Intelligent management system for kelp drying process based on AI algorithm

Abstract

The application provides a kelp drying process intelligent management system based on an AI algorithm. The system comprises a kelp hanging device, a spraying cleaning device, a mobile drying device and an AI temperature control device. The kelp hanging device, the spraying cleaning device and the mobile drying device are sequentially distributed in the same transportation channel. The AI temperature control device is connected with the kelp hanging device, the spraying cleaning device and the mobile drying device. The kelp hanging device is used for identifying the position of kelp and hanging the kelp based on the position of the kelp. The spraying cleaning device is used for spraying and cleaning the hung kelp. The mobile drying device is used for mobile drying of the kelp after spraying and cleaning. The AI temperature control device is used for acquiring real-time temperature data corresponding to the kelp hanging device, the spraying cleaning device and the mobile drying device respectively, and controlling the kelp hanging device, the spraying cleaning device and the mobile drying device to operate based on the multiple real-time temperature data.

Description

Technical Field

[0001] The present invention relates to the field of system control technology, and in particular to an intelligent management system for a kelp drying process based on an AI algorithm. Background Art

[0002] In order to improve the drying efficiency of kelp, an extrusion-type kelp dryer is often used. The unprocessed kelp is first dehydrated and then the dehydrated kelp is dried in one go.

[0003] However, when kelp is unprocessed, it is long and has a large area. In addition, the drying degree of the kelp needs to be consistent at all locations. In this case, one-time drying will cause the kelp to have a uniform drying degree.

[0004] Therefore, how to improve the drying quality of kelp by improving the drying effect is a technical problem that needs to be solved urgently by those skilled in the art. Summary of the Invention

[0005] The present invention provides an intelligent management system for kelp drying process based on AI algorithm, which is used to solve the problem that the existing technology has high requirements for kelp drying quality, the current one-time drying leads to poor kelp drying effect, and causes the kelp drying quality to fail to meet the standards.

[0006] In a first aspect, an embodiment of the present invention provides an intelligent management system for a kelp drying process based on an AI algorithm, wherein the handling robot comprises:

[0007] It includes a kelp hanging device, a spray cleaning device, a mobile drying device and an AI temperature control device. The kelp hanging device, the spray cleaning device and the mobile drying device are distributed in the same transportation channel in sequence. The AI ​​temperature control device is connected to the kelp hanging device, the spray cleaning device and the mobile drying device respectively.

[0008] The kelp hanging device is used to identify the position of the kelp and hang the kelp based on the position of the kelp;

[0009] The spray cleaning device is used to spray clean the hanging kelp;

[0010] The mobile drying device is used to carry out mobile drying of the kelp after spray cleaning;

[0011] The AI ​​temperature control device is used to obtain the real-time temperature data corresponding to the kelp hanging device, the spray cleaning device and the mobile drying device, and control the operation of the kelp hanging device, the spray cleaning device and the mobile drying device based on multiple real-time temperature data.

[0012] In a possible design, the mobile drying device corresponds to a plurality of drying sub-zones corresponding to a plurality of temperature intervals in the transportation channel.

[0013] In a possible design, each of the drying sub-zones is provided with a kelp moisture imaging module for collecting real-time temperature data corresponding to the kelp moisture imaging module.

[0014] In a possible design, the transportation channel is provided with at least two layers of drying lines, and the at least two layers of drying lines are parallel to each other.

[0015] In a second aspect, the present application provides a kelp drying method based on an AI algorithm, which is applied to the AI temperature control device in the first aspect, and the AI temperature control device is connected to the kelp hanging device, the spray cleaning device and the mobile drying device.

[0016] The method comprises the following steps.

[0017] Obtaining real-time temperature data corresponding to a target device, wherein the target device is any one of the kelp hanging device, the spray cleaning device and the mobile drying device.

[0018] According to the real-time temperature data corresponding to the target device, obtaining an execution parameter corresponding to the target device.

[0019] According to the execution parameter corresponding to the target device, controlling the target device to run.

[0020] In a possible implementation, when the target device is the mobile drying device, obtaining the real-time temperature data corresponding to the mobile drying device comprises obtaining real-time sub-data corresponding to a plurality of drying sub-zones.

