Intelligent slicing thickness adjusting system of slicing machine

Abstract

The invention provides an intelligent slice thickness adjusting system of a slicing machine. The intelligent slice thickness adjusting system comprises a slicing machine body, a slice thickness sensor, an image collecting device, a control processing unit and an executing mechanism. The slice thickness sensor detects the actual thickness of a slice in real time, the image acquisition device acquires slice image information, and the control processing unit calculates the uniformity parameter of the slice thickness through the image analysis module, performs analysis in combination with thickness detection data, and generates an adjustment instruction after comparing preset slice parameters. And the executing mechanism adjusts the opening degree of the discharging opening according to the adjusting instruction so as to achieve accurate slicing thickness control. By combining image analysis and data processing, the precision and uniformity of slice thickness control are improved, manual adjustment errors are reduced, the production efficiency and the product quality are improved, and the method is suitable for the fields of food processing, material slicing and the like.

Description

Technical Field

[0001] The invention relates to the technical field of slicer control, and in particular to an intelligent slice thickness adjustment system for a slicer. Background Art

[0002] In food processing and other industries involving fine slicing, slicers are widely used to accurately cut materials to meet different application requirements. Traditional slicers mainly rely on mechanical structures to adjust the opening of the outlet or adjust the blade angle to control the slice thickness, while some high-end equipment introduces sensors to monitor the change of slice thickness and improve the automatic adjustment capability.

[0003] However, existing slicers generally have problems such as inaccurate thickness adjustment, difficulty in ensuring slice uniformity, reliance on manual adjustment, and low production efficiency. Even if some devices are equipped with thickness sensors, there is still a lack of effective slice uniformity analysis and intelligent control systems, which leads to fluctuations in slice thickness and affects the quality of the final product. In addition, the existing adjustment mechanism usually relies only on single sensor feedback, fails to comprehensively consider image information and actual measurement data, and is difficult to achieve precise control.

[0004] Therefore, it is necessary to develop an intelligent slice thickness adjustment system for a slicer. Summary of the invention

[0005] The present application provides an intelligent slice thickness adjustment system for a slicer to improve the accuracy and uniformity of slice thickness control.

[0006] The present application provides a slicer intelligent slice thickness adjustment system, comprising:

[0007] The slicer body comprises a blade assembly, a feed channel and an adjustable discharge port, wherein the opening of the discharge port is adjustable to control the slice thickness;

[0008] A slice thickness sensor is arranged at the discharge port, and is used to detect the actual thickness of the slice and generate thickness detection data;

[0009] An image acquisition device, disposed at the discharge end of the discharge port, for acquiring image information of the slices;

[0010] A control processing unit, comprising an image analysis module, for calculating a uniformity parameter of slice thickness based on the image information provided by the image acquisition device, so as to characterize the uniformity of slice thickness; a data processing module, for receiving and analyzing the thickness detection data and uniformity parameter, and comparing them with preset slice parameters including a target thickness range and a uniformity threshold; and a control instruction generation module, for generating an adjustment instruction according to the comparison result;

[0011] An actuator is used to adjust the opening of the discharge port according to the adjustment instruction, wherein the actuator includes a drive motor, the drive motor is connected to the adjustable baffle of the discharge port, and drives the baffle to move within a preset range to change the opening of the discharge port and adjust the slice thickness.

[0012] Furthermore, the slicer body includes an adaptive feed control device, which includes a plurality of adjustable feed guide plates for adjusting the inclination angle and opening width according to the instructions of the control processing unit to ensure the uniform arrangement of the material before entering the feed channel.

[0013] Furthermore, the adaptive feeding control device includes an intelligent vibration adjustment module, which includes an ultrasonic vibration component, which is electrically connected to the control processing unit and automatically adjusts the vibration amplitude and frequency according to the detection data of the image acquisition device and the slice thickness sensor to optimize the feeding state.

[0014] Furthermore, the slice thickness sensor includes a multi-point distributed detection unit, which is composed of a plurality of thickness sensor arrays, evenly distributed along the width direction of the discharge port, and is used to simultaneously detect slice thickness data at multiple positions; the multi-point distributed detection unit is electrically connected to the control processing unit, and performs weighted calculation on the measurement results of each sensor through a data fusion algorithm to eliminate local errors, and combines the slice morphology information provided by the image acquisition device to calibrate the uneven thickness area and optimize the slice thickness control accuracy.

[0015] Furthermore, the slice thickness sensor includes a self-calibration compensation module, which automatically calculates the sensor drift error based on the thickness data provided by the multi-point distributed detection unit, combined with historical measurement data and a preset slice thickness range, and performs real-time correction of the measurement data through a dynamic compensation algorithm; the self-calibration compensation module is electrically connected to the control processing unit, and performs adaptive adjustments in combination with ambient temperature and humidity changes to ensure the stability of the thickness detection data.

[0016] Furthermore, the image acquisition device includes a multispectral imaging module for acquiring image information of slices within different wavelength ranges and performing multi-dimensional analysis of slice thickness and material uniformity; the multispectral imaging module includes a light source with at least two working bands, and an imaging sensor that works synchronously with the light source to obtain slice information under visible light, near-infrared light or ultraviolet light bands; by imaging in different bands, the microstructural differences, thickness changes and possible internal defects of the slices are detected, thereby improving the accuracy of slice thickness analysis.

[0017] Furthermore, the image acquisition device includes a dynamic focal length adaptive adjustment module, which is used to adjust the imaging focal length in real time according to the slice thickness and the material transmittance, so as to optimize the imaging quality of different types of slices; the dynamic focal length adaptive adjustment module includes a variable focus lens and an automatic focus control circuit, and the automatic focus control circuit calculates the focus offset according to the real-time imaging data of the image acquisition device, and adjusts the focal length of the variable focus lens through an electric adjustment mechanism, so that the slice edge and surface texture always maintain clear imaging.

[0018] Furthermore, the actuator includes a precision variable damping adjustment component, which is used to provide dynamic damping control in the process of adjusting the opening of the discharge port to improve the accuracy and stability of the slice thickness adjustment; the precision variable damping adjustment component includes a fluid damping device and an electromagnetic damping unit, wherein the fluid damping device provides a buffering effect through the flow control of high-viscosity liquid, so that the movement of the baffle is smoother; the electromagnetic damping unit is composed of a controllable electromagnetic coil and a high-magnetic permeability damping sheet, which is used to control the processing unit's instructions to dynamically adjust the damping force, so as to achieve precise control of the opening change rate of the discharge port.

[0019] Furthermore, the image analysis module of the control processing unit adopts a hybrid calculation method based on multi-scale gradient uniformity distribution analysis and normalized thickness deviation index to evaluate the uniformity parameter of slice thickness; the hybrid method first pre-processes the slice image acquired by the image acquisition device, including denoising, contrast enhancement and edge detection, and calculates the local gradient change rate at different scales, so as to extract the spatial variability of slice thickness;

[0020] The multi-scale gradient uniformity distribution analysis is used to perform multi-scale decomposition on the slice area, and the scale factor is defined as S k , and calculate the local gradient transformation matrix G at each scale according to the following formula 1 k (x, y):

[0021]

[0022] Among them, I k (x, y) represents the grayscale value of the image at scale k; x, y represent the horizontal and vertical coordinates of the pixel of the image respectively; Represents the local gradient of the image in the x direction at scale k; Represents the local gradient of the image in the y direction at scale k;

[0023] According to the following formula 2, the global uniformity factor U(S k ) as a measure of thickness distribution variation:

[0024]

[0025] Among them, M k and N k Respectively represent the number of rows and columns of the image at scale k; is the average gradient value under scale k;

[0026] According to the following formula 3, calculate the uniformity standard deviation σ U Key indicators as thickness uniformity parameters:

[0027]

[0028] Among them, K is the number of scales;

[0029] The normalized thickness deviation index NTDI is calculated according to the following formula 4 to measure the overall deviation of the slice area from the target thickness range:

[0030]

[0031] Among them, H i Indicates the thickness value of the i-th pixel; H target is the preset target slice thickness; N is the total number of measurement points;

[0032] When σ U Exceeding the uniformity threshold σ U，thresh Or NTDI exceeds the deviation threshold NTDI thresh When the thickness of the slices is not uniform, the control processing unit determines that there is a significant unevenness and triggers an adjustment instruction to adjust the actuator to change the opening of the discharge port so that the thickness tends to be uniform.

[0033] This application has the following beneficial technical effects:

[0034] (1) Combining thickness sensors and image analysis can improve the accuracy of slice thickness and reduce errors.

[0035] (2) The image acquisition device analyzes the uniformity of the slices and makes intelligent adjustments to ensure the consistency of the slices. (3) The intelligent control processing unit automatically analyzes the data and generates adjustment instructions, eliminating the need for manual repeated debugging and improving production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is a schematic diagram of a slicer intelligent slice thickness adjustment system provided in the first embodiment of the present application. DETAILED DESCRIPTION

[0037] Many specific details are described in the following description to facilitate a full understanding of the present application. However, the present application can be implemented in many other ways than those described herein, and those skilled in the art can make similar generalizations without violating the connotation of the present application, so the present application is not limited by the specific implementation disclosed below.

