Remote controller anti-crosstalk method and system and remote controller

Abstract

The invention relates to the technical field of remote control, and discloses a remote controller anti-crosstalk method and system and a remote controller. The method comprises the following steps: performing sub-channel division on a target frequency band to obtain a target frequency hopping sequence, and constructing a frequency hopping model according to the target frequency hopping sequence; performing segmentation and coding combination on the control instruction to obtain a plurality of sub-data packets, and calculating a coding dispersion scheme of the plurality of sub-data packets; performing multi-combination analysis on the frequency hopping model and the coding dispersion scheme to obtain a coding feature library; performing feature extraction and pattern matching on the received signal, and performing comparison in combination with a coding feature library to obtain an identification result and a reconstructed signal; performing neural network optimization on the coding dispersion scheme and the recognition result and generating an adaptive coding strategy; in a multi-remote-controller environment, anti-crosstalk performance evaluation is carried out on the self-adaptive coding strategy and the reconstructed signal, an anti-crosstalk performance evaluation report is obtained, and the anti-interference capability of the remote controller is improved.

Description

Technical Field

[0001] The present invention relates to the field of remote control technology, and in particular to a remote control crosstalk prevention method, system and 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 technology has been unable to meet the anti-interference requirements in a multi-device environment, and problems such as crosstalk and misoperation frequently occur, seriously affecting user experience and system reliability.

[0003] At the same time, most existing anti-crosstalk technologies rely on hardware upgrades or complex signal processing algorithms, which are costly and difficult to deploy on a large scale on existing equipment. In addition, these methods often lack the ability to adapt to dynamic environments and are difficult to cope with complex and changing interference scenarios. Summary of the invention

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

[0005] In a first aspect, the present invention provides a remote control crosstalk prevention method, the remote control crosstalk prevention method comprising:

[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 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;

[0008] Performing multiple combination analysis on the frequency hopping model and the coding dispersion scheme to obtain a coding feature library;

[0009] 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;

[0010] 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;

[0011] 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.

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

[0013] 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;

[0014] 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;

[0015] An analysis module, used for performing multiple combination analysis on the frequency hopping model and the coding dispersion scheme to obtain a coding feature library;

[0016] 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;

[0017] 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;

[0018] 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.

[0019] A third aspect of the present invention provides a remote controller, which is used to execute the above remote controller anti-crosstalk method.

[0020] A fourth aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute the above-mentioned remote control anti-crosstalk method.

[0021] In the technical solution provided by the present invention, by dividing the target frequency band into sub-channels and dynamically hopping the frequency, the method can more efficiently utilize limited spectrum resources and significantly improve the spectrum utilization rate and system capacity. The frequency hopping model and the coding dispersion scheme are adopted to make the signal transmission more random and dispersed, greatly reducing the probability of interference and interception. Through neural network optimization and dual time scale convergence-differential model, the system can automatically adjust the coding strategy according to environmental changes and realize dynamic adaptation to complex interference environments. Combined with feature extraction, pattern matching and coding feature library, the method can quickly and accurately identify and reconstruct the target signal in a complex multi-signal environment. By constructing a multi-remote control simulation environment and a multi-index evaluation mechanism, a quantitative basis is provided for system optimization, which is conducive to continuous improvement and performance enhancement. The group exploration neural dynamics network is adopted to effectively control the computational complexity while improving the anti-crosstalk performance, and achieve 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 accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0023] Figure 1 A schematic diagram of a flow chart of a remote control crosstalk prevention method provided in an embodiment of the present application;

[0024] Figure 2 A schematic block diagram of the structure of a remote control anti-crosstalk system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may also be decomposed, combined or partially merged, so the actual execution order may change based on actual conditions.

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

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

[0029] In conjunction with the accompanying drawings, some embodiments of the present application are described in detail below. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0030] See also Figure 1 , Figure 1 A flowchart of a remote control crosstalk prevention method provided in an embodiment of the present application is shown in FIG. Figure 1 As shown, the remote control crosstalk prevention method provided in the embodiment of the present application includes steps S100 to S600.

[0031] Step S100, 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;

[0032] It is understandable that the execution subject of the present invention may be a remote control crosstalk prevention system, or may be a terminal or a server, which is not limited here. The embodiment of the present invention is described by taking a server as the execution subject as an example.

