A method and system for preventing crosstalk of a remote controller and a remote controller

Abstract

The present invention relates to the field of remote control technology, and discloses a method, a system and a remote control for preventing crosstalk of remote controls. The method includes: dividing sub-channels for a target frequency band to obtain a target frequency hopping sequence, and constructing a frequency hopping model according to the target frequency hopping sequence; segmenting and encoding and combining control instructions to obtain a plurality of sub-data packets, and calculating an encoding dispersion scheme for the plurality of sub-data packets; performing multi-combination analysis on the frequency hopping model and the encoding dispersion scheme to obtain an encoding feature library; extracting features and performing pattern matching on a received signal, and comparing the received signal with the encoding feature library in combination to obtain an identification result and a reconstructed signal; optimizing the encoding dispersion scheme and the identification result through a neural network and generating an adaptive encoding strategy; in a multi-remote control environment, evaluating the anti-crosstalk performance of the adaptive encoding strategy and the reconstructed signal to obtain an anti-crosstalk performance evaluation report. The present invention improves the anti-interference ability of the remote control.

Description

Technical Field

[0001] The present invention relates to the technical field of remote control, and particularly to a method and system for preventing crosstalk of a remote control and a remote control. Background Art

[0002] With the rapid development of smart home and Internet of Things technologies, remote controls, as important human-computer interaction devices, have been widely used in daily life. However, with the complexity of usage scenarios and the increase in the number of remote control devices, the problem of signal interference between remote controls has become increasingly prominent. Traditional fixed-frequency or simple frequency-hopping technologies are difficult to meet the anti-interference requirements in a multi-device environment, and problems such as frequent crosstalk and misoperations often occur, seriously affecting the user experience and system reliability.

[0003] At the same time, most of the existing crosstalk prevention technologies rely on hardware upgrades or complex signal processing algorithms, which are costly and difficult to be deployed on a large scale on existing devices. In addition, these methods often lack the ability to adapt to dynamic environments and are difficult to cope with complex and changeable interference scenarios. Summary of the Invention

[0004] The present invention provides a method and system for preventing crosstalk of a remote control and a remote control, which are used to improve the anti-interference ability of the remote control.

[0005] In a first aspect, the present invention provides a method for preventing crosstalk of a remote control, and the method for preventing crosstalk of the remote control includes:

[0006] Dividing the target frequency band into sub-channels to obtain a target frequency hopping sequence, and constructing a frequency hopping model according to the target frequency hopping sequence;

[0007] Segmenting and encoding and combining control instructions to obtain a plurality of sub-packets, and calculating an encoding dispersion scheme for the plurality of sub-packets through a time dispersion algorithm;

[0008] Performing multi-combination analysis on the frequency hopping model and the encoding dispersion scheme to obtain an encoding feature library;

[0009] Extracting features and performing pattern matching on the received signal, and comparing with the encoding feature library to obtain an identification result and a reconstructed signal;

[0010] Optimizing the encoding dispersion scheme and the identification result through a neural network, and generating an adaptive encoding strategy through a double time-scale convergence-differential model;

[0011] In a multi-remote control environment, evaluating the anti-crosstalk performance of the adaptive encoding strategy and the reconstructed signal to obtain an anti-crosstalk performance evaluation report.

[0012] In a second aspect, the present invention provides a remote control anti-crosstalk system, and the remote control anti-crosstalk system includes:

[0013] A construction module, configured to divide sub-channels for a target frequency band to obtain a target frequency hopping sequence, and construct a frequency hopping model according to the target frequency hopping sequence;

[0014] A calculation module, configured to segment and encode-combine control instructions to obtain a plurality of sub-packets, and calculate an encoding dispersion scheme for the plurality of sub-packets through a time dispersion algorithm;

[0015] An analysis module, configured to perform multi-combination analysis on the frequency hopping model and the encoding dispersion scheme to obtain an encoding feature library;

[0016] A comparison module, configured to extract features and perform pattern matching on a received signal, and perform comparison in combination with the encoding feature library to obtain an identification result and a reconstructed signal;

[0017] An optimization module, configured to perform neural network optimization on the encoding dispersion scheme and the identification result, and generate an adaptive encoding strategy through a dual time-scale convergence-differential model;

[0018] An evaluation module, configured to evaluate the anti-crosstalk performance of the adaptive encoding strategy and the reconstructed signal in a multi-remote control environment to obtain an anti-crosstalk performance evaluation report.

[0019] In a third aspect of the present invention, there is provided a remote control, and the remote control is used to execute the above-mentioned remote control anti-crosstalk method.

[0020] In a fourth aspect of the present invention, there is provided a computer-readable storage medium, and instructions are stored in the computer-readable storage medium. When the instructions run on a computer, the computer is enabled to execute the above-mentioned remote control anti-crosstalk method.

[0021] In the technical solution provided by the present invention, by dividing sub-channels and dynamically hopping frequencies for the target frequency band, this method can utilize limited spectrum resources more efficiently, significantly improving spectrum utilization and system capacity. By adopting a frequency hopping model and a coding dispersion scheme, the signal transmission has stronger randomness and dispersion, greatly reducing the probability of being interfered with and intercepted. Through neural network optimization and a dual-time-scale convergence-differential model, the system can automatically adjust the coding strategy according to environmental changes, achieving dynamic adaptation to complex interference environments. Combining feature extraction, pattern matching, and a coding feature library, this method can quickly and accurately identify target signals and reconstruct them in a complex multi-signal environment. By constructing a multi-remote control simulation environment and a multi-index evaluation mechanism, it provides a quantitative basis for system optimization, facilitating continuous improvement and performance enhancement. By adopting a population exploration neural dynamics network, while improving the anti-crosstalk performance, it effectively controls the computational complexity, achieving a good balance between performance and resource consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following described drawings are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0023] Figure 1 It is a schematic flowchart of the method for preventing crosstalk of the remote control provided by the embodiment of the present application;

[0024] Figure 2 It is a schematic block diagram of the structure of the remote control anti-crosstalk system provided by the embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0025] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the protection scope of the present invention.

[0026] The flowcharts shown in the drawings are only illustrative examples, not necessarily including all contents and operations / steps, nor necessarily executed in the described order. For example, some operations / steps can also be decomposed, combined, or partially merged. Therefore, the actual execution order may change based on the actual situation.

[0027] It should also be understood that the terms used in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application. As used in the specification of this application and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include the plural forms.

[0028] It should be further understood that the term "and / or" used in the specification of this application and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0029] The following will, with reference to the accompanying drawings, elaborate on some embodiments of this application. Without conflict, the following embodiments and the features in the embodiments may be combined with each other.

[0030] Please refer to Figure 1 , Figure 1 which is a schematic flowchart of the remote control anti-interference method provided by the embodiment of this application. As Figure 1 shown, the remote control anti-interference method provided by the embodiment of this application includes steps S100 to S600.

[0031] Step S100: Divide the target frequency band into sub-channels to obtain a target frequency hopping sequence, and construct a frequency hopping model according to the target frequency hopping sequence;

[0032] It can be understood that the execution subject of the present invention can be a remote control anti-interference system, or a terminal or a server. Specifically, it is not limited here. The embodiment of the present invention will be described by taking the server as the execution subject as an example.