[0021] In a possible implementation, the execution parameter corresponding to the mobile drying device comprises execution sub-parameters corresponding to a plurality of drying zones,

[0022] According to the execution sub-parameters corresponding to the plurality of drying zones, obtaining an execution sub-parameter corresponding to each of the drying zones comprises the following steps.

[0023] For each of the drying zones, when there is a previous drying zone of the drying zone, obtaining a first drying degree of the previous drying zone.

[0024] According to the real-time sub-data corresponding to the drying zone, obtaining a second drying degree corresponding to the drying zone.

[0025] According to the first drying degree and the second drying degree, obtaining an adjustment ratio corresponding to the drying zone.

[0026] Obtain the real-time execution sub-parameters corresponding to the drying zone, and adjust the real-time execution sub-parameters according to the adjustment ratio corresponding to the drying zone to obtain new execution sub-parameters corresponding to the drying zone.

[0027] In a third aspect, the specific steps of the kelp drying process intelligent management include:

[0028] In response to the kelp drying instruction, a starting instruction corresponding to the kelp hanging device and the AI temperature control device is generated;

[0029] When it is detected that the kelp hanging device starts to run, a starting instruction corresponding to the spraying cleaning device is generated;

[0030] When it is detected that the spraying cleaning device starts to run, a starting instruction corresponding to the moving drying device is generated;

[0031] The starting instruction is used to trigger the device to start running.

[0032] In a fourth aspect, the present application provides a kelp drying process intelligent management based on an AI algorithm, which includes at least one processor and a memory;

[0033] The memory stores computer execution instructions;

[0034] The at least one processor executes the computer execution instructions stored in the memory.

[0035] The present application provides a kelp drying process intelligent management system based on an AI algorithm, which includes a kelp hanging device, a spraying cleaning device, a moving drying device and an AI temperature control device. The kelp hanging device, the spraying cleaning device and the moving drying device are sequentially distributed in the same transportation channel, and the AI temperature control device is connected with the kelp hanging device, the spraying cleaning device and the moving drying device. The kelp hanging device is used to identify the position of kelp and hang the kelp based on the position of kelp. The spraying cleaning device is used to spray and clean the hung kelp. The moving drying device is used to move and dry the kelp after spraying and cleaning. The AI temperature control device is used to obtain real-time temperature data corresponding to the kelp hanging device, the spraying cleaning device and the moving drying device, and control the kelp hanging device, the spraying cleaning device and the moving drying device to run based on the multiple real-time temperature data. The present application sequentially sets the kelp hanging device, the spraying cleaning device and the moving drying device, so that the untreated kelp can be hung, cleaned and dried directly to obtain the dried kelp product. Further, the AI temperature control device controls the operation of each device based on the real-time temperature of each device to ensure the consistency of the drying degree of the kelp product, thereby improving the drying quality of kelp. BRIEF DESCRIPTION OF DRAWINGS

[0036] The accompanying drawings, which are incorporated herein and constitute part of the specification, illustrate embodiments consistent with the present application and, together with the description, further serve to explain the principles of the application.

[0037] Figure 1 An AI temperature control device schematic diagram of a kelp drying process intelligent management system based on an AI algorithm provided for an embodiment of the present application;

[0038] Figure 2 A flowchart schematic diagram of kelp drying specific steps provided for an embodiment of the present application;

[0039] Figure 3 A flowchart schematic diagram of a kelp drying process intelligent management system based on an AI algorithm provided for an embodiment of the present application.

[0040] Figure 4 A structure schematic diagram of a kelp drying process intelligent management system based on an AI algorithm provided for an embodiment of the present application.

[0041] Reference signs:

[0042] 1 - kelp hanging device; 2 - spray cleaning device; 3 - moving drying device; 4 - transport channel;

[0043] 41 - cleaning area; 42 - drying area.

[0044] Through the above drawings, the specific embodiments of the present application have been shown, and more detailed descriptions will be given hereinafter. These drawings and textual descriptions are not intended to limit the scope of the concept of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solutions and advantages of the present application clearer, the technical solutions in the embodiments of the present application will be described in more detail below in combination with the drawings in the preferred embodiments of the present application. In the drawings, the same or similar reference signs represent the same or similar components or components with the same or similar functions throughout. The described embodiments are part of the embodiments of the present application, not all embodiments. The embodiments described below by referring to the drawings are exemplary and are intended to explain the present application, and cannot be understood as limiting the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application. The embodiments of the present application will be described in detail below in combination with the drawings.

