An efficient slicing slicing machine control method and system
By deploying intelligent thickness sensors on the slicer, analyzing slice thickness data in real time, dynamically updating the fuzzy subset, and optimizing the PID controller, the problem of inaccurate power control in the meat product slicing process is solved, and the uniformity of slice thickness and slice efficiency are improved.
Patent Information
- Application Number
- CN202410831744.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2024-06-26
- Publication Date
- 2025-05-27
- Estimated Expiration
- 2044-06-26
AI Technical Summary
During the slicing of meat products, traditional fuzzy PID control algorithms are prone to overshoot and slow convergence speed due to the highly nonlinear relationship between power and thickness, resulting in uneven slice thickness.
By deploying intelligent thickness sensors at the discharge end of the slicer, measuring slice thickness in real time, and analyzing using cutting deviation coefficients and extended cutting transfer variations, dynamically update the fuzzification subset, and optimizing the PID controller to achieve accurate control of slicer power.
The uniformity of slice thickness is achieved, the problem of slow control overshoot and convergence speed in traditional methods is avoided, and the slice efficiency and quality of the slicer are improved.
Smart Images
Figure CN118700239B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the technical field of slicing machine control, and particularly relates to a slicing machine control method and system for efficient slicing. Background Art
[0002] In the food processing process, it is often necessary to use a slicing machine to cut food materials into slices with uniform thickness, and the thickness and uniformity of the slices will affect the taste and appearance of the dishes. The food slicing process is usually completed by a slicing machine, and the thickness of the food slices is adjusted by controlling the power of the slicing machine. Since the fuzzy PID control algorithm has the advantages of strong adaptability, good robustness, and high control precision, this solution uses the fuzzy PID control algorithm for slicing machine power control.
[0003] This solution mainly targets meat slicing. Taking beef as an example, in the process of cutting beef, in order to facilitate cutting, the beef is first frozen and then cut using a slicing machine. However, in the actual slicing process, due to the differences in different parts of the beef and the freezing conditions, the relationship between the slice thickness and the slicing machine power is not linear. In the traditional fuzzy PID control algorithm, the fuzzification operation is performed through fixed fuzzification subsets, which cannot well match the highly non-linear characteristics of the power and thickness during the slicing process, resulting in the disadvantages of easy overshoot and slow convergence speed in power control, and uneven slice thickness. Summary of the Invention
[0004] In order to solve the above technical problems, this application provides a slicing machine control method and system for efficient slicing, and the specific technical solutions adopted are as follows:
[0005] In a first aspect, an embodiment of this application provides a slicing machine control method for efficient slicing, and the method includes the following steps:
[0006] 1) Deploy an intelligent thickness sensor directly above the discharge end of the slicing machine to measure the thickness of the slices at the discharge end. Use the intelligent thickness sensor to perform point-line scanning in the vertical direction perpendicular to the conveying direction of the object slices. Take the slicing center line as the reference to equally spaced scan several scanning points in the vertical direction, and number the scanning points from top to bottom to obtain the thickness of each scanning point at each sampling moment.
[0007] 2) Connect the output end of the intelligent thickness sensor to the input end of the A / D conversion module, and connect the output end of the A / D conversion module to the input end of the PID controller; the slicing machine control system analyzes the thickness data collected from the intelligent thickness sensor, and uses the analysis results to optimize the PID controller to control the power of the slicing machine.
[0008] The steps of analyzing the collected thickness data and optimizing the PID controller are specifically as follows:
[0009] A1. Positively fuse the deviation between the thickness of all scan points at each sampling moment obtained in step 1) and the preset target thickness, and the distribution similarity of the thickness of scan points on both sides of the slice, to obtain the cutting deviation coefficient at each sampling moment;
[0010] A2. Compose a sequence of the thicknesses of each scan point at the sampling moment obtained in step 1) and several adjacent sampling moments before it. Based on the distribution difference situation among the sequences composed of all scan points at each sampling moment, determine the extended cutting gradient at each sampling moment;
[0011] A3. Positively fuse the cutting deviation coefficient obtained in step A1) and the extended cutting gradient obtained in step A2) to obtain the fuzzification factor at each sampling moment;
[0012] A4. Use the fuzzification factor obtained in step A3) to update the fuzzification subset at each sampling moment and optimize the PID controller.
