A bus scheduling method and system based on a limited-edge camera device

Abstract

The present invention provides a bus scheduling method and system based on a limited-edge camera device, which relates to the technical field of bus management. A base for plugging and unplugging the edge camera device is provided on all buses, and the total number of the edge camera devices is less than the total number of buses, including: S1. Obtaining historical event information collected during the driving of the bus through the edge camera device; S2. Obtaining the user-preset attention events and the corresponding event attention degrees; S3. Performing weighted calculation on the attention events according to the historical event information, the attention events, and the corresponding event attention degrees; S4. Establishing a trajectory interaction graph with the bus station as the y-axis and time as the x-axis; S5. Establishing a trajectory directed graph according to the bus lines and the line fitting weight values in the trajectory interaction graph; S6. Replacing the number of edge camera devices carried by the buses in the bus scheduling table according to the trajectory directed graph to generate a new bus scheduling table.

Description

Technical Field

[0001] The present invention relates to the technical field of bus management, and in particular to a bus scheduling method and system based on limited edge camera equipment. Background Art

[0002] At present, cameras are installed on buses and corresponding edge computing devices are placed, so that they can sense events outside the bus through algorithms, such as illegal parking on the road and littering, and upload illegal events captured by the camera to relevant departments for timely processing. However, due to the large number of buses in a wide range, it is costly to install cameras and edge computing devices on all buses; if cameras and edge computing devices are installed on some buses, it is impossible to achieve full coverage of all time periods and all sections, resulting in low event collection efficiency and the inability of relevant departments to effectively control. Summary of the invention

[0003] The purpose of the present invention is to provide a bus scheduling method and system based on limited edge camera devices. While reducing costs, limited pluggable edge camera devices are used for flexibly setting on different buses. The edge camera devices are switched according to user needs and historical event information collected by the edge camera devices, so that the limited edge camera devices can capture more events, increase the probability of event discovery, and facilitate relevant departments to effectively control illegal time.

[0004] In order to solve the above technical problems, the present invention adopts the following solutions:

[0005] A bus scheduling method based on limited edge camera devices, wherein all buses are provided with bases for plugging and unplugging edge camera devices, and the total number of the edge camera devices is less than the total number of buses; the method comprises the following steps:

[0006] S1. Obtain the bus where the edge camera device is located according to the bus schedule, and obtain the historical event information collected during the driving of the bus through the edge camera device;

[0007] S2. Obtaining the user's preset attention events and the corresponding event attention levels;

[0008] S3, performing weighted calculation on the events of interest according to historical event information, events of interest, and corresponding event attention levels, to obtain weight values ​​of events of interest in different sections and time periods;

[0009] S4. Establish a trajectory interaction graph with the bus station as the y-axis and time as the x-axis, map all bus routes in the bus schedule onto the trajectory interaction graph, and calculate the route fitting weight values between adjacent bus stations on different bus routes according to the weight values of the concerned events in different sections and different time periods;

[0010] S5. Establish a trajectory directed graph based on the bus routes and route fitting weight values in the trajectory interaction graph, and obtain the route fitting weight values of the nodes and the connection relationships between the nodes in the trajectory directed graph;

[0011] S6. Replace the number of edge camera devices carried by the buses in the bus schedule according to the route fitting weight values of the nodes and the connection relationships between the nodes, and generate a new bus schedule.

[0012] Further, the bus schedule includes the bus shifts, bus routes, and the number of edge camera devices carried by the buses; the bus shifts indicate the scheduling of buses on the same bus route according to different time periods; the number of edge camera devices carried is 1 or 0.

[0013] Further, the historical event information includes the section where the event occurs, the time when the event occurs, and the event type, and the user can preset the event attention degree of the event through the event type of the concerned event.

[0014] Further, the S3 includes the following steps:

[0015] S31. Calculate the probability of the event occurrence according to the section where the event occurs and the time when the event occurs in the historical event information, and obtain the occurrence probabilities of the concerned events at different sections and different times;

[0016] S32. Calculate the weights of the concerned events according to the occurrence probabilities of the concerned events at different sections and different times and the corresponding event attention degrees, and obtain the weight values of the concerned events at different sections and different time periods.

