Method, System and Electronic Device for Angle Adaptive Adjustment of Scanning Code Camera

Abstract

The present invention discloses a method, a system and an electronic device for adaptively adjusting the angle of a code-scanning camera, which relates to the technical field of cameras and includes: detecting in real time the environmental parameters of the area to be code-scanned to obtain a set of dynamic parameters; performing deviation analysis based on the set of dynamic parameters and preset code-scanning reference parameters to generate an angle adjustment strategy; driving a multi-dimensional cloud platform to perform an angle compensation action according to the angle adjustment strategy to generate code-scanning camera image data; performing image quality evaluation based on the code-scanning camera image data to generate an evaluation result, and optimizing the angle adjustment strategy of the camera according to the evaluation result to determine an angle adaptive adjustment strategy. The present invention solves the technical problems of low efficiency and insufficient accuracy in adjusting the camera angle due to the dynamic change of environmental parameters, and achieves the technical effect of improving the accuracy and response speed of camera angle adjustment by detecting environmental parameters in real time and dynamically optimizing the adjustment strategy.

Description

Technical Field

[0001] The present invention relates to the technical field of cameras, and specifically to a method, system and electronic device for adaptively adjusting the angle of a code-scanning camera. Background Art

[0002] With the rapid development of the Internet of Things and intelligent devices, code-scanning technology, as an important means of information interaction, has been widely used in many fields such as retail, logistics, medical care, and manufacturing. However, in practical applications, the performance of code-scanning cameras is often significantly affected by environmental factors, such as changes in lighting conditions, uncertainties in the distance and angle of the target object, etc. These factors may lead to code-scanning failures or low recognition efficiency, thereby affecting the user experience and the overall performance of the system. Traditional code-scanning cameras usually adopt fixed-angle or manual adjustment methods, which are difficult to adapt to complex and changing actual scenarios, especially in dynamic environments, and cannot respond to changes in environmental parameters in real time, resulting in a decrease in code-scanning accuracy and efficiency. In addition, existing angle adjustment methods mostly rely on data from a single sensor, lack the ability to comprehensively analyze environmental parameters and dynamically optimize, and are difficult to achieve accurate angle compensation and efficient code-scanning operations. Therefore, there is an urgent need for a method for adaptively adjusting the angle of a code-scanning camera that can detect environmental parameters in real time, dynamically generate angle adjustment strategies, and automatically optimize the adjustment effect, so as to improve the adaptability, accuracy and response speed of the code-scanning system. Summary of the Invention

[0003] This application provides a method, system and electronic device for adaptively adjusting the angle of a code-scanning camera, which are used to solve the technical problems of low efficiency and insufficient accuracy of camera angle adjustment caused by dynamic changes in environmental parameters.

[0004] In the first aspect of this application, a method for adaptively adjusting the angle of a code-scanning camera is provided. The method includes:

[0005] Real-time detection of the environmental parameters of the area to be code-scanned to obtain a set of dynamic parameters; deviation analysis based on the set of dynamic parameters and preset code-scanning reference parameters to generate an angle adjustment strategy; driving a multi-dimensional gimbal to perform an angle compensation action according to the angle adjustment strategy to generate code-scanning camera image data; image quality evaluation based on the code-scanning camera image data to generate an evaluation result, and optimizing the angle adjustment strategy of the camera according to the evaluation result to determine an angle adaptive adjustment strategy.

[0006] In the second aspect of this application, a system for adaptively adjusting the angle of a code-scanning camera is provided. The system includes:

[0007] Real-time detection unit: Real-time detect the environmental parameters of the area to be scanned to obtain a set of dynamic parameters; Deviation analysis unit: Based on the set of dynamic parameters and preset scanning reference parameters, perform deviation analysis to generate an angle adjustment strategy; Angle compensation unit: According to the angle adjustment strategy, drive the multi-dimensional cloud platform to perform an angle compensation action to generate scanned camera image data; Strategy optimization unit: Based on the scanned camera image data, perform image quality evaluation to generate an evaluation result, and optimize the angle adjustment strategy of the camera according to the evaluation result to determine an angle adaptive adjustment strategy.

[0008] In a third aspect of the present application, an electronic device is provided, and the device includes: a processor; a memory for storing executable instructions of the processor; wherein, the processor is used to execute the method for adaptively adjusting the angle of a scanning camera provided by the present application.

[0009] One or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0010] The present application real-time detects the environmental parameters of the area to be scanned to obtain a set of dynamic parameters; based on the set of dynamic parameters and preset scanning reference parameters, performs deviation analysis to generate an angle adjustment strategy; according to the angle adjustment strategy, drives the multi-dimensional cloud platform to perform an angle compensation action to generate scanned camera image data; based on the scanned camera image data, performs image quality evaluation to generate an evaluation result, and optimizes the angle adjustment strategy of the camera according to the evaluation result to determine an angle adaptive adjustment strategy. The present invention solves the technical problems of low efficiency and insufficient accuracy in adjusting the angle of the camera due to the dynamic change of environmental parameters, and achieves the technical effect of improving the accuracy and response speed of camera angle adjustment by real-time detecting environmental parameters and dynamically optimizing the adjustment strategy. Description of the Drawings

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0012] Figure 1 It is a schematic flowchart of the method for adaptively adjusting the angle of a scanning camera provided by an embodiment of the present application.

[0013] Figure 2 It is a schematic structural diagram of the system for adaptively adjusting the angle of a scanning camera provided by an embodiment of the present application.

[0014] Figure 3 It is a schematic structural diagram of an exemplary electronic device of the present application.

[0015] Explanation of the accompanying drawings: real-time detection unit 11, deviation analysis unit 12, angle compensation unit 13, strategy optimization unit 14, processor 21, memory 22, input device 23, output device 24. DETAILED DESCRIPTION

[0016] The present application provides a method, system and electronic device for adaptively adjusting the angle of a code scanning camera, aiming to solve the technical problem of low efficiency and insufficient precision of camera angle adjustment due to dynamic changes in environmental parameters. By real-time detection of environmental parameters and dynamic optimization of the adjustment strategy, the technical effect of improving the accuracy and response speed of camera angle adjustment is achieved.

