A multifunctional on-site evidence investigation and evidence collection system based on an intelligent robot dog

Abstract

The present application relates to a kind of multifunctional scene evidence investigation evidence collection system based on intelligent robot dog, belong to criminal investigation trace detection technical field.The system includes: by communication device and positioner drive robot dog to reach trace detection site, the trace detection image data collected is sent to main control device;By the joint encoder equipped with angle drive architecture, posture precision adjustment is carried out in combination with the trace detection image data collected;The actual distance of microscope and target trace is calculated by trace detection distance model;Based on actual distance, in combination with corresponding initial focusing parameter, control focusing drive architecture moves;The trace detection image collected is carried out real-time definition evaluation by definition evaluation architecture, after evaluation, record the best focusing position and relevant trace detection image data, update database.The multi-scene application of trace detection is realized, the trace detection image blur problem caused by environmental interference or sample movement is solved, and the focusing quality and shooting success rate of trace detection photography are effectively improved.

Description

Technical Field

[0001] This invention belongs to the field of criminal investigation trace evidence technology, specifically relating to a multifunctional on-site physical evidence examination and collection system based on an intelligent robot dog. Background Technology

[0002] Trace evidence examination is of great significance for criminal investigations and other work, providing crucial clues for solving cases. Traditional trace evidence examination relies heavily on manual on-site investigation, requiring personnel to carry equipment to the scene to collect traces. In complex terrains such as mountains, ruins, or dangerous environments like crime scenes with numerous obstacles or toxic substances, manual investigation is inefficient and risky. Existing trace evidence microscopes are mostly fixed or portable. The former can only be used in laboratories and cannot quickly respond to on-site investigation needs; the latter, while portable, has limitations in stability, focusing speed, and accuracy in complex environments, affecting the quality of trace image acquisition. Existing gimbals are insufficient in stability, control precision, and adaptability to trace evidence requirements, making it difficult to achieve high-precision attitude adjustment and focusing control, failing to meet the requirements of photomicrography for detailed observation of tiny objects. Furthermore, existing focusing systems are slow and inaccurate in dynamically changing scenes, unable to accurately track depth changes of target objects. Additionally, existing robotic observation devices lack multi-sensor collaborative distance calculation and attitude compensation mechanisms. These deficiencies result in poor quality trace evidence images, affecting the accuracy and reliability of the examination results. Summary of the Invention

[0003] To address the aforementioned problems in the existing technology, this invention provides a multifunctional on-site evidence examination and collection system based on an intelligent robot dog. The objective of this invention can be achieved through the following technical solution:

[0004] A multifunctional on-site evidence examination and collection system based on an intelligent robot dog includes:

[0005] The remote control module receives remote control commands through the communication device integrated in the robot dog, and drives the robot dog to move to the trace inspection site in complex terrain in combination with the integrated locator. Based on the communication device, the collected data is transmitted to the main control device for clarity determination.

[0006] The attitude adjustment module, which integrates an angle drive architecture and a microscope component architecture, is mounted on top of the robot dog platform, electrically connected to and controlled by the robot dog platform; the angle drive architecture is equipped with a joint encoder, which adjusts the attitude accuracy in conjunction with the trace inspection image data acquired by the microscope component architecture.

[0007] The laser ranging module acquires the distance data between the robot dog and the target trace surface in real time through a laser rangefinder installed on the robot dog platform. Combined with the current attitude and position information of the microscopic gimbal, the actual distance between the microscope and the target trace is calculated through the trace ranging model.

[0008] The sharpness assessment module selects the corresponding initial focus parameters from the pre-established focus parameter database based on the actual distance, controls the focus drive architecture to drive the microscope component architecture to move along the optical axis according to the initialized focus parameters, and simultaneously starts the trace inspection image sensor to collect trace inspection image data in real time. The collected trace inspection image data is transmitted to the main control device for sharpness determination.

[0009] The focus drive module performs real-time sharpness evaluation on the acquired trace inspection images based on a sharpness evaluation architecture, calculates the sharpness index value of each trace inspection image, and compares the index value with a preset sharpness threshold. If the threshold is exceeded, it is determined that the focus is successful, the current position of the microscopic gimbal is recorded as the optimal focus position, and the focus drive is stopped. If the sharpness index value does not reach the threshold, the focus drive and trace inspection image acquisition evaluation process continues until the focus is successful. After successful focus, the acquired trace inspection images are subjected to image quality optimization processing.

[0010] As a preferred technical solution of the present invention, the angle driving architecture of the microscopic gimbal in the attitude adjustment module includes three mutually perpendicular rotary joints, which control the pitch angle, roll angle and yaw angle of the microscope respectively; the rotary joints are equipped with two joint encoders, a main encoder and a secondary encoder. During normal operation, the main encoder provides position data, and the secondary encoder serves as a redundant backup to monitor the working status of the main encoder in real time. When the main encoder fails, the system automatically switches to the secondary encoder.

