Method and device for determining device validity, storage medium, and electronic device

Abstract

Embodiments of the present invention provide a method and apparatus, storage medium, and electronic device for determining device validity. The method comprises: determining identification information of an identification device located in a target area from an image obtained through video surveillance of the target area, wherein the identification device is used to indicate an abnormality in the target area; using the identification information to determine N confidence levels for N abnormal events in the target area, where N is a natural number greater than or equal to 1; and determining the validity of the identification device based on the N confidence levels. This invention solves the problem of inaccurate detection of the validity of identification devices in related technologies, achieving the effect of accurately determining whether the identification device is valid.

Description

Technical Field

[0001] Embodiments of the present invention relate to the field of communications, and in particular, to a method and apparatus for determining device validity, a storage medium, and an electronic device. Background Art

[0002] Road reconstruction and maintenance work often occurs in various urban and highway traffic scenarios, inevitably disrupting normal traffic flow. Accurately identifying road construction areas is crucial for traffic management and guidance. However, the selection of construction markers for such projects often involves a degree of subjectivity and randomness. Construction signs, cones, fences, and other items may be used, making identification of construction areas more difficult. Summary of the Invention

[0003] The embodiments of the present invention provide a method and apparatus for determining the validity of a device, a storage medium, and an electronic device, so as to at least solve the problem of inaccurate detection of the validity of an identification device in the related art.

[0004] According to one embodiment of the present invention, a method for determining the effectiveness of a device is provided, comprising: determining identification information of an identification device located in a target area in an image obtained by video surveillance of the target area, wherein the identification device is used to indicate that an abnormality has occurred in the target area; determining N confidence levels of N abnormal events in the target area using the identification information, wherein N is a natural number greater than or equal to 1; and determining the effectiveness of the identification device based on the N confidence levels.

[0005] According to another embodiment of the present invention, a device for determining the effectiveness of a device is provided, comprising: a first determination module for determining identification information of an identification device located in the target area in an image obtained by video surveillance of the target area, wherein the identification device is used to indicate that an abnormality has occurred in the target area; a second determination module for determining N confidence levels of N abnormal events in the target area using the identification information, wherein N is a natural number greater than or equal to 1; and a third determination module for determining the effectiveness of the identification device based on the N confidence levels.

[0006] In an exemplary embodiment, the above-mentioned first determination module includes: a first determination unit, used to determine the coordinate information of the above-mentioned identification device in the above-mentioned target area in the above-mentioned image; a second determination unit, used to determine the coordinate section between the above-mentioned identification device and the M lane lines in the above-mentioned target area in the above-mentioned image according to the above-mentioned coordinate information, so as to determine the identification information of the above-mentioned identification device.

[0007] In an exemplary embodiment, the above-mentioned third determination module includes: a first calculation unit, used to calculate the product between each of the above-mentioned N confidence levels and the corresponding weight to determine the total confidence level; a third determination unit, used to determine that the above-mentioned identification device is in a valid state when the above-mentioned total confidence level is greater than or equal to a preset threshold value; and a fourth determination unit, used to determine that the above-mentioned identification device is in an invalid state when the above-mentioned total confidence level is less than the above-mentioned preset threshold value.

[0008] In an exemplary embodiment, the above-mentioned device also includes: a first issuing module, which is used to issue a prompt message after determining that the above-mentioned identification device is in an invalid state when the above-mentioned total confidence is less than the above-mentioned preset threshold, wherein the above-mentioned prompt message is used to prompt the adjustment of the placement position of the above-mentioned identification device in the above-mentioned target area.

[0009] In an exemplary embodiment, the above-mentioned device also includes: a fourth determination module, which is used to determine the abnormal area in the above-mentioned target area by using the coordinate information of the above-mentioned identification device and the intersection information between the above-mentioned identification device and the lane in the above-mentioned target area after determining that the above-mentioned identification device is in a valid state when the above-mentioned total confidence is greater than or equal to a preset threshold.

