Methods, products, equipment, and media for generating target trajectories based on infrared monitoring

Abstract

This application relates to the field of target monitoring technology, and more particularly to a method, product, device, and medium for generating target trajectories based on infrared monitoring. The method includes: during a drone's inspection along an initial inspection route, acquiring infrared monitoring data collected by an infrared detection device, image data collected by a camera device, and radar data collected by a radar, all mounted on the drone; determining a first position of the target based on the infrared monitoring data, a second position based on the image data, and a third position based on the radar data; inputting the first, second, and third positions into a preset fusion model to obtain the target position output by the preset fusion model; generating a target movement trajectory based on the target positions at each time point, and predicting the target's future movement trajectory based on the target movement trajectory. This application can improve the accuracy of target tracking.

Description

Technical Field

[0001] This application relates to the field of target monitoring technology, and in particular to a method, product, device and medium for generating target trajectories based on infrared monitoring. Background Technology

[0002] With the rapid development of modern technology, especially breakthroughs in avionics and sensor technology, the functions of drones have expanded to many fields such as environmental monitoring and search and rescue operations. In these applications, drones are typically equipped with a variety of sensors to collect detailed information about the surrounding environment.

[0003] Related drone target tracking technologies mainly rely on visible light cameras, lidar, and other types of optical sensors. However, these technologies often perform poorly in low-light or completely dark environments, making it difficult to ensure high accuracy and reliability in target tracking. Summary of the Invention

[0004] To address the issue of low accuracy in target tracking in existing technologies, this application provides a target trajectory generation method, product, device, and medium based on infrared monitoring.

[0005] Firstly, this application provides a target trajectory generation method based on infrared monitoring, employing the following technical solution:

[0006] A target trajectory generation method based on infrared monitoring includes:

[0007] During the inspection process of the UAV following the initial inspection route, infrared monitoring data collected by the infrared detection device, image data collected by the camera device, and radar data collected by the radar are acquired. The infrared detection device, the camera device, and the radar are all installed on the UAV.

[0008] The first position of the target is determined based on the infrared monitoring data, the second position of the target is determined based on the image data, and the third position of the target is determined based on the radar data;

[0009] Input the first position, the second position, and the third position into a preset fusion model to obtain the target position output by the preset fusion model;

[0010] Generate the target's movement trajectory based on the target's position at each time point, and predict the target's future movement trajectory based on the target's movement trajectory.

[0011] By adopting the above technical solution and integrating infrared monitoring data, image data, and radar data, the limitations of a single sensor in specific environments can be overcome. For example, the effectiveness of infrared sensors at night or in low light conditions, the penetration capability of radar in foggy weather, and the advantages of cameras in image recognition can all be overcome. This multi-sensor fusion method can provide more accurate and reliable target location information. By using a preset fusion model to process data from multiple sensors, the target location can be accurately determined. Based on the target location at each time point, the target movement trajectory can be generated and the future trajectory can be predicted, thus achieving accurate tracking of the target.

[0012] In a preferred embodiment, this application can be further configured such that, after predicting the future trajectory of the target based on the target's movement trajectory, the method further includes:

[0013] Acquire three-dimensional data of the inspection area, and dynamically determine the expected trajectory range of the UAV based on the three-dimensional data and the future movement trajectory of the target;

[0014] The expected trajectory is compared with the initial inspection path to determine whether the initial inspection path exceeds the range of the expected trajectory.

[0015] If there are inspection segments in the initial inspection path that exceed the expected trajectory range, then the inspection segments are adjusted based on the expected trajectory range to obtain a dynamic target inspection path;

[0016] Control the drone to perform inspections according to the dynamic target inspection path.

[0017] By adopting the above technical solutions, 3D data provides detailed terrain, obstacle, and altitude information of the inspection area, helping the UAV to understand the inspection environment more accurately, avoiding collisions during flight, and improving flight safety. Combined with the target's future movement trajectory, the expected trajectory range of the UAV is dynamically determined, ensuring that the UAV performs inspections at the optimal position and angle, improving inspection efficiency. Since the target's movement trajectory is dynamically predicted, the expected trajectory range of the UAV will also be adjusted accordingly. This allows the UAV to adapt to changes in the target in real time, verifying whether the initial inspection path meets the inspection requirements, whether it deviates too much from the target, or whether it cannot effectively cover the target's movement area. The initial inspection path is flexibly adjusted according to the expected range, ensuring that the UAV performs inspections at the optimal position and angle, reducing useless flight time and distance, and improving inspection efficiency.

