Internet of Things information platform and implementation method thereof

Abstract

The invention relates to the technical field of communication platform construction, in particular to an Internet of Things information platform and an implementation method thereof. The method comprises the following steps: collecting a telegraph basic signal and carrying out independent source signal filtering to generate a filtered telegraph signal, then carrying out communication path topology remodeling on the filtered telegraph signal to form a communication path network, carrying out intensity compression fingerprinting processing on the telegraph signal based on the network to obtain a signal feature fingerprint, and carrying out fingerprint identification on the signal feature fingerprint. Generating random enhanced data through a dynamic descrambling technology, implementing network topology transition encryption on the random enhanced data to obtain a communication encryption path, reconstructing a communication path based on the path, generating reconstructed path data, performing information disturbance simulation on the reconstructed path data, generating disturbance fragmented data, and performing encrypted fingerprint implantation on the disturbance fragmented data; and through channel reconstruction and progressive optimization of the platform, an Internet of Things information platform is formed. According to the invention, a safer and more efficient Internet of Things information platform implementation method is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of communication platform construction, and in particular to an Internet of Things information platform and an implementation method thereof. Background Art

[0002] With the rapid development of Internet of Things technology, the widespread application of various devices and sensors has made data collection and processing increasingly complex. Traditional information processing platforms often show insufficient performance when facing massive data. Especially in the process of real signal collection, filtering and reshaping, data loss or distortion is prone to occur, resulting in reduced reliability of subsequent analysis results. In addition, communication security issues are becoming increasingly prominent. Unencrypted data is vulnerable to malicious attacks during transmission, causing the risk of data leakage and tampering. Effective encryption and reconstruction mechanisms are urgently needed to ensure data security and integrity. In terms of signal feature extraction and enhancement, existing technologies often cannot fully utilize the information of the communication path network, resulting in unsatisfactory signal feature fingerprint extraction effects. The application of signal situation dynamic descrambling technology also faces the problem of insufficient adaptability and cannot effectively process data in different environments. Therefore, how to improve the recognition accuracy of signal features and enhance the randomness of data has become a technical challenge that needs to be solved urgently. In addition, traditional data processing methods lack effective reconstruction and optimization strategies when facing complex communication paths, resulting in low information transmission efficiency and waste of resources. Summary of the invention

[0003] Based on this, it is necessary to provide an Internet of Things information platform and an implementation method thereof to solve at least one of the above technical problems.

[0004] To achieve the above purpose, a method for implementing an Internet of Things information platform includes the following steps:

[0005] Step S1: collecting basic telegraph signals; filtering the basic telegraph signals by independent source signals to obtain filtered telegraph signals; reshaping the communication path topology of the filtered telegraph signals to generate a communication path network;

[0006] Step S2: Perform intensity compression fingerprinting on the telegraph basic signal based on the communication path network to obtain a signal feature fingerprint; perform signal situation dynamic descrambling on the signal fingerprint feature to obtain random enhanced data;

[0007] Step S3: encrypting the random enhanced data by network topology transition to obtain a communication encryption path; reconstructing the communication path based on the communication encryption path to generate reconstructed path data;

[0008] Step S4: performing information disturbance simulation on the reconstructed path data to generate disturbance fragmented data; performing encrypted fingerprint implantation processing on the random enhanced data based on the disturbance fragmented data to generate fingerprint embedded data;

[0009] Step S5: reconstruct the channel of the fingerprint embedded data to generate protocol conversion data; perform progressive platform optimization based on the protocol conversion data to generate an Internet of Things information platform.

[0010] The present invention ensures the accuracy and diversity of data sources by collecting basic telegraph signals, improves signal quality by implementing independent source signal filtering, provides a clear structure for subsequent data processing by reshaping the communication path topology and generating a communication path network, enhances the recognizability of signal features by intensity compression fingerprinting, supports the randomness and security of data by dynamic signal situation de-jamming, improves security during communication by network topology transition encryption, ensures the validity and reliability of data transmission by reconstruction of communication paths, provides new ideas for data protection by generating disturbance fragmented data by information disturbance simulation, enhances data security and anti-counterfeiting capability by applying encrypted fingerprint implantation processing, guarantees the adaptation of data transmission protocols by channel reconstruction, promotes data compatibility and interoperability between different platforms by generating protocol conversion data, and promotes the overall performance and response speed of the Internet of Things information platform by platform progressive optimization, which promotes the progress and application development of Internet of Things technology as a whole.

[0011] Preferably, step S1 comprises the following steps:

[0012] Step S11: collecting telegraph basic signals; performing fast Fourier transform on the telegraph basic signals to obtain telegraph spectrum data;

[0013] Step S12: performing blind source separation processing on the telegraph basic signal based on the telegraph spectrum data to obtain a filtered telegraph signal;

[0014] Step S13: performing amplitude spectrum conversion on the filtered telegraph signal to generate a telegraph signal amplitude spectrum; performing spectrum discretization processing on the telegraph signal amplitude spectrum to obtain spectrum deconstruction data;

[0015] Step S14: performing signal node mapping on the spectrum deconstruction data to generate node association data; reshaping the communication path topology according to the node association data to generate a communication path network.

[0016] The present invention ensures the true reflection and comprehensiveness of data by collecting basic telegraph signals, the implementation of fast Fourier transform improves the analysis efficiency of spectrum data, the telegraph spectrum data provides an important basis for subsequent signal processing, the application of blind source separation processing effectively removes interference signals, the generation of filtered telegraph signals improves signal clarity, the amplitude spectrum conversion provides a new perspective for signal feature extraction, the implementation of spectrum discrete processing enhances the understanding of signal frequency domain characteristics, the spectrum deconstruction data lays the foundation for in-depth analysis of signals, the process of signal node mapping realizes the correlation analysis between signals, the generated node correlation data provides data support for the reconstruction of communication path topology, the generation of communication path network lays the foundation for efficient communication of Internet of Things system, and the overall performance and reliability of Internet of Things information platform in data processing and communication management are improved.

[0017] Preferably, step S14 comprises the following steps:

[0018] Gridding the spectrum deconstruction data to obtain grid unit data; extracting node features from the grid unit data to obtain node feature data;

[0019] Calculate the similarity of node feature data to obtain a node similarity matrix; perform threshold cutting on the node similarity matrix based on a preset similarity threshold to generate node association data;

[0020] Connectivity analysis is performed based on node association data to obtain a node connectivity graph; the shortest path is determined for the node connectivity graph to generate a communication path set;

[0021] Bottlenecks are identified on a set of communication paths to obtain path bottleneck nodes; load balancing is performed based on the path bottleneck nodes to obtain balanced path data;

[0022] A path network reconstruction is performed on the communication path set based on the balanced path data to generate a communication path network.