[0010] In an exemplary embodiment, the above-mentioned second determination module includes: a fifth determination unit, used to determine the vehicle driving conditions in the above-mentioned target area within a preset time period as a first abnormal event; a first acquisition unit, used to obtain the total traffic flow of the above-mentioned vehicles in the above-mentioned target area within the above-mentioned preset time period, wherein the above-mentioned total traffic flow includes the first traffic flow of the abnormal lane identified by the above-mentioned identification device in the above-mentioned target area, and the second traffic flow of the normal lane in the above-mentioned target area; a sixth determination unit, used to determine the coordinate section between the coordinates of the above-mentioned identification device and the lane line in the above-mentioned target area; a second acquisition unit, used to obtain the cross-sectional traffic flow corresponding to the above-mentioned coordinate section from the above-mentioned image; a seventh determination unit, used to determine the first confidence level of the above-mentioned first abnormal event based on the above-mentioned total traffic flow, the above-mentioned second traffic flow and the above-mentioned cross-sectional traffic flow, wherein the above-mentioned N confidence levels include the above-mentioned first confidence level.

[0011] In an exemplary embodiment, the above-mentioned second determination module includes: an eighth determination unit, used to determine the lane change situation of the vehicle in the above-mentioned target area within a preset time period as a second abnormal event; a third acquisition unit, used to obtain the lane change information of the vehicle in the above-mentioned target area within the above-mentioned preset time period within a preset distance of the above-mentioned identification device, wherein the above-mentioned lane change information is used to indicate the change of the above-mentioned vehicle between the abnormal lane and the normal lane identified by the above-mentioned identification device; a ninth determination unit, used to determine the second confidence level of the above-mentioned second abnormal event based on the above-mentioned lane change information and the lane change position of the above-mentioned vehicle, wherein the above-mentioned N confidence levels include the above-mentioned second confidence level.

[0012] In an exemplary embodiment, the above-mentioned second determination module includes: a tenth determination unit, used to determine the object state of the first abnormal object included in the above-mentioned target area within a preset time period as a third abnormal event, wherein the above-mentioned first abnormal object is used to handle construction events in the above-mentioned target area; an eleventh determination unit, used to determine the third confidence level corresponding to the above-mentioned third abnormal event according to the degree of matching between the object state of the above-mentioned abnormal object and the preset object state, wherein the above-mentioned third confidence level is included in the above-mentioned N confidence levels.

[0013] In an exemplary embodiment, the above-mentioned second determination module includes: a twelfth determination unit, used to determine the object state of the second abnormal object included in the above-mentioned target area within a preset time period as a fourth abnormal event, wherein the above-mentioned second abnormal object is used to place the above-mentioned identification device; a thirteenth determination unit, used to determine the fourth confidence level of the object state of the above-mentioned second abnormal object.

[0014] According to yet another embodiment of the present invention, a computer-readable storage medium is provided, in which a computer program is stored. The computer program is configured to execute the steps of any one of the above method embodiments when run.

[0015] According to another embodiment of the present invention, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to perform the steps in any one of the above method embodiments.

[0016] The present invention determines identification information of an identification device located in a target area from an image obtained through video surveillance of the target area, where the identification device is used to indicate an abnormality in the target area; uses the identification information to determine N confidence levels for N abnormal events in the target area, where N is a natural number greater than or equal to 1; and determines the effectiveness of the identification device based on the N confidence levels. This achieves the goal of using the confidence levels of multiple events to determine whether the settings of the identification device are effective. Therefore, the problem of inaccurate identification device effectiveness detection in related technologies can be resolved, achieving the effect of accurately determining whether the identification device is effective. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a hardware structure block diagram of a mobile terminal according to a method for determining device validity according to an embodiment of the present invention;

[0018] Figure 2 is a flow chart of a method for determining device validity according to an embodiment of the present invention;

[0019] Figure 3 is an overall flow chart according to an embodiment of the present invention;

[0020] Figure 4 is a schematic diagram of a marker according to an embodiment of the present invention (1);

[0021] Figure 5 is a schematic diagram of a marker according to an embodiment of the present invention (II);

[0022] Figure 6 is a schematic diagram of road time according to an embodiment of the present invention;

[0023] Figure 7 is a schematic cross-sectional view according to an embodiment of the present invention;

[0024] Figure 8 4 is a structural block diagram of an apparatus for determining device validity according to an embodiment of the present invention. DETAILED DESCRIPTION

[0025] Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings and in combination with embodiments.