[0033] Specifically, according to the total bandwidth of the frequency band and the preset number of sub-channels, the bandwidth analysis of the target frequency band is performed 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 in the frequency hopping process and improves the anti-crosstalk effect. The initial frequency hopping sequence is periodically analyzed, and the sequence length is adjusted according to the preset repetition period threshold to obtain a periodic frequency hopping sequence. Through periodic analysis and adjustment, too frequent repetition in the frequency hopping sequence is avoided, and the complexity and security of the sequence are increased. The uniformity analysis of the periodic frequency hopping sequence is performed, and 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 to avoid overuse or underuse of some sub-channels. According to the channel usage distribution, the sub-channels with a usage frequency lower than the threshold are supplemented to obtain a uniform frequency hopping sequence to ensure 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 uniform frequency hopping sequence is calculated to obtain the sequence complexity index, and the sequence segments with entropy values ​​lower than the threshold are regenerated according to the sequence complexity index to obtain the target frequency hopping sequence. Through entropy calculation, the complexity of the frequency hopping sequence is evaluated to ensure its effectiveness in crosstalk prevention. If the entropy value of some sequence segments is too low, it means that its complexity is insufficient and it is easy to be predicted and attacked, so it needs to be regenerated to increase 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 frequency hopping rules and 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 frequency hopping. At the same time, the introduction of frequency hopping rules and time synchronization mechanisms ensures that in a multi-remote control environment, the frequency hopping between remote controls can be coordinated and consistent to avoid mutual interference, thereby realizing the anti-crosstalk function of the remote control.

[0034] Step S200, segmenting and encoding the control instruction to obtain multiple sub-data packets, and calculating the encoding dispersion scheme of the multiple sub-data packets through a time dispersion algorithm;

[0035] Specifically, according to the preset maximum sub-packet length, the control instruction is analyzed for length to obtain the number of segments. By analyzing the length of the control instruction, it is divided into the number of sub-packets, so that the length of each sub-packet does not exceed the preset maximum sub-packet length. According to the number of segments, the control instruction is divided into equal lengths to obtain multiple instruction fragments, and the remote control unique identifier is hashed, and the hash value is used as the identification code. According to the identification code and the multiple instruction fragments, interleaving encoding is performed to obtain an initial sub-packet sequence. The interleaving encoding improves the anti-interference ability of the data packet by interleaving the identification code and the instruction fragments, ensuring that even if some sub-packets are lost or erroneous during the transmission process, they can be effectively reconstructed through other data packets. The initial sub-packet sequence is analyzed for redundancy, and according to the preset error correction capability requirements, the number of check bits to be added is calculated to obtain the check bit length. According to the check bit length, cyclic redundancy check encoding is performed on each sub-packet to obtain a sub-packet sequence with check. Cyclic redundancy check encoding is a commonly used error correction encoding method. By adding check bits, errors occurring during the transmission process are detected and corrected. Perform timestamp encoding on the sub-packet sequence with checksum, embed the current time information into each sub-packet, and obtain the sub-packet sequence with timestamp. Timestamp encoding ensures the order and timeliness of the data packet by embedding the current time information in each sub-packet, and avoids the disorder or retransmission of the data packet during transmission. Perform time dispersion sequence calculation according to the preset pseudo-random sequence generation algorithm to obtain the sub-packet transmission order. The pseudo-random sequence generation algorithm obtains a pseudo-random sub-packet transmission order through calculation, ensuring the time dispersion of each sub-packet during transmission and reducing signal conflicts and interference. According to the sub-packet transmission order and frequency hopping model, calculate the sending time and corresponding frequency of each sub-packet, and obtain the time-frequency allocation scheme. By combining the transmission order and frequency hopping model of the sub-packet, determine the transmission mode of each sub-packet at a specific time point and frequency, and ensure reliable transmission of the signal and effective crosstalk prevention. According to the time-frequency allocation scheme, the sub-packet sequence with timestamps is sorted and scheduled to obtain a coding dispersion scheme. The sorting and scheduling steps form a final coding dispersion scheme by sorting and scheduling the sub-packets with timestamps according to predetermined time and frequency, thereby realizing the segmentation and coding combination of control instructions and ensuring the signal anti-crosstalk effect in a multi-remote control environment.

[0036] Step S300, performing multiple combination analysis on the frequency hopping model and the coding dispersion scheme to obtain a coding feature library;

[0037] Specifically, the frequency hopping model is parameter extracted to obtain a key parameter set, which includes a hopping period, a number of sub-channels and a hopping sequence. Based on the key parameter set, multiple sets of different frequency hopping model variants are generated to obtain a frequency hopping model set to ensure adaptability and anti-interference capabilities in different environments. The coding dispersion scheme is parameter extracted to obtain a characteristic parameter set, which includes the number of segments, the check bit length and the time dispersion sequence. Based on the characteristic parameter set, multiple sets of different coding dispersion scheme variants are generated to obtain a coding dispersion scheme set, so that signal dispersion and encoding can be effectively performed in different signal environments. The frequency hopping model set and the coding dispersion scheme set are orthogonally combined to obtain a multi-dimensional combination scheme matrix, and a simulation environment is constructed based on the multi-dimensional combination scheme matrix, and signal transmission simulation is performed on each combination to obtain 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. The simulation result data set is evaluated by multiple indicators to obtain a performance score table, and all combination schemes are ranked according to the performance score table, and the top N combinations are selected to obtain a target combination set. For each combination in the target combination set, the feature vector including the frequency hopping parameter and the coding dispersion parameter is extracted to obtain the feature vector set, and the index structure and query mechanism are constructed according to the feature vector set and the corresponding performance score to obtain the coding feature library. By combining the feature vector set and the performance score, 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 features and perform pattern matching on the received signal, and compare it with the coding feature library to obtain the recognition result and reconstruct the signal;