[0011] Specifically, the trace detection ranging model in the laser ranging module includes:

[0012] The attitude angle is measured by the main joint encoder of the microscopic gimbal combined with the attitude sensor. The position height difference is calculated according to the installation position of the microscopic gimbal and the current state of the terrain adaptability adjustment mechanism of the robot dog platform. The distance between the robot dog and the target trace surface is obtained by the laser rangefinder.

[0013] Based on the optical system parameters of the microscopic gimbal, the corresponding parameter values ​​are retrieved from the pre-established optical parameter database. The actual distance between the microscope and the target trace is calculated using the trace detection distance formula. The actual distance calculated now is compared with the average value of the previous calculation results. If the deviation exceeds the set threshold, the data re-acquisition and calculation process is triggered.

[0014] Specifically, the focus drive architecture in the sharpness assessment module is controlled by the main control device of the robot dog platform. The main control device sends drive commands to the focus drive architecture based on the actual distance and data in the focus parameter database, including the motor speed, rotation direction and rotation angle.

[0015] The focusing drive architecture is also equipped with a position sensor, which monitors the position information of the microscope component architecture along the optical axis in real time and feeds the position information back to the main control device. The main control device compares the feedback position information with the target focusing position and completes real-time correction of the focusing process.

[0016] Specifically, the sharpness evaluation architecture in the focus driving module performs real-time sharpness evaluation on the acquired trace inspection images, and the steps are as follows:

[0017] The acquired trace images are processed to grayscale, converting color trace images into grayscale trace images, retaining the brightness information of the trace images, removing color information, simplifying the three-dimensional color data into one-dimensional grayscale values, and generating preprocessed trace images through filtering and noise reduction.

[0018] Calculate the gradient magnitude and gradient direction of each pixel in the preprocessed trace evidence image, where the gradient magnitude is used to represent the intensity of the trace evidence image edge, and the gradient direction is used to represent the direction information of the trace evidence image edge;

[0019] The gradient histogram of the trace evidence image is calculated based on the gradient magnitude, and the distribution of the number of pixels with different gradient magnitudes is statistically analyzed. The overall clarity of the trace evidence image is evaluated by analyzing the concentration and peak position of the gradient histogram.

[0020] Based on gradient direction information, a direction histogram of the trace evidence image is constructed. The directional distribution characteristics of the trace evidence image edges are analyzed. Combining gradient magnitude and direction information, the sharpness index value of the trace evidence image is calculated.

[0021] Specifically, before calculating the sharpness index value, a local sharpness assessment of the trace evidence image is also included, including:

[0022] The trace evidence image is divided into multiple local regions, the sharpness index value of each local region is calculated, and the average of the sharpness index values ​​of all local regions is taken as the overall sharpness index value of the trace evidence image.

[0023] The local region is divided using an adaptive partitioning algorithm. Based on the principle that each local region contains sufficient edge detail information, the size and position of the local region are dynamically determined, and there is a certain overlap between adjacent local regions to avoid the loss of edge information.

[0024] Specifically, the preset sharpness threshold in the focusing drive module is set according to different types of traces and microscopic observation requirements, including:

[0025] Acquire trace evidence sample images, construct a training dataset, dynamically annotate the images in the training dataset, the dynamic annotation brightens the trace evidence location in the trace evidence sample images, and adds type label codes corresponding to the trace evidence location based on the trace evidence type: a preset database classification table integrates all classification types of the trace evidence sample images, associates the type labels with the corresponding classification types in the database classification table, and uses the type label codes as annotation labels for the trace evidence sample images in the training dataset;

[0026] A sharpness level model is generated using labeled sample trace images to learn the characteristics and sharpness index value distribution of trace images of different types of traces at different sharpness levels;

[0027] Based on the generated model, the sharpness threshold range corresponding to each type of trace is determined, and the median value is taken as the preset sharpness threshold to ensure that when the sharpness index value of the collected trace images exceeds the threshold during the actual trace inspection process, the trace images can meet the requirements of trace inspection analysis.

[0028] Specifically, during the focusing process, the sharpness evaluation architecture continuously acquires multiple trace images and calculates the sharpness index value of the trace images. Based on the change sequence of the sharpness index value, a curve fitting method is used to predict the position of the microscopic gimbal corresponding to the peak position of the sharpness index value. Based on the prediction result, the moving direction of the focusing drive architecture is adjusted to the direction of the peak position.

[0029] Specifically, after successful focusing, the acquired trace inspection images undergo trace inspection image quality optimization processing, including:

[0030] Based on the sharpness index value of the trace image and the optimal focus position information of the microscopic gimbal, the blurred details in the trace image are recovered by performing deconvolution processing on the trace image.

[0031] Based on the requirements of trace evidence analysis in terms of color and brightness, and combined with the illumination parameters of the robot dog's lighting device, the trace evidence images are color corrected and brightness adjusted; the optimized trace evidence images are compared with the original acquired trace evidence images, and the parameter data of the optimization process are recorded.

