Food raw material cleaning machine control system for food processing line

Abstract

The invention discloses a food raw material cleaning machine control system for a food processing assembly line, relates to the technical field of food processing, and solves the problem that nozzle angle adjustment lags behind due to the fact that dynamic characteristics of water surface fluctuation cannot be accurately obtained in real time. The characteristics of each corrugated concave section are verified and confirmed in sequence, the water ripples generated by the same impact point can be quickly and effectively confirmed in a gradual outward diffusion mode, and a better waveform confirmation effect can be achieved through gradual confirmation; according to the comprehensive characteristics of each adjacent water ripple, the impact state associated with each impact point is identified, so that the impact strength of each different area of the corresponding water surface is comprehensively evaluated, the angle of the associated water outlet nozzle is adaptively adjusted, and a better control effect is achieved; and the cleaning force applied to different positions of the corresponding water surface is consistent.

Description

Technical Field

[0001] The present invention relates to the technical field of food processing, in particular to a food raw material cleaning machine control system for a food processing line. Background Art

[0002] In the food processing industry, cleaning food ingredients is a critical step in ensuring food safety and product quality. Traditional food ingredient cleaning machines rely heavily on manual adjustments to the nozzle's spray angle and speed to mitigate cleaning uniformity issues caused by water surface fluctuations.

[0003] However, this manual adjustment method has significant defects: on the one hand, it is impossible to obtain the dynamic characteristics of water surface fluctuations in real time and accurately, resulting in a lag in the adjustment of the nozzle angle, which is prone to cleaning blind spots or excessive cleaning, affecting the cleaning effect and raw material quality; on the other hand, manual operation is difficult to quantify the specific parameters of water surface fluctuations (such as wave height, wavelength, diffusion range, etc.), and it is impossible to form a standardized cleaning process, resulting in large differences in the cleaning quality of different batches of raw materials, which is difficult to meet the requirements of industrial production for stability and consistency. Summary of the Invention

[0004] In response to the shortcomings of the existing technology, the present invention provides a food raw material cleaning machine control system for a food processing line, which solves the problem of being unable to obtain the dynamic characteristics of water surface fluctuations in real time and accurately, resulting in delayed adjustment of the nozzle angle.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: A food raw material cleaning machine control system for a food processing line, comprising:

[0006] The machine vision system detects and scans the water surface inside the washing tank of the food raw material washing machine to confirm the characteristic heights of multiple points corresponding to a single line of the water surface. The specific method is as follows:

[0007] From the detection scanning process, the time feature associated with the corresponding water surface point is determined. The time feature is the time difference between the laser emission time and the corresponding laser reception time. The associated time feature is recorded as T i , where i represents different water surface points;

[0008] Use: (v×T i )÷2=L i Confirm the distance parameter L associated with the corresponding water surface point i , where v is the speed of light, and then the emission angle A associated with the corresponding laser is determined from the detection scanning process i , using G i =L i ×cosA i Confirm the height data G associated with the corresponding pointi ;

[0009] Confirm the height G of several groups of points corresponding to the single line of the water surface i , select G i The water surface point associated with max is recorded as a feature point, and the point height G associated with the feature point is recorded as i Recorded as characteristic height, and transmitted to the characteristic image wave generating end;

[0010] The characteristic image wave generator constructs the associated characteristic image waves in a two-dimensional plane based on the different characteristic heights associated with different characteristic points. The specific method is as follows:

[0011] Construct a set of two-dimensional planes, and confirm related points of the same height in the two-dimensional plane based on the characteristic height of the feature points. According to the specific process of detection and scanning, the related points associated with the feature points are confirmed and calibrated in turn. A reference plane exists in the two-dimensional plane.

[0012] Connect the continuously marked related points to generate characteristic image waves associated with several related points;

[0013] The characteristic wave point calibration end confirms the concave wave in the characteristic image wave, and calibrates the impact points in the characteristic image wave in sequence based on the concave features associated with the concave wave and the standard features preset in the threshold module. The specific method is as follows:

[0014] Several concave waves in the characteristic image wave are calibrated in turn. There is a group of concave points in the concave wave. The front band of the concave point trends downward, and the back band trends upward.