[0033] Specifically, based on the total bandwidth of the frequency band and the preset number of sub-channels, the bandwidth of the target frequency band is analyzed to obtain the bandwidth value of a single sub-channel. By analyzing the bandwidth of the target frequency band, the bandwidth size of each sub-channel is determined, so that the target frequency band can be evenly divided into multiple sub-channels. According to the bandwidth value of a single sub-channel, the target frequency band is evenly divided to obtain multiple sub-channels, and the sub-channels are numbered to form a sub-channel index set. According to the preset pseudo-random number generation algorithm and seed value, the sub-channel index set is randomly sorted to generate an initial frequency hopping sequence. The random sorting method ensures the randomness and unpredictability during the frequency hopping process, and improves the anti-interference effect. The initial frequency hopping sequence is analyzed for periodicity, and according to the preset repetition period threshold, the sequence length is adjusted to obtain a periodic frequency hopping sequence. Through periodic analysis and adjustment, the overly frequent repetitions in the frequency hopping sequence are avoided, and the complexity and security of the sequence are increased. The periodic frequency hopping sequence is analyzed for uniformity, the usage frequency of each sub-channel is calculated to obtain the channel usage distribution. Through uniformity analysis, it is ensured that each sub-channel can be reasonably used during the frequency hopping process, and the overuse or underuse of certain sub-channels is avoided. According to the channel usage distribution, the sub-channels with usage frequencies lower than the threshold are supplemented to obtain a homogenized frequency hopping sequence, ensuring the uniformity of the frequency hopping sequence, so that each sub-channel has a relatively balanced usage opportunity during the hopping process. The entropy value of the homogenized frequency hopping sequence is calculated to obtain a sequence complexity index, and according to the sequence complexity index, the sequence segments with entropy values lower than the threshold are regenerated to obtain the target frequency hopping sequence. Through entropy value calculation, the complexity of the frequency hopping sequence is evaluated to ensure its effectiveness in anti-interference. If the entropy values of some sequence segments are too low, it means that their complexity is insufficient and they are easily predicted and attacked, so they need to be regenerated to improve the complexity of the entire sequence. According to the target frequency hopping sequence, a time-frequency mapping relationship is established, and a frequency hopping model including hopping rules and a time synchronization mechanism is constructed. Through the time-frequency mapping relationship, the hopping frequency corresponding to each time period is clarified to ensure the orderliness and controllability of the frequency hopping. At the same time, the introduction of hopping rules and a time synchronization mechanism ensures that in a multi-remote control environment, the frequency hopping between remote controls can be coordinated and consistent, avoiding mutual interference, thereby realizing the anti-interference function of the remote control.

[0034] Step S200: Segment and encode and combine the control instructions to obtain multiple sub-data packets, and calculate the encoding dispersion scheme of the multiple sub-data packets through the time dispersion algorithm;

[0035] Specifically, according to the preset maximum sub - data - packet length, the length of the control instruction is analyzed to obtain the number of segments. By analyzing the length of the control instruction, it is segmented into multiple sub - data - packets such that the length of each sub - data - packet does not exceed the preset maximum sub - data - packet length. According to the number of segments, the control instruction is equally divided to obtain multiple instruction segments, and the unique identifier of the remote control is hashed, and the hash value is used as the identification code. According to the identification code and multiple instruction segments, interleaving coding is performed to obtain the initial sub - data - packet sequence. Interleaving coding improves the anti - interference ability of the data - packet by interleaving and combining the identification code and instruction segments, ensuring that even if some sub - data - packets are lost or in error during transmission, they can be effectively reconstructed through other data - packets. The initial sub - data - packet sequence is analyzed for redundancy, and according to the preset error - correction ability requirements, the number of parity bits to be added is calculated to obtain the parity - bit length. According to the parity - bit length, cyclic redundancy check (CRC) coding is performed on each sub - data - packet to obtain the sub - data - packet sequence with checksums. Cyclic redundancy check coding is a commonly used error - correction coding method that detects and corrects errors occurring during transmission by adding parity bits. Timestamp coding is performed on the sub - data - packet sequence with checksums, embedding the current time information into each sub - data - packet to obtain the sub - data - packet sequence with timestamps. Timestamp coding ensures the order and timeliness of the data - packets by embedding the current time information into each sub - data - packet, avoiding out - of - order or re - transmission problems of the data - packets during transmission. According to the preset pseudo - random sequence generation algorithm, a time - dispersion sequence is calculated to obtain the transmission order of the sub - data - packets. The pseudo - random sequence generation algorithm calculates a pseudo - random transmission order of the sub - data - packets, ensuring the time - dispersion of each sub - data - packet during transmission and reducing signal conflicts and interference. According to the transmission order of the sub - data - packets and the frequency - hopping model, the transmission time and corresponding frequency of each sub - data - packet are calculated to obtain a time - frequency allocation scheme. By combining the transmission order of the sub - data - packets and the frequency - hopping model, the transmission method of each sub - data - packet at a specific time point and frequency is determined, ensuring reliable signal transmission and effective anti - crosstalk. According to the time - frequency allocation scheme, the sub - data - packet sequence with timestamps is sorted and scheduled to obtain an encoding - dispersion scheme. The sorting and scheduling steps sort and schedule the sub - data - packet sequence with timestamps according to the predetermined time and frequency to form the final encoding - dispersion scheme, realizing the segmentation and encoding combination of the control instruction and ensuring the anti - crosstalk effect of the signal in a multi - remote - control environment.

[0036] Step S300: Perform multi - combination analysis on the frequency - hopping model and the encoding - dispersion scheme to obtain an encoding feature library;

[0037] Specifically, parameter extraction is performed on the frequency hopping model to obtain a set of key parameters, which includes the hopping period, the number of sub-channels, and the hopping sequence. Based on the set of key parameters, multiple different variants of the frequency hopping model are generated to obtain a frequency hopping model set, ensuring adaptability and anti-interference ability in different environments. Parameter extraction is performed on the coding dispersion scheme to obtain a set of characteristic parameters, which includes the number of segments, the length of the parity bit, and the time dispersion sequence. Based on the set of characteristic parameters, multiple different variants of the coding dispersion scheme are generated to obtain a coding dispersion scheme set, so as to effectively disperse and encode signals in different signal environments. An orthogonal combination of the frequency hopping model set and the coding dispersion scheme set is performed to obtain a multi-dimensional combination scheme matrix, and based on the multi-dimensional combination scheme matrix, a simulation environment is constructed to perform signal transmission simulation for each combination, obtaining a simulation result data set. Through the construction of the simulation environment and the simulation of a large number of combination schemes, the performance of each combination in actual signal transmission is comprehensively evaluated. A multi-index evaluation is performed on the simulation result data set to obtain a performance scoring table, and based on the performance scoring table, all combination schemes are sorted, and the top N combinations are selected to obtain a target combination set. For each combination in the target combination set, a feature vector including frequency hopping parameters and coding dispersion parameters is extracted to obtain a feature vector set, and based on the feature vector set and the corresponding performance scores, an index structure and a query mechanism are constructed to obtain a coding feature library. By combining the feature vector set and the performance scores, an efficient index structure and query mechanism are established to ensure that the best coding scheme and frequency hopping model can be quickly found in practical applications.

