A method, apparatus, medium, and electronic device for identifying monitored vehicles.

Abstract

This application provides a method, apparatus, medium, and electronic device for identifying monitored vehicles. Multiple first vehicles report predicted information and pre-positioned locations of the same abnormal sound source. Based on this predicted information and pre-positioned locations, the precise location of the abnormal sound source is obtained. Then, by combining the pose information and monitoring performance information of multiple second vehicles surrounding the precise location with the precise location, the effective vehicles capable of monitoring the abnormal sound source are determined among the multiple second vehicles. This application utilizes the characteristics of vehicles being low-lying, unobstructed by tall objects, and widely distributed to identify effective monitoring vehicles around abnormal sound sources, compensating for the shortcomings of the Skynet monitoring system, improving monitoring density and the utilization rate of vehicles of various types, and enhancing the effectiveness of social management.

Description

Technical Field

[0001] This application relates to the field of computer technology, and more specifically, to a method, apparatus, medium, and electronic device for identifying monitored vehicles. Background Technology

[0002] A surveillance system consists of two main parts: front-end equipment and back-end equipment. Front-end equipment includes surveillance cameras, manual or motorized lenses, pan-tilt units, protective housings, listening devices, alarm detectors, and multi-functional decoders. Back-end equipment includes central control equipment and sub-control equipment. The front-end equipment communicates with the central control system, transmitting images captured by the surveillance cameras to the central control equipment. The Skynet surveillance system utilizes a large number of surveillance cameras installed in streets and alleys to form a surveillance network, providing security for residents' lives.

[0003] However, due to the limitations of the installation location of the surveillance cameras in the Skynet monitoring system, the target information is often blocked by tall objects, resulting in poor monitoring effect.

[0004] Therefore, this application provides a method for identifying monitored vehicles to solve the above-mentioned technical problems. Summary of the Invention

[0005] The purpose of this application is to provide a method, apparatus, medium, and electronic device for identifying monitored vehicles, which can solve at least one of the technical problems mentioned above. The specific solution is as follows:

[0006] According to a specific embodiment of this application, in a first aspect, this application provides a method for determining a monitored vehicle, comprising:

[0007] Obtain the prediction information and pre-positioning location of each of the multiple first vehicles for the same abnormal sound source;

[0008] The precise location of the abnormal sound source is obtained based on the predicted information and pre-positioned location of each of the plurality of first vehicles.

[0009] In response to determining multiple second vehicles around the precise positioning location, the pose information and monitoring performance information of each of the multiple second vehicles are acquired;

[0010] Based on the precise positioning location and the pose information and monitoring performance information of each of the plurality of second vehicles, the effective vehicle among the plurality of second vehicles that can monitor the abnormal sound source is determined.

[0011] Optionally, obtaining the precise location of the abnormal sound source based on the predicted information and pre-positioned location of each of the plurality of first vehicles includes:

[0012] The evaluation weight value of each first vehicle is obtained based on the prediction information of each first vehicle.

[0013] The precise location of the abnormal sound source is obtained based on the pre-positioned location and evaluation weight value of each of the plurality of first vehicles.

[0014] Optionally, the prediction information for each first vehicle includes: the predicted distance value between the corresponding first vehicle and the abnormal sound source, the predicted noise intensity value obtained by the sound of the abnormal sound source collected by the corresponding first vehicle, and the predicted signal-to-noise ratio obtained by the sound of the abnormal sound source collected by the corresponding first vehicle.

[0015] Accordingly, the process of obtaining the evaluation weight value of the corresponding first vehicle based on the prediction information of each first vehicle includes the following formula:

[0016]

[0017] Where i = positive integers from 1 to n, w i SNR represents the evaluation weight value of the i-th vehicle. i d represents the prediction signal-to-noise ratio in the prediction information of the i-th vehicle. i N represents the predicted distance value in the prediction information of the i-th first vehicle. i This represents the predicted noise intensity value in the prediction information of the i-th first vehicle.

