Vegetable quick-freezing control method and device and quick-freezing equipment

Abstract

The invention is suitable for the technical field of food processing, and particularly relates to a vegetable quick-freezing control method and device and quick-freezing device.The method comprises the steps that vegetables are subjected to quick-freezing treatment through the quick-freezing device, and quick-frozen vegetables to be sorted are obtained; obtaining the size, weight and appearance information of the quick-frozen vegetables to be sorted through a detection module; comparing the obtained size, weight and appearance information with size, weight and appearance information ranges in a preset standard information range in a one-to-one correspondence manner to obtain comparison results; based on a comparison result, if at least one piece of information exceeds corresponding information in a preset standard information range, triggering an abnormal signal; performing abnormal feature verification on the quick-frozen vegetables to be sorted based on the abnormal signals; and based on the result of the abnormal feature verification, an execution module is controlled to execute corresponding sorting operation, so that sorting of the quick-frozen vegetables to be sorted is completed. According to the embodiment of the invention, the quick-frozen vegetables can be accurately and efficiently sorted.

Description

Technical Field

[0001] This application belongs to the field of food processing technology, and in particular relates to a method, apparatus and quick-freezing equipment for controlling the quick-freezing of vegetables. Background Technology

[0002] Quick-freezing equipment is used to rapidly freeze vegetables and other foods. It generally includes a quick-freezing unit and a sorting unit. The quick-freezing unit is used to quickly freeze vegetables, while the sorting unit is a specialized device used to screen and grade the vegetables after quick-freezing. The sorting unit typically includes a conveying mechanism, a detection mechanism, a sorting execution mechanism, and a control unit. The working principle of the sorting unit is that the conveying mechanism sequentially conveys the quick-frozen vegetables to the detection mechanism. The detection mechanism collects the physical characteristic information of the vegetables and feeds it back to the control unit. The control unit judges the quality of the vegetables according to preset standards and then controls the sorting execution mechanism to guide the vegetables into different channels to complete the sorting operation.

[0003] However, the sorting devices in these technologies suffer from insufficient accuracy in some practical applications. For example, some qualified vegetables may be mistakenly identified as substandard, while some substandard vegetables may not be effectively identified. Furthermore, the sorting process often fails to detect batch-specific quality anomalies in a timely manner, with problems frequently only becoming apparent after the entire batch of frozen vegetables has been processed, impacting production efficiency and product quality stability. Summary of the Invention

[0004] This application provides a method, apparatus, and quick-freezing equipment for controlling vegetable quick-freezing, which can solve the problem of insufficient sorting accuracy of sorting devices.

[0005] In a first aspect, embodiments of this application provide a vegetable quick-freezing control method applied to quick-freezing equipment. The quick-freezing equipment includes a quick-freezing device and a sorting device. The sorting device includes a detection module for detecting characteristics of quick-frozen vegetables and an execution module for performing sorting operations. The method includes: The vegetables are quick-frozen using the quick-freezing device to obtain quick-frozen vegetables to be sorted. The detection module acquires the characteristic information of the quick-frozen vegetables to be sorted; wherein, the characteristic information includes the size information, weight information and appearance information of the quick-frozen vegetables to be sorted. The size, weight, and appearance information of the quick-frozen vegetables to be sorted are compared one by one with the size, weight, and appearance information ranges in the preset standard information range to obtain the comparison results. Based on the comparison results, if at least one of the size, weight, and appearance information of the frozen vegetables to be sorted exceeds the corresponding information in the preset standard information range, an abnormal signal is triggered; wherein, the abnormal signal includes a size abnormal signal, a weight abnormal signal, and an appearance abnormal signal. Based on the abnormal signal, the abnormal features of the frozen vegetables to be sorted are verified, including: In the case where the abnormal signal is the size abnormal signal and / or the appearance abnormal signal, the positional relationship of the abnormal features and the relationship between the abnormal features and the features of adjacent quick-frozen vegetables in the same batch are verified for the quick-frozen vegetables to be sorted. When the abnormal signal is the weight abnormal signal, the relationship between the abnormal characteristics of the quick-frozen vegetables to be sorted and the characteristics of adjacent quick-frozen vegetables in the same batch is verified. When the abnormal signal is two or more types of abnormal signals, including the weight abnormal signal, the quick-frozen vegetables to be sorted are subjected to superimposed verification; wherein, the superimposed verification is to verify the positional relationship of the abnormal features and the relationship between the abnormal features and the features of adjacent quick-frozen vegetables in the same batch for the size abnormal signal and / or the appearance abnormal signal, and at the same time, to verify the relationship between the abnormal features and the features of adjacent quick-frozen vegetables in the same batch for the weight abnormal signal. Based on the results of the abnormal feature verification, the execution module is controlled to perform the corresponding sorting operation to complete the sorting of the frozen vegetables to be sorted.

[0006] The technical solutions described in this application embodiment have at least the following technical effects: The vegetable quick-freezing control method provided in this application embodiment uses a quick-freezing device to quick-freeze vegetables to obtain quick-frozen vegetables to be sorted. Then, the detection module of the sorting device acquires the size, weight, and appearance information of the quick-frozen vegetables to be sorted. The acquired size, weight, and appearance information of the quick-frozen vegetables to be sorted is compared one-to-one with the size, weight, and appearance information ranges in the preset standard information ranges. Based on the comparison results, when at least one piece of information exceeds the corresponding preset standard information range, a corresponding size, weight, or appearance abnormality signal is triggered. Then, targeted abnormality feature verification is performed for different abnormal signals. For size and / or appearance abnormality signals, the positional relationship of abnormal features and the relationship between abnormal features and the features of adjacent quick-frozen vegetables in the same batch are verified simultaneously. For weight abnormality signals, only the relationship between abnormal features and the features of adjacent quick-frozen vegetables in the same batch is verified. Finally, based on the abnormal feature verification results, the control execution module performs the corresponding sorting operation, realizing accurate and efficient sorting of quick-frozen vegetables. This solves the problems of insufficient accuracy and difficulty in quickly identifying batch quality abnormalities in the sorting process of related technologies.

[0007] Secondly, embodiments of this application provide a vegetable quick-freezing control device, applied to quick-freezing equipment. The quick-freezing equipment includes a quick-freezing device and a sorting device. The sorting device includes a detection module for detecting characteristics of quick-frozen vegetables and an execution module for performing sorting operations. The device includes: A quick-freezing unit is used to quick-freeze vegetables through the quick-freezing device to obtain quick-frozen vegetables to be sorted. The acquisition unit is used to acquire the feature information of the quick-frozen vegetables to be sorted through the detection module; wherein, the feature information includes the size information, weight information and appearance information of the quick-frozen vegetables to be sorted. The comparison unit is used to compare the size information, weight information and appearance information of the quick-frozen vegetables to be sorted with the size information range, weight information range and appearance information range in the preset standard information range, and obtain the comparison result. A triggering unit is configured to trigger an abnormal signal based on the comparison result if at least one of the size information, weight information, and appearance information of the frozen vegetables to be sorted exceeds the corresponding information in the preset standard information range; wherein the abnormal signal includes a size abnormal signal, a weight abnormal signal, and an appearance abnormal signal. A verification unit is used to verify the abnormal features of the frozen vegetables to be sorted based on the abnormal signal, including: In the case where the abnormal signal is the size abnormal signal and / or the appearance abnormal signal, the positional relationship of the abnormal features and the relationship between the abnormal features and the features of adjacent quick-frozen vegetables in the same batch are verified for the quick-frozen vegetables to be sorted. When the abnormal signal is the weight abnormal signal, the relationship between the abnormal characteristics of the quick-frozen vegetables to be sorted and the characteristics of adjacent quick-frozen vegetables in the same batch is verified. When the abnormal signal is two or more types of abnormal signals, including the weight abnormal signal, the quick-frozen vegetables to be sorted are subjected to superimposed verification; wherein, the superimposed verification is to verify the positional relationship of the abnormal features and the relationship between the abnormal features and the features of adjacent quick-frozen vegetables in the same batch for the size abnormal signal and / or the appearance abnormal signal, and at the same time, to verify the relationship between the abnormal features and the features of adjacent quick-frozen vegetables in the same batch for the weight abnormal signal. The control unit is used to control the execution module to perform the corresponding sorting operation based on the result of the abnormal feature verification, so as to complete the sorting of the frozen vegetables to be sorted.

[0008] Thirdly, embodiments of this application provide a quick-freezing device, which includes a quick-freezing apparatus, a sorting apparatus, and a controller. The sorting apparatus includes a detection module for detecting the characteristics of quick-frozen vegetables and an execution module for performing sorting operations. The quick-freezing apparatus, the detection module, and the execution module are electrically connected to the controller. The controller includes a memory, a processor, and a computer program stored in the memory and executable on the processor.

[0009] It is understood that the beneficial effects of the second and third aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is a schematic diagram of the quick-freezing equipment provided in an embodiment of this application; Figure 2 This is a schematic flowchart of a vegetable quick-freezing control method provided in an embodiment of this application; Figure 3 This is a flowchart illustrating the positional relationship verification of abnormal features in a vegetable quick-freezing control method provided in an embodiment of this application; Figure 4 This is a flowchart illustrating the relationship verification between abnormal features and adjacent quick-frozen vegetables in the same batch in a vegetable quick-freezing control method provided in an embodiment of this application. Figure 5 This is a schematic diagram of the vegetable quick-freezing control device provided in the embodiments of this application; Detailed Implementation In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0012] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0013] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0014] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0015] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0016] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0017] In related technologies, quick-freezing equipment is used to rapidly freeze vegetables and other foods, and generally includes a quick-freezing device and a sorting device. The quick-freezing device is used to quickly freeze vegetables, while the sorting device is a specialized device in the food processing field used for quality screening and grading of quick-frozen vegetables. The sorting device typically includes a conveying mechanism, a detection mechanism, a sorting execution mechanism, and a control unit. The working principle of the sorting device is that the conveying mechanism sequentially transports the quick-frozen vegetables to the detection mechanism. The detection mechanism collects the physical characteristic information of the vegetables and feeds it back to the control unit. The control unit compares this characteristic information with preset standards and then controls the sorting execution mechanism to guide the vegetables into different channels through physical pushing or channel guidance, completing the sorting operation.

[0018] However, the sorting devices in related technologies still suffer from insufficient sorting accuracy in some practical applications. For example, some qualified vegetables may be mistakenly identified as unqualified, while some unqualified vegetables are not effectively identified. This problem mainly stems from the fact that testing institutions often rely on single characteristics of frozen vegetables to determine quality, failing to establish multi-feature collaborative verification. For instance, slight dimensional abnormalities in non-core edible parts of frozen vegetables are mistakenly identified as unqualified, and there is a lack of correlation analysis of the location of abnormal features in frozen vegetables. For example, the failure to differentiate between abnormalities in core and non-core parts leads to an increased rate of false rejection of qualified vegetables. Furthermore, because the control module lacks dynamic statistical and directional consistency analysis of abnormal features in adjacent vegetables within the same batch, it only judges the qualification of individual vegetables in isolation, failing to capture systematic deviations within the same batch. Often, problems are only discovered through manual sampling after the entire batch has been processed, affecting production efficiency and product quality stability.

