An automatic coverage adjustment device in a distributed multi-camera environment

Abstract

The application provides a kind of distributed multi-camera environment under the coverage range automation adjustment device, it is related to video monitoring and image data processing technical field including control optimization system, the control optimization system contains environmental information acquisition system, image information acquisition system, environmental evaluation system, coverage scheduling system, feedback optimization system and user control system, the application proposes a kind of distributed multi-camera environment under the coverage range automation adjustment device, effectively solves the problem that the adaptive capacity of existing monitoring system is insufficient in dynamic environment, multi-source data collaborative analysis ability is limited and lacks closed-loop feedback optimization.The device is through environmental data sensor group, computing processing core, mobile control base, view angle adjusting unit and image capture unit, cooperates with environmental information acquisition system, image information acquisition system, environmental evaluation system, coverage scheduling system, feedback optimization system and user control system, realizes the dynamic optimization of coverage range.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical fields of video monitoring and image data processing, in particular to an automatic coverage range adjustment device in a distributed multi-camera environment. BACKGROUND

[0002] In recent years, with the continuous growth of demand for smart cities, public safety and intelligent transportation, multi-camera monitoring systems have been widely applied in urban management, traffic monitoring, industrial park security and large event management, etc. These systems achieve extensive coverage of complex scenes through distributed camera networks, significantly improving monitoring efficiency and event response capabilities. However, most existing multi-camera monitoring systems still have significant limitations in practical applications. Traditional monitoring systems usually use fixedly installed cameras, whose angles and positions cannot be dynamically adjusted, making it difficult to adapt to dynamic environmental factors such as light intensity fluctuations, changes in pedestrian or vehicle flow density, and adverse weather conditions, resulting in blind spots or overlaps in the coverage area, reducing monitoring effectiveness. For example, in high-traffic areas during peak hours or scenes with rapidly changing light at dusk, fixed cameras cannot effectively capture key targets, affecting the accuracy of event detection.

[0003] In addition, existing systems have deficiencies in multi-source data processing and real-time analysis. Traditional systems mainly rely on single image data, lack comprehensive collection and fusion capabilities for environmental parameters (such as light, temperature, target movement speed), and are difficult to fully assess the dynamic state of the monitoring area, limiting the system's response speed and accuracy in complex scenes. In high-density crowds or complex traffic scenes, the target detection and tracking capabilities of existing systems decrease, making it easy to miss fast-moving targets or sudden events. Some monitoring systems have introduced intelligent algorithms, such as basic motion detection and target recognition functions, but still have the following problems: first, lack of adaptive adjustment mechanism, cameras cannot dynamically optimize angles and positions according to real-time environmental changes, leading to uneven resource allocation; second, insufficient multi-source data collaborative analysis capability, making it difficult to comprehensively utilize environmental data and image data to generate accurate adjustment strategies; finally, the system lacks closed-loop feedback and continuous optimization mechanism, and cannot dynamically improve performance through historical data or user intervention, limiting its intelligent level and long-term stability in dynamic and variable environments.

[0004] Therefore, an automatic coverage range adjustment device in a distributed multi-camera environment is needed to solve the above problems. SUMMARY

[0005] Technical problems solved

[0006] To solve the problems in the background art, the present application provides an automatic coverage range adjustment device in a distributed multi-camera environment.

[0007] Technical solution

[0008] To achieve the above object, the present application is implemented by the following technical solutions: A distributed multi-camera environment coverage range automatic adjustment device, comprising a control optimization system, the control optimization system includes an environment information acquisition system, an image information acquisition system, an environment evaluation system, a coverage scheduling system, a feedback optimization system and a user control system;

[0009] The environment information acquisition system collects environment data through light intensity sensors, temperature sensors and distance sensors, including illumination intensity fluctuation frequency, temperature value and target motion speed change rate; through an adaptive data acquisition frequency regulation unit, the acquisition frequency is adjusted based on the regional activity index based on the fixed threshold rule, to generate compressed environment data; the compressed environment data is transmitted to the environment evaluation system through a low-latency communication protocol;

[0010] The image information acquisition system collects real-time image data through monitoring cameras, adjusts the image frame rate according to the target motion speed change rate through an adaptive data acquisition frequency regulation unit, generates compressed image data, and transmits it to the environment evaluation system;

[0011] The environment information acquisition system and the image information acquisition system regularly push compressed environment data and compressed image data to the environment evaluation system, and if the environment evaluation system detects data anomalies, it feeds back control instructions to the environment information acquisition system or the image information acquisition system, requiring re-collection or adjusting the collection frequency.

[0012] Preferably, the environment evaluation system includes a regional activity index calculation unit and an environment interference dynamic compensation unit;

[0013] The environment interference dynamic compensation unit receives compressed environment data and compressed image data, compensates for light mutations and noise interference through a sliding window mean deviation correction method, and generates calibrated environment data and calibrated image data;

[0014] The regional activity index calculation unit calculates the activity index of each monitoring area based on the illumination intensity fluctuation frequency and the target motion speed change rate of the calibrated environment data through the ratio formula of standard deviation and mean value, generates a regional activity index table, and transmits it to the coverage scheduling system;

[0015] The environment evaluation system generates a regional activity index table and pushes it to the coverage scheduling system, and if the coverage scheduling system needs additional data, it requests the environment evaluation system to re-compensate or calculate the activity index through control instructions.

[0016] Preferably, the coverage scheduling system includes a task allocation unit and a cooperative motion constraint optimization unit;

[0017] The task allocation unit receives the area activity index table, generates a task priority table of each monitoring camera through an activity index sorting rule, and preferentially allocates the monitoring task of a high-activity-index area;

[0018] The task allocation unit generates the task priority table and transmits it to the cooperative motion constraint optimization unit. If the task execution effect does not reach the expectation, the overlay scheduling system requests the environment evaluation system to regenerate the area activity index table through a feedback optimization system.

[0019] Preferably, the cooperative motion constraint optimization unit of the overlay scheduling system calculates the motion trajectory of the adjustment device of each monitoring camera through a motion constraint algorithm based on vector analysis;

[0020] The cooperative motion constraint optimization unit generates an adjustment path with no collision and minimum field of view overlap based on the distance vector and angle vector between cameras according to the task priority table and the area activity index table, generates an adjustment strategy, and transmits it to the dynamic adjustment system;

[0021] If a collision risk is detected, the cooperative motion constraint optimization unit re-plans the path by adjusting the motion sequence;

[0022] The cooperative motion constraint optimization unit generates the adjustment strategy and issues it to the dynamic adjustment system through a control interface. If the adjustment fails, the dynamic adjustment system notifies the overlay scheduling system to regenerate the adjustment strategy through a feedback optimization system.

[0023] Preferably, the overlay scheduling system comprises an overlay effect evaluation unit that receives the area activity index table and calibration image data, analyzes the difference between the current coverage range and the preset optimal coverage range through a background modeling method, generates an overlay effect distribution map, and transmits it to the dynamic adjustment system;

[0024] The overlay effect evaluation unit generates the overlay effect distribution map and pushes it to the dynamic adjustment system and the user control system. If the overlay effect does not reach the expectation, the overlay scheduling system requests the environment evaluation system to supplement data through a control instruction.

[0025] Preferably, the dynamic adjustment system comprises an instruction analysis unit, a driving execution unit, and a cooperative scheduling unit;

[0026] The instruction analysis unit receives the adjustment strategy, analyzes it into a viewing angle adjustment parameter and a position offset parameter, and transmits them to the driving execution unit;

[0027] The driving execution unit executes the viewing angle adjustment and position offset through a driving motor to generate an adjustment execution result;

[0028] The cooperative scheduling unit coordinates the adjustment actions of multiple monitoring cameras through a time synchronization rule;

[0029] The dynamic adjustment system generates adjustment execution results and pushes them to the feedback optimization system. If the execution deviation exceeds the threshold value, the overlay scheduling system requires the dynamic adjustment system to re-adjust through control instructions.

[0030] Preferably, the dynamic adjustment system includes an adaptive visual angle fine-tuning feedback unit that receives adjustment execution results and posture feedback data, generates fine-tuning instructions through correction rules based on deviation threshold value comparison, and transmits them to the driving execution unit.

[0031] After the adaptive visual angle fine-tuning feedback unit generates fine-tuning instructions, the driving execution unit executes fine-tuning. If the deviation still exceeds the threshold value, the dynamic adjustment system requests the overlay scheduling system to regenerate adjustment strategies through the feedback optimization system.

[0032] Preferably, the user control system includes a visualization unit and an interactive control unit.

[0033] The visualization unit receives the overlay effect distribution map and the region activity index table, generates a dynamic monitoring region map, and transmits it to the interactive control unit.

[0034] The interactive control unit supports users to adjust the region activity index or task priority through the interactive interface, generates user instructions, and transmits them to the feedback optimization system.

[0035] The user instructions are issued to the environment assessment system or the overlay scheduling system through the feedback optimization system to adjust the corresponding parameters.

[0036] Preferably, the feedback optimization system includes a feedback analysis unit and a distributed task dynamic reallocation unit.

[0037] The feedback analysis unit receives user instructions, adjustment execution results, and overlay effect distribution maps, generates optimization instructions through deviation analysis, including data collection optimization instructions and task allocation optimization instructions, and issues them to the environment information collection system and the overlay scheduling system, respectively.

[0038] The distributed task dynamic reallocation unit coordinates the task allocation of each monitoring camera node through a low-latency communication protocol. When a high-activity index region is detected, the task priority of nearby monitoring cameras is adjusted.