[0038] Step S400: Extract the features and perform pattern matching on the received signal, and compare it with the coding feature library to obtain an identification result and a reconstructed signal;

[0039] Specifically, perform a fast Fourier transform on the received signal to obtain a frequency-domain representation, and analyze the signal energy distribution based on the frequency-domain representation to obtain an active sub-channel set. By performing a fast Fourier transform, the time-domain signal is converted into a frequency-domain signal, which is easier to analyze the frequency components and energy distribution of the signal and helps to identify active sub-channels. Perform a timing analysis on the active sub-channel set to obtain a frequency hopping pattern, and demodulate the received signal according to the frequency hopping pattern to obtain a time-domain data stream. Through timing analysis, the frequency hopping rule of the signal is identified, so as to correctly demodulate the time-domain data stream. According to the preset sub-packet length, segment the time-domain data stream to obtain multiple data segments to be identified, and verify the data segments to be identified to obtain a set of valid data segments. Through segmentation and verification, each sub-packet in the time-domain data stream is accurately extracted, and its integrity and correctness are ensured. According to the set of valid data segments, extract the timestamp information and identification code to obtain a list of candidate remote controls, and calculate the similarity between each candidate in the list of candidate remote controls and the coding feature library according to the characteristics of each candidate to obtain a matching degree ranking. By extracting the timestamp information and identification code, the possible source of the remote control is initially determined, and the similarity calculation and matching degree ranking further accurately identify the specific remote control. According to the matching degree ranking, select the best-matched remote control feature to obtain the recognition result, and reorder and decode the set of valid data segments according to the coding dispersion scheme corresponding to the recognition result to obtain a reconstructed signal. By selecting the best-matched remote control feature, the source of the received signal is accurately determined, and by reordering and decoding according to the corresponding coding dispersion scheme, the original control instruction can be restored.

[0040] Step S500: Optimize the coding dispersion scheme and the recognition result by a neural network, and generate an adaptive coding strategy through a dual-time-scale convergence-differential model;

[0041] Specifically, parameterize the coding dispersion scheme to obtain a coding parameter vector, and extract environmental interference features according to the recognition result to obtain an interference feature vector. By parameterizing the coding dispersion scheme, it is transformed into a processable vector form, while the interference feature vector reflects various interference factors in the current environment. Concatenate the coding parameter vector and the interference feature vector to obtain a neural network input tensor, and construct a population exploration neural dynamics network according to the preset network structure to obtain an initial neural network model. By concatenating the coding parameters and the interference features, they are input into the neural network as a whole so that the network can consider these two aspects of factors simultaneously. The population exploration neural dynamics network is a network structure that can efficiently handle complex nonlinear problems and is suitable for optimizing coding strategies. Perform forward propagation calculation on the initial neural network model to obtain the activation value of the output layer, and construct a double-time-scale convergence-differential model according to the activation value of the output layer. The forward propagation calculation enables the network to calculate the output result based on the input tensor, and the activation value of the output layer reflects the response of the network to the current input. The double-time-scale convergence-differential model can more accurately describe the dynamic behavior of the system by combining changes on different time scales. Numerically solve the double-time-scale convergence-differential model to obtain the neuron state trajectory, and calculate the global optimal solution according to the neuron state trajectory to obtain the optimized coding parameters. The numerical solution process enables the model to determine the state changes of neurons at different time points, thereby finding the optimal combination of coding parameters. The global optimal solution ensures that the obtained coding parameters have the best performance in the current environment. Perform inverse mapping on the optimized coding parameters to obtain a new coding dispersion scheme, and generate an adaptive coding strategy according to the new coding dispersion scheme and the current environmental characteristics. Through inverse mapping, the optimized coding parameters are transformed into a specific coding dispersion scheme so that it can be directly used in practical applications. The adaptive coding strategy is dynamically generated according to the new coding dispersion scheme and the current environmental characteristics, ensuring that the system can adapt to environmental changes at any time and improve the anti-crosstalk effect.

[0042] Step S600: In a multi-remote control environment, evaluate the anti-crosstalk performance of the adaptive coding strategy and the reconstructed signal to obtain an anti-crosstalk performance evaluation report.

[0043] Specifically, according to the preset number and distribution pattern of remote controls, a multi-remote-control simulation environment is constructed to obtain a test scenario model, simulating the multi-remote-control operation scenario in actual applications, and conducting a comprehensive anti-interference performance evaluation. For each remote control in the test scenario model, different adaptive coding strategies are assigned to obtain a strategy assignment scheme, and according to the strategy assignment scheme, control instruction sequences for multiple remote controls are generated. Each remote control has a unique coding strategy and instruction sequence in the simulation environment, which helps to test the anti-interference effect in various situations. According to the reconstructed signal and the control instruction sequence, the signal recovery rate is calculated to obtain a recovery rate index. The signal recovery rate is a key indicator for measuring the anti-interference effect. By comparing the reconstructed signal and the original control instruction, the proportion of the signal correctly recovered during transmission is determined. Statistical analysis is performed on the recovery rate index, and its average value and variance are calculated to obtain a stability evaluation result. The average value reflects the overall recovery effect, while the variance indicates the fluctuation degree of the recovery rate. According to the timestamp information of the reconstructed signal, the signal transmission delay is calculated to obtain a real-time performance index. The signal transmission delay reflects the response speed of the system in a multi-remote-control environment. A lower delay means that the system can respond quickly and effectively reduce the impact of interference. At the same time, complexity analysis is performed on the adaptive coding strategy, and the computational amount of encoding and decoding is calculated to obtain a resource consumption index. The resource consumption index includes the computational resources and time required during the encoding and decoding processes. By analyzing these indexes, the efficiency and resource occupancy of the system during actual operation are evaluated. According to the stability evaluation result, the real-time performance index, and the resource consumption index, an anti-interference performance evaluation report is comprehensively generated. Through the comprehensive analysis of these indexes, the anti-interference effect of the system and its performance in actual applications are obtained.

[0044] In the embodiments of the present invention, by performing sub-channel division and dynamic frequency hopping on the target frequency band, this method can more efficiently utilize limited spectrum resources, significantly improving the spectrum utilization rate and system capacity. Adopting a frequency hopping model and an encoding dispersion scheme makes the signal transmission have stronger randomness and dispersion, greatly reducing the probability of being interfered with and intercepted. Through neural network optimization and a dual-time-scale convergence-differential model, the system can automatically adjust the coding strategy according to environmental changes, achieving dynamic adaptation to a complex interference environment. Combining feature extraction, pattern matching, and an encoding feature library, this method can quickly and accurately identify target signals and reconstruct them in a complex multi-signal environment. By constructing a multi-remote-control simulation environment and a multi-index evaluation mechanism, it provides a quantitative basis for system optimization, which is beneficial to continuous improvement and performance enhancement. Adopting a population exploration neural dynamics network effectively controls the computational complexity while improving the anti-interference performance, achieving a good balance between performance and resource consumption.

[0045] In a specific embodiment, the process of executing step S100 may specifically include the following steps:

[0046] Perform bandwidth analysis on the target frequency band according to the total bandwidth of the frequency band and the preset number of sub-channels to obtain the bandwidth value of a single sub-channel;

[0047] According to the bandwidth value of a single sub-channel, evenly divide the target frequency band to obtain multiple sub-channels, and number the multiple sub-channels to obtain a sub-channel index set;

[0048] According to the preset pseudo-random number generation algorithm and seed value, randomly sort the sub-channel index set to obtain an initial frequency hopping sequence;

[0049] Perform periodic analysis on the initial frequency hopping sequence, and adjust the sequence length according to the preset repetition period threshold to obtain a periodic frequency hopping sequence;

[0050] Perform uniformity analysis on the periodic frequency hopping sequence, calculate the usage frequency of each sub-channel to obtain the channel usage distribution, and supplement the sub-channels with usage frequencies lower than the threshold according to the channel usage distribution to obtain a uniform frequency hopping sequence;

[0051] Calculate the entropy value of the uniform frequency hopping sequence to obtain a sequence complexity index, and regenerate the sequence segments with entropy values lower than the threshold according to the sequence complexity index to obtain a target frequency hopping sequence;

[0052] According to the target frequency hopping sequence, establish a time-frequency mapping relationship and construct a frequency hopping model including hopping rules and time synchronization mechanisms.