[0018] Optionally, obtaining the precise location of the abnormal sound source based on the pre-positioned location and evaluation weight value of each of the plurality of first vehicles includes calculating the precise location (x, y) using the following formula:

[0019]

[0020]

[0021] Where n is a positive integer, x represents the x-axis coordinate value of the precise positioning position, y represents the y-axis coordinate value of the precise positioning position, and w i Let x represent the evaluation weight value of the i-th first vehicle. i The x-axis coordinate of the predicted position of the i-th vehicle in the prediction information is represented by the y-axis coordinate. i This represents the y-axis coordinate value of the prepositioned position in the predicted information of the i-th first vehicle.

[0022] Optionally, determining the effective vehicle among the plurality of second vehicles capable of monitoring the abnormal sound source based on the precise positioning location and the respective pose information and monitoring performance information of the plurality of second vehicles includes:

[0023] Based on the precise positioning location and the pose information of each of the multiple second vehicles, a number of vehicles to be identified among the multiple second vehicles that can monitor the abnormal sound source are determined, as well as the calculated distance values ​​between each of the multiple vehicles to be identified and the precise positioning location.

[0024] Based on the calculated distance values ​​and monitoring performance information of each of the multiple undetermined vehicles, the effective vehicles among the multiple undetermined vehicles that can monitor the abnormal sound source are determined.

[0025] Optionally, determining the effective vehicle among the plurality of candidate vehicles capable of monitoring the abnormal sound source based on the calculated distance values ​​and monitoring performance information of each candidate vehicle includes:

[0026] The effective coefficient of each vehicle is obtained based on its calculated distance value and monitoring performance information.

[0027] When the effective coefficient of any undetermined vehicle is greater than the preset effective coefficient threshold, the undetermined vehicle is determined to be a valid vehicle.

[0028] Optionally, the effective coefficient for each vehicle is obtained based on its calculated distance value and monitoring performance information, including the following formula:

[0029] ;

[0030] Among them, EC j D represents the effective coefficient of the j-th vehicle to be determined. j D represents the calculated distance value for the j-th vehicle to be determined, Dmax represents the preset maximum distance value, and P represents the calculated distance value for the j-th vehicle to be determined. j This represents the pixel value of the monitoring camera in the monitoring performance information of the j-th vehicle to be determined, where Pmax represents the preset maximum pixel value, and L... j Lmax represents the light intensity value of the monitoring camera in the monitoring performance information of the j-th vehicle to be determined, and Lmin represents the preset maximum light intensity value.

[0031] According to a specific embodiment of this application, in a second aspect, this application provides a vehicle detection device, comprising:

[0032] The acquisition unit is used to acquire the prediction information and pre-positioning location of each of the multiple first vehicles for the same abnormal sound source;

[0033] The positioning unit is used to obtain the precise location of the abnormal sound source based on the prediction information and pre-positioning location of each of the plurality of first vehicles.

[0034] A triggering unit is configured to, in response to determining multiple second vehicles surrounding the precise positioning location, acquire the pose information and monitoring performance information of each of the multiple second vehicles;

[0035] The determining unit is used to determine, based on the precise positioning location and the pose information and monitoring performance information of each of the plurality of second vehicles, the effective vehicle among the plurality of second vehicles capable of monitoring the abnormal sound source.

[0036] Optionally, obtaining the precise location of the abnormal sound source based on the predicted information and pre-positioned location of each of the plurality of first vehicles includes:

[0037] The evaluation weight value of each first vehicle is obtained based on the prediction information of each first vehicle.

[0038] The precise location of the abnormal sound source is obtained based on the pre-positioned location and evaluation weight value of each of the plurality of first vehicles.

[0039] Optionally, the prediction information for each first vehicle includes: the predicted distance value between the corresponding first vehicle and the abnormal sound source, the predicted noise intensity value obtained by the sound of the abnormal sound source collected by the corresponding first vehicle, and the predicted signal-to-noise ratio obtained by the sound of the abnormal sound source collected by the corresponding first vehicle.

[0040] Accordingly, the process of obtaining the evaluation weight value of the corresponding first vehicle based on the prediction information of each first vehicle includes the following formula:

[0041]

[0042] Where i = positive integers from 1 to n, w i SNR represents the evaluation weight value of the i-th vehicle. i d represents the prediction signal-to-noise ratio in the prediction information of the i-th vehicle. i N represents the predicted distance value in the prediction information of the i-th first vehicle. i This represents the predicted noise intensity value in the prediction information of the i-th first vehicle.