[0018] In a preferred embodiment, this application can be further configured such that: dynamically determining the desired trajectory range of the UAV based on the three-dimensional data and the target's future movement trajectory includes:

[0019] Based on the target's future movement trajectory, preset angle, and preset distance, the initial expected trajectory range of the target that the UAV can monitor is determined;

[0020] The overlapping area between the three-dimensional data and the initial expected trajectory range is determined, and the overlapping area is removed from the initial expected trajectory range to obtain the expected trajectory range of the UAV.

[0021] By adopting the above technical solution, combined with the target's future movement trajectory, preset angle, and preset distance, it can be ensured that the initial expected trajectory range of the UAV closely surrounds the target's possible movement path, enabling the UAV to monitor the target more effectively. The three-dimensional data provides detailed terrain and obstacle information of the inspection area. By identifying the overlapping area between the initial expected trajectory range and the three-dimensional data and removing these overlapping parts, it can be ensured that the UAV avoids obstacles and collisions during flight, thereby improving flight safety.

[0022] In a preferred embodiment, this application can be further configured such that: the acquisition of infrared monitoring data collected by the infrared detection device includes:

[0023] Obtain the visibility of the current time and the current environment, and determine whether the current time is within a preset time period and whether the visibility is lower than a preset visibility threshold;

[0024] If the current time is within the preset time period and the visibility is lower than the preset visibility threshold, then the quality detection step is repeated until the quality parameters meet the preset quality standard.

[0025] The quality detection step includes: acquiring initial infrared monitoring data collected by the infrared detection device, determining whether the quality parameters of the initial infrared monitoring data meet the preset quality standard, and if not, adjusting the equipment parameters of the infrared detection device.

[0026] The initial infrared monitoring data that meets the preset quality standard is used as the infrared monitoring data collected by the infrared detection device.

[0027] By adopting the above technical solution and introducing the judgment of the current time and current environmental visibility, the data quality detection process can be automatically triggered in specific time periods and under low visibility conditions. The adaptive mechanism based on environmental conditions ensures that the infrared monitoring data can maintain a high quality level even in adverse environments. By repeatedly executing the quality detection steps and adjusting the equipment parameters of the infrared detection device when the quality parameters do not meet the preset standards, low-quality data caused by poor equipment condition or environmental interference is effectively avoided, ensuring that the final acquired infrared monitoring data meets the preset quality standards and improving the accuracy and reliability of the data.

[0028] In a preferred embodiment, this application can be further configured such that the quality parameters include signal-to-noise ratio and image sharpness;

[0029] The step of determining whether the quality parameters of the initial infrared monitoring data meet the preset quality standard, and adjusting the equipment parameters of the infrared detection device if they do not, includes:

[0030] The signal-to-noise ratio and the image sharpness are compared with the corresponding preset quality standards, respectively.

[0031] If any quality parameter does not meet the corresponding preset quality standard, the device parameter corresponding to the quality parameter that does not meet the preset quality standard is adjusted according to the preset correspondence. The preset correspondence represents the correspondence between quality parameters and device parameters, including signal-to-noise ratio corresponding to gain and image sharpness corresponding to focal length.

[0032] By adopting the above technical solution, signal-to-noise ratio and image sharpness are used as quality parameters, and corresponding preset quality standards are set, thus achieving precise control over the quality of infrared monitoring data.

[0033] In a preferred embodiment, this application can be further configured such that the method also includes:

[0034] Multiple sets of sample data are acquired. Each set of sample data includes the first sample position of the target sample collected by the infrared detection device, the second sample position of the target sample collected by the camera device, the third sample position of the target sample collected by the radar, and the actual sample position of the target sample.

