behavior of bus vehicles, and generates an abnormal type evaluation model based on big data analysis. The mechanism fills the gap in the physical verification level of the prior art, and provides technical support for safety hazard early warning and service dispute handling.

[0013] 3. The application establishes a periodic data collection mechanism in continuous data collection, continuously collects site flow data, vehicle stop data and facility state data, and forms a complete dynamic monitoring data set. The design provides data assets for long-term optimization of the bus system, enables the system to analyze passenger flow rules, equipment usage patterns and other deep information through historical data, and promotes the evolution of management decision from passive response to active optimization. BRIEF DESCRIPTION OF DRAWINGS

[0014] In order to make the technical solutions in the embodiments of the present application or the prior art clearer, the accompanying drawings needed in the embodiments or prior art description will be briefly introduced below. Obviously, the accompanying drawings in the following description only aim to some of the embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative effort on the basis of these drawings.

[0015] Figure 1 The schematic diagram for implementing the method of the present application is shown. DETAILED DESCRIPTION

[0016] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative effort belong to the protection scope of the present application.

[0017] Referring to Figure 1 The present application provides a bus station automatic detection method during bus operation, which comprises: S1. Station data detection, detecting the flow data of the target station.

[0018] In the specific embodiments of the present application, the flow data comprises the number of passengers, the traffic volume, the number of active interactions between each bus and the rest of the buses, a bidirectional perception system is constructed to obtain the actual carrying state of the station, which makes up for the deficiency of ignoring this aspect in the prior art, and a closed-loop monitoring network is formed in combination with the vehicle positioning data to provide a data basis for subsequent dynamic scheduling.

[0019] It should be noted that the number of active interactions is the total number of active interactions between each bus and the rest of the buses in a period, and the passenger X is monitored by the face information collection device at the target station to get off the A bus and transfer to the B bus at the target station, or the transfer behavior of the passenger at the target station between different lines is counted in combination with the bus card record or mobile application positioning data to form the number of active interactions.

[0020] S2. Departure parameter adjustment, obtaining the passing speed restriction factors of the buses at the target station, calculating the expected arrival time of the buses at the target station, adjusting the departure frequency of the buses at the target station, and adjusting and obtaining the corrected arrival time of the buses at the target station.

[0021] In the specific embodiments of the present application, the specific analysis method of the expected arrival time length of the several bus vehicles of the target station is as follows: the expected arrival time length of the several bus vehicles of the target station is obtained by using the passing speed restriction factors of the several bus vehicles of the target station and combining the data fed back by the vehicle-mounted speed monitor, and the time model is as follows: .

[0022] It should be noted that, The average speed of the mth bus vehicle on the conventional road section is the average speed of the non-congestion road section that has been traveled by the bus vehicle fed back by the vehicle-mounted speed monitor.

[0023] The expected arrival time length is obtained by processing , wherein is the number of each bus vehicle, , is an arbitrary integer greater than 2, is the number of each congestion road section, , is an arbitrary integer greater than 2, wherein is the total distance of the mth bus vehicle from the target station, is the length of the nth congestion road section on the way of the mth bus vehicle, is the passing time length of the nth congestion road section on the way of the mth bus vehicle.

[0024] In the specific embodiments of the present application, the specific method for regulating the departure frequency of the several bus vehicles of the target station is as follows: the carrying coefficient of the target station is determined according to the passenger flow and the vehicle flow of the target station , wherein is the average passenger flow of the target station in the detection time period, is the reference passenger flow of the target station stored in the database, is the average vehicle flow of the target station in the detection time period, is the reference vehicle flow of the target station stored in the database, is the weight coefficient of the passenger flow and the vehicle flow, and ; The regulated departure frequency is formed according to the carrying coefficient of the target station , wherein F is the adjusted bus vehicle departure frequency of the target station, is the basic departure frequency, is the departure frequency adjustment value corresponding to the unit carrying coefficient change amount, is the target carrying coefficient of the target station It should be noted that the bearing coefficient of the target station reflects the passenger flow and vehicle flow of the target station. If the bearing coefficient is too large, it means that the bearing capacity of the target station is insufficient, and the departure frequency needs to be increased to relieve the personnel and reduce the bearing number of the target station. Wherein is the basic departure frequency, i.e. the standard departure frequency when there is no load, is the departure frequency adjustment value corresponding to the unit bearing coefficient change amount, which specifically represents the influence of bearing coefficient change on the departure frequency. The departure frequency adjustment value corresponding to the unit bearing coefficient change amount is specifically set by a traffic management expert according to historical data, and is usually between 0.3-0.8, for example, in order to avoid too frequent adjustment of the departure frequency of the target station, and also to avoid the phenomenon of insufficient response of the departure frequency of the target station, usually set to 0.5, which can also be set to different parameters according to different time periods or regions of the target station, is the target bearing coefficient of the target station, i.e. the ideal load level that the target station wants to maintain. The safety operation and efficiency of the station need to be considered, and it is usually between 0.7-0.9, which avoids excessive congestion and maintains resource utilization, and guarantees the passenger riding experience.