[0043] Optionally, obtaining the precise location of the abnormal sound source based on the pre-positioned location and evaluation weight value of each of the plurality of first vehicles includes calculating the precise location (x, y) using the following formula:

[0044]

[0045]

[0046] Where n is a positive integer, x represents the x-axis coordinate value of the precise positioning position, y represents the y-axis coordinate value of the precise positioning position, and wi Let x represent the evaluation weight value of the i-th first vehicle. i The x-axis coordinate of the predicted position of the i-th vehicle in the prediction information is represented by the y-axis coordinate. i This represents the y-axis coordinate value of the prepositioned position in the predicted information of the i-th first vehicle.

[0047] Optionally, determining the effective vehicle among the plurality of second vehicles capable of monitoring the abnormal sound source based on the precise positioning location and the respective pose information and monitoring performance information of the plurality of second vehicles includes:

[0048] Based on the precise positioning location and the pose information of each of the multiple second vehicles, a number of vehicles to be identified among the multiple second vehicles that can monitor the abnormal sound source are determined, as well as the calculated distance values ​​between each of the multiple vehicles to be identified and the precise positioning location.

[0049] Based on the calculated distance values ​​and monitoring performance information of each of the multiple undetermined vehicles, the effective vehicles among the multiple undetermined vehicles that can monitor the abnormal sound source are determined.

[0050] Optionally, determining the effective vehicle among the plurality of candidate vehicles capable of monitoring the abnormal sound source based on the calculated distance values ​​and monitoring performance information of each candidate vehicle includes:

[0051] The effective coefficient of each vehicle is obtained based on its calculated distance value and monitoring performance information.

[0052] When the effective coefficient of any undetermined vehicle is greater than the preset effective coefficient threshold, the undetermined vehicle is determined to be a valid vehicle.

[0053] Optionally, the effective coefficient for each vehicle is obtained based on its calculated distance value and monitoring performance information, including the following formula:

[0054] ;

[0055] Among them, EC j D represents the effective coefficient of the j-th vehicle to be determined. j D represents the calculated distance value for the j-th vehicle to be determined, Dmax represents the preset maximum distance value, and P represents the calculated distance value for the j-th vehicle to be determined. j This represents the pixel value of the monitoring camera in the monitoring performance information of the j-th vehicle to be determined, where Pmax represents the preset maximum pixel value, and L... j Lmax represents the light intensity value of the monitoring camera in the monitoring performance information of the j-th vehicle to be determined, and Lmin represents the preset maximum light intensity value.

[0056] According to a specific embodiment of this application, in a third aspect, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for determining a monitored vehicle as described in any of the preceding claims.

[0057] According to a specific embodiment of this application, in a fourth aspect, this application provides an electronic device, including: one or more processors; and a storage device for storing one or more programs, which, when executed by the one or more processors, cause the one or more processors to implement the vehicle monitoring determination method as described in any of the preceding claims.

[0058] Compared with the prior art, the above-described solutions of this application have at least the following beneficial effects:

[0059] This application provides a method, apparatus, medium, and electronic device for identifying monitored vehicles. Multiple first vehicles report predicted information and pre-positioned locations of the same abnormal sound source. Based on this predicted information and pre-positioned locations, the precise location of the abnormal sound source is obtained. Then, by combining the pose information and monitoring performance information of multiple second vehicles surrounding the precise location with the precise location, the effective vehicles capable of monitoring the abnormal sound source are determined among the multiple second vehicles. This application utilizes the characteristics of vehicles being low-lying, unobstructed by tall objects, and widely distributed to identify effective monitoring vehicles around abnormal sound sources, compensating for the shortcomings of the Skynet monitoring system, improving monitoring density and the utilization rate of vehicles of various types, and enhancing the effectiveness of social management. Attached Figure Description

[0060] Figure 1 A flowchart of a method for determining a monitored vehicle according to an embodiment of this application is shown;

[0061] Figure 2 A unit block diagram of a vehicle detection device according to an embodiment of this application is shown. Detailed Implementation

[0062] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0063] The terminology used in the embodiments of this application is for the purpose of describing particular embodiments only and is not intended to limit the application. The singular forms “a,” “said,” and “the” used in the embodiments of this application and the appended claims are also intended to include the plural forms, and “multiple” generally includes at least two unless the context clearly indicates otherwise.

