A tunnel type freezing equipment for bamboo shoot pre-processing products and a control system thereof

Abstract

The present application relates to a kind of bamboo shoot pre-processing product with tunnel type freezing equipment and control system, it is related to food processing equipment technical field, the present application can be real-time, intuitively identify the complex low wind speed dead angle area and airflow short circuit area in tunnel by the synergy of flow field perception array and three-dimensional space modeling, effectively solve the problem that bamboo shoot freezing core temperature is not consistent due to airflow distribution uneven, innovatively introduce product quality inspection parameter as the feedback constraint of deep learning model, can be according to the actual quality feedback of pre-processing product, reverse optimization flow field compensation strategy, while ensuring quick freezing, through fine control wind, reduce the water loss on the surface of bamboo shoot, simultaneously based on the flow field simulation compensation model of deep learning, can automatically analyze complex flow field distribution diagram and be converted into the deflection instruction of multi-dimensional actuator, reduce the dependence on artificial experience, ensure that different batches, different stacking density bamboo shoot can obtain optimal and uniform quick-freezing effect.

Description

Technical Field

[0001] This invention relates to the field of food processing equipment technology, and in particular to a tunnel-type freezing equipment and control system for pre-processed bamboo shoot products. Background Technology

[0002] When bamboo shoots are slowly frozen, the internal water forms large ice crystals that pierce the cell walls, resulting in a mealy and watery texture after thawing. Tunnel freezing equipment, however, can form tiny ice crystals in a very short time, locking in the crispness and tenderness. Simultaneously, rapid cooling quickly inhibits enzyme activity, and combined with the preceding blanching process, it allows the bamboo shoots to retain their original white or pale yellow color. Therefore, tunnel freezing equipment is indispensable in the pre-processing of bamboo shoots. However, existing tunnel freezing equipment still has many pain points that urgently need to be addressed in terms of energy efficiency balance, hygiene maintenance, and product quality loss. I. Energy Efficiency Management and Heat Exchange Efficiency Issues: Traditional tunnel ventilation duct designs are relatively simple, easily leading to dead air zones and uneven heat exchange. Simultaneously, the heat generated by the fans is also part of the cooling load, increasing the burden on the refrigeration system. Furthermore, evaporator fins are prone to frosting, which increases thermal resistance and hinders airflow; frequent shutdowns for defrosting severely impact production continuity.

[0003] II. Product Quality and Physical Losses: When cold air flows rapidly over the surface of unpackaged products, it carries away moisture, resulting in reduced product weight and directly impacting the company's economic benefits. This is especially true when handling high-moisture or viscous products, which are prone to sticking to the conveyor belt or becoming stuck together, causing mechanical damage. Furthermore, in tunnels over ten meters long, the temperature difference between the inlet and outlet ends, as well as the wind speed differences across the tunnel cross-section, often lead to inconsistent freezing core temperatures for products from the same batch. To address the aforementioned technical deficiencies, a solution is proposed. Summary of the Invention

[0004] The purpose of this invention is to effectively solve the problem of inconsistent core freezing temperature of bamboo shoots caused by uneven airflow distribution by identifying complex low-wind-speed dead zones and airflow short-circuit zones in tunnels in real time and intuitively. It innovatively introduces product quality inspection parameters as feedback constraints for deep learning models, and can reverse-optimize the flow field compensation strategy based on the actual quality feedback of pre-processed products. While ensuring rapid freezing, it reduces the loss of moisture from the surface of bamboo shoots through refined airflow control.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a tunnel-type freezing equipment for pre-processed bamboo shoot products, comprising a tunnel shell, wherein a conveyor belt for carrying and transporting products to be frozen is provided inside the tunnel shell, and multiple freezing zones are arranged sequentially along the conveying direction of the conveyor belt, each of the freezing zones is provided with a cold air unit, the cold air unit is fixed to the inner wall of the tunnel shell, and an air outlet is opened on the bottom surface of the cold air unit, and a flexible flow guiding component is provided at the air outlet; The flexible airflow guide component is located at the air outlet of the air cooler unit. The flexible airflow guide component includes multiple independently adjustable deflection blades to eliminate dead airflow angles in the air duct.

