A control system for a food raw material washing machine in a food processing production line

Abstract

This invention discloses a control system for a food raw material washing machine in a food processing production line. This invention relates to the field of food processing technology and solves the problem of lagging nozzle angle adjustment due to the inability to accurately and in real-time acquire the dynamic characteristics of water surface fluctuations. The invention verifies the features of the constructed water ripple waveform surface, sequentially verifying the characteristics of each concave segment of the ripple. By gradually expanding outwards, it can quickly and effectively confirm the water ripple waves generated by the same impact point, achieving a better waveform confirmation effect. Based on the comprehensive characteristics of each adjacent water ripple, it identifies the impact state associated with each impact point, thereby comprehensively evaluating the impact intensity of each different area of ​​the corresponding water surface. This allows for adaptive adjustment of the angle of the associated water nozzles, achieving a better control effect and ensuring more consistent cleaning intensity at each different position on the corresponding water surface.

Description

Technical Field

[0001] This invention relates to the field of food processing technology, specifically to a control system for a food raw material cleaning machine used in a food processing production line. Background Technology

[0002] In the food processing industry, cleaning food raw materials is a crucial step in ensuring food safety and product quality. Traditional food raw material cleaning machines rely mainly on manual experience to adjust the spray angle and speed of the nozzles during the cleaning process to address the issue of uniform cleaning caused by water surface fluctuations.

[0003] However, this manual adjustment method has significant drawbacks: 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 nozzle angle adjustment, which can easily lead to blind spots or over-cleaning, affecting the cleaning effect and raw material quality; on the other hand, manual operation makes it difficult to quantify the specific parameters of water surface fluctuations (such as wave height, wavelength, diffusion range, etc.), making it impossible to form a standardized cleaning process, resulting in significant differences in the cleaning quality of different batches of raw materials, which makes it difficult to meet the requirements of stability and consistency for industrial production. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a control system for a food raw material washing machine used in a food processing production line, which solves the problem of the inability to obtain the dynamic characteristics of water surface fluctuations in real time and accurately, resulting in a lag in nozzle angle adjustment.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a control system for a food raw material washing machine in a food processing production line, comprising:

[0006] 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 height of multiple points on the single line of the water surface. The specific method is as follows:

[0007] From the detection and scanning process, the time characteristics associated with the corresponding water surface points are confirmed. These time characteristics are the time difference between the laser emission time and the corresponding laser reception time, and are denoted as T. i , where i represents different water surface locations;

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

[0009] Confirm the height G of several sets of points corresponding to the single line on the water surface. i Select G i The water surface points associated with max are denoted as feature points, and the height G of the points associated with the feature points is recorded. i Recorded as the feature height, and transmitted to the feature image wave generation end;

[0010] At the feature image wave generation end, based on the different feature heights associated with different feature points, associated feature image waves are constructed in a two-dimensional plane. Specifically:

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

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

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

[0014] Several concave waves existing within the characteristic image wave are sequentially calibrated. Within each concave wave, there is a set of concave points. The wave front of the concave points trendes downward, while the wave back trendes upward.

[0015] The concave features associated with the concave wave are determined as follows: the wave segment at the front end of the concave point of the concave wave is called the front feature segment, and the wave segment at the back end of the concave point of the concave wave is called the back feature segment. The trend features of the connected nodes in the front or back feature segments are confirmed by using the following formula: single-line trend = |the feature height of the back node - the feature height of the front node|. The average value of several groups of single-line trends confirmed in the front feature segment is then processed to confirm the trend feature TZ1 belonging to the front feature segment. The same processing method is used to confirm the trend feature TZ2 associated with the back feature segment. Concave waves that satisfy |TZ1-TZ2|≤Y1 are recorded as undetermined waves, where Y1 is a preset threshold. Concave waves that do not satisfy |TZ1-TZ2|≤Y1 are not calibrated.