[0064] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0065] It should be understood that although the terms first, second, third, etc., may be used in the embodiments of this application, these descriptions should not be limited to these terms. These terms are only used to distinguish the descriptions. For example, first may also be referred to as second without departing from the scope of the embodiments of this application, and similarly, second may also be referred to as first.

[0066] Depending on the context, the words “if” or “suppose” as used here can be interpreted as “when” or “in response to determination” or “in response to detection.” Similarly, depending on the context, the phrases “if determination” or “if detection (of the stated condition or event)” can be interpreted as “when determination” or “in response to determination” or “when detection (of the stated condition or event)” or “in response to detection (of the stated condition or event).”

[0067] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that an article or device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such an article or device. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the article or device that includes said element.

[0068] It should be noted that any symbols and / or numbers present in the specification that are not marked in the accompanying drawings are not reference numerals.

[0069] The optional embodiments of this application are described in detail below with reference to the accompanying drawings.

[0070] The embodiments provided in this application are embodiments of a method for determining a monitored vehicle.

[0071] The following is combined with Figure 1 The embodiments of this application will be described in detail.

[0072] Step S101: Obtain the prediction information and pre-positioning location of each of the multiple first vehicles for the same abnormal sound source.

[0073] An abnormal sound source is an object that emits abnormal sounds.

[0074] This application embodiment is applied to a monitoring center, which can monitor the abnormal sound source by observing the vehicles around the abnormal sound source when an abnormal sound source is detected.

[0075] Of course, abnormal sounds do not include sounds generated in normal daily life. The vehicle can identify abnormal sounds by collecting audio waveforms through its microphone. The process of identifying abnormal sounds is not detailed in this embodiment; it can be implemented using various methods found in existing technologies.

[0076] In this application embodiment, all positions and orientations are in a preset planar coordinate system.

[0077] In some specific embodiments, the prediction information for each first vehicle includes: the predicted distance value between the corresponding first vehicle and the abnormal sound source, the predicted noise intensity value obtained by the first vehicle collecting the sound from the abnormal sound source, and the predicted signal-to-noise ratio obtained by the first vehicle collecting the sound from the abnormal sound source.

[0078] In this embodiment, each first vehicle is equipped with a microphone array. The microphone array receives abnormal sounds emitted from the same abnormal sound source, and the relative position of the abnormal sound source is calculated based on the time difference between the arrival times of the abnormal sound at different microphones. Based on the global positioning system (GPS) and relative position of the corresponding first vehicle, the pre-positioning position of the corresponding first vehicle relative to the abnormal sound source can be obtained.

[0079] Step S102: Based on the prediction information and pre-positioning location of each of the plurality of first vehicles, the precise location of the abnormal sound source is obtained.

[0080] Because the accuracy of the GPS and microphone arrays are crucial, the pre-positioning of abnormal sound sources by each vehicle may have significant errors. Therefore, embodiments of this application provide a method for accurately locating abnormal sound sources to improve the effectiveness and accuracy of vehicle monitoring of abnormal sound sources.

[0081] In some specific embodiments, obtaining the precise location of the abnormal sound source based on the predicted information and pre-positioned location of each of the plurality of first vehicles includes the following steps:

[0082] Step S102-1: Obtain the evaluation weight value of the corresponding first vehicle based on the prediction information of each first vehicle.

[0083] The evaluation weight values ​​are used to assess the accuracy of the predictive information provided by each first vehicle.

[0084] In some specific embodiments, the prediction information for each first vehicle includes: the predicted distance value between the corresponding first vehicle and the abnormal sound source, the predicted noise intensity value obtained by the first vehicle collecting the sound from the abnormal sound source, and the predicted signal-to-noise ratio obtained by the first vehicle collecting the sound from the abnormal sound source.

[0085] Accordingly, the process of obtaining the evaluation weight value of the corresponding first vehicle based on the prediction information of each first vehicle includes the following formula:

[0086]

[0087] Where i = positive integers from 1 to n, w i SNR represents the evaluation weight value of the i-th vehicle. i d represents the prediction signal-to-noise ratio in the prediction information of the i-th vehicle. i N represents the predicted distance value in the prediction information of the i-th first vehicle. i This represents the predicted noise intensity value in the prediction information of the i-th first vehicle.