[0035] The first sample position, the second sample position, and the third sample position are used as model inputs, and the actual sample position is used as model outputs to train the model, thereby obtaining the trained preset fusion model.

[0036] By adopting the above technical solution, a model is constructed that integrates data from different sensors (infrared detection, camera, radar). This model can make comprehensive use of the advantages of each sensor, thereby more accurately determining the location of the target sample. Each sensor has its unique detection principle and limitations. Integrating data from multiple sensors can make up for the shortcomings of a single sensor and improve the overall positioning accuracy.

[0037] In a preferred embodiment, this application can be further configured such that inputting the first position, the second position, and the third position into a preset fusion model includes:

[0038] Determine the distance between every two positions among the first position, the second position, and the third position, and compare each distance with a preset distance threshold;

[0039] If each distance does not exceed the preset distance threshold, then the first position, the second position, and the third position are input into the preset fusion model.

[0040] By employing the above technical solution, the distances between locations collected by different sensors can be calculated and compared to initially verify the consistency of these data. If there are significant differences between the data from one sensor and the data from other sensors (i.e., the distance exceeds a preset threshold), it may indicate that the sensor data is abnormal or has a large error. This verification helps to identify and eliminate potential data quality problems in advance, thereby improving the accuracy and reliability of subsequent fusion models.

[0041] Secondly, this application provides a computer program product, which adopts the following technical solution:

[0042] A computer program product includes a computer program that, when executed by a processor, implements the target trajectory generation method based on infrared monitoring as described in any of the first aspects.

[0043] Thirdly, this application provides an electronic device that adopts the following technical solution:

[0044] One or more processors;

[0045] Memory;

[0046] At least one application, wherein the at least one application is stored in memory and configured to be executed by at least one processor, the at least one application being configured to: execute the target trajectory generation method based on infrared monitoring as described in any of the first aspects.

[0047] Fourthly, this application provides a computer-readable storage medium, which adopts the following technical solution:

[0048] A computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to perform the target trajectory generation method based on infrared monitoring as described in any of the first aspects.

[0049] In summary, this application includes the following beneficial technical effects:

[0050] This application overcomes the limitations of a single sensor in specific environments by fusing infrared monitoring data, image data, and radar data. For example, it addresses the effectiveness of infrared sensors at night or in low light conditions, the penetration capability of radar in foggy weather, and the advantages of cameras in image recognition. This multi-sensor fusion method can provide more accurate and reliable target location information. By using a preset fusion model to process data from multiple sensors, the target location can be accurately determined. Based on the target location at each time point, the target movement trajectory can be generated and the future trajectory can be predicted, thus achieving precise tracking of the target. Attached Figure Description

[0051] Figure 1 This is a flowchart illustrating a target trajectory generation method based on infrared monitoring provided in an embodiment of this application;

[0052] Figure 2 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0053] The following is in conjunction with the appendix Figure 1 -Appendix Figure 2 This application will be described in further detail.

[0054] This specific embodiment is merely an explanation of this application and is not intended to limit it. After reading this specification, those skilled in the art can make modifications to this embodiment without contributing any inventive step, but such modifications are protected by patent law as long as they fall within the scope of the claims of this application.

[0055] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0056] Furthermore, the term "and / or" 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, or B existing alone. Additionally, the character " / " in this article, unless otherwise specified, generally indicates that the preceding and following related objects have an "or" relationship.

[0057] It should be noted that, in the optional embodiments of this application, the data related to object information, when applied to specific products or technologies, requires the permission or consent of the object. Furthermore, the collection, use, and processing of this data must comply with the relevant laws, regulations, and standards of the relevant countries and regions. In other words, if the embodiments of this application involve data related to an object, it must be obtained with the object's authorization and consent, the authorization and consent of relevant departments, and in accordance with the relevant laws, regulations, and standards of the country and region. If the embodiments involve personal information, the acquisition of all personal information requires the individual's consent. If sensitive information is involved, the separate consent of the information subject is required. The embodiments also need to be implemented with the object's authorization and consent.

