Method, device, electronic device and storage medium for determining event authenticity

Abstract

The present disclosure provides a method, device, electronic device and storage medium for determining the authenticity of an event, which relates to the field of data processing technology, and in particular to the fields of artificial intelligence such as intelligent transportation, big data, computer vision, and knowledge graphs. The specific implementation scheme is: determining event information related to a target event; according to the event information and the graph, determining multiple associated nodes associated with the event information from multiple nodes of the graph, the multiple nodes including multiple camera device nodes, any two camera device nodes that meet predetermined position conditions in the multiple camera device nodes are connected via edges; and determining the authenticity of the target event according to the multiple associated nodes.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing technology, and in particular to artificial intelligence fields such as intelligent transportation, big data, computer vision, knowledge graphs, etc. More specifically, the present disclosure provides a method, device, electronic device, storage medium, and computer program product for determining the authenticity of an event. Background Art

[0002] Some cities use intelligent transportation technology to manage vehicles traveling on the road network. For example, multiple cameras can be installed in the road network to collect video data of vehicles, and then the video data can be processed using a target event detection algorithm to determine whether the vehicle has violated traffic regulations, such as driving in the wrong direction or illegally parking. Summary of the invention

[0003] The present disclosure provides a method, apparatus, electronic device, storage medium and computer program product for determining the authenticity of an event.

[0004] According to one aspect of the present disclosure, a method for determining the authenticity of an event is provided, comprising: determining event information related to a target event; determining, based on the event information and a graph, a plurality of associated nodes associated with the event information from a plurality of nodes of the graph, the plurality of nodes comprising a plurality of camera device nodes, any two camera device nodes among the plurality of camera device nodes that meet a predetermined position condition are connected via an edge; and determining the authenticity of the target event based on the plurality of associated nodes.

[0005] According to another aspect of the present disclosure, there is provided a device for determining the authenticity of an event, comprising an event information determination module, an associated node determination module and an authenticity determination module. The event information determination module is used to determine event information related to a target event. The associated node determination module is used to determine, based on the event information and a graph, a plurality of associated nodes associated with the event information from a plurality of nodes of the graph, the plurality of nodes comprising a plurality of camera device nodes, any two camera device nodes in the plurality of camera device nodes that meet a predetermined position condition are connected via an edge. The authenticity determination module is used to determine the authenticity of the target event based on the plurality of associated nodes.

[0006] According to another aspect of the present disclosure, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method provided by the present disclosure.

[0007] According to another aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable a computer to execute the method provided by the present disclosure.

[0008] According to another aspect of the present disclosure, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the method provided by the present disclosure is implemented.

[0009] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure.

[0011] Figure 1 is a schematic diagram of an application scenario of a method and device for determining the authenticity of an event according to an embodiment of the present disclosure;

[0012] Figure 2 is a schematic flow chart of a method for determining the authenticity of an event according to an embodiment of the present disclosure;

[0013] Figure 3 is a schematic diagram of a diagram according to an embodiment of the present disclosure;

[0014] Figure 4 is a schematic structural block diagram of an apparatus for determining the authenticity of an event according to an embodiment of the present disclosure; and

[0015] Figure 5 It is a structural block diagram of an electronic device used to implement the method for determining the authenticity of an event according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0016] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0017] Figure 1 It is a schematic diagram of an application scenario of the method and device for determining the authenticity of an event according to an embodiment of the present disclosure.

[0018] It should be noted that Figure 1What is shown is merely an example of a system architecture to which the embodiments of the present disclosure can be applied, in order to help those skilled in the art understand the technical content of the present disclosure, but it does not mean that the embodiments of the present disclosure cannot be used in other devices, systems, environments or scenarios.

[0019] like Figure 1 As shown, the system architecture 100 according to this embodiment may include terminal devices 101, 102, 103, a network 104 and a server 105. The network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired and / or wireless communication links, etc.