[0088] Step S102-2: Obtain the precise location of the abnormal sound source based on the predetermined location and evaluation weight value of each of the plurality of first vehicles.

[0089] In this specific embodiment, the weight of each of the pre-positioned positions of the first vehicles in the calculation of the precise positioning position is determined by the evaluation weight values ​​of each of the multiple first vehicles, thereby improving the accuracy of the precise positioning position.

[0090] In some specific embodiments, obtaining the precise location of the abnormal sound source based on the pre-positioned location and evaluation weight value of each of the plurality of first vehicles includes calculating the precise location (x, y) using the following formula:

[0091]

[0092]

[0093] Where n is a positive integer, x represents the x-axis coordinate value of the precise positioning position, y represents the y-axis coordinate value of the precise positioning position, and w i Let x represent the evaluation weight value of the i-th first vehicle. i The x-axis coordinate of the predicted position of the i-th vehicle in the prediction information is represented by the y-axis coordinate. iThis represents the y-axis coordinate of the predicted location of the i-th first vehicle in the prediction information. For example, if there are 3 first vehicles, i.e., n=3, and w1=0.5, x1=5, y1=10, w2=0.3, x2=4.8, y2=10.8, w3=0.2, x3=5.2, y3=9.2, then the precise location is (4.98, 10.08).

[0094] Step S103: In response to determining the multiple second vehicles around the precise positioning location, obtain the pose information and monitoring performance information of each of the multiple second vehicles.

[0095] The pose information includes the location of the second vehicle and the corresponding attitude information of the second vehicle.

[0096] Attitude information includes the heading of the second vehicle.

[0097] Monitoring performance information refers to the monitoring capabilities of the second vehicle. This information includes the pixel values ​​and light intensity values ​​of the monitoring cameras in the second vehicle.

[0098] Pixel value refers to the average brightness information of a small square in an image captured by a surveillance camera, which is also the average reflection (transmission) density information of that small square.

[0099] Illuminance value refers to the luminous flux of visible light received per unit area. It is used to indicate the intensity of light and the degree to which an object's surface is illuminated. In this specific embodiment, the illuminance value is collected by a light sensor in the surveillance camera.

[0100] The pose and monitoring performance information of the second vehicle are both uploaded to the monitoring center by the designated second vehicle. After identifying multiple second vehicles, the monitoring center instructs each second vehicle to upload its own pose and monitoring performance information.

[0101] Step S104: Based on the precise positioning location and the pose information and monitoring performance information of each of the plurality of second vehicles, determine the effective vehicle among the plurality of second vehicles that can monitor the abnormal sound source.

[0102] A valid vehicle refers to a second vehicle capable of providing effective information for monitoring abnormal sound sources. A valid vehicle is able to capture clear video of the abnormal sound source.

[0103] In some specific embodiments, determining the effective vehicle among the plurality of second vehicles capable of monitoring the abnormal sound source based on the precise positioning location and the respective pose information and monitoring performance information of the plurality of second vehicles includes the following steps:

[0104] Step S104-1: Based on the precise positioning location and the pose information of each of the multiple second vehicles, determine multiple undetermined vehicles among the multiple second vehicles that can monitor the abnormal sound source, and the calculated distance value between each of the multiple undetermined vehicles and the precise positioning location.

[0105] Although all second vehicles are located around the abnormal sound source, each second vehicle has a different position and the shooting direction of its surveillance camera is also different. Since each surveillance camera installed in the vehicle (such as a dashcam) can capture video of the area in front of the vehicle, the posture information in this specific embodiment includes the direction of the second vehicle's front end. The shooting direction of the surveillance camera can be determined by the direction of the front end. The abnormal sound source is located in the abnormal direction of the second vehicle by using the vehicle's location and precise location. When the abnormal direction of any second vehicle matches the shooting direction, that vehicle can be identified as a vehicle to be identified.

[0106] The calculated distance value is obtained by comparing the location and precise location of each vehicle to be determined.

[0107] Step S104-2: Based on the calculated distance values ​​and monitoring performance information of the multiple vehicles to be determined, determine the effective vehicles among the multiple vehicles to be determined that can monitor the abnormal sound source.

[0108] In some specific embodiments, the monitoring performance information includes the pixel value and light intensity value of the monitoring camera in the second vehicle.

