Infrared code dynamic generation and calibration method and system based on mobile terminal cooperation

Abstract

The invention discloses a mobile terminal collaborative infrared code dynamic generation and calibration method and system, and relates to the technical field of infrared code configuration. The method comprises the following steps: establishing a local data link between a panel and a mobile terminal; guiding a user to sequentially press at least three core anchor point instructions on a panel, and transmitting infrared waveform data of the core anchor point instructions to the mobile terminal through a local data link; after the mobile terminal receives the infrared waveform data of the core anchor point instruction, an instruction generation rule set and a corresponding confidence coefficient score are deduced through data field positioning and multi-point constraint solution; sending the instruction generation rule with the highest confidence coefficient score to the panel; and the panel calls the instruction generation rule to calculate and verify and generate a complete infrared instruction code. According to the infrared code dynamic generation and calibration method and system cooperating with the mobile terminal provided by the invention, derivation of the instruction generation rule set is realized by minimizing user input in an offline network-free environment, and dependence on the external Internet is avoided.

Description

A method and system for dynamic generation and calibration of infrared codes in collaboration with mobile devices Technical Field

[0001] This invention relates to the field of infrared code configuration technology, and in particular to a method and system for dynamic generation and calibration of infrared codes in collaboration with mobile terminals. Background Technology

[0002] In the deployment and application of smart homes, configuring infrared remote control devices to control appliances such as air conditioners and televisions is a necessary but challenging step. Currently, the industry mainly relies on two traditional solutions to accomplish this configuration. The first is cloud-based universal code library matching, where smart devices connect to the internet and download pre-stored instruction sets from a server. However, this solution can never achieve 100% coverage. When faced with newly launched, niche brand, or different batches of the same brand of devices, especially when checksum rules are slightly adjusted, the configuration immediately becomes invalid.

[0003] When cloud-based matching fails, the industry's only fallback option is to adopt the second solution: local key-by-key learning. This method requires the user to bring the original remote control close to the smart panel and press all the function keys on the remote control one by one. For air conditioner remote controls with complex functions and multiple temperature settings, this usually means that the user needs to repeat the button operations more than 100 times. This process is time-consuming, often taking 20 to 30 minutes, placing a huge operational burden on end users and professional installers, resulting in low configuration efficiency and a very high user abandonment rate.

[0004] Furthermore, existing technical solutions have fundamental flaws in terms of accuracy. Traditional learning methods merely statically record the infrared waveform of each button, failing to derive the underlying instruction generation rules, especially the core checksum logic. This means the generated code library is redundant and massive, and cannot handle complex proprietary protocols with timestamps or auto-incrementing counters. Simultaneously, checksum derivation typically relies on time-consuming manual differential analysis by professional engineers using desktop tools; this crucial step cannot be automated at the user's site.

[0005] In summary, existing infrared code configuration schemes face a fundamental contradiction between efficiency and compatibility. They are either highly efficient but have limited compatibility, or widely compatible but offer a poor user experience. Furthermore, the lack of a feedback verification mechanism to ensure that generated commands are correctly received by the target device makes it impossible to guarantee 100% usability of the commands after configuration. To overcome these technical shortcomings, this invention proposes a new solution. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to provide a method and system for dynamic generation and calibration of infrared codes in collaboration with mobile terminals. In an offline environment without network, it can derive a high-confidence instruction generation rule set by minimizing user input, thus avoiding dependence on the external Internet.

[0007] In a first aspect, the present invention provides a method for dynamic generation and calibration of infrared codes in collaboration with a mobile terminal, comprising: a local data link establishment process: establishing a local data link between a panel and a mobile terminal; an anchor point instruction acquisition process: guiding the user to press at least three core anchor point instructions sequentially on the panel, and transmitting the infrared waveform data of the core anchor point instructions to the mobile terminal through the local data link; an instruction generation rule derivation process: after the mobile terminal receives the infrared waveform data of the core anchor point instructions, it derives the instruction generation rule set and the corresponding confidence score through data field positioning and multi-point constraint solving; sending the instruction generation rule with the highest confidence score to the panel; and the panel calling the instruction generation rule to calculate and verify and generate a complete infrared instruction code.