[0006] Furthermore, the flexible flow guiding component includes a flow guiding frame, which is fixed to the inner wall of the air outlet. A plurality of insertion shafts are evenly distributed on the inner wall of the flow guiding frame. Insertion slots corresponding to the insertion shafts are provided at both ends of the deflection blades, and the insertion slots are movably connected to the insertion shafts. A deflection tension line is connected to the outer surface of each deflection blade. A plurality of deflection shafts in contact with the deflection tension lines are provided on the inner wall of the flow guiding frame. A magnetic block is connected to the end surface of each deflection tension line. A plurality of electromagnets corresponding to the magnetic blocks are fixed inside the flow guiding frame. Movable slots corresponding to the magnetic blocks are provided on the inner wall of the flow guiding frame, and each magnetic block is movably connected to its corresponding movable slot.

[0007] Furthermore, it also includes a heat exchanger defrosting module, which includes an ultrasonic vibration generator installed on the fins of the air cooler unit and a hot gas bypass valve connected in parallel with the fins of the air cooler unit.

[0008] Furthermore, it also includes a micro-mist humidification component for forming a micron-level ice film on the surface of the pre-processed bamboo shoot product. The micro-mist humidification component is disposed in the pre-cooling section adjacent to the inlet of the tunnel shell. The micro-mist humidification component includes a micro-mist water tank and a water pump. The micro-mist water tank is fixed to the inner wall of the tunnel shell, and the water pump is connected to the outer surface of the output end of the micro-mist water tank. A micro-mist distribution pipe is fixed to the inner wall of the pre-cooling section at the inlet of the tunnel shell, and the output end of the water pump is connected to the inner wall of the micro-mist distribution pipe.

[0009] This invention also provides a control system for a tunnel-type freezing equipment for pre-processed bamboo shoot products, comprising a flow field sensing array, a flow field analysis and parameter output unit, and a multi-dimensional adjustable execution unit, wherein: The flow field sensing array is used to set multiple monitoring points distributed in the tunnel cavity inside the tunnel shell according to the effective processing area of ​​the tunnel-type refrigeration equipment. Sensor groups are set at the monitoring points. The sensor groups are used to collect the raw airflow parameters of each monitoring point in real time and send them to the flow field analysis and parameter output unit. The airflow parameters include wind speed vector data and local static pressure value. The flow field analysis and parameter output unit includes a flow field analysis module and a parameter output module. The flow field analysis module is used to obtain the airflow parameters fed back by the flow field sensing array, and to establish a three-dimensional spatial model based on the effective processing area and freezing distribution points of the tunnel-type refrigeration equipment. The flow field distribution area is divided in the three-dimensional spatial model, and the low wind speed dead zone and airflow short-circuit zone in the tunnel cavity are identified based on the airflow parameters to output the flow field distribution map to the parameter output module. The parameter output module is used to construct a flow field simulation compensation model based on a deep learning network, obtain standard flow field parameters and product quality inspection parameters of bamboo shoot pre-processed products, extract features and use them as training samples to train the flow field simulation compensation model, obtain an optimized flow field simulation compensation model, input the flow field distribution map into the flow field simulation compensation model to obtain flow field compensation parameters, and send them to the multidimensional adjustable execution unit. A multi-dimensional adjustable execution unit is used to acquire flow field compensation parameters and parse them into adjustment commands. Based on the adjustment commands, it independently drives the deflection angle of each of the flexible flow guiding components to change the local flow direction in the tunnel.

[0010] Furthermore, the specific process of establishing a three-dimensional spatial model is as follows: S101. Obtain the geometric parameters of the tunnel-type refrigeration equipment, including the width and length of the conveyor belt and the height of the refrigeration chamber, and define the effective processing area based on the geometric parameters; S102. Based on the Cartesian coordinate system, establish a proportional digital model of the tunnel cavity, divide the effective processing area into several cubic grid units of equal volume, and map each grid unit to its nearest neighbor sensor group monitoring point. S103. Obtain airflow parameters from the flow field sensing array, and use a spatial interpolation algorithm to extend the data from the limited monitoring points to the full grid cells to complete the wind speed prediction values ​​in the non-monitoring point areas, so as to obtain a three-dimensional spatial model. S104. Combining Bernoulli's equation, the local static pressure value and wind speed vector are checked together to eliminate abnormal disturbance data caused by sensor noise.