[0019] To address the aforementioned problems, this application provides a method, apparatus, and quick-freezing equipment for controlling vegetable quick-freezing. In this method, vegetables are first quick-frozen using a quick-freezing device to obtain quick-frozen vegetables to be sorted. Then, a detection module acquires the characteristic information of the quick-frozen vegetables to be sorted, including size, weight, and appearance information. Next, the size, weight, and appearance information of the quick-frozen vegetables to be sorted are compared one-to-one with preset standard information ranges for size, weight, and appearance, respectively, to obtain comparison results. Based on the comparison results, if at least one of the size, weight, and appearance information of the quick-frozen vegetables to be sorted exceeds the corresponding information in the preset standard information range, an abnormal signal is triggered. The abnormal signal includes a size abnormality signal, a weight abnormality signal, and an appearance abnormality signal. Then, based on the abnormal signal, an abnormal feature verification is performed on the quick-frozen vegetables to be sorted, including: if the abnormal signal is a size abnormality signal... In the event of size and / or appearance anomalies, the system verifies the positional relationship of the abnormal features and their relationship with those of adjacent frozen vegetables in the same batch. If the anomaly is a weight anomaly, the system verifies the relationship between the abnormal features and those of adjacent frozen vegetables in the same batch. If the anomaly consists of two or more types of anomalies, including weight anomalies, the system performs a superimposed verification. This superimposed verification involves verifying the positional relationship of the abnormal features and their relationship with those of adjacent frozen vegetables in the same batch for size and / or appearance anomalies, and simultaneously verifying the relationship between the abnormal features and those of adjacent frozen vegetables in the same batch for weight anomalies. Finally, based on the results of the anomaly verification, the system controls the execution module to perform the corresponding sorting operation to complete the sorting of the frozen vegetables. This achieves accurate sorting of frozen vegetables with different anomalies, solving the problems of insufficient accuracy and difficulty in quickly identifying batch quality anomalies in related technologies.

[0020] The vegetable quick-freezing control method provided in this application embodiment can be applied to quick-freezing equipment. In this case, the quick-freezing equipment is the executing subject of the vegetable quick-freezing control method provided in this application embodiment. This application embodiment does not impose any restrictions on the specific type of quick-freezing equipment.

[0021] As we understand it, quick-freezing equipment is used to rapidly freeze vegetables and other foods. It generally includes a quick-freezing unit and a sorting unit. The quick-freezing unit is used to quickly freeze vegetables, while the sorting unit is a specialized device for screening and grading the quick-frozen vegetables. The working principle of the sorting unit is that a conveyor mechanism sequentially transports the quick-frozen vegetables to a detection unit. The detection unit collects the physical characteristics of the vegetables and feeds this information back to the control unit. The control unit judges the quality of the vegetables according to preset standards and then controls the sorting execution mechanism to guide the vegetables into different channels, completing the sorting operation.

[0022] Please see Figure 1 In some embodiments, the quick-freezing equipment 1 includes a quick-freezing device 2, a sorting device 3, and a controller 4. The quick-freezing device 2 includes a feeding module 20, a low-temperature refrigeration module 21, a temperature monitoring module 22, and a discharge transition module 23. The sorting device 3 includes a transition conveying mechanism 30, a pretreatment module 31, a conveying module 32, a detection module 33 for detecting the characteristics of quick-frozen vegetables, and an execution module 34 for performing sorting operations. The discharge transition module 23 of the quick-freezing device 2 is connected to the pretreatment module 31 or the conveying module 32 of the sorting device 3 through the transition conveying mechanism 30. The quick-freezing device 2 and the sorting device 3 are electrically connected to the controller 4.

[0023] It is understood that the feeding module 20 is located at the very front of the quick-freezing device 2. It is the starting module of the quick-freezing device 2 that receives fresh vegetables and evenly feeds them into the subsequent freezing stage, thereby achieving continuous and stable feeding of vegetables. The feeding module 20 may include, for example, a conveyor belt, guide baffles arranged along both sides of the conveyor belt, a material-pulling rod above the conveyor belt, and a variable frequency motor. The variable frequency motor drives the conveyor belt to run, the guide baffles along both sides of the conveyor belt limit the range of vegetable conveying to prevent them from falling, and the material-pulling rod above the conveyor belt rotates synchronously with the conveyor belt to separate the piled vegetables, so that the vegetables to be quick-frozen are continuously conveyed to the low-temperature refrigeration module in a uniform single-layer state.

[0024] The low-temperature refrigeration module 21 is located behind the feeding module 20 and is the core module of the quick-freezing device 2 to achieve rapid freezing. It creates a low-temperature environment of -30°C to -40°C through refrigerant circulation, so that the core temperature of the vegetables drops to below -18°C within 30 minutes.

[0025] The temperature monitoring module 22 is located at the end of the low-temperature refrigeration module 21. It is the control module of the quick-freezing device 2 to control the freezing quality. It is used to detect the core temperature of the vegetables after freezing in real time, so that they can enter the next stage after meeting the standard.

[0026] The feeding module 20, the low-temperature refrigeration module 21, and the temperature monitoring module 22 are all standard modules conventionally configured in existing quick-freezing devices 2. Their structure and function are widely used in various quick-freezing equipment such as tunnel quick-freezing machines and spiral quick-freezing machines, and they are mature basic components in the industry.

[0027] The discharge transition module 23, located at the end of the quick-freezing device, serves as the connection module between the quick-freezing device 2 and the sorting device 3. It smoothly delivers the quick-frozen vegetables and reduces external heat intrusion. For example, it may include a heat-insulating curtain installed at the outlet of the low-temperature channel in the low-temperature refrigeration module 21, a connecting conveyor belt installed behind the heat-insulating curtain, and a speed synchronization sensor installed on the motor shaft of the connecting conveyor belt. One end of the connecting conveyor belt extends into the low-temperature channel outlet, and the other end extends to the end of the quick-freezing device 2, receiving the quick-frozen vegetables and conveying them outwards. The heat-insulating curtain reduces the loss of low-temperature cold energy from inside the quick-freezing device 2 to the outside, while simultaneously preventing ambient air from intruding into the low-temperature channel, maintaining a low-temperature environment in the outlet area of ​​the quick-freezing device 2. The speed synchronization sensor is linked to the transition conveyor mechanism 30 of the sorting device 3 to match the conveying speed.

[0028] The conveyor belt of the feeding module 20 is aligned and connected with the conveyor belt of the cryogenic refrigeration module 21, and the outlet of the cryogenic refrigeration module 21 is connected to the connecting conveyor belt of the discharge transition module 23, forming a continuous material conveying path. At the same time, the temperature sensor of the temperature monitoring module 22 detects the temperature of the quick-frozen vegetables in real time and feeds the data back to the controller 4. The controller 4 dynamically adjusts the conveyor belt speed of the feeding module 20 and the refrigeration power of the cryogenic refrigeration module 21 according to whether the temperature meets the standard. The speed synchronization sensor monitors the running speed of the conveyor belts of each module in real time and feeds the signal back to the controller 4, so that the conveyor belt speeds of the feeding module 20, the cryogenic refrigeration module 21 and the discharge transition module 23 are synchronized to avoid material accumulation or excessive spacing.

[0029] The transition conveying mechanism 30 can be, for example, a horizontally arranged belt conveyor, with the input end of the belt conveyor aligned with the output end of the connecting conveyor belt of the discharge transition module 23, and the output end of the belt conveyor aligned with the inlet conveyor belt of the pretreatment module 31 or the conveying module 32 of the sorting device 3.

[0030] The pretreatment module 31 is the starting module of the sorting device 3, used to separate the sticky parts of the quick-frozen vegetables, allowing individual vegetables to enter the detection stage. For example, it may include a vibration unit 311 installed below the conveyor belt of the pretreatment module 31, a hot air nozzle 312 installed above the conveyor belt behind the vibration unit 311, and a stickiness detection sensor 313 installed above the inlet of the pretreatment module 31. The vibration unit 311 includes an eccentric wheel vibration motor and rubber shock-absorbing pads. The rubber shock-absorbing pads are attached to the bottom of the eccentric wheel vibration motor, which is fixed with bolts. The hot air nozzle 312 includes a metal nozzle body, an electric heating wire, and a temperature sensor. The electric heating wire is built into the air inlet of the metal nozzle body, and the temperature sensor is embedded in the inner wall of the metal nozzle body. The wires of the electric heating wire are connected to the power supply module, and the temperature sensor is connected to the controller 4 via wires.

[0031] The conveying module 32 is the material transport carrier of the sorting device 3, used to sequentially transport vegetables to the detection module 33 and the execution module 34. The conveying module 32 may include, for example, a stepping conveyor belt running through the sorting device 3 and a synchronous pulley drive motor installed at the end of the stepping conveyor belt.

[0032] The detection module 33 is a module in the sorting device 3 used to acquire the size, weight, and appearance information of the quick-frozen vegetables to be sorted. For example, it may include an image acquisition unit 331 installed above the middle section of the conveyor module 32 and a weight sensing unit 332 embedded in the stepping conveyor belt of the conveyor module 32. The image acquisition unit 331 is used to acquire image information of the quick-frozen vegetables to be sorted, and then extract size information such as the length, width, and curvature of the quick-frozen vegetables to be sorted, as well as appearance information such as abnormal color blocks. The image acquisition unit 331 may include, for example, an industrial camera with its lens facing downwards, which is vertically fixed above the stepping conveyor belt of the conveyor module 32 by a bracket; a ring LED light source coaxially mounted below the lens of the industrial camera at a distance of 30-40cm from the surface of the conveyor belt; a fixed-focus lens; and an image processing chip built into the housing of the industrial camera. The industrial camera is connected to the image processing chip via a coaxial cable or USB cable, the ring LED light source is connected to the controller 4 via a wire, and the fixed-focus lens is fixed to the front end of the industrial camera via a threaded structure. The weight sensing unit 332 is used to acquire the weight information of the quick-frozen vegetables to be sorted. The weight sensing unit 332 may include, for example, a strain gauge load cell embedded under the chain plate of the stepping conveyor belt of the conveying module 32, with the top and bottom of the chain plate closely attached; a signal conditioning module fixed to the side of the frame of the conveying module 32 (near the strain gauge load cell) by bolts; and a carrying tray mechanically fixed above the elastic body of the strain gauge. The strain gauge load cell is connected to the signal conditioning module through wires, and the signal conditioning module is connected to the controller 4 through an RS485 bus.