[0008] Furthermore, the method also includes: sending the instruction generation rule with the highest confidence score to the panel, guiding the user to execute the test instruction on the panel, and then receiving user feedback. If the user feedback indicates failure, the method automatically selects the instruction generation rule with the second highest confidence score and sends it back to the panel, guiding the user to perform the test again; until the user feedback indicates success.

[0009] Furthermore, the method also includes: if all high-confidence instruction generation rules are reported as failures by the user, the intelligent waveform copying process is automatically started; the intelligent waveform copying process is: using the data bit information obtained during the instruction generation rule derivation process, the user is guided to learn and store the key waveforms for use as static instructions.

[0010] Furthermore, the method also includes: for each key waveform learned, the panel immediately transmits the key waveform and asks the user to confirm whether the execution was successful; if the user reports success, it is marked as a valid waveform.

[0011] Furthermore, the local data link establishment process includes: after the user starts the configuration, the panel generates the network name and password required for local connection and displays the network name and password via a QR code; the user scans the QR code displayed on the panel with a mobile terminal, parses and obtains the network name and password, creates a Wi-Fi SoftAP hotspot, and the panel's Wi-Fi module connects to the SoftAP, thereby establishing a local data link with the mobile terminal.

[0012] Secondly, this invention provides a mobile-terminal collaborative infrared code dynamic generation and calibration system, including a panel and a mobile terminal. The panel and mobile terminal are used to perform the following interactive process: establishing a local data link between the panel and the mobile terminal; guiding the user to press at least three core anchor point commands sequentially on the panel, and transmitting the infrared waveform data of the core anchor point commands to the mobile terminal through the local data link; after receiving the infrared waveform data of the core anchor point commands, the mobile terminal derives the command generation rule set and the corresponding confidence score through data field positioning and multi-point constraint solving; sends the command generation rule with the highest confidence score to the panel; the panel calls the command generation rule to calculate and verify and generate a complete infrared command code.

[0013] Furthermore, the mobile terminal is also used to send the instruction generation rule with the highest confidence score to the panel, guide the user to execute the test instruction on the panel, and then receive user feedback. If the user feedback is failure, the terminal automatically selects the instruction generation rule with the second highest confidence score and sends it back to the panel, and guides the user to perform the test again; until the user feedback is success.

[0014] Furthermore, the mobile terminal is also used to automatically start the intelligent waveform copying process when all high-confidence instruction generation rules are reported as failures by the user; the intelligent waveform copying process is as follows: using the data bit information obtained during the instruction generation rule derivation process, the user is guided to learn and store the key waveforms for use as static instructions.

[0015] Furthermore, the panel is also used to immediately transmit each key waveform after it has been learned and to ask the user to confirm whether the execution was successful. If the user reports success, the waveform is marked as valid.

[0016] Furthermore, establishing a local data link between the panel and the mobile terminal specifically includes: after the user initiates the configuration, the panel generates the network name and password required for local connection and displays the network name and password via a QR code; the mobile terminal scans the QR code displayed on the panel, parses and obtains the network name and password, creates a Wi-Fi SoftAP hotspot, and the panel's Wi-Fi module connects to the SoftAP, thereby establishing a local data link with the mobile terminal.

[0017] The technical solution provided in this embodiment of the invention has at least the following technical effects: 1. A revolutionary improvement in configuration efficiency. Existing technologies require users to perform more than 100 button operations, taking up to 20 to 30 minutes. This invention simplifies user operation to only three core inputs through three-point anchoring command acquisition. Combined with offline computing of high-performance mobile terminals, the entire configuration process is shortened to less than 2 minutes. This improvement completely solves the user experience pain point of infrared code configuration and significantly improves the installation and deployment efficiency of smart home products.