[0011] Furthermore, the specific process for obtaining the output flow field distribution map is as follows: S201. Obtain a three-dimensional spatial model and compare the scalar wind speed value V of each grid cell with the preset minimum freezing threshold Vmin: When V < Vmin, and the area corresponding to the grid cell is located on the bamboo shoot material distribution path, it is marked as a low wind speed dead zone. The low wind speed dead zone is located below the conveyor belt support frame and at the tunnel corner, which can easily lead to insufficient cooling of bamboo shoots in some areas, resulting in quality deterioration. S202. The complete trajectory of airflow from the air outlet to the return air outlet of the air cooler is monitored by the sensor group. If it is found that part of the airflow does not pass through the material layer and returns directly through the gaps in the side wall and top, and the pressure drop on this path is lower than the design pressure drop through the material layer, it is determined to be an airflow short-circuit zone. In the airflow short-circuit zone, a large amount of cold energy is wasted and no effective heat exchange occurs with the bamboo shoots. S203. The identified low-wind-speed dead zones and airflow short-circuit zones are integrated into a visualized flow field distribution map. The flow field distribution map is represented in the form of a vector arrow field. In the three-dimensional spatial model, the low-wind-speed dead zones and airflow short-circuit zones are highlighted using a pseudo-color layer, and the center coordinates and coverage of the abnormal areas are calculated.

[0012] Furthermore, the specific process for obtaining the flow field compensation parameters is as follows: S301. Obtain the standard flow field parameters that are pre-stored in the system database. The standard flow field parameters represent the flow field numerical distribution under ideal working conditions, i.e., no dead zones, no short circuits, and the highest heat exchange efficiency. Obtain the quality inspection data of bamboo shoot pre-processed products after freezing. The quality inspection data includes cell wall damage rate (reflecting ice crystal size), surface whiteness (reflecting browning degree), water content loss (reflecting dry loss), and core temperature consistency. S302. Nonlinear feature encoding is performed on discrete quality inspection data, and the data are concatenated to form training samples containing the mapping relationship between the physical environment and the final quality. S303. Constructing a flow field simulation compensation model based on a deep learning architecture: During the training phase, a multi-objective loss function is defined, which includes the deviation between the predicted flow field value and the standard value. At the same time, a quality negative feedback penalty term is introduced, that is, if the quality inspection parameters show that the bamboo shoot dryness is too high, the penalty weight for the high wind speed characteristics of the area will be increased. By adjusting the weights of the neural network through the backpropagation algorithm, the flow field simulation compensation model can learn that under a specific bamboo shoot stacking density, different types of flow field distortion will lead to corresponding quality defect states, and finally obtain the optimized flow field simulation compensation model. S304. Input the spatial distribution characteristics of the low wind speed dead zone and airflow short-circuit zone in the tunnel generated in real time by the flow field analysis module into the optimized flow field simulation compensation model. Through the internal mapping matrix, automatically calculate the current flow field distortion to be eliminated and obtain the angle deviation value that needs to be adjusted for each multi-dimensional adjustable component.

[0013] The flow field compensation parameters are a set of instructions including the actuator number, target deflection angle, adjustment step size, and expected time to reach the target. The flow field compensation parameters not only take into account the elimination of dead angles, but also take into account the constraint of preventing bamboo shoots from drying out, ensuring that the adjusted airflow covers dead angles without causing excessive high-speed scouring on the product surface.