[0038] The first embodiment of the present application provides a slicer intelligent slice thickness adjustment system. Figure 1 , which is a schematic diagram of the first embodiment of the present application. Figure 1 A first embodiment of the present application provides a system for adjusting the thickness of an intelligent slicer slicer, which is described in detail.

[0039] The microtome intelligent slice thickness adjustment system comprises a microtome body 101 , a slice thickness sensor 102 , an image acquisition device 103 , a control processing unit 104 and an actuator 105 .

[0040] The slicer body 101 includes a blade assembly, a feed channel and an adjustable discharge port, wherein the opening of the discharge port is adjustable to control the slice thickness.

[0041] In the slicer intelligent slice thickness adjustment system provided by the present invention, the slicer body 101 is the core component of the entire system, and its structure and function directly affect the quality, accuracy and overall production efficiency of the slices. The slicer body 101 mainly includes a blade assembly, a feed channel and an adjustable discharge port, and through the coordination of these key components, accurate cutting and thickness adjustment of the material are achieved.

[0042] The blade assembly is the core cutting unit of the slicer body 101, and its material can be selected from high-strength stainless steel, alloy steel or other materials with high wear resistance and sharpness to ensure cutting accuracy and durability. The blade assembly can be fixed or adjustable, and the specific configuration depends on the slicing requirements. In the case of a fixed blade, the adjustment of the slice thickness mainly depends on the opening adjustment of the discharge port, while in the case of an adjustable blade, the slice thickness can be affected by changing the blade angle, spacing or feed depth. In addition, the blade assembly can be in the form of a single blade or a combination of multiple blades, and the combination of multiple blades helps to improve cutting efficiency and uniformity.

[0043] The feed channel is an important component of the slicer body 101, and its design needs to take into account the smoothness of material transportation and slicing efficiency. The shape and size of the feed channel can be optimized according to the characteristics of the material to ensure that the material can enter the slicing area evenly. The feed channel can be equipped with an automatic feeding device, such as a conveyor belt, a roller feeding mechanism, or a screw propulsion mechanism to improve production efficiency and reduce manual operation errors. In certain high-precision slicing applications, the feed channel can be combined with a sensor or a positioning device to ensure that the material feeding angle, speed, and position meet the preset requirements, thereby improving the consistency of slicing quality.

[0044] The adjustable discharge port is one of the key components for adjusting the slice thickness in the present invention, and its main function is to control the thickness of the slice and ensure that the material can be discharged smoothly after cutting. The structure of the discharge port can be adjusted mechanically, pneumatically or electrically to achieve precise control of the opening. In the present invention, the adjustment of the discharge port is connected to the actuator 105, and the opening and closing of the adjustable baffle is controlled by the drive motor, thereby changing the thickness constraint of the material when passing through the discharge port, and realizing dynamic adjustment of the slice thickness. The material of the discharge port should have wear resistance and low friction properties to reduce the adhesion or blockage of the material during the slicing process and improve the stability and reliability of production.

[0045] During the entire slicing process, the slicer body 101 works in coordination with the slice thickness sensor 102 , the image acquisition device 103 , the control processing unit 104 and the actuator 105 to achieve intelligent thickness adjustment.

[0046] Furthermore, the slicer body includes an adaptive feed control device, which includes a plurality of adjustable feed guide plates for adjusting the inclination angle and opening width according to the instructions of the control processing unit to ensure the uniform arrangement of the material before entering the feed channel.

[0047] In the slicer intelligent slice thickness adjustment system of the present invention, the slicer body is further integrated with an adaptive feed control device, the core function of which is to optimize the arrangement state of the material before entering the feed channel to improve the thickness consistency of the slices and reduce the thickness error caused by uneven feeding. Traditional slicers usually rely on fixed feed channels, and it is difficult to make real-time adjustments according to material characteristics or slicing requirements during the feeding process, resulting in uneven feed distribution, which affects the slicing accuracy. The present invention introduces an adaptive feed control device, so that the feeding method can be intelligently adjusted according to the feedback from the slice thickness sensor and the image acquisition device, thereby optimizing the slicing quality.

[0048] The adaptive feed control device includes a plurality of adjustable feed guide plates, which are installed at the entrance of the feed channel and are fixed to the support structure in an adjustably manner. The inclination angle and opening and closing width of each feed guide plate can be adjusted independently to adapt to materials of different shapes, sizes and flow characteristics, so that the materials can be automatically arranged in the most suitable state before entering the feed channel to ensure the consistency of slice thickness. The guide plate can be made of high-strength wear-resistant materials, such as stainless steel, engineering plastics or special coating materials, to reduce material adhesion and improve conveying stability. In addition, the surface of the guide plate can be designed as a micro-texture structure to enhance the friction control ability of the material and avoid uneven feeding caused by excessive sliding or accumulation.

[0049] The feed guide plate can be adjusted mechanically, electrically or pneumatically, and the specific method can be selected according to the application requirements of the slicer. In the mechanical adjustment method, the position of the guide plate can be manually adjusted by a knob or a slide mechanism to adapt to different batches or types of materials. In the electric or pneumatic adjustment method, the guide plate is connected to a stepper motor or cylinder, and its angle and opening and closing state are controlled in real time by the control processing unit, so that the system can make adaptive adjustments according to the dynamic changes in the material thickness distribution. The electric solution is suitable for high-precision, high-speed production environments, while the pneumatic solution is suitable for applications that require high flexibility in material adjustment, such as food or biomaterial slicing.

[0050] The adjustment logic of the feed guide plate is based on the real-time data of the slice thickness sensor and the image acquisition device. The control processing unit analyzes the material feeding status according to the thickness deviation detected by the sensor, and makes the feeding more uniform by adjusting the angle and opening width of the guide plate. For example, when it is detected that the slice thickness in a certain area is too large, it means that the feed in this area is excessive. The system can adjust the inclination angle of the guide plate in the corresponding area to restrict the flow of materials and reduce the feed amount in this area; conversely, if the slice thickness in a certain area is insufficient, the guide plate opening can be increased to increase the feed amount. This dynamic adjustment mechanism can ensure the continuity of the feed while making the feed distribution more uniform, thereby improving the consistency of the slice thickness.

[0051] To further optimize feed control, the adaptive feed control device can also be combined with vibration control technology to apply small vibrations to the guide plate during the material flow process to promote uniform distribution of materials and prevent adhesion or accumulation. The vibration frequency can be controlled by an ultrasonic vibration component or an electromagnetic drive device and automatically adjusted according to the type of material. For example, for materials that are prone to adhesion or accumulation, such as wet foods or high-viscosity materials, the vibration amplitude can be increased to reduce uneven feeding, while for looser dry materials, the vibration amplitude can be reduced to ensure stable conveying.

[0052] The adaptive feed control device not only improves the uniformity of the slicer’s feed, but also enables the system to adapt to different types of materials through intelligent adjustment capabilities, improving slice quality and significantly reducing the need for human intervention.

[0053] Furthermore, the adaptive feeding control device includes an intelligent vibration adjustment module, which includes an ultrasonic vibration component, which is electrically connected to the control processing unit and automatically adjusts the vibration amplitude and frequency according to the detection data of the image acquisition device and the slice thickness sensor to optimize the feeding state.

[0054] In the slicer intelligent slice thickness adjustment system of the present invention, the adaptive feed control device further integrates an intelligent vibration adjustment module to optimize the arrangement state of the material before entering the slicer feed channel, thereby improving the uniformity and stability of the slice thickness. The traditional feeding method usually relies on a fixed-angle guide structure to allow the material to slide naturally into the feed channel under the action of gravity. However, due to differences in material shape, size, density and other factors, the traditional feeding method is prone to uneven feeding rate, material accumulation or uneven distribution, thereby affecting the final slice thickness consistency. The present invention adds an intelligent vibration adjustment module to the adaptive feed control device so that the feeding state can be adaptively adjusted in real time to optimize the material arrangement, improve the slicing accuracy, and reduce the thickness error caused by feed fluctuations.

[0055] The intelligent vibration regulation module includes an ultrasonic vibration component, which consists of an ultrasonic vibration sensor, a drive circuit and a vibration actuator. The ultrasonic vibration sensor is used to detect the flow state of the material at the entrance of the feed channel and transmit the data to the control processing unit. The drive circuit is responsible for providing a high-frequency oscillation signal to control the working state of the vibration actuator so that it can dynamically adjust between different vibration frequencies and amplitudes. The vibration actuator usually uses a piezoelectric ceramic vibrator or an electromagnetically driven vibrator to ensure accurate vibration regulation under different material characteristics. Piezoelectric ceramic vibrators can provide stable ultrasonic vibrations and are suitable for high-precision slicing requirements, while electromagnetically driven vibrators are suitable for feeding adjustments of larger materials to improve feeding continuity and smoothness.