[0015] Determine the concave features associated with the concave wave: record the front-end band of the concave point of the concave wave as the front feature segment, and record the back-end band of the concave point of the concave wave as the back feature segment. Confirm the trend features of the connected nodes in the front feature segment or the back feature segment, and adopt: single-line trend = |feature height of the next node - feature height of the previous node|. Perform mean processing on several groups of single-line trends confirmed in the front feature segment to confirm the trend feature TZ1 belonging to the front feature segment. Use the same processing method to confirm the trend feature TZ2 associated with the back feature segment. Record the concave wave that meets the following conditions: |TZ1-TZ2|≤Y1 as the pending wave, where Y1 is the preset threshold value. The concave wave that does not meet the following conditions: |TZ1-TZ2|≤Y1 will not be calibrated.

[0016] Perform mean processing on the two sets of trend characteristics TZ1 and TZ2 associated with the pending wave to confirm the mean characteristics. The pending wave that satisfies the mean characteristic ≥ Y2 is recorded as the selected wave, and the concave point associated with the selected wave is recorded as the impact point. Y2 is the preset threshold, where Y1 and Y2 are both thresholds, which are set in advance by the relevant operators in the threshold module;

[0017] The diffusion wave verification processing end confirms the diffusion wave associated with each impact point, and then identifies the diffusion characteristics associated with each diffusion wave from the confirmed diffusion waves. Then, based on the multiple sets of diffusion characteristics associated with several diffusion waves, it determines whether the associated nozzle angle needs to be adjusted. The specific method is as follows:

[0018] Based on the impact points marked in the characteristic image wave, the concave wave associated with each impact point is confirmed. The concave wave is used as the reference wave, and the first two bands associated with the reference wave are verified: the trend characteristics of the two bands in the first group are confirmed using the same determination method of the concave wave trend characteristics, and marked as T1 k and T2 k , where k represents the group associated with the left and right sides of the reference wave, where k = 1, 2, ..., n. When k is 1, it represents the first group of bands with two bands located on the left and right sides of the concave wave. When k is 2, it represents the second group of bands with two bands located on the left and right sides of the concave wave. If T1 k and T2 k Satisfies: |T1 k -T2 k |≤X1, where Y3 is a preset value. If it is satisfied, the two bands in the first group are recorded as similar diffusion waves of the benchmark wave, and the bands of the subsequent low-k groups are continuously confirmed. If it is not satisfied, the confirmation process of the similar diffusion waves of the benchmark wave is completed;

[0019] The corresponding reference wave and the associated similar diffusion wave are recorded as a single diffusion wave, and the highest point and the lowest point associated with the single diffusion wave are confirmed. Based on the characteristic heights associated with the highest point and the lowest point, the height difference between the highest point and the lowest point is confirmed. If the height difference is greater than 0, the confirmed height difference is recorded as the first characteristic of the single diffusion wave. The two endpoints of the single diffusion wave are then calibrated, and a vertical plane perpendicular to the reference plane and passing through the corresponding endpoints is constructed. The vertical distance between the two vertical planes is confirmed, and the confirmed vertical distance is recorded as the second characteristic of the single diffusion wave.

[0020] The first feature confirmed by the different single diffusion waves is denoted as ZZ q , and the second feature is recorded as ZR q , where q represents different single diffusion waves, using: ZH q =ZZ q ×w1+ZR q×w2 confirms the comprehensive characteristics of the corresponding single diffusion wave ZH q , where w1 and w2 are preset fixed coefficient factors;

[0021] The multiple sets of comprehensive features ZH associated with several single diffusion waves q Perform variance processing to confirm the characteristic variance. If the characteristic variance is ≤X2, no control is required. If the characteristic variance is >X2, a control signal is generated and transmitted to the control center, where X2 is the preset value.

