Ice maker remote control system and method based on artificial intelligence

Abstract

The present invention belongs to the field of ice making technology. An ice making machine remote control system and method based on artificial intelligence are provided. The method comprises: analyzing the instruction type of the remote control instruction, and analyzing to obtain a safety instruction or a non-safety instruction; if the remote control instruction is a safety instruction, forwarding the remote control instruction to the ice making machine for execution; if the remote control instruction is a non-safety instruction, obtaining multi-source status data related to the ice making machine, inputting the multi-source status data and the remote control instruction into a matching analysis model based on artificial intelligence to obtain a predicted instruction matching degree; if the instruction matching degree is higher than the set matching value, forwarding the remote control instruction to the ice making machine for execution, otherwise feedback prompt information to the sending terminal of the remote control instruction. The present invention adopts different forwarding execution strategies according to the type of remote control instruction to avoid unreasonable instructions from having adverse effects on the normal production operation of the ice making machine.

Description

Technical Field

[0001] The present invention relates to the technical field of ice making, and in particular to an artificial intelligence-based remote control system and method for an ice making machine. Background Art

[0002] An ice maker is a refrigeration machine that generates ice by cooling water through an evaporator using the refrigerant in the refrigeration system.

[0003] In order to facilitate the operation of ice makers by relevant personnel, many ice makers have been equipped with remote communication modules, through which relevant personnel can remotely control the ice makers, such as adjusting the working parameters of the ice makers, starting or shutting down the ice makers, etc. In the existing remote control method, the ice maker directly executes the received remote control instructions. This method lacks consideration of many practical factors at the ice production site, which can easily lead to interference with the normal production of the ice maker factory, resulting in economic losses and even safety accidents. The present invention aims to solve this technical problem. Summary of the invention

[0004] In this regard, the present invention provides an artificial intelligence-based ice-making machine remote control method, system, electronic device, computer storage medium and computer program product to solve the above technical problems.

[0005] The present invention provides an ice-making machine remote control method based on artificial intelligence, which is applied to a field control device of the ice-making machine. The method comprises the following steps: in response to receiving a remote control instruction for the ice-making machine, analyzing the instruction type of the remote control instruction to obtain a safety instruction or a non-safety instruction; if the remote control instruction is a safety instruction, forwarding the remote control instruction to the ice-making machine for execution; if the remote control instruction is a non-safety instruction, acquiring multi-source status data related to the ice-making machine, inputting the multi-source status data and the remote control instruction into a matching analysis model based on artificial intelligence, and the matching analysis model outputting a predicted instruction matching degree; if the instruction matching degree is higher than a set matching value, forwarding the remote control instruction to the ice-making machine for execution, otherwise feeding back a prompt message to a sending terminal of the remote control instruction.

[0006] In some embodiments, the instruction type of the remote control instruction is analyzed to determine whether it is a safety instruction or a non-safety instruction, including: parsing the header of the remote control instruction to determine whether it contains a safety instruction tag, if so, determining it as a safety instruction, otherwise determining it as a non-safety instruction.

[0007] In some embodiments, the obtaining of multi-source status data related to the ice maker includes: communicating with the ice maker to determine the production operation stage of the ice maker, and determining the data source type included in the multi-source status data according to the production operation stage; and obtaining corresponding status data according to each of the determined data source types, that is, obtaining the multi-source status data related to the ice maker.

[0008] In some embodiments, the communicating with the ice maker to determine the production operation stage of the ice maker includes: communicating with the ice maker to obtain order data and ice making record data of the ice maker; wherein the order data includes ice making specifications and ice making demand, and the ice making record data includes water supply; calculating the theoretical ice making amount according to the water supply, determining the ice making loss coefficient according to the ice making specifications, and determining the predicted ice making amount based on the theoretical ice making amount and the ice making loss coefficient; comparing the predicted ice making amount with the ice making demand to determine the production operation stage of the ice maker.

[0009] In some embodiments, the multi-source status data and the remote control instructions are input into a matching analysis model based on artificial intelligence, and the matching analysis model outputs a predicted instruction matching degree, including: collecting several groups of historical data, each group of the historical data includes preset multi-source status data and preset remote control instructions, and configuring a matching degree label for each group of the historical data to form a training data set; using the training data set to fine-tune the general large model until the termination condition is reached, that is, obtaining the matching analysis model based on artificial intelligence; the multi-source status data and the remote control instructions are input into the matching analysis model, and the matching analysis model outputs a predicted instruction matching degree.