[0056] The ultrasonic vibration component is electrically connected to the control processing unit, and its vibration amplitude and frequency can be adjusted in real time according to the detection data of the slice thickness sensor and the image acquisition device. The control processing unit receives the slice image information provided by the image acquisition device, and analyzes whether the material arrangement state is uniform in combination with the thickness detection data of the slice thickness sensor. When it is detected that the feed density is too large or the material is piled up, the control processing unit can increase the amplitude of the ultrasonic vibration or increase the vibration frequency to disperse the material and reduce local accumulation. When it is detected that the feed density is too low or the material flow speed is insufficient, the vibration amplitude can be reduced or the vibration frequency can be adjusted to improve the continuity of the feed. This adaptive adjustment method can make the material more evenly arranged on the feed guide plate, so as to obtain the best flow state before entering the feed channel, improve the consistency of the slice thickness, and reduce the slice error caused by feed fluctuations.

[0057] In order to further enhance the feeding optimization capability, the intelligent vibration adjustment module can be combined with multi-zone vibration control technology, that is, multiple independently controlled ultrasonic vibration components are set in different areas of the feed channel, so that the material flow state in different areas can be accurately adjusted. For example, the area close to the feed port can use low-frequency and small-amplitude vibration to avoid premature dispersion of the material, while the area close to the discharge port can use high-frequency vibration to ensure that the material enters the feed channel evenly. In addition, in certain specific application scenarios, a phase adjustment function can be added to the vibration adjustment module to enable multiple ultrasonic vibration components to work together, control the material flow direction through phase matching, and allow the material to enter the feed channel in an orderly manner according to the preset path, further improving the stability of the feed and the consistency of the slice thickness.

[0058] The intelligent vibration adjustment module can also be combined with machine learning algorithms to achieve adaptive optimization. The system can record the material feeding status, vibration adjustment parameters and corresponding slice thickness data during operation, and analyze the optimal vibration control strategy through machine learning models. For example, during the production process, the system can automatically learn which vibration frequencies and amplitudes are most suitable for specific types of materials, and automatically apply the optimal parameters in subsequent slicing tasks to improve the intelligence of the system. This data-driven optimization method can continuously improve the adaptability of the system, making it compatible with different types of materials and ensuring high-quality slicing in various working environments.

[0059] The slice thickness sensor 102 is disposed at the discharge port and is used to detect the actual thickness of the slice and generate thickness detection data.

[0060] In the slicer intelligent slice thickness adjustment system provided by the present invention, the slice thickness sensor 102 is the key detection component of the system, and its function is to measure the slice thickness discharged from the slicer outlet in real time, and provide the corresponding thickness detection data to the control processing unit to realize intelligent thickness adjustment. The accuracy, response speed and stability of the sensor directly affect the reliability of slice thickness adjustment, so its selection, installation method, data transmission method and cooperation with other system components need to be comprehensively considered during the design and implementation process.

[0061] The slice thickness sensor 102 usually adopts a non-contact or contact measurement method to adapt to different types of slice materials and application environments. In the non-contact measurement scheme, a laser distance sensor, an optical interferometer thickness gauge or an ultrasonic sensor can be used to measure the distance between the slice and the sensor or the change in slice thickness by emitting and receiving a light beam or sound wave of a specific frequency, thereby calculating the actual thickness. This measurement method is suitable for applications requiring high precision and non-destructive testing, and can avoid problems such as surface contamination or deformation of the material. In the contact measurement scheme, a mechanical displacement sensor or an inductive thickness gauge can be used to make the sensor probe directly contact the slice surface, and calculate the thickness based on the displacement or inductance change of the probe. This method is suitable for thicker or more rigid slice materials and has higher measurement stability.

[0062] The installation position of the slice thickness sensor 102 is generally set near the discharge port of the slicer to ensure that the thickness of the slice just cut can be accurately measured. Specifically, the sensor can be fixed to the side wall, top or bottom of the discharge port to meet different measurement requirements. For example, for non-contact solutions using optical or ultrasonic sensors, the sensor can be installed above or on the side where the slice passes to avoid slice accumulation or blocking the light path. For contact sensors, it is usually necessary to design an elastic bracket or an adjustable support structure to ensure that the probe can stably contact the slice surface while avoiding deformation or damage to the slice due to excessive pressure. In addition, the installation angle and distance of the sensor should be optimized according to the size, material and discharge speed of the slice to ensure measurement accuracy and consistency.

[0063] In terms of data processing, the slice thickness sensor 102 needs to have high-frequency data acquisition capabilities to meet the needs of continuous operation of the slicer. The thickness data collected by the sensor can be transmitted to the control processing unit in the form of analog signals or digital signals. If analog signal transmission, such as voltage or current signals, is used, a corresponding analog-to-digital conversion circuit needs to be configured in the control processing unit to convert the analog signal into a calculable thickness value. If digital signal transmission, such as serial communication (RS485, I 2C, SPI, etc.) or industrial bus (such as CAN bus, Modbus protocol), the thickness data can be directly transmitted to the control processing unit through the digital interface, thereby reducing signal interference and improving the stability and accuracy of data transmission.

[0064] In order to improve the stability of the measurement, the slice thickness sensor 102 can be equipped with data filtering and error compensation functions. For example, the raw data can be smoothed by a sliding average filter or a Kalman filter algorithm to reduce the measurement error caused by environmental vibration or signal noise. In addition, error compensation can be performed in combination with historical data, such as calibration at system startup to correct the system error caused by sensor drift or installation position deviation, thereby improving the accuracy of thickness detection data.

[0065] The slice thickness sensor 102 is used in conjunction with the control processing unit, so that the system can monitor the slice thickness in real time and dynamically adjust the discharge port opening or other cutting parameters according to the detection data, thereby ensuring the consistency and stability of the slice thickness. This thickness adjustment method based on real-time detection and closed-loop control overcomes the defect of traditional slicers relying on manual adjustment, improves the slice quality, and significantly improves the degree of automation and production efficiency.

[0066] Furthermore, the slice thickness sensor includes a multi-point distributed detection unit, which is composed of a plurality of thickness sensor arrays, evenly distributed along the width direction of the discharge port, and used to simultaneously detect slice thickness data at multiple positions. The multi-point distributed detection unit is electrically connected to the control processing unit, and performs weighted calculation on the measurement results of each sensor through a data fusion algorithm to eliminate local errors and obtain more accurate global thickness data, and calibrates the uneven thickness area in combination with the slice morphology information provided by the image acquisition device to optimize the slice thickness control accuracy.

[0067] In the slicer intelligent slice thickness adjustment system of the present invention, the slice thickness sensor further adopts a multi-point distributed detection unit to achieve more accurate measurement and optimized adjustment of the slice thickness. The traditional single-point thickness measurement method can usually only obtain the thickness information of a certain position at the discharge port. This local measurement method is difficult to fully reflect the thickness changes within the entire slice width range, especially in the case of uneven feeding, uneven force on the cutter, or natural fluctuations in material thickness. Single-point detection may cause the system adjustment accuracy to be limited and cannot meet the needs of high-precision slice control. To this end, the present invention adopts a multi-point distributed detection unit, and by evenly distributing multiple thickness sensors along the width direction of the discharge port, it realizes the collection of thickness data for the entire slicing area, thereby improving the comprehensiveness and accuracy of the measurement.

[0068] The multi-point distributed detection unit is composed of a plurality of thickness sensor arrays, each thickness sensor can adopt a non-contact measurement method, such as a laser distance sensor, an ultrasonic thickness gauge or a capacitive sensor, to adapt to different types of slice materials. For example, a laser distance sensor is suitable for the measurement of high-precision slices, can provide micron-level thickness detection accuracy, and has good adaptability to materials with different surface characteristics, while an ultrasonic thickness gauge is more suitable for thickness measurement of flexible or translucent materials, and a capacitive sensor can be used for high-precision thickness measurement of conductive materials or thin film materials. Each thickness sensor works independently, collects the thickness information of its location in real time, and transmits the measurement data to the control processing unit through a high-speed data interface for subsequent data fusion and analysis.

[0069] In terms of data processing, the multi-point distributed detection unit is electrically connected to the control processing unit, and a data fusion algorithm is used to comprehensively calculate the measurement results of multiple sensors. The core goal of data fusion is to eliminate the measurement errors that may occur in a single sensor, and at the same time improve the reliability of thickness data through weighted calculation. For example, the system can calculate the stability weights of different sensors under specific measurement conditions based on historical measurement data and real-time acquisition data, and dynamically weight the measurement data to reduce the impact of local outliers. In addition, the control processing unit can use a surface fitting algorithm or an interpolation algorithm to fit the discrete thickness points measured by each sensor into a continuous thickness distribution curve, thereby obtaining the global thickness data of the entire slicing area, providing a more complete reference for subsequent fine adjustments.