[0039] Specifically, a fast Fourier transform is performed on the received signal to obtain a frequency domain representation, and a signal energy distribution analysis is performed based on the frequency domain representation to obtain an active sub-channel set. By fast Fourier transform, the time domain signal is converted into a frequency domain signal, which makes it easier to analyze the frequency component and energy distribution of the signal, and helps to identify active sub-channels. A timing analysis is performed on the active sub-channel set to obtain a frequency hopping pattern, and the received signal is demodulated according to the frequency hopping pattern to obtain a time domain data stream. Through timing analysis, the frequency hopping law of the signal is identified, so that the time domain data stream is correctly demodulated. According to the preset sub-data packet length, the time domain data stream is segmented to obtain multiple data segments to be identified, and the data segments to be identified are verified to obtain a valid data segment set. Through segmentation processing and verification, each sub-data packet in the time domain data stream is accurately extracted, and its integrity and correctness are ensured. According to the valid data segment set, the timestamp information and the identification code are extracted to obtain a candidate remote control list, and for each candidate item in the candidate remote control list, the similarity is calculated based on the characteristics of each candidate item and the encoding feature library to obtain a matching degree ranking. By extracting the timestamp information and identification code, the possible remote control source is preliminarily determined, and the similarity calculation and matching ranking further accurately identify the specific remote control. According to the matching ranking, the best matching remote control feature is selected to obtain the 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 the reconstructed signal. By selecting the best matching remote control feature, the source of the received signal is accurately determined, and according to the corresponding coding dispersion scheme, the original control instruction can be restored by reordering and decoding.

[0040] Step S500, 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;

[0041] Specifically, the coding dispersion scheme is parameterized to obtain a coding parameter vector, and the environmental interference features are extracted according to the recognition results to obtain an interference feature vector. The coding dispersion scheme is parameterized to convert it into a processable vector form, and the interference feature vector reflects the various interference factors in the current environment. The coding parameter vector and the interference feature vector are spliced ​​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. The coding parameters and interference features are spliced ​​and input into the neural network as a whole so that the network can consider both factors at the same time. The group exploration neural dynamics network is a network structure that can efficiently handle complex nonlinear problems and is suitable for optimizing coding strategies. The initial neural network model is forward propagated to obtain the output layer activation value, and a dual time scale convergence-differentiation model is constructed based on the output layer activation value. The forward propagation calculation enables the network to calculate the output result based on the input tensor, and the output layer activation value reflects the network's response to the current input. The dual time scale convergence-differentiation model can more accurately describe the dynamic behavior of the system by combining changes on different time scales. The dual-time-scale convergence-differential model is numerically solved to obtain the neuron state trajectory, and based on the neuron state trajectory, the global optimal solution is calculated to obtain the optimized coding parameters. The numerical solution process enables the model to determine the state changes of neurons at different time points, so as to find the optimal combination of coding parameters. The global optimal solution ensures that the obtained coding parameters have the best performance in the current environment. The optimized coding parameters are reverse mapped to obtain a new coding dispersion scheme, and an adaptive coding strategy is generated based on the new coding dispersion scheme and the current environmental characteristics. Through reverse mapping, the optimized coding parameters are converted into a specific coding dispersion scheme, so that it can be directly used in practical applications. The adaptive coding strategy is dynamically generated based on 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, anti-crosstalk performance evaluation is performed on 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 controllers, a multi-remote controller simulation environment is constructed to obtain a test scenario model, simulate the multi-remote controller operation scenario in actual applications, and conduct a comprehensive anti-crosstalk performance evaluation. Different adaptive coding strategies are assigned to each remote controller in the test scenario model to obtain a strategy allocation scheme, and control command sequences of multiple remote controllers are generated according to the strategy allocation scheme. Each remote controller has a unique coding strategy and command sequence in the simulation environment, which is helpful to test the anti-crosstalk effect in various situations. According to the reconstructed signal and the control command sequence, the signal recovery rate is calculated to obtain the recovery rate index. The signal recovery rate is a key indicator to measure the anti-crosstalk effect. By comparing the reconstructed signal and the original control command, the proportion of the signal correctly recovered during the transmission process is determined. The recovery rate index is statistically analyzed, and its average value and variance are calculated to obtain the stability evaluation result. The average value reflects the overall recovery effect, while the variance indicates the degree of fluctuation of the recovery rate. According to the timestamp information of the reconstructed signal, the signal transmission delay is calculated to obtain the real-time index. The signal transmission delay reflects the response speed of the system in a multi-remote controller environment. Lower delay means that the system can respond quickly and effectively reduce the impact of crosstalk. At the same time, the complexity analysis of the adaptive coding strategy is carried out, the computational amount of encoding and decoding is calculated, and the resource consumption index is obtained. The resource consumption index includes the computing resources and time required in the encoding and decoding process. By analyzing these indicators, the efficiency and resource occupancy of the system in actual operation are evaluated. According to the stability evaluation results, real-time indicators and resource consumption indicators, the anti-crosstalk performance evaluation report is comprehensively generated. Through the comprehensive analysis of these indicators, the anti-crosstalk effect of the system and its performance in actual applications are obtained.