[0032] Based on trace detection, the main control device organizes and analyzes the collected trace detection image data, extracts key feature information of the traces, and compares and matches them with the pre-stored trace database to complete the rapid identification and classification of traces.

[0033] Specifically, the main control device has a dynamic focusing compensation mechanism, including a compensation control subsystem based on attitude feedback and a compensation control subsystem based on distance feedback:

[0034] When the joint encoder of the angle drive architecture detects that the attitude change of the microscopic gimbal exceeds the attitude compensation threshold, it calculates the required focus compensation amount according to the direction and magnitude of the attitude change, and controls the focus drive architecture to drive the microscopic component architecture to move the corresponding compensation distance along the optical axis.

[0035] When the change in distance data acquired by the laser rangefinder exceeds the distance compensation threshold, the actual distance between the microscope and the target trace is recalculated based on the change in distance. Then, based on the corresponding compensation focus parameters selected from the focus parameter database, the focus drive architecture is controlled to adjust the focus.

[0036] The beneficial effects of this invention are as follows:

[0037] The robot dog can integrate communication devices and locators, move flexibly in complex terrain, and receive remote commands to reach the trace inspection site. Its multi-joint microscopic gimbal can adjust the microscope angle in all directions. Together with the laser rangefinder and trace inspection distance model, it can accurately calculate the distance, providing an accurate basis for subsequent dynamic focusing, which greatly improves the adaptability and work efficiency of trace inspection equipment in complex sites.

[0038] The dynamic focus compensation mechanism can monitor changes in posture and distance in real time, and immediately compensate for refocusing when the threshold is exceeded, ensuring that the focus accuracy is always maintained when the robot dog moves or the environment changes, thus ensuring continuous and stable trace inspection work.

[0039] Based on the focus parameter database, the system quickly selects initial focus parameters to start focusing according to the actual distance. Combined with the real-time acquisition of trace images by the trace image sensor and the sharpness evaluation by the main control device, the system calculates index values ​​in multiple dimensions through the sharpness evaluation architecture and compares them with the threshold to determine whether the focus is successful. If the target is not met, the system continues to adjust until the focus is accurate, which greatly improves the speed and accuracy of focusing.

[0040] The preset sharpness threshold is derived from training on different types of trace samples. Combined with curve fitting to predict the peak position, the focusing process is more intelligent and accurate, ensuring that the acquired trace images meet the requirements of trace analysis.

[0041] The trace evidence images are preprocessed by grayscale conversion and filtering to remove noise. Then, the gradient magnitude and direction are calculated, and a histogram is constructed to comprehensively evaluate the sharpness. The local sharpness evaluation adopts an adaptive partitioning algorithm to ensure the reliability of the overall sharpness index of the trace evidence images, thus providing a high-quality trace evidence image foundation for trace analysis.

[0042] After successful focusing, the blurry details are recovered by deconvolution using the sharpness index and the optimal focus position information. The color is corrected and the brightness is adjusted according to the brightness parameters to optimize the quality of the trace evidence image. The parameters are recorded by comparing with the original trace evidence image to provide more accurate and clear trace evidence image data for subsequent trace evidence analysis.

[0043] The rotary joint of the microscopic gimbal angle drive architecture is equipped with a main and a secondary encoder. Under normal conditions, the main encoder provides data and the secondary encoder provides backup monitoring. If the main encoder fails, the secondary encoder will automatically switch to ensure the smooth progress of trace inspection tasks and improve system reliability.

[0044] The focusing drive architecture is controlled by the main controller, which receives commands including motor speed, direction, and angle for precise driving. It is equipped with a position sensor to provide real-time position information, and the main controller corrects the focusing process accordingly, realizing closed-loop control and improving focusing accuracy and stability.

[0045] In summary, this invention achieves unmanned, high-precision, and highly interference-resistant operation of complex scene trace inspection through four-dimensional collaboration of robot dog, gimbal, sensor, and algorithm, providing disruptive technical support for the field of criminal investigation. Attached Figure Description

[0046] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0047] Figure 1 This is a flowchart illustrating a multifunctional on-site evidence examination and collection system based on an intelligent robot dog, according to the present invention.

[0048] Figure 2 This is a schematic diagram of an embodiment of the dynamic focusing compensation mechanism of the present invention. Detailed Implementation

[0049] To further illustrate the technical means and effects adopted by the present invention to achieve the intended purpose, the following detailed description of the specific implementation methods, structures, features and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided.

[0050] Please see Figure 1 A multifunctional on-site evidence examination and collection system based on an intelligent robot dog, comprising:

[0051] The remote control module receives remote control commands through the communication device integrated in the robot dog, and drives the robot dog to move to the trace inspection site in complex terrain in combination with the integrated locator. Based on the communication device, the collected data is transmitted to the main control device for clarity determination.

[0052] The attitude adjustment module, which integrates an angle drive architecture and a microscope component architecture, is mounted on top of the robot dog platform, electrically connected to and controlled by the robot dog platform; the angle drive architecture is equipped with a joint encoder, which adjusts the attitude accuracy in conjunction with the trace inspection image data acquired by the microscope component architecture.