[0020] Users can use terminal devices 101, 102, 103 to interact with server 105 through network 104 to receive or send messages, etc. Terminal devices 101, 102, 103 can be various electronic devices with display screens and supporting web browsing, including but not limited to smart phones, tablet computers, laptops, desktop computers, etc.

[0021] The server 105 may be a server that provides various services, such as a background management server (only an example) that provides support for websites browsed by users using the terminal devices 101, 102, and 103. The background management server may analyze and process the received data such as user requests, and feed back the processing results (such as the authenticity of the target event determined based on the event information and the graph) to the terminal device.

[0022] It should be noted that the method for determining the authenticity of an event provided in the embodiment of the present disclosure can generally be performed by the server 105. Accordingly, the device for determining the authenticity of an event provided in the embodiment of the present disclosure can generally be set in the server 105. The method for determining the authenticity of an event provided in the embodiment of the present disclosure can also be performed by a server or server cluster that is different from the server 105 and can communicate with the terminal devices 101, 102, 103 and / or the server 105. Accordingly, the device for determining the authenticity of an event provided in the embodiment of the present disclosure can also be set in a server or server cluster that is different from the server 105 and can communicate with the terminal devices 101, 102, 103 and / or the server 105.

[0023] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0024] Figure 2 is a schematic flowchart of a method for determining the authenticity of an event according to an embodiment of the present disclosure.

[0025] like Figure 2 As shown, the method 200 for determining the authenticity of an event may include operations S210 to S230.

[0026] In operation S210, event information related to a target event is determined.

[0027] Target events may include traffic violation events during vehicle driving, such as wrong-way driving, illegal parking, object throwing, and driver not wearing seat belts.

[0028] The event information may include at least one of event location information, vehicle information, time information, type information, and camera device information. The event location information indicates the location where the target event occurs, such as the intersection, road, GPS (Global Positioning System) coordinates, etc. where the target event occurs. The vehicle information indicates the vehicle where the target event occurs, and the vehicle information may include at least one of the vehicle identification, license plate number, body color, vehicle model, vehicle brand, and vehicle coordinates. The time information may indicate the time when the target event occurs. The type information may indicate the type of the target event, such as a vehicle reversing event is one type, and an illegal parking event is another type. The camera device information may indicate the relevant information of the camera device that captured the target event occurring in the vehicle, for example, it may include at least one of the identification of the camera device and the physical location of the camera device. The physical location of the camera device may be represented by the GPS coordinates of the camera device, or by the area where the camera device is located.

[0029] The target event detection algorithm can be used to process the video data captured by the camera device. For example, the target event detection algorithm extracts multiple frames of images from the video data and then processes the multiple frames of images to obtain event information. A variety of target event detection algorithms can be used to determine event information, and the present disclosure is not limited to this.

[0030] In operation S220, multiple associated nodes associated with the event information are determined from multiple nodes of the graph according to the event information and the graph. The multiple nodes may include multiple camera nodes, and any two camera nodes that meet the predetermined position condition among the multiple camera nodes are connected via an edge.

[0031] The graph may include multiple nodes and multiple edges, and the graph may be stored in a graph database, which may be JanusGraph.

[0032] The multiple nodes in the graph may also include a vehicle node, which may represent a vehicle in which a target event occurs. In the case where the associated node is a vehicle node, the association of the associated node with the event information may represent that the target event occurs in the vehicle represented by the vehicle node. In some embodiments, the multiple nodes in the graph may not include a vehicle node, but may only include a camera node, which may represent a camera for shooting video data.

[0033] In the case where the associated node is a camera node, the association of the associated node with the event information may indicate that the camera represented by the camera node photographed the target event, or may indicate that the camera a represented by the camera node is located near the camera b that photographed the target event. For example, the distance between the camera a and the camera b is less than a distance threshold (e.g., 10 meters), or the camera a and the camera b are located in the same predetermined area (e.g., at the same intersection).

[0034] In the embodiment of the present disclosure, any two camera device nodes satisfying the predetermined position condition may indicate that: of the two camera devices represented by the two camera device nodes, the physical position of the first camera device is located near the physical position of the second camera device.