[0013] Preferably, the slice center line is a straight line perpendicular to the discharge port and located at the center of the slice.
[0014] Preferably, the obtaining process of the cutting deviation coefficient at each sampling moment includes:
[0015] Based on the deviation between the thickness of all scan points at each sampling moment and the preset target thickness, determine the thickness deviation weight at each sampling moment;
[0016] Based on the distribution similarity of the thickness of scan points on both sides of the slice, determine the central extension coefficient at each sampling moment;
[0017] Positively fuse the thickness deviation weight and the central extension coefficient at each sampling moment to obtain the cutting deviation coefficient at each sampling moment.
[0018] Preferably, the method for determining the thickness deviation weight at each sampling moment includes:
[0019] Calculate the difference between the average thickness of all scan points at each sampling moment and the preset target thickness;
[0020] Calculate the fluctuation of the deviation between the thickness of all scan points at each sampling moment and the average thickness;
[0021] Positively fuse the fluctuation and the difference to obtain the thickness deviation weight at each sampling moment.
[0022] Preferably, the method for determining the central extension coefficient at each sampling moment includes:
[0023] Calculate the deviation value between the thickness of each scan point at each sampling moment and the average thickness of all scan points, construct a deviation sequence, and calculate the range of the deviation sequence;
[0024] According to the scan points at the position of the slice center line at each sampling moment, divide the deviation sequence into two segments, and calculate the similarity between the two segments;
[0025] Perform positive fusion on the range and the similarity to obtain the central extension coefficient at each sampling moment.
[0026] Preferably, the method for determining the extended cutting gradient at each sampling moment includes:
[0027] Based on the difference between the maximum value in the sequence and the sequence average value, determine the connective determination coefficient of the scan point where the sequence is located;
[0028] Perform clustering based on the distribution difference of the connective determination coefficients of all scan points at each sampling moment to determine the connective interference cluster;
[0029] Calculate the proportion of the number of scan points in the connective interference cluster to the number of all scan points; calculate the range value of the connective determination coefficients of all scan points;
[0030] Perform positive fusion on the proportion and the range value to determine the extended cutting gradient.
[0031] Preferably, the method for determining the connective interference cluster includes: clustering the connective determination coefficients of all scan points at each sampling moment to obtain several clustering clusters; and denoting the clustering cluster with the largest average value of the connective determination coefficients as the connective interference cluster.
[0032] Preferably, the method for obtaining the fuzzification factor at each sampling moment is: calculate the product value of the cutting deviation coefficient and the extended cutting gradient at each sampling moment, normalize the product value, and then multiply it by a preset scaling coefficient to obtain the fuzzification factor at each sampling moment.
[0033] Preferably, the specific process of updating the fuzzification subset at each sampling moment includes:
[0034] Set the initial domain of the fuzzy PID control algorithm to obtain the fuzzification subset;
[0035] Multiply the fuzzification factor at each sampling moment by each element in the fuzzification subset respectively to obtain the updated real-time adjustment fuzzification subset.
[0036] In a second aspect, an embodiment of the present application further provides a slicing machine control system for efficient slicing, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of the method described in any one of the above are implemented.