[0058] This application provides a target trajectory generation method based on infrared monitoring, such as... Figure 1 As shown, the method provided in this application embodiment is executed by an electronic device, which can be a server or a terminal device. The server can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The terminal device can be a smartphone, tablet, laptop, desktop computer, etc., but is not limited to these. The terminal device and the server can be directly or indirectly connected via wired or wireless communication. This application embodiment does not impose any limitations on this connection. The method includes steps S101-S104, wherein:

[0059] S101. During the inspection process of the UAV following the initial inspection route, infrared monitoring data collected by the infrared detection device, image data collected by the camera device, and radar data collected by the radar are acquired. The infrared detection device, camera device, and radar are all installed on the UAV.

[0060] The initial inspection path can be pre-set by technicians and stored in the drone or ground-based electronic equipment. The drone then performs the inspection according to this initial path. During the drone inspection, infrared detection devices, cameras, and radar collect data at the same acquisition frequency and time. The infrared detection devices collect infrared radiation data of the surrounding environment and convert it into infrared images or temperature distribution maps. Cameras (such as high-definition cameras) simultaneously capture visible light images of the surrounding environment, obtaining image information of the target. Radar (such as millimeter-wave radar or lidar) sends and receives signals, determining the target's distance, speed, and direction by measuring the time difference or phase difference of the signals.

[0061] S102. Determine the first position of the target based on infrared monitoring data, determine the second position of the target based on image data, and determine the third position of the target based on radar data.

[0062] Specifically, for any acquisition time, the infrared monitoring data acquired at that time is processed to identify the initial first position of the target in the infrared image or temperature distribution map. The image data acquired at that time is processed by image recognition or target tracking algorithms to identify the initial second position of the target in the image. The radar data acquired at that time is analyzed, and the initial third position of the target is calculated using the measured distance, speed and direction information.

[0063] Furthermore, the initial first, second, and third positions are transformed to a geographic coordinate system through coordinate transformation, resulting in the first position corresponding to the first initial position, the second position corresponding to the second initial position, and the third position corresponding to the third initial position. Thus, each acquisition moment corresponds to a set of location data, including the first, second, and third positions.

[0064] S103. Input the first position, the second position and the third position into the preset fusion model, and obtain the target position output by the preset fusion model.

[0065] After obtaining the location data at any acquisition time, the first, second, and third positions corresponding to that acquisition time are input into a preset fusion model to obtain the target position corresponding to that acquisition time.

[0066] The preset fusion model is pre-trained. The training process is as follows: multiple sets of sample data are acquired. Each set of sample data includes the first sample position of the target sample collected by the infrared detection device, the second sample position of the target sample collected by the camera device, the third sample position of the target sample collected by the radar, and the actual sample position of the target sample. The first, second, and third sample positions are used as model inputs, and the actual sample position is used as model outputs to train the model, resulting in the trained preset fusion model.

[0067] S104. Generate the target movement trajectory based on the target position at each time point, and predict the target's future movement trajectory based on the target movement trajectory.

[0068] The target locations are arranged in chronological order according to the time of data collection to generate the target movement trajectory. Based on the existing target movement trajectory, the future movement trajectory of the target is predicted using methods such as time series analysis, machine learning, or deep learning. The prediction algorithm can be flexibly selected according to actual needs, and this embodiment does not impose specific limitations.

[0069] This embodiment overcomes the limitations of a single sensor in specific environments by fusing infrared monitoring data, image data, and radar data. For example, it addresses the effectiveness of infrared sensors at night or in low light conditions, the penetration capability of radar in foggy weather, and the advantages of cameras in image recognition. This multi-sensor fusion method can provide more accurate and reliable target location information. By using a preset fusion model to process data from multiple sensors, the target location can be accurately determined. Based on the target location at each time point, the target movement trajectory can be generated and the future trajectory can be predicted, thus achieving accurate tracking of the target.

[0070] One possible implementation of this application embodiment, after predicting the future movement trajectory of the target based on the target's movement trajectory, the method further includes:

[0071] Acquire 3D data of the inspection area, and dynamically determine the expected trajectory range of the UAV based on the 3D data and the future movement trajectory of the target;

[0072] The expected trajectory is compared with the initial inspection path to determine whether the initial inspection path exceeds the range of the expected trajectory.