[0053] The laser ranging module acquires the distance data between the robot dog and the target trace surface in real time through a laser rangefinder installed on the robot dog platform. Combined with the current attitude and position information of the microscopic gimbal, the actual distance between the microscope and the target trace is calculated through the trace ranging model.

[0054] The sharpness assessment module selects the corresponding initial focus parameters from the pre-established focus parameter database based on the actual distance, controls the focus drive architecture to drive the microscope component architecture to move along the optical axis according to the initialized focus parameters, and simultaneously starts the trace inspection image sensor to collect trace inspection image data in real time. The collected trace inspection image data is transmitted to the main control device for sharpness determination.

[0055] The focusing drive module performs real-time sharpness evaluation on the acquired trace inspection images based on the sharpness evaluation architecture of the main control device, calculates the sharpness index value of each trace inspection image, and compares the index value with a preset sharpness threshold. If the threshold is exceeded, it is determined that the focus is successful, the current position of the microscopic gimbal is recorded as the optimal focus position, and the focusing drive is stopped. If the sharpness index value does not reach the threshold, the focusing drive and trace inspection image acquisition evaluation process continues until the focus is successful. After the focus is successful, the acquired trace inspection images are subjected to image quality optimization processing.

[0056] Specifically, the angle driving architecture of the microscopic gimbal in the attitude adjustment module includes three mutually perpendicular rotary joints, which control the pitch angle, roll angle and yaw angle of the microscope respectively; the rotary joints are equipped with two joint encoders, a main encoder and a secondary encoder. During normal operation, the main encoder provides position data, and the secondary encoder serves as a redundant backup to monitor the working status of the main encoder in real time. When the main encoder fails, the system automatically switches to the secondary encoder to ensure the smooth progress of the trace inspection task.

[0057] In this embodiment, a microscope gimbal integrating the angle drive architecture and the microscope component architecture is mounted above the robot dog platform. High-speed, stable electrical connections ensure control over the robot dog platform. The angle drive architecture includes three mutually perpendicular rotary joints, responsible for controlling the microscope's pitch angle (range ±45°), roll angle (range ±30°), and yaw angle (range ±60°). Each rotary joint is equipped with high-precision main and secondary joint encoders with a resolution of up to 10,000 pulses / revolution. The main encoder provides accurate position data during normal operation, while the secondary encoder monitors in real time and immediately switches over if the main encoder fails, ensuring continuous attitude adjustment.

[0058] The microscope assembly architecture uses a high-resolution microscope lens with an optical magnification of 50 to 200 times. It is equipped with a high-sensitivity trace image sensor, which can acquire and transmit trace image data to the main control device in real time. The focusing drive architecture uses a high-precision stepper motor with a minimum step angle of 0.9°. Together with the position sensor, it achieves precise optical axis direction movement control with a movement accuracy of ±10 micrometers, ensuring accurate focusing.

[0059] Remote operators send control commands containing the coordinates of the trace evidence inspection site to the robot dog via a dedicated control terminal. After receiving the commands, the robot dog's communication device, combined with its own locator data, plans the optimal path, autonomously navigates through complex terrain, avoids obstacles, and moves smoothly to the trace evidence inspection site. During this process, it transmits position, attitude, and other data back to the main control device in real time via the communication device. The main control device performs a preliminary clarity assessment of the transmitted data and estimates the impact of the site environment on the trace evidence inspection task.

[0060] Upon arrival at the site, the main control unit sends attitude adjustment commands to the microscopic gimbal. The angle drive architecture of the microscopic gimbal activates the rotation joints according to the commands, and the main encoder provides real-time feedback of position data. This drives the microscope to adjust its pitch, roll, and yaw angles in all directions. Combined with the trace inspection image data collected by the microscope components, the attitude accuracy adjustment algorithm is used to initially align the microscope with the target trace area, ensuring that subsequent trace inspection operations have a good starting angle. The entire attitude adjustment process is completed within 2 seconds, meeting the requirements for rapid response.

[0061] Specifically, the trace detection ranging model in the laser ranging module includes:

[0062] The attitude angle is measured by the joint encoder of the micro gimbal combined with the attitude sensor. The position height difference is calculated based on the installation position of the micro gimbal and the terrain adaptability adjustment mechanism of the robot dog platform. The distance between the robot dog and the target trace surface is obtained by the laser rangefinder.

[0063] Based on the optical system parameters of the microscopic gimbal, the corresponding parameter values ​​are retrieved from the pre-established optical parameter database. The actual distance between the microscope and the target trace is calculated using the trace detection distance formula. The actual distance calculated now is compared with the average value of the previous calculation results. If the deviation exceeds the set threshold, the data re-acquisition and calculation process is triggered, and the working status of the relevant sensors is checked.