[0039] The data transmission and control flow design is as follows: the feedback optimization system generates optimization instructions and issues them to the environment information collection system and the overlay scheduling system. If the optimization effect does not meet the expectation, the relevant systems request re-analysis through the feedback interface.

[0040] Preferably, the adjustment device components of the control optimization system include an environment data sensor group, a computing processing core, a mobile control base, a visual angle adjustment unit, and an image capture unit.

[0041] The environmental data sensor group integrates light intensity sensors, temperature sensors and distance sensors to collect light intensity fluctuation frequency, temperature value and target motion speed change rate data.

[0042] The computing processing core comprises a central processing unit running an environment evaluation system, a coverage scheduling system, a feedback optimization system and a user control system.

[0043] The mobile control base comprises a driving motor, a vibration suppression device and a sensor connection port, the driving motor performs view angle adjustment and position offset of the view angle adjustment unit, the vibration suppression device suppresses adjustment vibration through elastic elements and hydraulic mechanisms, and the sensor connection port supports dynamic connection of additional sensors and transmits configuration data to the computing processing core through a standard protocol.

[0044] The view angle adjustment unit comprises a control chip and an angle detector, the control chip receives instructions of the driving motor to perform view angle fine adjustment, and the angle detector collects adjusted angle data and feeds back to the computing processing core.

[0045] The image capture unit comprises an imaging sensor and a focal length adjustment assembly, the imaging sensor collects real-time image data through a monitoring camera, and the focal length adjustment assembly adjusts focal length according to instructions of the adaptive data collection frequency regulation unit.

[0046] The control optimization system adjustment process comprises the following steps: the environmental data sensor group collects environmental data and transmits to the computing processing core to generate a region activity index table and an adjustment strategy; the driving motor of the mobile control base drives the view angle adjustment unit to perform actions according to view angle adjustment and position offset parameters of the instruction analysis unit; the control chip of the view angle adjustment unit adjusts the angle according to the fine adjustment instruction of the adaptive view angle fine adjustment feedback unit, the angle detector collects feedback data, the vibration suppression device suppresses vibration to ensure adjustment accuracy; the coordinated scheduling unit coordinates the actions of multiple monitoring cameras through time synchronization rules, and if the deviation exceeds a threshold, the computing processing core regenerates an adjustment strategy.

[0047] Advantages

[0048] The application provides a distributed multi-camera environment coverage range automatic adjustment device.

[0049] 1. The present application proposes a kind of distributed multi-camera environment under the coverage range automation adjustment device, effectively solve the adaptive capacity of insufficient of existing monitoring system in dynamic environment, multi-source data collaborative analysis ability is limited and lacks closed-loop feedback optimization problem.The device passes through environmental data sensor group (light intensity sensor, temperature sensor, distance sensor), computing processing core, mobile control base, view angle adjusting unit and image capture unit, with environmental information acquisition system, image information acquisition system, environmental evaluation system, coverage scheduling system, feedback optimization system and user control system collaborative work, realize the dynamic optimization of coverage range, for the problem of existing system fixed camera causes coverage blind area and overlap, device passes through environmental information acquisition system and image information acquisition system Real-time acquisition illumination intensity fluctuation frequency (precision 0.1 hertz), temperature value (precision 0.1 degrees Celsius), target motion speed change rate (precision 0.01 meter per second square) and high-resolution image data (1920×1080 pixels), combine the mean deviation correction algorithm of sliding window to eliminate light mutation and noise interference, generate calibration data.Environmental evaluation system utilizes the ratio algorithm of standard deviation and mean to generate area active index table (100×100 meter grid, index 0 to 1), accurately identify high active area (such as the traffic dense area of index 0.85).Coverage scheduling system generates adjustment strategy (view angle ± 180 degrees / ± 90 degrees, position ± 0.5 meters / ± 0.3 meters) by motion constraint algorithm based on vector analysis, drive mobile control base and view angle adjusting unit to adjust camera, eliminate blind area and control view overlap within 10%, significantly improve monitoring comprehensiveness, especially in the complex scene of dusk light change or peak period.

[0050] 2. The present application is aimed at the problem of insufficient multi-source data collaborative analysis ability, device through computing processing core (64-bit processor, main frequency 2 gigahertz) fusion environmental data and image data, run background modeling method (Gaussian mixture model) to generate coverage effect distribution graph (100×100 pixels, coverage intensity 0 to 1), real-time evaluation of coverage effect.For example, the coverage intensity of northbound lane reaches 0.85 during peak period, and the coverage intensity of eastbound lane is improved to 0.8 during emergency.Dynamic adjustment system realizes accurate adjustment (deviation less than 0.2 degrees) by driving motor (100 watts, 100 revolutions per minute) and angle detector (precision 0.05 degrees), and vibration suppression device (vibration amplitude less than 0.01 meters) ensures stability, which is superior to conventional fixed camera system.

[0051] 3、The present application is aimed at the problem of lack of closed-loop feedback and optimization. The device performs deviation analysis (coverage intensity deviation greater than 0.2, viewing angle deviation greater than 0.5 degrees) through a feedback optimization system, generates optimization instructions to adjust data acquisition frequency (50 to 200 times per second) or task allocation (response time less than 5 milliseconds). For example, during an emergency, two cameras are re-allocated to monitor the eastbound lane, and the coverage intensity is increased from 0.5 to 0.8. The user control system supports manual adjustment of the activity index (e.g. from 0.85 to 0.9), and provides a coverage effect distribution map through the interactive interface (1920x1080 pixels), enhancing flexibility. The device's adaptive data acquisition frequency regulation (e.g. 25 frames per second during peak hours, 10 frames per second during low traffic periods) and distributed task reallocation (delay less than 5 milliseconds) ensure efficient use of resources, outperforming traditional feedback-free systems, significantly improving target detection and tracking accuracy in dynamic environments, especially in high-density crowds or complex traffic scenarios. BRIEF DESCRIPTION OF DRAWINGS

[0052] Figure 1 is the overall framework diagram of the present application;

[0053] Figure 2 is the overall workflow diagram of the present application. DETAILED DESCRIPTION

[0054] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application. Embodiment one:

[0056] As Figures 1-2 shown, the distributed multi-camera environment coverage automatic adjustment device works cooperatively through the environment information acquisition system, image information acquisition system, environment evaluation system, coverage scheduling system, feedback optimization system and user control system, combined with the environment data sensor group, computing processing core, mobile control base, viewing angle adjustment unit and image capture unit, to realize dynamic, accurate and automatic adjustment of the coverage range of multiple monitoring cameras. The complete working process of the system from data acquisition to control adjustment is described in detail below, covering the specific functions of each module and hardware component, data processing steps, transmission methods, control logic and hardware implementation details, to ensure clear definition of all links and highlight the engineering practicability and creativity.

[0057] The system first collects real-time environmental data and image data through an environmental data sensor group and an image capture unit. The environmental data sensor group integrates a light intensity sensor, a temperature sensor, and a distance sensor to collect light intensity fluctuation frequency, temperature value, and target motion speed change rate, respectively. The light intensity sensor uses a photoresistor element to collect 100 light intensity data per second, calculates the standard deviation and mean ratio of light intensity in a 10-second time window, obtains the light intensity fluctuation frequency, which is in hertz and has an accuracy of 0.1 hertz, and reflects the dynamic change of environmental light. The temperature sensor uses a thermistor element to collect 10 temperature data per second, records the environmental temperature value, which is in degrees Celsius and has an accuracy of 0.1 degrees Celsius, and is used to evaluate the environmental thermal stability. The distance sensor uses a laser ranging principle to collect 50 distance data per second with an accuracy of 0.01 meters, calculates the speed change rate of the target object in a 5-second time window, which is in meters per second square and has an accuracy of 0.01 meters per second square, and reflects the target dynamic behavior. The adaptive data collection frequency regulation unit of the environmental information collection system adjusts the collection frequency according to the regional activity index through a fixed threshold rule. When the regional activity index is greater than 0.8, the collection frequencies of the light intensity sensor and the distance sensor are increased to 150 and 80 times per second, respectively; when the regional activity index is less than 0.4, they are reduced to 50 and 20 times per second, respectively, to optimize the data volume. The collected environmental data are compressed by an embedded microcontroller, the data volume is reduced by 50% using a lossless compression algorithm, compressed environmental data are generated, and they are transmitted to the environmental evaluation system in the computing processing core through a low-latency communication protocol (based on the user datagram protocol, with a delay of less than 10 milliseconds).

[0058] The image information acquisition system collects real-time image data through the monitoring camera of the image capture unit. The monitoring camera uses a high-resolution complementary metal-oxide-semiconductor imaging sensor with a resolution of 1920x1080 pixels and a maximum frame rate of 30 frames per second. The adaptive data acquisition frequency control unit adjusts the frame rate according to the target motion speed change rate. When the target motion speed change rate is greater than 2 meters per second squared, the frame rate is increased to 25 frames per second; when the change rate is less than 0.5 meters per second squared, the frame rate is reduced to 10 frames per second to reduce the data processing burden. The collected image data is compressed by an embedded image processing chip, using the JPEG compression algorithm to reduce the data volume by 70%, generating compressed image data, which is transmitted to the environmental assessment system through a low-latency communication protocol. The environmental assessment system detects the integrity of the compressed environmental data and compressed image data through a data verification algorithm. If data is missing (no data for 5 seconds in a row) or abnormal (data values exceed the preset range, such as light intensity fluctuation frequency greater than 10 Hz or image pixel value deviation greater than 20%), feedback to the environmental information acquisition system or image information acquisition system through control instructions (based on controller area network bus protocol) is required to reacquire or adjust the acquisition frequency. For example, if light intensity data is detected to be abnormal, the instruction requires the light intensity sensor to reacquire 10 seconds of data at a frequency of 200 times per second.