[0026] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0027] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on a mobile terminal as an example, Figure 1FIG. 1 is a hardware structure block diagram of a mobile terminal according to an embodiment of the present invention, which is a method for determining device validity. Figure 1 As shown, the mobile terminal may include one or more ( Figure 1 Only one is shown) a processor 102 (the processor 102 may include but is not limited to a microprocessor MCU or a programmable logic device FPGA and other processing devices) and a memory 104 for storing data, wherein the mobile terminal may also include a transmission device 106 and an input and output device 108 for communication functions. It will be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the mobile terminal. Figure 1 More or fewer components than shown, or with Figure 1 Different configurations shown.

[0028] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the method for determining the effectiveness of the device in the embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, implementing the above-mentioned method. The memory 104 may include a high-speed random access memory and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely located relative to the processor 102, and these remote memories may be connected to the mobile terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0029] The transmission device 106 is used to receive or send data via a network. A specific example of the aforementioned network may include a wireless network provided by the mobile terminal's communications provider. In one embodiment, the transmission device 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station to enable communication with the Internet. In one embodiment, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0030] In this embodiment, a method for determining the effectiveness of a device is provided. Figure 2 FIG. 1 is a flow chart of a method for determining device validity according to an embodiment of the present invention. Figure 2 As shown, the process includes the following steps:

[0031] Step S202, determining identification information of an identification device located in the target area in an image obtained by video surveillance of the target area, wherein the identification device is used to indicate that an abnormality has occurred in the target area;

[0032] In this embodiment, the target area includes, but is not limited to, an area encompassing traffic roads. For example, signage equipment, such as warning signs, construction signs, cones, and fences, may be installed on roads requiring construction. Video surveillance can be captured using cameras installed in the target area.

[0033] Step S204, determining N confidence levels of N abnormal events in the target area using the identification information, where N is a natural number greater than or equal to 1;

[0034] In this embodiment, abnormal events include events that occur in the target area, such as a vehicle changing lanes on a construction road, a person placing a sign, a construction worker, or a construction vehicle.

[0035] Step S206: Determine the validity of the identification device based on the N confidence levels.

[0036] In this embodiment, the confidence levels of the N confidence levels and the comparison results with the preset values ​​may be combined to determine whether the identification device is valid.

[0037] The execution subject of the above steps may be a terminal, etc., but is not limited thereto.

[0038] Through the above steps, the identification information of an identification device located in a target area is determined from an image obtained through video surveillance of the target area, where the identification device is used to indicate an abnormality in the target area; N confidence levels for N abnormal events in the target area are determined using the identification information, where N is a natural number greater than or equal to 1; and the effectiveness of the identification device is determined based on the N confidence levels. This achieves the purpose of using the confidence levels of multiple events to determine whether the settings of the identification device are effective. Therefore, the problem of inaccurate identification device effectiveness detection in related technologies can be resolved, achieving the effect of accurately determining whether the identification device is effective.

[0039] In an exemplary embodiment, determining identification information of an identification device located in a target area from an image obtained by performing video surveillance on the target area includes:

[0040] S1, determining the coordinate information of the identification device in the target area in the image;

[0041] S2: Determine a coordinate section between the marking device and M lane lines in the target area in the image according to the coordinate information to determine marking information of the marking device.

[0042] In this embodiment, perpendicular lines are drawn to the lane lines in the same direction according to the coordinates of the marking devices, and the coordinate sections of M lanes are connected to obtain the traffic flow in each coordinate section.

[0043] In one exemplary embodiment, determining the validity of an identification device based on N confidence levels includes:

[0044] S1, calculate the product between each confidence level of N confidence levels and the corresponding weight to determine the total confidence level;

[0045] S2, when the total confidence level is greater than or equal to a preset threshold, determining that the identification device is in a valid state;

[0046] S3: When the total confidence level is less than a preset threshold, determine that the identification device is in an invalid state.

[0047] In this embodiment, the weight is preset and corresponds to each confidence level.

[0048] In an exemplary embodiment, when the total confidence level is less than a preset threshold, after determining that the identification device is in an invalid state, the method further includes:

[0049] S1, issuing a prompt message, wherein the prompt message is used to prompt the user to adjust the placement position of the identification device in the target area.

[0050] In this embodiment, modulating the identification device includes resetting the area indicated by the identification device, or replacing the identification device.

[0051] In an exemplary embodiment, when the total confidence level is greater than or equal to a preset threshold, after determining that the identification device is in a valid state, the method further includes:

[0052] S1, using coordinate information of the marking device and intersection information between the marking device and the lanes in the target area, determining an abnormal area in the target area.