[0018] 2. High accuracy is ensured in offline environments. By transferring complex verification and backfitting tasks to a high-performance local mobile terminal, the dependence on external internet connectivity is effectively overcome. Based on raw data collected on-site, the algorithm transforms verification and derivation into solving multi-point constraint equations, ensuring the mathematical rigor of the generated rules. This architecture not only guarantees the feasibility of offline operation but also solves the technical challenge of confirming command accuracy in the absence of reliable sensor feedback through a human-machine collaborative calibration mechanism.

[0019] 3. Hardware costs and storage efficiency have been optimized. Ultra-compressed rule set storage is used instead of traditional static waveform storage. This means the central control panel only needs to store tens of bytes of rule parameters, significantly reducing the capacity requirements of non-volatile memory (Flash) and effectively lowering hardware material costs. Simultaneously, the panel can dynamically calculate instruction codes in real time, avoiding delays in code table lookups and improving the real-time performance of instruction issuance.

[0020] 4. The system boasts strong technological barriers and compatibility advantages. Its "Mobile SoftAP Collaboration" architecture cleverly avoids dependence on on-site routers. More importantly, a dual-path closed-loop mechanism—"rule derivation first, intelligent waveform replication as a fallback"—ensures final configuration convergence. The derived rules exhibit strong resistance to protocol variations; and when rules fail, the intelligent waveform replication mode can complete the configuration of even the most complex protocols with minimal user cost, ensuring product availability in any complex protocol environment.

[0021] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0022] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0023] Figure 1 is a schematic diagram of the system framework of the present invention; Figure 2 is a flowchart of the method in Embodiment 1 of the present invention; Figure 3 is a schematic diagram of the system structure in Embodiment 2 of the present invention. Detailed Implementation

[0024] This application provides a mobile-coordinated method and system for dynamic generation and calibration of infrared codes. In an offline environment without network access, it can derive a high-confidence instruction generation rule set by minimizing user input, thus avoiding dependence on the external Internet.

[0025] The overall concept of the technical solution in this invention is as follows: It aims to fundamentally solve the technical challenges of existing infrared remote control configuration schemes in terms of efficiency, accuracy, and offline operation. Traditional cloud-based code libraries fail when encountering devices of specific models or batch differences, and the only alternative—local learning—requires users to perform more than 100 keystrokes, resulting in an extremely poor user experience. The core objective of this invention is to provide a solution that can achieve high-confidence instruction generation rule derivation even in offline environments without a network by minimizing user input.

[0026] This invention proposes a mobile-terminal collaborative infrared code dynamic generation and calibration system and method. The core of its technical solution lies in the decoupled computation and collaboration of tasks. First, the infrared receiving module of the central control panel only collects waveform data from three carefully designed anchor point commands to obtain all necessary differential information, thereby greatly simplifying the complex input process. Subsequently, the panel efficiently transmits the ultra-compressed waveform data to the mobile terminal using a local network link.

[0027] On the mobile terminal, the system runs a proprietary high-performance algorithm responsible for executing complex instruction rule reconstruction tasks. The algorithm first performs differential analysis to precisely locate the data fields in the instructions, then transforms the derivation of the checksum into solving a multi-point constraint equation. By hierarchically fitting mainstream checksum functions, the algorithm can automatically and with high confidence derive the complete instruction generation rule set. This process is completed entirely offline locally, avoiding dependence on the external internet.

[0028] The derived set of super-compression rules is then fed back to the panel, which stores it in a low-capacity memory and dynamically calculates and transmits infrared codes in real time when it receives user instructions, greatly optimizing hardware storage costs.

[0029] Finally, to ensure final accuracy in environments lacking reliable sensor feedback, the system employs a single-step human-machine calibration mechanism. The system guides the user to execute a critical instruction test, and based on the user's binary feedback (success or failure), if failure occurs, the system algorithm automatically backtracks and attempts the next highest confidence rule, guiding the user to retest until the rule is finally confirmed, thus ensuring 100% field availability.