[0022] Preferably, the control center adjusts the spray angle of the water outlet nozzle above the cleaning tank according to the received control signal and the comprehensive characteristics of the confirmed groups of single diffusion waves:

[0023] From the identified sets of comprehensive features, select the single diffusion waves associated with the minimum and maximum values, and identify the impact points of the corresponding single diffusion waves;

[0024] And according to the determination method from left to right, the sorting position of the corresponding impact point is identified, and then the water outlet nozzle at the same sorting position is confirmed. Based on the numerical comparison process of the trend characteristics of the two bands of the concave wave associated with the corresponding impact point, the angle adjustment direction is confirmed, and the trend characteristic TZ1 of the front characteristic segment and the trend characteristic TZ2 associated with the rear characteristic segment are compared and verified: if |TZ1|>|TZ2|, the corresponding water outlet nozzle is controlled to adjust the angle from one side of the front characteristic segment to the other side of the rear characteristic segment; if |TZ1|<|TZ2|, the reverse adjustment is performed; if |TZ1|=|TZ2|, an error signal is generated for display;

[0025] After the angle adjustment, if the subsequently confirmed feature variance still does not meet the requirement: feature variance ≤ X2, the adjustment will continue until the associated feature variance meets the standard.

[0026] The present invention provides a control system for a food raw material cleaning machine used in a food processing line. Compared with the prior art, it has the following advantages:

[0027] The present invention verifies the characteristics of the constructed water ripple waveform surface and verifies and confirms the characteristics of each concave section of the ripple in turn. By gradually diffusing outward, the water ripples generated by the same impact point can be quickly and effectively confirmed, and step-by-step confirmation can be performed to achieve a better waveform confirmation effect.

[0028] Subsequently, based on the comprehensive characteristics of each adjacent water ripple, the impact state associated with each impact point is identified, so as to comprehensively evaluate the impact strength of each different area of ​​the corresponding water surface, so as to adaptively adjust the angle of the associated water outlet nozzle, so as to achieve better control effect, make the cleaning strength of each different position of the corresponding water surface more consistent, and improve the overall cleaning effect of the corresponding cleaning machine. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 This is a schematic diagram of the framework principle of the present invention;

[0030] Figure 2 Schematic diagram for determining similar diffusion waves of the present invention. DETAILED DESCRIPTION

[0031] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0032] See also Figure 1 The present application provides a control system for a food raw material washing machine for a food processing line, comprising a machine vision terminal, a characteristic image wave generating terminal, a characteristic wave point calibration terminal, a threshold module, a diffusion wave verification processing terminal, and a control center, wherein the machine vision terminal, the characteristic image wave generating terminal, the characteristic wave point calibration terminal, the diffusion wave verification processing terminal, and the control center are electrically connected from an output node to an input node in sequence, and the threshold module is electrically connected to the input node of the characteristic wave point calibration terminal;

[0033] Among them, the machine vision end detects and scans the water surface inside the corresponding washing tank of the food raw material washing machine (the water flow impact surface during the normal washing process), and launches a laser radar to the water surface based on the corresponding laser radar. Based on the time from the laser emission to the laser being reflected back by the water surface and combined with the laser emission angle, the point height characteristics of the corresponding single line of the water surface are confirmed. Among them, its laser radar scans in sequence according to the preset angle, which can fully cover the water surface. When scanning, it is an axial scan, that is, when scanning from left to right, the scanned single line of the water surface is a multiple set of point height data of a single line on the corresponding plane, which is convenient for subsequent feature verification. During the scanning process, the spray angle and spray speed of the nozzle above the corresponding raw material washing machine are kept fixed. The angle of the nozzle will not be changed until after scanning and feature processing;

[0034] Among them, the specific method for determining the point height characteristics of the single line on the water surface is:

[0035] From the detection scanning process, the time feature associated with the corresponding water surface point is determined. The time feature is the time difference between the laser emission time and the corresponding laser reception time. The associated time feature is recorded as T i , where i represents different water surface points;

[0036] Use: (v×T i )÷2=L i Confirm the distance parameter L associated with the corresponding water surface point i , where v is the speed of light, and then the emission angle A associated with the corresponding laser is determined from the detection scanning process i (This angle data can be directly obtained from the corresponding processing process), using G i =L i ×cosA i Confirm the height data G associated with the corresponding point i ;