[0064] In this embodiment, the trace detection distance model satisfies the following formula:

[0065]

[0066] in, This indicates the straight-line distance measured by the laser rangefinder; The angle between the optical axis of the microscope gimbal and the horizontal plane is indicated by the gimbal tilt sensor. This represents the height of the robot dog's chassis from the ground; This represents the extension and retraction range of the pan-tilt mechanism.

[0067] Specifically, the focus drive architecture in the sharpness assessment module is controlled by the main control device of the robot dog platform. The main control device sends drive commands to the focus drive architecture based on the actual distance and data in the focus parameter database, including the motor speed, rotation direction and rotation angle.

[0068] The focusing drive architecture is also equipped with a position sensor, which monitors the position information of the microscope component architecture along the optical axis in real time and feeds the position information back to the main control device. The main control device compares the feedback position information with the target focusing position and completes real-time correction of the focusing process.

[0069] In this embodiment, during trace inspection, when the robot dog carrying the microscopic gimbal arrives near the target trace, the laser rangefinder first accurately measures the straight-line distance between the robot dog platform and the trace surface. Simultaneously, combining the attitude data of the microscopic gimbal and the state parameters of the terrain-adaptive adjustment mechanism, the actual working distance between the microscope and the trace is accurately calculated using the trace inspection ranging model, with an error range controlled within ±0.05 mm. Based on this actual distance, the main control device quickly retrieves matching initial focusing parameters from the focusing parameter database, including the motor's base speed setting of 300 rpm, the predetermined rotation direction, and the estimated rotation angle of 20 degrees. It then sends a drive command containing these parameters to the focusing drive architecture to initiate the focusing process.

[0070] Upon receiving the command, the focusing drive architecture initiates a rotation of the stepper motor at the set speed and direction, driving the microscope assembly to move along the optical axis. Simultaneously, the microscope's trace image sensor acquires trace image data in real-time at a frequency of 30 frames per second and transmits it to the main control unit. During the initial stage of motor rotation, the rapid change in microscope head position causes a rapid shift in the image sharpness of the traces. The main control unit's sharpness evaluation algorithm analyzes the sharpness index of each frame of the trace image in real time, providing dynamic feedback for subsequent focusing corrections.

[0071] Specifically, the sharpness evaluation architecture in the focus driving module performs real-time sharpness evaluation on the acquired trace inspection images, and the steps are as follows:

[0072] The acquired trace evidence images are processed to grayscale, converting color trace evidence images into grayscale trace evidence images, retaining the brightness information of the trace evidence images, removing color information, simplifying the three-dimensional color data into one-dimensional grayscale values, and generating preprocessed trace evidence images through filtering and noise reduction.

[0073] Calculate the gradient magnitude and gradient direction of each pixel in the preprocessed trace evidence image, where the gradient magnitude is used to represent the intensity of the trace evidence image edge, and the gradient direction is used to represent the direction information of the trace evidence image edge;

[0074] The gradient histogram of the trace evidence image is calculated based on the gradient magnitude, and the distribution of the number of pixels with different gradient magnitudes is statistically analyzed. The overall clarity of the trace evidence image is evaluated by analyzing the concentration and peak position of the gradient histogram.

[0075] Based on gradient direction information, a direction histogram of the trace evidence image is constructed. The directional distribution characteristics of the trace evidence image edges are analyzed. Combining gradient magnitude and direction information, the sharpness index value of the trace evidence image is calculated.

[0076] In this embodiment, after the microscope is aimed at the target trace and an inspection image is acquired, the acquired color trace image data is transmitted to the main control computer in real time. First, the program performs grayscale processing on the color trace image. In this way, the three-dimensional color data is simplified into a one-dimensional grayscale trace image, while retaining the brightness information of the trace image, laying the foundation for subsequent edge detection and sharpness evaluation. Then, a Gaussian filtering algorithm is applied to the grayscale trace image for noise reduction.

[0077] Gradient calculation is performed on the preprocessed grayscale trace image. The gradient magnitude reflects the intensity of the trace image edge, that is, the degree of drastic change in pixel value at the edge; the gradient direction indicates the directional information of the trace image edge, which helps in subsequent analysis of the directional distribution characteristics of the trace image edge.

[0078] Based on the calculated gradient magnitudes, a gradient histogram of the trace evidence image is constructed. The range of gradient magnitudes is divided into multiple intervals, and the number of pixels in each interval is counted and displayed in the form of a bar chart. By analyzing the concentration and peak positions of the gradient histogram, the overall sharpness of the trace evidence image can be evaluated. If the peaks of the gradient histogram are concentrated in the higher gradient magnitude intervals, it indicates that the edges of the trace evidence image are sharp and the overall sharpness of the image is high; conversely, if the peaks are concentrated in the lower gradient magnitude intervals, the trace evidence image may be blurry and have low sharpness.