[0046] In the description of the present application, it should be noted that unless otherwise explicitly specified and limited, the terms "mounting", "connecting", "connecting" should be understood in a broad sense, and can be used interchangeably. For example, "connection" can be direct connection or indirect connection through intermediate medium; it can be fixed connection or sliding connection. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0047] In the description of the present application, it should be understood that the terms "upper", "lower", "front", "rear", "vertical", "horizontal", "top", "bottom", "inner", "outer" and the like indicate the orientation or positional relationship based on the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the present application.

[0048] The terms "first", "second", "third" in the specification and claims of the present application and the above drawings are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the application described herein can be implemented in an order other than those illustrated or described herein.

[0049] In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or display including a series of steps or modules, and is not necessarily limited to those steps or modules clearly listed, but can include other steps or modules not clearly listed or inherent to the process, method, product or display.

[0050] In the processing of kelp, drying is a key step, which directly affects the quality and shelf life of kelp. In order to improve the drying efficiency of kelp, the industry often uses extrusion type kelp dryer. This equipment first dehydrates the untreated kelp by mechanical extrusion to reduce its water content, and then one-time dries the dehydrated kelp to further remove water and achieve the required drying degree.

[0051] However, kelp usually has a long length and a large area when it is not treated, which brings many challenges to the drying process. Due to the physical properties of kelp, the one-time drying method often cannot guarantee the consistency of the drying degree of kelp at different positions. The specific performance is that the drying degree of the edge and the center of the kelp is quite different, some areas may be over-dried, causing the kelp to become brittle and even break, while other areas may not be dried enough, leaving a lot of water, affecting the final quality and preservation effect of the kelp.

[0052] In addition, kelp may also be affected by various factors during the drying process, such as the air speed of the dryer, temperature distribution, and the placement of kelp. The unevenness of these factors further exacerbates the inconsistency of the drying degree. If there is residual moisture in the dried kelp, it is easy to mold and deteriorate during storage, thereby reducing the market value of the product.

[0053] Therefore, how to improve the drying effect to improve the drying quality of kelp has become a technical problem that technicians in the field urgently need to solve. Specifically, it is necessary to design a drying equipment or process that can adapt to the physical properties of kelp, uniformly distribute heat, and optimize drying parameters to ensure that the drying degree of kelp at each position is consistent, while improving drying efficiency and the quality of the final product. This not only helps to improve the production efficiency of kelp processing enterprises, but also significantly improves the market competitiveness of kelp products.

[0054] To solve the above technical problems, the present application provides a kelp drying process intelligent management system based on AI algorithm. The untreated kelp is hung, cleaned and dried in sequence by the kelp hanging device, the spray cleaning device and the mobile drying device, and the dried kelp product can be obtained directly. Further, the AI temperature control device controls the operation of each device based on the real-time temperature of each device to ensure the consistency of the drying degree of the kelp product, thereby improving the drying quality of kelp.

[0055] Some embodiments of the present application will be described in detail below with reference to the accompanying drawings. The following embodiments and features in the embodiments can be combined with each other without conflict between the embodiments.

[0056] Referring to Figure 1 The present application provides a kelp drying process intelligent management system based on AI algorithm. The system includes a kelp hanging device 1, a spray cleaning device 2, a mobile drying device 3 and an AI temperature control device. The kelp hanging device 1, the spray cleaning device 2 and the mobile drying device 3 are sequentially distributed in the same transport channel 4, and the AI temperature control device is connected with the kelp hanging device 1, the spray cleaning device 2 and the mobile drying device 3 respectively. The above system will be further explained in combination with the drawings and embodiments. The specific purposes and positions of the above devices are as follows:

[0057] The kelp hanging device 1 is used to identify the position of kelp and hang kelp based on the position of kelp. The kelp hanging device 1 is uniformly distributed on the drying line in the form of a character-shaped drying rod. The drying line is in the form of a conveyor belt, and the conveying direction is the same as the x-axis in the figure. It should be noted that the drying line runs through the entire transport channel 4. Figure 1

[0058] ​The spraying and cleaning device 2 is used for spraying and cleaning the kelp. The spraying and cleaning device 2 is arranged at the upper end of the cleaning area 41 in the drying line, that is, the conveying channel 4, and in view of the spraying effect and the water flow direction, the spraying is performed from top to bottom in this embodiment, that is, the spraying direction is the reverse direction of the z-axis. Alternatively, salt water is used for spraying and cleaning the kelp.