[0035] In one example, the predetermined location condition includes that the physical locations of the two cameras represented by any two camera nodes are in the same area. The areas may be divided according to intersections, for example, each intersection in the road network is determined as an area. The areas may be divided according to roads, for example, the same road is determined as an area. The areas may be divided according to distances, for example, a circular area with the physical location of a camera as the center and a predetermined radius as the radius is determined as an area.

[0036] In another example, the predetermined location condition includes that the physical locations of the two cameras represented by any two camera nodes are in two adjacent areas. For example, each intersection in the road network is determined as an area, and the two adjacent areas may be two adjacent intersections. For example, the same road is determined as an area, and the two adjacent areas may be two adjacent roads, such as a main road and a secondary road.

[0037] In another example, the predetermined location condition includes that the distance between the physical locations of two camera devices represented by any two camera device nodes is less than or equal to a distance threshold.

[0038] In the disclosed embodiment, when a camera device located in a certain area captures a target event occurring to a vehicle, other camera devices located in the area or nearby areas will also capture video data of the vehicle during its driving process. These video data can provide a reference for determining whether a target event occurs to the vehicle. Therefore, edges can be used in the graph to connect any two camera device nodes that meet predetermined position conditions, thereby establishing a connection between the two camera devices represented by the any two camera device nodes, thereby improving the accuracy of determining the target event.

[0039] In operation S230, the authenticity of the target event is determined according to the plurality of associated nodes.

[0040] It should be noted that the target event detection algorithm has some misjudgment rate, such as misjudging the normal driving of the vehicle as a wrong-way event. Authenticity can indicate the accuracy of the target event.

[0041] In some technical solutions, after the target event detection algorithm determines that a vehicle has committed a traffic violation, the traffic violation event can be pushed to a management personnel, who will watch the video related to the vehicle traffic violation event and manually check whether the traffic violation event determined by the target event detection algorithm is correct.

[0042] Compared with the above-mentioned technical solution in which verification is performed by management personnel, the technical solution provided by the embodiment of the present disclosure determines whether the target event is correct through graphs and event information, which can reduce the workload of management personnel and reduce labor costs.

[0043] In practical applications, taking the target event detection algorithm as an algorithm for detecting a vehicle that has experienced a reverse event as an example, if the target event is determined to be a real event, it means that the target event determined by the target event detection algorithm is correct, that is, the vehicle has experienced a reverse event. Event information related to the reverse event can be pushed to the traffic management department, thereby facilitating the traffic management department to handle the reverse event. If the target event is determined to be a non-real event, that is, the vehicle has not experienced a reverse event, the target event detection algorithm misjudges the normal driving of the vehicle as a reverse event. Event information related to the reverse event may not be pushed to the traffic management department.

[0044] According to another embodiment of the present disclosure, based on event information and a graph, the operation of determining multiple associated nodes associated with the event information from multiple nodes of the graph may include the following operations: for each camera node included in the graph, the physical position of the camera is set as the label information or attribute information of the camera node in advance. Then, the label information and attribute information of each of the multiple nodes in the graph are traversed to determine multiple camera nodes whose physical positions are near the position indicated by the event position information, and the multiple camera nodes are determined as multiple associated nodes.

[0045] According to another embodiment of the present disclosure, based on the event information and the graph, the operation of determining multiple associated nodes associated with the event information from multiple nodes of the graph may include the following operations: determining the node whose attribute information or label information matches the event information among the multiple nodes as the first associated node among the multiple associated nodes. Then, in the graph, determining the node whose edge path between the multiple nodes and the first associated node is less than or equal to the edge path threshold as the second associated node among the multiple associated nodes.

[0046] A node in a graph may include at least one of label information and attribute information.

[0047] For the camera node in the graph, in one example, the identification of the camera can be set as the label information of the camera node. In another example, the physical location of the camera can be set as the label information or attribute information of the camera node, and the physical location of the camera can include at least one of the area where the camera is located and the GPS coordinates of the camera. In another example, other information such as resolution and field of view can be set as the attribute information of the camera node.