[0017] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0018] It should be noted that any variations of the terms "include" and "have" are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules that are not explicitly listed or inherent to these processes, methods, products or devices.

[0019] Embodiment 1, as Figure 1 As shown, the present application provides a method for adaptively adjusting the angle of a barcode scanning camera, the method comprising:

[0020] The environmental parameters of the area to be scanned are detected in real time to obtain a set of dynamic parameters.

[0021] In the embodiments of the present application, during the barcode scanning process, real-time detection of the environmental parameters of the area to be scanned is a crucial step to ensure that the camera can accurately capture the target information. This process involves multiple sensors working together to obtain a set of dynamic parameters, mainly including three core parameters: target distance, camera tilt angle, and light intensity. First, through a TOF (Time of Flight) sensor array, the three-dimensional distance distribution between the surface of the target object and the camera can be measured in real time to obtain the target distance, providing data support for subsequent focus adjustment. Second, gyroscope sensors are used to collect the pitch angle and yaw angle data of the camera to determine the camera tilt angle, which is used to analyze the deviation between the current posture of the camera and the standard vertical angle. Finally, the ambient light sensor array samples multiple areas of the barcode scanning area to determine the light intensity and identify changes in the lighting conditions, especially the distribution of shadow areas. The acquisition of this set of dynamic parameters provides a comprehensive and real-time data basis for subsequent deviation analysis and the generation of angle adjustment strategies, ensuring that the camera can quickly and accurately adjust the angle according to environmental changes, improving the success rate and efficiency of barcode scanning.

[0022] Further, in the method provided by the application embodiments, the environmental parameters of the area to be scanned are detected in real time, and a set of dynamic parameters is obtained. The method includes:

[0023] Obtain the three-dimensional distance distribution from the surface of the target object to the camera through a TOF sensor array, and calculate the average target distance; use gyroscope sensors to collect the pitch angle data and yaw angle data of the camera in real time to construct a camera tilt angle matrix; activate the ambient light sensor array for multi-area sampling to generate a light intensity distribution map; integrate the average target distance, the camera tilt angle matrix, and the light intensity distribution map to obtain the set of dynamic parameters.

[0024] In the embodiment of the present application, during the process of adaptively adjusting the angle of the code scanning camera, the acquisition of the dynamic parameter set is a comprehensive process of multi-sensor collaborative work. First, the TOF sensor array is used to perform three-dimensional scanning on the surface of the target object to obtain the three-dimensional distance distribution data from the surface of the target object to the camera. Then, the average value of these distance distribution data is calculated to obtain the average target distance, providing an accurate distance reference for subsequent focus adjustment. Subsequently, the gyroscope sensor is used to collect the pitch angle and yaw angle data of the camera in real time, and a camera tilt matrix is constructed through these data to analyze the deviation between the current posture of the camera and the standard vertical angle. At the same time, the ambient light sensor array is activated to perform multi-region sampling on the code scanning area to generate an illumination intensity distribution map. This distribution map not only contains the illumination intensity data of each region but also particularly focuses on the illumination ratio between the central region and the edge region to identify the distribution of uneven illumination or shadow regions. Finally, the calculated average target distance, the camera tilt matrix, and the illumination intensity distribution map are integrated to form a complete dynamic parameter set. This set provides comprehensive and real-time data support for subsequent deviation analysis, generation of angle adjustment strategies, and dynamic adjustment of supplementary light, ensuring that the camera can quickly and accurately complete the angle adaptive adjustment according to environmental changes, improving the success rate and efficiency of code scanning.

[0025] Based on the deviation analysis of the dynamic parameter set and the preset code scanning reference parameters, an angle adjustment strategy is generated.

[0026] In the embodiment of the present application, based on the dynamically obtained parameter set detected in real time, it will be compared and analyzed with the preset code scanning reference parameters to identify the deviation between the current environment and the ideal code scanning conditions, and generate corresponding angle adjustment strategies. First, the difference between the target distance and the preset focus distance will be analyzed to determine whether the focus position of the camera needs to be adjusted to ensure that the target object is clearly imaged. Secondly, by comparing the camera tilt matrix with the standard vertical angle, the pitch angle and yaw angle deviations of the camera are calculated to determine the angle direction and amplitude that need to be adjusted, so that the camera can be aligned with the target area. Finally, in combination with the illumination intensity distribution map, uneven illumination or shadow areas in the code scanning area will be identified, and a dynamic adjustment instruction for supplementary light will be generated to optimize the illumination conditions. Based on the results of these deviation analyses, a multi-dimensional angle adjustment strategy will be comprehensively generated, including specific actions such as focus distance adjustment, camera angle compensation, and illumination optimization, providing precise guidance for the execution of subsequent compensation actions, thereby improving the accuracy and efficiency of code scanning.

[0027] Further, in the method provided by the application embodiment, based on the deviation analysis of the dynamic parameter set and the preset code scanning reference parameters, an angle adjustment strategy is generated. The method includes:

[0028] Retrieve the historical scanning code parameter set of the scanning code camera, and construct preset scanning code reference parameters according to the historical scanning code parameter set. The preset scanning code reference parameters include a preset optimal focusing distance and a standard vertical angle; calculate the difference between the average target distance and the preset optimal focusing distance to generate an axial displacement compensation amount; analyze according to the camera tilt matrix and the standard vertical angle to determine the compensation angle data set of the multi-dimensional gimbal. The compensation angle data set includes pitch compensation angle data and yaw compensation angle data; identify the proportion of the shadow area based on the light intensity distribution map to generate a fill light dynamic adjustment instruction; perform angle adjustment analysis on the axial displacement compensation amount, the pitch compensation angle data, and the yaw compensation angle data according to the fill light dynamic adjustment instruction to generate the angle adjustment strategy.