[0014] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: This pre-processed bamboo shoot product utilizes a tunnel-type freezing equipment and control system. Through the collaboration of a flow field sensing array and 3D spatial modeling, it can identify complex low-wind-speed dead zones and airflow short-circuit zones within the tunnel in real time and intuitively. This effectively solves the problem of inconsistent core freezing temperatures in bamboo shoots caused by uneven airflow distribution. It innovatively introduces product quality inspection parameters as feedback constraints for the deep learning model, enabling reverse optimization of the flow field compensation strategy based on the actual quality feedback of the pre-processed product. While ensuring rapid freezing, it reduces moisture loss from the bamboo shoot surface through refined airflow control. Furthermore, the deep learning-based flow field simulation compensation model can automatically analyze complex flow field distribution maps and convert them into deflection commands for multi-dimensional actuators, reducing reliance on manual experience and ensuring optimal and uniform rapid freezing results for bamboo shoots of different batches and stacking densities. Attached Figure Description

[0015] Figure 1 A schematic diagram of the overall external structure of the present invention is shown; Figure 2 A schematic diagram of the overall internal structure of the present invention is shown; Figure 3 A schematic diagram of the external structure of the air cooler unit of the present invention is shown; Figure 4 The present invention is shown Figure 3 Enlarged schematic diagram of section A in the middle; Figure 5 A schematic diagram of the control system structure of the present invention is shown; Legend: 1. Tunnel shell; 2. Conveyor belt; 3. Refrigeration compartment; 4. Air cooling unit; 5. Air outlet; 6. Baffle blades; 7. Guide frame; 8. Insert shaft; 9. Deflecting tension line; 10. Steering shaft; 11. Magnetic block; 12. Electromagnet; 13. Movable slot; 14. Micro-mist water tank; 15. Water pump; 16. Micro-mist distribution pipe. Detailed Implementation

[0016] 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. Example 1:

[0017] like Figure 1-4 As shown, a tunnel-type freezing equipment for pre-processed bamboo shoot products includes a tunnel shell 1. Inside the tunnel shell 1, there is a conveyor belt 2 for carrying and transporting the products to be frozen. Multiple freezing zones 3 are arranged sequentially along the conveying direction of the conveyor belt 2. Each freezing zone 3 is equipped with a cold air unit 4. The cold air unit 4 is fixed to the inner wall of the tunnel shell 1. An air outlet 5 is opened on the bottom surface of the cold air unit 4. A flexible flow guide component is provided at the air outlet 5. The flexible airflow guide component is installed at the air outlet 5 of the air cooler unit 4. The flexible airflow guide component includes multiple independently adjustable deflection blades 6, which are used to eliminate dead airflow angles in the air duct.

[0018] The flexible flow guiding assembly includes a flow guiding frame 7, which is fixed to the inner wall of the air outlet 5. Several insertion shafts 8 are evenly distributed on the inner wall of the flow guiding frame 7. Both ends of the turbulence blades 6 are provided with insertion slots corresponding to the insertion shafts 8. The insertion slots are movably connected to the insertion shafts 8. The outer surface of each turbulence blade 6 is connected with a deflection tension line 9. Several deflection shafts 10 that are in contact with the deflection tension lines 9 are provided on the inner wall of the flow guiding frame 7. The end surface of each deflection tension line 9 is connected with a magnetic block 11. Several electromagnets 12 corresponding to the magnetic blocks 11 are fixed inside the flow guiding frame 7. Movable slots 13 corresponding to the magnetic blocks 11 are provided on the inner wall of the flow guiding frame 7. Each magnetic block 11 is movably connected to the corresponding movable slot 13.

[0019] It also includes a heat exchanger defrosting module, which includes an ultrasonic vibration generator installed on the 4 fins of the air cooler unit and a hot gas bypass valve connected in parallel with the 4 fins of the air cooler unit.

[0020] It also includes a micro-mist humidification component for forming a micron-level ice film on the surface of the pre-processed bamboo shoot product. The micro-mist humidification component is set in the pre-cooling section adjacent to the inlet of the tunnel shell 1. The micro-mist humidification component includes a micro-mist water tank 14 and a water pump 15. The micro-mist water tank 14 is fixed to the inner wall of the tunnel shell 1, and the water pump 15 is connected to the outer surface of the output end of the micro-mist water tank 14. A micro-mist distribution pipe 16 is fixed to the inner wall of the pre-cooling section at the inlet of the tunnel shell 1, and the output end of the water pump 15 is connected to the inner wall of the micro-mist distribution pipe 16.