[0017] Further, in S4, the bus route includes several bus stations that the bus stops at in sequence during driving, and a part of the route between adjacent bus stations is formed by connecting several consecutive sections. Map all the bus routes of the buses in the bus schedule onto the trajectory interaction graph, substitute the weight values of the concerned events in different sections and different time periods into the corresponding part of the route, and perform weight fitting on the weight values of the concerned events on the part of the route to obtain the corresponding route fitting weight values.

[0018] Further, the trajectory interaction graph includes overlapping routes. When the time periods of different buses at the same bus station overlap, there are overlapping routes in the trajectory interaction graph.

[0019] Further, in S5, the process of establishing a trajectory directed graph based on the bus routes and route fitting weight values in the trajectory interaction graph is as follows:

[0020] Extract the bus routes with overlapping time periods according to the trajectory interaction graph, and process each bus route separately. Use the part of the route between adjacent bus stations as nodes, and use the corresponding route fitting weight value as the value of the node. Then connect the nodes in sequence according to their respective bus routes;

[0021] Then process different bus routes according to the overlapping routes, and connect the nodes corresponding to different bus routes in sequence according to the overlapping routes.

[0022] Further, in S6, the process of replacing the number of edge camera devices carried on the buses in the bus schedule according to the route fitting weight value of the nodes and the connection relationship between the nodes is as follows:

[0023] Obtain the route fitting weight value of the nodes and the connection relationship between the nodes corresponding to different bus routes according to the trajectory interaction graph. Compare the values of the corresponding nodes in sequence according to the connection relationship between the nodes, and obtain the nodes with larger values according to the comparison results; Replace the number of edge camera devices carried on the buses with the nodes with larger values, that is, update the number of edge camera devices on the part of the route corresponding to the nodes with larger values in the bus schedule to 1 to generate a new bus schedule.

[0024] A bus scheduling system based on a limited number of edge camera devices, which applies the bus scheduling method based on a limited number of edge camera devices. A base for plugging and unplugging edge camera devices is provided on all buses, and the total number of edge camera devices is less than the total number of buses; The system includes:

[0025] Historical event information collection module: Obtain the buses where the edge camera devices are located according to the bus schedule, and obtain the historical event information collected during the driving of the buses through the edge camera devices;

[0026] Attention event definition module: Obtain the attention events preset by the user and the corresponding event attention levels;

[0027] Attention event weight calculation module: Perform weighted calculation on the attention events according to the historical event information, attention events, and the corresponding event attention levels to obtain the weight values of the attention events in different road sections and different time periods;

[0028] Track Interaction Diagram Construction Module: Construct a track interaction diagram with the bus station as the y-axis and time as the x-axis, map all bus routes in the bus schedule onto the track interaction diagram, and calculate the line fitting weight values between adjacent bus stations on different bus routes according to the weight values of the concerned events in different sections and different time periods;

[0029] Track Directed Graph Construction Module: Construct a track directed graph based on the bus routes and line fitting weight values in the track interaction diagram, and obtain the line fitting weight values of the nodes in the track directed graph and the connection relationships between the nodes;

[0030] Bus Schedule Update Module: Replace the number of edge camera devices carried by the buses in the bus schedule according to the line fitting weight values of the nodes and the connection relationships between the nodes, and generate a new bus schedule.

[0031] Advantages of the present invention:

[0032] The present invention provides a bus scheduling method and system based on limited edge camera devices, which changes the fixed setting of cameras and edge computing devices on buses in the prior art to a flexible setting, integrates the existing cameras and edge computing devices into edge camera devices, and fixedly sets a base for plugging and unplugging edge camera devices on the buses, so that the same edge camera device can be set on different buses to collect event information outside the vehicle, without the need to set devices for all buses, reducing costs.

[0033] Under the condition of reducing costs, the edge camera devices are also switched according to user needs and historical event information collected by the edge camera devices, so that the limited edge camera devices can capture more events, improve the probability of event discovery, and facilitate effective control of illegal times by relevant departments. Description of the Drawings

[0034] Figure 1 It is a schematic flow chart of a bus scheduling method based on limited edge camera devices in Embodiment 1 of the present invention.