[0079] Using the calculated gradient direction information, a direction histogram of the trace evidence image is constructed. The gradient direction range is divided into multiple intervals, and the number of pixels in each interval is counted. By analyzing the direction histogram, the directional distribution characteristics of the trace evidence image edges can be understood. Combining gradient magnitude and direction information, a sharpness index value of the trace evidence image is comprehensively calculated using methods such as weighted averaging. The weights can be adjusted according to the actual application scenario and experience. For example, for trace types where edge intensity is more important, the weight of gradient magnitude can be appropriately increased; for trace types with significant directional distribution characteristics, the weight of direction information can be appropriately increased. The final sharpness index value can comprehensively reflect the sharpness status of the trace evidence image, providing a quantitative basis for focus adjustment.

[0080] Specifically, before calculating the sharpness index value, a local sharpness assessment of the trace evidence image is also included, including:

[0081] The trace evidence image is divided into multiple local regions, the sharpness index value of each local region is calculated, and the average of the sharpness index values ​​of all local regions is taken as the overall sharpness index value of the trace evidence image.

[0082] The local region is divided using an adaptive partitioning algorithm. Based on the principle that each local region contains sufficient edge detail information, the size and position of the local region are dynamically determined, and there is a certain overlap between adjacent local regions to avoid the loss of edge information.

[0083] Specifically, the preset sharpness threshold in the focusing drive module is set according to different types of traces and microscopic observation requirements, including:

[0084] Acquire trace evidence sample images, construct a training dataset, dynamically annotate the images in the training dataset, the dynamic annotation brightens the trace evidence location in the trace evidence sample images, and adds type label codes corresponding to the trace evidence location based on the trace evidence type: a preset database classification table integrates all classification types of the trace evidence sample images, associates the type labels with the corresponding classification types in the database classification table, and uses the type label codes as annotation labels for the trace evidence sample images in the training dataset;

[0085] A sharpness level model is generated using labeled sample trace images to learn the characteristics and sharpness index value distribution of trace images of different types of traces at different sharpness levels;

[0086] Based on the training results, the sharpness threshold range corresponding to each type of trace is determined, and the median value is taken as the preset sharpness threshold to ensure that when the sharpness index value of the collected trace images exceeds the threshold in the actual trace inspection process, the trace images can meet the requirements of trace inspection analysis.

[0087] In this embodiment, throughout the focusing process, the position sensor monitors the displacement of the microscope head along the optical axis in real time and sends the position data to the main controller at 0.1 millisecond intervals. The main controller precisely compares the actual position with the target focusing position and calculates the deviation between the two. When the detected position deviation exceeds a preset threshold, the main controller immediately adjusts the focusing drive command dynamically according to the magnitude and direction of the deviation. Assuming that the current actual position of the microscope head lags behind the target position by 3 micrometers, the main controller sends an acceleration command to the motor, increasing the motor speed to 400 rpm and appropriately adjusting the rotation angle to move the microscope head quickly toward the target position until the position deviation drops to within the threshold range. This achieves real-time fine correction of the focusing process, ensuring that the focusing accuracy reaches the micrometer level.

[0088] Specifically, during the focusing process, the sharpness evaluation architecture continuously acquires multiple trace images and calculates the sharpness index value of the trace images. Based on the change sequence of the sharpness index value, a curve fitting method is used to predict the position of the microscopic gimbal corresponding to the peak position of the sharpness index value. Based on the prediction result, the moving direction of the focusing drive architecture is adjusted to the direction of the peak position.

[0089] Specifically, after successful focusing, the acquired trace inspection images undergo trace inspection image quality optimization processing, including:

[0090] Based on the sharpness index value of the trace image and the optimal focus position information of the microscopic gimbal, the blurred details in the trace image are recovered by performing deconvolution processing on the trace image.

[0091] Based on the requirements of trace evidence analysis in terms of color and brightness, and combined with the illumination parameters of the lighting device of the robot dog platform, the trace evidence images are color corrected and brightness adjusted; the optimized trace evidence images are compared with the original acquired trace evidence images, and the parameter data of the optimization process are recorded.

[0092] Based on trace detection, the main control device organizes and analyzes the collected trace detection image data, extracts key feature information of the traces, and compares and matches them with the pre-stored trace database to complete the rapid identification and classification of traces.

[0093] Please see Figure 2 Specifically, the main control device has a dynamic focusing compensation mechanism, including a compensation control subsystem based on attitude feedback and a compensation control subsystem based on distance feedback:

[0094] When the joint encoder of the angle drive architecture detects that the attitude change of the microscopic gimbal exceeds the attitude compensation threshold, it calculates the required focus compensation amount according to the direction and magnitude of the attitude change, and controls the focus drive architecture to drive the microscopic component architecture to move the corresponding compensation distance along the optical axis.

[0095] When the change in distance data acquired by the laser rangefinder exceeds the distance compensation threshold, the actual distance between the microscope and the target trace is recalculated based on the change in distance. Then, based on the corresponding compensation focus parameters selected from the focus parameter database, the focus drive architecture is controlled to adjust the focus.

[0096] In this embodiment, a high-precision attitude sensor is installed on the photomicrography platform to monitor changes in the platform's tilt, rotation, and other attitudes in real time. Simultaneously, a laser rangefinder is equipped to accurately measure changes in the distance between the camera and the sample. Both the attitude sensor and the laser rangefinder are connected to the main control device wirelessly or via wired connection to ensure real-time data transmission.