[0021] The working principle is as follows: When in use, the angular deviation value output by the control system is used to control the magnitude of the magnetic force of the electromagnet 12, thereby changing the magnetic attraction between the electromagnet 12 and the magnetic block 11, which drives the deflection tension line 9 to pull the turbulence blade 6, thus achieving the deflection of the turbulence blade 6. Since the insertion shaft 8 and the turbulence blade 6 are connected by a torsion spring, the turbulence blade 6 is initially in a straight state. Example 2:

[0022] like Figure 5 As shown, a control system for a tunnel-type freezing equipment for pre-processed bamboo shoot products includes a flow field sensing array, a flow field analysis and parameter output unit, and a multi-dimensional adjustable execution unit, wherein: The flow field sensing array is used to set multiple monitoring points distributed in the tunnel cavity inside the tunnel shell 1 according to the effective processing area of ​​the tunnel-type refrigeration equipment. Sensor groups are set at the monitoring points. The sensor groups are used to collect the raw airflow parameters of each monitoring point in real time and send them to the flow field analysis and parameter output unit. The airflow parameters include wind speed vector data and local static pressure value. The flow field analysis and parameter output unit includes a flow field analysis module and a parameter output module. The flow field analysis module is used to obtain the airflow parameters fed back by the flow field sensing array, and to establish a three-dimensional spatial model based on the effective processing area and freezing distribution points of the tunnel-type refrigeration equipment. The flow field distribution area is divided in the three-dimensional spatial model, and the low wind speed dead zone and airflow short-circuit zone in the tunnel cavity are identified based on the airflow parameters to output the flow field distribution map to the parameter output module. The specific process of establishing a three-dimensional spatial model is as follows: S101. Obtain the geometric parameters of the tunnel-type refrigeration equipment, including the width and length of the conveyor belt 2 and the height of the refrigeration chamber, and define the effective processing area based on the geometric parameters; S102. Based on the Cartesian coordinate system, establish a proportional digital model of the tunnel cavity, divide the effective processing area into several cubic grid units of equal volume, and map each grid unit to its nearest neighbor sensor group monitoring point. S103. Obtain airflow parameters from the flow field sensing array, and use a spatial interpolation algorithm to extend the data from the limited monitoring points to the full grid cells to complete the wind speed prediction values ​​in the non-monitoring point areas, so as to obtain a three-dimensional spatial model. S104. Combining Bernoulli's equation, the local static pressure value and wind speed vector are checked together to eliminate abnormal disturbance data caused by sensor noise.

[0023] The specific process for obtaining the output flow field distribution map is as follows: S201. Obtain a three-dimensional spatial model and compare the scalar wind speed value V of each grid cell with the preset minimum freezing threshold Vmin: When V < Vmin, and the area corresponding to the grid cell is located on the bamboo shoot material distribution path, it is marked as a low wind speed dead zone. The low wind speed dead zone is located below the support frame of conveyor belt 2 and at the corner of the tunnel, which can easily lead to insufficient cooling of bamboo shoots in some areas, resulting in quality deterioration. S202. Monitor the complete trajectory of airflow from the air outlet 5 of the air cooler to the return air outlet through the sensor group. If it is found that some airflow does not pass through the material layer and returns directly through the gaps in the side wall and top, and the pressure drop on this path is lower than the design pressure drop through the material layer, it is determined to be an airflow short-circuit zone. In the airflow short-circuit zone, a large amount of cold energy is wasted and no effective heat exchange occurs with the bamboo shoots. S203. The identified low-wind-speed dead zones and airflow short-circuit zones are integrated into a visualized flow field distribution map. The flow field distribution map is represented in the form of a vector arrow field. In the three-dimensional spatial model, the low-wind-speed dead zones and airflow short-circuit zones are highlighted using a pseudo-color layer, and the center coordinates and coverage of the abnormal areas are calculated.