[0025] In a specific embodiment of the present application, the departure frequency of the several public transport vehicles of the target station is regulated, the arrival time of the several public transport vehicles of the target station is obtained after correction processing, and the driving speed is regulated. The specific method is: the estimated arrival time of the several public transport vehicles of the target station is substituted into the arrival time model , and the time when the several public transport vehicles are expected to arrive at the target station is output, wherein is the current time point.

[0026] The several public transport vehicles of the target station are sorted according to the order of the expected arrival time, and the sorted several public transport vehicles of the target station are obtained, and the expected arrival time of the several public transport vehicles of the target station is input into the correction processing time model The several public transport vehicles of the target station are sorted according to the order of the expected arrival time, and the sorted several public transport vehicles of the target station are obtained, and the expected arrival time of the several public transport vehicles of the target station is input into the correction processing time model , and the arrival time of the several public transport vehicles of the target station obtained after correction processing is output ; wherein is the number of each public transport vehicle, is any integer greater than 2, wherein is the time when the several public transport vehicles arrive at the target station, and d is the set minimum interval time.

[0027] It should be noted that in order to regulate the arrival time, the driving speed of the bus vehicles is regulated according to the arrival time of the bus vehicles of the target station obtained by the modified processing of the target station the number of each bus vehicle after sorting, , an arbitrary integer greater than 2, the number of each congestion section, , an arbitrary integer greater than 2.

[0028] S3. Data is continuously collected, and the flow data, bus vehicle stop data, and facility monitoring data of the target station for one period are continuously collected.

[0029] In specific embodiments of the present application, the bus vehicle stop data is the arrival time, departure time and stop model of each stop.

[0030] In specific embodiments of the present application, the facility monitoring data is a three-dimensional model of several devices of the target station, forming a complete dynamic monitoring data set, providing data assets for long-term optimization of the bus system.

[0031] S4. Data analysis, based on the flow data of the target station for one period, determine the transfer schedule of the target station, based on the bus vehicle stop data of the target station for one period, determine the several warning responsibility personnel of the target station, based on the bus vehicle stop data and facility monitoring data of the target station for one period, evaluate the abnormal type of the target station.

[0032] In specific embodiments of the present application, the determination of the transfer schedule of the target station is specifically as follows: according to the number of active interactions of each bus vehicle of the target station, the correlation degree between each bus vehicle is obtained . the number of each bus vehicle, , an arbitrary integer greater than 2, the number of the remaining bus vehicles related to each bus vehicle, , an arbitrary integer greater than 2, wherein is the correlation degree between the v-th bus vehicle and the v'-th bus vehicle, is the total number of active interactions between the v-th bus vehicle and the v'-th bus vehicle within one period, when , the remaining bus vehicles are recorded as the related bus vehicles of the bus vehicle, and the corresponding related bus vehicles of each bus vehicle after correlation are obtained, and j is a relevant maximum percentage specified according to historical operation data; Determine the maximum adjustable departure interval for each bus and each associated bus. , To link the bus numbers of each route, , Let be any integer greater than 2, where Let be the initial departure interval of the u-th associated bus corresponding to the v-th bus. The reference departure adjustment interval time corresponding to the unit correlation degree. Let v be the correlation between bus route v and bus route u.