[0044] In the embodiment of the present invention, by dividing the target frequency band into sub-channels and dynamically hopping the frequency, the method can more efficiently utilize limited spectrum resources and significantly improve the spectrum utilization rate and system capacity. The frequency hopping model and the coding dispersion scheme are adopted to make the signal transmission more random and dispersed, greatly reducing the probability of interference and interception. Through neural network optimization and dual time scale convergence-differential model, the system can automatically adjust the coding strategy according to environmental changes and realize dynamic adaptation to complex interference environments. Combined with feature extraction, pattern matching and coding feature library, the method can quickly and accurately identify and reconstruct the target signal in a complex multi-signal environment. By constructing a multi-remote control simulation environment and a multi-index evaluation mechanism, a quantitative basis is provided for system optimization, which is conducive to continuous improvement and performance enhancement. The group exploration neural dynamics network is adopted to effectively control the computational complexity while improving the anti-crosstalk performance, and achieve 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] 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;

[0047] According to the bandwidth value of a 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;

[0048] 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;

[0049] 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;

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

[0051] The entropy value of the uniform frequency hopping sequence is calculated to obtain a sequence complexity index, and the sequence segments with entropy values ​​lower than a threshold are regenerated according to the sequence complexity index to obtain a target frequency hopping sequence;

[0052] According to the target frequency hopping sequence, a time-frequency mapping relationship is established, and a frequency hopping model including frequency hopping rules and time synchronization mechanism is constructed.

[0053] Specifically, to determine the bandwidth value of a single sub-channel, assume that the total bandwidth of the target frequency band is B, and the number of preset sub-channels is 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. By using the formula, the entire frequency band is evenly divided into multiple sub-channels to ensure that the bandwidth of each sub-channel is consistent. According to the bandwidth value of a single sub-channel, the target frequency band is evenly divided to obtain multiple sub-channels, and these sub-channels are numbered to form a sub-channel index set. Suppose the target frequency band is [f min ,f max ], the total bandwidth is B = f max -f min By evenly dividing, 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 subchannel, ranging from 1 to N. At this time, the subchannel index set is {1,2,…,N}. In order to increase the randomness and unpredictability of frequency hopping, the subchannel index set is randomly sorted according to the preset pseudo-random number generation algorithm and seed value to obtain the initial frequency hopping sequence. The seed value of the pseudo-random number generation algorithm ensures that the random sequence generated each time 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 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 hopping in the sequence, adjust the sequence length according to the preset repetition period threshold T to obtain a periodic frequency hopping sequence. By counting the intervals at which each subchannel appears in the sequence, ensure that it meets the preset period requirements. If the interval of a sub-channel is less than the threshold T, the sequence needs to be readjusted and the length of the sequence needs to be increased until the interval of all sub-channels meets the requirement. Perform 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 subchannel i is:

[0056]

[0057] The channel usage distribution is obtained by calculating the usage frequency of all sub-channels. If the usage frequency of some sub-channels is found to be lower than the preset frequency threshold f min , then these sub-channels need to be supplemented so that the usage frequency of all sub-channels is within a reasonable range, and a uniform frequency hopping sequence is obtained. The entropy value of the uniform frequency hopping sequence is calculated to measure the complexity of the sequence. Suppose the usage probability of each sub-channel in the uniform frequency hopping sequence is 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 subchannel 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 the 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. According to the target frequency hopping sequence, a time-frequency mapping relationship is established, and a frequency hopping model including frequency hopping rules and time synchronization mechanism is constructed. The time-frequency mapping relationship can be realized by mapping each subchannel in the frequency hopping sequence to a specific time period. Suppose the frequency hopping sequence is {c1,c2,…,c L}, each subchannel c i The corresponding time period is [t i-1 ,t i ], where t i Indicates time point i. The hopping frequency in each time period is hopping according to the preset frequency hopping rules, thereby ensuring reliable signal transmission and anti-crosstalk effect. The time synchronization mechanism can be achieved 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, the control instruction is analyzed to obtain the number of segments;

[0062] According to the number of segments, the control instruction is divided into equal lengths to obtain multiple instruction fragments, and the remote control unique identifier is hashed to use the hash value as an identification code;

[0063] Interleaving and encoding are performed according to the identification code and the plurality of instruction fragments to obtain an initial sub-data packet sequence;

[0064] Performing redundancy analysis on the initial sub-data packet sequence, and calculating the number of check bits to be added according to the preset error correction capability requirements, to obtain the check bit length;

[0065] 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;

[0066] Performing timestamp encoding on the sub-data packet sequence with checksum, embedding the current time information into each sub-data packet, and obtaining a sub-data packet sequence with timestamp;

[0067] Perform time dispersion sequence calculation according to a preset pseudo-random sequence generation algorithm to obtain a sub-data packet transmission order;

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

[0069] According to the time-frequency allocation scheme, the sub-packet sequences with timestamps are sorted and scheduled to obtain a coding dispersion scheme.