[0059] The moving drying device 3 is used for moving drying the kelp after the spraying and cleaning. The conveying channel 4 is a ∩-shaped channel, and the moving drying devices 3 are symmetrically arranged on the two sides of the wall. Preferably, since the ∩-shaped drying rods in the kelp hanging device 1 are arranged at equal intervals, the moving drying devices 3 are vertically arranged on the wall and face the inner side of the conveying channel. In view of the large power consumption of drying, preferably, the interval between the moving drying devices 3 is based on the average arrangement distance between the ∩-shaped drying rods. In a possible implementation manner, the moving drying device 3 can move horizontally in the drying area where the moving drying device 3 is arranged, so as to adjust the interval with the adjacent moving drying device. The moving drying device is a high-temperature drying device, and a high-pressure air knife can be further arranged in front of the moving drying device to blow dry the kelp from both sides, so as to remove excess water and reduce the working load of the moving drying device.

[0060] The AI temperature control device is used for acquiring real-time temperature data corresponding to the kelp hanging device 1, the spraying and cleaning device 2 and the moving drying device 3 respectively, and controlling the kelp hanging device 1, the spraying and cleaning device 2 and the moving drying device 3 to operate based on the multiple real-time temperature data.

[0061] The AI algorithm-based intelligent management system for kelp drying process provided in this embodiment comprises a kelp hanging device, a spraying and cleaning device, a moving drying device and an AI temperature control device. The kelp hanging device, the spraying and cleaning device and the moving drying device are sequentially arranged in the same conveying channel, and the AI temperature control device is connected with the kelp hanging device, the spraying and cleaning device and the moving drying device respectively. The kelp hanging device is used for identifying the position of the kelp and hanging the kelp based on the position of the kelp. The spraying and cleaning device is used for spraying and cleaning the kelp. The moving drying device is used for moving drying the kelp after the spraying and cleaning. The AI temperature control device is used for acquiring real-time temperature data corresponding to the kelp hanging device, the spraying and cleaning device and the moving drying device respectively, and controlling the kelp hanging device, the spraying and cleaning device and the moving drying device to operate based on the multiple real-time temperature data. The kelp is directly obtained after drying by sequentially arranging the kelp hanging device, the spraying and cleaning device and the moving drying device, and the kelp is hung, cleaned and dried. Further, the AI temperature control device controls the operation of each device based on the real-time temperature of each device, so as to ensure the consistency of the drying degree of the kelp product and improve the drying quality of the kelp.

[0062] Further, the mobile drying device 3 corresponds to a plurality of temperature intervals in the transportation channel 4, and each temperature interval corresponds to a drying subzone. The mobile drying device is arranged in the drying zone 42 of the transportation channel 4, as shown in Figure 1 For example, there are four drying subzones, i.e., subzones 421, 422, 423, and 424, each of which corresponds to a different temperature interval.

[0063] Each drying subzone includes a preheating and warming-up stage, a balancing and dehumidifying stage, a warming-up and drying stage, and a cooling and shaping stage, each of which corresponds to a subzone. Each subzone is operated based on a drying curve. Specifically, the drying curve can be determined by experiments and data analysis, and the drying curve of kelp under different drying temperatures, humidity, and different wind speed conditions, i.e., the relationship between moisture content and time, can be determined.

[0064] Further, each drying subzone is provided with a kelp moisture imaging module, as shown in the shadow part of Figure 1 The arc-shaped position of the transportation channel 4 intersecting the I-shaped channel is the setting position of the kelp moisture imaging module. The kelp moisture imaging module is used to collect real-time temperature data corresponding to each kelp moisture imaging module.