[0053] Specifically, to determine the bandwidth value of a single sub-channel. Let the total bandwidth of the target frequency band be B, and the preset number of sub-channels be N. Then the bandwidth value W of a single sub-channel can be calculated by the formula:

[0054]

[0055] where B represents the total bandwidth of the target frequency band, and N represents the number of sub-channels. Through the formula, the entire frequency band is evenly divided into multiple sub-channels to ensure that the bandwidth of each sub-channel is the same. According to the bandwidth value of a single sub-channel, evenly divide the target frequency band to obtain multiple sub-channels, and number these sub-channels to form a sub-channel index set. Let the target frequency band be [f min , f max , and the total bandwidth is B = f max - f min . Through even division, the frequency band is divided into N sub-channels, and the frequency range of each sub-channel is [f min + (i - 1)W, f min+iW], where i represents the number of the sub-channel, ranging from 1 to N. At this time, the set of sub-channel indices is {1, 2, …, N}. To increase the randomness and unpredictability of frequency hopping, according to a preset pseudo-random number generation algorithm and seed value, the set of sub-channel indices is randomly sorted to obtain an initial frequency hopping sequence. The seed value of the pseudo-random number generation algorithm ensures that the randomly generated sequence is consistent under the same conditions. Let the pseudo-random number generation algorithm be PRNG(seed), the seed value be seed, and the initial frequency hopping sequence after random sorting be shuffle({1, 2, …, N}, PRNG(seed)). Perform a periodic analysis on the initial frequency hopping sequence to check the periodicity of frequency hopping in the sequence. If there are too many repeated frequency hops in the sequence, according to a preset repetition period threshold T, adjust the sequence length to obtain a periodic frequency hopping sequence. By counting the intervals at which each sub-channel appears in the sequence, ensure that it meets the preset period requirements. If the interval at which a certain sub-channel appears is less than the threshold T, then the sequence needs to be readjusted to increase the sequence length until the appearance intervals of all sub-channels meet the requirements. Perform a uniformity analysis on the periodic frequency hopping sequence, calculate the usage frequency of each sub-channel, and obtain the channel usage distribution. Let the number of times sub-channel i appears in the sequence be n i , the total length of the sequence is L, then the usage frequency of sub-channel i is:

[0056]

[0057] By calculating the usage frequencies of all sub-channels, obtain the channel usage distribution. If it is found that the usage frequencies of some sub-channels are lower than a preset frequency threshold f min , then these sub-channels need to be supplemented so that the usage frequencies of all sub-channels are within a reasonable range to obtain a homogenized frequency hopping sequence. Calculate the entropy value of the homogenized frequency hopping sequence to measure the complexity of the sequence. Let the usage probability of each sub-channel in the homogenized frequency hopping sequence be p i , then the entropy value H of the sequence can be calculated by the formula:

[0058]

[0059] where p i = f i is the usage frequency of sub-channel i. The entropy value H reflects the complexity of the sequence. The higher the entropy value, the stronger the randomness and unpredictability of the sequence. If the entropy value is lower than a preset threshold H min, it is necessary to regenerate the sequence segments with entropy values lower than the threshold to increase the complexity of the sequence and obtain the target frequency hopping sequence. Based on the target frequency hopping sequence, a time-frequency mapping relationship is established, and a frequency hopping model including hopping rules and a time synchronization mechanism is constructed. The time-frequency mapping relationship can be realized by mapping each sub-channel in the frequency hopping sequence to a specific time period. Let the frequency hopping sequence be {c 1 , c 2 , …, c L}, and the time period corresponding to each sub-channel c i is [t i-1 , t i , where t i represents the time point i. The hopping frequency within each time period hops according to the preset hopping rules, so as to ensure the reliable transmission of the signal and the anti-interference effect. The time synchronization mechanism can be realized by synchronizing the clock signal to ensure that all devices perform frequency hopping at the same time point.

[0060] In a specific embodiment, the process of executing step S200 may specifically include the following steps:

[0061] According to the preset maximum sub-packet length, analyze the length of the control instruction to obtain the number of segments;

[0062] According to the number of segments, equally divide the control instruction to obtain multiple instruction segments, and perform hash processing on the unique identifier of the remote control, and use the hash value as the identification code;

[0063] Perform interleaving coding according to the identification code and multiple instruction segments to obtain the initial sub-packet sequence;

[0064] Perform redundancy analysis on the initial sub-packet sequence, and calculate the number of parity bits to be added according to the preset error correction ability requirement to obtain the parity bit length;

[0065] According to the parity bit length, perform cyclic redundancy check coding on each sub-packet to obtain the sub-packet sequence with check;

[0066] Perform timestamp coding on the sub-packet sequence with check, embed the current time information into each sub-packet to obtain the sub-packet sequence with timestamp;

[0067] Calculate the time dispersion sequence according to the preset pseudo-random sequence generation algorithm to obtain the sub-packet transmission order;

[0068] According to the sub-packet transmission order and the frequency hopping model, calculate the sending time and corresponding frequency of each sub-packet to obtain the time-frequency allocation scheme;

[0069] According to the time-frequency allocation scheme, sort and schedule the timestamped sub-packet sequence to obtain the encoded dispersion scheme.

[0070] Specifically, determine the number of segments. Let the total length of the control instruction be L, and the preset maximum length of a sub-packet be M. Then the number of segments N can be calculated by the formula:

[0071]

[0072] where L represents the total length of the control instruction, M represents the maximum length of each sub-packet, and 「·| represents the ceiling operation. Through this formula, the control instruction is evenly divided into several sub-packets, and the length of each sub-packet does not exceed the preset maximum length. The control instruction is equally divided according to the number of segments to obtain multiple instruction segments. The control instruction is divided into segments of M characters each. If the length of the last segment is less than M, specific characters can be filled at its end to achieve a unified length, obtaining multiple equally long instruction segments. Hash process the unique identifier of the remote control, and use the hash value as the identification code. Assume the unique identifier of the remote control is ID. Then its hash value H(ID) can be generated using a standard hash algorithm (such as SHA-256) and used as the identification code to ensure that each remote control has a unique identifier during transmission. According to the identification code and multiple instruction segments, perform interleaved coding to obtain the initial sub-packet sequence. Interleaved coding interleaves the identification code and instruction segments so that each sub-packet contains part of the identification code and instruction segments, increasing the anti-interference ability of the data. After the identification code is divided into fixed-length segments, it is alternately arranged with the instruction segments to form a new data sequence. Then perform redundancy analysis on the initial sub-packet sequence, and according to the preset error correction ability requirement, calculate the number of parity bits to be added to obtain the parity bit length. Let the length of the sub-packet be m, and the error correction ability requirement be r. Then the number of parity bits k can be calculated by the formula Perform calculations. Determine the number of parity bits to be added to each sub-packet through a formula to meet the preset error correction capability requirements. According to the parity bit length, perform cyclic redundancy check (CRC) encoding on each sub-packet to obtain a sequence of sub-packets with checks. Cyclic redundancy check (CRC) is a commonly used error correction coding method. By adding parity bits at the end of each sub-packet, errors during transmission can be detected and corrected. Specifically, each sub-packet is CRC-encoded to obtain a sequence of sub-packets with checks. Perform timestamp encoding on the sequence of sub-packets with checks, embed the current time information into each sub-packet to obtain a sequence of sub-packets with timestamps. Timestamp encoding ensures the sequentiality and timeliness of data packets by embedding the current time information into each sub-packet. After encoding the current time information in a fixed format, it is added to the end of each sub-packet. Calculate the time dispersion sequence according to the preset pseudo-random sequence generation algorithm to obtain the transmission order of sub-packets. The pseudo-random sequence generation algorithm can generate a pseudo-random sequence. By sorting this sequence, the transmission order of each sub-packet is determined. Let the pseudo-random sequence generation algorithm be PRNG(seed), and the seed value be seed. Then the transmission order of sub-packets is shuffle({1,2,…,N}, PRNG(seed)). According to the transmission order of sub-packets and the frequency hopping model, calculate the transmission time and corresponding frequency of each sub-packet to obtain a time-frequency allocation scheme. Each sub-packet is assigned to different time points according to the transmission order, and the transmission frequency at each time point is determined according to the frequency hopping model. Let the transmission time interval be T and the frequency hopping model be Freq(t). Then the transmission time and corresponding frequency of sub-packet i are t i = i × T, f i = Freq(t i ). According to the time-frequency allocation scheme, sort and schedule the sequence of sub-packets with timestamps to obtain an encoding dispersion scheme.