[0030] This invention can achieve significant beneficial effects, including reducing configuration time from more than 20 minutes to less than 2 minutes; at the same time, even in extreme environments with no internet connection and unreliable sensor feedback, it can still achieve accurate configuration of complex private protocols through the rigor of the algorithm and human-machine collaboration, effectively solving the compatibility problem and significantly reducing the requirements for panel hardware storage space.

[0031] As shown in Figure 1, the system in this embodiment adopts an architecture that decouples high-performance computing from low-computing-power hardware, and mainly consists of the following units: Panel Unit: serving as a data acquisition end, instruction execution end, and network client. It includes a low-power microcontroller (MCU), an infrared transceiver module, a Wi-Fi module (Station mode), a liquid crystal display (LCD / LED), and low-capacity non-volatile memory.

[0032] Mobile Terminal: A mobile phone that serves as a high-performance computing terminal and network access point. It runs proprietary application software (App), utilizes its high-performance processor to execute complex algorithms, and has Wi-Fi SoftAP (software access point) functionality and a camera.

[0033] Local network link: Based on Wi-Fi SoftAP technology, created by the mobile terminal for panel connection, enabling high-speed, offline bidirectional exchange of raw data transmission and rule set backhaul. Example 1

[0034] This embodiment provides a method for dynamic generation and calibration of infrared codes in collaboration with a mobile terminal, as shown in Figure 2, including: S1, local data link establishment process: establishing a local data link between the panel and the mobile terminal.

[0035] Furthermore, the local data link establishment process includes: after the user starts the configuration, the panel generates the network name and password required for local connection and displays the network name and password via a QR code; the user scans the QR code displayed on the panel with a mobile terminal, parses and obtains the network name and password, creates a Wi-Fi SoftAP hotspot, and the panel's Wi-Fi module connects to the SoftAP, thereby establishing a local data link with the mobile terminal.

[0036] Panel Information Generation and Display: After the user starts the configuration, the panel MCU immediately generates the SSID (network name) and PSK (password) required for local connection, and displays a QR code containing this information on the LCD screen.

[0037] Mobile Terminal SoftAP Creation and Connection: The mobile terminal app uses its camera to scan the QR code displayed on the panel, parses and captures the SSID and PSK. The app then uses the captured information to automatically create a Wi-Fi SoftAP hotspot. The panel's Wi-Fi module, in Station mode, automatically connects to the SoftAP using the connection information displayed on its screen, establishing a secure local Wi-Fi data link.

[0038] S2. Anchor Point Command Acquisition Process: The user is guided to press at least three core anchor point commands sequentially on the panel. The infrared waveform data of these core anchor point commands is then transmitted to the mobile terminal via a local data link. Command Acquisition and Synchronization: Air conditioning is the most complex type of infrared control; therefore, air conditioning commands will be used as an example below. For instance, the panel guides the user to press three core anchor point commands sequentially (e.g., C1: 25℃, cooling; C2: 26℃, heating; C3: power off). The C1 and C2 core anchor point commands can be modified; one should include cooling and the other heating, and the temperatures should be different. After the panel acquires the infrared waveform data D1, D2, and D3, it transmits them synchronously to the mobile terminal app at high speed via a local Wi-Fi link.

[0039] S3. Command generation rule derivation process: After the mobile terminal receives the infrared waveform data of the core anchor point command, it derives the command generation rule set and the corresponding confidence score through data field positioning and multi-point constraint solving; the command generation rule with the highest confidence score is sent to the panel; the panel calls the command generation rule to calculate and verify and generate the complete infrared command code.

[0040] High-performance rule derivation (mobile execution): After receiving the data, the App algorithm immediately executes the following algorithm: Data field location (differential analysis): The algorithm compares D1 and D2 bit by bit to accurately locate the position of the temperature code bit (PT).