[0035] Figure 2 It is a schematic diagram of the track interaction diagram in Embodiment 1 of the present invention.

[0036] Figure 3 It is a schematic diagram of the track directed graph in Embodiment 1 of the present invention. Detailed Embodiments

[0037] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. The following description of at least one exemplary embodiment is actually only illustrative and in no way restrictive of the present invention or its application or use. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0038] Unless otherwise specifically stated, the relative arrangements of components and steps, numerical expressions, and numerical values set forth in these embodiments do not limit the scope of the present invention.

[0039] At the same time, it should be understood that, for the sake of convenience of description, the sizes of the various parts shown in the drawings are not drawn in actual proportional relationships.

[0040] In addition, for the sake of clarity and conciseness, the descriptions of well-known structures, functions, and configurations may be omitted. Those of ordinary skill in the art will recognize that various changes and modifications can be made to the examples described herein without departing from the spirit and scope of the present disclosure.

[0041] The techniques, methods, and devices known to those of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, the techniques, methods, and devices should be regarded as part of the authorization specification.

[0042] In all the examples shown and discussed here, any specific value should be construed as merely exemplary, rather than as a limitation. Therefore, other examples of the exemplary embodiments may have different values.

[0043] The present invention will be described in detail below with reference to the accompanying drawings and in conjunction with the embodiments:

[0044] Embodiment 1

[0045] In this embodiment, in order to solve the problem of high costs caused by fixedly installing cameras and edge computing devices on all buses in the prior art, the present invention mainly uses a limited number of cameras and edge computing devices, integrates the existing cameras and edge computing devices together as a camera perception module, that is, an edge camera device. By using a limited number of edge camera devices, the total number of edge camera devices is less than the total number of buses. Moreover, bases for plugging and unplugging the edge camera devices are provided on all buses, enabling the edge camera devices to be flexibly moved. When the bus arrives at the bus station, the driver will make a short stop at the current bus station. At this time, the driver can choose to remove the edge camera device on this bus and move it to another bus, allowing the other bus to insert the edge camera device for collection work. When the bus arrives at the final bus station, that is, the terminal station, the edge camera device on this bus can be removed, and the events collected on the edge camera device can be uploaded.

[0046] Specifically, the camera module is used to capture images outside the vehicle; the edge computing module is used to calculate the images captured by the camera module, generate corresponding events, obtain the event types of these events. And when the edge computing module collects the event type of the current event, it expands with the current collection time to obtain the time when the event occurs, and combines the GPS information of the bus itself to obtain the road section where the event occurs.

[0047] In one embodiment, bases for plugging and unplugging the edge camera devices are provided on all buses. The bases are fixedly installed on the buses, and a power supply is provided inside the bases, through which the edge camera devices can be charged.

[0048] In order to capture more events with a limited number of edge camera devices and improve the probability of event discovery, based on the above-mentioned edge camera devices, the present invention proposes a bus scheduling method based on limited edge camera devices, as Figure 1 shown. The method includes the following steps:

[0049] S1. Obtain the bus where the edge camera device is located according to the bus schedule, and obtain the historical event information collected during the driving of this bus through the edge camera device;

[0050] S2. Obtain the user-predefined concerned events and the corresponding event concern degrees;

[0051] S3. Perform weighted calculation on the concerned events according to the historical event information, concerned events, and the corresponding event concern degrees to obtain the weight values of the concerned events in different road sections and different time periods;

[0052] S4. Establish a trajectory interaction graph with the bus station as the y-axis and time as the x-axis, map all bus routes in the bus schedule onto the trajectory interaction graph, and calculate the line fitting weight values between adjacent bus stations on different bus routes according to the weight values of the concerned events in different road sections and different time periods.

[0053] S5. Establish a trajectory directed graph based on the bus routes and line fitting weight values in the trajectory interaction graph, and obtain the line fitting weight values of the nodes in the trajectory directed graph and the connection relationships between the nodes.

[0054] S6. Replace the number of edge camera devices carried by the buses in the bus schedule according to the line fitting weight values of the nodes and the connection relationships between the nodes, and generate a new bus schedule.