[0016] The two sets of trend features TZ1 and TZ2 associated with the undetermined wave are averaged to confirm the mean feature. The undetermined wave that satisfies the condition that the mean feature is ≥ Y2 is recorded as the selected wave. The concave point associated with the selected wave is recorded as the impact point. Y2 is a preset threshold. Both Y1 and Y2 are thresholds that are set in advance by relevant operators in the threshold module.

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

[0018] Based on several impact points marked within the characteristic image wave, the concave wave associated with each impact point is identified. Using the concave wave as the reference wave, the first group of two wavebands associated with the reference wave are verified: the trend characteristics of the two wavebands in the first group are confirmed using the method of determining that the concave wave trend characteristics are the same, and they are marked as T1. k and T2 k Where k represents the group associated with the left and right sides of the reference wave, and k = 1, 2, ..., n, when k is 1, it represents the first group of bands located on the left and right sides of the concave wave, and when k is 2, it represents the second group of bands located on the left and right sides of the concave wave. If T1 k and T2 k Satisfy: |T1 k -T2 k |≤X1, where Y3 is a preset value. If this condition is met, the two bands of the first group are recorded as similar spread waves of the reference wave, and the bands of the subsequent low k groups are continuously confirmed. If this condition is not met, the confirmation process of similar spread waves of the reference wave is completed.

[0019] The corresponding reference wave and the associated similar spread waves are denoted as a single spread wave. The highest and lowest points associated with the single spread wave are confirmed. Based on the characteristic height associated with the highest and lowest points, the height difference between the highest and lowest points is confirmed. If the height difference is greater than 0, the confirmed height difference is denoted as the first characteristic of the single spread wave. Then, the two endpoints of the single spread wave are 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 denoted as the second characteristic of the single spread wave.

[0020] The first characteristic identified by different monospread waves is denoted as ZZ. q The second feature is denoted 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-spread wave ZH q , where w1 and w2 are both preset fixed coefficient factors;

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

[0022] Preferably, the control center adjusts the spray angle of the water nozzles above the cleaning tank based on the received control signal and the comprehensive characteristics of several sets of single-diffusion waves.

[0023] From the confirmed sets of comprehensive characteristics, select the single spread wave associated with the minimum and maximum values, and confirm the impact point of the corresponding single spread wave.

[0024] Based on the left-to-right determination method, the corresponding impact point is identified in the sorting position, and the water nozzle in the same sorting position is confirmed. Based on the numerical comparison of the trend characteristics of the two concave wave bands associated with the corresponding impact point, the angle adjustment direction is confirmed. The trend characteristics TZ1 of the previous feature segment and the trend characteristics TZ2 associated with the subsequent feature segment are compared and verified: if |TZ1|>|TZ2|, the corresponding water nozzle is controlled to adjust its angle from one side of the previous feature segment to the other side of the subsequent feature segment; if |TZ1|<|TZ2|, the adjustment is reversed; if |TZ1|=|TZ2|, an error signal is generated and displayed.

[0025] If the confirmed characteristic variance still does not meet the requirement of characteristic variance ≤ X2 after the angle adjustment, the adjustment will continue until the associated characteristic variance meets the standard.

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

[0027] This invention verifies the features of the constructed water ripple waveform surface and verifies and confirms the features of each concave segment of the ripple sequentially. By gradually expanding outward, the water ripple generated by the same impact point can be quickly and effectively confirmed. By confirming step by step, a better waveform confirmation effect can be achieved.

[0028] Subsequently, based on the comprehensive characteristics of each adjacent water ripple, the impact state associated with each impact point is identified, thereby comprehensively evaluating the impact intensity of each different area of ​​the corresponding water surface. This allows for adaptive adjustments to the angle of the associated water nozzles, achieving better control and ensuring that the cleaning intensity received by each different position on the corresponding water surface is more consistent, thus improving the overall cleaning effect of the corresponding cleaning machine. Attached Figure Description

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

[0030] Figure 2 This is a schematic diagram illustrating the determination of similar diffused waves according to the present invention. Detailed Implementation

[0031] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0032] Please see Figure 1 This application provides a food raw material cleaning machine control system for a food processing production line, including a machine vision end, a feature image wave generation end, a feature wave point calibration end, a threshold module, a diffuse wave verification and processing end, and a control center. The machine vision end, the feature image wave generation end, the feature wave point calibration end, the diffuse wave verification and processing end, and the control center are electrically connected sequentially from the output node to the input node, and the threshold module is electrically connected to the input node of the feature wave point calibration end.