[0041] PT={k∣(D1[k]⊕D2[k])≠0} Checksum Backfit (Multi-point Constraint Solution): The algorithm transforms the checksum derivation into solving a system of multi-point constraint equations, seeking a checksum function F that can simultaneously satisfy the three constraints C1, C2, and C3: F(Data1)=CS1, F(Data2)=CS2, F(Data3)=CS3, where Data1-Data3 are the original waveform data of C1-C3 respectively, and CS1-CS3 are the check fields in the original waveform data respectively.

[0042] The purpose of the above formula is to derive the generation rules for the special field (checksum) from the acquired waveform data. The algorithm executes a hierarchical fitting process (prioritizing XOR sums, cumulative sums, and composite operations with constants), allocates confidence scores, and generates an ultra-compressed instruction generation rule set R. Final .

[0043] Preferably, the method further includes: sending the instruction generation rule with the highest confidence score to the panel, guiding the user to execute the test instruction on the panel, and then receiving user feedback. If the user feedback indicates failure, the method automatically selects the instruction generation rule with the second highest confidence score and sends it back to the panel, guiding the user to perform the test again; until the user feedback indicates success.

[0044] Single Human-Machine Calibration Verification (Rule Derivation Path): The App guide panel executes a challenging test command (e.g., heating at 16°C). The App receives success / failure feedback from the user. If the user reports failure, the App algorithm automatically selects the rule R with the second highest confidence level. Next The data is then sent back to the panel, and the user is guided to perform a single test again until the user reports success.

[0045] Preferably, the method further includes: if all high-confidence instruction generation rules are reported as failures by the user (i.e., mathematical derivation fails), automatically starting the intelligent waveform copying process; waveform copying mode activation: the mobile terminal App sends an instruction to the panel to switch to waveform copying mode and prompts the user for the reason for switching to this mode.

[0046] Intelligent guidance and targeted learning: The app's algorithm utilizes the data bit information determined in the previous derivation to guide the user to learn only the most critical and missing waveforms, minimizing user operations. For example, it guides the user to learn the most frequently used control commands (such as turning on the cooling to 26°C) and the waveform Wn for turning off the device.

[0047] Waveform closed-loop verification: For each learned waveform Wn, the panel immediately transmits the learned waveform. The app requires the user to confirm whether the air conditioner has executed successfully. If the user reports success, the waveform Wn is marked as a valid waveform.

[0048] Waveform library storage and dynamic retrieval: The system stores the finally confirmed valid waveforms in the panel's low-capacity memory (waveform library) for use as static instructions.

[0049] Based on the same inventive concept, this application also provides a system corresponding to the method in Embodiment 1, as detailed in Embodiment 2. Embodiment 2

[0050] This embodiment provides a mobile-terminal collaborative infrared code dynamic generation and calibration system, as shown in Figure 3. It includes a panel and a mobile terminal, which perform the following interactive process: establishing a local data link between the panel and the mobile terminal; guiding the user to press at least three core anchor point commands sequentially on the panel, transmitting the infrared waveform data of the core anchor point commands to the mobile terminal via the local data link; after receiving the infrared waveform data of the core anchor point commands, the mobile terminal derives the command generation rule set and corresponding confidence scores through data field positioning and multi-point constraint solving; sends the command generation rule with the highest confidence score to the panel; and the panel calls the command generation rule to calculate and verify the result and generate a complete infrared command code.

[0051] Preferably, the mobile terminal is further configured to send the instruction generation rule with the highest confidence score to the panel, guide the user to execute the test instruction on the panel, and then receive user feedback. If the user feedback indicates failure, the mobile terminal automatically selects the instruction generation rule with the second highest confidence score and sends it back to the panel, and guides the user to perform the test again; until the user feedback indicates success.