[0055] Specifically, the bus schedule includes the bus number, bus shift, bus route, and the number of edge camera devices carried by the bus. As shown in Table 1, according to the bus shifts, the buses on the same bus route are sequentially scheduled according to the time period, and then the bus shifts can be obtained. According to the bus route, the road sections and their section time periods during the driving of the bus can be obtained. For example, in section 1 time period, the number of edge camera devices carried is 1 or 0, and the number of edge camera devices carried corresponds to a road section. In Table 1, it can be seen that the bus route of bus A1 is Route 1, the bus shift is 1, the section time period in section 1 is 9:00 - 9:05, and the number of edge camera devices carried on section 1 is 1, that is, an edge camera device is inserted on the base of the bus A1 when it is driving on section 1. Through this edge camera device, the event information outside the vehicle can be collected.

[0056] Table 1

[0057]

[0058] In one embodiment, events can be collected with emphasis according to the needs of different users. For example, if the current user's need is to pay attention to the cleanliness of the trash cans around the road, then obtain the preset concerned events, their event types, and event attention degrees of the user. Then, increase the event attention degree of the concerned event corresponding to the cleanliness of the trash cans around the road, and appropriately reduce the event attention degrees of other events.

[0059] In one embodiment, the following steps are included in S3:

[0060] S31. Calculate the occurrence probability of the event according to the road section where the event occurs and the time when the event occurs in the historical event information, and obtain the occurrence probabilities of the concerned events at different road sections and different times.

[0061] S32. Calculate the weights of the concerned events based on the occurrence probabilities of the concerned events and the corresponding event concern degrees at different times on different road segments, so as to obtain the weight values of the concerned events at different times on different road segments.

[0062] Specifically, the historical event information is event information collected in advance by edge camera devices. For bus scheduling, the historical event information can be the event information of the previous period. The current bus scheduling is updated according to the event information of the previous period. The event information includes the road segment where the event occurs, the time when the event occurs, and the event type. Statistical calculations are performed on the occurrence probabilities of the same event type in the same road segment and the same time period in the event information of the previous period. This statistical calculation is a prior art means and will not be elaborated here. For example, through statistical calculations, it can be obtained that there is a 50% probability of the trash can being untidy on Road Segment 1 during the time period of 9:00 - 9:05 on Road Segment 1. Thus, the occurrence probabilities of the concerned events at different times on different road segments can be obtained.

[0063] Moreover, when the occurrence probabilities of the concerned events in the historical event information are obtained, since different users have different attention requirements, in this embodiment, the weights of the concerned events can also be calculated based on the occurrence probabilities of the concerned events and the corresponding event concern degrees at different times on different road segments. For example, it is preset that the optional range of the user's concern degree for an event is 1 - 10. If the current concern degree of the event that the trash can is untidy is 7, and there is a 50% probability of the trash can being untidy on Road Segment 1 during the time period of 9:00 - 9:05 on Road Segment 1, then the weight value of the event that the trash can is untidy at 9:00 - 9:05 on Road Segment 1 is 3.5. The weights of the concerned events are assigned according to the user's concern degree. The event concern degree corresponding to the cleanliness of the trash cans around the road is increased, and the event concern degrees of other events can be appropriately reduced, so as to focus on subsequent discovery of the untidiness of the trash cans and replace the number of edge camera devices carried by the buses.

[0064] In one embodiment, in S4, several bus stations that the buses stop at in sequence during the driving process are included in the bus route. A part of the route between adjacent bus stations is formed by connecting several consecutive road segments. Map the bus routes of all buses in the bus scheduling table to the trajectory interaction diagram, and substitute the weight values of the concerned events in different road segments and different time periods into the corresponding part of the route, and perform weight fitting on the weight values of the concerned events on the part of the route to obtain the corresponding route fitting weight value.

[0065] Such as Figure 2As shown, a trajectory interaction graph is established with the bus station as the y-axis and time as the x-axis. The bus routes of bus A1 and bus B1 are mapped in the trajectory interaction graph. Moreover, for the current bus route of bus A1, the route fitting weight values of some routes n1 are 1, some routes n2 are 2, some routes n3 are 3, and some routes n4 are 4; while for the current bus route of bus B1, the route fitting weight values of some routes m1 are 6, some routes m2 are 7, some routes m3 are 8, and some routes m4 are 9.