[0033] In the machine vision section, the water surface (the surface impacted by water flow during normal cleaning) inside the corresponding cleaning tank of the food raw material washing machine is detected and scanned. Based on the corresponding lidar, a lidar is emitted towards the water surface. Based on the time it takes for the laser to be emitted and reflected back by the water surface, combined with the laser's emission angle, the height characteristics of the corresponding single line on the water surface are confirmed. The lidar scans sequentially according to a preset angle, which can fully cover the water surface. During the scan, an axial scan is performed, that is, scanning from left to right. The scanned single line on the water surface is multiple sets of 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 raw material washing machine are kept fixed. The angle of the nozzle is only changed after scanning and feature processing.

[0034] The specific method for determining the positional height characteristics of a single line on the water surface is as follows:

[0035] From the detection and scanning process, the time characteristics associated with the corresponding water surface points are confirmed. These time characteristics are the time difference between the laser emission time and the corresponding laser reception time, and are denoted as T. i , where i represents different water surface locations;

[0036] Use: (v×T) i )÷2=L i Confirm the distance parameter L associated with the corresponding water surface location. i Where v is the speed of light, and the emission angle A associated with the corresponding laser is then determined from the detection and scanning process. i (This angle data can be obtained directly 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 sets of points corresponding to the single line on the water surface. i Select G i The water surface points associated with max are denoted as feature points, and the height G of the points associated with the feature points is recorded. i The characteristic height is recorded and transmitted to the characteristic image wave generation end. Specifically, when the laser beam is emitted, there is an emission angle. Based on the corresponding emission angle and the specific related detection process, the distance of the specified point can be confirmed. From the confirmed distances of multiple points along the axial direction, the distances of several points on the same horizontal axis of the water surface can be determined, which facilitates the subsequent specific construction of the image wave.

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

[0039] A set of two-dimensional planes is constructed, and related points with the same height are identified in the two-dimensional planes 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 identified and calibrated in turn. There is a reference plane in the two-dimensional plane. The specific height data of the corresponding related points can be identified by confirming the vertical distance to the reference plane.

[0040] Connecting consecutively marked relevant points (i.e., connecting adjacent relevant points) generates a characteristic image wave associated with several relevant points. This characteristic image wave can display the wave characteristics of the water surface, which facilitates the subsequent control and adjustment of the relevant parameters of the cleaning nozzle.

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

[0042] Several concave waves existing within the characteristic image wave are sequentially calibrated. Within each concave wave, there is a set of concave points. The wave front of the concave points trendes downward, while the wave back trendes upward.

[0043] The concave features associated with the concave wave are determined as follows: the wave segment at the front end of the concave point of the concave wave is called the front feature segment, and the wave segment at the back end of the concave point of the concave wave is called the back feature segment. The trend features of the connected nodes in the front or back feature segments are confirmed by using the following formula: single line trend = |the feature height of the back node - the feature height of the front node|. The average value of several groups of single line trends confirmed in the front feature segment is then processed to confirm the trend feature TZ1 belonging to the front feature segment. The same processing method is used to confirm the trend feature TZ2 associated with the back feature segment. The concave wave that satisfies |TZ1-TZ2|≤Y1 is recorded as a wave to be determined. Y1 is a preset threshold, generally taken as 0.02cm. The specific value is determined by the operator based on experience.