[0024] The parameter output module is used to build a flow field simulation compensation model based on a deep learning network, obtain standard flow field parameters and product quality inspection parameters of bamboo shoot pre-processed products, extract features and use them as training samples to train the flow field simulation compensation model, obtain the optimized flow field simulation compensation model, input the flow field distribution map into the flow field simulation compensation model to obtain the flow field compensation parameters, and send them to the multidimensional adjustable execution unit. The specific process for obtaining the flow field compensation parameters is as follows: S301. Obtain the standard flow field parameters that are pre-stored in the system database. The standard flow field parameters represent the flow field numerical distribution under ideal working conditions, i.e., no dead zones, no short circuits, and the highest heat exchange efficiency. Obtain the quality inspection data of bamboo shoot pre-processed products after freezing. The quality inspection data includes cell wall damage rate (reflecting ice crystal size), surface whiteness (reflecting browning degree), water content loss (reflecting dry loss), and core temperature consistency. S302. Nonlinear feature encoding is performed on discrete quality inspection data, and the data are concatenated to form training samples containing the mapping relationship between the physical environment and the final quality. S303. Constructing a flow field simulation compensation model based on a deep learning architecture: During the training phase, a multi-objective loss function is defined, which includes the deviation between the predicted flow field value and the standard value. At the same time, a quality negative feedback penalty term is introduced, that is, if the quality inspection parameters show that the bamboo shoot dryness is too high, the penalty weight for the high wind speed characteristics of the area will be increased. By adjusting the weights of the neural network through the backpropagation algorithm, the flow field simulation compensation model can learn that under a specific bamboo shoot stacking density, different types of flow field distortion will lead to corresponding quality defect states, and finally obtain the optimized flow field simulation compensation model. S304. Input the spatial distribution characteristics of the low wind speed dead zone and airflow short-circuit zone in the tunnel generated in real time by the flow field analysis module into the optimized flow field simulation compensation model. Through the internal mapping matrix, automatically calculate the current flow field distortion to be eliminated and obtain the angle deviation value that needs to be adjusted for each multi-dimensional adjustable component.

[0025] The flow field compensation parameters are a set of instructions including the actuator number, target deflection angle, adjustment step size, and expected time to reach the target. The flow field compensation parameters not only take into account the elimination of dead angles, but also take into account the constraint of preventing bamboo shoot dryness, ensuring that the adjusted airflow will cover dead angles without forming excessive high-speed scouring on the product surface.

[0026] The multi-dimensional adjustable execution unit is used to acquire flow field compensation parameters and parse them into adjustment commands. Based on the adjustment commands, it independently drives the deflection angle of each flexible flow guiding component to change the local flow direction in the tunnel.

[0027] This invention, through the synergy of a flow field sensing array and 3D spatial modeling, can identify complex low-wind-speed dead zones and airflow short-circuit zones within tunnels in real time and intuitively, effectively solving the problem of inconsistent core freezing temperatures in bamboo shoots caused by uneven airflow distribution. It innovatively introduces product quality inspection parameters as feedback constraints for the deep learning model, enabling reverse optimization of the flow field compensation strategy based on the actual quality feedback of pre-processed products. While ensuring rapid freezing, it reduces moisture loss from the bamboo shoot surface through refined airflow control. Furthermore, the deep learning-based flow field simulation compensation model can automatically analyze complex flow field distribution maps and convert them into deflection commands for multi-dimensional actuators, reducing reliance on manual experience and ensuring optimal and uniform rapid freezing results for bamboo shoots from different batches and with different stacking densities.

[0028] The size of the interval and threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by those skilled in the art for each set of sample data; as long as it does not affect the ratio between the parameter and the quantized value.