[0053] In this embodiment, the abnormal area includes the construction area in the target area.

[0054] In an exemplary embodiment, determining N confidence levels of N abnormal events in a target area using identification information includes:

[0055] S1, determining the vehicle driving situation in the target area within a preset time period as a first abnormal event;

[0056] S2, obtaining a total traffic flow of vehicles traveling in the target area within a preset time period, wherein the total traffic flow includes a first traffic flow in an abnormal lane identified by an identification device in the target area and a second traffic flow in a normal lane in the target area;

[0057] S3, determining a coordinate section between the coordinates of the marking device and the lane line in the target area;

[0058] S4, obtaining the cross-sectional traffic flow corresponding to the coordinate cross section from the image;

[0059] S5. Determine a first confidence level of the first abnormal event based on the total vehicle flow, the second vehicle flow, and the cross-sectional vehicle flow, wherein the N confidence levels include the first confidence level.

[0060] In this embodiment, when a construction area appears on the road, the vehicle volume in the area will inevitably change. The traffic volume of the lane occupied by the construction area will be basically zero, while the vehicle volume of other lanes will increase significantly.

[0061] In an exemplary embodiment, determining N confidence levels of N abnormal events in a target area using identification information includes:

[0062] S1, determining a vehicle lane change in a target area within a preset time period as a second abnormal event;

[0063] S2, obtaining lane change information of vehicles in a target area within a preset time period within a preset distance of the identification device, wherein the lane change information is used to indicate the change of the vehicle between an abnormal lane and a normal lane identified by the identification device;

[0064] S3 : Determine a second confidence level of the second abnormal event based on the lane change information and the lane change position of the vehicle, wherein the N confidence levels include the second confidence level.

[0065] In this embodiment, the vehicle will perform a lane change operation in the construction area.

[0066] In an exemplary embodiment, determining N confidence levels of N abnormal events in a target area using identification information includes:

[0067] S1, determining the object state of a first abnormal object included in a target area within a preset time period as a third abnormal event, wherein the first abnormal object is used to handle a construction event in the target area;

[0068] S2. Determine a third confidence level corresponding to a third abnormal event according to a degree of matching between an object state of the abnormal object and a preset object state, wherein the N confidence levels include the third confidence level.

[0069] In this embodiment, the first abnormal object includes but is not limited to construction workers or construction vehicles. When a certain number of construction workers or construction vehicles appear in a construction area, it indicates that the area is a construction area.

[0070] In an exemplary embodiment, determining N confidence levels of N abnormal events in a target area using identification information includes:

[0071] S1, determining the object state of a second abnormal object included in the target area within a preset time period as a fourth abnormal event, wherein the second abnormal object is used to place an identification device;

[0072] S2, determining a fourth confidence level of the object state of the second abnormal object.

[0073] In this embodiment, the second abnormal object includes but is not limited to a person who places the identification device, such as a police officer, a cleaning staff, and the like.

[0074] The present invention will be described below in conjunction with specific embodiments:

[0075] like Figure 3 FIG. 1 is an overall flow chart of this embodiment, which includes the following steps:

[0076] S301, detecting a marker from the received video (corresponding to the identification device mentioned above, such as Figure 4 、 Figure 5 It also detects non-targets such as people and vehicles. It mainly uses deep learning methods to detect and track landmarks and non-targets such as people and machines in the video image, and outputs target location trajectory information.

[0077] S302: When there are obvious signs such as construction signs or vehicles on the main road surface, motor vehicle drivers will generally proactively avoid them, changing lanes to merge into other normal lanes. Within a certain statistical period, there are a large number of lane changes from construction lanes to normal lanes. These lane changes are often unidirectional and regular. These proactive driver avoidance behaviors also cause uneven traffic flow distribution across lanes. By analyzing the historical status and trajectory information of each target, and monitoring abnormal events on the road, we can use this information to determine the effectiveness of subsequent landmarks, filter out invalid targets, and improve the recognition accuracy of construction areas.

[0078] The road event monitoring mentioned above includes at least one of the following: Figure 6 As shown:

[0079] S1, according to the lane direction, count the road entrance flow by lane and add up the total input flow.

[0080] Draw a perpendicular line from the lane line in the same direction to connect the position information of each marker output by S301 to obtain N sections, where N is equal to the number of lanes. Count the flow of each section separately, as shown in the following example: Figure 7 shown.