[0048] The matching of attribute information or tag information of the camera node with the event information may include at least one of the following situations: the physical location of the camera is the same as the event location information included in the event information, and the identification of the camera is the same as the identification of the camera included in the event information.

[0049] For a vehicle node in the graph, in one example, any one of the vehicle identification, license plate number, body color, vehicle model, vehicle brand, vehicle coordinates, and type information of the target event can be set as label information or attribute information of the vehicle node. For example, the license plate number is set as label information, and the body color is set as attribute information.

[0050] The matching of the attribute information or label information of the vehicle node with the event information may include: the license plate number of the vehicle represented by the vehicle node is the same as the vehicle information included in the event information.

[0051] When it is determined that at least one of the camera node and the vehicle node matches the event information, the camera node and the vehicle node can be searched in the graph, and the found camera node and vehicle node are determined as first associated nodes. Then, each first associated node is used as a starting point in the graph, and a predetermined edge path is used as a traversal range to traverse and find a second associated node.

[0052] The predetermined edge path represents the number of edges connecting the first associated node and the second associated node in the graph, and the predetermined edge path may be, for example, 2 to 10. The specific value of the predetermined edge path may be determined according to the type of the target event detection algorithm, for example, if the target event detection algorithm is an algorithm for detecting vehicle reversal, the predetermined edge path may be 4, and if the target event detection algorithm is an algorithm for detecting vehicle illegal parking, the predetermined edge path may be 3.

[0053] The technical solution provided by the embodiment of the present disclosure first determines the first associated node, and then determines the second associated node according to the predetermined edge path, without traversing each node in the graph, thereby improving the efficiency of determining the associated nodes and reducing the required computing resources.

[0054] According to another embodiment of the present disclosure, the multiple nodes in the graph may include multiple vehicle nodes, and each of the multiple vehicle nodes is connected to a camera device node among the multiple camera device nodes via an edge.

[0055] The camera node and the vehicle node are connected via an edge, indicating that the camera represented by the camera node captured a target event occurring in the vehicle represented by the vehicle node.

[0056] For example, three cameras capture the same vehicle driving in the wrong direction. In the graph, the three cameras correspond to a camera node respectively. The three camera nodes are each connected to a vehicle node via an edge. The three vehicle nodes have the same label information or attribute information (such as license plate number). The three vehicle nodes represent the same vehicle.

[0057] According to the technical solution provided by the embodiment of the present disclosure, since a vehicle node is established for the vehicle where the target event occurs, the authenticity of the target event can be determined based on the vehicle node, thereby simplifying the processing process.

[0058] According to another embodiment of the present disclosure, when multiple nodes in the graph include a vehicle node, the event information includes vehicle information representing the target vehicle; based on multiple associated nodes, the operation of determining the authenticity of the target event may include at least one of the following two operations.

[0059] In one example, the first operation includes: determining that the target event is a real event when the number of vehicle nodes representing the target vehicle among the multiple associated nodes is greater than or equal to a number threshold. For example, it can be determined whether the vehicles represented by each vehicle node are the same target vehicle based on the attribute information of each vehicle node. For example, the number threshold can be 4.

[0060] In another example, the second operation includes: determining that the target event is a real event when the ratio between the number of vehicle nodes representing the target vehicle and the number of the plurality of associated nodes is greater than or equal to a ratio threshold. For example, the ratio threshold may be 0.6.

[0061] Both of the above operations can indicate that multiple cameras capture the target vehicle experiencing the target event near the location of the target event. For example, four cameras capture the same vehicle experiencing a reverse driving event, and the authenticity of the vehicle experiencing the reverse driving is high.

[0062] The technical solution provided by the embodiment of the present disclosure can determine the authenticity of the target event according to the number of vehicle nodes, and the process of determining the authenticity is simple, convenient and highly accurate.