[0033] In a specific embodiment of the present invention, the method for determining the number of warning personnel responsible for the target station is as follows: importing the entry and exit times of several buses at the target station for each stop into the stop duration conformity judgment model. Among them , , , Let c be the standard parking duration for the z-th stop of the c-th bus. Let c be the minimum duration of the z-th stop of the c-th bus. Let R be the maximum duration of the c-th bus's z-th stop, a be the stopping duration per unit of passengers getting on and off the bus, b be the base stopping duration, e be the allowable error duration, and R be the maximum stopping duration of the c-th bus's z-th stop. cz1 R cz2 T cz2 T cz1 These represent the number of passengers boarding, the number of passengers alighting, the departure time, and the arrival time of the c-th bus at its z-th stop. Based on the bus stop model, a baseline stop line is constructed for the target station. Select two specified vertices for each bus and obtain the coordinates (x, y) of the first specified vertex for each bus. cz1 y cz1 ) and the coordinates (x) of the second specified vertex cz2 y cz2 The distance between the first designated vertex of each bus and the reference stopping line is calculated. Similarly, the distance between the second designated vertex of each bus and the reference stopping line is calculated. The distances between designated vertices of several buses and the baseline stopping line are substituted into the stopping position conformity judgment model. In the output, the stopping positions of each bus meet the judgment value, where Q is the allowable error distance. The maximum allowed docking distance in the database. The minimum allowed docking distance in the database. , They are logical symbols AND and OR, respectively.

[0034] If a bus at the target station and If so, the bus stop is recorded as a regular bus stop; if or If a bus is identified as a risk vehicle, and the stop is identified as a risk stop, the vehicle is identified based on the license plate. Buses with the same license plate are matched one-to-one with their drivers. The risk stops of each risk vehicle at the target station are then summarized, and the number of risk stops of each risk vehicle at the target station is counted.

[0035] If the number of risk stops of a certain risky vehicle exceeds the risk stop threshold, the driver of the risky vehicle will be marked as a warning person and an early warning will be issued, resulting in several warning persons being assigned to the target station.

[0036] It should be noted that the threshold for the number of risky stops is set by the relevant safety officer.

[0037] In a specific embodiment of the present invention, the assessment of the anomaly type of the target station is based on the analysis and judgment of the anomaly type according to the bus stop data and facility monitoring data of the target station, and the anomaly type is divided into: station layout anomaly and station facility anomaly.

[0038] Obtain a number of personnel responsible for issuing warnings at the target site, and count the number of personnel responsible for issuing warnings at the target site; if the number of personnel responsible for issuing warnings at the target site exceeds the threshold for personnel responsible for issuing warnings, then record the anomaly type of the target site as a layout anomaly; It should be noted that the threshold for warning personnel is set by the relevant safety officer.

[0039] The 3D images acquired at the target site are compared with the original 3D images of the target site stored in the database to obtain the volumes of the original 3D images of several devices at the target site and the overlap volumes between the 3D images of several devices at the target site and the original 3D images. These values ​​are then substituted into the tilt deformation judgment model. ,in Let A be the overlap volume between the A-th device and the original 3D image. Let F be the volume of the original 3D image of the F-th device; if the overlap volume ratio of a certain device at the target site is... If so, the anomaly type of the target site will be recorded as a facility anomaly; The anomaly types of the target site are summarized.

[0040] S5. Feedback processing: Display the corrected arrival times of several buses on the electronic display screen at the target station, and provide feedback on the anomaly type and transfer schedule of the target station.

[0041] It should be noted that the corrected arrival time of the several buses at the target station needs to be displayed in the electronic display screen of the target station, improving the passenger travel experience and helping passengers to better plan travel time; all abnormal types of the target station are fed back to the bus operation management system, a complete closed loop of "monitoring-analysis-feedback-optimization" is constructed, the service ability and brand image of the bus system are strengthened, and a technical scheme is provided for the digital transformation of urban public transportation.

[0042] It should be further pointed out that the application also includes a database for storing reference original data, including target station reference passenger flow, target station reference vehicle flow, allowed error time, allowed maximum distance of stopping, allowed minimum distance of stopping, original three-dimensional image of the target station, risk stopping number threshold, target station warning responsible personnel quantity, and minimum coincidence volume ratio of the equipment.