[0073] In a specific embodiment, the process of executing step S300 may specifically include the following steps:

[0074] Generate multiple different variants of the frequency hopping model according to the set of key parameters to obtain a set of frequency hopping models;

[0075] Extract parameters from the encoding dispersion scheme to obtain a set of characteristic parameters, which includes the number of segments, the parity bit length, and the time dispersion sequence;

[0076] Generate multiple different variants of the encoding dispersion scheme according to the set of characteristic parameters to obtain a set of encoding dispersion schemes;

[0077] Orthogonally combine the frequency hopping model set and the coding dispersion scheme set to obtain a multi-dimensional combination scheme matrix. According to the multi-dimensional combination scheme matrix, construct a simulation environment, perform signal transmission simulation for each combination, and obtain a simulation result data set;

[0078] Perform multi-index evaluation on the simulation result data set to obtain a performance score table. According to the performance score table, sort all combination schemes, select the top N combinations, and obtain a target combination set;

[0079] For each combination in the target combination set, extract the feature vector including the frequency hopping parameter and the coding dispersion parameter to obtain a feature vector set. According to the feature vector set and the corresponding performance score, construct an index structure and a query mechanism to obtain a coding feature library.

[0080] Specifically, analyze the key parameter set. Assume that the key parameter set includes the hopping period T, the number of sub-channels N, and the hopping sequence S. By different combinations of these parameters, multiple groups of different frequency hopping model variants are generated. Adjust the hopping period T within a certain range, for example, from 1 ms to 10 ms, increasing by 1 ms each time; adjust the number of sub-channels N, for example, from 5 to 20, increasing by 5 each time; randomly generate and adjust the hopping sequence S, for example, use different pseudo-random number generation seed values to generate different sequences. These combinations form a frequency hopping model set. Extract the parameters of the coding dispersion scheme to obtain a set of feature parameters. The set of feature parameters includes the number of segments D, the check bit length K, and the time dispersion sequence T s The number of segments D can be calculated according to the total length of the data packet and the maximum sub-data packet length. The check bit length K depends on the required error correction ability, and the time dispersion sequence T s is generated according to the pseudo-random number generation algorithm. Assume that the total data length is L and the maximum sub-data packet length is M, then the number of segments:

[0081]

[0082] The check bit length K is calculated according to the preset error correction ability requirement through the formula where r is the error correction ability. The time dispersion sequence T sIt can be obtained by setting different pseudo-random number generation seed values. According to the set of characteristic parameters, multiple groups of different coding dispersion scheme variants are generated. By different combinations of the number of segments, the length of the check bits, and the time dispersion sequence, multiple groups of coding dispersion schemes are generated. Suppose the number of segments ranges from 1 to 10, the length of the check bits ranges from 2 to 8, and the time dispersion sequence uses 5 different pseudo-random seed values for combination. Each combination will form a unique coding dispersion scheme, and a set of coding dispersion schemes is obtained. An orthogonal combination of the frequency hopping model set and the coding dispersion scheme set is performed to obtain a multi-dimensional combination scheme matrix. Let the frequency hopping model set be {F 1 , F 2 , …, F m}, and the coding dispersion scheme set be {E 1 , E 2 , …, E n}, then the multi-dimensional combination scheme matrix can be expressed as C =

[0083] {(F i , E j ) | 1 ≤ i ≤ m, 1 ≤ j ≤ n}. According to the multi-dimensional combination scheme matrix, a simulation environment is constructed, and signal transmission simulation is performed for each combination to obtain a simulation result data set. The simulation environment needs to simulate different interference scenarios and signal transmission conditions to evaluate the performance of each combination scheme. A multi-index evaluation is performed on the simulation result data set to obtain a performance score table. The multi-index evaluation includes indicators such as signal recovery rate, transmission delay, and resource consumption. The signal recovery rate R can be calculated by the formula:

[0084]

[0085] where N correct is the number of signals correctly recovered, and N total is the total number of signals. The transmission delay D can be obtained by calculating the difference between the reception time and the transmission time of each signal, and the resource consumption C is evaluated by calculating the computing resources and time used in the encoding and decoding processes. A performance score table is constructed according to the evaluation results, and all combination schemes are sorted. The top N combinations are selected to obtain a target combination set. For each combination in the target combination set, a feature vector including frequency hopping parameters and coding dispersion parameters is extracted to obtain a feature vector set. The feature vector set includes frequency hopping parameters {T, N, S} and coding dispersion parameters {D, K, T s}. Based on the set of feature vectors and the corresponding performance scores, an index structure and a query mechanism are constructed to obtain an encoded feature library. The index structure can use data structures such as inverted indexes or hash tables to quickly query encoding schemes that meet specific performance requirements. For example, assume there is a frequency band with a total bandwidth of 100 MHz, which is divided into 10 sub-channels, each with a bandwidth of 10 MHz. Let the hopping period T range from 1 ms to 10 ms, increasing by 1 ms each time, and the seed values of the pseudo-random number generation be {1001, 1002, 1003}, then multiple frequency hopping models can be generated. For the encoding dispersion scheme, assume the total data length is 200 bytes and the maximum sub-packet length is 50 bytes, then the number of segments D = 4. Assume the error correction ability r is 3, then the length of the parity bits Time dispersion sequence T s Generated using 3 different pseudo-random seed values. Through combinations of different parameters, multiple groups of encoding dispersion schemes are generated. In a simulation environment, simulate multiple remote controls working simultaneously and add interference signals of different intensities, conduct signal transmission simulations for each combination scheme, and record data such as signal recovery rate, transmission delay, and resource consumption. Assume that after simulation, the signal recovery rate of a certain combination scheme (F 3 , E 2 ) is 95%, the transmission delay is 5 ms, and the resource consumption is 10%, then record its score in the performance score table. According to the score table, sort all combination schemes, select the top 5 combinations, and form a target combination set. Extract the feature vectors from the target combination set, construct an index structure and a query mechanism, and form an encoded feature library.

[0086] In a specific embodiment, the process of executing step S400 may specifically include the following steps:

[0087] Perform a fast Fourier transform on the received signal to obtain a frequency-domain representation, and perform signal energy distribution analysis based on the frequency-domain representation to obtain a set of active sub-channels;

[0088] Perform timing analysis on the set of active sub-channels to obtain a frequency hopping pattern, and demodulate the received signal according to the frequency hopping pattern to obtain a time-domain data stream;

[0089] Segment the time-domain data stream according to a preset sub-packet length to obtain multiple data segments to be recognized, and perform verification on the data segments to be recognized to obtain a set of valid data segments;

[0090] Extract timestamp information and identification codes based on the set of valid data segments to obtain a list of candidate remote controls, and calculate the similarity between each candidate in the list of candidate remote controls and the encoded feature library according to the characteristics of each candidate to obtain a matching degree ranking;

[0091] Sort according to the matching degree, select the best-matching remote control features to obtain the recognition result, and reorder and decode the set of valid data segments according to the encoding dispersion scheme corresponding to the recognition result to obtain the reconstructed signal.