[0010] In some embodiments, feeding back prompt information to the terminal sending the remote control instruction includes: feeding back prompt information to the terminal sending the remote control instruction, wherein the prompt information includes the matching degree and the multi-source status data.

[0011] The present invention also provides an ice-making machine remote control system based on artificial intelligence, which is applied to a field control device of an ice-making machine. The system includes an instruction type analysis module, a first processing module, a second processing module, and a third processing module; the instruction type analysis module is used to analyze the instruction type of the remote control instruction in response to receiving a remote control instruction for the ice-making machine, and analyze to obtain a safety instruction or a non-safety instruction; the first processing module is used to forward the remote control instruction to the ice-making machine for execution if the remote control instruction is a safety instruction; the second processing module is used to obtain multi-source status data related to the ice-making machine if the remote control instruction is a non-safety instruction, input the multi-source status data and the remote control instruction into a matching analysis model based on artificial intelligence, and the matching analysis model outputs a predicted instruction matching degree; the third processing module is used to forward the remote control instruction to the ice-making machine for execution if the instruction matching degree is higher than a set matching value, otherwise feedback prompt information to the sending terminal of the remote control instruction.

[0012] The present invention also provides an electronic device, comprising: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor executes the computer program to implement the method described in any of the preceding items.

[0013] The present invention also provides a computer storage medium, wherein the computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the method as described in any of the above items.

[0014] The present invention also provides a computer program product, which includes a computer program stored in a computer storage medium, and when the computer program is executed by a processor of an electronic device, it implements the method described in any of the above items.

[0015] The beneficial effect of the present invention is that the scheme of the present invention can pre-analyze the remote control instructions and adopt different instruction forwarding execution strategies according to whether they are safety instructions or non-safety instructions. Such a setting can, on the one hand, ensure the timeliness of the execution of urgent remote control instructions, and on the other hand, avoid unreasonable remote control instructions, especially remote control instructions that are triggered by mistake, from having adverse effects on the normal production operation of the ice maker. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 The present invention is a flowchart of an artificial intelligence-based remote control method for an ice-making machine disclosed in an embodiment of the present invention.

[0018] Figure 2 It is a schematic diagram of upstream and downstream equipment of an ice-making machine disclosed in an embodiment of the present invention.

[0019] Figure 3 The invention discloses an artificial intelligence-based remote control system for an ice-making machine. DETAILED DESCRIPTION

[0020] The following is a description of the implementation of the present application by specific specific embodiments. People familiar with the technology can easily understand other advantages and effects of the present application from the contents disclosed in this specification. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0021] In addition, the technical features involved in the different embodiments of the present application described below can be combined with each other as long as they do not conflict with each other.

[0022] like Figure 1 As shown, an embodiment of the present invention discloses an artificial intelligence-based remote control method for an ice maker, which is applied to a field control device of an ice maker, and the method comprises the following steps: in response to receiving a remote control instruction for an ice maker, analyzing the instruction type of the remote control instruction to obtain a safety instruction or a non-safety instruction; if the remote control instruction is a safety instruction, forwarding the remote control instruction to the ice maker for execution; if the remote control instruction is a non-safety instruction, acquiring multi-source status data related to the ice maker, inputting the multi-source status data and the remote control instruction into a matching analysis model based on artificial intelligence, and the matching analysis model outputs a predicted instruction matching degree; if the instruction matching degree is higher than a set matching value, forwarding the remote control instruction to the ice maker for execution, otherwise feeding back a prompt message to the sending terminal of the remote control instruction.

[0023] Compared with the solution in the prior art that the remote control command is directly sent to the ice maker for execution, the present invention sets a field control device between the remote control terminal and the ice maker, and the field control device pre-analyzes the remote control command sent by the remote control terminal, and decides whether to forward the remote control command to the ice maker for execution based on the analysis result, thereby avoiding the interference of unreasonable remote control commands, especially remote control commands that are triggered by mistake, on the normal production operation of the on-site ice maker. Specifically, after receiving the remote control command to the ice maker, the field control device analyzes the command type of the remote control command, that is, determines whether it is a safety command or a non-safety command. If it is a safety command, it is directly forwarded to the ice maker, and the ice maker determines whether to execute and how to execute based on the preset command execution rules. If it is a non-safety instruction, then further multi-source status data related to the ice maker is obtained, and a matching analysis model based on artificial intelligence is used to conduct targeted and in-depth analysis of the multi-source status data and the remote control instruction, so as to obtain the predicted instruction matching degree. When the instruction matching degree is higher than the set matching value, the remote control instruction is deemed to be reasonable, and the remote control instruction is forwarded to the ice maker for execution. If the instruction matching degree is lower than the set matching value, the remote control instruction is deemed to be unreasonable, and a prompt message is fed back to the sending terminal of the remote control instruction to remind the person who sent the remote control instruction to verify the remote control instruction.