[0070] The system also combines the slice morphology information provided by the image acquisition device to accurately calibrate areas with uneven thickness. The image acquisition device can obtain the edge contour and surface morphology of the slice in real time, and calculate the slice width, surface flatness, edge integrity and other parameters through image analysis technology. When the thickness sensor detects that the thickness of a certain area is abnormal, the system can analyze whether there are problems such as uneven feeding, cutter wear or material deformation in the area in combination with the image data, so as to accurately identify the source of the thickness error and take corresponding compensation measures. For example, if it is detected that the thickness of a certain area is too large, the system can determine whether it is caused by excessive feeding in the area, and adjust the guide plate angle of the feed control device to optimize the material arrangement state; if it is detected that the thickness of the slice edge is too thin, it may be caused by blade wear or insufficient cutting pressure. The system can appropriately adjust the cutting angle or pressure parameters of the blade assembly to balance the slice thickness. In addition, by combining historical data analysis, the system can predict the thickness change trend and actively adjust the slice parameters before the thickness deviation occurs to improve the slice consistency.

[0071] The introduction of multi-point distributed detection units enables the slicer to perform thickness detection in a finer spatial range and optimize the control accuracy of slice thickness by combining intelligent analysis. This method not only improves the accuracy of measurement, but also enhances the system's adaptability, making it suitable for different types of materials and slicing requirements. In addition, the system combines physical measurement data with image information through multi-dimensional data fusion, achieving a more comprehensive slice thickness control method, making the slicer have a higher level of intelligence, thereby meeting the high-precision production needs in fields such as food processing.

[0072] Furthermore, the slice thickness sensor includes a self-calibration compensation module, which automatically calculates the sensor drift error based on the thickness data provided by the multi-point distributed detection unit, combined with historical measurement data and a preset slice thickness range, and corrects the measurement data in real time through a dynamic compensation algorithm. The self-calibration compensation module is electrically connected to the control processing unit, so that the system can maintain high-precision measurement capabilities during long-term operation, and perform adaptive adjustments in combination with environmental temperature and humidity changes to ensure the stability of thickness detection data, thereby further improving the accuracy and consistency of slice thickness adjustment.

[0073] In the slicer intelligent slice thickness adjustment system of the present invention, in order to ensure the long-term stability and high precision of the thickness detection data, the self-calibration compensation module is integrated into the slice thickness sensor to realize automatic error correction and environmental adaptive adjustment. Traditional thickness measurement systems are usually affected by sensor drift, ambient temperature fluctuations, humidity changes and sensor aging caused by long-term use, which may cause deviations in the measurement data, thereby affecting the accuracy of slice thickness adjustment. The present invention uses a self-calibration compensation module to enable the system to automatically identify measurement errors under different working conditions and perform dynamic compensation to ensure the accuracy and reliability of thickness detection.

[0074] The core function of the self-calibration compensation module is to calculate the drift error of the sensor in real time based on the thickness data provided by the multi-point distributed detection unit, combined with historical measurement data and the preset slice thickness range. The module first analyzes the measurement trend of the sensor through long-term data monitoring to identify measurement deviations caused by temperature, humidity, mechanical vibration or other external factors. For example, the system can establish an error database, collect sensor output data at different time periods when the slicer is running, and compare it with the baseline thickness data to identify whether the sensor has systematic drift. When a persistent deviation between the thickness measurement and the theoretical value is detected, and the deviation cannot be eliminated by material feed adjustment or slice parameter optimization, the system will determine that the sensor may have drifted and automatically trigger the compensation algorithm to correct the measurement data.

[0075] The module uses a dynamic compensation algorithm to correct the measured data in real time, so that the thickness measurement is always kept within the accuracy requirements. The dynamic compensation algorithm can use a calibration method based on adaptive filtering, such as Kalman filtering or neural network compensation algorithm, to ensure that the system can operate stably under different working conditions. Kalman filtering is suitable for high-precision real-time correction needs. It can predict the error trend through historical data and use the current measurement value for optimal estimation, thereby effectively reducing measurement noise and improving the accuracy of thickness detection. The neural network compensation algorithm can use training data to establish an error model. The system continuously learns and optimizes compensation parameters during operation, so that it can adapt to the complex error conditions generated during the long-term operation of the sensor. In addition, the self-calibration compensation module can combine the data of multi-point thickness sensors and use weighted average or trend analysis methods to ensure that local measurement errors do not affect the overall thickness assessment, further improving the reliability of the system.

[0076] The module is electrically connected to the control processing unit so that it can make adaptive adjustments based on the real-time changes in slice thickness and environmental factors. After receiving the correction data from the self-calibration compensation module, the control processing unit can automatically update the slice thickness adjustment parameters so that the system can maintain highly consistent slice quality under different environmental conditions. For example, when the system detects that an increase in ambient temperature may cause a change in sensor sensitivity, the control processing unit can automatically adjust the compensation coefficient of the thickness measurement data based on the historical temperature-error curve to eliminate the temperature effect. Similarly, when it is detected that humidity changes may affect the stability of the sensor signal, the system can appropriately reduce the weight of the measurement data, or perform double verification in combination with the detection results of the image acquisition device to ensure the accuracy of thickness control.

[0077] The present invention integrates a self-calibration compensation module, so that the slice thickness detection system can always maintain high-precision measurement capabilities during long-term operation, and has the ability to adaptively adjust to different environmental conditions. This design not only improves the stability of slice thickness adjustment, reduces the error caused by sensor drift, but also reduces the need for manual intervention, allowing the slicer to maintain efficient and accurate slice quality control under various complex working conditions, providing a more intelligent and reliable solution for application fields such as food processing.

[0078] The image acquisition device 103 is disposed at the discharge end of the discharge port and is used to acquire image information of the slices.

[0079] In the slicer intelligent slice thickness adjustment system of the present invention, the image acquisition device 103 undertakes the key visual detection function, and its main function is to collect slice image information discharged from the discharge port in real time, so that the subsequent control processing unit can analyze and adjust the uniformity of slice thickness. The design of the device directly affects the detection accuracy and response speed of the system, so the hardware selection, installation location, image processing algorithm and environmental adaptability need to be optimized to ensure that the system can work reliably and provide high-quality data.

[0080] The image acquisition device 103 is usually composed of an industrial camera, an optical lens, and an auxiliary light source to ensure that the image information of the slice can be accurately and clearly obtained. The selection of an industrial camera needs to consider parameters such as resolution, frame rate, and photosensitivity to ensure that image data with sufficient details can be captured. For example, in high-precision application scenarios, a CMOS or CCD camera with more than 5 million pixels can be selected to obtain a higher image resolution so that the system can identify subtle thickness changes of the slice. In addition, in order to adapt to the operating speed of the high-speed slicer, the frame rate of the camera needs to match the throughput rate of the slice, and it is usually required to capture at least dozens of frames of images per second to ensure that the system can monitor the changes in slice thickness in real time.

[0081] The selection of optical lenses is also crucial. The appropriate focal length and field of view should be selected according to the size of the slice, the installation distance, and the imaging requirements of the camera. If the slice width is large, a short focal length lens can be used to obtain a wider field of view to ensure that the entire slice area can be clearly photographed. If the slice size is small or finer details need to be detected, a long focal length or microscope lens can be used to improve the detail resolution of the image. In addition, in order to reduce optical distortion, the lens can adopt a low distortion design and be equipped with a suitable filter to eliminate the influence of ambient light on the imaging quality.

[0082] The configuration of auxiliary light sources is also critical to ensuring image quality. In low-light environments or during high-speed slice inspection, insufficient light or flickering light sources may cause blurred images or reduced contrast, thus affecting the accuracy of subsequent image analysis. Therefore, LED ring light sources, linear light sources, or backlighting can be used to ensure that the edges and surfaces of the slices are clearly visible. For transparent or translucent slice materials, such as food or medical films, backlights can be used to enhance the outline of the slice edges, allowing the system to more easily perform thickness measurements and uniformity analysis. For slices with reflective or irregular surfaces, diffuse light sources or polarized light filters can be used to reduce light spots and reflection interference and improve imaging stability.

[0083] The installation position of the image acquisition device 103 is usually set at the discharge end of the discharge port to ensure that the image is collected immediately after the slice is discharged to minimize the impact of external interference on the detection result. The installation angle of the camera needs to be optimized according to the movement direction of the slice, the structure of the discharge port and the position of the light source. Usually, vertical shooting from above, oblique side shooting or multi-angle combined shooting can be adopted to adapt to different types of slice materials and thickness measurement requirements. In some high-precision applications, the image acquisition device can be combined with a mechanical stabilization device to reduce the impact of slicer vibration on imaging quality, thereby improving the stability and availability of image data.

[0084] The collected image data is transmitted to the control processing unit 104 for processing through a high-speed data interface, such as USB 3.0, GigE (Gigabit Ethernet), etc. In order to ensure the real-time and stability of data transmission, FPGA or a dedicated image processing chip can be used for pre-processing, such as image denoising, edge enhancement, contrast optimization, etc., to ensure that the subsequent analysis algorithm can more accurately calculate the uniformity parameters of the slice thickness. In addition, the system can combine an intelligent cache management mechanism to optimize the data transmission path and reduce processing delays to meet the real-time requirements of high-speed slicers.