[0070] Specifically, determine the number of segments. Assuming the total length of the control instruction is L, and the preset maximum sub-data packet length is M, 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 rounding 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 divided into equal lengths according to the number of segments to obtain multiple instruction fragments. The control instruction is divided into M characters per segment. If the length of the last segment is less than M, specific characters can be padded at the end to achieve a uniform length, and multiple instruction fragments of equal length are obtained. The remote control unique identifier is hashed and the hash value is used as the identification code. Assuming that the remote control unique identifier is ID, a standard hash algorithm (such as SHA-256) can be used to generate its The hash value H(ID) is used as an identification code to ensure that each remote control has a unique identifier during the transmission process. Interleaved encoding is performed based on the identification code and multiple instruction fragments to obtain an initial sub-data packet sequence. Interleaved encoding interleaves the identification code and instruction fragments so that each sub-data packet contains part of the identification code and instruction fragments, thereby increasing the data's anti-interference ability. After the identification code is divided into a fixed length, it is arranged alternately with the instruction fragments to form a new data sequence. The initial sub-data packet sequence is then analyzed for redundancy, and the number of check bits that need to be added is calculated based on the preset error correction capability requirements to obtain the check bit length. Assuming the length of the sub-data packet is m and the error correction capability requirement is r, the number of check bits k can be calculated by the formula Calculate. The formula is used to determine the number of check bits that need to be added to each sub-packet to meet the preset error correction capability requirements. According to the check bit length, cyclic redundancy check encoding is performed on each sub-packet to obtain a sub-packet sequence with check bits. Cyclic redundancy check (CRC) is a commonly used error correction coding method. By adding check bits at the end of each sub-packet, errors in the transmission process are detected and corrected. The specific operation is to perform CRC encoding on each sub-packet to obtain a sub-packet sequence with check bits. Timestamp encoding is performed on the sub-packet sequence with check bits, and the current time information is embedded in each sub-packet to obtain a sub-packet sequence with timestamps. Timestamp encoding ensures the order and timeliness of the data packet by embedding the current time information in each sub-packet. After encoding the current time information in a fixed format, it is added to the end of each sub-packet. The time dispersion sequence is calculated according to the preset pseudo-random sequence generation algorithm to obtain the sub-packet transmission order. The pseudo-random sequence generation algorithm can generate a pseudo-random sequence, and the transmission order of each sub-packet is determined by sorting this sequence. Assume that the pseudo-random sequence generation algorithm is PRNG(seed), the seed value is seed, and the transmission order of the sub-packets is shuffle({1,2,…,N}, PRNG(seed)). According to the sub-packet transmission order and the frequency hopping model, the sending time and corresponding frequency of each sub-packet are calculated to obtain the time-frequency allocation scheme. Each sub-packet is assigned to different time points according to the transmission order, and the transmission frequency of each time point is determined according to the frequency hopping model. Assume that the transmission time interval is T and the frequency hopping model is Freq(t), then the sending time and corresponding frequency of sub-packet i is t i =i×T,f i =Freq(t i ). According to the time-frequency allocation scheme, the sub-packet sequences with timestamps are sorted and scheduled to obtain a coding dispersion scheme.

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

[0074] According to the key parameter set, a plurality of groups of different frequency hopping model variants are generated to obtain a frequency hopping model set;

[0075] Extracting parameters of the coding dispersion scheme to obtain a characteristic parameter set, the characteristic parameter set includes the number of segments, the check bit length and the time dispersion sequence;

[0076] According to the characteristic parameter set, a plurality of different coding dispersion scheme variants are generated to obtain a coding dispersion scheme set;

[0077] Performing orthogonal combination of the frequency hopping model set and the coding dispersion scheme set to obtain a multi-dimensional combination scheme matrix, and constructing a simulation environment based on the multi-dimensional combination scheme matrix, performing signal transmission simulation on each combination, and obtaining a simulation result data set;

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

[0079] 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 the corresponding performance score to obtain a coding feature library.