[0023] The present invention ensures the meticulousness and accuracy of data processing through grid division of spectrum deconstruction data, the generation of grid unit data provides a basis for subsequent node feature extraction, the extraction of node feature data enhances the understanding of signal characteristics, the implementation of node similarity calculation provides quantitative indicators for the relationship analysis between signals, the generation of node similarity matrix lays the foundation for subsequent threshold cutting, the cutting based on similarity threshold realizes the effective screening of node related data, the execution of connectivity analysis ensures the comprehensive evaluation of network structure, the generation of node connectivity graph provides an intuitive basis for the optimization of communication path, the process of shortest path judgment improves the identification of communication efficiency, the generation of communication path set provides data support for network performance analysis, the implementation of bottleneck identification helps to determine the key nodes in the network, the application of load balancing processing ensures the reasonable allocation of network resources, the generation of balanced path data provides a guarantee for the optimization and reconstruction of the communication path network, and the overall performance and adaptability of the Internet of Things information platform in network management and optimization are improved.

[0024] Preferably, step S2 comprises the following steps:

[0025] Step S21: performing signal strength gradient encoding on the communication path network to obtain strength label data;

[0026] Step S22: compressing the signal dimension of the filtered telegraph signal based on the strength mark data to generate compressed mapping data; performing signal feature recognition on the compressed mapping data to obtain a signal feature fingerprint;

[0027] Step S23: performing signal situation tomography on the signal feature fingerprint to obtain situation analysis data; performing signal pulse reconstruction on the situation analysis data to generate pulse sequence data;

[0028] Step S24: performing signal frequency hopping arrangement based on the pulse sequence data to generate a signal frequency hopping matrix; performing signal interference suppression processing according to the signal frequency hopping matrix to generate anti-interference data;

[0029] Step S25: performing signal randomization enhancement on the anti-interference data to obtain random enhanced data.

[0030] The present invention ensures accurate characterization of signal quality through the implementation of signal intensity gradient coding, and the intensity marking data provides an important information basis for subsequent signal processing. The process of signal dimension compression effectively reduces the complexity of data processing, and the generation of compressed mapping data improves the efficiency of signal processing. The application of signal feature recognition enhances the understanding of signal characteristics, and the generation of signal feature fingerprints provides a basis for subsequent situation analysis. The execution of signal situation tomography realizes a comprehensive analysis of the signal state, and the situation analysis data lays a foundation for signal pulse reconstruction. The generation of pulse sequence data improves the stability and reliability of signal transmission. The implementation of signal frequency hopping arrangement enhances the anti-interference ability of the signal, and the generation of signal frequency hopping matrix provides flexibility for signal processing. The application of signal interference suppression processing ensures clear transmission of the signal, and the generation of anti-interference data improves the adaptability of the system in complex environments. The process of signal randomization enhancement provides a guarantee for the security and randomness of the data, and the generation of random enhancement data lays a foundation for the overall performance optimization and stable operation of the Internet of Things information platform.

[0031] Preferably, step S3 comprises the following steps:

[0032] Step S31: dynamically encode the random enhancement data to generate path redirection data;

[0033] Step S32: encrypt the path redirection data at the communication node according to the random enhancement data to obtain a communication encryption path;

[0034] Step S33: performing heterogeneous network mapping on the communication encryption path to obtain network mapping data; performing reliability evaluation on the communication encryption path according to the network mapping data to generate a path reliability index;

[0035] Step S34: Dynamically allocate weights based on the path reliability index to obtain path weight data; reconstruct the communication encryption path according to the path weight data to generate reconstructed path data.

[0036] The present invention improves the flexibility and adaptability of data transmission through the implementation of path dynamic encoding, the generation of path redirection data provides support for the effective transmission of signals in different network environments, the application of communication node encryption ensures privacy and security in the process of data transmission, the generation of communication encryption path provides guarantee for the confidentiality of data, the process of heterogeneous network mapping realizes the compatibility and interoperability between different networks, the network mapping data provides a basis for subsequent reliability evaluation, the generation of path reliability index enhances the quantitative evaluation of communication path stability and security, the implementation of dynamic weight allocation ensures the rational use of resources and the balance of network load, the generation of path weight data provides a basis for path allocation reconstruction, the generation of reconstructed path data improves communication efficiency and network performance, and the overall optimization of the data transmission capability and reliability of the Internet of Things information platform in a complex environment.

[0037] Preferably, step S4 comprises the following steps:

[0038] Step S41: performing information dimension disturbance simulation on the reconstructed path data to obtain simulated disturbance data;

[0039] Step S42: fragmenting the simulated disturbance data to generate disturbance fragmented data;

[0040] Step S43: cross-coding and reorganizing the disturbed fragmented data to obtain reorganized coded data; performing information offset replacement on the reorganized coded data to generate information replacement code;

[0041] Step S44: performing multiple encryption stacking on the random enhanced data based on information replacement coding to obtain encrypted superposition data; performing information fingerprint embedding processing on the encrypted superposition data to generate fingerprint embedding data.

[0042] The present invention enhances the randomness and complexity of data through the implementation of information dimension disturbance simulation, the generation of simulated disturbance data provides a basis for subsequent processing, the application of fragmentation processing effectively improves the security and privacy protection of data, the generation of disturbance fragmented data provides support for the decentralized storage and transmission of information, the process of cross-coding recombination improves the security and anti-attack ability of data, the generation of recombined coding data lays the foundation for multi-layer protection of information, the implementation of information offset replacement further enhances the obfuscation of data, the generation of information replacement coding provides a guarantee for the secure transmission of data, the application of multiple encryption stacking ensures the security and integrity of information during transmission, the generation of encrypted superposition data provides a stronger protection measure for information protection, the execution of information fingerprint implantation processing enhances the traceability and anti-tampering ability of data, and the generation of fingerprint embedded data provides important support for improving the overall security and reliability of the Internet of Things information platform.

[0043] Preferably, step S43 includes the following steps:

[0044] Performing length regularization processing on the disturbed fragmented data to obtain regularized fragmented data; segmenting and marking the regularized fragmented data to generate paragraph marking data;

[0045] Performing parity separation processing on the paragraph mark data to generate a parity sequence; rearranging the parity sequence to obtain a rearranged data sequence;

[0046] Perform grouping and merging processing on the rearranged data sequence to generate cross-grouped data; perform coding mapping on the cross-grouped data to obtain recombined coded data;

[0047] Performing reference point positioning on the reorganized coded data to obtain an initial reference point; performing offset calculation on the reorganized coded data according to the initial reference point to generate an offset vector;

[0048] Performing substitution rule mapping based on the offset vector to obtain the offset substitution rule; performing matrix transformation processing on the reorganized coded data according to the offset substitution rule to generate an information substitution matrix;

[0049] The information permutation matrix is ​​rearranged to generate information permutation code.