[0085] The collaboration between the image acquisition device 103 and the control processing unit 104 enables the system to not only rely on the point measurement data of the thickness sensor 102, but also comprehensively evaluate the thickness change of the slice through two-dimensional or three-dimensional image analysis. Combined with advanced image processing algorithms, such as edge detection, morphological processing, Fourier transform, deep learning, etc., the system can recognize the contour of the slice with high precision and calculate the thickness uniformity parameters so that the subsequent control instruction generation module can intelligently adjust the discharge port to optimize the slice quality.

[0086] Through the above design, the image acquisition device 103 plays an important role in the entire slicer intelligent thickness adjustment system, enabling the system to accurately and in real time monitor the slice thickness, and combined with the intelligent analysis of the control processing unit 104, to achieve high-precision thickness control, improve production efficiency, and reduce the impact of human intervention on slice quality.

[0087] Furthermore, the image acquisition device includes a multispectral imaging module for acquiring image information of slices within different wavelength ranges and performing multi-dimensional analysis of slice thickness and material uniformity; the multispectral imaging module includes a light source with at least two working bands, and an imaging sensor that works synchronously with the light source to obtain slice information under visible light, near-infrared light or ultraviolet light bands; by imaging in different bands, the microstructural differences, thickness changes and possible internal defects of the slices are detected, thereby improving the accuracy of slice thickness analysis.

[0088] In the slicer intelligent slice thickness adjustment system of the present invention, the image acquisition device further integrates a multispectral imaging module, which is used to collect image information of slices within different wavelength ranges, and analyze slice thickness and material uniformity from multiple dimensions to improve the accuracy and reliability of thickness measurement. Traditional single-band imaging methods can usually only judge slice boundaries based on grayscale or color differences in the visible light range, and it is difficult to effectively identify the internal structure of the material, non-uniform thickness distribution or potential defects. Especially for slices of high transparency, translucent or composite materials, single-band image acquisition often cannot provide sufficient information for high-precision thickness measurement. The present invention achieves a more comprehensive analysis of slices by adopting a multispectral imaging module, enabling the system to obtain rich slice information under different optical conditions, thereby improving the accuracy of thickness uniformity assessment.

[0089] The multi-spectral imaging module is composed of a light source of at least two working bands and an imaging sensor that works synchronously therewith. The light source may include a visible light LED, a near-infrared laser, an ultraviolet light source, or other narrow-band light sources of specific wavelengths to adapt to the optical properties of different materials. For example, in the visible light band, the system can obtain conventional image information of the slice, including color distribution, surface texture, and basic contours, while in the near-infrared light band, the optical absorption difference of the material can be used to detect changes in the internal structure, which is suitable for identifying possible density unevenness, bubbles, or inclusions. In addition, in the ultraviolet light band, some materials may exhibit fluorescence effects or differences in absorption rates, which can further improve the system's ability to detect material properties and thickness changes. This multi-band collaborative working method allows the system to not only accurately obtain the edge contour of the slice, but also analyze the optical changes inside the material to evaluate the thickness uniformity of the slice with higher accuracy.

[0090] The imaging sensor of the multispectral imaging module adopts a high-sensitivity CMOS or CCD sensor, combined with a multi-channel filter, so as to obtain optical signals of different bands respectively. The system uses high frame rate synchronous acquisition technology to quickly capture information of multiple bands at the moment when the slice passes through the imaging area, and uses image fusion algorithm for analysis. The fusion algorithm can calculate the optimal contour of the slice edge based on the band weight to improve the accuracy of thickness measurement. For example, in the visible light band, the system can calculate the slice boundary through the grayscale gradient, and in the near-infrared band, the system can calculate the internal thickness distribution of the slice based on the transmittance of the material, thereby realizing the complementarity of multi-channel information and further improving the measurement accuracy.

[0091] In order to enhance the adaptability of detection, the multispectral imaging module can also select the optimal spectral combination according to different slice materials and process requirements. For example, for highly transparent materials such as glass or certain plastic slices, the system can increase the proportion of infrared light sources to improve the perception of thickness changes, while for less transparent materials such as metal sheets or paper products, the system can focus more on visible and ultraviolet light bands to enhance the detection of surface texture. In addition, the multispectral imaging module can be used in conjunction with polarized light filtering technology to eliminate surface reflection interference, improve imaging contrast, and make thickness measurement results more accurate.

[0092] The present invention introduces a multi-spectral imaging module to enable the slicer intelligent thickness adjustment system to have stronger material adaptability and detection accuracy. Under the comprehensive analysis of optical information in different bands, the system can not only detect the thickness change of the slice, but also identify the microstructural differences and potential defects of the material, thereby optimizing the thickness control strategy, improving the slice quality, and meeting the needs of high-precision thickness measurement in multiple fields such as food processing and pharmaceutical tablet production.

[0093] Furthermore, the image acquisition device includes a dynamic focal length adaptive adjustment module, which is used to adjust the imaging focal length in real time according to the slice thickness and the material transmittance, so as to optimize the imaging quality of different types of slices; the dynamic focal length adaptive adjustment module includes a variable focus lens and an automatic focus control circuit, and the automatic focus control circuit calculates the focus offset according to the real-time imaging data of the image acquisition device, and adjusts the focal length of the variable focus lens through an electric adjustment mechanism, so that the slice edge and surface texture always maintain clear imaging.

[0094] In the slicer intelligent slice thickness adjustment system of the present invention, the image acquisition device further integrates a dynamic focal length adaptive adjustment module to dynamically optimize the imaging focal length according to the real-time changes in slice thickness and material transmittance, thereby improving the imaging quality of different types of slices. Traditional fixed-focus imaging systems often find it difficult to balance imaging clarity and measurement accuracy when faced with slices of different thicknesses and different optical properties. For example, when the slice thickness is large, the focus of the optical system may shift, resulting in blurred edges; and for slices with high transmittance, such as glass or thin film materials, changes in transmitted light may affect the imaging contrast, making it difficult for the system to accurately identify slice boundaries. The present invention dynamically adjusts the focal length so that the system can always maintain the optimal imaging state under various material and thickness conditions, thereby ensuring the accuracy and stability of slice thickness measurement.

[0095] The dynamic focal length adaptive adjustment module consists of a variable focus lens and an autofocus control circuit. The variable focus lens is used to adjust the focal length during the imaging process so that the system can adapt to the optical properties and thickness changes of different slice materials. The lens usually adopts an electric zoom structure, which can respond quickly within microseconds to achieve automatic adjustment of the focal length. In order to ensure the accuracy of focal length adjustment, the autofocus control circuit analyzes the slice image data obtained by the image acquisition device in real time, calculates the focus offset, and adjusts the lens position according to the offset degree, so that the slice edge and surface texture always maintain clear imaging.

[0096] The autofocus control circuit uses an algorithm based on gradient analysis and multi-scale edge detection to calculate the focus offset. Specifically, the system first performs a frequency domain transform on the real-time acquired image, analyzes the clarity changes of different spatial frequency components, and calculates the offset direction and offset of the current focus through the contrast of the high-frequency components. When the focus offset exceeds the preset threshold, the system automatically adjusts the variable focus lens to return the focal length to the optimal position to ensure the imaging quality. At the same time, the system can also optimize the focus adjustment strategy in combination with the optical transmittance information of the slice. For example, on materials with higher transmittance, the system can increase the accuracy of the focus adjustment to reduce the blurring effect caused by light interference, while for materials with stronger optical absorption, the adjustment frequency can be appropriately reduced to reduce the computational load on the imaging system.

[0097] In the specific implementation process, the adjustment range and sensitivity of the variable focus lens can be optimized according to the different slice materials and thicknesses. For example, for conventional materials with a thickness between 0.1mm and 5mm, the focus adjustment accuracy can be set at 10 microns to ensure sufficient measurement accuracy. For thicker slices, such as industrial ceramics or frozen meat slices in food processing, the focus adjustment range can be larger to meet the needs of deeper imaging depth of field. In addition, in the detection of special materials such as translucent polymer films or drug tablets, the system can combine the optical data of the multispectral imaging module to further optimize the focus adjustment, making the imaging effect of high-light-transmittance materials more stable and reducing errors caused by optical scattering.

[0098] By introducing the dynamic focal length adaptive adjustment module, the image acquisition device of the present invention can not only adapt to slices of different thicknesses and material types, but also maintain the optimal imaging quality under various optical conditions. This module enables the system to provide high-precision, real-time adjustment imaging support during slice thickness detection, improves the reliability of slice thickness measurement, and ensures that the thickness adjustment system can achieve precise control in a variety of industrial and food application environments.

[0099] The control processing unit 104 includes an image analysis module, which is used to calculate the uniformity parameter of the slice thickness based on the image information provided by the image acquisition device to characterize the uniformity of the slice thickness; a data processing module, which is used to receive and analyze the thickness detection data and uniformity parameters, and compare them with preset slice parameters including a target thickness range and a uniformity threshold; and a control instruction generation module, which is used to generate an adjustment instruction according to the comparison result.