[0092] Specifically, perform a fast Fourier transform on the received signal to convert the time-domain signal into a frequency-domain signal. The formula for the fast Fourier transform is:

[0093]

[0094] where \(X(k)\) is the frequency-domain signal, \(x(n)\) is the time-domain signal, \(N\) is the length of the signal, and \(k\) is the frequency index. Through the fast Fourier transform, the amplitude and phase of the signal at different frequencies are obtained, forming a frequency-domain representation. Analyze the signal energy distribution based on the frequency-domain representation to determine the set of active sub-channels. By calculating the energy of each frequency component, identify the sub-channels on which the signal is more active. The formula for energy is:

[0095] \(E(k)=\vert X(k)\vert\) 2 ;

[0096] where \(E(k)\) is the energy at frequency \(k\), and \(\vert X(k)\vert\) is the amplitude of the frequency-domain signal. By setting an energy threshold, filter out the sub-channels with higher energy to form a set of active sub-channels. Perform a timing analysis on the set of active sub-channels to identify the frequency hopping pattern. The frequency hopping pattern reflects the frequency change law of the signal in different time periods. By analyzing these laws, determine the hopping sequence of the signal. According to the frequency hopping pattern, demodulate the received signal to obtain the time-domain data stream. The demodulation process includes converting the frequency-domain signal back to the time-domain signal and applying the hopping pattern for corresponding frequency correction. The demodulated time-domain data stream is segmented according to the preset sub-packet length to obtain multiple data segments to be recognized. Assume the sub-packet length is \(L\) p , then the time-domain data stream can be segmented every \(L\) pThe sampling points are divided into multiple segments. The data segments to be recognized are verified to determine their validity. Verification methods can use technologies such as cyclic redundancy check. By calculating the check code and comparing it with the received check code, the valid data segments are screened out. According to the set of valid data segments, the timestamp information and the recognition code are extracted. The timestamp information can record the reception time of each data segment. The recognition code can be obtained by hashing the unique identifier of the remote control. Assuming the unique identifier of the remote control is ID and the hash function is H, then the recognition code R = H(ID). After obtaining the timestamp information and the recognition code, a candidate remote control list is generated based on this information. Each candidate remote control has a set of characteristic parameters, including the timestamp and the recognition code. For each candidate item in the candidate remote control list, the similarity is calculated between the characteristics of each candidate item and the coding feature library to obtain a matching degree ranking. Similarity calculation methods can include Euclidean distance, cosine similarity, etc. Assuming the feature vector of the candidate item is V and the feature vector in the coding feature library is U, then the similarity S can be expressed as:

[0097]

[0098] where, · represents the vector inner product, and ∥·∥ represents the vector norm. According to the matching degree ranking, the best-matched remote control characteristics are selected to obtain the recognition result. The coding dispersion scheme corresponding to the recognition result is used to reorder and decode the set of valid data segments. Reordering restores the data segments to the correct order, and decoding converts the encoded data back to the original control instruction. Through the above steps, the reconstructed signal is finally obtained.

[0099] In a specific embodiment, the process of executing step S500 may specifically include the following steps:

[0100] The coding dispersion scheme is parameterized to obtain a coding parameter vector, and according to the recognition result, the environmental interference characteristics are extracted to obtain an interference feature vector;

[0101] The coding parameter vector and the interference feature vector are concatenated to obtain a neural network input tensor, and according to the preset network structure, a population exploration neural dynamics network is constructed to obtain an initial neural network model;

[0102] The initial neural network model is calculated by forward propagation to obtain the output layer activation value, and according to the output layer activation value, a double time-scale convergence-differential model is constructed;

[0103] The double time-scale convergence-differential model is numerically solved to obtain the neuron state trajectory, and according to the neuron state trajectory, the global optimal solution is calculated to obtain the optimized coding parameters;

[0104] Perform inverse mapping on the optimized coding parameters to obtain a new coding dispersion scheme, and generate an adaptive coding strategy based on the new coding dispersion scheme and the current environmental characteristics.

[0105] Specifically, parameterize the coding dispersion scheme to obtain a coding parameter vector. Assume that the coding dispersion scheme includes the number of segments D, the length of the parity bit K, and the time dispersion sequence T. s . Through parameterization, convert these parameters into vector form to obtain the coding parameter vector E = [D, K, T]. s . The purpose of parameterization is to convert the coding dispersion scheme into an input form that can be processed by a neural network. According to the recognition result, extract the environmental interference characteristics to obtain an interference feature vector. The environmental interference characteristics can include signal strength, the number of interference signals, the frequency of interference signals, etc. Assume that these characteristics include signal strength S. i , the number of interference signals N. i and the frequency of interference signals F. i , then the interference feature vector can be expressed as I = [S. i , N. i , F. i . Concatenate the coding parameter vector E and the interference feature vector I to obtain the neural network input tensor X = [E, I]. The concatenation operation can be achieved through vector connection, combining the coding parameters and interference characteristics into an overall input. According to the preset network structure, construct a population exploration neural dynamics network to obtain an initial neural network model. The population exploration neural dynamics network is a neural network structure that can efficiently handle complex non-linear optimization problems. By simulating the combination of swarm intelligence and neural dynamics, it uses information sharing and mutual cooperation among swarm members to explore the global optimal solution. Perform forward propagation calculation on the initial neural network model to obtain the activation value of the output layer. The process of forward propagation is to calculate the input tensor X layer by layer through the network, and finally obtain the result of the output layer. Assume that the activation value of the output layer of the neural network is 0, then the formula for forward propagation can be expressed as:

[0106] 0 = f(W:X + b);

[0107] where W represents the weight matrix of the network, b represents the bias vector, and f(:) represents the activation function. According to the activation value 0 of the output layer, construct a double time-scale convergence-differential model. The double time-scale convergence-differential model can more accurately describe the dynamic behavior of the system by combining changes on different time scales. Assume that the state variables of the double time-scale model are y and z, then its update formula can be expressed as:

[0108] y t+1 = y t + α(O - y t )

[0109] z t+1 = z t + β(y t - z t )

[0110] Where α and β are time scale factors representing the change rates at different time scales. Numerically solve the dual-time-scale convergence-differential model to obtain the neuron state trajectory, and calculate the global optimal solution based on the neuron state trajectory. The process of numerical solution is to iteratively calculate the changes of state variables y and z until convergence to a stable state. The global optimal solution can be found by analyzing the change trend of the neuron state trajectory to find the parameter combination that optimizes the system performance. Obtain the optimized coding parameter E*, perform inverse mapping on it, and convert the optimized parameter vector back to a specific coding scheme, including the number of segments, the length of the parity bit, and the time dispersion sequence, etc., to obtain a new coding dispersion scheme. Generate an adaptive coding strategy according to the new coding dispersion scheme and the current environmental characteristics. The adaptive coding strategy is a coding scheme dynamically adjusted according to the optimized parameters and the current environmental conditions to ensure that the system can maintain the best performance in various environments.

[0111] In a specific embodiment, the process of executing step S600 may specifically include the following steps:

[0112] Construct a multi-remote control simulation environment according to the preset number and distribution pattern of remote controls to obtain a test scenario model;

[0113] For each remote control in the test scenario model, assign different adaptive coding strategies to obtain a strategy assignment scheme, and generate a control instruction sequence for multiple remote controls according to the strategy assignment scheme;

[0114] Calculate the signal recovery rate according to the reconstructed signal and the control instruction sequence to obtain a recovery rate index, and perform statistical analysis on the recovery rate index to calculate the average value and variance to obtain a stability evaluation result;

[0115] Calculate the signal transmission delay according to the timestamp information of the reconstructed signal to obtain a real-time index, and perform complexity analysis on the adaptive coding strategy to calculate the computational amount of encoding and decoding to obtain a resource consumption index;

[0116] Comprehensively generate an anti-crosstalk performance evaluation report according to the stability evaluation result, the real-time index, and the resource consumption index.