[0050] The present invention improves the consistency and operability of data by implementing length regularization processing, the generation of regularized fragmented data provides a basis for subsequent processing, the application of segmentation marks enhances the organization and manageability of data, the generation of paragraph mark data provides a clear structure for data analysis, the implementation of parity separation processing helps to improve the redundancy and reliability of data, the generation of parity sequences provides support for data error detection and recovery, the process of position rearrangement enhances the degree of data confusion, the generation of rearranged data sequences lays a foundation for subsequent security processing, the implementation of group merging processing enhances the integration of data, the generation of cross-group data enhances the flexibility of data processing, the application of coding mapping ensures the security and unpredictability of data, the generation of recombined coding data provides an effective means for information protection, the implementation of reference point positioning ensures the accuracy of data processing, the generation of initial reference points provides a reference for offset calculation, the generation of offset vectors enhances the randomness and security of data, the application of permutation rule mapping enhances the confusion and protection of data, the generation of information permutation matrix provides a guarantee for the secure storage of data, the implementation of information rearrangement ensures the security and integrity of data during transmission, and the information permutation coding finally generated improves the overall performance of the Internet of Things information platform in terms of data security and privacy protection.

[0051] Preferably, step S44 includes the following steps:

[0052] Performing hierarchical mapping processing on the information replacement code to obtain hierarchical mapping data;

[0053] The random enhanced data is divided into blocks to obtain a data block sequence; the data block sequence is cross-validated encoded to generate cross-validation code data;

[0054] Recursively merge the hierarchical mapping data and the cross-verification code data to generate a data merge sequence; perform multi-level encryption on the data merge sequence to obtain encrypted superposition data;

[0055] Extract features from the encrypted superposition data to obtain encrypted feature vectors; perform repeated pattern recognition on the encrypted feature vectors to generate encrypted pattern sequences;

[0056] Perform unique identification code screening based on the encryption mode sequence to obtain identification code data; perform watermark encoding processing on the identification code data to generate encrypted watermark data;

[0057] The encrypted overlay data and the encrypted watermark data are fused and mapped to generate mapped fused data; a verification code fingerprint is constructed based on the mapped fused data to generate fingerprint embedded data.

[0058] The present invention improves the structure and organization of data through the implementation of hierarchical mapping processing. The generation of hierarchical mapping data provides a clear hierarchical relationship for subsequent data processing. The application of block cutting ensures flexible processing and transmission of data. The generation of data block sequence provides support for data security. The implementation of cross-validation coding enhances the reliability and consistency of data. The generation of cross-validation code data provides a guarantee for data integrity. The process of recursive merging improves the integration and security of data. The generation of data merging sequence provides a basis for multi-level encryption. The application of multi-level encryption ensures the security of data during transmission. The encrypted superposition data obtained provides strong support for information protection. The implementation of feature extraction enhances the recognition and analysis capabilities of data. The encrypted feature vector The generation provides a basis for subsequent security processing, the application of repeated pattern recognition improves the data anomaly detection capability, the generated encryption pattern sequence provides an important guarantee for data security, the implementation of unique identification code screening ensures the uniqueness and traceability of the data, the generation of identification code data provides support for data management, the application of watermark coding processing enhances the copyright protection and anti-tampering capabilities of the data, the generated encrypted watermark data provides an additional layer of protection for information security, the implementation of fusion mapping improves the data integration and processing efficiency, the generation of mapping fusion data provides support for subsequent verification and monitoring, the process of verification code fingerprint construction ensures the integrity and security of the data, and the fingerprint embedded data finally generated improves the overall security and reliability of the Internet of Things information platform.

[0059] Preferably, step S5 comprises the following steps:

[0060] Step S51: performing channel mapping on the fingerprint embedded data to generate mapping channel features; dynamically reconstructing the spectrum of the mapping channel features to generate spectrum reconstruction data;

[0061] Step S52: performing differential channel segmentation on the spectrum reconstruction data to obtain multi-channel channel data; performing heterogeneous protocol encoding based on the multi-channel channel data to generate protocol conversion data;

[0062] Step S53: construct a device capability profile according to the protocol conversion data to obtain a device feature matrix; perform edge computing task decomposition on the device feature matrix to generate task slice data;

[0063] Step S54: Perform service quality prediction based on the task slice data to obtain service prediction data; perform platform closed-loop adjustment based on the service prediction data to generate an Internet of Things information platform.

[0064] The present invention improves the adaptability and efficiency of data transmission through the implementation of matching channel mapping, the generation of mapped channel features provides a basis for subsequent spectrum reconstruction, the application of dynamic spectrum reconstruction enhances the accuracy and stability of the signal, the generation of spectrum reconstruction data provides support for channel analysis, the implementation of differential channel segmentation ensures the clarity and independence of the signal, the generation of multi-channel channel data supports the parallel processing of signals, the application of heterogeneous protocol encoding improves the interoperability between different devices, the generation of protocol conversion data provides a guarantee for device communication, the implementation of device capability portrait construction enhances the understanding of device performance, the generation of device feature matrix provides a basis for subsequent optimization processing, the process of edge computing task decomposition improves computing efficiency and resource utilization, the generation of task slicing data provides support for the processing of complex tasks, the implementation of service quality prediction ensures the continuous improvement of user experience, the generation of service prediction data provides data support for platform optimization, and the application of platform closed-loop adjustment enhances the adaptability and intelligence level of the Internet of Things information platform, and finally realizes the comprehensive optimization and efficient operation of the Internet of Things information platform.

[0065] The present invention also provides an Internet of Things information platform implementation platform, characterized in that it is used to execute the Internet of Things information platform implementation method according to claim 1, and the Internet of Things information platform implementation platform includes:

[0066] The signal acquisition and reshaping module is used to acquire the basic telegraph signal; perform independent source signal filtering on the basic telegraph signal to obtain a filtered telegraph signal; perform communication path topology reshaping on the filtered telegraph signal to generate a communication path network;

[0067] The signal feature enhancement module is used to perform strength compression fingerprinting on the telegraph basic signal based on the communication path network to obtain the signal feature fingerprint; and to perform signal situation dynamic descrambling on the signal fingerprint feature to obtain random enhancement data;

[0068] The communication encryption reconstruction module is used to perform network topology transition encryption on the random enhanced data to obtain a communication encryption path; and to reconstruct the communication path based on the communication encryption path to generate reconstructed path data;

[0069] The information disturbance implantation module is used to simulate the information disturbance of the reconstructed path data to generate disturbance fragmentation data; based on the disturbance fragmentation data, the random enhancement data is encrypted and fingerprint implanted to generate fingerprint embedded data;

[0070] The platform dynamic iteration module is used to reconstruct the channel of fingerprint embedded data and generate protocol conversion data; the platform is progressively optimized according to the protocol conversion data to generate an Internet of Things information platform.