[0081] By analyzing the traffic flow distribution over a specific period—namely, the relationship between the traffic flow in the marker section, the total input traffic flow, and the traffic flow in the normal lanes—the confidence level is output, which serves as one of the criteria for determining whether the marker is effective. If there is an effective construction area, the traffic flow in the section corresponding to the marker will be significantly lower than that in other lanes. In other words, the total traffic flow = the traffic flow in the construction lane + the traffic flow in other normal lanes, and the traffic flow in the construction lane approaches zero.

[0082] S2: Within a certain distance before and after the marker, a count of lane-changing and lane-crossing events is collected periodically, recording the location of the triggering event target and lane change information. Based on the cached lane positions and lane change patterns, a confidence score is output.

[0083] S3, searching for special vehicles or construction workers within a certain range of each marker position outputted in S301, and whether the target is in a slow-moving or stopped state, and outputting its confidence level according to the matching degree.

[0084] S4, combining the historical trajectory information of the marker and the person, based on the matching degree of distance and time analysis, determines whether the marker was placed by a person and outputs its confidence.

[0085] S303 , multiplying each confidence level of the marker output in S302 by the corresponding set weight to obtain a final total confidence level.

[0086] S304, comparing the total confidence with the set confidence threshold. If it is greater than the threshold, it is considered a valid target; otherwise, it is an invalid target and is filtered out.

[0087] S305: Calculate the intersection of the valid marker coordinates, marker positions, and lane sections using a convex hull algorithm to obtain a construction area, which is periodically updated synchronously.

[0088] In summary, this embodiment leverages the inherent impact of construction events on lane traffic flow by analyzing differences in events such as cross-sectional traffic flow at construction markers. This enhances the effectiveness of construction target filtering, reduces false detections in the detection model, and improves the effectiveness and accuracy of construction area identification. Taking into account the close association between construction events and construction personnel and vehicles, false detections are further filtered out by analyzing the historical trajectories of markers and personnel and vehicles, and matching temporal and spatial distance factors. A weighted factor comparison method is used to weight the confidence of each factor to derive an overall confidence score for the marker, which is used to determine target validity. This weighted factor approach allows lane area identification in traffic scenarios to be configured based on factors such as scene and time, dynamically adjusting weights to adapt to changing scenarios. For example, at night, when traffic volume is relatively low, event statistics such as traffic flow may not reflect differences, but information related to markers and construction personnel can be valuable. In this case, weights can be adjusted to achieve scenario adaptation, ensuring both versatility and recognition accuracy. Based on the effective marker coordinate points, the effective intersection points of the marker cross-section line and the lane are added, and the construction area is obtained through the convex hull algorithm, so that the construction area is more consistent with the actual lane road surface and the accuracy of area recognition is improved.

[0089] Through the description of the above embodiments, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus the necessary general hardware platform, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in each embodiment of the present invention.

[0090] This embodiment also provides a device validity determination device, which is used to implement the above-mentioned embodiments and preferred embodiments. Details already described are omitted. As used below, the term "module" may refer to a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation using hardware, or a combination of software and hardware, is also possible and contemplated.

[0091] Figure 8 FIG. 1 is a structural block diagram of an apparatus for determining device validity according to an embodiment of the present invention. Figure 8 As shown, the device includes:

[0092] A first determining module 82 is configured to determine identification information of an identification device located in a target area from an image obtained by video surveillance of the target area, wherein the identification device is used to indicate that an abnormality has occurred in the target area;

[0093] A second determining module 84 is configured to determine N confidence levels of N abnormal events in the target area using the identification information, where N is a natural number greater than or equal to 1;

[0094] The third determination module 86 is configured to determine the validity of the identification device based on the N confidence levels.

[0095] In an exemplary embodiment, the first determining module includes:

[0096] A first determining unit is configured to determine coordinate information of the identification device in the target area in the image;

[0097] The second determining unit is configured to determine, in the image according to the coordinate information, a coordinate section between the marking device and the M lane lines in the target area, so as to determine the marking information of the marking device.

[0098] In an exemplary embodiment, the third determining module includes:

[0099] A first calculation unit is used to calculate the product between each of the N confidence levels and the corresponding weight to determine the total confidence level;

[0100] A third determining unit is configured to determine that the identification device is in a valid state when the total confidence level is greater than or equal to a preset threshold;

[0101] The fourth determining unit is configured to determine that the identification device is in an invalid state when the total confidence level is less than the preset threshold.