[0037] The present application has at least the following beneficial effects:
[0038] The present application realizes a control method for efficient slicing of a slicing machine. Compared with the prior art, in which fuzzyfication operations are performed through fixed fuzzyfication subsets and cannot well match the highly non-linear characteristics of power and thickness during the slicing process, resulting in disadvantages such as easy overshoot and slow convergence speed in power control, and uneven slicing thickness. The present application uses an intelligent sensor to obtain the slicing thickness during the slicing process. By analyzing the change of thickness data of all scanning points at a single sampling moment of the slice, the cutting deviation coefficient is obtained, which reflects the degree of influence of the central freezing effect caused by uneven heat transfer of the meat product at the current sampling moment; by forming a sequence of thickness data of all scanning points at the sampling moment and several adjacent sampling moments before it, the extended cutting gradient is obtained by analyzing the sequence, which reflects the content degree of connective tissue in the meat; by analyzing the connective tissue determination coefficient of the sequence of all sampling points at each sampling moment, it is used to reflect the interference situation of the connective tissue on the slicing thickness; combining the cutting deviation coefficient and the extended cutting gradient to further obtain the fuzzyfication factor at each sampling moment, evaluating the influence of the central freezing effect and connective tissue on the slice, so as to realize the dynamic update and adjustment of the fuzzyfication subset, solve the problem that only fixed fuzzyfication subsets are used in the traditional fuzzy PID control algorithm and cannot match the thickness fluctuation changes caused by interference factors during slicing. This solution dynamically updates the fuzzyfication subset according to the interference phenomenon during slicing, avoiding the disadvantages of overshoot and slow convergence speed caused by fixed fuzzyfication subsets, thereby ensuring the precise control of the power of the slicing machine and achieving the effect of uniform slicing thickness of the slicing machine. Description of the Drawings
[0039] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained according to these drawings.
[0040] Figure 1 It is a flowchart of a slicing machine control method for efficient slicing provided by an embodiment of the present application;
[0041] Figure 2 It is a schematic diagram of a scanning structure provided by an embodiment of the present application;
[0042] Figure 3 Schematic diagram of the control process of the slicer system provided by an embodiment of the present application;
[0043] Figure 4 Flowchart of the steps for obtaining the fuzzification factor at each sampling moment provided by an embodiment of the present application;
[0044] Figure 5 Schematic diagram of the normal thickness data and the central freezing effect thickness data provided by an embodiment of the present application;
[0045] Figure 6 Flowchart of the process for obtaining the cutting deviation coefficient at each sampling moment provided by an embodiment of the present application;
[0046] Figure 7 Flowchart of the process for obtaining the extended cutting gradient at each sampling moment provided by an embodiment of the present application. Detailed implementation manners
[0047] In order to further elaborate on the technical means and effects adopted by the present application to achieve the intended invention purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation manners, structures, features, and effects of a slicer control method and system for efficient slicing proposed according to the present application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.
[0048] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which this application belongs.
[0049] The following specifically describes the specific solutions of a slicer control method and system for efficient slicing provided by the present application with reference to the accompanying drawings.
[0050] A slicer control method and system for efficient slicing provided by an embodiment of the present application.
[0051] Specifically, the following slicer control method for efficient slicing is provided. Please refer to Figure 1 , and this method includes the following steps:
[0052] The first step is to obtain the thickness of each scan point at each sampling moment during the cutting process of the slicer for the slice.
[0053] This solution mainly focuses on power control of a numerically controlled slicing machine during the processing of meat products. Thus, an intelligent thickness sensor is deployed directly above the discharge end of the slicing machine to measure the thickness of the slices at the discharge end, with the data accurate to millimeters (mm). The intelligent thickness sensor is used to perform point-line scanning in the direction perpendicular to the food slicing and conveying direction. Taking the center line of the slice as a reference, Q scanning points in the vertical direction are scanned at equal intervals for the slice at each sampling moment. In this embodiment, the number of scanning points is set to 7, and the scanning points are numbered from top to bottom. The specific scanning method is as shown in Appendix Figure 2 as shown. Therefore, the thickness data of 7 scanning points of the slice are obtained at a single sampling moment. And the slicing machine discharges materials at a constant speed at the discharge end, with the sampling interval set to 50 ms.
[0054] Among them, the center line of the slice is a straight line perpendicular to the discharge port and located at the center of the slice.
[0055] In an embodiment of this application, the schematic diagram of the scanning structure is as shown in Appendix Figure 2 as shown. In Appendix Figure 2 , 1 represents the conveyor belt, 2 represents the conveying direction, 3 represents the discharge end of the slicing machine, 4 represents the object slice, 5 represents the No. 1 scanning point, 6 represents the Qth scanning point, and 7 represents the intelligent thickness sensor.