[0029] In the embodiment of the present application, the historical scanning code parameter set of the scanning code camera is retrieved from the operation log, including the historical focusing distance and historical angle data. Then, the mean value calculation and standard deviation calculation are respectively performed on the historical focusing distance and historical angle data in the historical scanning code parameter set to obtain the mean value and standard deviation of the historical focusing distance, and the mean value and standard deviation of the historical angle data. Subsequently, the mean value and variance of the historical focusing distance are used to set the focusing distance cleaning threshold [μ - kσ, μ + kσ], where μ is the mean value; σ is the standard deviation; k is a constant, usually taking 2 or 3, which is used to control the strictness of the cleaning. After obtaining the focusing distance cleaning threshold, this cleaning threshold is used to judge the historical focusing distance, remove the data points outside this range, and then perform a mean value calculation on the cleaned historical focusing distance to obtain the preset optimal focusing distance; for the historical angle data, the same operation is performed to obtain the standard vertical angle. Then, calculate the difference between the average target distance and the preset optimal focusing distance to generate an axial displacement compensation amount; use the standard vertical angle as the standard vertical direction, extract the direction vector of the current direction of the camera from the camera tilt matrix. If the standard vertical angle is (0, 0, 1), representing the Z-axis of the world coordinate system, at this time, the data in the third column of the camera tilt matrix is used as the current direction vector of the camera. By performing an arcsine calculation on the x-axis component of the current direction vector, the yaw compensation angle data is obtained, and the pitch compensation angle data is obtained through the formula Calculation, where, Is the pitch compensation angle data, Is the y-axis component of the current direction vector, The calculated yaw compensation angle data and pitch compensation angle data are stored in a data set to form a compensation angle data set. Then, the light intensity distribution map is analyzed to identify the coordinate data of the shadow area, understand the proportion of the shadow area, and then dynamically adjust the brightness and angle of the fill light according to the coordinate data of the shadow area to generate a fill light dynamic adjustment instruction. Finally, the axial displacement compensation amount, pitch compensation angle data and yaw compensation angle data are combined with the fill light dynamic adjustment instruction to form a comprehensive adjustment strategy, namely the angle adjustment strategy, including axial displacement adjustment (adjusting the position of the camera according to the axial displacement compensation amount), pitch angle adjustment (adjusting the pitch angle of the camera according to the pitch compensation angle data), yaw angle adjustment (adjusting the yaw angle of the camera according to the yaw compensation angle data), fill light adjustment (adjusting the brightness and angle of the fill light according to the fill light dynamic adjustment instruction). This angle adjustment strategy will be sent to the multi-dimensional gimbal to adjust the position, angle and fill light parameters of the camera in real time to ensure that the camera can accurately aim at the target area and optimize the scanning effect.

[0030] Furthermore, in the method provided in the embodiment of the application, the shadow area ratio is identified based on the light intensity distribution diagram, and a fill light dynamic adjustment instruction is generated, and the method includes:

[0031] Shadow recognition is performed according to the light intensity distribution diagram to determine the shadow area, the shadow area is traversed to mark points, and the coordinate data of the shadow area is obtained; the adjustable-focus LED array is controlled to generate a directional fill light beam according to the coordinate data of the shadow area; the directional fill light beam is used to deploy a ring-shaped auxiliary light source at the edge of the code scanning area to generate the fill light dynamic adjustment instruction.

[0032] In the embodiment of the present application, shadow recognition is performed on the light intensity distribution map according to the light intensity threshold, and the area below the threshold in the light intensity distribution map is marked as the shadow area, where the light intensity threshold can be dynamically adjusted according to the average intensity of the ambient light. Subsequently, each pixel inside each shadow area is traversed one by one, the coordinates of each pixel point are recorded, and then the contour extraction algorithm (such as the findContours function in OpenCV) is used to obtain the coordinates of the boundary points of the shadow area. By storing the pixel point coordinates and boundary point coordinates inside the shadow area, the shadow area coordinate data is obtained. After that, according to the position of the shadow area, the LED units that can cover this area are selected from the LED array, and then the direction vector from the LED to the center point of the shadow area is calculated, and the focusing angle of the LED is adjusted so that the light beam accurately points to the target area. Then, according to the size of the shadow area, the focusing range of the LED is adjusted to ensure that the light beam covers the entire shadow area. Then, according to the difference between the light intensity of the shadow area and the surrounding environment, the brightness of the LED is adjusted so that the supplementary lighting effect matches the ambient light. Then, the selected LED units are controlled to generate a directional supplementary lighting beam according to the adjusted direction, focusing range and brightness. Then, a ring-shaped auxiliary light source is evenly deployed at the edge of the code scanning area to ensure that the light source covers the entire code scanning area, and then the initial brightness, color temperature and light beam angle of the ring-shaped auxiliary light source are set to be coordinated with the directional supplementary lighting beam. Among them, the ring-shaped auxiliary light source uses PWM (pulse width modulation) technology to dynamically adjust the color temperature of the light source to match the spectral distribution of the background ambient light and avoid visual discomfort or lighting disharmony caused by color temperature differences. Finally, the parameter settings of the ring-shaped auxiliary light source and the parameters of the directional supplementary lighting beam of the LED array are combined to generate a supplementary lighting dynamic adjustment instruction to achieve accurate supplementary lighting.

[0033] Drive the multi-dimensional pan-tilt to perform an angle compensation action according to the angle adjustment strategy, and generate code scanning camera image data.

[0034] In the embodiment of the present application, according to the generated angle adjustment strategy, the multi-dimensional pan-tilt will be driven to perform a series of angle compensation actions to accurately adjust the posture and lighting conditions of the camera, so as to generate high-quality code scanning camera image data. First of all, the multi-dimensional pan-tilt will synchronously adjust the pitch angle and yaw angle of the camera according to the analysis result of the camera tilt deviation, so that it keeps the best alignment angle with the target area and ensures that the code scanning area is within the central field of view of the camera. At the same time, the focusing position of the camera will be dynamically adjusted in combination with the deviation of the target distance to ensure that the target object is clearly imaged. In addition, in order to optimize the lighting conditions, the light source parameters of the supplementary lighting module will be synchronously adjusted. For example, the brightness, angle and irradiation range of the light source are adjusted to eliminate the influence of the shadow area on the code scanning effect. Through the collaborative work of the multi-dimensional pan-tilt and the supplementary lighting module, it can respond to environmental changes in real time, accurately perform angle compensation actions, and finally generate clear and stable code scanning camera image data, providing high-quality input for subsequent code scanning recognition.