[0080] Specifically, the key parameter set is analyzed. Assume that the key parameter set includes a hopping period T, a number of sub-channels N, and a hopping sequence S. By combining these parameters in different ways, multiple sets of different frequency hopping model variants are generated. The hopping period T is adjusted within a certain range, for example, from 1ms to 10ms, increasing by 1ms each time; the number of sub-channels N is adjusted, for example, from 5 to 20, increasing by 5 each time; the hopping sequence S is randomly generated and adjusted, for example, different sequences are generated using different pseudo-random number generation seed values. These combinations form a frequency hopping model set. Parameters are extracted for the coding dispersion scheme to obtain a characteristic parameter set. The characteristic parameter set 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 based on the total length of the data packet and the maximum sub-packet length. The check bit length K depends on the required error correction capability. The time dispersion sequence T s It is generated according to the pseudo-random number generation algorithm. Assuming the total data length is L and the maximum sub-packet length is M, the number of segments is:

[0081]

[0082] The check bit length K is calculated according to the preset error correction capability requirements by the formula Calculated, where r is the error correction capability. Time-dispersed sequence T sIt can be obtained by setting different pseudo-random number generation seed values. According to the characteristic parameter set, multiple groups of different coding dispersion scheme variants are generated. Multiple groups of coding dispersion schemes are generated by different combinations of the number of segments, check bit lengths and time dispersion sequences. Assuming that the number of segments ranges from 1 to 10, the check bit lengths range from 2 to 8, and the time dispersion sequence are combined using 5 different pseudo-random seed values, each combination will form a unique coding dispersion scheme, and a set of coding dispersion schemes is obtained. The frequency hopping model set and the coding dispersion scheme set are orthogonally combined to obtain a multi-dimensional combination scheme matrix. Assume that the frequency hopping model set is {F1, F2,…, F m}, the set of coding distribution schemes is {E1,E2,…,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 to simulate the signal transmission of each combination to obtain a data set of realistic results. The realistic 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 sheet. 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 correctly recovered signals, N total is the total number of signals. The transmission delay D can be obtained by calculating the difference between the receiving time and the sending time of each signal, and the resource consumption C is evaluated by calculating the computing resources and time used in the encoding and decoding process. A performance score table is constructed based on the evaluation results, and all combination schemes are sorted. The top N combinations are selected to obtain the target combination set. For each combination in the target combination set, the feature vector including the frequency hopping parameters and the coding dispersion parameters is extracted to obtain the feature vector set. The feature vector set includes the frequency hopping parameters {T, N, S} and the coding dispersion parameters {D, K, T s}. According to the feature vector set and the corresponding performance score, an index structure and query mechanism are constructed to obtain the coding feature library. The index structure can use data structures such as inverted index or hash table to quickly query the coding scheme that meets specific performance requirements. For example, suppose there is a frequency band with a total bandwidth of 100MHz, and it is divided into 10 sub-channels, and the bandwidth of each sub-channel is 10MHz. Assume that the hopping period T increases from 1ms to 10ms, increasing by 1ms each time, and the pseudo-random number generation seed value is {1001, 1002, 1003}, then multiple frequency hopping models can be generated. For the coding dispersion scheme, assume that the total data length is 200 bytes and the maximum sub-packet length is 50 bytes, then the number of segments D = 4. Assuming that the error correction capability r is 3, the check bit length Time-dispersed series T s Generated using 3 different pseudo-random seed values. Generate multiple sets of coding dispersion schemes through the combination of different parameters. In the simulation environment, simulate multiple remote controls working at the same time, add interference signals of different strengths, simulate signal transmission for each combination scheme, and record data such as signal recovery rate, transmission delay, and resource consumption. Assuming that after simulation, a certain combination scheme (F3, E2) has a signal recovery rate of 95%, a transmission delay of 5ms, and a resource consumption of 10%, then record its score in the performance score table. According to the score table, sort all the combination schemes, select the top 5 combinations, and form the target combination set. Extract the feature vectors in the target combination set, build an index structure and query mechanism, and form a coding feature library.

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

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

[0088] Performing 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;

[0089] According to the 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;

[0090] Extract timestamp information and identification codes according to the valid data fragment set to obtain a candidate remote control list, and perform similarity calculation on each candidate item in the candidate remote control list according to the features of each candidate item and the encoding feature library to obtain a matching ranking;

[0091] According to the matching degree sorting, the best matching remote control feature is selected to obtain the 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 the reconstructed signal.

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

[0093]

[0094] Among them, 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 fast Fourier transform, the amplitude and phase of the signal at different frequencies are obtained to form a frequency domain representation. According to the frequency domain representation, the signal energy distribution is analyzed to determine the active sub-channel set. By calculating the energy of each frequency component, it is possible to identify which sub-channels the signal is more active on. The energy calculation formula is:

[0095] E(k)=|X(k)| 2 ;