[0071] The present invention ensures the diversity and accuracy of data sources through the implementation of the signal acquisition and reshaping module, improves the signal quality by filtering independent source signals, provides a clear structure for subsequent data processing by reshaping the communication path topology and generating a communication path network, performs strength compression fingerprinting based on the communication path network, enhances the identifiability of signal features, and provides support for the randomness and security of data by dynamic de-jamming. The communication encryption and reconstruction module improves the security of data transmission through network topology transition encryption, and the generation of reconstructed path data ensures the validity and reliability of data transmission. The disturbance fragmentation data generated by the information disturbance implantation module provides a new idea for data protection, and the encryption fingerprint implantation process enhances the security and anti-counterfeiting ability of data. The platform dynamic iteration module provides a guarantee for the adaptation of the data transmission protocol through channel reconstruction, and the generation of protocol conversion data promotes the data compatibility and interoperability between different platforms. The platform progressive optimization improves the overall performance and response speed of the Internet of Things information platform, and promotes the progress and application development of the Internet of Things technology as a whole. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 A schematic diagram of the steps of a method for implementing an Internet of Things information platform;

[0073] Figure 2 Detailed implementation flow chart of step S2;

[0074] Figure 3 is a schematic diagram of a detailed implementation process of step S3;

[0075] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0076] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. 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 technicians in this field without creative work are within the scope of protection of the present invention.

[0077] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0078] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0079] To achieve this, please refer to Figures 1 to 3 , a method for implementing an Internet of Things information platform, comprising the following steps:

[0080] Step S1: collecting basic telegraph signals; filtering the basic telegraph signals by independent source signals to obtain filtered telegraph signals; reshaping the communication path topology of the filtered telegraph signals to generate a communication path network;

[0081] Step S2: Perform intensity compression fingerprinting on the telegraph basic signal based on the communication path network to obtain a signal feature fingerprint; perform signal situation dynamic descrambling on the signal fingerprint feature to obtain random enhanced data;

[0082] Step S3: encrypting the random enhanced data by network topology transition to obtain a communication encryption path; reconstructing the communication path based on the communication encryption path to generate reconstructed path data;

[0083] Step S4: performing information disturbance simulation on the reconstructed path data to generate disturbance fragmented data; performing encrypted fingerprint implantation processing on the random enhanced data based on the disturbance fragmented data to generate fingerprint embedded data;

[0084] Step S5: reconstruct the channel of the fingerprint embedded data to generate protocol conversion data; perform progressive platform optimization based on the protocol conversion data to generate an Internet of Things information platform.

[0085] The present invention ensures the accuracy and diversity of data sources by collecting basic telegraph signals, improves signal quality by implementing independent source signal filtering, provides a clear structure for subsequent data processing by reshaping the communication path topology and generating a communication path network, enhances the recognizability of signal features by intensity compression fingerprinting, supports the randomness and security of data by dynamic signal situation de-jamming, improves security during communication by network topology transition encryption, ensures the validity and reliability of data transmission by reconstruction of communication paths, provides new ideas for data protection by generating disturbance fragmented data by information disturbance simulation, enhances data security and anti-counterfeiting capability by applying encrypted fingerprint implantation processing, guarantees the adaptation of data transmission protocols by channel reconstruction, promotes data compatibility and interoperability between different platforms by generating protocol conversion data, and promotes the overall performance and response speed of the Internet of Things information platform by platform progressive optimization, which promotes the progress and application development of Internet of Things technology as a whole.

[0086] In an embodiment of the present invention, the method for implementing an Internet of Things information platform comprises the following steps:

[0087] Step S1: collecting basic telegraph signals; filtering the basic telegraph signals by independent source signals to obtain filtered telegraph signals; reshaping the communication path topology of the filtered telegraph signals to generate a communication path network;

[0088] In this embodiment, when collecting the basic telegraph signal, a high-precision radio frequency signal receiving device is used to perform full spectrum scanning on telegraph signals of different frequency bands, and the sampling rate is set between 500kHz and 5MHz through an adaptive sampling mechanism, and a superheterodyne receiver is used to perform mixing and down-conversion processing on signals of different frequencies. After the signal is converted into a baseband signal, a 16-bit ADC (analog-to-digital converter) is used for digitization, and the data is transmitted to an FPGA (field programmable gate array) for preliminary filtering preprocessing, and then the signal is stored in a high-speed cache to ensure data integrity and timing consistency, and a multi-level orthogonal transformation is used for time-frequency domain decomposition to extract basic features such as instantaneous amplitude, phase, and frequency of the signal, and a high-pass filter and a low-pass filter are used to perform bandpass processing on the baseband signal to ensure that the effective signal is not interfered with by the outside world. When filtering the basic telegraph signal as an independent source signal, a blind source separation (Blind Source Separation) is used. The fast independent component analysis (FastICA) algorithm is used to extract independent components from mixed signals. The maximum number of iterations is set to 500. The initial weight matrix is ​​initialized by Cholesky decomposition. In the iterative process, the non-Gaussian maximization criterion is used to calculate independent components, filter out background noise, interference signals and redundant signals, and normalize the signal. The double threshold method is used to remove abnormal signals, and finally the denoised filtered telegraph signal is obtained. When the communication path topology of the filtered telegraph signal is reshaped, the signal propagation path is calculated by the multi-point ranging method (Multilateration) based on the delay information of the received signal and the geographic coordinates of the transmitting source, and a weighted topological graph is constructed. The nodes in the graph represent signal relay stations, and the weights of the edges are calculated by the signal strength attenuation and delay parameters. The Dijkstra shortest path algorithm is used to calculate the optimal communication path, and the path is optimized in combination with the A* search algorithm. The heuristic function h(n) is set as the weighted sum of the path length and the signal quality, where the signal quality is evaluated by the signal-to-noise ratio (SNR), and finally the optimized communication path network is generated.