[0043] The above is only an example and description of the concept of the application, and those skilled in the art can make various modifications or supplements or use similar ways to replace the described specific embodiments, as long as they do not deviate from the concept of the application or exceed the scope defined by the application, which shall belong to the protection scope of the application.

Claims

1. A method for automatic detection of bus stops during bus operation, characterized in that, include: S1, Site Data Detection: Detects the flow data of the target site; S2. Adjusting departure parameters: Obtain the traffic speed constraints of several buses at the target station, calculate the estimated arrival time of several buses at the target station, adjust the departure frequency of several buses at the target station, obtain the arrival time of several buses at the target station after correction, and adjust the driving speed. S3. Continuous data collection: Continuously collect circulation data, bus stop data, and facility monitoring data of the target station for one cycle. S4. Data analysis: Based on the circulation data of the target station for one period, determine the transfer schedule of the target station; based on the bus stop data of the target station for one period, determine several warning personnel responsible for the target station; based on the bus stop data and facility monitoring data of the target station for one period, assess the anomaly type of the target station. S5. Feedback processing: Display the arrival times of several buses at the target station on the electronic display screen after correction processing, and provide feedback on the anomaly type and transfer schedule of the target station.

2. The method for automatic detection of bus stops during operation according to claim 1, characterized in that, The circulation data includes the average pedestrian flow, average vehicle flow, and the number of people actively interacting with each bus route during the detection period.

3. The method for automatic station detection during bus operation according to claim 2, characterized in that, The factors limiting the travel speed of several buses at the target station are the length and travel time of each congested road segment obtained using map applications.

4. The method for automatic station detection during bus operation according to claim 3, characterized in that, The specific prediction method for calculating the estimated arrival times of several buses at the target station is as follows: By utilizing the factors constraining the travel speed of several buses at the target station and the data fed back from the onboard speed monitors, the estimated arrival time of several buses at the target station is calculated. , For each bus, , It is any integer greater than 2. Number each congested road section. , Let be any integer greater than 2, where Let m be the total distance between the m-th bus and the target stop. Let be the length of the nth congested section along the route of the mth bus. Let m be the average speed of the m-th bus on the regular road section. Let be the travel time of the m-th bus on the n-th congested road segment.

5. The method for automatic detection of bus stops during operation according to claim 4, characterized in that, The specific steps of adjusting the departure frequency of several buses at the target station, obtaining the arrival time of several buses at the target station after correction processing, and adjusting the driving speed include: B1. Determine the carrying capacity of the target site based on the pedestrian and vehicular traffic flow. ,in The average foot traffic at the target site during the detection period. The target site's reference traffic is stored in the database. The average traffic flow at the target site during the detection period. The target site reference traffic flow is stored in the database. The weighting coefficients for pedestrian and vehicle traffic. ; The adjusted departure frequency is determined based on the carrying capacity of the target station. Where F represents the adjusted bus departure frequency at the target station. Based on the basic departure frequency, The departure frequency adjustment value corresponding to the unit change in load factor. The target carrying capacity of the target site; B2. Substitute the estimated arrival times of several buses at the target station into the arrival time model. Output the estimated arrival times of several buses at the target station, among which This refers to the current time point; The buses at the target station are sorted according to their estimated arrival times, resulting in a sorted list of buses at the target station. The estimated arrival times of these buses are then imported into the corrected time processing model. In the process, the output, after correction, shows the arrival times of several buses at the target station. ;in These are the serial numbers of each bus after sorting. Let be any integer greater than 2, where Given a number of buses, the estimated arrival time at the target station is d, where d is the set minimum interval. Based on the corrected arrival times of several buses at the target station, the travel speeds of several buses are adjusted. , The numbers assigned to each bus after sorting. , It is any integer greater than 2. Number each congested road section. , It can be any integer greater than 2.

6. The method for automatic detection of bus stops during operation according to claim 1, characterized in that, The bus stop data includes the arrival time, departure time, and stop model for each stop.