[0096] Among them, E(k) is the energy at frequency k, and |X(k)| is the amplitude of the frequency domain signal. By setting the energy threshold, sub-channels with higher energy are screened out to form a set of active sub-channels. The active sub-channel set is subjected to timing analysis 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, the hopping sequence of the signal is determined. According to the frequency hopping pattern, the received signal is demodulated to obtain a time domain data stream. The demodulation process includes converting the frequency domain signal back to the time domain signal, and applying the hopping pattern to perform corresponding frequency correction. The demodulated time domain data stream is segmented according to the preset sub-packet length to obtain multiple data fragments to be identified. Assume that the sub-packet length is L p , then the time domain data stream can be calculated per L pThe sampling points are divided into multiple segments. The data segments to be identified are verified to determine their validity. The verification method can use technologies such as cyclic redundancy check, and the valid data segments are screened out by calculating the check code and comparing it with the received check code. According to the set of valid data segments, the timestamp information and identification code are extracted. The timestamp information can record the receiving time of each data segment. The identification code can be obtained by hashing the unique identifier of the remote control. Assuming that the unique identifier of the remote control is ID and the hash function is H, the identification code R = H (ID). After obtaining the timestamp information and identification code, a list of candidate remote controls is generated based on this information. Each candidate remote control has a set of feature parameters, including timestamp and identification code. For each candidate in the candidate remote control list, the similarity is calculated based on the features of each candidate and the encoding feature library to obtain a matching ranking. The similarity calculation can use methods such as Euclidean distance and cosine similarity. Assuming that the feature vector of the candidate is V and the feature vector in the encoding feature library is U, the similarity S can be expressed as:

[0097]

[0098] Among them, · represents the inner product of the vector, and ∥·∥ represents the norm of the vector. According to the matching degree sorting, the best matching remote control feature is selected to obtain the recognition result. The encoding dispersion scheme corresponding to the recognition result is used to reorder and decode the valid data segment set. Reordering restores the data segments to the correct order, and decoding converts the encoded data back to the original control instructions. 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] Parameterize the coding dispersion scheme to obtain a coding parameter vector, and extract environmental interference features based on the recognition results to obtain an interference feature vector;

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

[0102] Perform forward propagation calculation on the initial neural network model to obtain the output layer activation value, and build a dual time scale convergence-differentiation model based on the output layer activation value;

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

[0104] 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 the current environment characteristics.

[0105] Specifically, the coding dispersion scheme is parameterized to obtain a coding parameter vector. Assume that the coding dispersion scheme includes the number of segments D, the check bit length K and the time dispersion sequence T s Through parameterization, these parameters are converted into vector form, and the encoding 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 the neural network. According to the recognition results, the environmental interference features are extracted to obtain the interference feature vector. The environmental interference features can include signal strength, number of interference signals, frequency of interference signals, etc. Assume that these features include signal strength S i 、Number of interference signals i and the interference signal frequency F i , then the interference feature vector can be expressed as I = [S i ,N i ,F i ]. Concatenate the encoding parameter vector E and the interference feature vector I to obtain the neural network input tensor X=E,I]. The concatenation operation can be implemented by vector connection, combining the encoding parameters and interference features into an overall input. According to the preset network structure, a group exploration neural dynamics network is constructed to obtain the initial neural network model. The group exploration neural dynamics network is a neural network structure that can efficiently handle complex nonlinear optimization problems. It simulates the combination of group intelligence and neural dynamics, and uses information sharing and mutual collaboration among group members to explore the global optimal solution. Perform forward propagation calculations on the initial neural network model to obtain the output layer activation value. The forward propagation process is to calculate the input tensor X through the network layer by layer, and finally obtain the output layer result. Assuming that the output layer activation value of the neural network is 0, the forward propagation formula 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 output layer activation value 0, a dual-time scale convergence-differential model is constructed. The dual-time scale convergence-differential model can more accurately describe the dynamic behavior of the system by combining changes on different time scales. Assuming that the state variables of the dual-time scale model are y and z, its update formula can be expressed as:

[0108] y t+1 =y t +α(Oy t )

[0109] z t+1 =z t +β(y t -z t )

[0110] Among them, α and β are time scale factors, which represent the rate of change on different time scales. The dual time scale convergence-differential model is numerically solved to obtain the neuron state trajectory, and the global optimal solution is calculated 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 find the parameter combination that optimizes the system performance by analyzing the change trend of the neuron state trajectory. The optimized coding parameter E* is obtained, and reverse mapping is performed on it. The optimized parameter vector is converted back to a specific coding scheme, including the number of segments, the length of the check bit, and the time dispersion sequence, etc., to obtain a new coding dispersion scheme. According to the new coding dispersion scheme and the current environmental characteristics, an adaptive coding strategy is generated. The adaptive coding strategy is a coding scheme that is dynamically adjusted according to the optimized parameters and the current environmental conditions to ensure that the system can maintain optimal performance in various environments.