[0063] According to another embodiment of the present disclosure, the multiple nodes in the graph may not include a vehicle node.

[0064] In the case where the multiple nodes in the atlas do not include a vehicle node, the authenticity of the target event can be determined by the following operations. For example, in the case where it is determined that the camera device has captured the target event of the vehicle, label information or attribute information can be set for the camera device node that captured the target event. Taking attribute information as an example below, the attribute information can include vehicle information of the target vehicle where the target event occurs, such as the license plate number of the target vehicle. Then, multiple associated nodes can be determined from the multiple camera device nodes in the atlas. Then, the attribute information of the multiple associated nodes is counted, and the associated node whose attribute information includes the vehicle information of the target vehicle is determined as a reference node. Then, in the case where it is determined that the number of reference nodes is greater than or equal to the number threshold, or in the case where it is determined that the ratio between the number of reference nodes and the number of multiple associated nodes is greater than or equal to the ratio threshold, it is determined that the target event is a real event. For example, the ratio threshold can be set to 0.6.

[0065] According to another embodiment of the present disclosure, in response to detecting that a start condition is met, an operation of determining multiple associated nodes associated with the event information from multiple nodes of the graph according to the event information and the graph is performed. The start condition may include at least one of the following three start conditions.

[0066] In one example, the first start condition includes that a usage rate of predetermined resource data is less than or equal to a usage rate threshold.

[0067] It should be noted that the operations of determining the associated nodes from multiple nodes and the operations of determining the authenticity of the target event both require the use of the predetermined resource data configured by the server, such as using at least one of the GPU and the CPU configured by the server. The Grafana (an open source metric analysis and visualization tool) monitoring platform can be used to monitor the usage rate of the predetermined resources of the server.

[0068] The first start condition may specifically include, for example, at least one of a GPU usage rate being less than or equal to 60% and a CPU usage rate being less than or equal to 50%.

[0069] The technical solution provided in this example performs an operation of determining multiple associated nodes from multiple nodes when the usage rate of the predetermined resource data is less than the usage rate threshold, thereby improving the utilization rate of the predetermined data resources configured by the server. In addition, when the target event detection algorithm uses the predetermined resource data to run, the technical solution provided in this example can also ensure that the server has more resources to run the target event detection algorithm, thereby ensuring the normal operation of the target event detection algorithm.

[0070] In another example, the second start condition includes that the duration is greater than or equal to a duration threshold, and the duration is the duration during which the usage rate of the predetermined resource data is less than or equal to the usage rate threshold.

[0071] For example, the duration threshold may be 15 minutes. If the duration is greater than or equal to the duration threshold, it indicates that the server is in an idle state.

[0072] In another example, the third start condition includes that the current time is within a predetermined time period based on the day. The predetermined time period may be a time period at night, such as 23:00 to 5:00. Due to the poor light at night, the clarity of the video data captured by the camera device will be reduced, which will lead to an increase in the misjudgment rate of the target event detection algorithm. Determining the authenticity of the target event can improve the accuracy of the target event. In addition, there are fewer vehicles at night, and the number of calls to the target event detection algorithm and the predetermined resource data used will also decrease accordingly. Determining the authenticity of the target event can improve the utilization rate of the predetermined resource data.

[0073] Figure 3 It is a schematic diagram of a diagram according to an embodiment of the present disclosure.

[0074] Figure 3 The circle in the figure represents a node, the characters inside the circle represent the name of the node, and the straight line connecting two nodes represents an edge.

[0075] Figure 3There are 6 intersections and 8 cameras involved. The 6 intersections are distributed in sequence along the road direction. The camera node N_M1 represents the camera M1 installed at the first intersection, the camera nodes N_M2_1 and N_M2_2 represent the two cameras M2_1 and M2_2 installed at the second intersection, the camera node N_M3 represents the camera M3 installed at the third intersection, and the camera node N_M4 represents the camera M4 installed at the fourth intersection. The camera nodes N_M5_1 and N_M5_2 represent the two cameras M5_1 and M5_2 installed at the fifth intersection. The camera node N_M6 represents the camera M6 installed at the sixth intersection.