[0117] Specifically, construct a multi-remote control simulation environment according to the preset number and distribution pattern of remote controls. Assume that the preset number of remote controls is N, and the position and distribution pattern of each remote control can be generated by random distribution or fixed pattern. Set the position (x i , y i), where \(i = 1, 2, \ldots, N\). In this way, a test scenario model simulating the real environment is obtained. For each remote control in the test scenario model, different adaptive coding strategies are assigned to obtain a strategy assignment scheme. The adaptive coding strategies can include parameters such as frequency hopping sequences, segment lengths, and parity bit lengths. Suppose there are \(M\) different adaptive coding strategies \(\{S 1 , S 2 , \ldots, S M \}\), then these strategies can be assigned to \(N\) remote controls in a cyclic or random assignment manner to form a strategy assignment scheme. According to the strategy assignment scheme, control instruction sequences for multiple remote controls are generated. Each control instruction sequence is encoded and segmented according to the corresponding coding strategy to form the final transmitted data. According to the reconstructed signal and the control instruction sequences, the signal recovery rate is calculated. The signal recovery rate \(R\) is an important indicator to measure the anti-interference crosstalk effect, representing the proportion of the received valid signal in the total transmitted signal. Statistical analysis is performed on the recovery rate index to calculate the average value and variance. The average value represents the overall recovery rate level, and the variance reflects the fluctuation degree of the recovery rate. Through these statistical analyses, the stability evaluation result of the system is obtained. According to the timestamp information of the reconstructed signal, the signal transmission delay is calculated. The signal transmission delay \(D\) is another key indicator, representing the time experienced by the signal from transmission to reception. Suppose the transmission time is \(t send \), and the reception time is \(t recv \), then the transmission delay \(D\) can be calculated by the formula \(D = t recv - t send \). Complexity analysis is performed on the adaptive coding strategies to calculate the computational workload of encoding and decoding, and the resource consumption index is obtained. The resource consumption index includes the computational resources and time required in the encoding and decoding processes. Through complexity analysis, the efficiency of each coding strategy in actual operation is evaluated. Combining the stability evaluation result, real-time index, and resource consumption index, an anti-interference crosstalk performance evaluation report is generated. The report details the performance of the system in different environments, including the signal recovery rate, transmission delay, and resource consumption. Through the comprehensive analysis of these indicators, the anti-interference crosstalk effect and overall performance of the system are comprehensively evaluated.

[0118] Please refer to Figure 2 , Figure 2 which is the structural schematic block diagram of the remote control anti-interference crosstalk system 200 provided by the embodiment of the present application. As shown in Figure 2 , the remote control anti-interference crosstalk system 200 includes:

[0119] A construction module 210, configured to divide sub-channels for a target frequency band to obtain a target frequency hopping sequence, and construct a frequency hopping model according to the target frequency hopping sequence;

[0120] A calculation module 220, configured to segment and encode and combine control instructions to obtain a plurality of sub-packets, and calculate an encoding dispersion scheme for the plurality of sub-packets through a time dispersion algorithm;

[0121] An analysis module 230, configured to perform multi-combination analysis on a frequency hopping model and an encoding dispersion scheme to obtain an encoding feature library;

[0122] A comparison module 240, configured to extract features and perform pattern matching on a received signal, and perform comparison in combination with the encoding feature library to obtain an identification result and a reconstructed signal;

[0123] An optimization module 250, configured to perform neural network optimization on the encoding dispersion scheme and the identification result, and generate an adaptive encoding strategy through a dual-time-scale convergence-differential model;

[0124] An evaluation module 260, configured to evaluate the anti-interference performance of the adaptive encoding strategy and the reconstructed signal in a multi-remote control environment to obtain an anti-interference performance evaluation report.

[0125] Through the collaborative cooperation of the above-mentioned various components, by performing sub-channel division and dynamic frequency hopping on the target frequency band, this method can more efficiently utilize limited spectrum resources, significantly improve the spectrum utilization rate and system capacity. By adopting a frequency hopping model and an encoding dispersion scheme, the signal transmission has stronger randomness and dispersion, greatly reducing the probability of being interfered with and intercepted. Through neural network optimization and a dual-time-scale convergence-differential model, the system can automatically adjust the encoding strategy according to environmental changes, achieving dynamic adaptation to a complex interference environment. Combining feature extraction, pattern matching, and an encoding feature library, this method can quickly and accurately identify a target signal and reconstruct it in a complex multi-signal environment. By constructing a multi-remote control simulation environment and a multi-index evaluation mechanism, it provides a quantitative basis for system optimization, which is conducive to continuous improvement and performance enhancement. By adopting a population exploration neural dynamics network, while improving the anti-interference performance, it effectively controls the computational complexity and achieves a good balance between performance and resource consumption.

[0126] This application also provides a remote control, which is used to execute the steps of the remote control anti-interference method in the above-mentioned embodiments.

[0127] This application also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions run on a computer, the computer is made to execute the steps of the remote control anti-interference method.

[0128] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described systems, systems, and units can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0129] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The foregoing storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs.

[0130] As described above, the above embodiments are only used to illustrate the technical solutions of the present application and are not intended to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present application.

Claims

1. A remote control crosstalk prevention method, characterized in that: include: Dividing the target frequency band into sub-channels to obtain a target frequency hopping sequence, and constructing a frequency hopping model according to the target frequency hopping sequence; Segmenting and encoding the control instruction to obtain a plurality of sub-data packets, and calculating the encoding dispersion scheme of the plurality of sub-data packets by a time dispersion algorithm; Performing multiple combination analysis on the frequency hopping model and the coding dispersion scheme to obtain a coding feature library; Extracting features and performing pattern matching on the received signal, and comparing it with the coding feature library to obtain a recognition result and a reconstructed signal; Performing neural network optimization on the coding dispersion scheme and the recognition result, and generating an adaptive coding strategy through a dual time scale convergence-differentiation model; In a multi-remote control environment, an anti-crosstalk performance evaluation is performed on the adaptive coding strategy and the reconstructed signal to obtain an anti-crosstalk performance evaluation report.

2. The remote control crosstalk prevention method according to claim 1, characterized in that: The sub-channel division of the target frequency band to obtain a target frequency hopping sequence, and constructing a frequency hopping model according to the target frequency hopping sequence includes: According to the total bandwidth of the frequency band and the number of preset sub-channels, bandwidth analysis is performed on the target frequency band to obtain the bandwidth value of a single sub-channel; According to the bandwidth value of the single sub-channel, the target frequency band is evenly divided to obtain a plurality of sub-channels, and the plurality of sub-channels are numbered to obtain a sub-channel index set; According to a preset pseudo-random number generation algorithm and a seed value, the sub-channel index set is randomly sorted to obtain an initial frequency hopping sequence; Performing periodic analysis on the initial frequency hopping sequence, and adjusting the sequence length according to a preset repetition period threshold to obtain a periodic frequency hopping sequence; Performing uniformity analysis on the periodic frequency hopping sequence, calculating the usage frequency of each sub-channel to obtain a channel usage distribution, and supplementing sub-channels whose usage frequency is lower than a threshold according to the channel usage distribution to obtain a uniform frequency hopping sequence; Calculating the entropy value of the uniform frequency hopping sequence to obtain a sequence complexity index, and regenerating the sequence segments with entropy values ​​lower than a threshold according to the sequence complexity index to obtain a target frequency hopping sequence; According to the target frequency hopping sequence, a time-frequency mapping relationship is established, and a frequency hopping model including frequency hopping rules and a time synchronization mechanism is constructed.