[0033] The execution module 34 is a module used to perform sorting operations. For example, it may include a pneumatic push rod 341 installed on the side of the conveying module 32, a diversion baffle 342 installed at the end of the conveying module 32, and electrically controlled rotating shafts 343 installed on both sides of the frame at the end of the conveying module 32. The pneumatic push rod 341 includes a cylinder, a solenoid directional valve, and a pressure sensor. The solenoid directional valve is connected to the cylinder inlet via an air pipe, and the pressure sensor is installed at the cylinder outlet and connected to the controller 4 via a wire. The diversion baffle 342 includes a metal plate and a fixed bushing. The fixed bushing is welded to one side of the metal plate and keyed to the electrically controlled rotating shaft 343. The electrically controlled rotating shaft 343 includes a stepper motor, a reducer, and an angle encoder. The input end of the reducer is connected to the output shaft of the stepper motor via a coupling. The output end of the reducer is mechanically connected to the fixed bushing of the diverter baffle 342. The angle encoder is installed on the output shaft end of the reducer and connected to the controller 4 via a wire. The axis of the electrically controlled rotating shaft 343 is parallel to the width direction of the conveyor belt of the conveyor module 32. Both ends are fixed to the frame via bearing seats. The middle part is rigidly connected to the middle position of the diverter baffle 342 (such as by welding or keying). The diverter baffle 342 is driven to rotate around the axis by forward and reverse rotation to realize the switching of the sorting channel.

[0034] Controller 4 is the core device for executing the control logic of sorting device 3. Its core functions are to receive feature information transmitted by detection module 33, perform comparative analysis based on preset standard information ranges, trigger abnormal signals, complete abnormal feature verification, and send control commands to execution module 34 according to the verification results to achieve precise control of the sorting operation. It can also record sorting data, generate reports, and trigger equipment maintenance prompts. Controller 4 can be, for example, a PLC or an industrial control computer.

[0035] To better understand the vegetable quick-freezing control method provided in the embodiments of this application, the specific implementation process of the vegetable quick-freezing control method provided in the embodiments of this application will be described by way of example below.

[0036] Figure 2 This paper presents a schematic flowchart of a vegetable quick-freezing control method provided in an embodiment of this application. The vegetable quick-freezing control method includes: S100, the vegetables are quick-frozen by quick-freezing device 2 to obtain quick-frozen vegetables to be sorted.

[0037] Quick-freezing, as we understand it, refers to a process that uses a low-temperature environment of -30°C to -40°C to rapidly lower the core temperature of vegetables to below -18°C within 30 minutes, quickly freezing the water inside the cells and reducing the damage to cell structure caused by ice crystals. Quick-frozen vegetables awaiting sorting refer to vegetables that have undergone quick-freezing and whose shape and quality have stabilized, ready to enter the sorting process. Examples include quick-frozen broccoli, quick-frozen carrots, and quick-frozen peas.

[0038] For example, after being washed and cut, fresh vegetables are conveyed by a conveyor belt into the low-temperature channel of the quick-freezing device 2. The refrigeration system rapidly cools the vegetables by spraying liquid nitrogen or circulating cold air, causing the surface and internal moisture of the vegetables to freeze quickly. Temperature sensors monitor the core temperature of the vegetables in real time. Once it reaches below -18°C, the conveyor belt transports them to the outlet, forming quick-frozen vegetables ready for sorting. For example, after being processed by the quick-freezing device 2, the core temperature of fresh carrot strips drops to -20°C, resulting in a firm texture and fixed shape, thus becoming quick-frozen carrots ready for sorting.

[0039] S200, the detection module 33 acquires the characteristic information of the quick-frozen vegetables to be sorted; wherein, the characteristic information includes the size information, weight information and appearance information of the quick-frozen vegetables to be sorted.

[0040] It is understandable that feature information is a set of key parameters used to measure the quality of frozen vegetables. Among them, size information is a parameter describing the physical morphology of frozen vegetables. For example, size information may include length, width, and curvature. Length refers to the straight-line distance between the two ends of the frozen vegetable, width refers to the straight-line distance at the widest point of the frozen vegetable, and curvature refers to the degree of curvature of the curved part of the frozen vegetable. Weight information is the magnitude of the gravity on the frozen vegetable, in grams or kilograms. Appearance information is the visual characteristics presented on the surface of the frozen vegetable, such as the color, integrity, and presence of abnormal color patches on the surface of the frozen vegetable.

[0041] For example, the image acquisition unit 331 in the detection module 33 can take multi-angle photos of the quick-frozen vegetables to be sorted to obtain high-definition images, thereby obtaining size and appearance information. Then, when the quick-frozen vegetables are placed on the support tray, the weight sensing unit 332 converts the deformation of the strain gauge into an electrical signal to obtain weight data. For example, for quick-frozen carrots, after the image acquisition unit 331 takes a picture of the whole carrot, the image processing chip in the image acquisition unit 331 extracts the length, width and curvature of the quick-frozen carrot. The weight sensing unit 332 directly weighs the quick-frozen carrot. At the same time, if there are white patches in the image caused by frost damage, they are abnormal color patches in the appearance information.

[0042] S300: The size, weight and appearance information of the frozen vegetables to be sorted are compared one by one with the size, weight and appearance information ranges in the preset standard information range to obtain the comparison results.

[0043] It is understandable that the preset standard information range is a qualified range of characteristic parameters pre-set according to product quality requirements or industry standards. For example, the size information range of a certain type of frozen peas may be set as length 5-8mm, width 4-6mm, weight information range 2-3g, and the area of ​​abnormal color patches in the appearance information range must be less than 0.5mm². The comparison result refers to the conclusion of the comparison between the various characteristic information of the frozen vegetables to be sorted and the corresponding preset range. For example, if the size information is within the range, it is qualified; if it exceeds the range, it is unqualified.

[0044] For example, the controller 4 can call the preset standard information range stored in the memory to compare the size information, weight information, and appearance information transmitted by the detection module 33 with the corresponding preset standard information range. For instance, if the actual length of the frozen peas is 9mm, exceeding the preset range of 5-8mm, the size information comparison result is unqualified; if the weight is 2.5g, within the range of 2-3g, the weight information comparison result is qualified; if there are white patches caused by frostbite with an area of ​​0.8mm², exceeding the preset 0.5mm², the appearance information comparison result is unqualified. The results are integrated through logical operations to form the final comparison result.

[0045] S400, based on the comparison results, if at least one of the size information, weight information, and appearance information of the frozen vegetables to be sorted exceeds the corresponding information in the preset standard information range, an abnormal signal is triggered; wherein, the abnormal signal includes a size abnormal signal, a weight abnormal signal, and an appearance abnormal signal.

[0046] As can be understood, anomaly signals are electrical signals or data identifiers used to indicate that a certain characteristic of the frozen vegetables to be sorted does not meet the standards. Size anomaly signals are triggered when the length, width, curvature, or other dimensional information of the frozen vegetables exceeds the preset standard range. Weight anomaly signals are triggered when the weight information of the frozen vegetables exceeds the preset standard range. Appearance anomaly signals are triggered when the appearance information of the frozen vegetables exceeds the preset standard range.

[0047] For example, the controller 4 can perform logical judgment on the comparison results obtained by S300. When at least one of the features of size, weight, and appearance exceeds the corresponding preset range, a corresponding abnormal signal is immediately generated and output. For example, if the length of the frozen carrot is 12cm and the preset standard length range is 8-10cm, a size abnormal signal is triggered; if the weight is 150g and the preset standard weight range is 100-130g, a weight abnormal signal is triggered; if a moldy patch with an area of ​​2mm² is detected in the image and the preset standard appearance range is less than 1mm², an appearance abnormal signal is triggered; if both length and weight exceed the standard, both size and weight abnormal signals are triggered simultaneously.

[0048] S500 performs anomaly feature verification on the quick-frozen vegetables to be sorted based on abnormal signals, including: When the abnormal signal is a size abnormal signal and / or an appearance abnormal signal, the positional relationship of the abnormal features and the relationship between the abnormal features and the features of adjacent quick-frozen vegetables in the same batch are verified for the quick-frozen vegetables to be sorted. When the abnormal signal is a weight abnormal signal, the relationship between the abnormal characteristics of the quick-frozen vegetables to be sorted and the characteristics of adjacent quick-frozen vegetables in the same batch is verified. When there are two or more abnormal signals, including weight abnormal signals, the quick-frozen vegetables to be sorted are subjected to superimposed verification. The superimposed verification is to verify the positional relationship of abnormal features and the relationship between abnormal features and adjacent quick-frozen vegetables in the same batch for size abnormal signals and / or appearance abnormal signals, and at the same time, to verify the relationship between abnormal features and adjacent quick-frozen vegetables in the same batch for weight abnormal signals.

[0049] It is understandable that anomaly feature verification is a process of further verifying the features that trigger anomaly signals to confirm the authenticity and severity of the anomaly. Anomaly feature positional verification determines the specific location of the anomalous part within the overall frozen vegetables and the degree of influence that location has on the frozen vegetables. Anomaly feature relationship verification with adjacent frozen vegetables in the same batch determines the specificity or prevalence of the anomaly by comparing the characteristics of adjacent vegetables in the same batch. Adjacent frozen vegetables in the same batch refer to vegetables from the same production batch as those that have triggered anomaly signals and are adjacent to each other on the conveyor belt. Overlay verification is a combined verification method that integrates multiple verifications when multiple anomaly signals exist.

[0050] For example, the controller 4 can invoke the corresponding verification logic based on the type of triggered abnormal signal. For instance, if a size abnormality signal is triggered, the location of the abnormal portion is first determined through image analysis acquired by the image acquisition unit 331, such as the top, middle, or end of the frozen vegetable. The impact of the abnormal position on the vegetable quality is then assessed, such as whether it affects the overall shape or edibility. Simultaneously, the size information of five adjacent frozen vegetables from the same batch is extracted, and the difference between the size of these five adjacent vegetables and the size of the currently abnormal frozen vegetable is compared. If both size and weight abnormality signals are triggered simultaneously, the weight of the adjacent frozen vegetables from the same batch is also compared while verifying the location of the size abnormality and comparing it with the size of adjacent frozen vegetables from the same batch, to comprehensively determine the nature of the abnormality.

[0051] S600, based on the result of the abnormal feature verification, controls the execution module 34 to perform the corresponding sorting operation to complete the sorting of the frozen vegetables to be sorted.

[0052] It is understandable that the sorting operation is the action performed by module 34 to classify frozen vegetables based on the anomaly verification results, such as importing them into different channels or triggering an alarm. The anomaly feature verification result refers to the specific conclusion obtained through verification in S500.

[0053] For example, the controller 4 can convert the verification result into control commands to drive the execution module 34 to operate. For instance, if the verification result indicates an isolated, accidental occurrence, the controller 4 sends a signal to the electrically controlled rotating shaft 343, causing it to drive the diversion baffle 342 to rotate to a specific angle, guiding the vegetables into the corresponding channel. If the verification result indicates a common anomaly in the same batch, the controller 4 sends a stop signal to the conveyor belt drive motor and triggers an audible and visual alarm, pausing the sorting process and prompting the operator to intervene. The electrically controlled rotating shaft 343 is a rotating shaft driven by the control module to adjust the angle of the diversion baffle 342, which is a plate-shaped component that guides frozen vegetables into different channels by adjusting its angle.