[0052] Preferably, the mobile terminal is also used to automatically start the intelligent waveform copying process when all high-confidence instruction generation rules are reported as failures by the user; the intelligent waveform copying process is as follows: using the data bit information obtained during the instruction generation rule derivation process, the user is guided to learn and store the key waveforms for use as static instructions.

[0053] Preferably, the panel is also used to immediately transmit each key waveform after it has been learned and to ask the user to confirm whether the execution was successful. If the user reports success, the waveform is marked as valid.

[0054] Preferably, establishing a local data link between the panel and the mobile terminal specifically includes: after the user initiates the configuration, the panel generates the network name and password required for local connection and displays the network name and password via a QR code; the mobile terminal scans the QR code displayed on the panel, parses and obtains the network name and password, creates a Wi-Fi SoftAP hotspot, and the panel's Wi-Fi module connects to the SoftAP, thereby establishing a local data link with the mobile terminal.

[0055] Since the system described in Embodiment 2 of this invention is a system used to implement the method of Embodiment 1 of this invention, those skilled in the art can understand the specific structure and variations of this system based on the method described in Embodiment 1 of this invention, and therefore will not be repeated here. All systems used in the method of Embodiment 1 of this invention fall within the scope of protection of this invention.

[0056] The technical solution provided in this embodiment of the invention has at least the following technical effects: 1. A revolutionary improvement in configuration efficiency. Existing technologies require users to perform more than 100 button operations, taking up to 20 to 30 minutes. This invention simplifies user operation to only three core inputs through three-point anchoring command acquisition. Combined with offline computing of high-performance mobile terminals, the entire configuration process is shortened to less than 2 minutes. This improvement completely solves the user experience pain point of infrared code configuration and significantly improves the installation and deployment efficiency of smart home products.

[0057] 2. High accuracy is ensured in offline environments. By transferring complex verification and backfitting tasks to a high-performance local mobile terminal, the dependence on external internet connectivity is effectively overcome. Based on raw data collected on-site, the algorithm transforms verification and derivation into solving multi-point constraint equations, ensuring the mathematical rigor of the generated rules. This architecture not only guarantees the feasibility of offline operation but also solves the technical challenge of confirming command accuracy in the absence of reliable sensor feedback through a human-machine collaborative calibration mechanism.

[0058] 3. Hardware costs and storage efficiency have been optimized. Ultra-compressed rule set storage is used instead of traditional static waveform storage. This means the central control panel only needs to store tens of bytes of rule parameters, significantly reducing the capacity requirements of non-volatile memory (Flash) and effectively lowering hardware material costs. Simultaneously, the panel can dynamically calculate instruction codes in real time, avoiding delays in code table lookups and improving the real-time performance of instruction issuance.

[0059] 4. The system boasts strong technological barriers and compatibility advantages. Its "Mobile SoftAP Collaboration" architecture cleverly avoids dependence on on-site routers. More importantly, a dual-path closed-loop mechanism—"rule derivation first, intelligent waveform replication as a fallback"—ensures final configuration convergence. The derived rules exhibit strong resistance to protocol variations; and when rules fail, the intelligent waveform replication mode can complete the configuration of even the most complex protocols with minimal user cost, ensuring product availability in any complex protocol environment.

[0060] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0061] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in one or more blocks of the flowchart illustrations and / or one or more blocks of the block diagrams.

[0062] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means that implement the functions specified in one or more flowcharts and / or one or more block diagrams.

[0063] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, such that the instructions, which execute on the computer or other programmable apparatus, provide steps for implementing the functions specified in one or more flowcharts and / or one or more block diagrams.