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

[0112] 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;

[0113] For each remote controller in the test scenario model, a different adaptive coding strategy is assigned to obtain a strategy assignment scheme, and control instruction sequences of multiple remote controllers are generated according to the strategy assignment scheme;

[0114] According to the reconstructed signal and the control instruction sequence, the signal recovery rate is calculated to obtain the recovery rate index, and the recovery rate index is statistically analyzed to calculate the mean value and variance to obtain the stability evaluation result;

[0115] According to the timestamp information of the reconstructed signal, the signal transmission delay is calculated to obtain the real-time index, and the complexity analysis of the adaptive coding strategy is performed to calculate the encoding and decoding calculation amount to obtain the resource consumption index;

[0116] Based on the stability evaluation results, real-time indicators and resource consumption indicators, an anti-crosstalk performance evaluation report is comprehensively generated.

[0117] Specifically, a multi-remote control simulation environment is constructed according to the preset number and distribution pattern of remote controls. Assuming that the preset number of remote controls is N, the position and distribution pattern of each remote control can be generated by random distribution or fixed pattern. Set the position of each remote control (x i ,y i), where i = 1, 2, ..., N. In this way, a test scenario model simulating a real environment is obtained. For each remote control in the test scenario model, a different adaptive coding strategy is assigned to obtain a strategy allocation scheme. The adaptive coding strategy may include parameters such as frequency hopping sequence, segment length, and check bit length. Assume that there are M different adaptive coding strategies {S1, S2, ..., S M}, these strategies can be assigned to N remote controllers in a cyclic or random manner to form a strategy allocation scheme. According to the strategy allocation scheme, control command sequences for multiple remote controllers are generated. Each control command sequence is encoded and segmented according to the corresponding coding strategy to form the final transmission data. According to the reconstructed signal and the control command sequence, the signal recovery rate is calculated. The signal recovery rate R is an important indicator to measure the anti-crosstalk effect, which indicates the proportion of the received valid signal to the total transmitted signal. The recovery rate indicator is statistically analyzed to calculate the mean and variance. The mean value indicates the overall recovery rate level, and the variance reflects the degree of fluctuation of the recovery rate. Through these statistical analyses, the stability evaluation results of the system are obtained. The signal transmission delay is calculated based on the timestamp information of the reconstructed signal. The signal transmission delay D is another key indicator, which indicates the time it takes for the signal to be sent and received. Assume that the sending time is t send , the receiving time is t recv , then the transmission delay D can be calculated by the formula D = t recv -t send Calculation. Perform complexity analysis on the adaptive coding strategy, calculate the amount of computation for encoding and decoding, and obtain resource consumption indicators. Resource consumption indicators include the computing resources and time required for encoding and decoding. Through complexity analysis, evaluate the efficiency of each coding strategy in actual operation. Generate an anti-crosstalk performance evaluation report based on the comprehensive stability evaluation results, real-time indicators, and resource consumption indicators. The report describes in detail the performance of the system in different environments, including signal recovery rate, transmission delay, and resource consumption. Through the comprehensive analysis of these indicators, comprehensively evaluate the anti-crosstalk effect and overall performance of the system.

[0118] See also Figure 2 , Figure 2 A schematic block diagram of the structure of the remote control anti-crosstalk system 200 provided in an embodiment of the present application is shown in FIG. Figure 2 As shown, the remote control crosstalk prevention system 200 includes:

[0119] A construction module 210 is 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;

[0120] A calculation module 220, configured to segment and code the control instruction to obtain a plurality of sub-data packets, and calculate coding dispersion schemes of the plurality of sub-data packets by a time dispersion algorithm;

[0121] The analysis module 230 is used to perform multiple combination analyses on the frequency hopping model and the coding dispersion scheme to obtain a coding feature library;

[0122] The comparison module 240 is used to extract features and perform pattern matching on the received signal, and compare it with the coding feature library to obtain the recognition result and reconstruct the signal;

[0123] An optimization module 250, 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-differentiation model;

[0124] The evaluation module 260 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.

[0125] Through the cooperation of the above components, by dividing the target frequency band into sub-channels and dynamically hopping, this method can more efficiently utilize limited spectrum resources and significantly improve spectrum utilization and system capacity. The frequency hopping model and coding dispersion scheme make the signal transmission more random and dispersed, greatly reducing the probability of interference and interception. Through neural network optimization and dual time scale convergence-differential model, the system can automatically adjust the coding strategy according to environmental changes and achieve dynamic adaptation to complex interference environments. Combined with feature extraction, pattern matching and coding feature library, this method can quickly and accurately identify and reconstruct target signals in complex multi-signal environments. By constructing a multi-remote control simulation environment and a multi-index evaluation mechanism, a quantitative basis is provided for system optimization, which is conducive to continuous improvement and performance enhancement. The group exploration neural dynamics network is used to effectively control the computational complexity while improving the anti-crosstalk performance, achieving a good balance between performance and resource consumption.

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

[0127] The present application also provides a computer-readable storage medium, which may 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 are executed on a computer, the computer executes the steps of the remote control anti-crosstalk 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 aforementioned method embodiments and will not be repeated here.

[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 is essentially 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, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program code.

[0130] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the 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 analysis 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.

Images