[0024] Therefore, the scheme of the present invention can pre-analyze the remote control instructions and adopt different instruction forwarding execution strategies according to whether they are safety instructions or non-safety instructions. Such a setting can, on the one hand, ensure the timeliness of the execution of urgent remote control instructions, and on the other hand, reduce the adverse effects of unreasonable remote control instructions, especially mistaken remote control instructions, on the normal production operation of the ice maker.

[0025] In some embodiments, the instruction type of the remote control instruction is analyzed to determine whether it is a safety instruction or a non-safety instruction, including: parsing the header of the remote control instruction to determine whether it contains a safety instruction tag, if so, determining it as a safety instruction, otherwise determining it as a non-safety instruction.

[0026] In an embodiment of the present invention, the remote control instruction sent by the remote control terminal includes three parts: a header, a payload, and a trailer. The header is the beginning of the data packet, which contains metadata of the data packet used for routing and control. The header usually includes a source address and a destination address (identifying the sender and receiver of the data packet), a protocol type (indicating the protocol used by the data packet, such as TCP, UDP), a sequence number and an acknowledgment number (used for the order and reliability control of the data packet, especially in TCP), a data packet length (the total length of the header and data part), a flag and a flag bit, etc. When generating a remote control instruction, the remote control terminal will choose to set a "safety instruction label" at the "flag" of the data header according to a preset rule. Correspondingly, the field control device can obtain or not obtain the above-mentioned "safety instruction label" by parsing the data header, thereby determining the remote control instruction as a safety instruction or a non-safety instruction.

[0027] In some embodiments, the obtaining of multi-source status data related to the ice maker includes: communicating with the ice maker to determine the production operation stage of the ice maker, and determining the data source type included in the multi-source status data according to the production operation stage; and obtaining corresponding status data according to each of the determined data source types, that is, obtaining the multi-source status data related to the ice maker.

[0028] In the embodiment of the present invention, Figure 2 As shown, the multi-source status data related to the ice maker includes three types of data sources: the status data of the ice maker itself, the status data of the upstream equipment of the ice maker, and the status data of the downstream equipment of the ice maker. The status data of each data source type is as follows: 1) The status data of the ice maker itself.

[0029] Refrigeration system status: including the operating status of the compressor, the temperature and pressure of the condenser and evaporator, etc.

[0030] Ice-making process parameters: such as ice generation speed, ice thickness, ice shape and size, etc.

[0031] Equipment operating status: such as equipment startup time, shutdown time, fault alarm records, etc.

[0032] Maintenance and cleaning cycle: remind users to perform regular maintenance and cleaning to ensure the normal operation of the equipment.

[0033] Environmental parameters: such as the temperature and humidity of the surrounding environment, these factors may affect the performance of the ice maker.

[0034] Water quality monitoring: Monitor the quality of water entering the ice machine to ensure the hygiene and safety of the ice.

[0035] 2) Status data of the upstream equipment of the ice maker: Water supply system status: This involves parameters such as water pressure, water flow rate and water quality, because a stable water supply is essential for the normal operation of the ice maker. Any abnormality in the water supply system may cause the ice maker to fail to produce ice cubes that meet the standards.

[0036] Refrigerant and refrigeration system status: including the pressure, temperature and flow of the refrigerant, as well as the operating efficiency of the refrigeration system. The type of refrigerant (such as environmentally friendly refrigerant) and the efficiency of the refrigeration system directly affect the energy efficiency ratio of the ice maker and the quality of the ice.

[0037] Electrical system status: The ice machine requires a stable power supply, so the status data of the electrical system, including voltage, current, power factor and frequency, are key factors to ensure the normal operation of the equipment.

[0038] 3) Status data of downstream equipment of ice making machine: mainly includes working status data of downstream equipment such as ice compactor, packaging machine and ice delivery system (including conveyor belt, delivery pipeline, etc.).