[0037] Confirm the height G of several groups of points corresponding to the single line of the water surface i , select G i The water surface point associated with max is recorded as a feature point, and the point height G associated with the feature point is recorded as i It is recorded as the characteristic height and transmitted to the characteristic image wave generating end. Specifically, the laser beam has an emission angle when it is emitted. Based on the corresponding emission angle and the specific related detection process, the distance to the specified point can be confirmed, and from the distance of the confirmed axial multiple points, the distance of several points on the same horizontal axis of the water surface can be determined, which is convenient for the subsequent specific construction of the image wave.

[0038] The characteristic image wave generation end constructs related points in a two-dimensional plane based on the different characteristic heights associated with different characteristic points, and then connects several groups of related points to generate characteristic image waves associated with the current detection and scanning process. The characteristic image wave generation process includes:

[0039] Construct a set of two-dimensional planes, and confirm related points of the same height in the two-dimensional plane based on the characteristic height of the feature points. According to the specific process of detection and scanning, the related points associated with the feature points are confirmed and calibrated in turn. There is a reference plane in the two-dimensional plane, and the specific height data of the corresponding related points can be confirmed by confirming the vertical distance to the reference plane.

[0040] Connect the continuously calibrated related points (that is, connect the adjacent related points) to generate characteristic image waves associated with several related points. This characteristic image wave can display the wave characteristics of the water surface, which is convenient for the subsequent control and adjustment of the relevant parameters of the cleaning nozzle.

[0041] Among them, the characteristic wave point calibration end confirms the concave wave in the characteristic image wave, and calibrates the impact points in the characteristic image wave in turn based on the concave features associated with the concave wave and the standard features preset in the threshold module. The specific calibration method is as follows:

[0042] Several concave waves in the characteristic image wave are calibrated in turn. There is a group of concave points in the concave wave. The front band of the concave point trends downward, and the back band trends upward.

[0043] Determine the concave features associated with the concave wave: record the front-end band of the concave point of the concave wave as the front feature segment, and record the back-end band of the concave point of the concave wave as the back feature segment. Confirm the trend features of the connected nodes in the front feature segment or the back feature segment using: single-line trend = |feature height of the next node - feature height of the previous node|, and perform average processing on several groups of single-line trends confirmed in the front feature segment to confirm the trend feature TZ1 belonging to the front feature segment. Use the same processing method to confirm the trend feature TZ2 associated with the back feature segment. Record the concave wave that satisfies: |TZ1-TZ2|≤Y1 as the undetermined wave. Y1 is the preset threshold, generally 0.02cm, and its specific value is determined by the operator based on experience.

[0044] The two sets of trend characteristics TZ1 and TZ2 associated with the undetermined wave are averaged to confirm the average characteristics. The undetermined wave that meets the following conditions: average characteristics ≥ Y2 is recorded as the selected wave, and the concave point associated with the selected wave is recorded as the impact point. Y2 is the preset threshold, generally 0.5cm, and its specific value is determined by the operator based on experience. Y1 and Y2 are both thresholds, which are set in advance by the relevant operator in the threshold module;

[0045] Specifically, the so-called impact point is the direct point of the nozzle facing the water surface. The radiation waves on both sides of the direct point have corresponding radiation characteristics. The direct point is generally the deepest point, and the trend of the bands on both sides is the largest. The trends between the bands on both sides are also relatively consistent. Because such characteristics are the most obvious, such impact points can be effectively confirmed according to the preset threshold, so as to carry out subsequent band verification and realize the specific control process.