[0054] By employing the steps S100 to S600 described above, vegetables are first quick-frozen using the quick-freezing device 2 to obtain quick-frozen vegetables to be sorted. Then, the detection module 33 acquires the size, weight, and appearance information of the quick-frozen vegetables. The information is compared with the preset standard information range to trigger an anomaly signal. Then, the nature of the anomaly is clarified through multi-dimensional verification, including location influence and individual / batch attributes. Finally, based on the verification results, the execution module 34 is controlled to accurately sort the vegetables, forming a closed-loop control logic of "feature acquisition - anomaly identification - deep verification - accurate execution". This avoids the limitations of single feature judgment, reduces misjudgment, missed judgment, and over-processing, thereby significantly improving the sorting accuracy of the sorting device 3.

[0055] As an optional embodiment of this application, in step S200, the characteristic information of the quick-frozen vegetables to be sorted is obtained through the detection module 33, including: S210, image information of the quick-frozen vegetables to be sorted is acquired through image acquisition unit 331.

[0056] It can be understood that image information refers to the visual data of the frozen vegetables to be sorted, which is captured by the image acquisition unit 331. The visual data may include, for example, color images and grayscale images. The image information of the frozen vegetables to be sorted may include, for example, the visual features of the frozen vegetables such as shape, color, and texture.

[0057] For example, the ring-shaped LED light source in the image acquisition unit 331 can provide uniform illumination to the quick-frozen vegetables to be sorted, avoiding shadows that affect image quality. The industrial camera focuses on the quick-frozen vegetables through a fixed-focus lens to capture multi-angle, high-resolution images. The image data is transmitted to the image processing chip via a coaxial cable. For instance, when photographing quick-frozen green beans, the light source illuminates from all sides, and the camera captures one image from above and one from the side, capturing the overall shape of the green beans and whether there are mold spots on the surface. The image information is transmitted to the subsequent processing unit in real time.

[0058] S211, extract the size and appearance information of the quick-frozen vegetables to be sorted based on image information; wherein, the size information includes length, width and curvature, and the appearance information includes abnormal color blocks, which are local color blocks formed by frost damage, mold or impurity contamination of the quick-frozen vegetables to be sorted.

[0059] It can be understood that size information extraction is the process of parsing morphological parameters such as length, width, and curvature of frozen vegetables from an image. Appearance information extraction is the identification of abnormal color areas on the surface of vegetables in an image. Abnormal color patches are local areas that differ significantly from the normal color of vegetables; for example, they could be white patches caused by frost damage, black spots caused by mold, or discolored particles caused by impurities.

[0060] For example, image information can be preprocessed using an image processing chip. An edge detection algorithm can be used to identify the outline of frozen vegetables, calculating the longest distance as the length and the widest distance as the width. Curvature of curved sections can be calculated using curve fitting. Simultaneously, a color thresholding algorithm is used to mark areas where the color difference from normal areas exceeds a preset threshold as abnormal color blocks, recording their area and location. Preprocessing can be, for example, noise reduction or enhancement. The edge detection algorithm identifies the boundary lines between the frozen vegetables and the background by calculating the brightness differences of pixels in the image, thus outlining the overall contour of the vegetables, similar to drawing lines along the edges of an object with a pencil. Curve fitting approximates the actual shape of curved sections of frozen vegetables using a mathematical curve model; for example, fitting the curved edge of a frozen spinach leaf into an arc curve and then calculating the degree of curvature using the curve radius. The color thresholding algorithm sets a threshold value for the normal color of frozen vegetables, segmenting pixel areas in the image where the color value exceeds this range, similar to comparing with a specific color chart and picking out areas with mismatched colors.

[0061] S212, the weight information of the quick-frozen vegetables to be sorted is obtained through the weight sensing unit 332.

[0062] For example, frozen vegetables to be sorted are conveyed onto a support tray. The tray transfers its weight to the elastic body of the strain gauge load cell, causing the strain gauge to deform and its resistance to change. An amplifier in the signal conditioning module amplifies the resistance change signal, and an A / D converter converts the analog signal into a digital signal, which is then transmitted via an RS485 bus. For instance, when frozen green peas are placed on the support tray, the weight signal detected by the sensor is processed to obtain a weight of 25g, which is sent to controller 4 in real time for subsequent comparison.

[0063] By employing the steps S210 to S212 described above, the size, appearance, and weight information of the quick-frozen vegetables to be sorted can be obtained comprehensively and accurately through the coordinated work of image acquisition and weight sensing. The various feature information complement each other, reducing the limitations of judging a single feature, and providing comprehensive data support for subsequent comparison and sorting, thereby improving the sorting accuracy of the sorting device 3.

[0064] Please see Figure 3 In one possible implementation, S500, the positional relationship verification of abnormal features, includes: S510, when the abnormal signal is a size abnormality signal, analyze the position of the size abnormal part in the whole quick-frozen vegetables to be sorted.

[0065] As can be understood, the size abnormality refers to a localized area in the frozen vegetables to be sorted that exceeds the preset size standard range; for example, it could be an excessively long stem or an excessively thick root. Location analysis determines the specific location of this abnormal part within the overall frozen vegetable.

[0066] For example, the controller 4 can access images of frozen vegetables processed by the image processing chip. An image segmentation algorithm can then be used to divide the vegetables into different parts, such as the tender tips and lower stems of frozen asparagus. The pixel locations of the abnormally sized parts can be determined using coordinate positioning and compared with the pixel regions of each part. Image segmentation algorithms are used to divide a pre-processed image into multiple regions with unique attributes based on features such as color, brightness, or texture. For example, separating the green body of frozen broccoli from the yellow diseased parts, or distinguishing the soil and impurities on the surface of a carrot from its orange body. For instance, the standard length of frozen asparagus is 15-20cm. If a frozen asparagus is detected to be 22cm long, image analysis reveals that the extra 2cm is located in the lower stem, thus completing the analysis of the abnormal size location.

[0067] S511, if the abnormal size part is located in a non-core edible part, it is judged as a non-critical location abnormality; whereby, the non-core edible part refers to the edible but not the main part.

[0068] It is understandable that non-core edible parts refer to areas in frozen vegetables that are edible but not typically considered the primary food source. Examples include the stems of frozen broccoli or the top leaves of frozen carrots. It should be understood that the definition of non-core edible parts varies depending on the type of frozen vegetable. Non-critical location anomalies refer to anomalies where the size abnormality is located in a non-core edible part and has a relatively minor impact on the overall quality of the frozen vegetable.

[0069] For example, the controller 4 can match the location of the size-abnormal part determined in S510 with a pre-stored feature library of non-core edible parts. If they match perfectly, the abnormality is determined to be a non-critical location. The pre-stored feature library of non-core edible parts can be formed by collecting a large number of samples of the same variety of quick-frozen vegetables, extracting and labeling the image features of their non-core edible parts, such as location coordinates, shape parameters, and proportion to the whole, and storing them after classification and organization.

[0070] S512, if the abnormal size part is located in the core edible part, it is judged as a critical position abnormality; where the core edible part refers to the part that is mainly eaten.

[0071] It's understandable that the core edible part is the most important edible area in frozen vegetables, such as the florets of frozen broccoli or the fleshy root of frozen carrots. Critical location anomalies refer to anomalies where the sized portion is located in the core edible part, significantly impacting the quality of the vegetable.

[0072] For example, the controller 4 can compare the location of the size-abnormal part determined in S510 with the pre-stored core edible part feature library. If a match is found, it is determined to be a key location abnormality. The pre-stored core edible part feature library can be formed by collecting a large number of samples of the same variety of quick-frozen vegetables, extracting and labeling the image features of their core edible parts, such as location coordinates, shape parameters, and proportion to the whole, and storing them after classification and organization.

[0073] S513, when the abnormal signal is an appearance abnormal signal, analyze the position of the abnormal color block in the whole of the quick-frozen vegetables to be sorted.

[0074] For example, controller 4 uses image recognition technology to locate the pixel coordinates of the abnormal color block, and then combines this with the overall image outline of the frozen vegetable to determine the relative position of the abnormal color block within the vegetable. This position may include, for example, the distance from the core edible part and the proportion of the core part it occupies. For instance, the fleshy root of a frozen carrot is the core edible part. If a brown moldy color block is detected in the image, and coordinate calculations show that it is located in the central area of ​​the fleshy root, then the analysis of the abnormal color block's position is complete.

[0075] S514. If the abnormal color block is located in a non-core edible part, it is judged as an abnormality in a non-critical location.

[0076] For example, the controller 4 compares the location of the abnormal color block determined in S513 with the coordinate range of the non-core edible part. If the center coordinate of the abnormal color block falls within the range of the non-core part and the area of ​​the abnormal color block is less than the proportion of the area of ​​the non-core part to a preset threshold, it is determined to be an abnormal non-critical location.

[0077] S515, if the abnormal color block is located in the core edible part, it is judged as an abnormality in a critical position.

[0078] For example, the controller 4 compares the location of the abnormal color block determined in S513 with the coordinate range of the core edible part. If the center coordinates of the abnormal color block are within the core part, or the area of ​​the abnormal color block accounts for more than a preset threshold, it is determined to be an abnormality in the key position.

[0079] By adopting the above steps S510 to S515, the location of size and appearance abnormalities can be accurately determined, and the abnormalities in critical and non-critical locations can be distinguished, so that the sorting standards are more in line with the actual quality requirements, avoiding over-sorting or under-sorting, thereby improving the sorting accuracy of the sorting device 3.

[0080] Please see Figure 4 As an optional embodiment of this application, S500, the relationship verification between abnormal features and the features of adjacent quick-frozen vegetables in the same batch includes: S520: Based on the quick-frozen vegetables to be sorted that have triggered abnormal signals, the detection module 33 extracts the feature information of a preset number of adjacent quick-frozen vegetables in the same batch of quick-frozen vegetables that have triggered abnormal signals.

[0081] It is understandable that the preset quantity is the number of adjacent vegetables to be extracted based on statistical needs.

[0082] For example, the controller 4 can record the location information of vegetables that have triggered an abnormal signal, and then control the detection module 33 to collect feature information of a preset number of vegetables from the same batch before and after that location. The collection method is the same as in S200. For example, after a frozen corn triggers an abnormal signal, the controller 4 instructs the detection module 33 to extract feature information such as the length, diameter, weight, and appearance color of two frozen corns from the same batch before and after it.

[0083] S521, based on the extracted feature information of adjacent preset quantity quick-frozen vegetables in the same batch, count the first number of quick-frozen vegetables in the same batch that trigger the same type of abnormal signal as the quick-frozen vegetables to be sorted; wherein, the same type of abnormal signal refers to a signal that is completely consistent with the type of abnormal signal triggered by the quick-frozen vegetables to be sorted that have already triggered an abnormal signal, and the same type of abnormal signal includes the same signal among size abnormal signal, weight abnormal signal or appearance abnormal signal.

[0084] It can be understood that the first quantity refers to the number of vegetables in the same batch with adjacent preset quantities that have the same type of abnormal signal as the vegetables that have triggered the abnormal signal. The similarity of abnormal signals emphasizes the consistency of the abnormality type, such as all being size abnormal signals, rather than a mixture of size and weight abnormal signals.

[0085] For example, the controller 4 can compare the feature information of adjacent vegetables with a preset standard range to filter out vegetables that have triggered abnormal signals, and then count the number of vegetables with the same type of abnormal signal as those that have been triggered. For example, if the abnormal signal that has been triggered is a size abnormal signal, and two of the four adjacent vegetables have also triggered a size abnormal signal, then the first count is 2.