[0056] The second step is to analyze the influence of the central freezing effect and connective tissue on the slice according to the thickness data, and obtain the fuzzification factor at each sampling moment.
[0057] In the traditional PID control method, the P, I, and D parameters are constant and cannot be applied to complex slicing processes. Therefore, a fuzzification controller is added on the basis of PID control to achieve adaptive control of the power of the slicing machine. The specific control flow of the slicing machine system is as shown in Appendix Figure 3 as shown.
[0058] However, during the process of slicing beef, different beef parts result in different fiber orientations and fat contents of the meat, and different freezing states affect the texture of the meat. These factors will interfere with the adjustment ability of fuzzy PID. When using a fixed fuzzification subset in traditional fuzzy PID, there are problems such as slow convergence speed and weak anti-interference ability of the algorithm, which may lead to inaccurate regulation of the power of the slicing machine, resulting in uneven slice thickness.
[0059] Accordingly, this application analyzes the influence of the central freezing effect and connective tissue on the slice according to the thickness data, and obtains the fuzzification factor at each sampling moment. Among them, the flow chart of the acquisition steps of the fuzzification factor at each sampling moment is as shown in Appendix Figure 4 as shown, specifically as follows:
[0060] In the first small step, based on the deviation between the thicknesses of all scanned points at each sampling moment and the preset target thickness, as well as the distribution similarity of the thicknesses of the scanned points on both sides of the slice, the cutting deviation coefficient at each sampling moment is obtained.
[0061] During the food processing process, in order to facilitate cutting, meat products are shaped by rapid freezing. However, during the freezing process, due to the non-uniformity of heat transfer, the freezing speed of the meat products gradually decreases from the inside to the outside, resulting in the central part being frozen more severely than the edge part, thus there is a central freezing effect. Due to the existence of the central freezing effect, the texture of the meat product at the edge is relatively tender, which is convenient for the cutting knife of the slicing machine to cut. The closer to the center, the harder the texture, which is not conducive to the cutting knife of the slicing machine to cut.
[0062] For a single sampling moment, in an ideal situation, the thicknesses of Q scanned points are relatively consistent, and there will only be small fluctuations due to the influence of equipment noise during acquisition. However, if affected by the central freezing effect, the greater the thickness fluctuation is closer to the center of the slice. The schematic diagrams of the normal thickness data and the thickness data affected by the central freezing effect are specifically as shown in the appendix Figure 5 as follows.
[0063] In the appendix Figure 5 , for the normal thickness data (a) and the thickness data affected by the central freezing effect (b), the abscissa is each scanned point, and the ordinate is the thickness of the corresponding scanned point. Among them, the deviation between the thickness of each scanned point and the position of the dotted line where the target thickness is located reflects the difference between the normal situation and the occurrence of the central freezing effect.
[0064] Therefore, in this application, the cutting deviation coefficient at the current sampling moment is obtained by using the thickness data of Q scanned points at the current sampling moment, and is specifically obtained by the positive fusion of the thickness deviation weight and the central extension coefficient at the current sampling moment.
[0065] It can be understood that positive fusion is a fusion method such as addition and multiplication between data. The specific positive fusion method is determined by the implementer according to the actual situation to select a suitable fusion method, and this application does not make special restrictions.
[0066] In an embodiment of this application, the product of the thickness deviation weight and the central extension coefficient at the current sampling moment is used as the cutting deviation coefficient at the current sampling moment. In other embodiments of this application, the sum of the thickness deviation weight and the central extension coefficient at the current sampling moment can also be used as the cutting deviation coefficient at the current sampling moment.
[0067] Preferably, the thickness deviation weight is calculated from the deviation between the thicknesses of all scanned points at the current sampling moment and the preset target thickness, and is used to evaluate whether the thickness of the slice being scanned at the current sampling moment is uniform. Among them, the target thickness is obtained through the slicing machine parameters.