[0059] The environmental evaluation system receives compressed environmental data and compressed image data, and analyzes and processes them through an environmental interference dynamic compensation unit and a regional activity index calculation unit. The environmental interference dynamic compensation unit adopts a mean deviation correction method of a sliding window, sets a 5-second sliding window, and calculates the mean value of the data in the window and the deviation of the actual value. If the deviation is greater than 2 times the standard deviation, the abnormal value is corrected. For example, the illumination intensity fluctuation frequency is abnormal due to external light source flickering, and the unit replaces the abnormal value with the window mean value to generate calibrated environmental data; the image data has abnormal pixel values due to noise, and the unit corrects them through mean filtering to generate calibrated image data. The regional activity index calculation unit calculates the activity index of each monitoring area based on the illumination intensity fluctuation frequency and the target motion speed change rate of the calibrated environmental data through the ratio formula of the standard deviation and the mean value. The formula is: activity index = 0.6 x illumination fluctuation frequency standard deviation ÷ illumination fluctuation frequency mean + 0.4 x target motion speed change rate standard deviation ÷ target motion speed change rate mean, the weight coefficients 0.6 and 0.4 are optimized through experiments, and the activity index range is 0 to 1. For example, the illumination fluctuation frequency standard deviation of a certain area is 5 Hz, the mean value is 20 Hz, the target motion speed change rate standard deviation is 1 meter per second square, and the mean value is 4 meters per second square. The activity index is 0.6 x 5 ÷ 20 + 0.4 x 1 ÷ 4 = 0.25. The unit divides the monitoring area into a 100x100 meter grid, calculates the activity index of each grid, generates a regional activity index table, and pushes it to the coverage scheduling system. If the coverage scheduling system needs more detailed data (for example, the activity index of a certain grid is lower than 0.2), it requests the environmental evaluation system to recompensate the interference or collect data at a higher frequency (200 times per second) through a control instruction to update the activity index.

[0060] The coverage scheduling system receives the region activity index table, generates monitoring tasks and adjustment strategies through the task allocation unit and the cooperative motion constraint optimization unit. The task allocation unit marks the regions with an activity index greater than 0.7 as high priority according to the activity index sorting rule, and generates a task priority table for each monitoring camera. High activity index regions (greater than 0.7) are assigned two monitoring cameras, and low activity index regions (less than 0.3) are assigned one monitoring camera. The task priority table is transmitted to the cooperative motion constraint optimization unit, which calculates the motion trajectory of the adjustment device of each monitoring camera through the motion constraint algorithm based on vector analysis. The algorithm generates an adjustment path with no collision and an overlapping area of less than 10% of the field of view based on the distance vector (measured in real time by the distance sensor, accuracy 0.1 meters) and the angle vector (measured by the angle detector, accuracy 0.5 degrees) between the cameras. For example, if the distance between two monitoring cameras is less than 1 meter, there is a risk of collision, and the unit delays the adjustment of one camera by 0.5 seconds to re-plan the path. The adjustment strategy includes the viewing angle adjustment parameters (horizontal angle ±180 degrees, vertical angle ±90 degrees, accuracy 0.1 degrees) and the position offset parameters (horizontal offset ±0.5 meters, vertical offset ±0.3 meters, accuracy 0.01 meters), which are transmitted to the dynamic adjustment system. If the task execution effect does not meet the expectation (for example, the high-priority area is not covered enough), the coverage scheduling system requests the environment evaluation system to regenerate the region activity index table with higher accuracy (grid refinement to 50x50 meters) through the feedback optimization system.

[0061] The coverage effect evaluation unit of the coverage scheduling system receives the region activity index table and the calibration image data, and analyzes the difference between the current coverage range and the preset optimal coverage range through the background modeling method. The background modeling method uses a Gaussian mixture model to extract the image background and calculate the coverage intensity of each region (the proportion of covered pixels to the total number of pixels in the region), generating a coverage effect distribution map with a resolution of 100x100 pixels and a coverage intensity range of 0 to 1. For example, if the coverage intensity of a high activity index region is less than 0.6, it indicates that the coverage is insufficient. The distribution map is pushed to the dynamic adjustment system to guide the adjustment, and is also transmitted to the user control system for user viewing. If the coverage effect does not meet the expectation (the coverage intensity of the high activity index region is less than 0.7), the coverage scheduling system requests the environment evaluation system to supplement the image data with a higher frame rate (30 frames per second) through the control instruction to re-analyze the activity index.

[0062] The dynamic adjustment system receives an adjustment strategy, and realizes accurate adjustment of the monitoring camera through an instruction analysis unit, a driving execution unit, a collaborative scheduling unit, and a self-adaptive view angle fine-tuning feedback unit. The instruction analysis unit parses the adjustment strategy into view angle adjustment parameters and position offset parameters, and transmits them to the driving execution unit. The driving execution unit drives the view angle adjustment unit through the driving motor (a direct-current brushless motor, power 100 watts, maximum speed 100 revolutions per minute) of the mobile control base to execute view angle adjustment and position offset. The control chip (a 32-bit microcontroller, operating frequency 200 megahertz) of the view angle adjustment unit controls angle adjustment, and the angle detector (an optical encoder, accuracy 0.05 degrees) collects real-time angle data after adjustment and feeds back to the computing processing core. The collaborative scheduling unit coordinates the adjustment actions of multiple monitoring cameras through time synchronization rules (based on the network time protocol, synchronization error less than 1 millisecond) to ensure action consistency. The vibration suppression device of the mobile control base suppresses adjustment vibration through elastic elements (spring stiffness coefficient 50 newtons per meter) and hydraulic mechanisms (damping coefficient 10 newton seconds per meter), and the vibration amplitude is controlled within 0.01 meters. The self-adaptive view angle fine-tuning feedback unit receives adjustment execution results and angle detector data, generates fine-tuning instructions through deviation threshold comparison rules (deviation greater than 0.2 degrees triggers fine-tuning), and transmits them to the driving execution unit. For example, if the view angle deviation is 0.3 degrees, a fine-tuning instruction is generated to adjust 0.3 degrees. If the deviation is still greater than 0.2 degrees, the dynamic adjustment system requests the overlay scheduling system to generate an adjustment strategy again through the feedback optimization system. The adjustment execution results are pushed to the feedback optimization system for subsequent optimization.

[0063] The user control system realizes user interaction through a visualization unit and an interactive control unit. The visualization unit receives the overlay effect distribution map and the region activity index table, and generates a dynamic monitoring region mapping (resolution 1920x1080 pixels, refresh rate 1 times per second) on the interactive interface to display the coverage intensity and the activity index of each region. The interactive control unit supports users to adjust the region activity index (for example, adjusting the index of a certain region from 0.5 to 0.8) or the task priority (for example, specifying a certain region for priority monitoring) through a touch screen or a keyboard, generates user instructions, and transmits them to the feedback optimization system. The user instructions are issued to the environment evaluation system (adjust the activity index calculation parameters) or the overlay scheduling system (update the task priority table) through the feedback optimization system. For example, the user improves the priority of a certain region, and the overlay scheduling system reassigns two monitoring cameras to monitor the region.

[0064] The feedback optimization system optimizes system performance through a feedback analysis unit and a distributed task dynamic reallocation unit. The feedback analysis unit receives user instructions, adjustment execution results, and coverage effect distribution maps, generates optimization instructions through deviation analysis (coverage intensity deviation greater than 0.2 or viewing angle deviation greater than 0.5 degrees), including data collection optimization instructions (such as increasing the collection frequency to 200 times per second) and task allocation optimization instructions (such as increasing the number of monitoring cameras in high-activity areas), and respectively issuing to the environmental information collection system and the coverage scheduling system. The distributed task dynamic reallocation unit coordinates the task allocation of each monitoring camera node through a low-latency communication protocol (delay less than 5 milliseconds). When a high-activity index area (index greater than 0.8) is detected, the task priority of nearby monitoring cameras is adjusted, such as reallocating two monitoring cameras from low-activity areas to high-activity areas. If the optimization effect is not as expected (coverage intensity is less than 0.7), the environmental information collection system or the coverage scheduling system requests the feedback optimization system to re-analyze through the feedback interface and generates new optimization instructions.

[0065] The hardware components support the above processes. The environmental data sensor group collects environmental data through light intensity sensors (photoresistors, measurement range 0 to 1000 lux, accuracy 1 lux), temperature sensors (thermistors, measurement range -20 to 60 degrees Celsius, accuracy 0.1 degrees Celsius), and distance sensors (laser ranging, measurement range 0.1 to 50 meters, accuracy 0.01 meters), and transmits the data to the computing processing core. The central processing unit (64-bit processor, main frequency 2 gigahertz, memory 8 gigabytes) of the computing processing core runs the environmental evaluation system, the coverage scheduling system, the feedback optimization system, and the user control system, and processes all data analysis and control logic. The drive motor of the mobile control base executes the adjustment action of the viewing angle adjustment unit, and the vibration suppression device ensures stability through elastic elements and hydraulic mechanisms. The sensor connection port (supports I2C protocol, data transmission rate 400 kilobits per second) dynamically connects additional sensors (such as infrared sensors, measurement range 0.5 to 10 meters), and transmits configuration data to the computing processing core. The control chip and angle detector of the viewing angle adjustment unit realize fine tuning of the viewing angle, the imaging sensor of the image capture unit collects image data through the monitoring camera, the focal length adjustment assembly (stepper motor driven, focal length range 4 to 12 millimeters, accuracy 0.1 millimeters) adjusts the focal length according to the instructions of the adaptive data collection frequency adjustment unit, and optimizes the image quality.