[0035] Furthermore, in the method provided by the application embodiment, based on the light intensity distribution map, the proportion of the shadow area is identified, and a supplementary light dynamic adjustment instruction is generated. The method includes:

[0036] Convert the axial displacement compensation amount into a pulse drive signal for the Z-axis stepper motor; control the camera to move along the guide rail to the target focus position through the pulse drive signal, and determine the focus compensation data; use the PID control algorithm to convert the pitch compensation angle and the yaw compensation angle into servo rotation control instructions for the multi-dimensional gimbal; determine the angle compensation data of the camera through the servo rotation control instructions, and start the code scanning camera for code scanning imaging according to the focus compensation data combined with the angle compensation data, and generate the code scanning imaging data.

[0037] In the embodiment of the present application, the basic parameters of the Z-axis stepper motor are obtained, including the step angle (the angle rotated per step) and the lead screw pitch (the distance that the lead screw drives the camera to move when the motor rotates one circle). The lead screw pitch is divided by the number of steps per circle to obtain the displacement per step. Among them, the number of steps per circle is the ratio of 360° to the step angle. Then, the axial displacement compensation amount is divided by the displacement per step to obtain the number of pulses. According to the calculated number of pulses, a corresponding pulse drive signal is generated to control the rotation of the stepper motor. Each pulse signal corresponds to one step of rotation of the stepper motor, so as to accurately move the camera along the Z-axis to the target focus position. Subsequently, the PID control algorithm is used to convert the pitch compensation angle and the yaw compensation angle into servo rotation control instructions for the multi-dimensional gimbal. Specifically, the angle compensation amount is directly used as the input error of the PID controller, and the control output is calculated through: ; ; where , , are the proportional, integral, and differential coefficients of the PID controller, and are the current angle compensation amounts, and are the integrals of the angle compensation amount, which are used to eliminate the steady-state error, and are the differentials of the angle compensation amount, which are used to predict the change trend of the error. After that, the PID control output , is converted into a servo rotation control instruction. The servo is usually controlled by a pulse width modulation (PWM) signal. The pulse width of the control signal is proportional to the rotation angle of the servo, that is, , where It refers to the output of the PID controller, representing the control quantity calculated based on the current error (angle compensation amount). Then, through the steering gear rotation control instruction, the multi-dimensional cloud platform is driven to adjust the pitch angle and yaw angle of the camera, so that it maintains the best alignment angle with the target area, thereby determining the angle compensation data and ensuring the accurate posture of the camera. Finally, combining the focus compensation data and the angle compensation data, the code scanning camera is started to perform code scanning and imaging. Based on accurate focusing and angle compensation, the camera captures the image of the target area and generates high-quality code scanning imaging data. Through the above steps, the accurate control of the position and posture of the camera is realized, ensuring the efficiency and accuracy of the code scanning process and generating the code scanning imaging data that meets the requirements.

[0038] Based on the code scanning imaging data, image quality evaluation is carried out to generate an evaluation result, and according to the evaluation result, the angle adjustment strategy of the camera is optimized to determine the angle adaptive adjustment strategy.

[0039] In the embodiment of the present application, based on the code scanning imaging data, image quality evaluation will be carried out to determine whether the current image meets the requirements of code scanning recognition. The evaluation process will analyze key indicators such as the clarity and contrast of the image, and through the convolution analysis of the convolutional neural network model, an evaluation result will be generated. If the evaluation result does not reach the threshold, it means that the current image quality is not sufficient to support efficient code scanning. At this time, an iterative optimization process will be entered. Specifically, the angle adjustment strategy will be optimized based on the historical adjustment records. For example, the pitch angle, yaw angle and focus distance of the camera will be further adjusted to improve the image quality. This process will be iterated continuously until the generated code scanning imaging data meets the requirements of code scanning recognition. Finally, an angle adaptive adjustment strategy will be determined, which can dynamically adjust the camera parameters according to the real-time environmental changes to ensure high-quality imaging data can be obtained under various complex conditions, thereby improving the accuracy and stability of code scanning.

[0040] Furthermore, in the method provided by the application embodiment, based on the code scanning imaging data, image quality evaluation is carried out to generate an evaluation result, and according to the evaluation result, the angle adjustment strategy of the camera is optimized to determine the angle adaptive adjustment strategy. The method includes:

[0041] Extract the clarity index, contrast index, and distortion coefficient of the imaging data based on the scanned code camera image data, and construct a quality evaluation vector; synchronize the quality evaluation vector to a pre-trained convolutional neural network model for convolutional analysis to generate the evaluation result, where the evaluation result includes a comprehensive evaluation value; compare the comprehensive evaluation value with the expected evaluation value to determine whether the comprehensive evaluation value is less than the expected evaluation value; when the comprehensive evaluation value is less than the expected evaluation value, generate a parameter optimization path based on the historical adjustment record; update the compensation coefficient of the angle adjustment strategy of the camera according to the parameter optimization path to determine the angle adaptive adjustment strategy.