[0068] In this application, the method for determining the thickness deviation weight is specifically as follows: Calculate the fluctuation of the deviation between the thicknesses of Q scanning points at the current sampling moment and the average thickness, and calculate the difference between the average thickness of the Q scanning points and the target thickness. Positively fuse the said fluctuation and the said difference to obtain the thickness deviation weight at the current sampling moment.
[0069] In an embodiment of this application, the method for determining the thickness deviation weight is specifically as follows: Calculate the variance of the absolute value of the difference between the thicknesses of Q scanning points at the current sampling moment and the average thickness, and calculate the absolute value of the difference between the average thickness of the Q scanning points and the target thickness. Take the product of the said variance and the said absolute value of the difference as the thickness deviation weight at the current sampling moment.
[0070] In other embodiments of this application, the method for determining the thickness deviation weight is specifically as follows: Calculate the information entropy of the difference between the thicknesses of Q scanning points at the current sampling moment and the average thickness, and calculate the absolute value of the difference between the average thickness of the Q scanning points and the target thickness. Take the sum value of the said information entropy and the said absolute value of the difference as the thickness deviation weight at the current sampling moment. Among them, the calculation process of the information entropy is well-known technology and will not be elaborated.
[0071] It should be understood that if the current sampling moment is affected by the central freezing effect, the thicknesses of each scanning point show large fluctuations, so that the deviation between the thickness value of each scanning point and the average thickness is large. Due to the inconsistent fluctuations, the value of the said variance is large. And it makes the deviation between the average thickness of all data points at the current sampling moment and the target thickness large, obtaining a large thickness deviation weight value at the current sampling moment.
[0072] Among them, the central extension coefficient is obtained from the change trend characteristics of the thickness data of the scanning points on both sides of the slice at the current sampling moment by the central freezing effect, and is used to evaluate whether there is a central freezing effect at the current sampling moment of the slice, so as to effectively estimate the cutting deviation coefficient at the current sampling moment. In an embodiment of this application, the method for determining the central extension coefficient is specifically as follows:
[0073] At the current sampling moment, calculate the deviation value between the thickness of each scanning point and the average thickness of all scanning points, construct a deviation sequence, and calculate the range of the deviation sequence; divide the deviation sequence into two segments according to the scanning points at the position of the slice center line at the current sampling moment, calculate the similarity between the two segments of the sequence, and perform positive fusion on the range and the similarity to obtain the central extension coefficient at the current sampling moment. Among them, in this embodiment, the Pearson correlation coefficient is used to calculate the similarity between the two segments of the sequence. In other embodiments of the present application, negative correlation mapping of DTW distance, cosine similarity, etc. can also be used for correlation analysis. The calculations of Pearson correlation coefficient, DTW distance, and cosine similarity are all well-known technologies and will not be elaborated here.
[0074] In an embodiment of the present application, the product of the range and the similarity is used as the central extension coefficient at the current sampling moment. In other embodiments of the present application, the sum value of the range and the similarity is used as the central extension coefficient at the current sampling moment.
[0075] It should be understood that if the current sampling moment is greatly affected by the central freezing effect of the meat product, since the texture of the meat is harder closer to the center, the deviation of the thickness value is larger, the range value of the deviation sequence is larger, and at the same time, the deviation sequence shows a symmetric trend about the central scanning point, making the similarity value of the two segments of the sequence larger, and the central extension coefficient at the current sampling moment is obtained.
[0076] Thus, the cutting deviation coefficient is obtained by using the thickness deviation weight and the central extension coefficient at the current sampling moment, reflecting the influence degree of the central freezing effect on the slicing of the meat product. If the freezing effect of the current meat product is relatively uniform and there is no influence of the central freezing effect on the slicing of the meat product, the thickness of each scanning point is relatively uniform and the deviation from the target thickness is small, resulting in a small value of the thickness deviation weight. At the same time, the range of the deviation sequence at the current sampling moment is small. Due to the influence of noise in the thickness data acquisition and different influences on each scanning point, the symmetry of the deviation sequence distance about the central scanning point is poor, making the similarity of the two segments of the sequence small, and the obtained central extension coefficient is small. Finally, the value of the cutting deviation coefficient at the current sampling moment is small.