[0073] If there are inspection segments in the initial inspection path that exceed the expected trajectory range, the inspection segments are adjusted based on the expected trajectory range to obtain a dynamic target inspection path.

[0074] Control the drone to perform inspections according to the dynamic target inspection path.

[0075] In this embodiment, the inspection area can be scanned in advance using a drone equipped with sensors (such as LiDAR, stereo cameras, etc.) to collect three-dimensional data of the inspection area. The three-dimensional data includes information such as three-dimensional terrain, buildings, vegetation, and obstacles. The collected three-dimensional data is then converted into a format suitable for processing, such as point cloud data, three-dimensional mesh, or digital elevation model.

[0076] The target's future trajectory includes information such as coordinates, velocity, and direction of each point in the trajectory. Based on the requirements of the UAV monitoring mission, monitoring parameters are preset for the UAV, including preset angles and preset distances. The preset angles include both top-down and side-down monitoring angles, and the preset distance represents a safe and effective monitoring range. Centered on the target's future trajectory, a cylindrical or sector-shaped area conforming to the monitoring parameters is drawn as the UAV's desired trajectory range. This desired trajectory range represents the flight range within which the UAV can monitor the target.

[0077] Furthermore, there may be one or more inspection segments that exceed the expected trajectory range. For any one of these inspection segments, a spatial search algorithm (KD tree, R tree, or spatial hash table) is used to determine the point in the inspection segment that is closest to the expected trajectory range. The points that are closest to the expected trajectory range are then combined to form the adjusted inspection segment.

[0078] In this embodiment, the 3D data provides detailed terrain, obstacle, and altitude information of the inspection area, helping the UAV to more accurately understand the inspection environment, avoid collisions during flight, and improve flight safety. Combined with the target's future movement trajectory, the expected trajectory range of the UAV is dynamically determined, ensuring that the UAV performs inspections at the optimal position and angle, thus improving inspection efficiency. Since the target's movement trajectory is dynamically predicted, the expected trajectory range of the UAV will also be adjusted accordingly. This allows the UAV to adapt to changes in the target in real time, verify whether the initial inspection path meets the inspection requirements, whether it deviates too much from the target, or whether it cannot effectively cover the target's movement area. The initial inspection path is flexibly adjusted according to the expected range to ensure that the UAV performs inspections at the optimal position and angle, reducing useless flight time and distance and improving inspection efficiency.

[0079] One possible implementation of this application embodiment involves dynamically determining the desired trajectory range of the UAV based on three-dimensional data and the target's future movement trajectory, including:

[0080] Based on the target's future movement trajectory, preset angle, and preset distance, determine the initial expected trajectory range that the UAV can monitor for the target;

[0081] The overlapping area between the 3D data and the initial desired trajectory range is determined, and the overlapping area is removed from the initial desired trajectory range to obtain the desired trajectory range of the UAV.

[0082] In this embodiment, for each point on the target's future trajectory, a fan-shaped region is drawn centered on that point, based on a preset angle and a preset distance. The preset distance represents the radius of the fan-shaped region. The fan-shaped regions of each point are then stitched together along the direction of the target's future trajectory to form a continuous fan-shaped region. This region is the initial expected trajectory range, which covers the predicted path of the target's future trajectory and takes into account the monitoring angle and distance limitations of the UAV. Furthermore, GIS software or spatial analysis libraries (such as ArcGIS, QGIS, GDAL, Shapely, etc.) are used to perform spatial alignment and remove overlapping areas from the initial expected trajectory range.

[0083] This embodiment combines the target's future movement trajectory, preset angle, and preset distance to ensure that the initial expected trajectory range of the UAV closely follows the target's possible movement path, enabling the UAV to monitor the target more effectively. The three-dimensional data provides detailed terrain and obstacle information of the inspection area. By identifying the overlapping area between the initial expected trajectory range and the three-dimensional data and removing these overlapping parts, the UAV can avoid obstacles and collisions during flight, thus improving flight safety.