[0042] In the embodiment of the present application, after obtaining the scanned code camera image data, the clarity index, contrast index, and distortion coefficient of the imaging data are extracted therefrom. Among them, the clarity index is obtained by calculating the gradient amplitude of the image; the contrast index is obtained by calculating the variance of the gray level distribution of the image; the distortion coefficient is obtained by detecting whether the straight lines in the image are bent. By combining the clarity, contrast, and distortion coefficients, a quality evaluation vector is obtained. Subsequently, activate the pre-constructed convolutional neural network model, input the quality evaluation vector into the convolutional neural network model, and the convolutional neural network model performs feature extraction and analysis on the input vector through convolutional layers, pooling layers, and fully connected layers to generate a comprehensive evaluation result. This evaluation result is a comprehensive evaluation value, which is obtained by weighting the resolution score and the recognition probability. Then, compare the comprehensive evaluation value with the preset expected evaluation value. If the comprehensive evaluation value is less than the expected evaluation value, it means that the current image quality does not meet the expectation and parameter optimization is required. At this time, the similarity between the quality evaluation vector of the historical scene and the current quality evaluation vector will be calculated by cosine similarity, and the records whose similarity meets the similarity threshold and their corresponding adjustment parameters, such as the camera angle compensation coefficient , , . Then, perform a weighted average on the adjustment parameters in the similar historical records to generate a new camera angle compensation coefficient, and use these coefficients as the parameter optimization path. Finally, update the compensation coefficient of the angle adjustment strategy of the camera according to the parameter optimization path, that is, update the proportional, integral, and differential coefficients in the PID controller. This process will be repeated until the comprehensive evaluation value reaches or exceeds the expected evaluation value, so as to generate a new angle adaptive adjustment strategy for subsequent camera attitude adjustment.

[0043] For the convolutional neural network model, a convolutional neural network (CNN) is used to construct an initial model structure, including an input layer, a convolutional layer, a pooling layer, a fully connected layer, and an output layer. Then, the weights of the CNN model are initialized using random numbers or other methods, and the training set (including historical quality evaluation vectors, historical resolution scores, and historical recognition probabilities) is input into the initialized CNN model for forward propagation. The data is passed layer by layer through the input layer, convolutional layer, pooling layer, fully connected layer, output layer, etc., and prediction results including resolution scores and recognition probabilities are calculated. Subsequently, the mean squared error (MSE) loss function is used to calculate the loss value between the prediction results and the historical data, and the gradients of the loss with respect to the weights of each layer are calculated layer by layer through backpropagation. Then, the Adam optimizer is used to optimize the model parameters and adjust the weights to minimize the value of the loss function. The above process is repeated until the maximum number of iterations is reached. After training, the validation set is used to test the model performance, and the accuracy of the model in the resolution score and recognition probability prediction tasks is evaluated. If the accuracy meets the expected accuracy, the current CNN model is output as the final convolutional neural network model; otherwise, hyperparameters such as the learning rate and the number of training batches are adjusted to further improve the prediction effect of the model. After training is completed, the fusion weights determined based on business requirements and expert decisions are built into the output layer to fuse the predicted resolution scores and recognition probabilities to determine the comprehensive evaluation value.

[0044] Further, in the method provided by the application embodiment, based on the data of the scanned code camera image, the clarity index, contrast index, and distortion coefficient of the imaging data are extracted to construct a quality evaluation vector. The method includes:

[0045] The data of the scanned code camera image is divided into N×N pixel blocks to generate an image block matrix; the improved Sobel gradient operator is used to calculate the horizontal and vertical direction gradient amplitudes of the image block matrix to generate a local clarity index; the gray histogram of the image block matrix is extracted, and the gray variance ratio between the central region and the edge region of the gray histogram is calculated to generate a contrast index; the preset reference marker points in the data of the scanned code camera image are identified, and the spatial coordinate offset is obtained by matching the preset reference marker points with the standard template; the data of the scanned code camera image is divided into M×M detection grids, and the radial distortion coefficient of the feature point distribution in each grid is calculated; the local clarity indexes of each pixel block in the image block matrix are aggregated to generate a clarity index; the radial distortion coefficient and the coordinate offset are weighted and fused to generate a distortion coefficient; the clarity index, the contrast index, and the distortion coefficient are combined to form the quality evaluation vector.

[0046] In the embodiments of the present application, after obtaining the data of the scanned code camera image, the data of the scanned code camera image will be divided into pixel blocks of size N×N. For example, for an image of 640×480, if N = 32, it will be divided into 20×15 pixel blocks, thereby obtaining an image block matrix, where each element is an N×N pixel block. For the clarity index, an improved Sobel operator is used to calculate the horizontal gradient of each pixel block and the vertical gradient , that is, obtained by multiplying the traditional Sobel operator by a Gaussian weight, where the Gaussian weight is determined according to the requirements of the specific task. For example, the weight of the central pixel is enhanced to reduce the influence of noise, and then based on the horizontal gradient and the vertical gradient, the gradient amplitude of each pixel is calculated, and the proportion of the number of pixels whose gradient amplitude exceeds the preset gradient threshold in the total number of pixels in each pixel block is statistically calculated to obtain the local clarity index. Then, the local clarity indexes of all pixel blocks in the image block matrix are aggregated, that is, by means of mean calculation, the clarity index is obtained. For the contrast index, the gray histogram is extracted for each pixel block, the number of pixels at each gray level is statistically calculated, and then the gray histogram is divided into a central region (such as gray levels [64, 192]) and an edge region (such as gray levels [0, 63] and [193, 255]). Then, the gray variance of the central region and the gray variance of the edge region are calculated, and the ratio of the gray variance of the central region to the gray variance of the edge region is calculated to obtain the contrast index. For the distortion coefficient, preset reference marker points (such as the positioning markers of the two-dimensional code) are identified in the data of the scanned code camera image, and the identified reference marker points are matched with the standard template, and the spatial coordinate offset is calculated by means of taking the difference. Subsequently, the data of the scanned code camera image is divided into detection grids of M×M, and feature points (such as corner points or edge points) are detected in each grid, and the radial distortion coefficient of the feature points in each grid is calculated through the radial distortion coefficient calculation formula. The specific radial distortion coefficient is as follows: ; where k is the radial distortion coefficient, is the distance from the feature point to the center of the image, is the average distance, and n is the number of feature points. After that, the distortion coefficient is calculated through the formula , where is the distortion coefficient, , are the weight coefficients, and are the spatial coordinate offsets. Finally, the clarity index, the contrast index, and the distortion coefficient are concatenated to obtain a quality evaluation vector. Through the above steps, the data of the scanned code camera image can be divided into pixel blocks, the local clarity index, the contrast index, and the distortion coefficient can be calculated, and finally the quality evaluation vector can be generated. This method can comprehensively evaluate the quality of the image and provide data support for the subsequent optimization of the camera adjustment strategy.