[0076] Multiple camera device nodes are connected according to the following rules: any two camera devices located at two adjacent intersections are connected via an edge, and any two camera devices located at the same intersection are connected via an edge.

[0077] Figure 3 There are also three vehicles involved in the incident. Vehicle nodes N_C1_1, N_C1_2 and N_C1_3 represent the same vehicle C1 that has a target event (e.g., a wrong-way event). Vehicle node N_C2 represents vehicle C2 that has a target event (e.g., a parking violation event). Vehicle node N_C3 represents vehicle C3 that has a target event (e.g., a driver not wearing a seat belt event).

[0078] The edge connection between the camera node and the vehicle node indicates that the camera represented by the camera node captures the target event of the vehicle represented by the vehicle node. For example, the three cameras MI, M2_1, and M2_2 capture the reverse driving event of vehicle C1, the camera M2_1 captures the illegal parking event of vehicle C2, and the camera M5_1 captures the driver of vehicle C3 not wearing a seat belt.

[0079] The following uses the target event, which is the driver not wearing a seat belt event related to vehicle C3 as an example, to illustrate the process of determining the authenticity of the target event.

[0080] The intersection where the camera is located can be set as the label information of the camera node, and the license plate number of the vehicle can be set as the attribute information of the vehicle node.

[0081] Event information related to the target event can be determined. For example, the target event detection algorithm is used to process the video data of the camera device to obtain the above-mentioned driver not wearing a seat belt event and event information related to the event. The event information can include event location information, such as the fifth intersection, and the event information can also include vehicle information, such as the license plate number of vehicle C3.

[0082] According to the event information and the graph, multiple associated nodes associated with the event information can be determined from multiple nodes of the graph. For example, the camera node N_M5_1 can be found from the graph according to the event location information, and the vehicle node N_C3 can be found from the graph according to the vehicle information. The camera node N_M5_1 and the vehicle node N_C3 are respectively used as the first associated nodes described above, and each first associated node is used as a starting point, and the second associated node is determined by traversing with a predetermined edge path of 3. The first associated node and the second associated node are determined as multiple associated nodes, and the multiple associated nodes include 8 nodes, namely, the camera node N_M2_1, N_M2_2, N_M3, N_M4, N_M5_1, N_M5_2, N_M6 and the vehicle node N_C3.

[0083] Then, the authenticity of the target event can be determined based on the multiple associated nodes. For example, the ratio between the number of vehicle nodes where the target event occurs and the number of multiple associated nodes is calculated, and the calculated ratio result is 0.125, which is less than the ratio threshold (e.g., 0.6), so the above driver not wearing a seat belt event is determined as a non-real event.

[0084] Figure 4 It is a schematic structural block diagram of an apparatus for determining the authenticity of an event according to an embodiment of the present disclosure.

[0085] like Figure 4 As shown, the device 400 for determining the authenticity of an event may include an event information determination module 410 , a related node determination module 420 and an authenticity determination module 430 .

[0086] The event information determination module 410 is used to determine event information related to a target event.

[0087] The associated node determination module 420 is used to determine multiple associated nodes associated with the event information from multiple nodes of the graph based on the event information and the graph, the multiple nodes include multiple camera device nodes, and any two camera device nodes among the multiple camera device nodes that meet the predetermined position conditions are connected via edges.

[0088] The authenticity determination module 430 is used to determine the authenticity of the target event according to a plurality of associated nodes.

[0089] According to another embodiment of the present disclosure, the predetermined location condition includes one of the following: the physical locations of the two cameras represented by any two camera nodes are in the same area; and the physical locations of the two cameras represented by any two camera nodes are in two adjacent areas.