[0086] S522, based on the statistically obtained first quantity, calculate the proportion of the first quantity to the quantity of adjacent preset quantities of quick-frozen vegetables in the same batch.

[0087] As can be understood, the ratio refers to the ratio of the number of adjacent vegetables with the same type of abnormality to the total number of adjacent vegetables with the same type of abnormality. For example, if the first quantity is 2 and the preset quantity is 4, then the ratio is 50%, which is used to reflect the degree of concentration of the same type of abnormality.

[0088] For example, controller 4 calculates the ratio by division, that is, the ratio is equal to the first quantity divided by the adjacent preset quantity in the same batch and then multiplied by 100%. For example, if the preset quantity is 5 and the first quantity is 3, then the ratio is equal to 3 divided by 5 and then multiplied by 100%, which equals 60%.

[0089] S523, based on the abnormal characteristics of the frozen vegetables to be sorted that have triggered abnormal signals and the extracted characteristic information of the adjacent preset number of frozen vegetables in the same batch, select adjacent frozen vegetables from the adjacent preset number of frozen vegetables in the same batch that have triggered the same type of abnormal signal as the frozen vegetables to be sorted that have triggered abnormal signals.

[0090] It is understandable that screening refers to identifying individuals that meet the same abnormal signal conditions from adjacent preset quantities of quick-frozen vegetables in the same batch.

[0091] For example, the controller 4 compares the abnormal characteristics of the vegetables that have triggered the abnormal signal with the characteristic information of a preset number of adjacent quick-frozen vegetables in the same batch, and selects quick-frozen vegetables with the same type of characteristic information and consistent parameter deviation trends. For example, if the triggered abnormality is that the length exceeds the standard, the adjacent quick-frozen vegetables in the preset number of adjacent quick-frozen vegetables in the same batch that also exceed the standard are selected as adjacent quick-frozen vegetables.

[0092] S524, based on the abnormal features of the selected adjacent quick-frozen vegetables, determine the direction in which the feature value corresponding to the abnormal feature of each selected adjacent quick-frozen vegetable deviates from the standard value of the corresponding feature; wherein, the feature value is the specific value of the size information, weight information and appearance information of the quick-frozen vegetables to be sorted obtained by the detection module 33, the standard value is set based on the preset standard information range, the standard value includes the size standard value, weight standard value and appearance standard value of the quick-frozen vegetables to be sorted, and the direction includes the feature value being greater than the standard value of the corresponding feature or the feature value being less than the standard value of the corresponding feature.

[0093] It can be understood that the standard value is a reference value within a preset standard range. For example, the standard value for a size can be the midpoint of the size information range within the preset standard range. The deviation direction refers to the magnitude relationship of the feature value relative to the standard value. For example, a length of 5.2cm is considered a positive deviation if it is greater than the standard value of 5cm, and a negative deviation if it is less than the standard value.

[0094] For example, the controller 4 calculates the difference between the feature value of each selected adjacent vegetable and the standard value. If the difference is positive, the direction is greater than the standard value; if it is negative, the direction is less than the standard value. For example, if the standard size is 5cm, and the length of an adjacent vegetable is 5.3cm, then the deviation direction is greater than the standard value; if the length of another adjacent vegetable is 4.7cm, then the deviation direction is less than the standard value.

[0095] S525, based on the abnormal characteristics of the quick-frozen vegetables to be sorted that have triggered abnormal signals and the standard values ​​of the corresponding characteristics, determine the direction in which the characteristic value corresponding to the abnormal characteristics of the quick-frozen vegetables to be sorted that has triggered abnormal signals deviates from the standard value of the corresponding characteristics.

[0096] For example, controller 4 calculates the difference between the characteristic value of the vegetable that has triggered the abnormal signal and the standard value, and determines the direction of deviation based on the sign of the difference. For instance, if the abnormal vegetable weighs 12g and the standard value is 10g, then the direction of deviation is greater than the standard value.

[0097] S526, based on the direction of the deviation of the feature value corresponding to the abnormal feature of the quick-frozen vegetables to be sorted from the standard value of the corresponding feature, and the direction of the deviation of the feature value corresponding to the abnormal feature of each selected adjacent quick-frozen vegetable from the standard value of the corresponding feature, it is determined whether the feature value corresponding to the abnormal feature of the quick-frozen vegetables to be sorted that has triggered the abnormal signal and the feature value corresponding to the abnormal feature of all adjacent quick-frozen vegetables that have triggered the same type of abnormal signal deviate from the standard value of the corresponding feature in the same direction, and the consistency result of the direction of deviation of the feature value corresponding to the abnormal feature from the standard value of the corresponding feature is obtained.

[0098] It can be understood that the direction consistency result refers to the judgment conclusion that the deviation direction of the characteristic value corresponding to the abnormal feature of the frozen vegetables to be sorted that has triggered the abnormal signal is exactly the same as that of the characteristic value corresponding to the abnormal feature of all adjacent frozen vegetables that have triggered the same type of abnormal signal. For example, if the characteristic values ​​are all positive deviations, they are consistent; if there is a mixture of positive and negative values, they are inconsistent.

[0099] For example, controller 4 compares the deviation direction of the frozen vegetables to be sorted that have triggered an abnormal signal in S525 with the deviation direction of all adjacent frozen vegetables that have triggered the same type of abnormal signal in S524. If all are the same, the result is consistent; otherwise, it is inconsistent. For example, if the deviation direction of the frozen vegetables to be sorted that have triggered an abnormal signal is greater than the standard value, and the deviation directions of two adjacent frozen vegetables that have triggered the same type of abnormal signal are also greater than the standard value, then the direction consistency result is consistent; if one of them is less than the standard value, then the result is inconsistent.

[0100] S527, when the calculated number of adjacent preset quantity quick-frozen vegetables in the same batch that triggers the same type of abnormal signal as the quick-frozen vegetables to be sorted is less than the total number of adjacent preset quantity quick-frozen vegetables in the same batch, it is determined to be an individual accidental abnormality.

[0101] It is understandable that an individual random anomaly refers to an anomaly that only occurs in the current batch of frozen vegetables, and the proportion of the same type of anomaly is low in adjacent batches of frozen vegetables, which is an individual phenomenon that occurs by chance.

[0102] For example, controller 4 compares the ratio calculated in S522 with a preset ratio. If the ratio is less than the preset ratio, it is determined to be an individual random anomaly. For example, if the ratio is 25% and the preset ratio is 30%, then the current abnormal vegetable is an individual random anomaly.

[0103] S528, when the calculated number of adjacent preset quantity quick-frozen vegetables in the same batch that triggers the same type of abnormal signal as the quick-frozen vegetables to be sorted accounts for a proportion greater than or equal to a preset proportion, and the consistency result of the direction of the characteristic value corresponding to the abnormal feature deviating from the standard value of the corresponding feature is that they all deviate from the standard value of the corresponding feature in the same direction, it is determined to be a batch systemic abnormality.

[0104] It is understandable that batch systematic anomalies refer to a high proportion of similar anomalies in adjacent quick-frozen vegetables within the same batch, with the deviations being consistent, which may be caused by systematic problems in the quick-freezing time or vegetable batch during the production process.

[0105] For example, controller 4 determines a batch as systematically abnormal when both conditions are met: the proportion is greater than or equal to a preset proportion and the direction consistency results are consistent. For instance, if the proportion is 40%, the preset proportion is 30%, and all abnormal vegetables are longer than the standard value, then the batch is determined to be systematically abnormal.

[0106] S529, when the calculated number of adjacent preset quantity quick-frozen vegetables in the same batch triggering the same type of abnormal signal as the quick-frozen vegetables to be sorted accounts for a proportion greater than or equal to the preset proportion of the total number of adjacent preset quantity quick-frozen vegetables in the same batch, but the characteristic value corresponding to the abnormal feature deviates from the standard value of the corresponding feature in a direction of at least one deviation direction is inconsistent, it is determined to be an individual abnormal cluster; wherein, the individual abnormal cluster is multiple independent individual abnormalities that are accidentally adjacent.

[0107] It is understandable that individual anomalous clustering refers to a high proportion of similar anomalous individuals, but with inconsistent deviations in direction. It is a phenomenon in which multiple independent individual anomalous individuals happen to appear in adjacent positions, rather than a batch-wide systemic problem.

[0108] For example, controller 4 determines an abnormal clustering of individuals when the proportion is greater than or equal to a preset proportion but the directional consistency results are inconsistent. For instance, if the proportion is 40%, but some of the abnormal vegetables are longer than the standard value while others are shorter than the standard value, then it is determined to be an abnormal clustering of individuals.

[0109] By employing the steps S520 to S529 described above, the nature of the anomaly can be accurately determined by comparing the characteristics of adjacent quick-frozen vegetables in the same batch. This includes individual accidental anomalies, batch-wide systematic anomalies, and individual clustering anomalies. This provides a scientific basis for subsequent sorting operations, reduces sorting errors caused by misjudgment of the nature of the anomaly, and thus improves the sorting accuracy of the sorting device 3.

[0110] In one possible implementation, S600, based on the result of the anomaly feature verification, the execution module 34 is controlled to perform the corresponding sorting operation to complete the sorting of the frozen vegetables to be sorted, including: S610, when the result of feature verification is an individual random anomaly, or a non-critical location anomaly and an individual random anomaly, the control execution module 34 adjusts the angle of the diversion baffle 342 to the first preset angle to introduce the quick-frozen vegetables into the secondary product channel.

[0111] It is understandable that the secondary product channel is used to transport frozen vegetables that are slightly abnormal but still usable. The first preset angle is the angle formed between the diversion baffle 342 and the conveyor belt after rotation, and this angle must be precisely aligned with the inlet direction of the secondary product channel.

[0112] For example, the controller 4 can send a signal to the electrically controlled rotating shaft 343 to drive the diversion baffle 342 to rotate to a first preset angle. At this time, the quick-frozen vegetables conveyed by the conveyor belt will enter the secondary product channel along the baffle after contacting the baffle.

[0113] S611, when the result of feature verification is that the key position is abnormal and the individual is occasionally abnormal, the control execution module 34 triggers the pneumatic push rod 341 to push the quick-frozen vegetables to the rejection channel.

[0114] It is understood that the pneumatic pusher 341 is a mechanical rod driven by air pressure, used to push frozen vegetables off the conveyor belt to the rejection channel. The rejection channel is used to transport vegetables that do not meet basic quality requirements due to abnormalities in critical locations.

[0115] For example, controller 4 sends a signal to the solenoid valve of pneumatic push rod 341 to control compressed air to enter the cylinder, pushing the piston rod to extend and push the frozen vegetables on the conveyor belt to the rejection channel. After completion, the piston rod returns to its original position. For example, if the fleshy root of a frozen carrot is moldy, the pneumatic push rod 341 will push it to the rejection channel.

[0116] S612, when the result of feature verification is a batch systemic anomaly, the control execution module 34 stops the transmission action and triggers the audible and visual alarm device.