[0039] When performing order-based ice-making operations, the ice-making operation of the ice-making machine involves multiple production stages, such as the shutdown stage, the startup stage, the operation stage, the end of the operation stage, etc. In different operation stages, the upstream and downstream equipment that may be involved in the remote control instructions for the ice-making machine are different.

[0040]

[0041] During the shutdown phase, the user's remote control of the ice maker mainly involves the startup, self-test, and equipment preheating of the ice maker. During this phase, it is necessary to simultaneously obtain the status data of the upstream and downstream devices to determine whether the startup conditions are met, such as whether the water quality of the upstream equipment meets the standards and the downstream equipment is installed in place.

[0042] In the startup phase, the user's remote control of the ice maker mainly involves the setting of the ice maker's operating parameters, such as ice making speed, ice thickness, ice shape and size, etc. In this phase, it is only necessary to obtain the status data of the upstream equipment to determine whether the upstream equipment has completed the ice making operation preparation. Since ice making has not yet started, there is no need to pay attention to the status data of the downstream equipment, such as whether the ice compactor and packaging machine have entered the working state, whether there is ice accumulation in the ice delivery system, etc. For example, when the set ice making speed exceeds the water supply speed of the upstream water supply system (the water supply system needs to filter the water, etc.), it can be determined that the instruction is unreasonable.

[0043] During the operation phase, the user's remote control of the ice maker mainly involves the adjustment of the ice maker's operating parameters and emergency stop control. At this stage, it is necessary to obtain the status data of the upstream and downstream devices at the same time to determine whether the instruction is suitable for execution. For example, the adjusted ice-making speed is too fast. Although it does not exceed the rated ice-making speed of the ice maker, it exceeds the water supply speed of the water supply system (the water supply system needs to filter the water, etc.). At this time, it can be determined that the instruction is unreasonable; or, the adjusted ice-making speed is too fast and exceeds the transmission rate of the ice delivery system. At this time, it can be determined that the instruction is unreasonable.

[0044] At the end of the operation, the user's remote control of the ice maker mainly involves the adjustment of the ice maker's operating parameters and shutdown control. Since the upstream equipment automatically shuts down when entering the end of the operation, the ice maker actually only maintains an operation linkage state with the downstream equipment, so at this stage, only the status data of the downstream equipment needs to be obtained. For example, if the adjusted ice-making speed is too fast and exceeds the delivery capacity of the ice delivery system, the instruction can be determined to be unreasonable.

[0045] According to the upstream and downstream equipment involved in the above production operation stage, the present invention determines the data source types included in the multi-source status data, and then obtains the status data corresponding to these data source types, that is, obtains the multi-source status data related to the ice maker.

[0046] In some embodiments, the communicating with the ice maker to determine the production operation stage of the ice maker includes: communicating with the ice maker to obtain order data and ice making record data of the ice maker; wherein the order data includes ice making specifications and ice making demand, and the ice making record data includes water supply; calculating the theoretical ice making amount according to the water supply, determining the ice making loss coefficient according to the ice making specifications, and determining the predicted ice making amount based on the theoretical ice making amount and the ice making loss coefficient; comparing the predicted ice making amount with the ice making demand to determine the production operation stage of the ice maker.

[0047] In the embodiment of the present invention, since the control system of the ice maker often does not have the indicator data of "production operation stage", the present invention indirectly determines the current production operation stage of the ice maker by prediction. The field control device communicates with the ice maker to obtain the order data and ice making record data of the ice maker; wherein the order data is input into the ice maker by the order system of the ice maker based on customer demand, including ice making specifications and ice making demand. The ice making specifications refer to the shape (block ice, shavings, tube ice, etc.) and size of the ice products. The ice making record data includes the water supply.

[0048] When the parameters such as the refrigeration efficiency (COP) of the ice maker, the performance of the refrigeration system, and the heat exchange efficiency of the evaporator and condenser are fixed and known, the conversion relationship or comparison relationship between the water supply and the theoretical ice making amount can be determined in advance. Also, different ice making specifications require different ice making processes. For example, block ice requires a pressing process, which leads to inevitable ice making losses. For this reason, the ice making loss coefficient corresponding to different ice making specifications is determined in advance. At this point, the predicted ice making amount can be determined based on the theoretical ice making amount and the ice making loss coefficient, that is, the actual amount of ice made (predicted value) under the above water supply conditions.