[0065] Further, at least two layers of drying lines are arranged in the transportation channel 4, and the at least two layers of drying lines are parallel to each other. Optionally, the at least two layers of drying lines are vertically and parallelly arranged. Preferably, the I-shaped drying rods of the upper layer of drying lines and the I-shaped drying rods of the lower layer of drying lines are staggered, so as to avoid the contact between the sewage after spraying of the upper layer and the kelp of the lower layer, and to avoid errors in the real-time temperature data caused by the overlap of the upper and lower layers when the kelp moisture imaging module collects the real-time temperature data.

[0066] In this embodiment, the fresh kelp is hung in the drying chamber by using an automatic device during automatic hanging, thereby reducing manual operation and improving production efficiency. In the saltwater spraying and cleaning process, the kelp is sprayed and cleaned with saltwater to remove the surface mud and impurities. Before drying, the kelp is blown by a high-pressure air knife to remove excess moisture, thereby preparing for the subsequent drying process. In the preheating and warming-up stage, the kelp is preheated to evaporate the moisture inside the kelp, thereby forming a "sweating" phenomenon, which is beneficial to the subsequent drying process. In the balancing and dehumidifying stage, the kelp is dehumidified under constant temperature conditions to gradually remove moisture and form micro-channels, thereby further reducing the moisture content of the kelp. In the warming-up and drying stage, the kelp is gradually warmed up and dried until the predetermined moisture content is reached. In the cooling and shaping stage, the dried kelp is shaped by reducing the temperature, thereby maintaining the shape and texture of the kelp. Finally, in the discharging and sorting process, the dried kelp is discharged from the drying chamber, and is sorted and graded. In the grading and packaging process, the kelp is graded and packaged according to the quality and specifications of the kelp, thereby facilitating storage and sales.

[0067] The execution subject of the method in this embodiment can be Figure 1 The AI temperature control device shown in FIG. 1. As shown in FIG. Figure 2 The kelp drying method based on the AI algorithm in this embodiment can include the following steps:

[0068] Step 201, obtaining real-time temperature data corresponding to a target device, wherein the target device is any one of a kelp hanging device, a spraying cleaning device, and a mobile drying device.

[0069] The real-time temperature data includes environmental real-time temperature, kelp real-time temperature, and kelp real-time moisture content.

[0070] In a possible design, when the target device is the mobile drying device, obtaining the real-time temperature data corresponding to the mobile drying device includes a plurality of drying sub-zones each corresponding to real-time sub-data.

[0071] Step 202, obtaining execution parameters corresponding to the target device according to the real-time temperature data corresponding to the target device.

[0072] The execution parameters corresponding to the mobile drying device include execution sub-parameters corresponding to a plurality of drying zones. The execution parameters corresponding to the kelp hanging device include hanging speed and hanging density. The execution parameters corresponding to the spraying cleaning device include spraying water pressure and spraying concentration. The execution parameters corresponding to the mobile drying device include wind temperature and wind intensity.

[0073] Step 203, controlling the target device to run according to the execution parameters corresponding to the target device.

[0074] The kelp drying method based on the AI algorithm provided in the embodiment of the present application includes the following steps: obtaining real-time temperature data corresponding to a target device, wherein the target device is any one of a kelp hanging device, a spraying cleaning device, and a mobile drying device; obtaining execution parameters corresponding to the target device according to the real-time temperature data corresponding to the target device; and controlling the target device to run according to the execution parameters corresponding to the target device. In this embodiment, the real-time temperature data of the target device is used to control the target device to run, so that the running state of the target device can be adjusted in real time to ensure the quality of the dried product.

[0075] In a possible design, the execution parameters corresponding to the mobile drying device include execution sub-parameters corresponding to a plurality of drying zones. According to the execution sub-parameters corresponding to the plurality of drying zones, the execution sub-parameters corresponding to each drying zone are obtained, including:

[0076] For each drying zone, when there is a previous drying zone of the drying zone, a first drying degree of the previous drying zone is obtained;

[0077] According to the real-time sub-data corresponding to the drying zone, a second drying degree corresponding to the drying zone is obtained;

[0078] According to the first drying degree and the second drying degree, an adjustment ratio corresponding to the drying area is obtained;

[0079] The real-time execution sub-parameters corresponding to the drying area are obtained, and the real-time execution sub-parameters are adjusted according to the adjustment ratio corresponding to the drying area, to obtain new execution sub-parameters corresponding to the drying area.