[0090] According to another embodiment of the present disclosure, the associated node determination module includes a first associated node determination submodule and a second associated node determination submodule. The first associated node determination submodule is used to determine a node whose attribute information or label information matches the event information among multiple nodes as a first associated node among multiple associated nodes. The second associated node determination submodule is used to determine, in the graph, a node whose edge path between the multiple nodes and the first associated node is less than or equal to an edge path threshold as a second associated node among multiple associated nodes.

[0091] According to another embodiment of the present disclosure, in response to detecting that one of the following conditions is met, the operation of the associated node determination module is executed: the usage rate of the scheduled resource data is less than or equal to the usage rate threshold; the duration is greater than or equal to the duration threshold, and the duration is the duration during which the usage rate of the scheduled resource data is less than or equal to the usage rate threshold; and the current time is within the scheduled time period based on the day.

[0092] According to another embodiment of the present disclosure, the multiple nodes also include multiple vehicle nodes, each of the multiple vehicle nodes is connected to a camera device node among the multiple camera device nodes via an edge; the camera device represented by a camera device node captures the target event occurring in the vehicle represented by each vehicle node.

[0093] According to another embodiment of the present disclosure, the event information includes vehicle information representing the target vehicle; the authenticity determination module includes at least one of a first authenticity determination submodule and a second authenticity determination submodule. The first authenticity determination submodule is used to determine that the target event is a real event when the number of vehicle nodes representing the target vehicle among the multiple associated nodes is greater than or equal to a quantity threshold. The second authenticity determination submodule is used to determine that the target event is a real event when the ratio between the number of vehicle nodes representing the target vehicle and the number of multiple associated nodes among the multiple associated nodes is greater than or equal to a ratio threshold.

[0094] In the technical solution of the present disclosure, the collection, storage, use, processing, transmission, provision and disclosure of user personal information involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0095] In the technical solution of the present disclosure, the user's authorization or consent is obtained before obtaining or collecting the user's personal information.

[0096] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

[0097] Figure 5A schematic block diagram of an example electronic device 500 that can be used to implement an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0098] like Figure 5 As shown, the device 500 includes a computing unit 501, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 502 or a computer program loaded from a storage unit 508 into a random access memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the device 500 can also be stored. The computing unit 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0099] A number of components in the device 500 are connected to the I / O interface 505, including: an input unit 506, such as a keyboard, a mouse, etc.; an output unit 507, such as various types of displays, speakers, etc.; a storage unit 508, such as a disk, an optical disk, etc.; and a communication unit 509, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 509 allows the device 500 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0100] The computing unit 501 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 501 performs the various methods and processes described above, such as a method for determining the authenticity of an event. For example, in some embodiments, the method for determining the authenticity of an event may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 508. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 500 via ROM 502 and / or communication unit 509. When the computer program is loaded into RAM 503 and executed by the computing unit 501, one or more steps of the method for determining the authenticity of an event described above may be performed. Alternatively, in other embodiments, the computing unit 501 may be configured to perform a method for determining the authenticity of an event by any other appropriate means (e.g., by means of firmware).

[0101] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0102] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0103] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0104] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0105] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communications network). Examples of communications networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0106] A computer system may include clients and servers. Clients and servers are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship to each other.

[0107] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0108] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A method for determining the authenticity of an event, comprising: Determining event information related to a target event, the event information including vehicle information of a target vehicle; According to the event information and the graph, a plurality of associated nodes associated with the event information are determined from a plurality of nodes of the graph; wherein the plurality of nodes include a plurality of camera nodes and a plurality of vehicle nodes, any two camera nodes among the plurality of camera nodes that meet a predetermined position condition are connected via an edge, each of the plurality of vehicle nodes is connected to a camera node among the plurality of camera nodes via an edge, and the camera represented by the one camera node captures the occurrence of the target event on the vehicle represented by each vehicle node; as well as The authenticity of the target event is determined according to the number of vehicle nodes representing the target vehicle in the plurality of associated nodes.

2. The method according to claim 1, wherein: The predetermined location condition includes one of the following: The physical locations of the two camera devices represented by the arbitrary two camera device nodes are in the same area; and The physical locations of the two camera devices represented by the arbitrary two camera device nodes are in two adjacent areas.