[0102] In an exemplary embodiment, the apparatus further comprises:

[0103] The first issuing module is used to issue a prompt message after determining that the above-mentioned identification device is in an invalid state when the above-mentioned total confidence is less than the above-mentioned preset threshold, wherein the above-mentioned prompt message is used to prompt the placement position of the above-mentioned identification device in the above-mentioned target area to be adjusted.

[0104] In an exemplary embodiment, the above-mentioned device also includes: a fourth determination module, which is used to determine the abnormal area in the above-mentioned target area by using the coordinate information of the above-mentioned identification device and the intersection information between the above-mentioned identification device and the lane in the above-mentioned target area after determining that the above-mentioned identification device is in a valid state when the above-mentioned total confidence is greater than or equal to a preset threshold.

[0105] In an exemplary embodiment, the second determining module includes:

[0106] a fifth determining unit, configured to determine the vehicle driving condition in the target area within a preset time period as a first abnormal event;

[0107] a first acquiring unit, configured to acquire a total traffic volume of the vehicles traveling in the target area within the preset time period, wherein the total traffic volume includes a first traffic volume of an abnormal lane identified by the identification device in the target area and a second traffic volume of a normal lane in the target area;

[0108] a sixth determining unit, configured to determine a coordinate section between the coordinates of the marking device and the lane line in the target area;

[0109] A second acquiring unit is configured to acquire the cross-sectional traffic flow corresponding to the coordinate cross-section from the image;

[0110] The seventh determination unit is used to determine a first confidence level of the first abnormal event based on the total vehicle flow, the second vehicle flow and the cross-sectional vehicle flow, wherein the N confidence levels include the first confidence level.

[0111] In an exemplary embodiment, the second determining module includes:

[0112] an eighth determining unit, configured to determine a lane change of a vehicle in the target area within a preset time period as a second abnormal event;

[0113] a third acquiring unit, configured to acquire lane change information of a vehicle in the target area within the preset time period within a preset distance from the identification device, wherein the lane change information indicates the vehicle's movement between an abnormal lane and a normal lane identified by the identification device;

[0114] A ninth determination unit is configured to determine a second confidence level of the second abnormal event based on the lane change information and the lane change position of the vehicle, wherein the N confidence levels include the second confidence level.

[0115] In an exemplary embodiment, the second determining module includes:

[0116] a tenth determining unit, configured to determine an object state of a first abnormal object included in the target area within a preset time period as a third abnormal event, wherein the first abnormal object is used to handle a construction event in the target area;

[0117] An eleventh determining unit is configured to determine a third confidence level corresponding to the third abnormal event according to a degree of matching between the object state of the abnormal object and a preset object state, wherein the third confidence level is included in the N confidence levels.

[0118] In an exemplary embodiment, the second determining module includes:

[0119] a twelfth determining unit, configured to determine the object state of a second abnormal object included in the target area within a preset time period as a fourth abnormal event, wherein the second abnormal object is used to place the identification device;

[0120] The thirteenth determining unit is configured to determine a fourth confidence level of the object state of the second abnormal object.

[0121] It should be noted that the above modules can be implemented through software or hardware. For the latter, it can be implemented in the following ways, but not limited to: the above modules are all located in the same processor; or the above modules are located in different processors in any combination.

[0122] An embodiment of the present invention further provides a computer-readable storage medium, in which a computer program is stored. The computer program is configured to execute the steps of any one of the above method embodiments when running.

[0123] In this embodiment, the computer-readable storage medium may be configured to store a computer program for executing the above steps.

[0124] In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to, various media that can store computer programs, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk, or an optical disk.

[0125] An embodiment of the present invention further provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0126] In an exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0127] In an exemplary embodiment, the processor may be configured to execute the above steps through a computer program.

[0128] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail here.