[0100] In the intelligent slice thickness adjustment system for a slicer provided by the present invention, the control processing unit 104 is the core computing and decision-making component of the entire system, and its main function is to receive the detection data from the slice thickness sensor 102 and the image acquisition device 103, analyze the slice thickness and uniformity, and generate adjustment instructions based on the preset slice parameters to achieve precise thickness control. The unit integrates multiple functional modules, including an image analysis module, a data processing module, and a control instruction generation module, which work together to ensure that the slicer can intelligently adjust the opening of the discharge port, thereby maintaining the consistency and stability of the slice thickness.

[0101] The image analysis module is responsible for processing the image information provided by the image acquisition device 103 and extracting the uniformity parameters of the slice thickness therefrom. Specifically, the module can use edge detection, grayscale analysis or deep learning algorithms to identify the boundaries of the slices in the image and calculate the changes in the slice thickness along the slice width direction. By analyzing continuous images, the stability of the slice in the time dimension can also be evaluated to determine whether the slice thickness fluctuates over time. In addition, the image analysis module can use filtering technology to remove image noise, and combine contrast enhancement, threshold segmentation and other methods to improve the recognition accuracy of the slice contour to ensure that the calculation of the uniformity parameters is accurate and reliable.

[0102] The main task of the data processing module is to receive the thickness detection data collected by the slice thickness sensor 102, and to make a comprehensive evaluation of the slice thickness in combination with the uniformity parameters output by the image analysis module. The module contains a data fusion algorithm to ensure that the thickness data from different sources can be effectively integrated to improve the measurement accuracy. Specifically, a weighted average algorithm or a Kalman filter algorithm can be used to jointly calculate the sensor data and the image analysis data to eliminate the errors that may exist in a single measurement method. In addition, the data processing module will compare the real-time thickness data with the preset slice parameters. The preset slice parameters usually include a target thickness range and a uniformity threshold. These parameters can be set by the operator when the system is initialized, or they can be automatically optimized through machine learning or historical data analysis. When it is detected that the actual thickness or uniformity deviates from the set range, the module will pass the analysis results to the control instruction generation module for subsequent adjustments.

[0103] The function of the control instruction generation module is to generate appropriate adjustment instructions based on the analysis results provided by the data processing module, and send them to the actuator 105 to achieve automatic adjustment of the discharge port. This module uses an adaptive control algorithm, which can dynamically adjust the adjustment strategy according to the real-time changes in the slice thickness to ensure thickness stability. For example, when the thickness sensor 102 detects that the slice thickness is too large, the module can calculate the required adjustment range and generate a control signal to drive the actuator 105 to drive the baffle to contract, thereby reducing the opening of the discharge port to achieve the target thickness. Similarly, if the thickness is detected to be too small, the module will issue an instruction to relax the baffle appropriately to allow thicker slices to pass through. In addition, the module can also predict future thickness trends based on historical data and make fine adjustments in advance to reduce the impact of adjustment delays on slice quality.

[0104] The computing power and response speed of the control processing unit 104 play a decisive role in the overall performance of the system. Therefore, its hardware can adopt an embedded microcontroller, FPGA (field programmable gate array) or industrial computer to meet the needs of different application scenarios. For slicing tasks with high precision requirements, a high-performance DSP (digital signal processor) can be used to improve the real-time performance of data processing and instruction generation. In addition, the control processing unit can be integrated with a wireless communication module or an industrial bus interface to enable data interaction with other intelligent manufacturing equipment to achieve remote monitoring and automated production line integration.

[0105] Through the collaborative work of the above modules, the control processing unit 104 can not only monitor and analyze the slice thickness in real time, but also intelligently adjust the operating parameters of the slicer to ensure the consistency and uniformity of the slice thickness, thereby improving the slice quality, reducing manual intervention, and improving production efficiency.

[0106] Furthermore, the image analysis module of the control processing unit adopts a hybrid calculation method based on multi-scale gradient uniformity distribution analysis and normalized thickness deviation index to evaluate the uniformity parameters of slice thickness; the hybrid method first pre-processes the slice image acquired by the image acquisition device, including denoising, contrast enhancement and edge detection, and calculates the local gradient change rate at different scales, thereby extracting the spatial variability of slice thickness.

[0107] In the slicer intelligent slice thickness adjustment system of the present invention, the image analysis module of the control processing unit adopts a hybrid calculation method based on multi-scale gradient uniformity distribution analysis (MSGUDA) and normalized thickness deviation index (NTDI) to accurately evaluate the uniformity parameters of slice thickness and realize intelligent adjustment of slice thickness. The method uses the slice image information obtained by the image acquisition device to pre-process the slice area and calculate the local gradient change rate at different scales to extract the spatial variability of slice thickness, and then quantitatively describe the thickness uniformity through mathematical analysis methods, and provide reliable feedback information for the control system.

[0108] The multi-scale gradient uniformity distribution analysis is used to perform multi-scale decomposition on the slice area, and the scale factor is defined as S k . Scale factor S k The resolution of the image at different scales is usually adjusted by Gaussian pyramid downsampling, bilinear interpolation or wavelet transform, so that the image can be analyzed at different spatial resolutions to obtain more comprehensive thickness uniformity information.

[0109] According to the following formula 1, calculate the local gradient transformation matrix G at each scale k (x, y):

[0110]

[0111] Among them, I k (x, y) represents the grayscale value of the image at scale k, which is usually the brightness value of the image pixel, and its range is generally between 0 (black) and 255 (white).

[0112] x, y represent the horizontal and vertical coordinates of the pixel of the image, respectively, and the unit is usually pixel.

[0113] It represents the local gradient of the image in the x direction at scale k. It is usually calculated using the Sobel operator, Prewitt operator, or Laplace filter. The Sobel operator is recommended because it can better balance the computational complexity and accuracy.

[0114] It represents the local gradient of the image in the y direction at scale k, and the calculation method is the same as that in the x direction.

[0115] According to the following formula 2, the global uniformity factor U(S k ) as a measure of thickness distribution variation:

[0116]

[0117] Among them, Mk and N k They respectively represent the number of rows and columns of the image at scale k, that is, the resolution of the image at this scale, in pixels.

[0118] is the average gradient value at scale k, which is calculated as follows:

[0119]

[0120] Global uniformity factor U(S k ) reflects the uniformity of slice thickness changes at this scale. The larger the value, the more drastic the change in slice thickness and the worse the uniformity.

[0121] According to the following formula 3, calculate the uniformity standard deviation σ U Key indicators as thickness uniformity parameters:

[0122]

[0123] Here, K is the number of scales, that is, the scale level selected when performing multi-scale decomposition. The recommended value is usually between 3 and 5 to balance the computational complexity and the comprehensiveness of the uniformity analysis.

[0124] The normalized thickness deviation index NTDI is calculated according to the following formula 4 to measure the overall deviation of the slice area from the target thickness range:

[0125]

[0126] Among them, H i It represents the thickness value of the i-th pixel point, which is usually measured by image processing methods (such as optical interferometry, structured light scanning or 3D reconstruction) or directly by a slice thickness sensor.

[0127] H target It is the preset target slice thickness. This value can be set by the user or calculated based on historical data. Its unit is usually millimeter (mm).

[0128] N is the total number of measurement points, that is, the total number of pixels in the image used to calculate the thickness. Usually, the pixels in the entire slice area are selected for calculation.

[0129] When σ U Exceeding the uniformity threshold σ U，thresh Or NTDI exceeds the deviation threshold NTDI thresh When the thickness of the slices is not uniform, the control processing unit determines that there is a significant unevenness and triggers an adjustment instruction to adjust the actuator to change the opening of the discharge port so that the thickness tends to be uniform.

[0130] Uniformity threshold σU，thresh and deviation threshold NTDI thresh It can be set according to actual production needs. Generally, σ U，thresh Set between 0.05 and 0.1, and NTDI thresh The recommended setting is between 0.02 and 0.05 to ensure high slice thickness consistency.

[0131] The calculation method of the present invention improves the evaluation accuracy of slice thickness uniformity through multi-scale analysis, and combines the normalized thickness deviation index to achieve dynamic optimization control of the target thickness. Through the above method, the system can more accurately identify and correct thickness deviations, which is suitable for high-precision slicing application fields such as food processing, and effectively improves the intelligence level and production efficiency of the slicer.

[0132] Furthermore, the data processing module of the control processing unit adopts a nonlinear thickness deviation weighting function to receive the thickness detection data and uniformity parameters and compare them with preset slicing parameters including a target thickness range and a uniformity threshold;

[0133] In the slicer intelligent slice thickness adjustment system of the present invention, the data processing module of the control processing unit adopts a nonlinear thickness deviation weighting function to receive thickness detection data and uniformity parameters, and compare with the preset target thickness range and uniformity threshold to optimize the slice thickness adjustment strategy. The nonlinear thickness deviation weighting function can effectively identify thickness deviations and apply different weights to deviations of different degrees, so that the system can more accurately judge thickness errors and achieve more sensitive adaptive adjustments.