[0046] The diffusion wave verification processing end verifies the diffusion wave associated with each impact point based on the confirmed characteristic image wave and the internally calibrated impact point, and then identifies the diffusion characteristics associated with each diffusion wave from the confirmed diffusion waves. Then, based on multiple groups of diffusion characteristics associated with several diffusion waves, the associated nozzle angle is adjusted. The specific method of adjustment is as follows:

[0047] Based on the impact points marked in the characteristic image wave, the concave wave associated with each impact point is confirmed. The concave wave is used as the reference wave, and the first two bands associated with the reference wave are verified: the trend characteristics of the two bands in the first group are confirmed using the same determination method of the concave wave trend characteristics, and marked as T1 k and T2 k , where k represents the group associated with the left and right sides of the base wave, where k = 1, 2, ..., n. When k is 1, it represents the first group of bands with two bands located on the left and right sides of the concave wave. When k is 2, it represents the second group of bands with two bands located on the left and right sides of the concave wave. Here is the specific confirmation of the symmetrical feature. The single band is the band with unchanged trend, that is, the band with upward trend. Its trend has not changed, and the band with downward trend has not changed synchronously. If T1 k and T2 k Satisfies: |T1 k -T2 k |≤X1, where Y3 is a preset value. Its specific value is determined by the operator in advance based on experience, and is generally 0.05cm. If it is satisfied, the two bands of the first group are recorded as similar diffusion waves of the reference wave, and the bands of the subsequent low-k group are continuously confirmed. If it is not satisfied, the confirmation process of the similar diffusion waves of the reference wave is completed. Figure 2 As shown in the figure, on both sides of the concave wave associated with an impact point, there are two groups of similar diffusion waves. Figure 2 The same type of diffusion waves marked in are the two same type of diffusion waves in the state where k is 1. And so on, and then continue to conduct external confirmation to confirm the existing diffusion waves;

[0048] The corresponding reference wave and the associated similar diffusion wave are recorded as a single diffusion wave, and the highest point and the lowest point associated with the single diffusion wave are confirmed. Based on the characteristic heights associated with the highest point and the lowest point, the height difference between the highest point and the lowest point is confirmed. If the height difference is greater than 0, the confirmed height difference is recorded as the first characteristic of the single diffusion wave. Then, the two endpoints of the single diffusion wave are calibrated (that is, an initial point and an end point), a vertical plane perpendicular to the reference plane and passing through the corresponding endpoints is constructed, and the vertical distance between the two vertical planes is confirmed. The confirmed vertical distance is recorded as the second characteristic of the single diffusion wave.

[0049] The first feature confirmed by the different single diffusion waves is denoted as ZZ q , and the second feature is recorded as ZR q , where q represents different single diffusion waves, using: ZH q =ZZ q ×w1+ZR q ×w2 confirms the comprehensive characteristics of the corresponding single diffusion wave ZH q, where w1 and w2 are both preset fixed coefficient factors, and their specific values ​​are determined by the operator in advance based on experience. The value of w1 is generally 0.463, and the value of w2 is generally 0.537;

[0050] The multiple sets of comprehensive features ZH associated with several single diffusion waves q Perform variance processing to confirm the characteristic variance. If the characteristic variance is ≤X2, no control is required. If the characteristic variance is >X2, a control signal is generated and transmitted to the control center. X2 is a preset value, and its specific value is determined by the operator based on experience, generally 2cm.

[0051] The control center adjusts the spray angle of the water outlet nozzle above the cleaning tank based on the received control signal and the comprehensive characteristics of several groups of confirmed single diffusion waves:

[0052] From the identified sets of comprehensive features, select the single diffusion waves associated with the minimum and maximum values, and identify the impact points of the corresponding single diffusion waves;

[0053] And according to the determination method from left to right, the sorting position of the corresponding impact point is identified, and then the water outlet nozzle at the same sorting position is confirmed, and based on the numerical comparison process of the trend characteristics of the two bands of the concave wave associated with the corresponding impact point, the angle adjustment direction is confirmed, and the trend characteristic TZ1 of the front characteristic segment and the trend characteristic TZ2 associated with the rear characteristic segment are compared and verified: if |TZ1|>|TZ2|, the corresponding water outlet nozzle is controlled to adjust the angle from one side of the front characteristic segment to the other side of the rear characteristic segment; if |TZ1|<|TZ2|, the reverse adjustment is performed; if |TZ1|=|TZ2|, an error signal is generated for display; each time an adjustment process is executed, the angle changes by 1°; based on the actual processing adjustment process, the angles associated with different water outlet nozzles can be adjusted and confirmed in turn, so as to achieve a better angle adjustment effect;

[0054] After the angle adjustment, if the subsequently confirmed feature variance still does not meet the requirement: feature variance ≤ X2, the adjustment will continue until the associated feature variance meets the standard.