[0066] The whole adjustment process is as follows: the environmental data sensor group collects the light intensity fluctuation frequency, temperature value and target motion speed change rate, the image capture unit collects image data through the monitoring camera, the adaptive data acquisition frequency control unit adjusts the frequency according to the activity index, generates compressed data and transmits to the computing processing core. The core runs the environment evaluation system, compensates for interference and generates a regional activity index table. The coverage scheduling system generates a task priority table and adjustment strategy according to the activity index table, and the dynamic adjustment system executes the adjustment through the driving motor and the angle adjustment unit. The vibration suppression device and the adaptive angle fine adjustment feedback unit ensure the accuracy. The coverage effect distribution map and user instructions are optimized by the feedback optimization system to optimize the acquisition, analysis and adjustment strategy, and finally realize the automatic and accurate adjustment of the coverage range of multiple monitoring cameras. Specific embodiment two:

[0068] As shown in Figures 1-2 The distributed multi-camera environment coverage range automatic adjustment device works cooperatively through the environmental information acquisition system, image information acquisition system, environmental evaluation system, coverage scheduling system, feedback optimization system and user control system, combined with the environmental data sensor group, computing processing core, mobile control base, angle adjustment unit and image capture unit, to realize precise control and coordinated adjustment of single monitoring camera and multiple monitoring cameras, and ensure dynamic optimization of coverage range. The control mechanism runs each system module through the computing processing core, coordinates data flow and control flow through low-latency communication protocol, and completes data acquisition, analysis, task allocation, path planning, execution adjustment and feedback optimization. The following details the implementation of the control mechanism, the specific process of single monitoring camera control and multiple monitoring camera coordinated control, and measures to ensure better control effect, covering the functions of each module and hardware component, data processing, transmission method, control logic and hardware execution details.

[0069] The control mechanism adopts a distributed architecture, with each monitoring camera node performing independent computation and collaborative control through a low-latency communication protocol (delay less than 5 milliseconds, based on User Datagram Protocol), avoiding the single-point failure risk of centralized control. The environmental information acquisition system collects light intensity fluctuation frequency, temperature value, and target motion speed change rate through light intensity sensors, temperature sensors, and distance sensors, generating compressed environmental data. The image information acquisition system collects real-time image data through the imaging sensors of the monitoring camera, generating compressed image data. The environmental assessment system analyzes the data, generating a regional activity index table. The coverage scheduling system generates a task priority table and adjustment strategy based on the activity index table. The dynamic adjustment system performs perspective and position adjustment, and the feedback optimization system optimizes control effectiveness through deviation analysis. The user control system provides a manual intervention interface, enhancing flexibility. Hardware support includes environmental data sensor groups collecting data, computing processing cores running control logic, mobile control bases and perspective adjustment units performing adjustments, and image capture units providing image feedback. The closed-loop feedback mechanism adjusts the collection frequency, task allocation, and adjustment path through real-time deviation analysis and optimization instructions, ensuring control accuracy.

[0070] The single monitoring camera control is responsible for the dynamic adjustment system, involving the instruction analysis unit, the driving execution unit and the adaptive view angle fine-tuning feedback unit, which adjusts through the mobile control base and the view angle adjustment unit. The coverage scheduling system generates adjustment strategies, including view angle adjustment parameters (horizontal angle ±180 degrees, vertical angle ±90 degrees, accuracy 0.1 degrees) and position offset parameters (horizontal offset ±0.5 meters, vertical offset ±0.3 meters, accuracy 0.01 meters). For example, the active index of a certain area is 0.85, and the coverage scheduling system allocates a monitoring camera to monitor preferentially, generating an adjustment strategy: horizontal angle adjustment to 45 degrees, vertical angle adjustment to 20 degrees, horizontal offset 0.2 meters. The instruction analysis unit converts the adjustment strategy into motor control signals through a 32-bit microcontroller (running frequency 200 megahertz), and converts it into a step pulse sequence (0.1 degrees or 0.01 meters per step) to drive the motor, and transmits it to the driving execution unit. The driving execution unit drives the view angle adjustment unit through the driving motor (direct current brushless motor, power 100 watts, maximum speed 100 revolutions per minute) of the mobile control base, and the control chip controls the motor to rotate to complete the angle and position adjustment. For example, it takes 0.9 seconds to adjust the 45-degree horizontal angle. The vibration suppression device of the mobile control base suppresses vibration through elastic elements (spring stiffness coefficient 50 newtons per meter) and hydraulic mechanisms (damping coefficient 10 newton seconds per meter), and the vibration amplitude is controlled within 0.01 meters. The angle detector (optical encoder, accuracy 0.05 degrees) of the view angle adjustment unit collects real-time angle data after adjustment and feeds back to the computing processing core. For example, the actual horizontal angle is 45.2 degrees, with a deviation of 0.2 degrees. The adaptive view angle fine-tuning feedback unit generates fine-tuning instructions through the deviation threshold comparison rule (deviation greater than 0.2 degrees triggers fine-tuning), and the control chip drives the motor to fine-tune at a speed of 10 revolutions per minute, which takes 0.04 seconds to complete 0.2-degree adjustment. The adjustment execution result is pushed to the feedback optimization system, if the deviation exceeds 0.2 degrees, the dynamic adjustment system requests the coverage scheduling system to generate an adjustment strategy again through the feedback optimization system, for example, adjust to 46 degrees. If the adjustment fails (deviation continues to exceed 0.2 degrees), the coverage scheduling system requests the environment evaluation system to collect data at a higher frequency (200 times per second) to update the active index. The data flow is: the environment evaluation system generates the active index table, the coverage scheduling system generates the adjustment strategy, the instruction analysis unit converts it into control signals, and the angle detector feeds back the results. The control flow is: the deviation exceeds the threshold to trigger re-planning or data supplement, to ensure the accuracy of the single monitoring camera adjustment.

[0071] Coordinated control of multiple surveillance cameras is achieved by the collaborative motion constraint optimization unit of the overlay scheduling system and the collaborative scheduling unit of the dynamic adjustment system, ensuring collision-free operation and minimal field of view overlap. The task allocation unit generates a task priority table based on the regional activity index table. For example, if the activity index of region A is 0.85 and that of region B is 0.6, surveillance cameras C1 and C2 are assigned to monitor region A, while C3 is assigned to monitor region B. The collaborative motion constraint optimization unit uses a motion constraint algorithm based on vector analysis to calculate the motion trajectory of each surveillance camera's adjustment mechanism. Based on the inter-camera distance vectors (measured by distance sensors with an accuracy of 0.1 meters) and angle vectors (measured by angle detectors with an accuracy of 0.5 degrees), it generates an adjustment path that is collision-free and has a field of view overlap of less than 10%. For example, if the distance between C1 and C2 is 0.8 meters, posing a collision risk, the algorithm delays C2's adjustment by 0.5 seconds, resulting in a path where C1's horizontal angle is adjusted to 50 degrees and C2's to 48 degrees, with a vertical angle difference of 2 degrees. The adjustment strategy is transmitted to the dynamic adjustment system. The collaborative scheduling unit allocates time slices based on time synchronization rules (based on the Network Time Protocol, with a synchronization error of less than 1 millisecond). For example, C1 is adjusted first for 0.5 seconds, followed by C2. The drive execution unit uses the drive motors of each surveillance camera to perform the adjustment, and a vibration suppression device ensures stability. For example, adjusting C1 to 50 degrees takes 1 second, while adjusting C2 to 48 degrees takes 0.96 seconds. The angle detector collects the adjusted angle data, such as the actual angle of C1 is 50.1 degrees and the actual angle of C2 is 48.2 degrees. The adaptive view angle fine-tuning feedback unit generates fine-tuning instructions (C1 adjusts -0.1 degrees, C2 adjusts -0.2 degrees), and the drive motors execute the fine-tuning at 10 revolutions per minute. If the field of view overlap exceeds 10% or a collision occurs, the dynamic adjustment system requests the coverage scheduling system to replan the path through the feedback optimization system, for example, adjusting C2 to 47 degrees. The data flow is as follows: the activity index table generates a task priority table, the collaborative motion constraint optimization unit generates the adjustment strategy, the angle detector provides feedback, and the coverage effect evaluation unit generates a coverage effect distribution map. The control flow is: coordination adjustment failure triggers path replanning or data supplementation to ensure efficient collaboration among multiple surveillance cameras.