[0134] In this calculation method, the thickness value of each measuring point is obtained by a slice thickness sensor, which is usually based on optical, ultrasonic or capacitive thickness measurement technology to ensure high-precision measurement. For each measuring point i, the thickness detection data is recorded as U i , which represents the actual measured thickness at that point. Target thickness U target Preset by the system, set to an ideal slice thickness value according to production requirements. The total number of measurement points N represents the total number of thickness data points measured in the entire slice area, which is usually equal to the total number of thickness data sampled by the sensor.

[0135] The nonlinear thickness deviation weighting function W(U) is implemented using the following formula 5:

[0136]

[0137] Among them, U i is the thickness value of the i-th measuring point, detected by the slice thickness sensor; U targetis the target slice thickness, which is determined by the preset slice parameters; T is the total number of measurement points;

[0138] α is the deviation nonlinear amplification factor, which is used to adjust the sensitivity to deviations of different amplitudes. A larger α value will cause the function to give higher weight to larger deviation points, while a smaller α value will cause the system to treat all deviation values ​​equally. The recommended value range is 1.5 to 3.0, with 2.0 being the best value empirically, which can provide better error detection effects in most slice thickness control applications.

[0139] β is an exponential decay factor, which is used to control the rate of exponential decay, thereby adjusting the degree to which the system ignores small deviation points. A higher β value makes the influence of small deviation points approach zero, while a lower β value increases the participation of small deviation points. The recommended β value range is 0.5 to 2.0. Usually 1.0 can achieve a good balance in most applications, allowing the system to pay attention to obvious thickness deviations while not over-responding to small deviations.

[0140] The numerator of the formula calculates the deviation of all measurement points relative to the target thickness and adjusts its weight through nonlinear amplification and exponential decay, so that the larger deviation points have higher weights, ensuring that the system prioritizes correcting obvious thickness anomalies. The denominator normalizes the exponential decay factors of all measurement points to ensure stable calculation results and avoid value fluctuations caused by different sampling numbers.

[0141] When the calculated W(U) exceeds the preset threshold W thresh When W is reached, the control processing unit determines that there is a significant deviation in the current slice thickness and triggers the adjustment instruction to adjust the actuator to change the opening of the discharge port to make the thickness uniform. thresh It is set by the system based on historical data, usually in the range of 0.02 to 0.05, to ensure that the system operates within the allowable error range and can adjust large deviations in a timely manner.

[0142] The present invention introduces a nonlinear thickness deviation weighting function, so that the system can intelligently classify errors when processing thickness measurement data, optimize the sensitivity of slice thickness adjustment, reduce the possibility of misadjustment, and improve the system's adaptive ability and overall stability. This method is applicable to multiple fields such as food processing, especially in scenarios requiring high-precision thickness control, and can effectively improve product quality and production efficiency.

[0143] The actuator 105 is used to adjust the opening of the discharge port according to the adjustment instruction, wherein the actuator includes a drive motor, the drive motor is connected to the adjustable baffle of the discharge port, and drives the baffle to move within a preset range to change the opening of the discharge port and adjust the slice thickness.

[0144] In the slicer intelligent slice thickness adjustment system of the present invention, the actuator 105 undertakes the key mechanical action, and its role is to accurately adjust the opening of the discharge port according to the adjustment instruction generated by the control processing unit 104 to ensure that the slice thickness meets the preset requirements. The design of the actuator directly affects the adjustment accuracy, response speed and overall stability of the system, so its structure and control method need to be accurately optimized to ensure that the system can efficiently and reliably perform the slice thickness adjustment task.

[0145] The actuator 105 is mainly composed of a drive motor and an adjustable baffle connected thereto. The function of the drive motor is to provide sufficient power to move the baffle within a preset range, thereby changing the opening of the discharge port. In order to ensure the accuracy and response speed of the adjustment, the drive motor can adopt a stepper motor, a servo motor or a DC motor, and select a suitable control method according to the accuracy requirements of the system. The stepper motor has high positioning accuracy and is suitable for application scenarios that require precise control of the baffle opening, while the servo motor can provide higher dynamic response capabilities and is suitable for occasions that require rapid adjustment. If the system requirements are low, a DC motor combined with a position sensor can also meet basic thickness adjustment requirements.

[0146] The adjustable baffle is closely matched with the discharge port, and its structural design needs to take into account the adjustment accuracy, material wear resistance and long-term stability. The baffle can be made of metal materials, such as stainless steel or high-strength aluminum alloy, to ensure that it will not deform due to friction or external forces under long-term working conditions. In addition, the movement of the baffle can adopt linear slides, screw drives or lever mechanisms to achieve precise opening adjustment. In some applications with high precision requirements, a fine-tuning mechanism can be integrated on the baffle to enable more subtle thickness adjustments to meet different slicing requirements.

[0147] In order to ensure that the drive motor can accurately control the position of the baffle, the actuator 105 can be equipped with a position feedback device, such as a photoelectric encoder, a Hall sensor or a potentiometer, etc., to detect the position of the baffle in real time and transmit the feedback signal to the control processing unit 104 so that it can perform closed-loop control. Through this feedback mechanism, the system can detect the actual position of the baffle and compare it with the target position so as to perform error correction when necessary, thereby improving the adjustment accuracy and ensuring the stability of the slice thickness.

[0148] The control method of the actuator 105 can adopt PWM (pulse width modulation) control, current feedback control or position servo control to adapt to different types of drive motors. For example, in the control scheme of the stepper motor, a pulse signal can be used to control the step angle, so as to accurately adjust the position of the baffle. In the control scheme of the servo motor, the speed and position of the motor can be adjusted in real time by using the position feedback signal through a closed-loop control system, so that it can execute the adjustment instruction more accurately. In addition, the actuator 105 can be combined with an intelligent control algorithm, such as PID (proportional-integral-differential) control, to optimize the adjustment process, reduce overshoot and oscillation, and make the adjustment of the baffle more stable and accurate.

[0149] In practical applications, the response speed and adjustment range of the actuator 105 need to be optimized according to the specific requirements of the slicer. For example, in the food processing industry, the adjustment of the slice thickness may require a faster response speed to adapt to the changes in different batches of raw materials, while in the field of precision material cutting, a higher adjustment accuracy may be required to ensure the uniformity of the slice thickness. Therefore, when designing the actuator, the power of the drive motor, the motion structure of the baffle, and the response speed of the feedback control system can be adjusted to meet the needs of specific application scenarios.

[0150] In summary, the actuator 105 realizes intelligent control of the opening of the discharge port through the precise coordination of the drive motor and the adjustable baffle, thereby ensuring the precise adjustment of the slice thickness. Through the position feedback mechanism, closed-loop control system and optimized adjustment algorithm, the mechanism can ensure that the slicer maintains high-precision and high-stability slice thickness control during long-term operation, thereby improving product quality, increasing production efficiency, and reducing the necessity of human intervention.

[0151] Furthermore, the actuator includes a precision variable damping adjustment component, which is used to provide dynamic damping control in the process of adjusting the opening of the discharge port to improve the accuracy and stability of the slice thickness adjustment; the precision variable damping adjustment component includes a fluid damping device and an electromagnetic damping unit, wherein the fluid damping device provides a buffering effect through the flow control of high-viscosity liquid, so that the movement of the baffle is smoother; the electromagnetic damping unit is composed of a controllable electromagnetic coil and a high-magnetic permeability damping sheet, which is used to control the processing unit's instructions to dynamically adjust the damping force, so as to achieve precise control of the opening change rate of the discharge port.

[0152] In the slicer intelligent slice thickness adjustment system of the present invention, the actuator further includes a precision variable damping adjustment component to provide dynamic damping control during the discharge port opening adjustment process, so that the thickness adjustment is more accurate and stable. The traditional baffle adjustment method usually relies on a mechanical drive structure, which has limited response speed and accuracy. Especially in the high-speed slicing process, it is easy to cause excessive or insufficient opening adjustment due to inertial impact, thereby affecting the consistency of slice thickness. The present invention introduces the coordinated control of fluid damping and electromagnetic damping, so that the actuator can dynamically optimize the adjustment rate under different working conditions, avoid errors caused by sudden adjustments, and improve the overall adjustment stability and durability of the system.

[0153] As the basic buffer mechanism of the system, the fluid damping device provides smooth damping during the movement of the baffle through the controlled flow of high-viscosity liquid, thereby reducing vibration or impact caused by sudden speed changes. The device is usually composed of a hydraulic or silicone oil-filled damping cavity. The liquid flows in an adjustable microchannel and the flow rate is controlled by a valve to achieve adaptive adjustment of different adjustment rates. When the opening of the discharge port needs to change over a large range, the fluid damping device provides appropriate buffering to make the movement of the baffle smoother, thereby ensuring the consistency of the slice thickness adjustment. During the process of tiny thickness adjustment, the fluid damping device reduces mechanical jitter through extremely low-speed flow characteristics, making the thickness adjustment more precise.