[0044] The two sets of trend features TZ1 and TZ2 associated with the undetermined wave are averaged to confirm the mean feature. The undetermined wave that satisfies the condition that the mean feature is ≥ Y2 is recorded as the selected wave. The concave point associated with the selected wave is recorded as the impact point. Y2 is a preset threshold, generally 0.5cm. The 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 point where the nozzle directly hits the water surface. The radiation waves on both sides of the impact point have corresponding radiation characteristics. The impact point is generally the deepest point, and the trend of the wave bands on both sides is the largest. At the same time, the trend of the wave bands on both sides is also relatively consistent. Because this kind of characteristic is the most obvious, the impact point can be effectively confirmed according to the preset threshold, so as to carry out subsequent wave band verification and realize the specific control process.

[0046] The diffusion wave verification processing unit, based on the confirmed characteristic image wave and the internally marked impact points, confirms the diffusion wave associated with each impact point. Then, from the confirmed diffusion waves, it identifies the diffusion characteristics associated with each diffusion wave. Finally, based on multiple sets of diffusion characteristics associated with several diffusion waves, it adjusts the associated nozzle angle. The specific method of adjustment is as follows:

[0047] Based on several impact points marked within the characteristic image wave, the concave wave associated with each impact point is identified. Using the concave wave as the reference wave, the first group of two wavebands associated with the reference wave are verified: the trend characteristics of the two wavebands in the first group are confirmed using the method of determining that the concave wave trend characteristics are the same, and they are marked as T1. k and T2 k Where k represents the group associated with the left and right sides of the reference wave, and k = 1, 2, ..., n. When k is 1, it represents the first group of wave segments located on the left and right sides of the concave wave. When k is 2, it represents the second group of wave segments located on the left and right sides of the concave wave. This is where the symmetry feature is specifically confirmed. Each individual wave segment is a wave segment whose trend has not changed, that is, an upward wave segment whose trend has not changed. The downward wave segment has also not changed synchronously. If T1 k and T2 k Satisfy: |T1 k -T2 k |≤X1, where Y3 is a preset value, the specific value of which is determined in advance by the operator based on experience, generally 0.05cm. If this condition is met, the two bands of the first group are recorded as similar spread waves of the reference wave, and the bands of the subsequent low-k groups are continuously confirmed. If this condition is not met, the confirmation process of similar spread waves of the reference wave is completed. Figure 2 As shown, on both sides of the concave wave associated with an impact point, there exist a first set of similar spreading waves. Figure 2 The similar spreading waves marked in the text are two similar spreading waves in the state where k is 1. And so on, and continue to perform external confirmation to confirm the existing spreading waves.

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

[0049] The first characteristic identified by different monospread waves is denoted as ZZ. q The second feature is denoted 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-spread wave ZH qw1 and w2 are preset fixed coefficient factors, and their specific values ​​are determined in advance by the operator based on experience. w1 is generally 0.463 and w2 is generally 0.537.

[0050] The multiple sets of integrated features ZH associated with several single-diffusion waves q Variance processing is performed to confirm the characteristic variance. If the characteristic variance is ≤ X2, no adjustment is required. If the characteristic variance is > X2, an adjustment 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, based on the received control signals and the comprehensive characteristics of several sets of single-diffusion waves, adjusts the spray angle of the water nozzles above the cleaning tank.

[0052] From the confirmed sets of comprehensive characteristics, select the single spread wave associated with the minimum and maximum values, and confirm the impact point of the corresponding single spread wave.

[0053] Based on the left-to-right determination method, the corresponding impact point is identified in the sorting position, and the water nozzles in the same sorting position are confirmed. Based on the numerical comparison of the trend characteristics of the two concave wave bands associated with the corresponding impact point, the angle adjustment direction is confirmed. The trend characteristics TZ1 of the previous feature segment and the trend characteristics TZ2 associated with the subsequent feature segment are compared and verified: if |TZ1|>|TZ2|, the corresponding water nozzle is controlled to adjust its angle from one side of the previous feature segment to the other side of the subsequent feature segment; if |TZ1|<|TZ2|, the adjustment is reversed; if |TZ1|=|TZ2|, an error signal is generated and displayed. Each adjustment process changes the angle by 1°. Based on the actual processing adjustment process, the angles associated with different water nozzles can be adjusted and confirmed in sequence to achieve a better angle adjustment effect.