[0084] One possible implementation of this application embodiment involves acquiring infrared monitoring data collected by an infrared detection device, including:

[0085] Obtain the visibility of the current time and the current environment, and determine whether the current time is within a preset time period and whether the visibility is lower than a preset visibility threshold;

[0086] If the current time is within the preset time period and the visibility is lower than the preset visibility threshold, the quality detection steps are repeated until the quality parameters meet the preset quality standards.

[0087] The quality inspection steps include: acquiring the initial infrared monitoring data collected by the infrared detection device, determining whether the quality parameters of the initial infrared monitoring data meet the preset quality standards, and adjusting the equipment parameters of the infrared detection device if they do not meet the standards.

[0088] Initial infrared monitoring data whose quality parameters meet the preset quality standards are used as the infrared monitoring data collected by the infrared detection device.

[0089] In this embodiment, visibility data of the current environment is acquired using weather station data, environmental sensors, or a visibility sensor integrated into an infrared detection device. The current time is compared with a preset time period (used to characterize periods of high low visibility, such as nighttime and early morning) to determine whether the current time falls within that time period. Simultaneously, the current visibility is compared with a preset visibility threshold (e.g., less than 500 meters indicates low visibility) to determine whether the visibility is below the preset visibility threshold. If the current time is within the preset time period and the visibility is below the preset visibility threshold, the process proceeds to a repetitive quality check.

[0090] This embodiment introduces the determination of the current time and current environmental visibility, which can automatically trigger the data quality detection process under specific time periods and low visibility conditions. The adaptive mechanism based on environmental conditions ensures that the infrared monitoring data can maintain a high quality level even under adverse environments. By repeatedly executing the quality detection steps and adjusting the equipment parameters of the infrared detection device when the quality parameters do not meet the preset standards, low-quality data caused by poor equipment condition or environmental interference is effectively avoided, ensuring that the final acquired infrared monitoring data meets the preset quality standards and improving the accuracy and reliability of the data.

[0091] One possible implementation of this application embodiment includes quality parameters such as signal-to-noise ratio and image sharpness;

[0092] Determine whether the quality parameters of the initial infrared monitoring data meet the preset quality standards. If not, adjust the equipment parameters of the infrared detection device, including:

[0093] The signal-to-noise ratio and image sharpness are compared with the corresponding preset quality standards.

[0094] If any quality parameter does not meet the corresponding preset quality standard, the device parameter corresponding to the quality parameter that does not meet the preset quality standard will be adjusted according to the preset correspondence. The preset correspondence represents the correspondence between quality parameters and device parameters, including signal-to-noise ratio corresponding to gain and image sharpness corresponding to focal length.

[0095] In this embodiment, the signal-to-noise ratio (SNR) can be extracted from the initial infrared monitoring data using software analysis tools. Gain is a parameter that directly controls the degree of signal amplification, therefore its impact on the SNR is more significant. By adjusting the gain, the signal strength can be improved to a certain extent while relatively reducing the impact of noise. Image sharpness can be represented by calculating image sharpness, which typically involves high-pass filtering the image and then calculating the intensity or energy of the filtered image. A higher sharpness value indicates a sharper image.

[0096] The preset quality standards include signal-to-noise ratio (SNR) range and sharpness range. If the SNR is outside the SNR range, it means the SNR does not meet the preset quality standards; adjust the gain parameter. Similarly, if the image sharpness is outside the sharpness range, it means the image sharpness does not meet the preset quality standards; adjust the focus parameter.

[0097] Gain difference and focal length difference can be preset. When the gain needs to be adjusted, the single adjustment method is to increase the gain, and the increased difference is the preset gain difference. When the focal length needs to be adjusted, the single adjustment method is to increase the focal length, and the increased difference is the preset focal length difference.

[0098] This embodiment uses signal-to-noise ratio and image sharpness as quality parameters and sets corresponding preset quality standards to achieve precise control over the quality of infrared monitoring data.

[0099] One possible implementation of this application embodiment includes:

[0100] Multiple sets of sample data are acquired. Each set of sample data includes the first sample position of the target sample collected by the infrared detection device, the second sample position of the target sample collected by the camera device, the third sample position of the target sample collected by the radar, and the actual sample position of the target sample.