[0080] Specifically, taking the drying sub-area 422 as an example, when the drying area is the sub-area 422, the previous drying area is the sub-area 421, the first drying degree is the moisture content of the kelp collected by the kelp moisture imaging module of the sub-area 421, and the real-time sub-data is the real-time moisture content and real-time temperature of the kelp hung on each of the plurality of one-shaped drying areas in the drying area; the moisture content variance of the real-time moisture content and the temperature variance of the real-time temperature are calculated, the mean value of the moisture content variance and the temperature variance is taken, and then the mean value plus 1 is taken as the above-mentioned adjustment ratio; the execution sub-parameters are multiplied by the above-mentioned adjustment ratio to obtain new execution sub-parameters.

[0081] Figure 3 A flowchart of a kelp drying process intelligent management method based on an AI algorithm provided by the embodiment of the present application. In this embodiment, the execution subject of the method is a terminal device with control function, hereinafter referred to as an electronic device. As shown in the figure, the kelp drying process intelligent management method based on an AI algorithm in this embodiment is used to control the kelp drying process intelligent management system based on an AI algorithm. The method can include: Figure 3

[0082] Step 301, in response to a kelp drying instruction, and generating a starting instruction corresponding to the kelp hanging device and the AI temperature control device respectively.

[0083] When the kelp drying instruction is received, the starting instruction corresponding to the kelp hanging device and the AI temperature control device is generated immediately, and then the kelp hanging device and the AI temperature control device are started based on the starting instruction corresponding to the kelp hanging device and the AI temperature control device respectively.

[0084] Step 302, when it is detected that the kelp hanging device starts to run, a starting instruction corresponding to the spray cleaning device is generated.

[0085] It can be understood that when there is no kelp on the kelp hanging device, spray cleaning is meaningless, and therefore, starting the spray cleaning device after the kelp hanging device runs can save energy to a certain extent.

[0086] Step 303, when it is detected that the spray cleaning device starts to run, a starting instruction corresponding to the moving drying device is generated. The starting instruction is used to trigger the device to start running.

[0087] ​The embodiment of the present application also provides a computer readable storage medium, and the computer readable storage medium stores computer execution instructions, and when a processor executes the computer execution instructions, the method of any one of the foregoing embodiments is realized.

[0088] The computer readable storage medium can be a read only memory (ROM), a random access memory (RAM), a compact disc read only memory (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, or the like.

[0089] The present application also provides a program product, which comprises an executable computer program stored in a readable storage medium. At least one processor of the kelp drying process intelligent management equipment based on an AI algorithm can read the computer program from the readable storage medium, and the at least one processor executes the computer program to realize the kelp drying process intelligent management method based on the AI algorithm provided in the various embodiments.

[0090] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative, for example, the division of the modules is only a logical function division, and actual implementation can have another division mode, for example, a plurality of modules can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed modules can be indirect coupling or communication connection through some interfaces, devices or modules, and can be electrical, mechanical or other forms.

[0091] The modules described as separate components can or can not be physically separated, and the components shown as modules can or can not be physical units, that is, they can be located in one place, or can be distributed on a plurality of network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the embodiment.

[0092] In addition, the functional modules in each embodiment of the present application can be integrated in one processing unit, or each module can be physically present alone, or two or more modules can be integrated in one unit. The unit formed by the modules can be realized in the form of hardware, or in the form of hardware plus software function unit.

[0093] The integrated module realized in the form of the software function module can be stored in a computer readable storage medium. The software function module is stored in a storage medium and includes a plurality of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to execute partial steps of the method of each embodiment of the present application.

[0094] It should be understood that the processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), etc. The general-purpose processor can be a microprocessor or can also be any conventional processor, etc. The steps of the method disclosed in the present application can be directly embodied as hardware processor execution, or executed by a combination of hardware and software modules in the processor.

[0095] The memory can include a high-speed RAM memory, and can also include a non-volatile storage NVM, for example, at least one disk memory, and can also be a U disk, a mobile hard disk, a read-only memory, a magnetic disk or an optical disk, etc.