7. The method for automatic detection of bus stops during operation according to claim 6, characterized in that, The facility monitoring data consists of three-dimensional models of several devices at the target site.

8. The method for automatic detection of bus stops during operation according to claim 1, characterized in that, The specific method for determining the transfer schedule of the target station is as follows: The total number of passengers actively interacting with each bus route within a given period is summarized, and the correlation between each bus route and other bus routes is calculated. , For the numbering of each bus route, , It is any integer greater than 2. For the numbering of the remaining buses associated with each bus route, , Let be any integer greater than 2, where The degree of correlation between bus routes v and v'. The total number of passengers actively interacting between bus routes V and V' within one cycle, when Then the remaining buses are considered as associated buses, and each associated bus is obtained, where j is the maximum percentage of associated buses as specified by historical operating data. Determine the maximum adjustable departure interval for each bus and each associated bus. , To link the bus numbers of each route, , Let be any integer greater than 2, where Let be the initial departure interval of the u-th associated bus corresponding to the v-th bus. The reference departure adjustment interval time corresponding to the unit correlation degree. To determine the correlation between buses on route v and buses on route u, a scheduling table is generated based on each bus and each associated bus, along with their corresponding longest adjusted departure interval.

9. The method for automatic detection of bus stops during operation according to claim 7, characterized in that, The specific method for assigning several warning personnel to the target site is as follows: Import the arrival and departure times of several buses at the target station into the stop duration compliance judgment model. Among them , , , Let c be the standard parking duration for the z-th stop of the c-th bus. Let c be the minimum duration of the z-th stop of the c-th bus. Let R be the maximum duration of the c-th bus's z-th stop, a be the stopping duration per unit of passengers getting on and off the bus, b be the base stopping duration, e be the allowable error duration, and R be the maximum stopping duration of the c-th bus's z-th stop. cz1 R cz2 T cz2 T cz1 These represent the number of passengers boarding, the number of passengers alighting, the departure time, and the arrival time of the c-th bus at its z-th stop. Based on the bus stop model, a baseline stop line is constructed for the target station. Select two specified vertices for each bus and obtain the coordinates (x, y) of the first specified vertex for each bus. cz1 y cz1 ) and the coordinates (x) of the second specified vertex cz2 y cz2 The distance between the first designated vertex of each bus and the reference stopping line is calculated. Similarly, the distance between the second designated vertex of each bus and the reference stopping line is calculated. , Substituting the distances between designated vertices of several buses and the baseline stopping line into the stopping position conformity judgment model. In the output, the stopping positions of each bus meet the judgment value, where Q is the allowable error distance. The maximum allowed docking distance in the database. The minimum allowed docking distance in the database. , These are the logical symbols AND and OR, respectively; If a bus at the target station and If so, the bus stop is recorded as a regular bus stop; if or If the bus is marked as a risk vehicle, the stop is marked as a risk stop, and the risk stops of each risk vehicle at the target station are summed up to obtain the number of risk stops of each risk vehicle at the target station. If the number of risk stops of a certain risky vehicle exceeds the risk stop threshold, the driver of the risky vehicle will be marked as a warning person and an early warning will be issued, resulting in several warning persons being assigned to the target station.

10. The method for automatic detection of bus stops during operation according to claim 9, characterized in that, The specific evaluation method for assessing the anomaly types of the target site is as follows: C1. Obtain a number of personnel responsible for issuing warnings at the target site, and count the number of personnel responsible for issuing warnings at the target site; if the number of personnel responsible for issuing warnings at the target site exceeds the threshold for personnel responsible for issuing warnings, then record the anomaly type of the target site as a layout anomaly; C2. Compare the 3D images acquired at the target site with the original 3D images of the target site stored in the database to obtain the volumes of the original 3D images of several devices at the target site and the overlap volumes between the 3D images of several devices at the target site and the original 3D images, and substitute them into the tilt deformation judgment model. ,in Let A be the overlap volume between the A-th device and the original 3D image. Let F be the volume of the original 3D image of the F-th device; if the overlap volume ratio of a certain device at the target site is... If so, the anomaly type of the target site will be recorded as a facility anomaly; The anomaly types of the target site are summarized.

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