[0047] Further, in the method provided by the application embodiment, after optimizing the angle adjustment strategy of the camera according to the evaluation result and determining the angle adaptive adjustment strategy, the method includes:

[0048] Establish an association database between the code scanning success rate and the adjustment parameters, record the parameter combinations and recognition results of each adjustment process, and obtain historical adjustment combination data; analyze the historical adjustment combination data through a reinforcement learning algorithm, and dynamically optimize the matching rules of the multi-dimensional pan-tilt control parameters and the fill light parameters; when a new code scanning target is detected, call the matching rules to initialize the angle adaptive adjustment strategy.

[0049] In the embodiments of the present application, during each adjustment process, multi-dimensional pan-tilt control parameters (such as pitch angle, yaw angle, focal length, etc.) and fill light parameters (such as fill light intensity, fill light angle, etc.) are recorded. The code scanning recognition results (success or failure) after each adjustment are recorded, and then the parameter combinations and recognition results of each adjustment are stored in the associated database to form historical adjustment combination data. Subsequently, a reinforcement learning model is constructed. Common algorithms include Q-learning or Deep Q-Network (DQN). These algorithms can dynamically optimize the matching rules of multi-dimensional pan-tilt control parameters and fill light parameters by learning the patterns in historical data. In the design of the model, the state is defined as the current multi-dimensional pan-tilt control parameters (such as pitch angle, yaw angle, focal length, etc.) and fill light parameters (such as fill light intensity, fill light angle, etc.). The action is the adjustment operation on these parameters. For example, increasing or decreasing the value of a certain parameter. The reward is set based on the code scanning result. If the code scanning is successful, a reward of +1 is given. If it fails, a reward of -1 is given. This reward mechanism can guide the model to optimize in the direction of improving the code scanning success rate. During the training process, the state, action, and reward are extracted from the historical adjustment combination data and used as training samples to be input into the reinforcement learning model. The model gradually optimizes its strategy through continuous trial and error and learning, and finds the parameter adjustment rule that can maximize the reward. This process is dynamic. As more historical data accumulates, the model can continuously update and improve its matching rules. Finally, the trained model will generate a set of optimized matching rules, which can guide the subsequent adjustment process and help quickly find the optimal parameter combination when facing different code scanning targets. In this way, the reinforcement learning algorithm can not only effectively utilize historical data but also continuously optimize the parameter matching rules in a dynamic environment, thereby improving the overall performance and efficiency of the code scanning system. When a new type of code scanning target is detected, the features of the target (such as size, shape, reflectivity, etc.) are extracted, and then according to the features of the target, the optimized matching rules are called to initialize the multi-dimensional pan-tilt control parameters and fill light parameters. By directly initializing a parameter combination close to the optimal through the matching rules, the number of iterations of adaptive adjustment is reduced, and the adjustment efficiency is improved. In summary, by establishing an associated database of the code scanning success rate and adjustment parameters, combining the reinforcement learning algorithm to analyze historical data, dynamically optimizing the matching rules, and calling the matching rules to initialize the adjustment strategy when a new type of code scanning target is detected, the number of iterations of adaptive adjustment can be significantly shortened, and the code scanning efficiency and success rate can be improved.

[0050] In the embodiments of the present application, in summary, the embodiments of the present application have at least the following technical effects:

[0051] This application performs real-time detection on the environmental parameters of the area to be scanned to obtain a set of dynamic parameters; conducts deviation analysis based on the set of dynamic parameters and preset scanning benchmark parameters to generate an angle adjustment strategy; drives a multi-dimensional gimbal to perform an angle compensation action according to the angle adjustment strategy to generate scanned camera image data; conducts image quality evaluation based on the scanned camera image data to generate an evaluation result, and optimizes the angle adjustment strategy of the camera according to the evaluation result to determine an angle adaptive adjustment strategy. The present invention solves the technical problems of low efficiency and insufficient accuracy in the angle adjustment of the camera due to the dynamic change of environmental parameters, and achieves the technical effects of improving the accuracy and response speed of the camera angle adjustment by real-time detecting environmental parameters and dynamically optimizing the adjustment strategy.

[0052] Embodiment 2, based on the same inventive concept as the method for angle adaptive adjustment of a scanning camera in the foregoing embodiment, as Figure 2 shown, this application provides a system for angle adaptive adjustment of a scanning camera. The system in the embodiments of this application and the method embodiments are based on the same inventive concept. Among them, the system includes:

[0053] A real-time detection unit 11: performs real-time detection on the environmental parameters of the area to be scanned to obtain a set of dynamic parameters; a deviation analysis unit 12: conducts deviation analysis based on the set of dynamic parameters and preset scanning benchmark parameters to generate an angle adjustment strategy; an angle compensation unit 13: drives a multi-dimensional gimbal to perform an angle compensation action according to the angle adjustment strategy to generate scanned camera image data; a strategy optimization unit 14: conducts image quality evaluation based on the scanned camera image data to generate an evaluation result, and optimizes the angle adjustment strategy of the camera according to the evaluation result to determine an angle adaptive adjustment strategy.

[0054] Furthermore, the system is also used to implement the following functions:

[0055] Obtain the three-dimensional distance distribution from the surface of the target object to the camera through a TOF sensor array, and calculate the average target distance; use a gyroscope sensor to collect the pitch angle data and yaw angle data of the camera in real time to construct a camera tilt angle matrix; activate the ambient light sensor array for multi-region sampling to generate an illumination intensity distribution map; integrate the average target distance, the camera tilt angle matrix, and the illumination intensity distribution map to obtain the set of dynamic parameters.

[0056] Furthermore, the system is also used to implement the following functions:

[0057] Retrieve the historical scanning code parameter set of the scanning code camera, and construct preset scanning code reference parameters according to the historical scanning code parameter set. The preset scanning code reference parameters include a preset optimal focusing distance and a standard vertical angle; calculate the difference between the average target distance and the preset optimal focusing distance to generate an axial displacement compensation amount; analyze according to the camera tilt matrix and the standard vertical angle to determine the compensation angle data set of the multi-dimensional cloud platform. The compensation angle data set includes pitch compensation angle data and yaw compensation angle data; identify the proportion of the shadow area based on the light intensity distribution map to generate a fill light dynamic adjustment instruction; according to the fill light dynamic adjustment instruction, perform angle adjustment analysis on the axial displacement compensation amount, the pitch compensation angle data, and the yaw compensation angle data to generate the angle adjustment strategy.