[0064] While specific embodiments of the present invention have been described above, those skilled in the art should understand that the specific embodiments described are merely illustrative and not intended to limit the scope of the present invention. Equivalent modifications and variations made by those skilled in the art in accordance with the spirit of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for dynamic generation and calibration of infrared codes in collaboration with mobile terminals, characterized in that, include: Local data link establishment process: Establish a local data link between the panel and the mobile terminal; Anchor point command acquisition process: Guide the user to press at least three core anchor point commands sequentially on the panel, and transmit the infrared waveform data of the core anchor point commands to the mobile terminal via the local data link; Command generation rule derivation process: After the mobile terminal receives the infrared waveform data of the core anchor point commands, it derives the command generation rule set and corresponding confidence scores through data field positioning and multi-point constraint solving; The command generation rule with the highest confidence score is sent to the panel; The panel calls the command generation rule to calculate and verify and generate a complete infrared command code.

2. The method according to claim 1, characterized in that, The method further includes: sending the instruction generation rule with the highest confidence score to the panel, guiding the user to execute the test instruction on the panel, and then receiving user feedback. If the user feedback indicates failure, the instruction generation rule with the second highest confidence score is automatically selected and sent back to the panel, and the user is guided to perform the test again; until the user feedback indicates success.

3. The method according to claim 2, characterized in that, The method further includes: if all high-confidence instruction generation rules are reported as failures by the user, the intelligent waveform copying process is automatically started; the intelligent waveform copying process is: using the data bit information obtained during the instruction generation rule derivation process, the user is guided to learn and store the key waveforms for use as static instructions.

4. The method according to claim 3, characterized in that, The method further includes: for each key waveform learned, the panel immediately transmits the key waveform and asks the user to confirm whether the execution was successful. If the user reports success, it is marked as a valid waveform.

5. The method according to claim 1, characterized in that: The local data link establishment process includes: after the user starts the configuration, the panel generates the network name and password required for local connection and displays the network name and password via a QR code; the user scans the QR code displayed on the panel with a mobile terminal, parses and obtains the network name and password, creates a Wi-Fi SoftAP hotspot, and the panel's Wi-Fi module connects to the SoftAP, thereby establishing a local data link with the mobile terminal.

6. A mobile-terminal collaborative infrared code dynamic generation and calibration system, characterized in that, The system includes a control panel and a mobile terminal, which are used to perform the following interactive process: establishing a local data link between the control panel and the mobile terminal; guiding the user to press at least three core anchor point commands sequentially on the control panel, and transmitting the infrared waveform data of the core anchor point commands to the mobile terminal through the local data link; after receiving the infrared waveform data of the core anchor point commands, the mobile terminal derives the command generation rule set and the corresponding confidence score by locating data fields and solving multi-point constraints; sending the command generation rule with the highest confidence score to the control panel; and the control panel calls the command generation rule to calculate and verify and generate a complete infrared command code.

7. The system according to claim 6, characterized in that: The mobile terminal is also used to send the instruction generation rule with the highest confidence score to the panel, guide the user to execute the test command on the panel, and then receive user feedback. If the user feedback is failure, it automatically selects the instruction generation rule with the second highest confidence score and sends it back to the panel, and guides the user to perform the test again; until the user feedback is success.

8. The system according to claim 7, characterized in that: The mobile terminal is also used to automatically start the intelligent waveform copying process when all high-confidence instruction generation rules are reported as failures by the user. The intelligent waveform copying process is as follows: using the data bit information obtained during the instruction generation rule derivation process, the user is guided to learn and store the key waveforms for use as static instructions.

9. The system according to claim 8, characterized in that: The panel is also used to immediately transmit each key waveform after it has been learned and to ask the user to confirm whether the execution was successful. If the user reports success, the waveform is marked as valid.

10. The system according to claim 6, characterized in that, Establishing a local data link between the panel and the mobile terminal specifically includes: after the user starts the configuration, the panel generates the network name and password required for local connection and displays the network name and password via a QR code; the mobile terminal scans the QR code displayed on the panel, parses and obtains the network name and password, creates a Wi-Fi SoftAP hotspot, and the panel's Wi-Fi module connects to the SoftAP, thereby establishing a local data link with the mobile terminal.

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