[0049] Finally, by comparing the predicted ice-making amount with the ice-making demand, the production operation stage of the ice-making machine can be determined. For example, if the ratio of the predicted ice-making amount to the ice-making demand is approximately 1 / 2, then the production operation stage of the ice-making machine is determined to be the operation stage; if the ratio of the predicted ice-making amount to the ice-making demand is approximately 9 / 10, then the production operation stage of the ice-making machine is determined to be the operation end stage.

[0050] In some embodiments, the multi-source status data and the remote control instructions are input into a matching analysis model based on artificial intelligence, and the matching analysis model outputs a predicted instruction matching degree, including: collecting several groups of historical data, each group of the historical data includes preset multi-source status data and preset remote control instructions, and configuring a matching degree label for each group of the historical data to form a training data set; using the training data set to fine-tune the general large model until the termination condition is reached, that is, obtaining the matching analysis model based on artificial intelligence; the multi-source status data and the remote control instructions are input into the matching analysis model, and the matching analysis model outputs a predicted instruction matching degree.

[0051] In an embodiment of the present invention, a general large model (Large Language Models, LLMs for short) is a type of machine learning model with large-scale parameters and complex computing structure, usually constructed by a deep neural network. Compared with vertical large models in specific fields, general large models pay more attention to the generalization ability of the model, that is, the ability to be applied in a variety of different tasks and fields. For example, OpenAI's GPT model, Alibaba Cloud's Tongyi Qianwen, Huawei's PanGu, etc. Vertical large models are optimized for the needs of specific industries or fields, and are pre-trained using industry-related data to improve performance and accuracy in this field.

[0052] In order to reduce the difficulty of model construction and improve construction efficiency, the matching analysis model of the present invention is obtained by fine-tuning and training the general large model, without the need to manually build a vertical model locally. Specifically, several groups of historical data of the ice maker are collected in advance, each group of training data includes a preset multi-source status data and a preset remote control instruction, and a matching degree label is configured for each group of training data automatically or manually, thus forming a training data set, and all training data are integrated into a training data set. The integrated training data set is used to fine-tune the general large model until the termination condition is reached, that is, a matching analysis model that is suitable for whether the remote control instruction matches the multi-source status data of the ice maker is obtained.

[0053] In some embodiments, feeding back prompt information to the terminal sending the remote control instruction includes: feeding back prompt information to the terminal sending the remote control instruction, wherein the prompt information includes the matching degree and the multi-source status data.

[0054] In the embodiment of the present invention, when the matching degree between the remote control instruction and the multi-source status data is lower than the set matching value, the field control device feeds back a prompt message to the terminal sending the remote control instruction. The prompt message includes the matching degree and the multi-source status data. After seeing the prompt message, the relevant personnel of the sending terminal can know the reason for the mismatch and can adjust the remote control instruction.

[0055] like Figure 3 As shown, an embodiment of the present invention further discloses an ice-making machine remote control system based on artificial intelligence, which is applied to a field control device of an ice-making machine. The system includes an instruction type analysis module, a first processing module, a second processing module, and a third processing module; the instruction type analysis module is used to analyze the instruction type of the remote control instruction in response to receiving a remote control instruction for the ice-making machine, and analyze to obtain a safety instruction or a non-safety instruction; the first processing module is used to forward the remote control instruction to the ice-making machine for execution if the remote control instruction is a safety instruction; the second processing module is used to obtain multi-source status data related to the ice-making machine if the remote control instruction is a non-safety instruction, input the multi-source status data and the remote control instruction into a matching analysis model based on artificial intelligence, and the matching analysis model outputs a predicted instruction matching degree; the third processing module is used to forward the remote control instruction to the ice-making machine for execution if the instruction matching degree is higher than a set matching value, otherwise feedback prompt information to the sending terminal of the remote control instruction.

[0056] An embodiment of the present invention further discloses an electronic device, comprising: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor executes the computer program to implement the method described in the above embodiment.

[0057] An embodiment of the present invention further discloses a computer storage medium, wherein the computer storage medium stores a computer program, and the computer program is executed by a processor to implement the method described in the above embodiment.

[0058] The embodiment of the present invention further discloses a computer program product. When the computer program product is run on a terminal, the terminal implements the method described in the above embodiment.

[0059] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0060] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0061] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0062] The above description is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention.