[0058] Further, the system is also used to implement the following functions:

[0059] Convert the axial displacement compensation amount into a pulse drive signal of the Z-axis stepper motor; control the camera to move along the guide rail to the target focusing position through the pulse drive signal to determine the focusing compensation data; use the PID control algorithm to convert the pitch compensation angle and the yaw compensation angle into servo rotation control instructions of the multi-dimensional cloud platform; determine the angle compensation data of the camera through the servo rotation control instructions, and start the scanning code camera for scanning and imaging according to the focusing compensation data combined with the angle compensation data to generate the scanning and imaging map data.

[0060] Further, the system is also used to implement the following functions:

[0061] Perform shadow recognition according to the light intensity distribution map to determine the shadow area, traverse the shadow area for point marking to obtain the shadow area coordinate data; control the adjustable focus LED array to generate a directional fill light beam according to the shadow area coordinate data; use the directional fill light beam to deploy a ring-shaped auxiliary light source at the edge of the scanning code area to generate the fill light dynamic adjustment instruction.

[0062] Further, the system is also used to implement the following functions:

[0063] Extract the clarity index, contrast index, and distortion coefficient of the imaging data based on the scanning and imaging map data to construct a quality evaluation vector; synchronize the quality evaluation vector to a pre-trained convolutional neural network model for convolutional analysis to generate the evaluation result. The evaluation result includes a comprehensive evaluation value; compare the comprehensive evaluation value with the expected evaluation value to determine whether the comprehensive evaluation value is less than the expected evaluation value; when the comprehensive evaluation value is less than the expected evaluation value, generate a parameter optimization path based on the historical adjustment record; update the compensation coefficient of the angle adjustment strategy of the camera according to the parameter optimization path to determine the angle adaptive adjustment strategy.

[0064] Further, the system is also used to implement the following functions:

[0065] Divide the scanned code camera image data into N×N pixel blocks to generate an image block matrix; use an improved Sobel gradient operator to calculate the horizontal and vertical direction gradient amplitudes of the image block matrix to generate a local sharpness index; extract the gray histogram of the image block matrix, calculate the gray variance ratio between the central region and the edge region of the gray histogram to generate a contrast index; identify the preset reference marker points in the scanned code camera image data, match the preset reference marker points with the standard template to obtain the spatial coordinate offset; divide the scanned code camera image data into M×M detection grids, calculate the radial distortion coefficients of the feature point distributions within each grid; aggregate the local sharpness indices of each pixel block in the image block matrix to generate a sharpness index; perform weighted fusion of the radial distortion coefficients and the coordinate offset to generate a distortion coefficient; form the quality evaluation vector from the sharpness index, the contrast index, and the distortion coefficient.

[0066] Further, the system is also used to implement the following functions:

[0067] Establish an association database between the code scanning success rate and the adjustment parameters, record the parameter combinations and recognition results of each adjustment process to obtain historical adjustment combination data; analyze the historical adjustment combination data through a reinforcement learning algorithm to dynamically optimize the matching rules of the multi-dimensional pan-tilt control parameters and the fill light parameters; when a new code scanning target is detected, call the matching rules to initialize the angle adaptive adjustment strategy.

[0068] Embodiment 3, based on the same inventive concept as the method for adaptively adjusting the angle of a code scanning camera in the foregoing embodiments, this embodiment provides an electronic device Figure 3 FIG. is a schematic structural diagram of the electronic device provided in Embodiment 3 of the present invention, showing a block diagram of an exemplary electronic device suitable for implementing the embodiments of the present invention. Figure 3 The displayed electronic device is only an example and should not bring any limitations to the functions and usage scope of the embodiments of the present invention. As Figure 3 shown, the electronic device includes a processor 21, a memory 22, an input device 23, and an output device 24; the number of processors 21 in the electronic device can be one or more, Figure 3 Taking one processor 21 as an example, the processor 21, the memory 22, the input device 23, and the output device 24 in the electronic device can be connected through a bus or other means, Figure 3 Taking the connection through the bus as an example.

[0069] It should be noted that the above order of the embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. And the above specific embodiments of this specification have been described. The processes depicted in the drawings do not necessarily require the specific order and continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0070] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included within the protection scope of the present application.

[0071] This specification and the drawings are only exemplary descriptions of the present application and are considered to have covered any and all modifications, variations, combinations, or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to include these changes and modifications.

Claims

1. A method for adaptively adjusting the angle of a barcode scanning camera, characterized in that: The method comprises: Perform real-time detection of environmental parameters of the area to be scanned to obtain a dynamic parameter set; Perform deviation analysis based on the dynamic parameter set and the preset code scanning reference parameters to generate an angle adjustment strategy; According to the angle adjustment strategy, the multi-dimensional gimbal is driven to perform angle compensation action to generate scanned code camera image data; Performing image quality assessment based on the scanned code camera image data to generate an assessment result, optimizing the angle adjustment strategy of the camera according to the assessment result, and determining an angle adaptive adjustment strategy; Wherein, the angle adjustment strategy of the camera is optimized according to the evaluation result, and after the angle adaptive adjustment strategy is determined, the method includes: Establish a database associating the scanning success rate with the adjustment parameters, record the parameter combination and recognition results of each adjustment process, and obtain historical adjustment combination data; The historical adjustment combination data is analyzed by a reinforcement learning algorithm to dynamically optimize the matching rules between the multi-dimensional gimbal control parameters and the fill light parameters; When a new type of scanning target is detected, the matching rule is called to initialize the angle adaptive adjustment strategy.