3. The remote control crosstalk prevention method according to claim 1, characterized in that: The step of segmenting and encoding the control instructions to obtain a plurality of sub-data packets, and calculating the encoding dispersion scheme of the plurality of sub-data packets by a time dispersion algorithm includes: According to the preset maximum sub-packet length, the control instruction is analyzed to obtain the number of segments; According to the number of segments, the control instruction is divided into equal lengths to obtain a plurality of instruction segments, and a unique identifier of the remote controller is hashed to use the hash value as an identification code; Perform interleaving encoding according to the identification code and the multiple instruction fragments to obtain an initial sub-data packet sequence; Performing redundancy analysis on the initial sub-data packet sequence, and calculating the number of check bits to be added according to a preset error correction capability requirement, to obtain a check bit length; According to the check bit length, cyclic redundancy check encoding is performed on each sub-data packet to obtain a sub-data packet sequence with check bits; Performing timestamp encoding on the sub-data packet sequence with checksums, embedding current time information into each sub-data packet, and obtaining a sub-data packet sequence with timestamps; Perform time dispersion sequence calculation according to a preset pseudo-random sequence generation algorithm to obtain a sub-data packet transmission order; According to the sub-packet transmission order and the frequency hopping model, the transmission time and the corresponding frequency of each sub-packet are calculated to obtain a time-frequency allocation scheme; According to the time-frequency allocation scheme, the sub-data packet sequence with timestamps is sorted and scheduled to obtain a coding dispersion scheme.

4. The remote control crosstalk prevention method according to claim 1, characterized in that: The performing of multiple combination analysis on the frequency hopping model and the coding dispersion scheme to obtain a coding feature library includes: Extracting parameters from the frequency hopping model to obtain a key parameter set, wherein the key parameter set includes a hopping period, a number of sub-channels, and a hopping sequence; Generating a plurality of different frequency hopping model variants according to the key parameter set to obtain a frequency hopping model set; Extracting parameters of the coding dispersion scheme to obtain a characteristic parameter set, wherein the characteristic parameter set includes the number of segments, the check bit length and the time dispersion sequence; According to the characteristic parameter set, a plurality of groups of different coding dispersion scheme variants are generated to obtain a coding dispersion scheme set; Orthogonally combining the frequency hopping model set and the coding dispersion scheme set to obtain a multi-dimensional combination scheme matrix, and constructing a simulation environment according to the multi-dimensional combination scheme matrix, performing signal transmission simulation on each combination, and obtaining a simulation result data set; Performing a multi-index evaluation on the simulation result data set to obtain a performance score table, and ranking all combination schemes according to the performance score table, selecting the top N combinations, and obtaining a target combination set; For each combination in the target combination set, feature vectors including frequency hopping parameters and coding dispersion parameters are extracted to obtain a feature vector set, and an index structure and query mechanism are constructed based on the feature vector set and corresponding performance scores to obtain a coding feature library.

5. The remote control crosstalk prevention method according to claim 1, characterized in that: The feature extraction and pattern matching of the received signal and the comparison with the coding feature library to obtain the recognition result and the reconstructed signal include: Performing a fast Fourier transform on the received signal to obtain a frequency domain representation, and performing a signal energy distribution analysis based on the frequency domain representation to obtain an active sub-channel set; Performing a timing analysis on the active sub-channel set to obtain a frequency hopping pattern, and demodulating the received signal according to the frequency hopping pattern to obtain a time domain data stream; According to a preset sub-data packet length, the time domain data stream is segmented to obtain a plurality of data segments to be identified, and the data segments to be identified are verified to obtain a set of valid data segments; Extracting timestamp information and identification codes according to the valid data segment set to obtain a candidate remote control list, and calculating similarity between each candidate item in the candidate remote control list and the encoding feature library according to the features of each candidate item to obtain a matching ranking; According to the matching degree sorting, the best matching remote control feature is selected to obtain a recognition result, and according to the coding dispersion scheme corresponding to the recognition result, the valid data segment set is reordered and decoded to obtain a reconstructed signal.

6. The remote control crosstalk prevention method according to claim 1, characterized in that: The neural network optimization of the coding dispersion scheme and the recognition result and the generation of an adaptive coding strategy through a dual time scale convergence-differential model include: Parameterizing the coding dispersion scheme to obtain a coding parameter vector, and extracting environmental interference features according to the recognition result to obtain an interference feature vector; The encoding parameter vector and the interference feature vector are concatenated to obtain a neural network input tensor, and a group exploration neural dynamics network is constructed according to a preset network structure to obtain an initial neural network model; Performing forward propagation calculation on the initial neural network model to obtain an output layer activation value, and constructing a dual time scale convergence-differentiation model according to the output layer activation value; Numerically solving the dual-time-scale convergence-differential model to obtain a neuron state trajectory, and calculating a global optimal solution based on the neuron state trajectory to obtain optimized encoding parameters; The optimized coding parameters are reversely mapped to obtain a new coding dispersion scheme, and an adaptive coding strategy is generated according to the new coding dispersion scheme and current environment characteristics.

7. The remote control crosstalk prevention method according to claim 1, characterized in that: The anti-crosstalk performance evaluation is performed on the adaptive coding strategy and the reconstructed signal in a multi-remote control environment to obtain an anti-crosstalk performance evaluation report, including: According to the preset number and distribution pattern of remote controllers, a multi-remote controller simulation environment is constructed to obtain a test scenario model; Allocating a different adaptive coding strategy to each remote controller in the test scenario model to obtain a strategy allocation scheme, and generating control instruction sequences for multiple remote controllers according to the strategy allocation scheme; Calculating the signal recovery rate according to the reconstructed signal and the control instruction sequence to obtain a recovery rate index, and performing statistical analysis on the recovery rate index to calculate the average value and variance to obtain a stability evaluation result; According to the timestamp information of the reconstructed signal, the signal transmission delay is calculated to obtain a real-time index, and the complexity analysis of the adaptive coding strategy is performed to calculate the computational amount of encoding and decoding to obtain a resource consumption index; An anti-crosstalk performance evaluation report is comprehensively generated according to the stability evaluation result, the real-time index and the resource consumption index.

8. A remote control anti-crosstalk system, characterized in that: The method for executing the remote control crosstalk prevention method according to any one of claims 1 to 7 comprises: A construction module, used to divide the target frequency band into sub-channels, obtain a target frequency hopping sequence, and construct a frequency hopping model according to the target frequency hopping sequence; A calculation module, used for segmenting and coding the control instruction to obtain a plurality of sub-data packets, and calculating the coding dispersion scheme of the plurality of sub-data packets by a time dispersion algorithm; An analysis module, used for performing multiple combination analyses on the frequency hopping model and the coding dispersion scheme to obtain a coding feature library; A comparison module is used to extract features and perform pattern matching on the received signal, and compare it with the coding feature library to obtain a recognition result and a reconstructed signal; An optimization module, used for performing neural network optimization on the coding dispersion scheme and the recognition result, and generating an adaptive coding strategy through a dual time scale convergence-differential model; The evaluation module is used to perform anti-crosstalk performance evaluation on the adaptive coding strategy and the reconstructed signal in a multi-remote control environment to obtain an anti-crosstalk performance evaluation report.

9. A remote controller, characterized in that: The remote controller is used to execute the remote controller anti-crosstalk method according to any one of claims 1 to 7.

10. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by the processor, the remote control anti-crosstalk method according to any one of claims 1 to 7 is implemented.

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