Claims

1. An artificial intelligence-based remote control method for an ice-making machine, applied to an on-site control device for an ice-making machine, characterized in that: The method comprises the following steps: in response to receiving a remote control instruction for an ice maker, analyzing the instruction type of the remote control instruction to determine whether it is a safety instruction or a non-safety instruction; if the remote control instruction is a safety instruction, forwarding the remote control instruction to the ice maker for execution; If the remote control instruction is a non-safety instruction, multi-source status data related to the ice maker is obtained, and the multi-source status data and the remote control instruction are input into a matching analysis model based on artificial intelligence, and the matching analysis model outputs a predicted instruction matching degree; if the instruction matching degree is higher than a set matching value, the remote control instruction is forwarded to the ice maker for execution, otherwise a prompt message is fed back to the sending terminal of the remote control instruction; Acquiring multi-source status data related to the ice maker, including: communicating with the ice maker to determine the production operation stage of the ice maker, and determining the data source type included in the multi-source status data according to the production operation stage; Acquire corresponding status data according to the determined data source types, that is, acquire the multi-source status data related to the ice maker; wherein the multi-source status data related to the ice maker includes the status data of the ice maker itself, the status data of the upstream equipment of the ice maker, and the status data of the downstream equipment of the ice maker; Communicating with the ice maker to determine the production operation stage of the ice maker includes: communicating with the ice maker to obtain order data and ice making record data of the ice maker; wherein the order data includes ice making specifications and ice making demand, and the ice making record data includes water supply; calculating the theoretical ice making amount according to the water supply, determining the ice making loss coefficient according to the ice making specifications, and determining the predicted ice making amount based on the theoretical ice making amount and the ice making loss coefficient; comparing the predicted ice making amount with the ice making demand to determine the production operation stage of the ice maker; The instruction type of the remote control instruction is analyzed to determine whether it is a safety instruction or a non-safety instruction, including: parsing the header of the remote control instruction to determine whether it contains a safety instruction tag, if so, determining it as a safety instruction, otherwise determining it as a non-safety instruction.

2. According to the artificial intelligence-based ice machine remote control method of claim 1, it is characterized by: The multi-source status data and the remote control instructions are input into a matching analysis model based on artificial intelligence, and the matching analysis model outputs a predicted instruction matching degree, including: collecting several groups of historical data, each group of the historical data includes preset multi-source status data and preset remote control instructions, and configuring a matching degree label for each group of the historical data to form a training data set; using the training data set to fine-tune the general large model until the termination condition is reached, that is, obtaining the matching analysis model based on artificial intelligence; the multi-source status data and the remote control instructions are input into the matching analysis model, and the matching analysis model outputs a predicted instruction matching degree.

3. The ice-making machine remote control method based on artificial intelligence according to claim 2, characterized in that: Feeding back prompt information to the terminal sending the remote control instruction includes: feeding back prompt information to the terminal sending the remote control instruction, wherein the prompt information includes the matching degree and the multi-source status data.

4. An artificial intelligence-based ice-making machine remote control system for implementing the method described in any one of claims 1 to 3, applied to an on-site control device of an ice-making machine, the system comprising an instruction type analysis module, a first processing module, a second processing module, and a third processing module; characterized in that: The instruction type analysis module is used for analyzing the instruction type of the remote control instruction in response to receiving the remote control instruction for the ice maker, and analyzing to obtain a safety instruction or a non-safety instruction; The first processing module is used for forwarding the remote control instruction to the ice maker for execution if the remote control instruction is a safety instruction; The second processing module is used for obtaining multi-source status data related to the ice maker if the remote control instruction is a non-safety instruction, inputting the multi-source status data and the remote control instruction into a matching analysis model based on artificial intelligence, and the matching analysis model outputs a predicted instruction matching degree; The third processing module is used for forwarding the remote control instruction to the ice maker for execution if the instruction matching degree is higher than a set matching value, and otherwise feeding back prompt information to the sending terminal of the remote control instruction.

5. An electronic device comprising: At least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor executes the computer program to implement the method according to any one of claims 1 to 3.

6. A computer storage medium storing a computer program, characterized in that: The computer program is executed by a processor to implement the method according to any one of claims 1 to 3.

7. A computer program product, comprising a computer program stored in a computer storage medium, characterized in that: When the computer program is executed by a processor of an electronic device, the method according to any one of claims 1 to 3 is implemented.

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