[0029] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation. In the two embodiments provided in this application, it should be understood that the disclosed apparatus and system can be implemented in other ways; for example, the apparatus embodiments described above are merely illustrative, for example, the division of modules is merely a logical functional division, and there may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed; another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the apparatus or module can be electrical, mechanical or other forms. The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A tunnel-type freezing device for pre-processed bamboo shoot products, comprising a tunnel shell (1), characterized in that, The tunnel shell (1) is provided with a conveyor belt (2) for carrying and transporting products to be frozen. Multiple freezing zones (3) are arranged in sequence along the conveying direction of the conveyor belt (2). Each freezing zone (3) is provided with a cold air unit (4). The cold air unit (4) is fixed to the inner wall of the tunnel shell (1). An air outlet (5) is opened on the bottom surface of the cold air unit (4). A flexible flow guide component is provided at the air outlet (5). The flexible flow guide assembly includes multiple independently adjustable deflection blades (6) to eliminate dead airflow angles in the duct. The flexible flow guiding component includes a flow guiding frame (7), which is fixed to the inner wall of the air outlet (5). A plurality of insertion shafts (8) are evenly distributed on the inner wall of the flow guiding frame (7). Both ends of the turbulence blade (6) are provided with insertion slots corresponding to the insertion shafts (8). The insertion slots are movably connected to the insertion shafts (8). The outer surface of each turbulence blade (6) is connected with a deflection tension line (9). A plurality of steering shafts (10) that are in contact with the deflection tension line (9) are provided on the inner wall of the flow guiding frame (7). A magnetic block (11) is connected to the end surface of each deflection tension line (9). A plurality of electromagnets (12) corresponding to the magnetic block (11) are fixed inside the flow guiding frame (7). A movable slot (13) corresponding to the magnetic block (11) is provided on the inner wall of the flow guiding frame (7). Each magnetic block (11) is movably connected to the corresponding movable slot (13). It also includes a micro-mist humidification component, which is located in the pre-cooling section adjacent to the feed inlet of the tunnel shell (1).

2. The tunnel-type freezing equipment for pre-processed bamboo shoot products according to claim 1, characterized in that, It also includes a heat exchanger defrosting module, which includes an ultrasonic vibration generator installed on the fins of the air cooler (4) and a hot gas bypass valve connected in parallel with the fins of the air cooler (4).

3. The tunnel-type freezing equipment for pre-processed bamboo shoot products according to claim 1, characterized in that, The micro-mist humidification component includes a micro-mist water tank (14) and a water pump (15). The micro-mist water tank (14) is fixed to the inner wall of the tunnel shell (1). The water pump (15) is connected to the outer surface of the output end of the micro-mist water tank (14). A micro-mist distribution pipe (16) is fixed to the inner wall of the pre-cooling section of the inlet of the tunnel shell (1). The output end of the water pump (15) is connected to the inner wall of the micro-mist distribution pipe (16).

4. A control system for a tunnel-type freezing equipment for pre-processed bamboo shoot products, characterized in that, A tunnel-type freezing device for bamboo shoot pre-processing products, applicable to any one of claims 1-3, comprises a flow field sensing array, a flow field analysis and parameter output unit, and a multi-dimensional adjustable execution unit, wherein: The flow field sensing array is used to set multiple monitoring points distributed in the tunnel cavity inside the tunnel shell (1) according to the effective processing area of ​​the tunnel-type refrigeration equipment. A sensor group is set at the monitoring point. The sensor group is used to collect the original airflow parameters of each monitoring point in real time and send them to the flow field analysis and parameter output unit. The airflow parameters include wind speed vector data and local static pressure value. The flow field analysis and parameter output unit includes a flow field analysis module and a parameter output module. The flow field analysis module is used to obtain the airflow parameters fed back by the flow field sensing array, and to establish a three-dimensional spatial model based on the effective processing area and freezing distribution points of the tunnel-type refrigeration equipment. The flow field distribution area is divided in the three-dimensional spatial model, and the low wind speed dead zone and airflow short-circuit zone in the tunnel cavity are identified based on the airflow parameters to output the flow field distribution map to the parameter output module. The parameter output module is used to construct a flow field simulation compensation model based on a deep learning network, obtain standard flow field parameters and product quality inspection parameters of bamboo shoot pre-processed products, extract features and use them as training samples to train the flow field simulation compensation model, obtain an optimized flow field simulation compensation model, input the flow field distribution map into the flow field simulation compensation model to obtain flow field compensation parameters, and send them to the multidimensional adjustable execution unit. The multi-dimensional adjustable execution unit is used to acquire flow field compensation parameters and parse them into adjustment commands. Based on the adjustment commands, it independently drives the deflection angle of each flexible flow guiding component to change the local flow direction in the tunnel.