[0129] Obviously, those skilled in the art will appreciate that the various modules or steps of the present invention described above can be implemented using a general-purpose computing device, can be centralized on a single computing device, or can be distributed across a network of multiple computing devices. They can be implemented using program code executable by the computing device, and thus, can be stored in a storage device and executed by the computing device. In some cases, the steps shown or described herein can be performed in a different order than that shown, or can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0130] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A method for determining the effectiveness of a device, characterized in that: include: Determining identification information of an identification device located in a target area from an image obtained by video surveillance of the target area, wherein the identification device is used to indicate that an abnormality has occurred in the target area; Determining N confidence levels of N abnormal events in the target area using the identification information, where N is a natural number greater than or equal to 1; determining the validity of the identification device based on the N confidence levels; Among them, determining the identification information of the identification device located in the target area in the image obtained by video surveillance of the target area includes: determining the coordinate information of the identification device in the target area in the image; determining the coordinate section between the identification device and M lane lines in the target area in the image according to the coordinate information to determine the identification information of the identification device.

2. The method according to claim 1, characterized in that Determining the validity of the identification device based on the N confidence levels includes: Calculating the product between each confidence level of the N confidence levels and the corresponding weight to determine a total confidence level; When the total confidence level is greater than or equal to a preset threshold, determining that the identification device is in a valid state; When the total confidence level is less than the preset threshold, it is determined that the identification device is in an invalid state.

3. The method according to claim 2, characterized in that When the total confidence level is less than the preset threshold, after determining that the identification device is in an invalid state, the method further includes: A prompt message is issued, wherein the prompt message is used to prompt the user to adjust the placement position of the identification device in the target area.

4. The method according to claim 2, characterized in that When the total confidence level is greater than or equal to a preset threshold, after determining that the identification device is in a valid state, the method further includes: An abnormal area in the target area is determined by using the coordinate information of the identification device and the intersection information between the identification device and the lane in the target area.

5. The method according to claim 1, wherein Determining N confidence levels of N abnormal events in the target area using the identification information includes: Determining the vehicle driving conditions in the target area within a preset time period as a first abnormal event; Obtaining a total traffic flow of vehicles traveling in the target area within the preset time period, wherein the total traffic flow includes a first traffic flow of an abnormal lane identified by the identification device in the target area and a second traffic flow of a normal lane in the target area; Determining a coordinate section between the coordinates of the marking device and a lane line in the target area; Obtaining the cross-sectional traffic flow corresponding to the coordinate cross-section from the image; A first confidence level of the first abnormal event is determined based on the total vehicle flow, the second vehicle flow, and the cross-sectional vehicle flow, wherein the N confidence levels include the first confidence level.

6. The method according to claim 1, characterized in that Determining N confidence levels of N abnormal events in the target area using the identification information includes: determining a lane change of a vehicle in the target area within a preset time period as a second abnormal event; Acquiring lane change information of a vehicle in the target area within the preset time period within a preset distance of the identification device, wherein the lane change information is used to indicate the change of the vehicle between an abnormal lane and a normal lane identified by the identification device; A second confidence level of the second abnormal event is determined based on the lane change information and the lane change position of the vehicle, wherein the N confidence levels include the second confidence level.

7. The method according to claim 1, characterized in that Determining N confidence levels of N abnormal events in the target area using the identification information includes: determining an object state of a first abnormal object included in the target area within a preset time period as a third abnormal event, wherein the first abnormal object is used to handle a construction event in the target area; A third confidence level corresponding to the third abnormal event is determined according to a degree of matching between the object state of the abnormal object and a preset object state, wherein the N confidence levels include the third confidence level.

8. The method according to claim 1, characterized in that Determining N confidence levels of N abnormal events in the target area using the identification information includes: determining an object state of a second abnormal object included in the target area within a preset time period as a fourth abnormal event, wherein the second abnormal object is used to place the identification device; A fourth confidence level for the object state of the second abnormal object is determined.

9. A device for determining the effectiveness of a device, characterized in that: include: A first determining module is configured to determine identification information of an identification device located in a target area from an image obtained by video surveillance of the target area, wherein the identification device is used to indicate that an abnormality has occurred in the target area; a second determining module, configured to determine N confidence levels of N abnormal events in the target area using the identification information, wherein N is a natural number greater than or equal to 1; a third determining module, configured to determine the validity of the identification device based on the N confidence levels; Among them, the first determination module is also used to determine the coordinate information of the identification device in the target area in the image; determine the coordinate section between the identification device and the M lane lines in the target area in the image according to the coordinate information to determine the identification information of the identification device.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, wherein the computer program implements the method described in any one of claims 1 to 8 when executed by a processor.

11. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to run the computer program to perform the method according to any one of claims 1 to 8.

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