[0072] Measures to ensure better control effects include real-time deviation analysis, multi-level feedback optimization, dynamic data collection adjustment, and user intervention mechanisms. The coverage effect evaluation unit analyzes the difference between the coverage range and the preset optimal coverage range using a background modeling method (Gaussian Mixture Model) to generate a coverage effect distribution map (resolution 100x100 pixels, coverage intensity 0 to 1). For example, if the high activity index area has a coverage intensity less than 0.7, an optimization instruction is triggered. The feedback analysis unit of the feedback optimization system generates optimization instructions through deviation analysis (coverage intensity deviation greater than 0.2, viewing angle deviation greater than 0.5 degrees), such as increasing the collection frequency to 200 times per second or redistributing the monitoring cameras. The distributed task dynamic redistribution unit adjusts task priorities through a low-latency communication protocol, such as redistributing two monitoring cameras from a low-activity area (index less than 0.3) to a high-activity area (index greater than 0.8). The adaptive data collection frequency regulation unit of the environmental information collection system and the image information collection system adjusts the frequency according to the activity index, such as increasing the frequency of light intensity sensors and distance sensors to 150 times per second and 80 times per second, and increasing the image frame rate to 25 frames per second when the activity index is greater than 0.8, ensuring sufficient data. The user control system supports users to adjust the region activity index or task priority through the interactive interface, such as adjusting the index of a certain area from 0.5 to 0.8, triggering the coverage scheduling system to redistribute resources. Hardware optimization includes vibration suppression devices (vibration amplitude less than 0.01 meters) and angle detectors (accuracy 0.05 degrees) to ensure adjustment stability. The data flow is: environmental data and image data generate an activity index table, the coverage scheduling system generates adjustment strategies, the dynamic adjustment system executes and feeds back results, and the coverage effect distribution map and user instruction optimize control. The control flow is: deviation threshold or coverage deficiency triggers data supplement, path re-planning, or task redistribution to ensure accurate and efficient coverage range. Embodiment Three:

[0074] As shown in the following detailed analysis of the key algorithms mentioned in Embodiment One: Figures 1-2

[0075] The distributed multi-camera environment coverage range automatic adjustment device relies on multiple key algorithms, including the standard deviation to mean ratio algorithm, the motion constraint algorithm based on vector analysis, the sliding window mean deviation correction algorithm, and the background modeling method. These algorithms run in the environmental evaluation system, the coverage scheduling system, and the dynamic adjustment system, combined with the environmental data sensor group, the computing processing core, the mobile control base, the viewing angle adjustment unit, and the image capture unit, to realize dynamic optimization and adjustment of the monitoring camera coverage range. The following details the input data, output results, and specific applications of each algorithm in the system, covering how the algorithm processes data, supports the control process, and optimizes the coverage effect.

[0076] ​The standard deviation to mean ratio algorithm runs in the environmental assessment system's area activity index calculation unit to calculate the activity index of each monitoring area, guiding task allocation and adjustment strategy generation. The input data includes the light intensity fluctuation frequency in the calibration environment data and the target motion speed variation rate. The light intensity fluctuation frequency is collected by the light intensity sensor (100 times per second, 10-second time window, accuracy 0.1 hertz), and the target motion speed variation rate is collected by the distance sensor (50 times per second, 5-second time window, accuracy 0.01 meters per second square). The algorithm processes these data, analyzes the dynamic characteristics of each monitoring area (100x100 meter grid), generates an area activity index table, and outputs the result as the activity index of each grid (range 0 to 1). For example, if the light fluctuation is frequent and the target moving speed changes greatly in a certain area, the activity index is 0.85, indicating that it needs to be monitored first. In the system, the area activity index table is transmitted to the task allocation unit of the coverage scheduling system to generate a task priority table, and the monitoring cameras are preferentially allocated to high activity index areas (for example, areas with an index greater than 0.7 are allocated two monitoring cameras). If the activity index is abnormal (for example, less than 0.2), the coverage scheduling system requests the environmental assessment system to re-collect data (200 times per second) through control instructions to ensure the accuracy of the index. The application of this algorithm ensures that the system dynamically identifies key monitoring areas, which is better than the conventional uniform coverage, and improves the coverage efficiency.

[0077] The motion constraint algorithm based on vector analysis runs in the cooperative motion constraint optimization unit of the coverage scheduling system, which is used to calculate the motion trajectory of the multi-monitor camera adjustment device to ensure no collision and minimum field of view overlap. The input data includes the distance vector between cameras (measured by distance sensors, accuracy 0.1 meters), angle vector (measured by angle detectors, accuracy 0.5 degrees), and task priority table (containing the monitoring area assigned to each monitoring camera). The algorithm analyzes the position and angle relationship of multiple monitoring cameras, generates an adjustment path with no collision and a field of view overlap area less than 10%, and outputs the adjustment strategy, including the viewing angle adjustment parameters (horizontal angle ±180 degrees, vertical angle ±90 degrees, accuracy 0.1 degrees) and position offset parameters (horizontal offset ±0.5 meters, vertical offset ±0.3 meters, accuracy 0.01 meters) of each monitoring camera. For example, two monitoring cameras are 0.8 meters apart, the algorithm detects the risk of collision, delays the adjustment of one camera by 0.5 seconds, and generates a path: one adjusts to 50 degrees horizontal angle, the other adjusts to 48 degrees, with a 2-degree vertical angle difference. In the system, the adjustment strategy is transmitted to the instruction analysis unit of the dynamic adjustment system, converted into motor control signals, and drives the drive motor (power 100 watts, maximum speed 100 revolutions per minute) of the mobile control base to execute the adjustment. If the adjustment fails (for example, the overlap area reaches 15%), the dynamic adjustment system requests the coverage scheduling system to re-plan the path through the feedback optimization system. The application of this algorithm realizes the cooperative adjustment of multiple monitoring cameras, avoids collision and redundant coverage, and is superior to the conventional time scheduling.

[0078] The mean deviation correction algorithm of the sliding window runs in the environmental interference dynamic compensation unit of the environmental evaluation system, used to correct the interference in environmental data and image data, ensuring data reliability. The input data includes compressed environmental data (light intensity fluctuation frequency, temperature value, target motion speed change rate) and compressed image data (1920x1080 pixels, frame rate 10 to 25 frames per second). The algorithm analyzes the data fluctuation within a 5-second sliding window, calculates the deviation of the mean value and the actual value, and corrects the abnormal value if the deviation is greater than 2 times the standard deviation, generating calibrated environmental data and calibrated image data. For example, the light intensity fluctuation frequency is abnormal due to external light source flickering, and the algorithm replaces the abnormal value with the window mean value; the image data deviates due to noise, and the algorithm corrects it through mean filtering. The output results are calibrated environmental data (fluctuation frequency accuracy 0.1 Hz, temperature accuracy 0.1°C, speed change rate accuracy 0.01 m / s^2) and calibrated image data (pixel deviation less than 5%). In the system, the calibration data is transmitted to the regional activity index calculation unit for calculating the activity index. If the calibration data is still abnormal (e.g. fluctuation frequency greater than 10 Hz), the environmental evaluation system requests the environmental information acquisition system or the image information acquisition system to re-acquire at a higher frequency (200 times per second or 30 frames per second) through control instructions. The application of this algorithm ensures data accuracy, which is superior to conventional fixed filtering, improving the reliability of subsequent analysis and control.

[0079] The background modeling method runs in the coverage effect evaluation unit of the coverage scheduling system, used to analyze the difference between the current coverage range and the preset optimal coverage range, and generate a coverage effect distribution map. The input data includes calibrated image data (1920x1080 pixels, frame rate 10 to 25 frames per second) and regional activity index table (100x100 meter grid, activity index 0 to 1). The algorithm extracts the image background through Gaussian mixture model, calculates the coverage intensity of each region (the proportion of covered pixels to total pixels), and generates a coverage effect distribution map (resolution 100x100 pixels, coverage intensity 0 to 1). For example, the high activity index region (index 0.85) has a coverage intensity lower than 0.7, indicating insufficient coverage. The output result is the coverage effect distribution map, which is transmitted to the dynamic adjustment system for guidance and pushed to the visualization unit of the user control system for display. If the coverage intensity is lower than 0.7, the coverage scheduling system requests the environmental evaluation system to supplement image data at a higher frame rate (30 frames per second) through control instructions, and reanalyzes the activity index. In the system, the distribution map supports the dynamic adjustment system to optimize the monitoring camera angle and position, and the user can view and adjust the task priority through the interactive control unit. The application of this algorithm realizes quantitative evaluation of coverage effect, which is superior to conventional visual inspection, ensuring the pertinence of adjustment.

[0080] These algorithms synergistically support system control flow. The environmental information acquisition system collects environmental data through light intensity sensors (measurement range 0 to 1000 lux), temperature sensors (measurement range -20 to 60 degrees Celsius), and distance sensors (measurement range 0.1 to 50 meters), the image information acquisition system collects image data through monitoring cameras (imaging sensors, resolution 1920x1080 pixels), the adaptive data acquisition frequency control unit adjusts the frequency according to the activity index (light intensity and distance sensors 50 to 200 times per second, image frame rate 10 to 25 frames per second), generates compressed data (environmental data reduced by 50%, image data reduced by 70%), and transmits it to the computing processing core through a low-latency communication protocol. The computing processing core (64-bit processor, main frequency 2 GHz, memory 8 GB) runs the environmental evaluation system, executes the sliding window mean deviation correction algorithm and the standard deviation to mean ratio algorithm, generates calibration data and regional activity index table. The coverage scheduling system runs the motion constraint algorithm based on vector analysis and the background modeling method, generates the task priority table and adjustment strategy. The dynamic adjustment system executes the adjustment through the instruction analysis unit (32-bit microcontroller, running frequency 200 MHz), the drive execution unit (drive motor, power 100 W), and the cooperative scheduling unit (time synchronization error less than 1 ms), the view angle adjustment unit (control chip and angle detector, accuracy 0.05 degrees), and the movement control base (vibration suppression device, vibration amplitude less than 0.01 meters) to ensure accuracy. The feedback optimization system generates optimization instructions through deviation analysis (coverage intensity deviation greater than 0.2, view angle deviation greater than 0.5 degrees), adjusts the acquisition frequency or task allocation. The user control system supports users to adjust the activity index or priority through the visualization unit (resolution 1920x1080 pixels) and the interactive control unit to ensure flexibility. The data flow is: environmental and image data generate calibration data and activity index table, converted into adjustment strategy, executed and feedback results, distribution graph optimization control. The control flow is: deviation threshold or insufficient coverage triggers data supplement, path re-planning or task reallocation to ensure optimal results. Specific embodiment four:

[0082] As Figures 1-2 shown below is a detailed hardware composition and hardware description of a distributed multi-camera environment automatic coverage adjustment device:

[0083] The hardware components of the distributed multi-camera environment automatic coverage adjustment device include an environmental data sensor group, a computing processing core, a mobile control base, a view angle adjustment unit, and an image capture unit. These hardware components work with the environmental information collection system, the image information collection system, the environmental assessment system, the coverage scheduling system, the feedback optimization system, and the user control system to achieve dynamic and precise automatic adjustment of the coverage range of multiple monitoring cameras. The following describes the composition, function, technical parameters, connection method, and specific role of each hardware component in the system, covering how hardware supports data collection, processing, control execution, and feedback optimization to ensure efficient and stable coverage.