[0097] When the photomicrography platform undergoes an attitude change due to external interference, the attitude sensor detects the magnitude of the change. If the change exceeds a preset attitude compensation threshold, the attitude feedback subsystem is triggered. Based on the direction and magnitude of the attitude change, the main controller calculates the required focus compensation amount using a focus compensation model. Then, the main controller sends a command to the focus drive architecture, causing the photomicrography camera to move a corresponding compensation distance along the optical axis to counteract the impact of the attitude change on the focus position. During this movement, the position sensor provides real-time feedback on the camera's position information to ensure the accuracy of the compensation movement.

[0098] Simultaneously, the laser rangefinder continuously monitors the distance between the camera and the sample. When the distance change exceeds a preset distance compensation threshold, the distance feedback subsystem is activated. The main control unit, based on the new distance data and the focus parameter curves in the focus parameter database, recalculates the optimal focus position for the current distance. Based on the calculation results, the main control unit adjusts the focus drive architecture, moving the camera to the new focus position to ensure the clarity of the trace evidence images. This process, through real-time data analysis and rapid focus adjustment, effectively addresses distance changes caused by sample position variations or platform instability.

[0099] After each compensation adjustment, the main control device evaluates the compensation effect by assessing the sharpness of the trace image. If the sharpness of the trace image still does not meet the preset threshold, the main control device will continue to perform compensation adjustments until the sharpness of the trace image meets the requirements. In this way, the dynamic focusing compensation mechanism continuously optimizes the focus position and improves the quality of trace images in photomicrography.

[0100] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A multifunctional on-site evidence examination and collection system based on an intelligent robot dog, characterized in that, include: The remote control module receives remote control commands through the communication device integrated in the robot dog, and drives the robot dog to move to the trace inspection site in complex terrain in combination with the integrated locator. Based on the communication device, the collected data is transmitted to the main control device for clarity determination. The attitude adjustment module, which integrates an angle driving architecture and a microscope component architecture, is mounted on top of the robot dog platform and is electrically connected to and controlled by the robot dog platform. The angle driving architecture is equipped with a joint encoder, which is used to adjust the attitude accuracy in conjunction with the trace inspection image data acquired by the microscope component architecture. The laser ranging module acquires the distance data between the robot dog and the target trace surface in real time through a laser rangefinder installed on the robot dog platform. Combined with the current attitude and position information of the microscopic gimbal, the actual distance between the microscope and the target trace is calculated through the trace ranging model. The sharpness assessment module selects the corresponding initial focus parameters from the pre-established focus parameter database based on the actual distance, controls the focus drive architecture to drive the microscope component architecture to move along the optical axis according to the initialized focus parameters, and simultaneously starts the trace inspection image sensor to collect trace inspection image data in real time. The collected trace inspection image data is transmitted to the main control device for sharpness determination. The focus drive module performs real-time sharpness evaluation on the acquired trace inspection images based on a sharpness evaluation architecture, calculates the sharpness index value of each trace inspection image, and compares the index value with a preset sharpness threshold. If the threshold is exceeded, it is determined that the focus is successful, the current position of the microscopic gimbal is recorded as the optimal focus position, and the focus drive is stopped. If the sharpness index value does not reach the threshold, the focus drive and trace inspection image acquisition evaluation process continues until the focus is successful. After successful focus, the acquired trace inspection images are subjected to image quality optimization processing.

2. The system according to claim 1, characterized in that, The angle drive architecture of the microscopic gimbal in the attitude adjustment module includes three mutually perpendicular rotary joints, which control the pitch angle, roll angle and yaw angle of the microscope respectively. The rotary joints are equipped with two joint encoders, a main encoder and a secondary encoder. During normal operation, the main encoder provides position data, and the secondary encoder serves as a redundancy backup to monitor the working status of the main encoder in real time. When the main encoder fails, the system automatically switches to the secondary encoder to ensure the smooth progress of the trace inspection task.

3. The system according to claim 1, characterized in that, The trace detection ranging model in the laser ranging module includes: The attitude angle is measured by the main joint encoder of the microscopic gimbal combined with the attitude sensor. The position height difference is calculated according to the installation position of the microscopic gimbal and the current state of the terrain adaptability adjustment mechanism of the robot dog platform. The distance between the robot dog and the target trace surface is obtained by the laser rangefinder. Based on the optical system parameters of the microscopic gimbal, the corresponding parameter values ​​are retrieved from the pre-established optical parameter database. The actual distance between the microscope and the target trace is calculated using the trace detection distance formula. The actual distance calculated now is compared with the average value of the previous calculation results. If the deviation exceeds the set threshold, the data re-acquisition and calculation process is triggered.