[0054] If the confirmed characteristic variance still does not meet the requirement of characteristic variance ≤ X2 after the angle adjustment, the adjustment will continue until the associated characteristic variance meets the standard.

[0055] Some of the data in the above formulas are numerical calculations with dimensions removed, and the contents not described in detail in this specification are all prior art known to those skilled in the art.

[0056] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A food raw material cleaning machine control system for a food processing line, characterized by, The application relates to a machine vision system for a food raw material cleaning machine. The machine vision end detects and scans the water surface in a corresponding cleaning tank of the food raw material cleaning machine, confirms the feature heights of a plurality of point positions of the corresponding water surface single line, and specifically adopts the following method: From the detection scanning process, the time characteristics associated with the corresponding water surface point are confirmed, and the time characteristics are the time difference between the laser emission time and the corresponding laser receiving time. The associated time characteristics are denoted as T i where i represents different water surface points. Adopt: (v x 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 confirm the emission angle A associated with the corresponding laser from the detection scanning process i Adopt G i = L i x cos A i Confirm the height data G associated with the corresponding point i ; Confirming several groups of point heights G corresponding to the water surface single line i , selecting G i max associated with the water surface point as a feature point, and recording the point height G i associated with the feature point as a feature height, and transmitting to the feature image wave generation end; A feature image wave generating end constructs feature image waves associated with different feature heights in a two-dimensional plane; A feature wave point marking end confirms concave waves existing in the feature image waves, and sequentially marks impact points existing in the feature image waves based on concave features associated with the concave waves and standard features preset in a threshold module; A diffusion wave checking and processing end confirms diffusion waves associated with each impact point, identifies diffusion features associated with each diffusion wave from the confirmed diffusion waves, and determines whether the angle of a corresponding nozzle needs to be adjusted based on a plurality of groups of diffusion features associated with the diffusion waves.

2. The food raw material cleaning machine control system for a food processing line according to claim 1, characterized by, The feature image wave generating end generates the feature image waves in the following manner: A two-dimensional plane is constructed, and relevant point positions of the same height are confirmed in the two-dimensional plane based on the feature heights of the feature points; and the relevant point positions associated with the feature points are sequentially confirmed and marked based on the specific progress of the detection and scanning, and a reference surface exists in the two-dimensional plane; The continuously marked relevant point positions are connected to generate the feature image waves associated with a plurality of relevant point positions.

3. The food raw material cleaning machine control system for a food processing line according to claim 1, characterized by, The feature wave point marking end marks the impact points in the following manner: A plurality of concave waves existing in the feature image waves are sequentially marked, and a group of concave points exist in the concave waves; the front end wave segment of the concave points trends downward, and the rear end wave segment trends upward; The concave features associated with the concave waves are determined: the front end wave segment of the concave points is recorded as a front feature segment, and the rear end wave segment of the concave points is recorded as a rear feature segment; the trend feature of the connected nodes in the front feature segment or the rear feature segment is confirmed by adopting a single line trend = |the feature height of a next node - the feature height of a previous node|; a plurality of groups of single line trends confirmed in the front feature segment are processed by taking an average value to confirm the trend feature TZ1 of the front feature segment; the trend feature TZ2 associated with the rear feature segment is confirmed by adopting the same processing manner; the concave waves that satisfy |TZ1-TZ2|<=Y1 are recorded as to-be-determined waves, and Y1 is a preset threshold value; The two groups of trend features TZ1 and TZ2 associated with the to-be-determined waves are processed by taking an average value to confirm an average value feature; the to-be-determined waves that satisfy the average value feature >=Y2 are recorded as selected waves, and the concave points associated with the selected waves are recorded as impact points; Y2 is a preset threshold value; Y1 and Y2 are both thresholds, which are preset in the threshold module by relevant operating personnel in advance.