[0109] Accordingly, determining the effective vehicle among the plurality of undetermined vehicles that can monitor the abnormal sound source based on the calculated distance values ​​and monitoring performance information of each of the plurality of undetermined vehicles includes the following steps:

[0110] Step S104-2-1: Obtain the effective coefficient of each vehicle based on its calculated distance value and monitoring performance information.

[0111] In some specific embodiments, the process of obtaining the effective coefficient of each vehicle based on its calculated distance value and monitoring performance information includes the following formula:

[0112] ;

[0113] Among them, EC j D represents the effective coefficient of the j-th vehicle to be determined. j D represents the calculated distance value for the j-th vehicle to be determined, Dmax represents the preset maximum distance value, and P represents the calculated distance value for the j-th vehicle to be determined. j This represents the pixel value of the monitoring camera in the monitoring performance information of the j-th vehicle to be determined, where Pmax represents the preset maximum pixel value, and L... jLmax represents the light intensity value of the monitoring camera in the monitoring performance information of the j-th vehicle to be determined, and Lmin represents the preset maximum light intensity value.

[0114] Step S104-2-2: When the effective coefficient of any pending vehicle is greater than the preset effective coefficient threshold, the pending vehicle is determined to be a valid vehicle.

[0115] For example, if the preset effective coefficient threshold is 0.4, when the effective coefficient of any pending vehicle is greater than 0.4, the pending vehicle is determined to be a valid vehicle; when the effective coefficient of any pending vehicle is less than or equal to 0.4, the pending vehicle is determined to be an invalid vehicle.

[0116] In this embodiment, multiple first vehicles report predicted information and pre-positioned locations of the same abnormal sound source. Based on this predicted information and pre-positioned locations, the precise location of the abnormal sound source is obtained. Then, by combining the pose information and monitoring performance information of multiple second vehicles surrounding the precise location with the precise location, the effective vehicles capable of monitoring the abnormal sound source are determined among the multiple second vehicles. This embodiment utilizes the characteristics of vehicles being low-lying, unobstructed by tall objects, and widely distributed to determine the effective monitoring vehicles around the abnormal sound source, compensating for the shortcomings of the Skynet monitoring system, improving monitoring density and the utilization rate of vehicles of various types, and enhancing the effectiveness of social management.

[0117] This application also provides an apparatus embodiment that follows the above embodiments, used to implement the method steps described in the above embodiments. The interpretation of the same names is the same as that in the above embodiments, and the same technical effects are achieved. Therefore, it will not be repeated here.

[0118] like Figure 2 As shown, this application provides a vehicle detection device 200, comprising:

[0119] The acquisition unit 201 is used to acquire the prediction information and prepositioning position of each of the multiple first vehicles for the same abnormal sound source;

[0120] The positioning unit 202 is used to obtain the precise location of the abnormal sound source based on the prediction information and pre-positioning location of each of the plurality of first vehicles;

[0121] Triggering unit 203 is used to obtain the pose information and monitoring performance information of each of the multiple second vehicles in response to determining the precise positioning position;

[0122] The determining unit 204 is used to determine, based on the precise positioning location and the pose information and monitoring performance information of each of the plurality of second vehicles, the effective vehicle among the plurality of second vehicles that can monitor the abnormal sound source.

[0123] Optionally, obtaining the precise location of the abnormal sound source based on the predicted information and pre-positioned location of each of the plurality of first vehicles includes:

[0124] The evaluation weight value of each first vehicle is obtained based on the prediction information of each first vehicle.

[0125] The precise location of the abnormal sound source is obtained based on the pre-positioned location and evaluation weight value of each of the plurality of first vehicles.

[0126] Optionally, the prediction information for each first vehicle includes: the predicted distance value between the corresponding first vehicle and the abnormal sound source, the predicted noise intensity value obtained by the sound of the abnormal sound source collected by the corresponding first vehicle, and the predicted signal-to-noise ratio obtained by the sound of the abnormal sound source collected by the corresponding first vehicle.