[0101] The first, second, and third sample positions are used as model inputs, and the actual sample positions are used as model outputs to train the model, resulting in a pre-trained fusion model.

[0102] In this embodiment, the data collection time for each data set is equal, and a neural network can be used as the model. This embodiment does not impose any specific limitations.

[0103] This embodiment constructs a model that integrates data from different sensors (infrared detection, camera, radar). This model can make comprehensive use of the advantages of each sensor, thereby more accurately determining the location of the target sample. Each sensor has its unique detection principle and limitations. Integrating data from multiple sensors can make up for the shortcomings of a single sensor and improve the overall positioning accuracy.

[0104] One possible implementation of this application embodiment involves inputting the first position, the second position, and the third position into a preset fusion model, including:

[0105] Determine the distance between any two positions in the first, second, and third positions, and compare each distance with a preset distance threshold;

[0106] If each distance does not exceed the preset distance threshold, then the first position, the second position, and the third position are input into the preset fusion model.

[0107] In this embodiment, the distance between any two locations can be determined using the Euclidean distance formula. The preset distance threshold is determined manually based on the device's accuracy. If the distance does not exceed the preset distance threshold, it indicates that the error is within the allowable range. If the distance between two locations exceeds the preset distance threshold, it indicates that there is a device error, and relevant personnel can be prompted to conduct an inspection.

[0108] This embodiment calculates and compares the distances between locations collected by different sensors to initially verify the consistency of these data. If there are significant differences between the data from one sensor and the data from other sensors (i.e., the distance exceeds a preset threshold), it may indicate that the sensor data is abnormal or has a large error. This verification helps to identify and eliminate potential data quality problems in advance, thereby improving the accuracy and reliability of the subsequent fusion model.

[0109] This application provides a computer program product, including a computer program that, when executed by a processor, implements the content shown in the aforementioned embodiment of the target trajectory generation method based on infrared monitoring.

[0110] This application provides an electronic device, such as... Figure 2 As shown, Figure 2The illustrated electronic device 200 includes a processor 201 and a memory 203. The processor 201 and the memory 203 are connected, for example, via a bus 202. Optionally, the electronic device 200 may also include a transceiver 204. It should be noted that in practical applications, the transceiver 204 is not limited to one type, and the structure of this electronic device 200 does not constitute a limitation on the embodiments of this application.

[0111] Processor 201 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 201 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0112] Bus 202 may include a pathway for transmitting information between the aforementioned components. Bus 202 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 202 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 2 The symbol is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0113] The memory 203 may be a ROM (Read Only Memory) or other type of static storage device capable of storing static information and instructions, RAM (Random Access Memory) or other type of dynamic storage device capable of storing information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto.

[0114] The memory 203 is used to store the application code that executes the scheme of this application, and its execution is controlled by the processor 201. The processor 201 is used to execute the application code stored in the memory 203 to implement the content shown in the aforementioned embodiment of the target trajectory generation method based on infrared monitoring.

[0115] Figure 2 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0116] This application provides a computer-readable storage medium storing a computer program that, when run on a computer, enables the computer to execute the contents shown in the aforementioned embodiment of the target trajectory generation method based on infrared monitoring.