4. The food raw material cleaning machine control system for a food processing line according to claim 3, characterized by, The concave waves that do not satisfy |TZ1-TZ2|<=Y1 are not marked.

5. The food raw material cleaning machine control system for a food processing line according to claim 1, characterized by, The diffusion wave checking and processing end confirms the diffusion waves associated with each impact point in the following manner: Based on the calibrated impact points in the feature image wave, the concave wave associated with each impact point is determined, and the first group of two wave bands associated with the reference wave on the left and right is verified: the trend characteristics of the two wave bands in the first group are confirmed in the same way as the trend characteristics of the concave wave, and they are calibrated as T1 k and T2 k , where k represents the group associated with the reference wave on the left and right, where k = 1, 2, …, n, k = 1 represents the first group of two wave bands located on the left and right sides of the concave wave, k = 2 represents the second group of two wave bands located on the left and right sides of the concave wave, if T1 k and T2 k satisfy: |T1 k -T2 k |≤X1, X1 is a preset value, if it is satisfied, the two wave bands of the first group are recorded as the same type of diffusion wave of the reference wave, and the wave bands of the subsequent low k group are continuously confirmed, if it is not satisfied, the confirmation process of the same type of diffusion wave of the reference wave is completed.

6. A food raw material cleaning machine control system for a food processing line according to claim 5, characterized by, The diffusion wave checking and processing end determines whether the angle of the corresponding nozzle needs to be adjusted in the following manner: The corresponding reference wave and the associated similar diffusion wave are recorded as a single diffusion wave, the highest point and the lowest point associated with the single diffusion wave are determined, the height difference between the highest point and the lowest point is determined based on the characteristic height associated with the highest point and the lowest point, the height difference between the highest point and the lowest point is > 0, the determined height difference is recorded as the first characteristic of the single diffusion wave, the two end points of the single diffusion wave are calibrated, the vertical plane perpendicular to the reference plane and passing through the corresponding end point is constructed, and the vertical distance between the two vertical planes is determined, and the determined vertical distance is recorded as the second characteristic of the single diffusion wave; The first feature identified by different single diffraction waves is denoted as ZZ q The second feature is denoted as ZR q The comprehensive feature ZH of the corresponding single diffraction wave is identified by using: q = ZZ q × w1+ ZR q × w2 q wherein w1 and w2 are both preset fixed coefficient factors a plurality of sets of comprehensive features ZH associated with a plurality of single diffraction waves q The variance is processed to confirm the feature variance. If the feature variance is ≤ X2, no regulation is needed. If the feature variance is > X2, a regulation signal is generated and transmitted to the regulation center, and X2 is a preset value.

7. A food raw material cleaning machine control system for a food processing line according to claim 6, characterized by 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 plurality of groups of single diffusion waves confirmed: From the plurality of groups of comprehensive characteristics confirmed, the single diffusion wave associated with the minimum value and the maximum value is selected, and the impact point of the corresponding single diffusion wave is determined; And according to the determination method from left to right, the sorting position of the corresponding impact point is identified, the water outlet nozzle at the same sorting position is determined, and the angle adjustment direction is determined based on the numerical comparison process of the two wave segment trend characteristics of the concave wave associated with the impact point. The trend characteristics TZ1 of the front characteristic segment and the trend characteristics TZ2 associated with the rear characteristic segment are compared and verified: if |TZ1|>|TZ2|, adjust the angle of the corresponding water outlet nozzle from the front characteristic segment to the rear characteristic segment, if |TZ1|<|TZ2|, adjust in the opposite direction, if |TZ1|=|TZ2|, generate an error signal for display; After the angle adjustment, if the subsequent confirmed characteristic variance still does not satisfy: characteristic variance≤X2, continue to adjust until the associated characteristic variance meets the standard.

Images