[0127] Accordingly, the process of obtaining the evaluation weight value of the corresponding first vehicle based on the prediction information of each first vehicle includes the following formula:

[0128]

[0129] Where i = positive integers from 1 to n, w i SNR represents the evaluation weight value of the i-th vehicle. i d represents the prediction signal-to-noise ratio in the prediction information of the i-th vehicle. i N represents the predicted distance value in the prediction information of the i-th first vehicle. i This represents the predicted noise intensity value in the prediction information of the i-th first vehicle.

[0130] Optionally, obtaining the precise location of the abnormal sound source based on the pre-positioned location and evaluation weight value of each of the plurality of first vehicles includes calculating the precise location (x, y) using the following formula:

[0131]

[0132]

[0133] Where n is a positive integer, x represents the x-axis coordinate value of the precise positioning position, y represents the y-axis coordinate value of the precise positioning position, and w i Let x represent the evaluation weight value of the i-th first vehicle. i The x-axis coordinate of the predicted position of the i-th vehicle in the prediction information is represented by the y-axis coordinate. i This represents the y-axis coordinate value of the prepositioned position in the predicted information of the i-th first vehicle.

[0134] Optionally, determining the effective vehicle among the plurality of second vehicles capable of monitoring the abnormal sound source based on the precise positioning location and the respective pose information and monitoring performance information of the plurality of second vehicles includes:

[0135] Based on the precise positioning location and the pose information of each of the multiple second vehicles, a number of vehicles to be identified among the multiple second vehicles that can monitor the abnormal sound source are determined, as well as the calculated distance values ​​between each of the multiple vehicles to be identified and the precise positioning location.

[0136] Based on the calculated distance values ​​and monitoring performance information of each of the multiple undetermined vehicles, the effective vehicles among the multiple undetermined vehicles that can monitor the abnormal sound source are determined.

[0137] Optionally, determining the effective vehicle among the plurality of candidate vehicles capable of monitoring the abnormal sound source based on the calculated distance values ​​and monitoring performance information of each candidate vehicle includes:

[0138] The effective coefficient of each vehicle is obtained based on its calculated distance value and monitoring performance information.

[0139] When the effective coefficient of any undetermined vehicle is greater than the preset effective coefficient threshold, the undetermined vehicle is determined to be a valid vehicle.

[0140] Optionally, the effective coefficient for each vehicle is obtained based on its calculated distance value and monitoring performance information, including the following formula:

[0141] ;

[0142] Among them, EC j D represents the effective coefficient of the j-th vehicle to be determined. j D represents the calculated distance value for the j-th vehicle to be determined, Dmax represents the preset maximum distance value, and P represents the calculated distance value for the j-th vehicle to be determined. j This represents the pixel value of the monitoring camera in the monitoring performance information of the j-th vehicle to be determined, where Pmax represents the preset maximum pixel value, and L... j Lmax represents the light intensity value of the monitoring camera in the monitoring performance information of the j-th vehicle to be determined, and Lmin represents the preset maximum light intensity value.

[0143] In this embodiment, multiple first vehicles report predicted information and pre-positioned locations of the same abnormal sound source. Based on this predicted information and pre-positioned locations, the precise location of the abnormal sound source is obtained. Then, by combining the pose information and monitoring performance information of multiple second vehicles surrounding the precise location with the precise location, the effective vehicles capable of monitoring the abnormal sound source are determined among the multiple second vehicles. This embodiment utilizes the characteristics of vehicles being low-lying, unobstructed by tall objects, and widely distributed to determine the effective monitoring vehicles around the abnormal sound source, compensating for the shortcomings of the Skynet monitoring system, improving monitoring density and the utilization rate of vehicles of various types, and enhancing the effectiveness of social management.

[0144] This embodiment provides an electronic device, 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, the instructions being executed by the at least one processor to enable the at least one processor to perform the method steps described in the above embodiment.

[0145] This application provides a non-volatile computer storage medium storing computer-executable instructions that can perform the steps described in the above embodiments.

[0146] Finally, it should be noted that the various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems or apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple, and relevant parts can be referred to the method section.