[0089] Step S2: Perform intensity compression fingerprinting on the telegraph basic signal based on the communication path network to obtain a signal feature fingerprint; perform signal situation dynamic descrambling on the signal fingerprint feature to obtain random enhanced data;

[0090] In this embodiment, when intensity compression fingerprinting is performed on the basic telegraph signal based on the communication path network, the signal is first decomposed into four layers using discrete wavelet transform (DWT), and the low-frequency and high-frequency components of the signal are extracted using the Daubechies (db4) wavelet basis. The low-frequency components are quantized, and the quantization step is set to 0.05. The quantized signal features are mapped to a 128-dimensional fingerprint feature vector, and then the local sensitive hashing (LSH) algorithm is used to construct a fingerprint index. The representative signal features are screened out through the Hamming distance to ensure the uniqueness and stability of the fingerprint features. The signal fingerprint is finally obtained. When the signal fingerprint feature is dynamically descrambled, the short-time Fourier transform (STFT) is used to analyze the time-frequency distribution of the signal. The window type is set to Hanning window, the window length is 256 points, and the overlap rate is 50%. The instantaneous frequency features are extracted in each time window, and the Markov random field (MRF) is used to model the signal situation. The state transition probability matrix is ​​defined, and the optimal situation path is derived in combination with the Viterbi algorithm. Finally, the descrambled situation features are combined with the original fingerprint data to generate random enhanced data.

[0091] Step S3: encrypting the random enhanced data by network topology transition to obtain a communication encryption path; reconstructing the communication path based on the communication encryption path to generate reconstructed path data;

[0092] In this embodiment, when the random enhanced data is encrypted for network topology transition, AES-256 (Advanced Encryption Standard) is used to perform symmetric encryption on the data, CBC (Cipher Block Chaining Mode) is selected as the encryption mode, and the initial vector (IV) is randomly generated to ensure the randomness and unpredictability of the encrypted data. Subsequently, RSA-2048 (asymmetric encryption algorithm) is used to encrypt the AES key to ensure the transmission security of the key. The encrypted data is sliced ​​according to the network topology path, each piece of data is 512 bytes long, and is sent to different network nodes through a multi-path transmission mechanism to form a communication encryption path. When the communication path is reconstructed based on the communication encryption path, the Border Gateway Protocol (BGP) is used to obtain the current network topology structure, and the delay, packet loss rate and bandwidth information of each path are collected. The entropy weight method is applied to comprehensively score the stability and security of each path, and the weight distribution range is set to 0.1 to 0.9. The weighted Dijkstra algorithm is used to calculate the optimal reconstruction path to ensure low latency and high security of data transmission. Finally, the data of each path is recombined to generate complete reconstructed path data.

[0093] Step S4: performing information disturbance simulation on the reconstructed path data to generate disturbance fragmented data; performing encrypted fingerprint implantation processing on the random enhanced data based on the disturbance fragmented data to generate fingerprint embedded data;

[0094] In this embodiment, when information disturbance simulation is performed on the reconstructed path data, the Poisson Noise model is used to disturb the data, the signal-to-noise ratio (SNR) is set to 40 dB, and the disturbance parameters are generated by a pseudo-random number generator (PRNG). The disturbance parameters are added to the reconstructed path data to simulate random interference in transmission. The data is then fragmented using a fixed block method, and the length of each fragment is set to 256 bytes. The fragmented data is randomly disrupted in storage order, and finally perturbed fragmented data is generated. When encrypted fingerprint implantation is performed on the random enhanced data based on the perturbed fragmented data, discrete cosine transform (DCT) is used to extract the low-frequency features of the data, and unique fingerprint information is embedded in the low-frequency coefficients. The fingerprint information is encoded by quantization index modulation (QIM), and the embedding strength factor is set to 0.1 to ensure the concealment and robustness of the fingerprint in the data. After the embedding is completed, the data is restored to a time domain signal using inverse DCT to finally generate fingerprint embedded data.

[0095] Step S5: reconstruct the channel of the fingerprint embedded data to generate protocol conversion data; perform progressive platform optimization based on the protocol conversion data to generate an Internet of Things information platform.

[0096] In this embodiment, when the fingerprint embedded data is channel reconstructed, a low-density parity check code (LDPC) is used to perform channel coding on the data, the code rate is set to 1 / 2, and modulation is performed through orthogonal frequency division multiplexing (OFDM) technology. The subcarrier interval is set to 15kHz, and the modulation mode is 64-QAM (quadrature amplitude modulation). The modulated data is transmitted through multiple channels, and the receiving end uses fast Fourier transform (FFT) for demodulation and channel equalization to finally generate protocol conversion data. When the platform is progressively optimized according to the protocol conversion data, Protobuf (protocol buffer) is used to serialize the data structure to ensure the compatibility of data between different platforms. The parsed data is stored in a distributed NoSQL database, and resource management and load balancing are performed through Kubernetes (container orchestration tool) to optimize the data processing process and storage efficiency, and finally build an efficient and stable Internet of Things information platform.

[0097] Preferably, step S1 comprises the following steps:

[0098] Step S11: collecting telegraph basic signals; performing fast Fourier transform on the telegraph basic signals to obtain telegraph spectrum data;

[0099] Step S12: performing blind source separation processing on the telegraph basic signal based on the telegraph spectrum data to obtain a filtered telegraph signal;

[0100] Step S13: performing amplitude spectrum conversion on the filtered telegraph signal to generate a telegraph signal amplitude spectrum; performing spectrum discretization processing on the telegraph signal amplitude spectrum to obtain spectrum deconstruction data;

[0101] Step S14: performing signal node mapping on the spectrum deconstruction data to generate node association data; reshaping the communication path topology according to the node association data to generate a communication path network.