2. The method for adaptively adjusting the angle of a barcode scanning camera according to claim 1, characterized in that: The environmental parameters of the area to be scanned are detected in real time to obtain a dynamic parameter set. The method includes: The three-dimensional distance distribution from the target object surface to the camera is obtained through the TOF sensor array, and the average target distance is calculated; Use the gyroscope sensor to collect the camera's pitch angle data and yaw angle data in real time and build the camera tilt angle matrix; Activate the ambient light sensor array to perform multi-area sampling and generate a light intensity distribution map; The average target distance, the camera tilt matrix, and the light intensity distribution map are integrated to obtain the dynamic parameter set.

3. The method for adaptively adjusting the angle of a barcode scanning camera according to claim 2, characterized in that: Based on the deviation analysis between the dynamic parameter set and the preset code scanning reference parameter, an angle adjustment strategy is generated, and the method includes: Retrieve a historical scanning parameter set of the scanning camera, and construct preset scanning benchmark parameters according to the historical scanning parameter set, wherein the preset scanning benchmark parameters include a preset optimal focus distance and a standard vertical angle; Calculate the difference between the average target distance and the preset optimal focus distance to generate an axial displacement compensation amount; Analyze the camera tilt angle matrix and the standard vertical angle to determine a compensation angle data set of the multi-dimensional gimbal, wherein the compensation angle data set includes pitch compensation angle data and yaw compensation angle data; Identify the shadow area ratio based on the light intensity distribution diagram, and generate a fill light dynamic adjustment instruction; According to the fill light dynamic adjustment instruction, the axial displacement compensation amount, the pitch compensation angle data, and the yaw compensation angle data are subjected to angle adjustment analysis to generate the angle adjustment strategy.

4. The method for adaptively adjusting the angle of a barcode scanning camera according to claim 3, characterized in that: According to the angle adjustment strategy, the multi-dimensional gimbal is driven to perform angle compensation action to generate scanned code camera image data, and the method includes: Converting the axial displacement compensation amount into a pulse drive signal of the Z-axis stepper motor; The pulse driving signal is used to control the camera to move along the guide rail to the target focus position, and determine the focus compensation data; The pitch compensation angle and the yaw compensation angle are converted into a steering gear rotation control instruction of the multi-dimensional gimbal by using a PID control algorithm; The angle compensation data of the camera is determined by the steering gear rotation control instruction, and the code scanning camera is started to scan and take pictures according to the focus compensation data and the angle compensation data to generate the code scanning picture data.

5. The method for adaptively adjusting the angle of a barcode scanning camera according to claim 3, characterized in that: Identifying the shadow area ratio based on the light intensity distribution diagram and generating a fill light dynamic adjustment instruction, the method includes: Perform shadow recognition according to the light intensity distribution map, determine the shadow area, traverse the shadow area to mark points, and obtain coordinate data of the shadow area; Controlling the focus-adjustable LED array to generate a directional fill light beam according to the shadow area coordinate data; The directional fill light beam is used to deploy a ring-shaped auxiliary light source at the edge of the code scanning area to generate the fill light dynamic adjustment instruction.

6. The method for adaptively adjusting the angle of a barcode scanning camera according to claim 1, characterized in that: Performing image quality evaluation based on the scanned code camera image data, generating an evaluation result, optimizing the angle adjustment strategy of the camera according to the evaluation result, and determining an angle adaptive adjustment strategy, the method includes: Extracting the clarity index, contrast index and distortion coefficient of the imaging data based on the scanned code camera image data, and constructing a quality assessment vector; Synchronizing the quality assessment vector to a pre-trained convolutional neural network model for convolutional analysis to generate the assessment result, which includes a comprehensive assessment value; Based on the comparison between the comprehensive evaluation value and the expected evaluation value, determining whether the comprehensive evaluation value is less than the expected evaluation value; When the comprehensive evaluation value is less than the expected evaluation value, generating a parameter optimization path based on historical adjustment records; The compensation coefficient of the angle adjustment strategy of the camera is updated according to the parameter optimization path to determine the angle adaptive adjustment strategy.

7. The method for adaptively adjusting the angle of a barcode scanning camera according to claim 6, characterized in that: Extracting the clarity index, contrast index and distortion coefficient of the imaging data based on the scanned code camera image data, and constructing a quality assessment vector, the method includes: Divide the scanned image data into N×N pixel blocks to generate an image block matrix; The improved Sobel gradient operator is used to calculate the horizontal and vertical gradient amplitudes of the image block matrix to generate a local clarity index; Extracting the grayscale histogram of the image block matrix, calculating the grayscale variance ratio between the central area and the edge area of ​​the grayscale histogram, and generating a contrast index; Identify preset reference mark points in the scanned camera image data, and match the preset reference mark points with a standard template to obtain a spatial coordinate offset; Divide the scanned code camera image data into M×M detection grids, and calculate the radial distortion coefficient of the feature point distribution in each grid; Aggregating the local clarity index of each pixel block in the image block matrix to generate a clarity index; Performing weighted fusion of the radial distortion coefficient and the coordinate offset to generate a distortion coefficient; The clarity index, the contrast index, and the distortion coefficient are combined into the quality assessment vector.

8. A camera angle adaptive adjustment system for scanning barcodes, characterized in that: The system is used to execute the method for adaptively adjusting the angle of a barcode scanning camera according to any one of claims 1 to 7, and the system includes: Real-time detection unit: performs real-time detection of environmental parameters of the area to be scanned to obtain a dynamic parameter set; Deviation analysis unit: performs deviation analysis based on the dynamic parameter set and the preset code scanning reference parameter to generate an angle adjustment strategy; Angle compensation unit: drives the multi-dimensional gimbal to perform angle compensation actions according to the angle adjustment strategy, and generates scanned image data; Strategy optimization unit: perform image quality evaluation based on the scanned code camera image data, generate evaluation results, optimize the angle adjustment strategy of the camera according to the evaluation results, and determine the angle adaptive adjustment strategy.

9. An electronic device, characterized in that: The electronic device comprises: a processor; and a memory for storing instructions executable by the processor; wherein the processor is used to execute the method for adaptively adjusting the angle of a barcode scanning camera as described in any one of claims 1 to 7.

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