[0117] It is understandable that a batch-wide systematic anomaly refers to a quality problem in which adjacent quantities of quick-frozen vegetables in the same batch exhibit the same type of abnormal characteristics, the same location and direction of the abnormality, and the proportion of abnormalities exceeds a preset threshold, indicating a systematic deviation in the production process. An audible and visual alarm device is a device that simultaneously emits an alarm using sound (such as a buzzer) and light (such as a red warning light).

[0118] For example, controller 4 sends a stop signal to the drive motor of the conveyor belt to stop its operation, and simultaneously sends a signal to the audible and visual alarm device to activate the buzzer and flash the red warning light. For instance, if a batch of frozen corn is determined to have a common size abnormality, the conveyor belt stops, the alarm device is activated, and the operator is prompted to check the raw materials or equipment.

[0119] S613, when the feature verification result is an abnormal clustering of individuals, the control execution module 34 adjusts the angle of the diversion baffle 342 to the second preset angle to guide adjacent quick-frozen vegetables of the same batch into the re-inspection channel; wherein, the first preset angle and the second preset angle correspond to the entrance direction of the secondary product channel and the re-inspection channel, respectively, and the angle adjustment of the diversion baffle 342 is achieved by driving the electric control shaft 343, and the rotation angle of the electric control shaft 343 is controlled in a closed loop according to the feature verification result.

[0120] It is understandable that the re-inspection channel is used to transport quick-frozen vegetables that require manual or more precise equipment re-inspection. The second preset angle is the rotation angle of the diversion baffle 342 corresponding to the entrance of the re-inspection channel. Closed-loop control means that the controller 4 adjusts the rotation of the electric control shaft 343 in real time according to the deviation between the actual angle of the baffle and the preset angle to make the angle accurate. For example, when the controller 4 detects a 5° deviation between the actual angle of the diversion baffle 342 and the second preset angle, it drives the electric control shaft 343 to rotate counterclockwise by 5°, and compares the actual angle again after adjustment. If there is still a 0.5° deviation, it continues to fine-tune until the angle error is less than ±0.1°.

[0121] For example, the controller 4 uses an angle encoder to detect the current angle of the diversion baffle 342 in real time, and then compares it with a second preset angle. If there is a deviation, the controller adjusts the rotation of the electrically controlled rotating shaft 343 until the baffle angle reaches the second preset angle. At this time, adjacent abnormally concentrated quick-frozen vegetables from the same batch enter the re-inspection channel along the baffle guide. The angle encoder is a sensor that can convert mechanical rotation angle into an electrical signal (such as a digital signal). In the sorting device, it is used to detect the rotation angle of the diversion baffle 342 in real time and feed the angle information back to the controller 4 to realize closed-loop control of the baffle angle, so that it is accurately adjusted to the preset angle, thereby allowing the vegetables to be accurately introduced into the target channel.

[0122] By employing the steps S610 to S613 described above, targeted sorting operations can be performed based on different verification results, enabling various abnormal quick-frozen vegetables to be accurately assigned to the corresponding processing channels, avoiding sorting chaos, and thereby improving the sorting accuracy of the sorting device 3.

[0123] As an optional embodiment of this application, the method includes: S700, when the result of feature verification is that the key position is abnormal and the individual is occasionally abnormal, the thrust parameter of the pneumatic push rod 341 is dynamically adjusted according to the crispness level of the quick-frozen vegetables to be sorted; wherein, the crispness level is determined based on the feature information obtained by the detection module 33.

[0124] It is understandable that the crispness grade describes the fragility of frozen vegetables, such as high crispness, medium crispness, and low crispness. The higher the crispness, the easier it is to break due to external force. The thrust parameter is the amount of force exerted by the pneumatic push rod 341 when pushing the vegetables, which may include, for example, air pressure value and push rod extension speed.

[0125] For example, the detection module 33 determines the crispness level by analyzing the texture of the vegetables (such as the fiber density of frozen spinach leaves) and hardness-related dimensional parameters (such as stem thickness) through image analysis. For example, leafy vegetables with fine fibers and thin stems are judged as high crispness, while those with loose fibers and thick stems are judged as low crispness. The controller 4 adjusts the air pressure of the pneumatic push rod 341 according to the crispness level, so that the pushing force can push the vegetables away without causing damage. For example, when high-crisp frozen spinach triggers S611, the pneumatic push rod 341 gently pushes with an air pressure of 0.3 MPa to avoid the leaves breaking.

[0126] By adopting the above step S700, the thrust can be adjusted according to the brittleness characteristics of quick-frozen vegetables. While removing abnormal quick-frozen vegetables, the damage to vegetables caused by excessive thrust can be reduced, and damaged vegetables can be prevented from affecting the sorting of other channels, thereby improving the sorting accuracy of the sorting device 3.

[0127] As an optional embodiment of this application, prior to S200, the method includes: S800 identifies the sticky areas of adjacent quick-frozen vegetables through the image acquisition unit 331 of the detection module 33.

[0128] As can be understood, the sticky area refers to the contact area where two or more adjacent quick-frozen vegetables stick together due to the freezing of surface ice crystals or moisture.

[0129] For example, the image acquisition unit 331 can capture images of adjacent frozen vegetables. The image processing chip identifies the contours of the vegetables using an edge detection algorithm. If there is a continuous pixel connection area between two contours, i.e., without obvious gaps, the area is considered an adhesion area, and its area is calculated. For example, frozen green beans stick together due to surface ice. In the image, there is a 0.8 mm² connection area between the contours of two green beans, which is considered an adhesion area.

[0130] S810, when the area of ​​the adhered region exceeds a preset threshold, the preprocessing module 31 is controlled to perform a grading and separation operation on the adhered quick-frozen vegetables; wherein the grading and separation operation includes: S8101, the vibration unit 311 is controlled to apply vibration at a first preset frequency to the sticky quick-frozen vegetables, and the vibration impact force is used to initially separate the slightly sticky quick-frozen vegetables with surface ice crystals.

[0131] It's understandable that the preset threshold is the critical area for determining whether the adhesion needs to be separated; if it's exceeded, separation is necessary. The graded separation operation involves using different methods to separate the adhesion in stages, based on the degree of adhesion.

[0132] For example, the controller 4 compares the area of ​​the adhesion region identified in S800 with a preset threshold. If the threshold is exceeded, the controller 4 instructs the preprocessing module 31 to start the separation operation.

[0133] It is understood that the vibration unit 311 is a device that generates impact force through mechanical vibration. The first preset frequency is a vibration frequency set for slight adhesion. Primary separation refers to breaking the surface ice crystals through vibration, thus separating the slightly adhered frozen vegetables.

[0134] For example, the controller 4 sends a signal to the vibration unit 311 to make it vibrate at a first preset frequency. The vibration is transmitted to the sticky quick-frozen vegetables through the conveyor belt. The surface ice crystals are broken under the impact force, and the slightly sticky vegetables are separated during the vibration.

[0135] S8102, if an adhesion area is still detected after the primary separation, the hot air nozzle 312 is controlled to spray hot air onto the adhesion area to melt the local ice crystals. Then, the vibration unit 311 applies vibration at a second preset frequency to complete the secondary separation. The second preset frequency is dynamically adjusted according to the effect of the primary separation.

[0136] It is understandable that the hot air nozzle 312 is a device capable of ejecting hot air at a certain temperature, such as air heated by an electric heating wire and then ejected through the nozzle. Localized ice crystal melting refers to the hot air melting the ice crystals at the adhesion points, reducing viscosity. The second preset frequency is a vibration frequency (usually higher than the first preset frequency) set for more severe adhesion remaining after primary separation. Its magnitude is adjusted according to the degree of remaining adhesion after primary separation; for example, a larger remaining adhesion area requires a higher second preset frequency.

[0137] For example, after the initial separation, the detection module 33 takes another image. If there is still an adhered area, the controller 4 instructs the hot air nozzle 312 to spray hot air at 50°C for 2 seconds into the area to cause the local ice crystals to melt slightly. Then, the controller controls the vibration unit 311 to vibrate at a second preset frequency to further separate the adhered quick-frozen vegetables.

[0138] By adopting the above steps S800 to S810 and S8101 to S8102, the adhesion problem of quick-frozen vegetables can be effectively handled through the grading and separation operation, so that each quick-frozen vegetable can be detected and sorted individually, avoiding misjudgment of features caused by adhesion, thereby improving the sorting accuracy of the sorting device 3.

[0139] As an optional embodiment of this application, the method further includes: The S900 records sorting data in real time during the sorting process of quick-frozen vegetables. The sorting data includes the quantity of quick-frozen vegetables in each channel, the abnormal feature type corresponding to each abnormal signal, and the frequency of abnormal occurrence.

[0140] As we can understand, sorting data is a dataset that records various types of information during the sorting process. The number of vegetables quick-frozen in each channel refers to the number of vegetables entering different processing channels. The anomaly frequency refers to the number of times a certain type of anomaly signal is triggered per unit of time.

[0141] For example, the controller 4 uses a photoelectric counter at the channel entrance to count the number of vegetables in each channel in real time, while recording the type and time of each abnormal signal, calculating the frequency of abnormal occurrence per unit time, and storing this data in the memory.

[0142] S910 generates a sorting report based on the sorting data. The sorting report is used to analyze the overall quality of quick-frozen vegetables and the operating status of the detection module 33 and the execution module 34.

[0143] It is understandable that a sorting report is a document formed after sorting data is organized, statistically analyzed, and includes charts and text descriptions. Overall quality analysis refers to evaluating the pass rate and anomaly distribution of the same batch of quick-frozen vegetables through data assessment. Equipment operation status analysis refers to judging the detection accuracy of the detection module 33 and the accuracy of the actions of the execution module 34.

[0144] For example, controller 4 calls sorting data in memory and performs statistics through a preset algorithm, such as calculating the proportion of each abnormality type and the proportion of each channel, to generate a sorting report containing data tables and trend charts.

[0145] By adopting the above steps S900 to S910, it is possible to promptly identify potential equipment problems or unreasonable sorting standards by recording and analyzing sorting data, providing data support for equipment debugging and parameter optimization, thereby improving the sorting accuracy of sorting device 3.

[0146] As an optional embodiment of this application, S910, generating a sorting report based on the sorting data, further includes: S911, when the sorting report shows that the frequency of one or more abnormal signal types exceeds a third preset frequency threshold, a maintenance prompt is issued; wherein, the maintenance prompt includes: S9111 If an abnormal signal with a frequency exceeding the third preset frequency threshold is a size abnormal signal and / or an appearance abnormal signal, it prompts that the image acquisition unit 331 of the detection module 33 should be calibrated or maintained.

[0147] It is understandable that the third preset frequency threshold is a critical value for determining whether an abnormal signal exceeds the preset frequency. Equipment maintenance prompts are warnings issued to operators that the equipment may have a malfunction and requires inspection; these can be displayed on a screen or accompanied by audible and visual cues.

[0148] For example, the controller 4 compares the frequency of each abnormal signal type in the sorting report with a third preset frequency threshold. If the frequency exceeds the threshold, a maintenance prompt is triggered. For instance, if the frequency of the size abnormal signal is 40 times / hour and the third preset frequency threshold is 30 times / hour, the controller 4 displays "Please check the image acquisition unit" on the device operation screen.