[0096] The bus can be an industry standard architecture (ISA) bus, a peripheral component (PCI) bus, or an extended industry standard architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For the convenience of representation, the bus in the drawings of the present application is not limited to only one bus or one kind of bus.

[0097] The storage medium may be implemented by any type of volatile or nonvolatile memory device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), ROM, magnetic storage, flash memory, magnetic disk, or optical disk. The storage medium may be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0098] An exemplary storage medium is coupled to a processor, such that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be an integral part of the processor. The processor and storage medium can be located in an ASIC. Of course, the processor and storage medium can also exist as discrete components in an electronic device or a host control device.

[0099] Those skilled in the art will appreciate that all or part of the steps in the above-described method embodiments can be implemented using hardware associated with program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0100] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. An intelligent management system for kelp drying process based on AI algorithm, characterized in that: It includes a kelp hanging device, a spray cleaning device, a mobile drying device and an AI temperature control device. The kelp hanging device, the spray cleaning device and the mobile drying device are distributed in the same transportation channel in sequence. The AI ​​temperature control device is connected to the kelp hanging device, the spray cleaning device and the mobile drying device respectively. The kelp hanging device is used to identify the position of the kelp and hang the kelp based on the position of the kelp; The spray cleaning device is used to spray clean the hanging kelp; The mobile drying device is used to carry out mobile drying of the kelp after spray cleaning; an AI temperature control device, configured to obtain real-time temperature data corresponding to each of the kelp hanging device, the spray cleaning device, and the mobile drying device, and control the operation of the kelp hanging device, the spray cleaning device, and the mobile drying device based on the plurality of real-time temperature data; The AI ​​temperature control device is connected to the kelp hanging device, the spray cleaning device and the mobile drying device respectively. The specific steps of kelp drying include: Acquiring real-time temperature data corresponding to a target device, wherein the target device is any one of the kelp hanging device, the spray cleaning device, and the mobile drying device; Obtaining execution parameters corresponding to the target device according to real-time temperature data corresponding to the target device; controlling the target device to operate according to execution parameters corresponding to the target device; Each drying zone is provided with a kelp moisture imaging module, and the kelp moisture imaging module is used to collect the real-time temperature data corresponding to each kelp moisture imaging module; When the target device is the mobile drying device, obtaining the real-time temperature data corresponding to the mobile drying device includes real-time sub-data corresponding to each of the plurality of drying zones; The execution parameters corresponding to the mobile drying device include execution sub-parameters corresponding to the multiple drying zones. According to the execution sub-parameters corresponding to the plurality of drying zones, the execution sub-parameters corresponding to each drying zone are obtained, including: For each drying zone, when there is a previous drying zone of the drying zone, obtaining the first drying degree of the previous drying zone; Obtaining a second drying degree corresponding to the drying zone according to the real-time sub-data corresponding to the drying zone; Obtaining an adjustment ratio corresponding to the drying zone according to the first drying degree and the second drying degree; The real-time execution sub-parameter corresponding to the drying partition is obtained, and the real-time execution sub-parameter is adjusted according to the adjustment ratio corresponding to the drying partition to obtain a new execution sub-parameter corresponding to the drying partition.

2. The intelligent management system for kelp drying process based on AI algorithm according to claim 1 is characterized in that: The mobile drying device corresponds to a plurality of drying zones corresponding to respective temperature intervals in the transport channel.

3. The intelligent management system for kelp drying process based on AI algorithm according to claim 1 is characterized in that: At least two layers of drying lines are arranged in the transport channel, wherein the at least two layers of drying lines are parallel to each other.

4. The intelligent management system for kelp drying process based on AI algorithm according to claim 3 is characterized in that: The specific steps of intelligent management of kelp drying process include: Responding to the kelp drying instruction, generating start instructions corresponding to the kelp hanging device and the AI ​​temperature control device respectively; When it is detected that the kelp hanging device starts to operate, a start instruction corresponding to the spray cleaning device is generated; When it is detected that the spray cleaning device starts to operate, a start instruction corresponding to the mobile drying device is generated; The start instruction is used to trigger the device to start running.

5. The intelligent management system for kelp drying process based on AI algorithm according to claim 4 is characterized in that: include: at least one processor and memory; The memory stores computer-executable instructions; The at least one processor executes computer-executable instructions stored in the memory.

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