[0084] The environmental data sensor group is the core hardware of the environmental information collection system, used to collect light intensity fluctuation frequency, temperature value, and target motion speed change rate, support regional activity index calculation and task allocation. The environmental data sensor group consists of a light intensity sensor, a temperature sensor, and a distance sensor. The light intensity sensor uses a photoresistor element, with a measurement range of 0 to 1000 lux, an accuracy of 1 lux, and a sampling frequency of 50 to 200 times per second (default 100 times). It calculates the light intensity fluctuation frequency (accuracy 0.1 Hz) through a 10-second time window, reflecting the dynamic changes of environmental light. The temperature sensor uses a thermistor element, with a measurement range of -20 to 60 degrees Celsius, an accuracy of 0.1 degrees Celsius, and a sampling frequency of 5 to 20 times per second (default 10 times), used to assess environmental thermal stability. The distance sensor uses a laser ranging module, with a measurement range of 0.1 to 50 meters, an accuracy of 0.01 meters, and a sampling frequency of 20 to 80 times per second (default 50 times), calculating the target motion speed change rate (accuracy 0.01 meters per second squared) through a 5-second time window, reflecting the target dynamic behavior. The sensor group is connected to an embedded microcontroller (32-bit, running frequency 100 MHz) through the I2C protocol (data transmission rate 400 kilobits per second). The microcontroller executes a lossless compression algorithm (data volume reduction 50%) to generate compressed environmental data, which is transmitted to the computing processing core through a low-latency communication protocol (based on the User Datagram Protocol, delay less than 10 milliseconds). In the system, the environmental data sensor group supports the environmental information collection system to generate input data for the environmental assessment system to calculate the regional activity index. For example, a high activity index region (index greater than 0.8) triggers a higher sampling frequency (200 times per second), ensuring accurate data capture, superior to regular fixed frequency collection.

[0085] The computing processing core is the central processing unit of the system, running the environment evaluation system, coverage scheduling system, feedback optimization system, and user control system, processing data analysis, task allocation, path planning, and control instruction generation. The computing processing core uses a 64-bit processor with a main frequency of 2 gigahertz and a memory of 8 gigabytes, equipped with a 16-gigabyte solid-state storage for storing calibration data, activity index table, and adjustment strategy. The core receives compressed data from the environment data sensor group and image capture unit through a high-speed bus (data rate of 1 gigabit per second), runs the standard deviation to mean ratio algorithm to generate the regional activity index table, the motion constraint algorithm based on vector analysis to generate the adjustment strategy, the sliding window mean deviation correction algorithm to correct interference, and the background modeling method to generate the coverage effect distribution map. The core communicates with the mobile control base and the view angle adjustment unit through the controller area network bus protocol (delay less than 5 milliseconds), issues control signals, and receives feedback data. For example, the core generates an adjustment strategy (horizontal angle 45 degrees, vertical angle 20 degrees) and transmits it to the dynamic adjustment system for execution. The core also supports the user control system's interactive interface, processing user instructions (such as adjusting the regional activity index). In the system, the computing processing core coordinates all data processing and control logic, ensuring efficient operation of the distributed architecture, superior to the single-point failure risk of centralized control.

[0086] The mobile control base is the execution hardware of the dynamic adjustment system, responsible for monitoring the physical movement and stability control of the camera, including drive motors, vibration suppression devices, and sensor connection ports. The drive motor uses a brushless DC motor with a power of 100 watts, a maximum speed of 100 revolutions per minute, and a torque of 2 newton meters, supporting the view angle adjustment (horizontal ±180 degrees, vertical ±90 degrees, accuracy 0.1 degrees) and position offset (horizontal ±0.5 meters, vertical ±0.3 meters, accuracy 0.01 meters) of the view angle adjustment unit. The vibration suppression device consists of an elastic element (spring, stiffness coefficient 50 newtons per meter) and a hydraulic mechanism (damping coefficient 10 newton seconds per meter), suppressing adjustment vibration, with a vibration amplitude controlled within 0.01 meters, ensuring stability. The sensor connection port supports the I2C protocol (rate 400 kilobits per second), dynamically connecting additional sensors (such as infrared sensors, measuring range 0.5 to 10 meters), and transmitting configuration data to the computing processing core. For example, the additional sensor detects obstacles, and the core adjusts the path to avoid collisions. The base receives control signals from the dynamic adjustment system through the controller area network bus protocol, and the drive motor executes adjustment actions, such as completing a 45-degree horizontal angle adjustment in 0.9 seconds. In the system, the mobile control base supports the dynamic adjustment system to execute precise movement, and the vibration suppression device improves adjustment stability, superior to conventional undamped designs.

[0087] The view angle adjustment unit is the precision control hardware of the dynamic adjustment system, responsible for monitoring the fine adjustment of the camera view angle, including a control chip and an angle detector. The control chip uses a 32-bit microcontroller, with a running frequency of 200 megahertz, receiving the step pulse sequence of the dynamic adjustment system (0.1 degrees per step), controlling the driving motor to perform view angle adjustment, with a response time less than 10 milliseconds. The angle detector uses an optical encoder with an accuracy of 0.05 degrees, collecting real-time angle data after adjustment, such as an actual angle of 45.2 degrees, and feeding back to the computing processing core. The control chip generates fine adjustment instructions through the deviation threshold comparison rule (deviation greater than 0.2 degrees triggers fine adjustment), such as adjusting -0.2 degrees, and drives the motor to complete fine adjustment at a speed of 10 revolutions per minute (0.04 seconds). The view angle adjustment unit communicates with the computing processing core through the controller area network bus protocol, and the feedback data supports the adaptive view angle fine adjustment feedback unit to optimize adjustment. In the system, the view angle adjustment unit ensures that the monitoring camera view angle is accurately aligned with the high active index area (e.g., index 0.85), which is superior to the conventional single adjustment mechanism.

[0088] The image capture unit is the core hardware of the image information acquisition system, responsible for acquiring real-time image data, supporting coverage effect evaluation and active index calculation, including an imaging sensor and a focal length adjustment component. The imaging sensor uses a high-resolution complementary metal oxide semiconductor sensor with a resolution of 1920x1080 pixels and a maximum frame rate of 30 frames per second, acquiring image data through the monitoring camera. The focal length adjustment component is driven by a stepper motor, with a focal length range of 4 to 12 millimeters and an accuracy of 0.1 millimeters, adjusting the focal length according to the instructions of the adaptive data acquisition frequency control unit, such as when the target motion speed change rate is greater than 2 meters per second squared, the frame rate is increased to 25 frames per second, and the focal length is adjusted to 6 millimeters to clearly capture fast-moving targets. Image data is compressed by an embedded image processing chip (running frequency 150 megahertz) in JPEG format (data volume reduced by 70%), generating compressed image data, which is transmitted to the computing processing core through a low-latency communication protocol. If the environmental evaluation system detects data anomalies (e.g., pixel deviation greater than 5%), it requires reacquisition through control instructions (frame rate 30 frames per second). In the system, the image capture unit provides calibration image data for the coverage scheduling system to generate a coverage effect distribution map, ensuring accurate coverage effect evaluation, which is superior to conventional fixed frame rate acquisition.

[0089] These hardware components work together to support the system control process. The environmental data sensor group collects light intensity fluctuation frequency (accuracy 0.1 Hz), temperature (accuracy 0.1 degrees Celsius), and target motion velocity change rate (accuracy 0.01 meters per second squared), transmitting these data to the microcontroller via the I2C protocol for compression, generating compressed environmental data. The image capture unit collects image data (1920×1080 pixels) from a surveillance camera, compresses it, and transmits it to the computing core. The computing core runs an algorithm to generate a regional activity index table (100×100 meter grid, index 0 to 1) and an adjustment strategy (viewing angle and position parameters). The mobile control base and viewing angle adjustment unit perform adjustments, driving the motor and control chip to achieve viewing angle adjustment (accuracy 0.1 degrees) and position offset (accuracy 0.01 meters). A vibration suppression device and angle detector ensure stability (vibration less than 0.01 meters, angle accuracy 0.05 degrees). The feedback optimization system generates optimization instructions based on deviation analysis (coverage intensity deviation greater than 0.2, viewing angle deviation greater than 0.5 degrees), adjusting the acquisition frequency (50 to 200 times per second) or task allocation. The user control system allows users to adjust activity indexes or priorities through an interactive interface (resolution 1920×1080 pixels). The data flow is as follows: environmental and image data generate calibration data and activity index tables, which are converted into adjustment strategies, executed and fed back, and coverage distribution map optimization control. The control flow is as follows: deviations exceeding thresholds or insufficient coverage trigger data replenishment, path replanning, or task reallocation to ensure accurate and efficient coverage. Specific embodiment five:

[0091] like Figures 1-2 As shown, the following provides specific use cases:

[0092] Case 1: Daytime cargo handling monitoring in a large logistics warehouse

[0093] Scenario description: A large logistics warehouse performs cargo loading and unloading operations during the day. The lighting inside the warehouse is composed of a combination of natural light and artificial lighting, with light intensity fluctuating between 300-1000 lux. Forklifts frequently shuttle back and forth, and workers move goods back and forth. Real-time monitoring of the cargo loading and unloading process, as well as the safety of personnel and equipment, is required to prevent collisions and cargo loss.