[0066] When the bus route of bus A1 running according to Route 1 is mapped in the trajectory interaction graph, and the bus route of bus B1 running according to Route 2 is also mapped in the trajectory interaction graph, the two bus routes overlap in the time period in the trajectory interaction graph and both run in the time period T0 - T7, indicating that bus A1 and bus B1 are buses departing and running in the same time period and may have overlapping routes. Then, in another embodiment, when establishing the trajectory interaction graph, multiple trajectory interaction graphs can be established according to the time period, and the bus routes of buses running in the same time period are mapped in the same trajectory interaction graph.

[0067] The current bus A1 arrives at bus station 1 from bus station 2, and bus B1 arrives at bus station 1 from bus station 0, and the residence times of bus A1 and bus B1 at bus station 1 overlap. Then there is an overlapping route in this trajectory interaction graph, indicating that there is a time for bus A1 and bus B1 to exchange the carrying quantity of the edge camera device. At this time, if the carrying quantity of the edge camera device on some routes between bus station 2 and bus station 1 of the current bus A1 is 1, and the carrying quantity of the edge camera device on some routes between bus station 0 and bus station 1 of the current bus B1 is 0, then the edge camera device of bus A1 can be transferred to bus B1 at bus station 1, enabling the present invention to judge whether there is time for edge camera device transfer between buses according to the overlapping route in the trajectory interaction graph, and then performing weight fitting on some routes between bus stations in the trajectory interaction graph to obtain the corresponding route fitting weight value, which is convenient for subsequent scheduling of edge camera device transfer.

[0068] In one embodiment, in S5, the process of establishing a trajectory directed graph according to the bus route and the route fitting weight value in the trajectory interaction graph is as follows:

[0069] Extract the bus routes with overlapping time periods according to the trajectory interaction graph, and process each bus route separately. Take the part of the route between adjacent bus stations as nodes, use the corresponding route fitting weight value as the value of the node, and connect the nodes in sequence according to their respective bus routes;

[0070] Then, process different bus routes according to the repeated routes, and connect the nodes corresponding to different bus routes in sequence according to the repeated routes.

[0071] Such as Figure 3 shown, a trajectory directed graph is established based on the Figure 2 trajectory interaction graph in. The establishment process is as follows: First, process the bus routes of bus A1 and bus B1 separately. Take part of the routes as nodes, use the route fitting weight value of the part of the routes as the value of the nodes, and connect the nodes in sequence according to their respective bus routes. At this time, two paths can be obtained, namely 1-2-3-4 and 6-7-8-9; Then, process the bus routes of bus A1 and bus B1 according to the repeated routes in the trajectory interaction graph. For example: in Figure 2 it can be obtained that the route between part of route n1 and part of route n2 and the route between part of route m1 and part of route m2 form a repeated route, indicating that bus A1 and bus B1 can transfer the edge camera device at the corresponding bus station 1. Then connect the node corresponding to part of route n1 with the node corresponding to part of route m2, and connect the node corresponding to part of route m1 with the node corresponding to part of route n2 to form a trajectory directed graph.

[0072] In one embodiment, in S6, the process of replacing the number of edge camera devices carried by the buses in the bus schedule according to the route fitting weight value of the nodes and the connection relationship between the nodes is as follows:

[0073] Obtain the route fitting weight value of the nodes and the connection relationship between the nodes corresponding to different bus routes according to the trajectory interaction graph. Compare the values of the corresponding nodes in sequence according to the connection relationship between the nodes, and obtain the node with a larger value according to the comparison result; Replace the number of edge camera devices carried on the bus with the node with a larger value, that is, update the number of edge camera devices on the part of the route corresponding to the node with a larger value to 1 in the bus schedule to generate a new bus schedule.