4. The system according to claim 1, characterized in that, The focus drive architecture in the sharpness assessment module is controlled by the main control device of the robot dog platform. The main control device sends drive commands to the focus drive architecture based on the actual distance and data in the focus parameter database, including the motor speed, rotation direction and rotation angle. The focusing drive architecture is also equipped with a position sensor, which monitors the position information of the microscope component architecture along the optical axis in real time and feeds the position information back to the main control device. The main control device compares the feedback position information with the target focusing position and completes real-time correction of the focusing process.

5. The system according to claim 1, characterized in that, The sharpness evaluation architecture in the focus drive module performs real-time sharpness assessment on the acquired trace inspection images. The steps are as follows: The acquired trace images are processed to grayscale, converting color trace images into grayscale trace images, retaining the brightness information of the trace images, removing color information, simplifying the three-dimensional color data into one-dimensional grayscale values, and generating preprocessed trace images through filtering and noise reduction. Calculate the gradient magnitude and gradient direction of each pixel in the preprocessed trace evidence image, where the gradient magnitude is used to represent the intensity of the trace evidence image edge, and the gradient direction is used to represent the direction information of the trace evidence image edge; The gradient histogram of the trace evidence image is calculated based on the gradient magnitude, and the distribution of the number of pixels with different gradient magnitudes is statistically analyzed. The overall clarity of the trace evidence image is evaluated by analyzing the concentration and peak position of the gradient histogram. Based on gradient direction information, a direction histogram of the trace evidence image is constructed. The directional distribution characteristics of the trace evidence image edges are analyzed. Combining gradient magnitude and direction information, the sharpness index value of the trace evidence image is calculated.

6. The system according to claim 1, characterized in that, Before calculating the sharpness index value, a local sharpness assessment of the trace evidence images is also included, including: The trace evidence image is divided into multiple local regions, the sharpness index value of each local region is calculated, and the average of the sharpness index values ​​of all local regions is taken as the overall sharpness index value of the trace evidence image. The local region is divided using an adaptive partitioning algorithm. Based on the principle that each local region contains sufficient edge detail information, the size and position of the local region are dynamically determined, and there is a certain overlap between adjacent local regions to avoid the loss of edge information.

7. The system according to claim 1, characterized in that, The preset sharpness threshold in the focusing drive module is set according to different types of traces and microscopic observation requirements, including: Acquire trace evidence sample images, construct a training dataset, and process the images in the training dataset. Dynamic annotation is used to brighten the areas where traces are detected in the trace detection sample image. The display shows that, based on the trace detection type, a type label code corresponding to the trace detection location is added: preset data. The library classification table integrates all classification types of the trace evidence sample images, and associates the type labels with... The database classification table is associated with the corresponding classification types, and the type label encoding is used as... The annotation labels of the trace evidence sample images in the training dataset; A sharpness level model is generated using labeled sample trace images to learn the characteristics and sharpness index value distribution of trace images of different types of traces at different sharpness levels; Based on the generated model, the sharpness threshold range corresponding to each type of trace is determined, and the median value is taken as the preset sharpness threshold to ensure that when the sharpness index value of the collected trace images exceeds the threshold during the actual trace inspection process, the trace images can meet the requirements of trace inspection analysis.

8. The system according to claim 1, characterized in that, During the focusing process, the sharpness evaluation architecture continuously acquires multiple trace images and calculates the sharpness index value of the trace images. Based on the change sequence of the sharpness index value, a curve fitting method is used to predict the position of the microscopic gimbal corresponding to the peak position of the sharpness index value. Based on the prediction result, the moving direction of the focusing drive architecture is adjusted to the direction of the peak position.

9. The system according to claim 1, characterized in that, The image quality optimization processing method in the focus driving module is as follows: Based on the sharpness index value of the trace image and the optimal focus position information of the microscopic gimbal, the blurred details in the trace image are recovered by performing deconvolution processing on the trace image. Based on the requirements of trace evidence analysis in terms of color and brightness, and combined with the illumination parameters of the lighting device of the robot dog platform, the trace evidence images are color corrected and brightness adjusted; the optimized trace evidence images are compared with the original acquired trace evidence images, and the parameter data of the optimization process are recorded. Based on trace detection, the main control device organizes and analyzes the collected trace detection image data, extracts key feature information of the traces, and compares and matches them with the pre-stored trace database to complete the rapid identification and classification of traces.

10. The system according to claim 1, characterized in that, The main control device has a dynamic focusing compensation mechanism, including a compensation control subsystem based on attitude feedback and a compensation control subsystem based on distance feedback: When the joint encoder of the angle drive architecture detects that the attitude change of the microscopic gimbal exceeds the attitude compensation threshold, it calculates the required focus compensation amount according to the direction and magnitude of the attitude change, and controls the focus drive architecture to drive the microscopic component architecture to move the corresponding compensation distance along the optical axis. When the change in distance data acquired by the laser rangefinder exceeds the distance compensation threshold, the actual distance between the microscope and the target trace is recalculated based on the change in distance. Then, based on the corresponding compensation focus parameters selected from the focus parameter database, the focus drive architecture is controlled to adjust the focus.

Images