3. The method according to claim 1, wherein: The step of determining, based on the event information and the graph, a plurality of associated nodes associated with the event information from a plurality of nodes of the graph comprises: Determine a node among the plurality of nodes whose attribute information or label information matches the event information as a first associated node among the plurality of associated nodes; and In the graph, a node among the plurality of nodes whose edge path with the first associated node is less than or equal to an edge path threshold is determined as a second associated node among the plurality of associated nodes.

4. The method according to claim 1, wherein: In response to detecting that one of the following conditions is met, an operation of determining, based on the event information and the graph, a plurality of associated nodes associated with the event information from a plurality of nodes of the graph is performed: The usage rate of the reserved resource data is less than or equal to the usage rate threshold; The duration is greater than or equal to a duration threshold, and the duration is a duration during which the usage rate of the predetermined resource data is less than or equal to the usage rate threshold; as well as The current time is within the day-based scheduled time period.

5. The method according to claim 1, wherein: Determining the authenticity of the target event according to the number of vehicle nodes representing the target vehicle in the plurality of associated nodes comprises at least one of the following: In the case where the number of vehicle nodes representing the target vehicle among the plurality of associated nodes is greater than or equal to a number threshold, determining that the target event is a real event; as well as When, in determining the plurality of associated nodes, a ratio between the number of vehicle nodes representing the target vehicle and the number of the plurality of associated nodes is greater than or equal to a ratio threshold, it is determined that the target event is a real event.

6. A device for determining the authenticity of an event, comprising: An event information determination module, used to determine event information related to a target event, wherein the event information includes vehicle information representing a target vehicle; An associated node determination module is used to determine, based on the event information and the graph, a plurality of associated nodes associated with the event information from a plurality of nodes in the graph; wherein the plurality of nodes include a plurality of camera device nodes and a plurality of vehicle nodes, any two camera device nodes in the plurality of camera device nodes that meet a predetermined position condition are connected via an edge, each of the plurality of vehicle nodes is connected to a camera device node in the plurality of camera device nodes via an edge, and the camera represented by the one camera device node captures the occurrence of the target event in the vehicle represented by each of the vehicle nodes; as well as The authenticity determination module is used to determine the authenticity of the target event according to the number of vehicle nodes representing the target vehicle in the multiple associated nodes.

7. The device according to claim 6, wherein: The predetermined location condition includes one of the following: The physical locations of the two camera devices represented by the arbitrary two camera device nodes are in the same area; and The physical locations of the two camera devices represented by the arbitrary two camera device nodes are in two adjacent areas.

8. The device according to claim 6, wherein: The associated node determination module includes: a first associated node determination submodule, configured to determine a node among the plurality of nodes whose attribute information or label information matches the event information as a first associated node among the plurality of associated nodes; and The second associated node determination submodule is used to determine, in the graph, a node among the multiple nodes whose edge path with the first associated node is less than or equal to an edge path threshold as a second associated node among the multiple associated nodes.

9. The device according to claim 6, wherein: In response to detecting that one of the following conditions is met, the operation of the associated node determination module is executed: The usage rate of the reserved resource data is less than or equal to the usage rate threshold; The duration is greater than or equal to a duration threshold, and the duration is a duration during which the usage rate of the predetermined resource data is less than or equal to the usage rate threshold; as well as The current time is within the day-based scheduled time period.

10. The device according to claim 6, wherein: The authenticity determination module includes at least one of the following: A first authenticity determination submodule, configured to determine that the target event is a real event when the number of vehicle nodes representing the target vehicle among the plurality of associated nodes is greater than or equal to a number threshold; as well as The second authenticity determination submodule is used to determine that the target event is a real event when the ratio between the number of vehicle nodes representing the target vehicle and the number of the multiple associated nodes is greater than or equal to a ratio threshold.

11. An electronic device, comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 5.

12. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 5.

13. A computer program product comprising a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 5.

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