[0074] Specifically, when the one-day operation time of the bus ends, the bus schedule and the collected historical event information can be obtained, and Figure 3The trajectory digraph, and, according to the number of edge camera devices carried in the bus schedule, the current path carrying the edge camera is 1-2-3-4, that is, edge cameras are inserted in all buses of Bus A1 on the current bus line. And through the historical event information, it is found that the attention degree of events that users are concerned about may be higher in other parts of the line. Then, the transfer of the path carrying the edge camera can be realized through the trajectory digraph. At this time, compare the sizes between nodes in turn. It is found that node 7 is greater than node 1, and the path is transferred. Then, update the path carrying the edge camera to 1-7-8-9. Then, in the bus schedule, update the number of edge camera devices carried on the corresponding part of the route with the path 1-7-8-9 to 1 to generate a new bus schedule, so that the buses on the next day run based on the new bus schedule. When Bus A1 travels to Bus Station 1, send a notice of edge camera device transfer to Bus A1, and notify the driver of Bus B1 to insert the edge camera device into Bus B1 to complete the subsequent event collection work, realizing the flexible switching of edge camera devices. Under the limited number of edge camera devices, certain events can be focused on according to user needs, and the sections with a higher probability of such events can be monitored to achieve dynamic update.

[0075] Embodiment 2

[0076] A bus scheduling system based on limited edge camera devices, applying the described bus scheduling method based on limited edge camera devices. A base for plugging and unplugging edge camera devices is set on all buses, and the total number of the edge camera devices is less than the total number of buses; including:

[0077] Historical event information collection module: Obtain the buses where the edge camera devices are located according to the bus schedule, and obtain the historical event information collected during the driving of the buses through the edge camera devices;

[0078] Concerned event definition module: Obtain the concerned events preset by the user and the corresponding event attention degrees;

[0079] Concerned event weight calculation module: Perform weighted calculation on the concerned events according to the historical event information, the concerned events and the corresponding event attention degrees to obtain the weight values of the concerned events in different sections and different time periods;

[0080] Trajectory interaction graph construction module: Establish a trajectory interaction graph with the bus station as the y-axis and time as the x-axis, map all bus lines in the bus schedule in the trajectory interaction graph, and calculate the line fitting weight values between adjacent bus stations on different bus lines according to the weight values of the concerned events in different sections and different time periods;

[0081] Trajectory directed graph construction module: construct a trajectory directed graph based on the bus routes and route fitting weight values in the trajectory interaction graph, and obtain the route fitting weight values of the nodes in the trajectory directed graph and the connection relationships between the nodes;

[0082] Bus schedule update module: replace the number of on-board edge camera devices in the bus schedule according to the route fitting weight values of the nodes and the connection relationships between the nodes, and generate a new bus schedule.

[0083] The above is only a preferred embodiment of the present invention, and does not impose any form of limitation on the present invention. Based on the technical essence of the present invention, any simple modifications, equivalent replacements, and improvements made to the above embodiments within the spirit and principles of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A bus scheduling method based on limited edge camera equipment, characterized in that: All buses are provided with bases for plugging and unplugging edge camera devices, and the total number of edge camera devices is less than the total number of buses; the method comprises the following steps: S1. Obtain the bus where the edge camera device is located according to the bus schedule, and obtain the historical event information collected during the driving of the bus through the edge camera device; S2. Obtaining the user's preset attention events and the corresponding event attention levels; S3, performing weighted calculation on the events of interest according to historical event information, events of interest, and corresponding event attention levels, to obtain weight values ​​of events of interest in different sections and time periods; The S3 includes the following steps: S31, calculating the probability of the event occurring according to the road section where the event occurred and the time when the event occurred in the historical event information, and obtaining the probability of the event occurring at different sections and at different times; S32, weighting the events of interest according to the probability of occurrence of the events of interest at different road sections and different times and the corresponding event attention levels, to obtain weight values ​​of the events of interest at different road sections and different time periods; S4, establishing a trajectory interaction diagram with the bus station as the y-axis and time as the x-axis, mapping all bus routes in the bus schedule into the trajectory interaction diagram, and calculating the route fitting weight values ​​between adjacent bus stations on different bus routes according to the weight values ​​of the events of interest in different sections and different time periods; S5. Establish a trajectory directed graph according to the bus routes and route fitting weight values ​​in the trajectory interaction graph, and obtain the route fitting weight values ​​of the nodes in the trajectory directed graph and the connection relationship between the nodes; S6. According to the line fitting weight value of the node and the connection relationship between the nodes, the number of edge camera devices carried by the buses in the bus schedule is replaced to generate a new bus schedule.