[0094] Device deployment: Distributed multi-camera devices are deployed in the four corners of the warehouse and key locations of shelf aisles. They include an environmental data sensor group, a computing processing core, a mobile control base, and eight surveillance cameras. The mobile control base and viewing angle adjustment unit enable multi-angle and multi-position adjustment of the cameras.

[0095] Usage process:

[0096] Data collection and environment perception: The environment information collection system detects the light intensity fluctuation in the warehouse through the light intensity sensor, records the temperature in the warehouse (26°C) through the temperature sensor, and measures the motion speed change rate of the forklift and personnel through the distance sensor. The adaptive data collection frequency regulation unit adjusts the collection frequency of the light intensity sensor and the distance sensor to 80 times per second according to the preliminary calculation of the region activity index, generates compressed environment data, and transmits it to the environment assessment system through a low-latency communication protocol. The image information collection system's surveillance camera collects real-time image data, and the adaptive data collection frequency regulation unit adjusts the image frame rate to 20 frames per second according to the motion speed change rate of the forklift and personnel, generates compressed image data, and transmits it to the environment assessment system.

[0097] Environment assessment and index calculation: The environment disturbance dynamic compensation unit of the environment assessment system receives compressed environment data and compressed image data, uses a 5-second sliding window mean deviation correction method to compensate for light mutations and noise interference caused by light flickering and equipment vibration in the warehouse, generates calibrated environment data and calibrated image data. The region activity index calculation unit calculates the activity index of each monitoring area based on the calibrated environment data through the ratio of standard deviation to mean formula, where the forklift loading and unloading area and the goods stacking area have higher activity indexes, generates a region activity index table and transmits it to the coverage scheduling system.

[0098] Coverage scheduling and strategy generation: The task allocation unit of the coverage scheduling system receives the region activity index table, marks the forklift loading and unloading area and personnel-intensive passageway with an activity index greater than 0.7 as high priority according to the activity index sorting rule, generates a task priority table for each surveillance camera, and preferentially allocates monitoring tasks for these areas. The cooperative motion constraint optimization unit generates an adjustment path with no collision and minimal field of view overlap based on the task priority table and region activity index table, distance vector and angle vector between cameras, determines the viewing angle adjustment parameters (horizontal angle ±30 degrees, vertical angle ±20 degrees) and position offset parameters of each camera, and generates an adjustment strategy to transmit to the dynamic adjustment system.

[0099] Dynamic adjustment and feedback optimization: The dynamic adjustment system's instruction analysis unit parses the adjustment strategy into specific parameters and transmits them to the driving execution unit. The driving execution unit adjusts the view angle and position through motor-driven view angle adjustment and movement control base. The cooperative scheduling unit coordinates the adjustment actions of each camera through time synchronization rules, and the vibration suppression device suppresses the vibration during the adjustment process. The adaptive view angle fine-tuning feedback unit receives the adjustment execution results and posture feedback data, and generates fine-tuning instructions to correct the deviation if it exceeds the threshold. The feedback optimization system receives the adjustment execution results and coverage effect distribution map, and generates optimization instructions such as adjusting the image acquisition frame rate to 25 frames per second, and sends them to the relevant system. The user control system displays the coverage effect through the visualization unit for management personnel to view.

[0100] Effect: The coverage rate of the high-activity area in the warehouse has increased from 65% to 92%, the target tracking accuracy has reached 96%, and the response time is 150ms. The full-range monitoring of the cargo handling process has been successfully achieved, and 3 potential forklift collision accidents have been avoided, ensuring the safe and efficient operation of the warehouse.

[0101] Case 2: Monitoring of high-speed rail station waiting hall during morning and evening peak

[0102] Scene description: A high-speed rail station waiting hall has a large number of passengers during the morning and evening peak periods, and passengers carry luggage back and forth. The light is composed of natural light and indoor lighting, and the light intensity changes significantly with time. It is necessary to monitor the passenger flow in real time to avoid congestion, stampede and other safety accidents, and to pay attention to the order of key areas such as ticket gates and security checkpoints.

[0103] Device deployment: A distributed multi-camera device is hung on the ceiling of the waiting hall, including 12 monitoring cameras, an environmental data sensor group, a computing processing core, and a dynamic adjustment system. The camera can realize 360-degree rotation and view angle scaling through the pan-tilt head, and fully cover each area of the waiting hall.

[0104] Usage process:

[0105] Data acquisition and environmental perception: The light intensity sensor of the environmental information acquisition system monitors the fluctuation frequency of the light intensity in the hall in real time, the temperature sensor records the environmental temperature (24°C), and the distance sensor measures the passenger's motion speed change rate. The adaptive data acquisition frequency regulation unit increases the environmental data acquisition frequency of high-activity areas (such as near the ticket gate) to 120 times per second according to the regional activity index, and generates compressed environmental data to transmit to the environmental assessment system. The camera of the image information acquisition system collects real-time images, adjusts the image frame rate according to the passenger's motion speed change rate, and sets the frame rate to 25 frames per second in areas with dense passenger flow, and generates compressed image data and transmits it.

[0106] Environment assessment and index calculation: The environmental interference dynamic compensation unit processes the received compressed data, compensates for data anomalies caused by changes in external light and crowd shielding through the mean deviation correction method of the sliding window, and generates calibrated data. The regional activity index calculation unit calculates the activity index of each region based on the calibrated data, and the activity index of the ticket checking and security checking regions exceeds 0.8, generating a regional activity index table that is transmitted to the coverage scheduling system.

[0107] Coverage scheduling and strategy generation: The task allocation unit of the coverage scheduling system sets the ticket checking and security checking regions as the highest priority based on the regional activity index table, generates a task priority table, and assigns multiple cameras to monitor these regions. The collaborative motion constraint optimization unit calculates the adjustment path with the least collision and the minimum field of view overlap based on the task priority table and the position information of each camera, generates an adjustment strategy, and ensures that the key regions are always within the best monitoring range.

[0108] Dynamic adjustment and feedback optimization: The dynamic adjustment system drives each camera to adjust the viewing angle and position according to the adjustment strategy. During the adjustment process, the collaborative scheduling unit ensures coordinated action of each camera to avoid mutual interference. If it is detected that the coverage effect of a certain region is poor, the feedback optimization system generates optimization instructions, such as increasing the task priority of the camera near the region and redistributing the monitoring tasks. The user control system allows staff to adjust the key monitoring regions through the interactive interface and issue user instructions to related systems.

[0109] Use effect: The coverage rate of key areas in the waiting hall during peak hours reaches 95%, the passenger flow trajectory tracking accuracy is 97%, the response time is 180ms, and 5 local congestion situations are successfully alleviated, ensuring smooth passage of passengers and improving the management efficiency and safety of the waiting hall. Specific embodiment six:

[0111] As Figures 1-2 shown, the following is an experiment to verify the automatic adjustment device of the coverage range in a distributed multi-camera environment, which is the application of the device in the urban traffic intersection monitoring scene and provides the following data:

[0112] Time of day Monitoring area Activity index Coverage strength Viewing angle adjustment deviation (degrees) Light intensity sensor frequency (Hz) Distance sensor frequency (Hz) Image frame rate (Hz) Adjustment response time (seconds) Task reassignment time (milliseconds) Peak time of day Northbound lane 0.85 0.85 0.1 150 80 25 1.0 4.5 Peak time of day Eastbound lane 0.60 0.75 0.15 100 50 15 0.95 4.8 Peak time of day Southbound sidewalk 0.30 0.55 0.2 50 20 10 0.9 5.0 Low flow time of day Northbound lane 0.40 0.65 0.18 50 20 10 0.92 4.7 Low flow time of day Eastbound lane 0.35 0.60 0.2 50 20 10 0.9 4.9 Low flow time of day Southbound sidewalk 0.20 0.50 0.25 50 20 10 0.88 5.1 Incident Eastbound lane 0.90 0.80 0.12 200 80 30 1.05 4.3 Incident Northbound lane 0.70 0.78 0.14 100 50 15 0.98 4.6 Incident Southbound sidewalk 0.25 0.52 0.22 50 20 10 0.89 5.0

[0113] The experimental data is derived from a field test conducted in February 2025 at a city intersection (500x500 meters), using four monitoring cameras (C1 to C4), each equipped with an environmental data sensor group (light intensity sensor, temperature sensor, distance sensor), a movement control base, a viewing angle adjustment unit, and an image capture unit. The data is collected by an environmental information collection system and an image information collection system, and the computing processing core (64-bit processor, 2 GHz main frequency) runs the environmental evaluation system, the coverage scheduling system, and the feedback optimization system, recording indicators such as the activity index and coverage intensity. The test covers the peak period (8 am, lasting 30 minutes), the low-flow period (2 am, lasting 30 minutes), and the emergency period (3 pm, traffic accident, lasting 15 minutes). Sensor data is transmitted through the I2C protocol (400 kbit / s), image data is transmitted through the low-latency communication protocol (delay less than 10 ms), and adjustment instructions are issued through the controller area network bus protocol.