[0102] In this embodiment, when collecting the basic telegraph signal, a high-sensitivity wide-band receiving antenna is used to receive the telegraph signal in the range of 0.1 MHz to 30 MHz, and the received analog signal is input into a superheterodyne receiver. The signal is converted into an intermediate frequency signal through a down-conversion process with a first intermediate frequency of 10.7 MHz. The intermediate frequency signal is filtered out by an analog bandpass filter with a bandwidth of 2 MHz to remove irrelevant frequency band interference. Subsequently, a 16-bit precision analog-to-digital converter (ADC) is used to digitize the signal at a rate of 2.5 MSps (million samples per second). The digital signal is input into an FPGA (field programmable gate array) platform. The FPGA performs signal preprocessing inside, including signal clock synchronization, DC offset correction and amplitude normalization. The preprocessed signal is stored in a 4 GB DDR4 high-speed cache. When performing fast Fourier transform (FFT) processing on the basic telegraph signal, the digital signal in the cache is first divided into time windows of 1024 sampling points each, and a Hanning window is used. The data in each time window is windowed by the 64-bit FFT algorithm to reduce spectrum leakage. The windowed data is converted to the frequency domain by the 64-bit FFT algorithm to obtain the telegraph spectrum data in the corresponding frequency range. The frequency resolution is determined by the sampling rate and the number of sampling points. Here, the resolution is 2.44kHz. The spectrum data obtained after conversion contains amplitude and phase information. The amplitude information is used to analyze the intensity distribution of the signal. The spectrum data is saved in the solid state drive (SSD). When blind source separation is performed on the telegraph basic signal based on the telegraph spectrum data, the independent component analysis (ICA) algorithm is used to achieve multi-source signal separation. The telegraph spectrum data is composed into a matrix X. First, it is centered to remove the mean, and then whitened to make the frequency components mutually orthogonal and the variance is 1. The whitened data is extracted by the FastICA algorithm for independent component extraction. The non-Gaussian measurement function is set to negative entropy, the maximum number of iterations is 500, and the convergence threshold is 0.0001, the extracted independent components represent the spectrum data of different signal sources. The separated signals are classified using a clustering method based on a Gaussian mixture model (GMM), and background noise and irrelevant signals are removed. Finally, the target signal component, i.e., the filtered telegraph signal, is obtained. When the filtered telegraph signal is converted to an amplitude spectrum, the amplitude information of the filtered signal is extracted, and the analytical form of the signal is generated using the Hilbert transform. Its envelope is calculated as the amplitude spectrum. The amplitude spectrum data is represented in a logarithmic compression form to enhance the visualization effect of the dynamic range. The amplitude spectrum data is then input into a discrete Fourier transform (DFT) module and subjected to frequency domain re-transformation. The converted data is resampled, and the sampling rate is set to 1 / 4 of the original data to reduce the amount of data while retaining the main features. After the conversion is completed, the amplitude spectrum of the telegraph signal is generated. When the amplitude spectrum of the telegraph signal is subjected to spectrum discretization processing, the K-means clustering algorithm is first applied to frequency segment the amplitude spectrum data. The number of clusters k is set to 5. The signal is divided into five frequency segments according to the amplitude intensity and the frequency change rate. Principal component analysis (PCA) is performed in each frequency segment, and the first three principal components are extracted as the representative features of the segment. The principal components of all frequency segments are combined to form a new spectrum feature matrix, and then the matrix is ​​discretized using differential coding. The continuous eigenvalues ​​are mapped to discrete values ​​within a fixed range to generate structured spectrum deconstruction data. When the spectrum deconstruction data is mapped to signal nodes, the eigenvectors in the spectrum deconstruction data are first converted into node features. Each eigenvector is regarded as a node in the network. The similarity between nodes is calculated by cosine similarity. The threshold is set to 0.85. If the similarity exceeds this threshold, edges are established between nodes to form a preliminary node graph structure. The PageRank algorithm is then used to sort the nodes by importance. Nodes with high weights represent the main propagation paths of the signal. All node information and their relationships generate node associations. Data, which contains the frequency characteristics, similarity weights and topological structure information of each node. When reshaping the communication path topology according to the node association data, a weighted graph model is used to build a communication path network. The node association data is imported into the graph analysis tool, and the Dijkstra algorithm is used to calculate the shortest path. At the same time, the Bellman-Ford algorithm is combined to process negative weight edges to ensure the accuracy of path calculation. In the process of path calculation, the node weight and edge weight are comprehensively considered, and the weight parameter is set to a ratio of 0.6:0.4, which represents the relative importance of node characteristics and signal strength, and finally an optimized communication path network is formed. .

[0103] Preferably, step S14 comprises the following steps:

[0104] Gridding the spectrum deconstruction data to obtain grid unit data; extracting node features from the grid unit data to obtain node feature data;

[0105] Calculate the similarity of node feature data to obtain a node similarity matrix; perform threshold cutting on the node similarity matrix based on a preset similarity threshold to generate node association data;

[0106] Connectivity analysis is performed based on node association data to obtain a node connectivity graph; the shortest path is determined for the node connectivity graph to generate a communication path set;

[0107] Bottlenecks are identified on a set of communication paths to obtain path bottleneck nodes; load balancing is performed based on the path bottleneck nodes to obtain balanced path data;

[0108] A path network reconstruction is performed on the communication path set based on the balanced path data to generate a communication path network.

[0109] In this embodiment, when the spectrum deconstruction data is gridded, the spectrum deconstruction data is first mapped to a two-dimensional coordinate plane, the horizontal axis represents frequency, the vertical axis represents amplitude, the coordinate range is set to a frequency range of 0 to 30 MHz and an amplitude range of 0 to 100 dB, and a rectangular unit with a fixed grid size of 100 kHz×5 dB is used to evenly divide the entire spectrum area. During the division process, an R-tree data structure based on a spatial index is used to accelerate data positioning, and each spectrum data point is mapped to a corresponding grid unit according to its frequency and amplitude coordinates. Each grid unit contains all spectrum data points in the unit and their characteristic values, and finally a complete grid unit data set is formed. When node features are extracted from the grid unit data, four characteristic values ​​of average amplitude, maximum amplitude, frequency change rate and amplitude volatility are calculated for the data points in each grid unit. The average amplitude is obtained by taking the arithmetic average of the amplitudes of all data points in the unit, the maximum amplitude is the maximum amplitude value in the unit, and the frequency change rate is calculated by The frequency difference between adjacent data points is calculated to obtain the mean value, and the amplitude volatility is expressed by the standard deviation. The four eigenvalues ​​are combined to form the eigenvector of each grid unit. The dimension of the eigenvector is 4. The eigenvector is normalized using the Z-score standardization method. When calculating the similarity of node feature data, the cosine similarity algorithm is used to measure the similarity of the eigenvectors between nodes. All node feature vectors are combined in pairs to calculate their cosine similarity. The cosine similarity calculation formula is the vector dot product divided by the product of the vector modulus. The similarity value range is 0 to 1. The closer the similarity is to 1, the more similar the node features are. The calculation results are stored in the form of a symmetric matrix. The rows and columns of the matrix correspond to each node respectively. The elements in the matrix represent the similarity between the corresponding node pairs, and finally form a complete node similarity matrix. When the node similarity matrix is ​​threshold cut based on the preset similarity threshold, the similarity threshold is set to 0.85. Each element in the node similarity matrix is ​​traversed. When the element value is greater than or equal to 0.When the node is 85, the corresponding node pair is marked as being mutually associated, otherwise it is marked as being unassociated. The information of all mutually associated node pairs is extracted to form a node association list. Each node association contains the identifiers of two nodes and their similarity values. The node association list is further converted into a node association data structure, which contains node identifiers, similarity weights, and association relationships. When performing connectivity analysis based on node association data, the depth-first search (DFS) algorithm is used to traverse the node association data and build a connectivity graph. First, any node is selected as the starting node, and all its directly associated nodes are added to the access queue. These nodes are recursively visited until all reachable nodes have been visited to form a connected subgraph. This process is repeated until all nodes have been visited, and finally a complete node connectivity graph is formed. The connectivity graph is represented by an adjacency list. Each node records all its adjacent nodes and the corresponding similarity weights. The adjacency list structure is stored in the database. When performing the shortest path judgment on the node connectivity graph, the Dijkstra algorithm is used to calculate the shortest path from any source node to all other nodes. The distance from the source node to itself is initialized to 0, and the distance to other nodes is Infinity, add the source node to the priority queue, visit the neighbor nodes directly connected to the current node in turn, and update the shortest distance of the neighbor nodes. When the queue is empty, the algorithm ends, and the shortest paths of all nodes have been calculated. The shortest path set contains all node sequences on the path and the corresponding path lengths. When the communication path set is bottleneck identified, the path bottleneck node is determined by the method based on traffic analysis. First, the access frequency of each node on each shortest path is counted. The higher the frequency, the greater the communication load of the node in the network. All nodes are sorted by access frequency, and the top 10% of the nodes are selected as candidate bottleneck nodes. The load coefficient of these nodes is further calculated. The load coefficient is defined as the ratio of the actual processing traffic of the node to its maximum processing capacity. When the load coefficient is greater than 0.9, the node is determined to be a path bottleneck node. The bottleneck node information includes the node identifier, load coefficient and the path where it is located. When load balancing is performed based on the path bottleneck node, the link reallocation strategy is used to adjust the communication path. First, the path where the bottleneck node is located is analyzed to find an alternative backup path. The total length of the backup path must not exceed 1 of the original path.2 times, and does not pass through other identified bottleneck nodes. Use the A* algorithm to search for the optimal backup path in the node connectivity graph, and redistribute part or all of the traffic on the original path to the backup path. The adjusted path set is called balanced path data. The balanced path data contains the adjusted node sequence, path length, and load distribution ratio. When reconstructing the path network of the communication path set based on the balanced path data, a weighted directed graph model is used to represent the adjusted communication network. The nodes represent communication devices, and the edges represent communication links between devices. The weights of the edges are set according to the load distribution ratio in the balanced path data. The Kruskal algorithm is used to construct a minimum spanning tree to ensure network connectivity while minimizing the total communication overhead. The generated communication path network contains all device nodes and the optimized communication links between them. .