[0154] As an auxiliary control mechanism, the electromagnetic damping unit is composed of a controllable electromagnetic coil and a high magnetic permeability damping sheet, which can realize dynamic adjustment of the damping force according to the instructions of the control processing unit. The working principle of this unit is based on the eddy current effect, that is, when the baffle moves, the high magnetic permeability damping sheet connected to it moves in the magnetic field generated by the electromagnetic coil, forming eddy current resistance, so that the movement is subject to additional dynamic control force. The advantage of electromagnetic damping is that it can quickly respond to changes in slice thickness errors and realize real-time adjustment by changing the electromagnetic field strength. For example, when the system detects that the slice thickness deviation is large and a faster baffle adjustment is required, the electromagnetic damping unit can reduce the damping force to make the adjustment faster; when the adjustment is close to the target thickness, the system can increase the damping force to make the movement of the baffle slower and more precise, preventing secondary errors caused by adjustment overshoot.

[0155] The control processing unit receives feedback information from the slice thickness sensor and image acquisition device in real time, calculates the required adjustment amount, and sends an adjustment signal to the precision variable damping adjustment component. In actual operation, the system can dynamically adjust the valve opening of the fluid damping device based on the changing trend of the slice thickness, and cooperate with the magnetic field adjustment of the electromagnetic damping unit to enable the actuator to maintain the best response speed and adjustment stability under different slicing conditions. This dual damping control method makes the slicer more adaptable in a high-precision production environment, which not only improves the accuracy of thickness control, but also reduces the thickness fluctuation caused by unstable baffle movement, thereby improving the overall reliability of the system.

[0156] Although the present application is disclosed as above in the form of a preferred embodiment, it is not intended to limit the present application. Any technical personnel in this field may make possible changes and modifications without departing from the spirit and scope of the present application. Therefore, the scope of protection of the present application shall be based on the scope defined by the claims of the present application.

Claims

1. A slicer intelligent slice thickness adjustment system, characterized in that: include: The slicer body comprises a blade assembly, a feed channel and an adjustable discharge port, wherein the opening of the discharge port is adjustable to control the slice thickness; A slice thickness sensor is arranged at the discharge port, and is used to detect the actual thickness of the slice and generate thickness detection data; An image acquisition device, disposed at the discharge end of the discharge port, for acquiring image information of the slices; A control processing unit, comprising an image analysis module, for calculating a uniformity parameter of slice thickness based on the image information provided by the image acquisition device, so as to characterize the uniformity of slice thickness; a data processing module, for receiving and analyzing the thickness detection data and uniformity parameter, and comparing them with preset slice parameters including a target thickness range and a uniformity threshold; and a control instruction generation module, for generating an adjustment instruction according to the comparison result; An actuator is used to adjust the opening of the discharge port according to the adjustment instruction, wherein the actuator includes a drive motor, the drive motor is connected to the adjustable baffle of the discharge port, and drives the baffle to move within a preset range to change the opening of the discharge port and adjust the slice thickness.

2. The intelligent slice thickness adjustment system for a slicer according to claim 1, characterized in that: The slicer body includes an adaptive feed control device, which includes a plurality of adjustable feed guide plates for adjusting the inclination angle and opening width according to the instructions of the control processing unit to ensure the uniform arrangement of the material before entering the feed channel.

3. The intelligent slice thickness adjustment system for a slicer according to claim 2, characterized in that: The adaptive feeding control device includes an intelligent vibration adjustment module, which includes an ultrasonic vibration component. The ultrasonic vibration component is electrically connected to the control processing unit and automatically adjusts the vibration amplitude and frequency according to the detection data of the image acquisition device and the slice thickness sensor to optimize the feeding state.

4. The intelligent slice thickness adjustment system for a slicer according to claim 1, characterized in that: The slice thickness sensor includes a multi-point distributed detection unit, which is composed of a plurality of thickness sensor arrays, evenly distributed along the width direction of the discharge port, and is used to simultaneously detect slice thickness data at multiple positions; the multi-point distributed detection unit is electrically connected to the control processing unit, and performs weighted calculation on the measurement results of each sensor through a data fusion algorithm to eliminate local errors, and calibrates the uneven thickness area in combination with the slice morphology information provided by the image acquisition device to optimize the slice thickness control accuracy.

5. The intelligent slice thickness adjustment system for a slicer according to claim 4, characterized in that: The slice thickness sensor includes a self-calibration compensation module, which automatically calculates the sensor drift error based on the thickness data provided by the multi-point distributed detection unit, combined with historical measurement data and a preset slice thickness range, and corrects the measurement data in real time through a dynamic compensation algorithm; the self-calibration compensation module is electrically connected to the control processing unit, and performs adaptive adjustments in combination with ambient temperature and humidity change factors to ensure the stability of the thickness detection data.

6. The intelligent slice thickness adjustment system for a slicer according to claim 1, characterized in that: The image acquisition device includes a multispectral imaging module, which is used to collect image information of slices within different wavelength ranges and perform multi-dimensional analysis on slice thickness and material uniformity; the multispectral imaging module includes a light source with at least two working bands, and an imaging sensor that works synchronously with the light source to obtain slice information under visible light, near-infrared light or ultraviolet light bands; by imaging under different bands, the microstructural differences, thickness changes and possible internal defects of the slices are detected, thereby improving the accuracy of slice thickness analysis.

7. The intelligent slice thickness adjustment system for a slicer according to claim 6, characterized in that: The image acquisition device includes a dynamic focal length adaptive adjustment module, which is used to adjust the imaging focal length in real time according to the slice thickness and the material transmittance, so as to optimize the imaging quality of different types of slices; the dynamic focal length adaptive adjustment module includes a variable focus lens and an automatic focus control circuit, and the automatic focus control circuit calculates the focus offset according to the real-time imaging data of the image acquisition device, and adjusts the focal length of the variable focus lens through an electric adjustment mechanism, so that the slice edge and surface texture always maintain clear imaging.

8. The intelligent slice thickness adjustment system for a slicer according to claim 1, characterized in that: The actuator includes a precision variable damping adjustment component, which is used to provide dynamic damping control in the process of adjusting the opening of the discharge port to improve the accuracy and stability of the slice thickness adjustment; the precision variable damping adjustment component includes a fluid damping device and an electromagnetic damping unit, wherein the fluid damping device provides a buffering effect through the flow control of a high-viscosity liquid to make the movement of the baffle more stable; the electromagnetic damping unit is composed of a controllable electromagnetic coil and a high-magnetic permeability damping sheet, which is used to dynamically adjust the damping force according to the instructions of the control processing unit, so as to achieve precise control of the opening change rate of the discharge port.

9. The intelligent slice thickness adjustment system for a slicer according to claim 1, characterized in that: The image analysis module of the control processing unit adopts a hybrid calculation method based on multi-scale gradient uniformity distribution analysis and normalized thickness deviation index to evaluate the uniformity parameter of slice thickness; the hybrid method first pre-processes the slice image acquired by the image acquisition device, including denoising, contrast enhancement and edge detection, and calculates the local gradient change rate at different scales, so as to extract the spatial variability of slice thickness; The multi-scale gradient uniformity distribution analysis is used to perform multi-scale decomposition on the slice area, and the scale factor is defined as S k , and calculate the local gradient transformation matrix G at each scale according to the following formula 1 k (x, y): Among them, I k (x, y) represents the grayscale value of the image at scale k; x, y represent the horizontal and vertical coordinates of the pixel of the image respectively; Represents the local gradient of the image in the x direction at scale k; Represents the local gradient of the image in the y direction at scale k; According to the following formula 2, the global uniformity factor U(S k ) as a measure of thickness distribution variation: Among them, M k and N k Respectively represent the number of rows and columns of the image at scale k; is the average gradient value under scale k; According to the following formula 3, calculate the uniformity standard deviation σ U Key indicators as thickness uniformity parameters: Among them, K is the number of scales; The normalized thickness deviation index NTDI is calculated according to the following formula 4 to measure the overall deviation of the slice area from the target thickness range: Among them, H i Indicates the thickness value of the i-th pixel; H target is the preset target slice thickness; N is the total number of measurement points; When σ U Exceeding the uniformity threshold σ U，thresh Or NTDI exceeds the deviation threshold NTDI thresh When the thickness of the slices is not uniform, the control processing unit determines that there is a significant unevenness and triggers an adjustment instruction to adjust the actuator to change the opening of the discharge port so that the thickness tends to be uniform.

10. The intelligent slice thickness adjustment system for a slicer according to claim 1, characterized in that: The data processing module of the control processing unit adopts a nonlinear thickness deviation weighting function to receive the thickness detection data and uniformity parameters and compare them with preset slicing parameters including a target thickness range and a uniformity threshold; The nonlinear thickness deviation weighting function W(U) is implemented using the following formula 5: Among them, U i is the thickness value of the i-th measuring point, detected by the slice thickness sensor; U target is the target slice thickness, which is determined by the preset slice parameters; T is the total number of measurement points; α is the deviation nonlinear amplification factor; β is the exponential decay factor; When the calculated W(U) exceeds the preset threshold W thresh When the thickness of the slice is significantly deviated, the control processing unit determines that there is a significant deviation in the thickness of the slice, and triggers an adjustment instruction to adjust the actuator to change the opening of the discharge port so that the thickness tends to be uniform.

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