[0114] The experimental data shows that the device efficiently covers high-activity areas during peak periods (coverage intensity 0.85), saves resources during low-flow periods (frame rate 10 frames per second), quickly adjusts during emergencies (task redistribution 4.3 ms), and has a viewing angle deviation of less than 0.2 degrees, with an adjustment response time of about 1 second, a task redistribution time of less than 5 ms, verifying the dynamic optimization capability and stability of the device. The data supports that the device is superior to conventional fixed coverage (e.g., uniform resource allocation) or single adjustment (e.g., no fine-tuning feedback) techniques, embodying the synergistic effect of the standard deviation-to-mean ratio algorithm, vector analysis motion constraint algorithm, sliding window correction algorithm, and background modeling method.

[0115] It should be noted that, in the present document, relational terms such as first and second and the like can be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises", "comprising", or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by "comprises... a" does not, without more constraints, exclude the existence of additional identical elements in the process, method, article, or apparatus that comprises the element.

[0116] While embodiments of the present application have been shown and described with reference to particular embodiments thereof, it will be understood by those skilled in the art that various changes in form and details can be made therein without departing from the spirit and scope of the application. The scope of the application is defined by the appended claims and their equivalents.

Claims

1. A device for automatically adjusting coverage in a distributed multi-camera environment, including a control optimization system, characterized in that: The control optimization system includes an environmental information acquisition system, an image information acquisition system, an environmental assessment system, a coverage scheduling system, a dynamic adjustment system, a feedback optimization system and a user control system; The environmental information acquisition system collects environmental data through light intensity sensors, temperature sensors and distance sensors, including light intensity fluctuation frequency, temperature value and target motion speed change rate; Through the adaptive data collection frequency control unit, the collection frequency is adjusted based on the fixed threshold rule according to the regional activity index to generate compressed environmental data; Transmit compressed environmental data to the environmental assessment system via a low-latency communication protocol; The image information acquisition system collects real-time image data through a monitoring camera, adjusts the image frame rate according to the target motion speed change rate through an adaptive data acquisition frequency control unit, generates compressed image data, and transmits it to the environmental assessment system; The environmental information acquisition system and the image information acquisition system regularly push compressed environmental data and compressed image data to the environmental assessment system. If the environmental assessment system detects data anomalies, it feeds back control instructions to the environmental information acquisition system and the image information acquisition system, requiring re-acquisition and adjustment of the acquisition frequency; The environmental assessment system comprises a regional activity index calculation unit and an environmental interference dynamic compensation unit; The environmental interference dynamic compensation unit receives compressed environmental data and compressed image data, compensates for illumination mutations and noise interference through a mean deviation correction method of a sliding window, and generates calibrated environmental data and calibrated image data; The regional activity index calculation unit calculates the activity index of each monitoring area based on the light intensity fluctuation frequency and target motion speed change rate of the calibration environment data using the standard deviation to mean ratio formula, generates a regional activity index table, and transmits it to the coverage scheduling system; The environmental assessment system generates a regional activity index table and pushes it to the coverage scheduling system. If the coverage scheduling system needs to supplement data, it requests the environmental assessment system to re-compensate and recalculate the activity index through control instructions; The coverage scheduling system includes a task allocation unit and a collaborative motion constraint optimization unit; The task allocation unit receives the regional activity index table, generates a task priority table for each surveillance camera according to the activity index sorting rule, and gives priority to allocating surveillance tasks to areas with high activity indexes; The task allocation unit generates a task priority table and transmits it to the collaborative motion constraint optimization unit. If the task execution effect does not meet expectations, the coverage scheduling system requests the environmental assessment system to regenerate the regional activity index table through the feedback optimization system; The collaborative motion constraint optimization unit of the coverage scheduling system calculates the motion trajectory of each surveillance camera adjustment device through a motion constraint algorithm based on vector analysis; The collaborative motion constraint optimization unit generates an adjustment path with no collision and minimal field of view overlap based on the distance vectors and angle vectors between cameras according to the task priority table and the regional activity index table, generates an adjustment strategy, and transmits it to the dynamic adjustment system; if a collision risk is detected, the collaborative motion constraint optimization unit replans the path by adjusting the motion sequence; After the collaborative motion constraint optimization unit generates the adjustment strategy, it sends it to the dynamic adjustment system through the control interface. If the adjustment fails, the dynamic adjustment system notifies the coverage scheduling system through the feedback optimization system to regenerate the adjustment strategy.

2. The device for automatically adjusting coverage in a distributed multi-camera environment according to claim 1, characterized in that: The coverage scheduling system includes a coverage effect evaluation unit that receives the regional activity index table and calibration image data, analyzes the difference between the current coverage range and the preset optimal coverage range through a background modeling method, generates a coverage effect distribution map, and transmits it to the dynamic adjustment system; The coverage effect evaluation unit generates a coverage effect distribution map and pushes it to the dynamic adjustment system and the user control system. If the coverage effect does not meet expectations, the coverage scheduling system requests the environmental evaluation system to supplement data through control instructions.

3. The device for automatically adjusting coverage in a distributed multi-camera environment according to claim 1, characterized in that: The dynamic adjustment system includes an instruction parsing unit, a drive execution unit and a collaborative scheduling unit; The instruction parsing unit receives the adjustment strategy, parses it into viewing angle adjustment parameters and position offset parameters, and transmits them to the driving execution unit; The driving execution unit executes the viewing angle adjustment and position shift by driving the motor to generate an adjustment execution result; The collaborative scheduling unit coordinates the adjustment actions of multiple surveillance cameras through time synchronization rules; The dynamic adjustment system generates an adjustment execution result and pushes it to the feedback optimization system. If the execution deviation exceeds a threshold, the coverage scheduling system requires the dynamic adjustment system to readjust through a control instruction.

4. The device for automatically adjusting coverage in a distributed multi-camera environment according to claim 3, characterized in that: The dynamic adjustment system includes an adaptive viewing angle fine-tuning feedback unit, which receives the adjustment execution result and posture feedback data, generates a fine-tuning instruction through a correction rule based on deviation threshold comparison, and transmits it to the drive execution unit; After the adaptive viewing angle fine-tuning feedback unit generates a fine-tuning instruction, it drives the execution unit to perform fine-tuning. If the deviation still exceeds the threshold, the dynamic adjustment system requests the coverage scheduling system to regenerate the adjustment strategy through the feedback optimization system.

5. The device for automatically adjusting coverage in a distributed multi-camera environment according to claim 1, characterized in that: The user control system includes a visualization unit and an interaction control unit; The visualization unit receives the coverage effect distribution map and the regional activity index table, generates a dynamic monitoring area map, and transmits it to the interactive control unit; The interactive control unit supports users to adjust the regional activity index and task priority through the interactive interface, generates user instructions, and transmits them to the feedback optimization system; The user instructions are sent to the environmental assessment system and coverage scheduling system through the feedback optimization system to adjust the corresponding parameters.

6. The device for automatically adjusting coverage in a distributed multi-camera environment according to claim 1, characterized in that: The feedback optimization system includes a feedback analysis unit and a distributed task dynamic reallocation unit; The feedback analysis unit receives user instructions, adjustment execution results and coverage effect distribution diagrams, generates optimization instructions through deviation analysis, including data collection optimization instructions and task allocation optimization instructions, and sends them to the environmental information collection system and coverage scheduling system respectively; The distributed task dynamic reallocation unit coordinates the task allocation of each surveillance camera node through a low-latency communication protocol, and adjusts the task priority of nearby surveillance cameras when a high activity index area is detected; The data transmission and control flow are designed as follows: the feedback optimization system generates optimization instructions and sends them to the environmental information collection system and coverage scheduling system. If the optimization effect does not meet expectations, the relevant systems request re-analysis through the feedback interface.

7. The device for automatically adjusting coverage in a distributed multi-camera environment according to claim 1, characterized in that: The adjustment device components of the control optimization system include an environmental data sensor group, a computing processing core, a mobile control base, a viewing angle adjustment unit and an image capture unit; The environmental data sensor group integrates a light intensity sensor, a temperature sensor and a distance sensor to collect data on light intensity fluctuation frequency, temperature value and target motion speed change rate; The computing and processing core includes a central processing unit, an operating environment assessment system, a coverage scheduling system, a feedback optimization system, and a user control system; The mobile control base includes a drive motor, a vibration suppression device, and a sensor connection port. The drive motor performs the viewing angle adjustment and position shift of the viewing angle adjustment unit. The vibration suppression device suppresses the adjustment vibration through an elastic element and a hydraulic mechanism. The sensor connection port supports the dynamic connection of additional sensors and transmits configuration data to the computing processing core through a standard protocol. The angle adjustment unit includes a control chip and an angle detector. The control chip receives instructions from the drive motor to perform angle fine adjustment. The angle detector collects adjusted angle data and feeds it back to the calculation processing core. The image capture unit includes an imaging sensor and a focus adjustment component. The imaging sensor collects real-time image data through a monitoring camera, and the focus adjustment component adjusts the focus according to the instructions of the adaptive data acquisition frequency control unit; The control optimization system adjustment process includes the following steps: an environmental data sensor group collects environmental data and transmits it to a computing processing core to generate a regional activity index table and an adjustment strategy; The driving motor of the mobile control base drives the viewing angle adjustment unit to perform an action according to the viewing angle adjustment and position offset parameters of the instruction parsing unit; The control chip of the viewing angle adjustment unit adjusts the angle according to the fine-tuning instruction of the adaptive viewing angle fine-tuning feedback unit, the angle detector collects feedback data, and the vibration suppression device suppresses vibration to ensure adjustment accuracy; The collaborative scheduling unit coordinates the actions of multiple surveillance cameras through time synchronization rules. If the deviation exceeds the threshold, the computing processing core regenerates the adjustment strategy.

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