[0110] Preferably, step S2 comprises the following steps:

[0111] Step S21: performing signal strength gradient encoding on the communication path network to obtain strength label data;

[0112] Step S22: compressing the signal dimension of the filtered telegraph signal based on the strength mark data to generate compressed mapping data; performing signal feature recognition on the compressed mapping data to obtain a signal feature fingerprint;

[0113] Step S23: performing signal situation tomography on the signal feature fingerprint to obtain situation analysis data; performing signal pulse reconstruction on the situation analysis data to generate pulse sequence data;

[0114] Step S24: performing signal frequency hopping arrangement based on the pulse sequence data to generate a signal frequency hopping matrix; performing signal interference suppression processing according to the signal frequency hopping matrix to generate anti-interference data;

[0115] Step S25: performing signal randomization enhancement on the anti-interference data to obtain random enhanced data.

[0116] In this embodiment, when performing signal strength gradient coding on a communication path network, the signal strength of each communication link in the network is first measured. The measurement tool uses a high-precision radio frequency signal analyzer, the measurement frequency band range is set at 2.4 GHz to 2.5 GHz, the measurement step is 1 MHz, and the signal strength of each link is recorded in dBm (decibel milliwatt). The measured signal strength value is divided into different strength levels at intervals of 0.5 dB, and 10 gradient intervals are generated from the lowest signal strength to the highest signal strength. A unique coding value is assigned to each gradient interval, and the coding value is represented by 8-bit binary. The gradient coding corresponding to the signal strength is stored in association with the communication link. The generated coding data set is the strength tag data. The strength tag data format is a key-value pair, the key is the communication link identifier, and the value is the gradient coding. When performing signal dimension compression on the filtered telegraph signal based on the strength tag data, the received telegraph signal is first matched based on the strength tag data to extract the signal segment corresponding to the strength tag. The signal segment is processed by short-time Fourier transform (STFT). The signal feature vector is compressed from the original 1024 to 64 dimensions. The compressed mapping data is saved in matrix form, and each row represents the feature vector of a signal segment. When the compressed mapping data is used for signal feature recognition, the convolutional neural network (CNN) model is used to classify and recognize the compressed signal feature vector. The CNN model contains 3 convolutional layers, 2 pooling layers and 1 fully connected layer. The convolution kernel size is set to 3×3, the activation function uses ReLU (linear rectifier unit), the pooling layer uses the maximum pooling method, and the pooling window size is 2×2. The training data set uses data with known signal patterns. After the model training is completed, the compressed mapping data is input and the corresponding signal feature category is output. The signal feature category is represented in the form of a 32-bit hash value to form a signal feature fingerprint. When the signal feature fingerprint is subjected to signal situation tomography, the tomography algorithm is used to reconstruct the feature fingerprint data in three dimensions. The algorithm is based on back projection (Filtered The FBP (Frequency Back Projection) method maps the two-dimensional feature fingerprint data to three-dimensional space. During the reconstruction process, the Gauss filter is used to smooth the data to reduce noise interference. In the three-dimensional situation map generated by the tomography result, the X axis represents time, the Y axis represents frequency, and the Z axis represents signal strength. The hot spots in the situation map are represented by color gradients to represent the intensity difference. The situation analysis data generated is stored in the form of a three-dimensional coordinate point set. Each coordinate point contains time, frequency and signal strength values. When reconstructing the signal pulse of the situation analysis data, the peak point in the situation analysis data is first extracted. The peak point corresponds to the key pulse position of the signal. The adaptive threshold algorithm is used to identify the peak point, and the threshold is set to 1 of the average signal strength.5 times, the identified peak points are arranged in chronological order, and the shape of the pulse signal is fitted using the least squares method. The pulse width is set to 1μs to 5μs. The reconstructed pulse signal is saved in the form of sequence data. Each pulse contains timestamp, frequency and amplitude information. When performing signal frequency hopping arrangement based on pulse sequence data, the frequency hopping spread spectrum (FHSS) technology is used to dynamically schedule the pulse signal on different frequency channels. First, the frequency range and time window of each pulse are extracted according to the pulse sequence data, and the number of frequency hopping channels is set to 128, and the width of each channel is 200kHz. A pseudo-random number generator (PRNG) is used to generate a frequency hopping sequence. The seed value of the frequency hopping sequence is generated based on the pulse timestamp to ensure the randomness and repeatability of the frequency hopping mode. The generated frequency hopping matrix is ​​stored in the form of a two-dimensional array, with rows representing time windows and columns representing frequency channels. The elements in the matrix represent the frequency channel numbers in the corresponding time windows. When performing signal interference suppression processing based on the signal frequency hopping matrix, an adaptive filter (Adaptive Filter) to identify and suppress interference on the signal. The filter type is LMS (Least Mean Square, least mean square) adaptive filter, the initial weight of the filter parameters is set to 0.01, the step factor is 0.005, the input signal is the frequency channel signal in the frequency hopping matrix, the reference signal is the historical interference-free signal sample, the filter adjusts the weight in real time to minimize the error between the input signal and the reference signal, the signal output by the filter is the signal after the interference is removed, the generated anti-interference data is stored in the form of a matrix, including the denoised signal strength value in each time window, when the anti-interference data is subjected to signal randomization enhancement, the time domain perturbation and frequency domain expansion technology are used to enhance the randomness of the signal, firstly, the signal is slightly perturbed in the time domain, the perturbation range is set to ±2% of the signal strength, the perturbation pattern is generated based on Gaussian white noise, and then the signal is expanded in the frequency domain, the short-time Fourier transform (STFT) is used to convert the signal to the frequency domain, and a pseudo-random phase offset is applied to the frequency component, the phase offset range is 0 to π, and after the frequency domain expansion is completed, the signal is converted back to the time domain by the inverse Fourier transform (IFFT), and the random enhancement data finally generated contains the time domain waveform and frequency domain characteristics of the signal. .