2. A bus scheduling method based on a limited edge camera device according to claim 1, characterized in that: The bus schedule includes bus schedules, bus routes, and the number of edge camera devices carried by the bus; the bus schedule indicates that buses on the same bus route are scheduled according to different time periods; The number of edge camera devices carried is 1 or 0.

3. A bus scheduling method based on a limited edge camera device according to claim 1, characterized in that: The historical event information includes the road section where the event occurred, the time when the event occurred, and the event type. The user can preset the event attention level of the event by focusing on the event type.

4. A bus scheduling method based on a limited edge camera device according to claim 1, characterized in that: In S4, the bus route includes several bus stations where the bus stops in sequence during its travel, and some routes between adjacent bus stations are formed by connecting several continuous road sections. The bus routes of all buses in the bus schedule are mapped in the trajectory interaction diagram, and the weight values ​​of the events of interest in different sections and different time periods are substituted into the corresponding partial routes, and the weight values ​​of the events of interest on the partial routes are weighted fitted to obtain the corresponding route fitting weight values.

5. A bus scheduling method based on a limited edge camera device according to claim 1, characterized in that: The trajectory interaction diagram includes overlapping routes. When different buses overlap in time periods at the same bus station, overlapping routes exist in the trajectory interaction diagram.

6. A bus scheduling method based on a limited edge camera device according to claim 5, characterized in that: In S5, the process of establishing a trajectory directed graph according to the bus routes and route fitting weight values ​​in the trajectory interaction graph is as follows: According to the trajectory interaction diagram, the bus routes with overlapping time periods are extracted, and the bus routes are processed separately. Some routes between adjacent bus stations are used as nodes, and the corresponding route fitting weight values ​​are used as node values. The nodes are connected in sequence according to their respective bus routes. Then, different bus lines are processed according to the overlapping routes, and the nodes corresponding to different bus lines are connected in sequence according to the overlapping routes.

7. A bus scheduling method based on a limited edge camera device according to claim 6, characterized in that: In S6, the process of changing the number of edge camera devices carried by buses in the bus schedule according to the line fitting weight value of the node and the connection relationship between the nodes is as follows: According to the trajectory interaction diagram, the line fitting weight value of the node and the connection relationship between the nodes corresponding to different bus lines are obtained. According to the connection relationship between the nodes, the values ​​of the corresponding nodes are compared in turn, and the nodes with larger values ​​are obtained according to the comparison results; the number of edge camera devices on the bus is replaced to the nodes with larger values, that is, the number of edge camera devices on some routes corresponding to the nodes with larger values ​​in the bus schedule is updated to 1 to produce a new bus schedule.

8. A bus scheduling system based on limited edge camera equipment, characterized in that: A bus scheduling method based on limited edge camera devices as described in any one of claims 1 to 7 is applied, wherein all buses are provided with bases for plugging and unplugging edge camera devices, and the total number of edge camera devices is less than the total number of buses; comprising: Historical event information collection module: obtains the bus where the edge camera device is located according to the bus schedule, and obtains the historical event information collected during the driving of the bus through the edge camera device; Focus event definition module: obtains the focus events preset by the user and the corresponding event focus levels; Concern event weight calculation module: weighted calculation of concerned events is performed according to historical event information, concerned events and corresponding event attention levels, and weight values ​​of concerned events in different sections and time periods are obtained; Trajectory interaction graph construction module: establish a trajectory interaction graph with bus stations as the y-axis and time as the x-axis, map all bus routes in the bus schedule into the trajectory interaction graph, and calculate the route fitting weight values ​​between adjacent bus stations on different bus routes according to the weight values ​​of the events of interest in different sections and time periods; Trajectory directed graph construction module: establishes a trajectory directed graph according to the bus routes and route fitting weight values ​​in the trajectory interaction graph, and obtains the route fitting weight values ​​of the nodes in the trajectory directed graph and the connection relationship between the nodes; Bus schedule update module: replaces the number of edge camera devices carried by buses in the bus schedule according to the line fitting weight value of the node and the connection relationship between the nodes to generate a new bus schedule.

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