[0077] In an embodiment of the present application, the flowchart for obtaining the cutting deviation coefficient at each sampling moment is as shown in the appendix Figure 6 as follows.
[0078] The second small step is to determine the extended cutting gradient at each sampling moment based on the sequence composed of the thicknesses at each scanning point at the current sampling moment and several adjacent sampling moments before it, and based on the distribution difference situation between the sequences composed of all scanning points at each sampling moment.
[0079] When slicing meat products, in addition to the influence of the central freezing effect on the slice thickness, there are also interferences from the meat products themselves. Especially in beef, there are muscle fibers. Usually, the muscle fibers are evenly distributed in the beef block. However, in order to connect the muscle fibers, there are some connective tissues distributed in the beef block. The connective tissues are distributed in strips. Due to the relatively high toughness of the connective tissues, it will affect the slice thickness during slicing. By analyzing the influence of the connective tissues in the beef block on the thickness, the power of the slicing machine is adjusted in real time to avoid the influence of the internal tissues of the slice on the working state of the slicing machine.
[0080] Thus, taking the current sampling moment as the time end point, n sampling moments are intercepted forward for a single scanning point to form the thickness extension sequence of this scanning point. In this embodiment, n = 10 is set. It should be noted that if the length of the thickness extension sequence is less than n, the regression filling method is used for filling. Among them, the regression filling method is a well-known technology and will not be elaborated here.
[0081] According to the thickness extension sequence of a single scanning point intercepted at the current sampling moment, the connective determination coefficient is obtained. Combining the connective determination coefficients of all scanning points, the extension cutting gradient at the current moment is obtained, which is used to determine the overall content of the connective tissues in the slice and evaluate the interference degree of the connective tissues on the slice at the current sampling moment. The specific process is as follows:
[0082] In an embodiment of the present application, the difference between the maximum value and the mean value in the thickness extension sequence is used to obtain the connective determination coefficient of this scanning point, which is used to judge the connective tissue content of this scanning point.
[0083] It should be understood that since the main function of the connective tissue is to connect the muscle fibers, the connective tissue is distributed in strips in the meat block and the extension direction is related to the trend of the muscle fibers. Therefore, when the slice position corresponding to the thickness extension sequence of this scanning point intersects with the connective tissue, the thickness value at the intersection point may be relatively large, far deviating from the normal slice thickness. At this time, the value of the connective determination coefficient of this scanning point is relatively large.
[0084] In the present application, the extension cutting gradient at the current sampling moment is calculated from the distribution of the connective determination coefficients of all scanning points at the current sampling moment, which is used to evaluate the interference degree of the connective tissues on the slice at the current sampling moment. Specifically:
[0085] According to the connective determination coefficients of all scanning points at the current sampling moment, they are divided into two clusters based on the magnitude of the connective determination coefficient values. The cluster with the largest mean value of the connective determination coefficient is marked as the connective interference cluster, and the remaining is marked as the normal cluster. Calculate the extreme difference value of the connective determination coefficients of all scanning points, and obtain the proportion of the scanning points within the connective interference cluster in the total scanning points. Positively fuse the extreme difference value and the proportion value to obtain the extended cutting gradient at the current sampling moment. Among them, the selectable clustering and cluster splitting methods are: K-Means, K-Medoids, BIRCH clustering, all of which are well-known technologies and will not be elaborated in this solution.
[0086] In an embodiment of the present application, the product of the extreme difference value and the proportion value is used as the extended cutting gradient at the current sampling moment. In other embodiments of the present application, the sum value of the extreme difference value and the proportion value can also be used as the extended cutting gradient at the current sampling moment.
[0087] It should be understood that since the overall content of connective tissue is relatively small and is sporadically distributed in the meat block, if the current sampling moment is greatly interfered by connective tissue, the extreme difference value of the connective determination coefficients of all scanning points is relatively large, and the proportion value of the scanning points within the connective interference cluster in the total number is relatively large, resulting in a relatively large value of the extended cutting gradient.
[0088] In an embodiment of the present application, the flowchart of the acquisition process of the extended cutting gradient at each sampling moment is as shown in the appendix Figure 7 as shown.