[0055] Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0056] The above embodiments are only used to illustrate the technical method of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A food raw material cleaning machine control system for a food processing line, characterized in that: include: On the machine vision side, the water surface inside the corresponding washing tank of the food raw material washing machine is detected and scanned to confirm the characteristic heights of multiple points corresponding to a single line of the water surface; The characteristic image wave generating end constructs the associated characteristic image waves in a two-dimensional plane according to the different characteristic heights associated with different characteristic points; The characteristic wave point calibration end confirms the concave wave in the characteristic image wave, and calibrates the impact points in the characteristic image wave in sequence based on the concave features associated with the concave wave and the standard features preset in the threshold module; The diffusion wave verification processing end confirms the diffusion wave associated with each impact point, and then identifies the diffusion characteristics associated with each diffusion wave from the confirmed diffusion waves. Then, based on multiple groups of diffusion characteristics associated with several diffusion waves, it determines whether the associated nozzle angle needs to be adjusted.

2. The food processing line food raw material cleaning machine control system according to claim 1, characterized in that: The specific method for the machine vision end to confirm the characteristic heights of multiple points corresponding to a single line on the water surface is: From the detection scanning process, the time feature associated with the corresponding water surface point is determined. The time feature is the time difference between the laser emission time and the corresponding laser reception time. The associated time feature is recorded as T i , where i represents different water surface points; Use: (v×T i )÷2=L i Confirm the distance parameter L associated with the corresponding water surface point i , where v is the speed of light, and then the emission angle A associated with the corresponding laser is determined from the detection scanning process i , using G i =L i ×cosA i Confirm the height data G associated with the corresponding point i ; Confirm the height G of several groups of points corresponding to the single line of the water surface i , select G i The water surface point associated with max is recorded as a feature point, and the point height G associated with the feature point is recorded as i It is recorded as the characteristic height and transmitted to the characteristic image wave generating end.

3. The food processing line food raw material cleaning machine control system according to claim 1, characterized in that: The specific method for generating the characteristic image wave at the characteristic image wave generating end is as follows: Construct a set of two-dimensional planes, and confirm related points of the same height in the two-dimensional plane based on the characteristic height of the feature points. According to the specific process of detection and scanning, the related points associated with the feature points are confirmed and calibrated in turn. A reference plane exists in the two-dimensional plane. Connect the continuously marked related points to generate characteristic image waves associated with several related points.

4. The food processing line food raw material cleaning machine control system according to claim 1, characterized in that: The specific method of calibrating the impact points in sequence at the characteristic wave point calibration end is as follows: Several concave waves in the characteristic image wave are calibrated in turn. There is a group of concave points in the concave wave. The front band of the concave point trends downward, and the back band trends upward. Determine the concave features associated with the concave wave: record the front-end band of the concave point of the concave wave as the front feature segment, and record the back-end band of the concave point of the concave wave as the back feature segment. Confirm the trend features of the connected nodes in the front feature segment or the back feature segment using: single-line trend = |feature height of the next node - feature height of the previous node|, and perform mean processing on several groups of single-line trends confirmed in the front feature segment to confirm the trend feature TZ1 belonging to the front feature segment. Use the same processing method to confirm the trend feature TZ2 associated with the back feature segment. Record the concave wave that satisfies: |TZ1-TZ2|≤Y1 as the pending wave, where Y1 is the preset threshold. The two sets of trend characteristics TZ1 and TZ2 associated with the pending wave are averaged to confirm the mean characteristics, and the pending wave that meets the following conditions: mean characteristic ≥ Y2 is recorded as the selected wave, and the concave point associated with the selected wave is recorded as the impact point, where Y2 is the preset threshold, where Y1 and Y2 are both thresholds, which are set in advance in the threshold module by relevant operators.