[0147] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for determining a monitored vehicle, characterized in that, include: Obtain the prediction information and pre-positioning location of each of the multiple first vehicles for the same abnormal sound source; The precise location of the abnormal sound source is obtained based on the predicted information and pre-positioned location of each of the plurality of first vehicles. In response to determining multiple second vehicles around the precise positioning location, the pose information and monitoring performance information of each of the multiple second vehicles are acquired; Based on the precise positioning location and the pose information and monitoring performance information of the plurality of second vehicles, the effective vehicle among the plurality of second vehicles that can monitor the abnormal sound source is determined. Specifically, based on the precise positioning location and the pose information of each of the multiple second vehicles, a number of vehicles to be identified among the multiple second vehicles that can monitor the abnormal sound source are determined, as well as the calculated distance values ​​between each of the multiple vehicles to be identified and the precise positioning location. Based on the calculated distance values ​​and monitoring performance information of the multiple vehicles to be identified, the effective vehicles among the multiple vehicles to be identified that can monitor the abnormal sound source are determined. The effective coefficient of each vehicle is obtained based on its calculated distance value and monitoring performance information. When the validity coefficient of any undetermined vehicle is greater than the preset validity coefficient threshold, the undetermined vehicle is determined to be a valid vehicle. The effective coefficient for each vehicle is obtained based on its calculated distance value and monitoring performance information, including the following formula: ； Among them, EC j D represents the effective coefficient of the j-th vehicle to be determined. j D represents the calculated distance value for the j-th vehicle to be determined, Dmax represents the preset maximum distance value, and P represents the calculated distance value for the j-th vehicle to be determined. j This represents the pixel value of the monitoring camera in the monitoring performance information of the j-th vehicle to be determined, where Pmax represents the preset maximum pixel value, and L... j Lmax represents the light intensity value of the monitoring camera in the monitoring performance information of the j-th vehicle to be determined, and Lmin represents the preset maximum light intensity value.

2. The method according to claim 1, characterized in that, The step of obtaining the precise location of the abnormal sound source based on the predicted information and pre-positioned location of each of the plurality of first vehicles includes: The evaluation weight value of each first vehicle is obtained based on the prediction information of each first vehicle. The precise location of the abnormal sound source is obtained based on the pre-positioned location and evaluation weight value of each of the plurality of first vehicles.

3. The method according to claim 2, characterized in that, The prediction information for each first vehicle includes: the predicted distance value between the corresponding first vehicle and the abnormal sound source, the predicted noise intensity value obtained by the first vehicle collecting the sound from the abnormal sound source, and the predicted signal-to-noise ratio obtained by the first vehicle collecting the sound from the abnormal sound source. Accordingly, the process of obtaining the evaluation weight value of the corresponding first vehicle based on the prediction information of each first vehicle includes the following formula: Where i = positive integers from 1 to n, w i SNR represents the evaluation weight value of the i-th vehicle. i d represents the prediction signal-to-noise ratio in the prediction information of the i-th vehicle. i N represents the predicted distance value in the prediction information of the i-th first vehicle. i This represents the predicted noise intensity value in the prediction information of the i-th first vehicle.

4. The method according to claim 2, characterized in that, The precise location of the abnormal sound source is obtained based on the pre-positioned location and evaluation weight value of each of the plurality of first vehicles, including the following formula for calculating the precise location (x, y): Where n is a positive integer, x represents the x-axis coordinate value of the precise positioning position, y represents the y-axis coordinate value of the precise positioning position, and w i Let x represent the evaluation weight value of the i-th vehicle. i The x-axis coordinate of the predicted position of the i-th vehicle in the prediction information is represented by the y-axis coordinate. i This represents the y-axis coordinate value of the prepositioned position in the predicted information of the i-th first vehicle.

5. A vehicle detection device, characterized in that, include: The acquisition unit is used to acquire the prediction information and pre-positioning location of each of the multiple first vehicles for the same abnormal sound source; The positioning unit is used to obtain the precise location of the abnormal sound source based on the prediction information and pre-positioning location of each of the plurality of first vehicles. A triggering unit is configured to, in response to determining multiple second vehicles surrounding the precise positioning location, acquire the pose information and monitoring performance information of each of the multiple second vehicles; The determining unit is configured to determine, based on the precise positioning location and the pose information and monitoring performance information of the plurality of second vehicles, the effective vehicle among the plurality of second vehicles capable of monitoring the abnormal sound source; The vehicle identification device is used to perform the identification method as described in claim 1.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1 to 4.

7. An electronic device, characterized in that, include: One or more processors; Storage device for storing one or more programs. Wherein, when the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1 to 4.

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