[0149] For example, after the controller 4 identifies a signal that exceeds the frequency threshold as an abnormal signal in size and / or appearance, it generates a prompt message, such as "Image acquisition unit calibration prompt: lens may be misaligned", which is displayed on the screen or printed out as a repair order.

[0150] S9112, if an abnormal signal with a frequency exceeding the third preset frequency threshold is a weight abnormal signal, it prompts the weight sensing unit 332 of the detection module 33 to be calibrated or maintained.

[0151] For example, after the controller 4 identifies a weight abnormality signal that exceeds the frequency threshold, it generates a prompt message, such as "Weight sensor unit maintenance prompt: Please calibrate the strain gauge", and attracts the operator's attention through an audible and visual alarm.

[0152] S9113 If the abnormal signal type with a frequency exceeding the third preset frequency threshold is two or more types of abnormal signals, including weight abnormal signals, the image acquisition unit 331 and weight sensing unit 332 of the detection module 33 are respectively prompted to be calibrated or maintained.

[0153] For example, after the controller 4 identifies multiple signals, including weight abnormalities, that exceed the frequency threshold, it generates a comprehensive prompt message, such as "Both the image acquisition unit and the weight sensing unit need to be calibrated," and lists specific inspection items, such as "Image acquisition unit: clean the lens; Weight sensing unit: check the wiring."

[0154] S9114 If the same abnormal signal type exceeds the third frequency threshold multiple times in a row, a mechanical inspection is prompted for the pneumatic push rod 341, the diversion baffle 342, and the electrically controlled rotating shaft 343 of the execution module 34.

[0155] For example, the controller 4 records the number of times the same abnormal signal type exceeds the frequency threshold consecutively. If the set number is reached, a prompt message is generated, such as "Execution module mechanical check prompt: pneumatic push rod may be stuck, diverter baffle angle deviation".

[0156] By adopting the above steps S911, S9111 to S914, the direction of equipment maintenance can be accurately indicated according to the frequency of abnormal signals, equipment faults can be eliminated in time, and the detection and execution module 34 can be made to work normally, thereby improving the sorting accuracy of the sorting device 3.

[0157] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0158] Corresponding to the vegetable quick-freezing control method described in the above embodiments, this application also provides a vegetable quick-freezing control device, the various modules of which can realize the various steps of the vegetable quick-freezing control method. Figure 5 A structural block diagram of the vegetable quick-freezing control device provided in the embodiments of this application is shown. For ease of explanation, only the parts related to the embodiments of this application are shown.

[0159] Reference Figure 5 The device includes: The quick-freezing unit is used to quick-freeze vegetables using a quick-freezing device to obtain quick-frozen vegetables to be sorted. The acquisition unit is used to acquire the feature information of the quick-frozen vegetables to be sorted through the detection module; wherein, the feature information includes the size information, weight information and appearance information of the quick-frozen vegetables to be sorted. The comparison unit is used to compare the size information, weight information and appearance information of the quick-frozen vegetables to be sorted with the size information range, weight information range and appearance information range in the preset standard information range, and obtain the comparison results. The triggering unit is used to trigger an abnormal signal if, based on the comparison result, at least one of the size information, weight information, and appearance information of the frozen vegetables to be sorted exceeds the corresponding information in the preset standard information range; wherein, the abnormal signal includes a size abnormal signal, a weight abnormal signal, and an appearance abnormal signal. The verification unit is used to verify abnormal features of the quick-frozen vegetables to be sorted based on abnormal signals, including: When the abnormal signal is a size abnormal signal and / or an appearance abnormal signal, the positional relationship of the abnormal features and the relationship between the abnormal features and the features of adjacent quick-frozen vegetables in the same batch are verified for the quick-frozen vegetables to be sorted. When the abnormal signal is a weight abnormal signal, the relationship between the abnormal characteristics of the quick-frozen vegetables to be sorted and the characteristics of adjacent quick-frozen vegetables in the same batch is verified. When the abnormal signal is two or more types of abnormal signals, including weight abnormal signal, the quick-frozen vegetables to be sorted are subjected to superimposed verification. The superimposed verification is to verify the positional relationship of abnormal features and the relationship between abnormal features and adjacent quick-frozen vegetables in the same batch for size abnormal signal and / or appearance abnormal signal, and at the same time, to verify the relationship between abnormal features and adjacent quick-frozen vegetables in the same batch for weight abnormal signal. The control unit is used to control the execution module to perform the corresponding sorting operation based on the result of the abnormal feature verification, so as to complete the sorting of the frozen vegetables to be sorted.

[0160] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0161] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0162] This application also provides a quick-freezing device 1. Figure 1 This is a schematic diagram of the structure of a quick-freezing device 1 provided in an embodiment of this application. Figure 1As shown, the quick-freezing equipment 1 in this embodiment includes: a quick-freezing device 2, a sorting device 3, and at least one controller 4. The quick-freezing device 2 includes a feeding module 20, a low-temperature refrigeration module 21, a temperature monitoring module 22, and a discharge transition module 23. The sorting device 3 includes a transition conveying mechanism 30, a pretreatment module 31, a conveying module 32, a detection module 33, and an execution module 34. The controller 4 includes at least one processor 41. Figure 1 Only one is shown in the image), at least one memory 42 ( Figure 1 (Only one is shown in the image) and a computer program 43 stored in the at least one memory 42 and executable on the at least one processor 41. When the processor 41 executes the computer program 43, it causes the quick-freezing device 1 to perform the steps in any of the above-described vegetable quick-freezing control method embodiments, or causes the quick-freezing device 1 to perform the functions of each module / unit in the above-described device embodiments.

[0163] Exemplarily, the computer program 43 may be divided into one or more modules / units, which are stored in the memory 42 and executed by the processor 41 to complete this application. The one or more modules / units may be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program 43 in the quick-freezing device 1.

[0164] The quick-freezing equipment 1 may include, but is not limited to, a quick-freezing device 2, a sorting device 3, a controller 4, a feeding module 20, a low-temperature refrigeration module 21, a temperature monitoring module 22, a discharge transition module 23, a transition conveying mechanism 30, a pretreatment module 31, a conveying module 32, a detection module 33, an execution module 34, a processor 41, and a memory 42. Those skilled in the art will understand that... Figure 1 This is merely an example of quick-freezing equipment 1 and does not constitute a limitation on quick-freezing equipment 1. It may include more or fewer components than shown in the figure, or combine certain components, or different components. For example, it may also include cleaning and pretreatment devices, temperature control and monitoring devices, etc.

[0165] The processor 41 can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0166] In some embodiments, the memory 42 may be an internal storage unit of the quick-freezing equipment 1, such as a hard disk or memory of the quick-freezing equipment 1. In other embodiments, the memory 42 may be an external storage device of the quick-freezing equipment 1, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the quick-freezing equipment 1. Furthermore, the memory 42 may include both internal and external storage units of the quick-freezing equipment 1. The memory 42 is used to store operating systems, applications, bootloaders, data, and other programs, such as the program code of computer programs. The memory 42 can also be used to temporarily store data that has been output or will be output.

[0167] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in any of the above method embodiments.

[0168] This application provides a computer program product that, when run on an electronic device, causes the electronic device to perform the steps in any of the above method embodiments.

[0169] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying the computer program code to a photographing device / terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0170] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0171] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0172] In the embodiments provided in this application, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. For example, the device / electronic device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings or direct couplings or communication connections may be through some interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0173] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0174] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for controlling the quick-freezing of vegetables, characterized in that, The method is applied to quick-freezing equipment, which includes a quick-freezing device and a sorting device. The sorting device includes a detection module for detecting characteristics of quick-frozen vegetables and an execution module for performing sorting operations. The vegetables are quick-frozen using the quick-freezing device to obtain quick-frozen vegetables to be sorted. The detection module acquires the characteristic information of the quick-frozen vegetables to be sorted; wherein, the characteristic information includes the size information, weight information and appearance information of the quick-frozen vegetables to be sorted. The size, weight, and appearance information of the quick-frozen vegetables to be sorted are compared one by one with the size, weight, and appearance information ranges in the preset standard information range to obtain the comparison results. Based on the comparison results, if at least one of the size, weight, and appearance information of the frozen vegetables to be sorted exceeds the corresponding information in the preset standard information range, an abnormal signal is triggered; wherein, the abnormal signal includes a size abnormal signal, a weight abnormal signal, and an appearance abnormal signal. Based on the abnormal signal, the abnormal features of the frozen vegetables to be sorted are verified, including: In the case where the abnormal signal is the size abnormal signal and / or the appearance abnormal signal, the positional relationship of the abnormal features and the relationship between the abnormal features and the features of adjacent quick-frozen vegetables in the same batch are verified for the quick-frozen vegetables to be sorted. When the abnormal signal is the weight abnormal signal, the relationship between the abnormal characteristics of the quick-frozen vegetables to be sorted and the characteristics of adjacent quick-frozen vegetables in the same batch is verified. When the abnormal signal is two or more types of abnormal signals, including the weight abnormal signal, the quick-frozen vegetables to be sorted are subjected to superimposed verification; wherein, the superimposed verification is to verify the positional relationship of the abnormal features and the relationship between the abnormal features and the features of adjacent quick-frozen vegetables in the same batch for the size abnormal signal and / or the appearance abnormal signal, and at the same time, to verify the relationship between the abnormal features and the features of adjacent quick-frozen vegetables in the same batch for the weight abnormal signal. Based on the results of the abnormal feature verification, the execution module is controlled to perform the corresponding sorting operation to complete the sorting of the frozen vegetables to be sorted.

2. The vegetable quick-freezing control method as described in claim 1, characterized in that, The detection module includes an image acquisition unit and a weight sensing unit. The detection module acquires feature information of the quick-frozen vegetables to be sorted, including: The image acquisition unit acquires image information of the quick-frozen vegetables to be sorted. Based on the image information, the size information and appearance information of the quick-frozen vegetables to be sorted are extracted; wherein, the size information includes length, width and curvature, and the appearance information includes abnormal color blocks, which are local color blocks formed on the quick-frozen vegetables to be sorted due to frost damage, mold or impurity contamination. The weight information of the quick-frozen vegetables to be sorted is obtained through the weight sensing unit.

3. The vegetable quick-freezing control method as described in claim 2, characterized in that, The positional relationship verification of the abnormal features includes: In the case where the abnormal signal is the size abnormal signal, analyze the position of the size abnormal part in the whole of the quick-frozen vegetables to be sorted; If the abnormal size is located in a non-core edible part, it is determined to be an abnormality in a non-critical location; wherein, the non-core edible part refers to the edible but not the main part. If the abnormally sized portion is located in the core edible part, it is determined to be a critical location abnormality; wherein, the core edible part refers to the main edible part. In the case where the abnormal signal is the appearance abnormal signal, analyze the position of the abnormal color block in the whole of the quick-frozen vegetables to be sorted; If the abnormal color block is located in a non-core edible part, it is determined to be an abnormality in a non-critical location; If the abnormal color block is located in the core edible part, it is determined to be an abnormality in a critical location.