5. The food raw material cleaning machine control system for a food processing line according to claim 4, characterized in that: Concave waves that do not satisfy |TZ1-TZ2|≤Y1 are not calibrated.

6. The food processing line food raw material cleaning machine control system according to claim 1, characterized in that: The specific method of the diffusion wave verification processing end for confirming the diffusion wave associated with each impact point is as follows: Based on the impact points marked in the characteristic image wave, the concave wave associated with each impact point is confirmed. The concave wave is used as the reference wave, and the first two bands associated with the reference wave are verified: the trend characteristics of the two bands in the first group are confirmed using the same determination method of the concave wave trend characteristics, and marked as T1 k and T2 k , where k represents the group associated with the left and right sides of the reference wave, where k = 1, 2, ..., n. When k is 1, it represents the first group of bands with two bands located on the left and right sides of the concave wave. When k is 2, it represents the second group of bands with two bands located on the left and right sides of the concave wave. If T1 k and T2 k Satisfies: |T1 k -T2 k |≤X1, where Y3 is the preset value. If it is satisfied, the two bands of the first group are recorded as similar diffusion waves of the benchmark wave, and the bands of subsequent low-k groups are continuously confirmed. If it is not satisfied, the confirmation process of the similar diffusion waves of the benchmark wave is completed.

7. The food material cleaning machine control system for a food processing line according to claim 6, characterized in that: The specific method for the diffusion wave verification processing end to determine whether the associated nozzle angle needs to be adjusted is: The corresponding reference wave and the associated similar diffusion wave are recorded as a single diffusion wave, and the highest point and the lowest point associated with the single diffusion wave are confirmed. Based on the characteristic heights associated with the highest point and the lowest point, the height difference between the highest point and the lowest point is confirmed. If the height difference is greater than 0, the confirmed height difference is recorded as the first characteristic of the single diffusion wave. The two endpoints of the single diffusion wave are then calibrated, and a vertical plane perpendicular to the reference plane and passing through the corresponding endpoints is constructed. The vertical distance between the two vertical planes is confirmed, and the confirmed vertical distance is recorded as the second characteristic of the single diffusion wave. The first feature confirmed by the different single diffusion waves is denoted as ZZ q , and the second feature is recorded as ZR q , where q represents different single diffusion waves, using: ZH q =ZZ q ×w1+ZR q ×w2 confirms the comprehensive characteristics of the corresponding single diffusion wave ZH q , where w1 and w2 are preset fixed coefficient factors; The multiple sets of comprehensive features ZH associated with several single diffusion waves q Perform variance processing to confirm the characteristic variance. If the characteristic variance is ≤X2, no control is required. If the characteristic variance is >X2, a control signal is generated and transmitted to the control center, where X2 is the preset value.

8. The food raw material cleaning machine control system for a food processing line according to claim 7, characterized in that: The control center adjusts the spray angle of the water outlet nozzle above the cleaning tank based on the received control signal and the comprehensive characteristics of the confirmed groups of single diffusion waves: From the identified sets of comprehensive features, select the single diffusion waves associated with the minimum and maximum values, and identify the impact points of the corresponding single diffusion waves; And according to the determination method from left to right, the sorting position of the corresponding impact point is identified, and then the water outlet nozzle at the same sorting position is confirmed. Based on the numerical comparison process of the trend characteristics of the two bands of the concave wave associated with the corresponding impact point, the angle adjustment direction is confirmed, and the trend characteristic TZ1 of the front characteristic segment and the trend characteristic TZ2 associated with the rear characteristic segment are compared and verified: if |TZ1|>|TZ2|, the corresponding water outlet nozzle is controlled to adjust the angle from one side of the front characteristic segment to the other side of the rear characteristic segment; if |TZ1|<|TZ2|, the reverse adjustment is performed; if |TZ1|=|TZ2|, an error signal is generated for display; After the angle adjustment, if the subsequently confirmed feature variance still does not meet the requirement: feature variance ≤ X2, the adjustment will continue until the associated feature variance meets the standard.

Images