[0089] The third small step is to combine the cutting deviation coefficient and the extended cutting gradient at each sampling moment to obtain the fuzzy factor at each sampling moment.
[0090] The cutting deviation coefficient is obtained by the thickness data deviation characteristics of each scanning point at the current sampling moment, and the thickness extension sequence is obtained by the thickness change coefficient of each scanning point within the time window to obtain the demonstration cutting gradient at the current moment. Thus, the fuzzy factor at the current sampling moment is further analyzed.
[0091] In an embodiment of the present application, calculate the product value of the cutting deviation coefficient and the extended cutting gradient at the current sampling moment, normalize the product value, and use the product of the normalized result and the preset scaling coefficient as the fuzzy factor at the current sampling moment. Among them, the preset scaling coefficient is a fixed constant between [1, 3], and the implementer can make adaptive adjustments according to the actual control situation. In this embodiment, the value is 2.
[0092] It should be understood that if the slices at the current sampling moment are affected by the central freezing effect and connective tissue, resulting in a large deviation in thickness, the value of the fuzzification factor will be large. At this time, it is necessary to increase the adjustment of the slicer thickness. If the thickness changes relatively evenly at the current sampling moment, the slicer power needs to remain stable, and the value of the fuzzification factor will be small.
[0093] In the third step, the fuzzy factor is used to update the fuzzy subset at each sampling moment, and the fuzzy PID control algorithm is optimized to control the power of the slicer to ensure the uniformity of the slice thickness during the operation of the slicer.
[0094] In this scheme, the domain of the fuzzy PID control algorithm is initialized to [-6, 6], and the fuzzy subset is obtained as {-6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6}. The fuzzy factor at the current sampling time is multiplied by each element in the fuzzy subset to obtain the updated real-time adjustment fuzzy subset, so as to realize the dynamic adjustment of the fuzzy subset, thereby adjusting the slicer power through the analysis of the slice thickness.
[0095] The specific process of adjusting the slicer power is as follows: the real-time adjustment fuzzy subset is transmitted to the fuzzy controller, the fuzzy controller outputs the fuzzy quantities of P, I, and D, which are used as the input of the PID controller, and the real-time PID components are output to dynamically adjust the slicer power control.
[0096] The fuzzy subset is scaled up or down through the fuzzification factor, thereby realizing dynamic adjustment of the fuzzy subset, avoiding the shortcomings of control overshoot and slow convergence caused by only a fixed fuzzy subset, thereby ensuring precise control of the slicer power and achieving uniform thickness of the slicer slices.
[0097] Based on the same inventive concept as the above method, an embodiment of the present application also provides a slicer control system for efficient slicing, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of a slicer control method for efficient slicing described in any one of the above methods when executing the computer program.
[0098] The various embodiments in the present application are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.
[0099] It should be noted that, unless otherwise specified and limited, terms such as "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a circuit structure, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such article or device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of another identical element in the article or device including the said element. In addition, the term "and / or" used herein includes any and all combinations of any one of one or more related listed items.
[0100] Those skilled in the art will readily conceive of other embodiments of the present application after considering the specification and practicing the invention herein. The present application is intended to cover any variations, uses or adaptations of the present application, which follow the general principles of the present application and include common general knowledge or conventional technical means in the technical field not invented by the present application.
[0101] It should be understood that the present application is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope.