4. The vegetable quick-freezing control method as described in claim 3, characterized in that, The verification of the relationship between the abnormal characteristics and the characteristics of adjacent quick-frozen vegetables in the same batch includes: Based on the quick-frozen vegetables that have triggered abnormal signals in the quick-frozen vegetables to be sorted, the detection module extracts the feature information of a preset number of adjacent quick-frozen vegetables in the same batch of the quick-frozen vegetables that have triggered abnormal signals. Based on the extracted feature information of adjacent preset quantity quick-frozen vegetables in the same batch, the first number of adjacent preset quantity quick-frozen vegetables in the same batch that trigger the same type of abnormal signal as the quick-frozen vegetables to be sorted is counted; wherein, the same type of abnormal signal refers to a signal that is completely consistent with the type of abnormal signal triggered by the quick-frozen vegetables to be sorted that have already triggered an abnormal signal, and the same type of abnormal signal includes the same signal among the size abnormal signal, the weight abnormal signal, or the appearance abnormal signal. Based on the first quantity obtained from statistics, calculate the proportion of the first quantity to the adjacent preset quantity of quick-frozen vegetables in the same batch; Based on the abnormal characteristics of the frozen vegetables to be sorted that have triggered abnormal signals and the extracted characteristic information of the adjacent preset number of frozen vegetables in the same batch, adjacent frozen vegetables that have triggered the same type of abnormal signal as the frozen vegetables to be sorted that have triggered abnormal signals are selected from the adjacent preset number of frozen vegetables in the same batch. Based on the abnormal features of the selected adjacent frozen vegetables, the direction in which the feature value corresponding to the abnormal feature of each selected adjacent frozen vegetable deviates from the standard value of the corresponding feature is determined; wherein, the feature value is a specific numerical value of the size information, weight information and appearance information of the frozen vegetables to be sorted obtained by the detection module, the standard value is set based on the preset standard information range, the standard value includes the size standard value, weight standard value and appearance standard value of the frozen vegetables to be sorted, and the direction includes the feature value being greater than the standard value of the corresponding feature or the feature value being less than the standard value of the corresponding feature; Based on the abnormal characteristics of the quick-frozen vegetables to be sorted that have triggered abnormal signals and the standard values ​​of the corresponding characteristics, determine the direction in which the characteristic value corresponding to the abnormal characteristics of the quick-frozen vegetables to be sorted that has triggered abnormal signals deviates from the standard value of the corresponding characteristics. Based on the direction of the deviation of the feature value corresponding to the abnormal feature of the frozen vegetables to be sorted that have triggered the abnormal signal from the standard value of the corresponding feature, and the direction of the deviation of the feature value corresponding to the abnormal feature of each of the adjacent frozen vegetables selected from the abnormal signal from the standard value of the corresponding feature, it is determined whether the feature value corresponding to the abnormal feature of the frozen vegetables to be sorted that have triggered the abnormal signal and the feature value corresponding to the abnormal feature of all the adjacent frozen vegetables that have triggered the same type of abnormal signal deviate from the standard value of the corresponding feature in the same direction, so as to obtain the consistency result of the direction of deviation of the feature value corresponding to the abnormal feature from the standard value of the corresponding feature; When the calculated proportion of the number of adjacent preset quantity quick-frozen vegetables in the same batch that triggers the same type of abnormal signal as the quick-frozen vegetables to be sorted to the total number of adjacent preset quantity quick-frozen vegetables in the same batch is less than a preset proportion, it is determined to be an individual random abnormality. When the calculated proportion of the number of adjacent preset quantity quick-frozen vegetables in the same batch that triggers the same type of abnormal signal as the quick-frozen vegetables to be sorted to the total number of adjacent preset quantity quick-frozen vegetables in the same batch is greater than or equal to a preset proportion, and the consistency result of the direction of the deviation of the feature value corresponding to the abnormal feature from the standard value of the corresponding feature is that they all deviate from the standard value of the corresponding feature in the same direction, it is determined to be a batch systemic abnormality. When the calculated proportion of the number of adjacent preset quantity quick-frozen vegetables in the same batch that triggers the same type of abnormal signal as the quick-frozen vegetables to be sorted to the total number of adjacent preset quantity quick-frozen vegetables in the same batch is greater than or equal to a preset proportion, but the characteristic value corresponding to the abnormal feature deviates from the standard value of the corresponding feature in a direction of at least one deviation direction is inconsistent, it is determined to be an individual abnormal cluster; wherein, the individual abnormal cluster is multiple independent individual abnormalities that are accidentally adjacent.

5. The vegetable quick-freezing control method as described in claim 4, characterized in that, The execution module includes a pneumatic push rod, a flow divider, and an electrically controlled rotating shaft. Based on the result of the abnormal feature verification, the execution module is controlled to perform the corresponding sorting operation to complete the sorting of the frozen vegetables to be sorted, including: When the result of the abnormal feature verification is that the individual is occasionally abnormal, or when the result of the abnormal feature verification is that the non-critical location is abnormal and the individual is occasionally abnormal, the execution module is controlled to adjust the angle of the diversion baffle to the first preset angle to introduce the quick-frozen vegetables into the secondary product channel. When the result of the abnormal feature verification is that the key position is abnormal and the individual is occasionally abnormal, the execution module is controlled to trigger the pneumatic push rod action to push the quick-frozen vegetables to the rejection channel. When the result of the abnormal feature verification is that the batch is systematically abnormal, the execution module is controlled to stop the transmission action and the audible and visual alarm device is triggered. When the result of the abnormal feature verification is that the individual abnormal clusters, the execution module is controlled to adjust the angle of the diversion baffle to the second preset angle and introduce the adjacent quick-frozen vegetables of the same batch into the re-inspection channel. The first preset angle and the second preset angle correspond to the entrance directions of the secondary product channel and the re-inspection channel, respectively. The angle adjustment of the diversion baffle is achieved by the electric control rotating shaft drive. The rotation angle of the electric control rotating shaft is controlled in a closed loop according to the abnormal feature verification result.

6. The vegetable quick-freezing control method as described in claim 1, characterized in that, The sorting device includes a pre-processing module, which includes a vibration unit and a hot air nozzle. Before obtaining the characteristic information of the quick-frozen vegetables to be sorted through the detection module, the method includes: The image acquisition unit of the detection module identifies the sticky areas of adjacent quick-frozen vegetables; When the area of ​​the adhesion region exceeds a preset threshold, the preprocessing module is controlled to perform a grading and separation operation on the adhesion-bound quick-frozen vegetables; wherein, the grading and separation operation includes: The vibration unit is controlled to apply vibration at a first preset frequency to the sticky quick-frozen vegetables, and the vibration impact force is used to initially separate the slightly sticky quick-frozen vegetables with surface ice crystals connected. If the adhesion area is still detected after the primary separation, the hot air nozzle is controlled to spray hot air into the adhesion area to melt the local ice crystals. Then, the vibration unit applies vibration at a second preset frequency to complete the secondary separation. The second preset frequency is dynamically adjusted according to the effect of the primary separation.

7. The vegetable quick-freezing control method as described in claim 1, characterized in that, The method further includes: During the sorting process of quick-frozen vegetables, sorting data is recorded in real time; wherein, the sorting data includes the quantity of quick-frozen vegetables in each channel, the abnormal feature type corresponding to each abnormal signal, and the frequency of abnormal occurrence. A sorting report is generated based on the sorting data. The sorting report is used to analyze the overall quality of the quick-frozen vegetables and the operating status of the detection module and the execution module.

8. The vegetable quick-freezing control method as described in claim 7, characterized in that, Generating a sorting report based on the sorting data also includes: When the sorting report shows that the frequency of one or more abnormal signal types exceeds a third preset frequency threshold, a device maintenance prompt is issued; wherein, the device maintenance prompt includes: If the abnormal signal type that occurs more frequently than the third preset frequency threshold is the size abnormal signal and / or the appearance abnormal signal, it prompts that the image acquisition unit of the detection module should be calibrated or maintained. If the abnormal signal type that occurs more frequently than the third preset frequency threshold is the weight abnormal signal, it prompts that the weight sensing unit of the detection module should be calibrated or maintained. If the abnormal signal type whose frequency exceeds the third preset frequency threshold is two or more types of abnormal signals including the weight abnormal signal, the image acquisition unit and weight sensing unit of the detection module are respectively prompted to be calibrated or maintained. If the same abnormal signal type exceeds the third preset frequency threshold multiple times consecutively, a mechanical inspection of the pneumatic push rod, diverter baffle, and electrically controlled shaft of the execution module is prompted.

9. A vegetable quick-freezing control device, characterized in that, This device is applied to quick-freezing equipment, which includes a quick-freezing unit and a sorting unit. The sorting unit includes a detection module for detecting the characteristics of quick-frozen vegetables and an execution module for performing sorting operations. The vegetable quick-freezing control device includes: A quick-freezing unit is used to quick-freeze vegetables through the quick-freezing device to obtain quick-frozen vegetables to be sorted. The acquisition unit is used to acquire the feature information of the quick-frozen vegetables to be sorted through the detection module; wherein, the feature information includes the size information, weight information and appearance information of the quick-frozen vegetables to be sorted. The comparison unit is used to compare the size information, weight information and appearance information of the quick-frozen vegetables to be sorted with the size information range, weight information range and appearance information range in the preset standard information range, and obtain the comparison result. A triggering unit is configured to trigger an abnormal signal based on the comparison result if at least one of the size information, weight information, and appearance information of the frozen vegetables to be sorted exceeds the corresponding information in the preset standard information range; wherein the abnormal signal includes a size abnormal signal, a weight abnormal signal, and an appearance abnormal signal. A verification unit is used to verify the abnormal features of the frozen vegetables to be sorted based on the abnormal signal, including: In the case where the abnormal signal is the size abnormal signal and / or the appearance abnormal signal, the positional relationship of the abnormal features and the relationship between the abnormal features and the features of adjacent quick-frozen vegetables in the same batch are verified for the quick-frozen vegetables to be sorted. When the abnormal signal is the weight abnormal signal, the relationship between the abnormal characteristics of the quick-frozen vegetables to be sorted and the characteristics of adjacent quick-frozen vegetables in the same batch is verified. When the abnormal signal is two or more types of abnormal signals, including the weight abnormal signal, the quick-frozen vegetables to be sorted are subjected to superimposed verification; wherein, the superimposed verification is to verify the positional relationship of the abnormal features and the relationship between the abnormal features and the features of adjacent quick-frozen vegetables in the same batch for the size abnormal signal and / or the appearance abnormal signal, and at the same time, to verify the relationship between the abnormal features and the features of adjacent quick-frozen vegetables in the same batch for the weight abnormal signal. The control unit is used to control the execution module to perform the corresponding sorting operation based on the result of the abnormal feature verification, so as to complete the sorting of the frozen vegetables to be sorted.

10. A quick-freezing device, comprising a quick-freezing unit, a sorting unit, and a controller, wherein the sorting unit includes a detection module for detecting characteristics of quick-frozen vegetables and an execution module for performing sorting operations, the quick-freezing unit, the detection module, and the execution module are electrically connected to the controller, and the controller includes a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1 to 8.

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