[0117] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0118] The above are only some embodiments of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A target trajectory generation method based on infrared monitoring, characterized in that, include: During the inspection process of the UAV following the initial inspection route, infrared monitoring data collected by the infrared detection device, image data collected by the camera device, and radar data collected by the radar are acquired. The infrared detection device, the camera device, and the radar are all installed on the UAV. The first position of the target is determined based on the infrared monitoring data, the second position of the target is determined based on the image data, and the third position of the target is determined based on the radar data; Input the first position, the second position, and the third position into a preset fusion model to obtain the target position output by the preset fusion model; Generate the target's movement trajectory based on the target's position at each time point, and predict the target's future movement trajectory based on the target's movement trajectory; After predicting the future trajectory of the target based on the target's movement trajectory, the method further includes: Acquire three-dimensional data of the inspection area, and dynamically determine the expected trajectory range of the UAV based on the three-dimensional data and the future movement trajectory of the target; The expected trajectory is compared with the initial inspection path to determine whether the initial inspection path exceeds the range of the expected trajectory. If there are inspection segments in the initial inspection path that exceed the expected trajectory range, then the inspection segments are adjusted based on the expected trajectory range to obtain a dynamic target inspection path; Control the drone to perform inspections according to the dynamic target inspection path; The step of adjusting the inspection segment based on the expected trajectory range to obtain a dynamic target inspection path includes: determining the point in the inspection segment that is closest to the expected trajectory range for each point; selecting the closest points to form the adjusted inspection segment; and combining the inspection segments in the initial inspection path that do not exceed the expected trajectory range with the adjusted segment to obtain the dynamic target inspection path. The step of dynamically determining the expected trajectory range of the UAV based on the three-dimensional data and the future movement trajectory of the target includes: Based on the target's future movement trajectory, preset angle, and preset distance, the initial expected trajectory range of the target that the UAV can monitor is determined; The overlapping area between the three-dimensional data and the initial expected trajectory range is determined, and the overlapping area is removed from the initial expected trajectory range to obtain the expected trajectory range of the UAV.

2. The target trajectory generation method based on infrared monitoring according to claim 1, characterized in that, The acquisition of infrared monitoring data collected by the infrared detection device includes: Obtain the visibility of the current time and the current environment, and determine whether the current time is within a preset time period and whether the visibility is lower than a preset visibility threshold; If the current time is within the preset time period and the visibility is lower than the preset visibility threshold, then the quality detection step is repeated until the quality parameters meet the preset quality standard. The quality detection step includes: acquiring initial infrared monitoring data collected by the infrared detection device, determining whether the quality parameters of the initial infrared monitoring data meet the preset quality standard, and if not, adjusting the equipment parameters of the infrared detection device. The initial infrared monitoring data that meets the preset quality standard is used as the infrared monitoring data collected by the infrared detection device.

3. The target trajectory generation method based on infrared monitoring according to claim 2, characterized in that, The quality parameters include signal-to-noise ratio and image sharpness; The step of determining whether the quality parameters of the initial infrared monitoring data meet the preset quality standard, and adjusting the equipment parameters of the infrared detection device if they do not, includes: The signal-to-noise ratio and the image sharpness are compared with the corresponding preset quality standards, respectively. If any quality parameter does not meet the corresponding preset quality standard, the device parameter corresponding to the quality parameter that does not meet the preset quality standard is adjusted according to the preset correspondence. The preset correspondence represents the correspondence between quality parameters and device parameters, including signal-to-noise ratio corresponding to gain and image sharpness corresponding to focal length.

4. The target trajectory generation method based on infrared monitoring according to claim 1, characterized in that, The method further includes: Multiple sets of sample data are acquired. Each set of sample data includes the first sample position of the target sample collected by the infrared detection device, the second sample position of the target sample collected by the camera device, the third sample position of the target sample collected by the radar, and the actual sample position of the target sample. The first sample position, the second sample position, and the third sample position are used as model inputs, and the actual sample position is used as model outputs to train the model, thereby obtaining the trained preset fusion model.

5. The target trajectory generation method based on infrared monitoring according to claim 1, characterized in that, The step of inputting the first position, the second position, and the third position into a preset fusion model includes: Determine the distance between every two positions among the first position, the second position, and the third position, and compare each distance with a preset distance threshold; If each distance does not exceed the preset distance threshold, then the first position, the second position, and the third position are input into the preset fusion model.

6. A computer program product, characterized in that, It includes a computer program, which, when executed by a processor, implements the steps of the target trajectory generation method based on infrared monitoring as described in any one of claims 1-5.

7. An electronic device, characterized in that, include: At least one processor; Memory; At least one application, wherein the at least one application is stored in memory and configured to be executed by at least one processor, said at least one application being configured to: perform the target trajectory generation method based on infrared monitoring as described in any one of claims 1-5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed in the computer, the computer is instructed to perform the target trajectory generation method based on infrared monitoring as described in any one of claims 1-5.

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