5. The control system for a tunnel-type freezing equipment for pre-processed bamboo shoot products according to claim 4, characterized in that, The specific process of establishing a three-dimensional spatial model is as follows: S101. Obtain the geometric parameters of the tunnel-type refrigeration equipment, including the width and length of the conveyor belt (2) and the height of the refrigeration chamber, and define the effective processing area according to the geometric parameters; S102. Based on the Cartesian coordinate system, establish a proportional digital model of the tunnel cavity, divide the effective processing area into several cubic grid units of equal volume, and map each grid unit to its nearest neighbor sensor group monitoring point. S103. Obtain airflow parameters from the flow field sensing array, and use a spatial interpolation algorithm to extend the data from the limited monitoring points to the full grid cells to complete the wind speed prediction values ​​in the non-monitoring point areas, so as to obtain a three-dimensional spatial model. S104. Combining Bernoulli's equation, the local static pressure value and wind speed vector are checked together to eliminate abnormal disturbance data caused by sensor noise.

6. The control system for a tunnel-type freezing equipment for pre-processed bamboo shoot products according to claim 4, characterized in that, The specific process for obtaining the output flow field distribution map is as follows: S201. Obtain a three-dimensional spatial model and compare the scalar wind speed value V of each grid cell with the preset minimum freezing threshold Vmin: When V < Vmin, and the area corresponding to the grid cell is located on the bamboo shoot material distribution path, it is marked as a low wind speed dead zone. S202. Monitor the complete trajectory of airflow from the air outlet (5) of the air cooler to the return air outlet through the sensor group. If it is found that some airflow does not pass through the material layer and returns directly through the side wall and top gap, and the pressure drop on this path is lower than the design pressure drop through the material layer, it is determined to be the airflow short-circuit area. S203. The identified low-wind-speed dead zones and airflow short-circuit zones are integrated into a visualized flow field distribution map. The flow field distribution map is represented in the form of a vector arrow field. In the three-dimensional spatial model, the low-wind-speed dead zones and airflow short-circuit zones are highlighted using a pseudo-color layer, and the center coordinates and coverage of the abnormal areas are calculated.

7. The control system for a tunnel-type freezing equipment for pre-processed bamboo shoot products according to claim 4, characterized in that, The specific process for obtaining the flow field compensation parameters is as follows: S301. Obtain standard flow field parameters pre-stored in the system database. The standard flow field parameters represent the flow field numerical distribution under ideal working conditions, i.e., no dead angles, no short circuits, and the highest heat exchange efficiency. Obtain the quality inspection data of bamboo shoot pre-processed products after freezing. The quality inspection data includes cell wall damage rate, surface whiteness, water content loss, and core temperature consistency. S302. Nonlinear feature encoding is performed on discrete quality inspection data, and the data are concatenated to form training samples containing the mapping relationship between the physical environment and the final quality. S303. Constructing a flow field simulation compensation model based on a deep learning architecture: During the training phase, a multi-objective loss function is defined, which includes the deviation between the predicted flow field value and the standard value. At the same time, a quality negative feedback penalty term is introduced, that is, if the quality inspection parameters show that the bamboo shoot dryness is too high, the penalty weight for the high wind speed characteristics of the area will be increased. By adjusting the weights of the neural network through the backpropagation algorithm, the flow field simulation compensation model can learn that under a specific bamboo shoot stacking density, different types of flow field distortion will lead to corresponding quality defect states, and finally obtain the optimized flow field simulation compensation model. S304. Input the spatial distribution characteristics of the low wind speed dead zone and airflow short-circuit zone in the tunnel generated in real time by the flow field analysis module into the optimized flow field simulation compensation model. Through the internal mapping matrix, automatically calculate the current flow field distortion to be eliminated and obtain the angle deviation value that needs to be adjusted for each flexible guide component.

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