Claims
1. A method for controlling a slicer for efficient slicing, characterized in that: The method comprises the following steps: 1) deploying an intelligent thickness sensor (7) directly above the discharging end (3) of the slicer to measure the thickness of the slice at the discharging end, using the intelligent thickness sensor (7) to perform point-line scanning in a direction perpendicular to the object slice (4) and the conveying direction (2), scanning the slice at a plurality of scanning points in the vertical direction at equal intervals based on the slice centerline as a reference, numbering the scanning points from top to bottom, and obtaining the thickness of each scanning point at each sampling moment; 2) The output end of the intelligent thickness sensor (7) is connected to the input end of the A / D conversion module (8), and the output end of the A / D conversion module (8) is connected to the input end of the PID controller (9); the slicer control system (10) analyzes the thickness data collected from the intelligent thickness sensor (7), uses the analysis result to optimize the PID controller (9), and controls the power of the slicer; The steps of analyzing the collected thickness data and optimizing the PID controller (9) are as follows: A1, forward fusion is performed on the deviation between the thickness of all scanning points at each sampling moment obtained in step 1) and the preset target thickness, as well as the distribution similarity of the thickness of the scanning points on both sides of the slice, to obtain the cutting deviation coefficient at each sampling moment; A2, forming a sequence of the thickness of each scanning point at the sampling moment and the previous several adjacent sampling moments obtained in step 1), and determining the extension cutting gradient at each sampling moment based on the distribution difference between the sequences composed of all scanning points at each sampling moment; A3, forward fusion of the cutting deviation coefficient obtained in step A1) and the extended cutting gradient obtained in step A2) to obtain a fuzzy factor at each sampling moment; A4, using the fuzzy factors obtained in step A3) to update the fuzzy subset at each sampling time, and optimize the PID controller (9); The process of obtaining the cutting deviation coefficient at each sampling moment includes: Calculate the difference between the mean thickness of all scanning points at each sampling moment and the preset target thickness; calculate the fluctuation of the deviation between the thickness of all scanning points at each sampling moment and the mean thickness; forward fuse the fluctuation and the difference to obtain the thickness deviation weight at each sampling moment; Calculate the deviation between the thickness of each scanning point at each sampling moment and the mean thickness of all scanning points, construct a deviation sequence, and calculate the range of the deviation sequence; divide the deviation sequence into two segments according to the scanning point at the position of the slice center line at each sampling moment, and calculate the similarity between the two segments; forwardly fuse the range and the similarity to obtain the center extension coefficient at each sampling moment; The thickness deviation weight at each sampling moment is forwardly fused with the center extension coefficient to obtain the cutting deviation coefficient at each sampling moment; The method for determining the extension cutting gradient at each sampling moment includes: Based on the difference between the maximum value in the sequence and the mean value of the sequence, the determination coefficient of the connection at the scanning point of the sequence is determined; Clustering is performed based on the distribution differences of the association determination coefficients of all scanning points at each sampling moment to determine the association interference clusters; Calculate the ratio of the number of scanning points in the connection interference cluster to the number of all scanning points; calculate the extreme value of the connection determination coefficient of all scanning points; The ratio is forwardly merged with the extreme value to determine the extension cutting gradient.
2. A method for controlling a microtome for efficient slicing as claimed in claim 1, characterized in that: The slice center line is a straight line that is perpendicular to the discharge port and located at the center of the slice.
3. A method for controlling a microtome for efficient slicing as claimed in claim 1, characterized in that: The method for determining the connection interference cluster includes: clustering the connection determination coefficients of all scanning points at each sampling moment to obtain a plurality of clusters; and recording the cluster with the largest mean value of the connection determination coefficient as the connection interference cluster.
4. The method for controlling a microtome for efficient slicing as claimed in claim 1, characterized in that: The method for obtaining the fuzzy factor at each sampling moment is: calculating the product value of the cutting deviation coefficient and the extended cutting gradient at each sampling moment, normalizing the product value and multiplying it by a preset scaling factor to obtain the fuzzy factor at each sampling moment.
5. A method for controlling a microtome for efficient slicing as claimed in claim 4, characterized in that: The specific process of updating the fuzzy subset at each sampling moment includes: Set the initialization domain of the fuzzy PID control algorithm to obtain the fuzzified subset; The fuzzification factor at each sampling moment is multiplied by each element in the fuzzification subset to obtain an updated real-time adjusted fuzzification subset.
6. A slicer control system for efficient slicing, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the microtome control method for efficient slicing as described in any one of claims 1-5 are implemented.
Citation Information
Patent Citations
Full-automatic beefsteak slice measuring mechanism and beefsteak volume measuring system
CN216568008U
Pid control method with fuzzy inference
JP1995281710A