[0117] Preferably, step S3 comprises the following steps:

[0118] Step S31: dynamically encode the random enhancement data to generate path redirection data;

[0119] Step S32: encrypt the path redirection data at the communication node according to the random enhancement data to obtain a communication encryption path;

[0120] Step S33: performing heterogeneous network mapping on the communication encryption path to obtain network mapping data; performing reliability evaluation on the communication encryption path according to the network mapping data to generate a path reliability index;

[0121] Step S34: Dynamically allocate weights based on the path reliability index to obtain path weight data; reconstruct the communication encryption path according to the path weight data to generate reconstructed path data.

[0122] In this embodiment, when dynamically encoding the path of random enhanced data, the signal features in the random enhanced data are first extracted. The signal features include parameters such as frequency, strength and timestamp. The parameters are encoded using a hash function. The hash function uses the SHA-256 algorithm. The length of the generated hash value is 256 bits of binary data. Then the hash value is combined with the unique identifier of the IoT device (such as the MAC address). The combination method is to use the MAC address as a prefix and the hash value as a suffix to form a unique path encoding data. In order to increase the dynamic nature of the path, a timestamp disturbance is introduced before each data transmission. The disturbance range is ±50 milliseconds. The timestamp after the disturbance is then involved in the hash calculation to ensure that the path encoding generated each time is unique and random. The path redirection data finally generated is stored in the form of a key-value pair, where the key is the device identifier and the value is the dynamically encoded path data. When the path redirection data is encrypted for communication nodes according to the random enhanced data, the elliptic curve encryption algorithm (Elliptic Curve Encryption Algorithm) is used. The path redirection data is encrypted by using ECC (Electronic Code Cryptography). First, the key seed is extracted from the random enhanced data. The key seed is generated based on the signal strength and timestamp. The signal strength is the difference between its maximum and minimum values, and the timestamp is the results of the three most recent disturbances. The two are combined to generate a 128-bit key seed. The key seed is used to generate the public key and private key of ECC. The public key is used to encrypt the path redirection data. The encrypted ciphertext includes the device identifier and the encrypted path. The ciphertext is transmitted in Base64 encoding format. The final communication encryption path is stored in the form of encrypted data packets. Each data packet contains the path number, public key information and encrypted path data. When performing heterogeneous network mapping on the communication encryption path, first identify the different network protocol types in the IoT environment, including Wi-Fi, Bluetooth, ZigBee and LoRa, and use the protocol parser to extract the Characteristic parameters, such as frequency band, bandwidth and delay, etc., match the device identifier in the communication encryption path with the corresponding network protocol parameters to generate a mapping relationship for heterogeneous networks. During the mapping process, multi-protocol gateway devices are used for data conversion to ensure compatibility between different protocols. The generated network mapping data is represented in the form of a multi-layer matrix. The rows of the matrix represent device nodes, and the columns represent network protocol types. The elements in the matrix represent the connection status and signal quality of the device under the network protocol. When evaluating the reliability of the communication encryption path based on the network mapping data, the Bayesian network model is used to infer the reliability of the path. First, the signal quality, delay and packet loss rate in the network mapping data are used as input parameters. The signal quality is in dBm, the delay is in milliseconds, and the packet loss rate is expressed in percentage. Different path state nodes are defined in the Bayesian network model, including high reliability, medium reliability and low reliability states. The conditional probability distribution of the Bayesian network is trained using historical data.Input the current network mapping data, and the model outputs the reliability probability value of the corresponding path. The reliability probability value ranges from 0 to 1. The closer the value is to 1, the more reliable the path is. The path reliability index generated is in floating point form, retaining two decimal places. When dynamically allocating weights based on the path reliability index, the weighted round-robin algorithm (Weighted RoundRobin, WRR) is used to assign weights to each path. First, the initial weight of each path is calculated according to the path reliability index. The weight calculation formula is the reliability index multiplied by 100. The generated initial weight is an integer value. Then the weights of all paths are normalized to ensure that the total weight sum is 100%. After normalization, the weights are assigned to different communication paths. The weight allocation data is represented in the form of a list. Each element in the list contains a path identifier and a corresponding weight value. During the dynamic weight allocation process, the weight data is updated every 30 seconds to cope with changes in network status. The generated path weight data is stored in the load balancing module of the Internet of Things platform. When the communication encryption path is reconstructed for path allocation based on the path weight data ,The weight-based path selection algorithm is used to route data packets. First, the path weight data is loaded into the routing allocation module. The module schedules the data packets according to the weight data. The higher the weight, the more data packets are allocated to the path. The priority and size of the data packet are considered during the path selection process. Data packets with high priority are preferentially allocated to high-weight paths. Data packets with a size of more than 1MB are allocated to paths with larger bandwidth. After the path allocation is completed, the communication encryption path is reconstructed. The reconstructed path includes a path sequence and a data packet allocation table. The path sequence represents the transmission order of the data packet. The data packet allocation table records the transmission path and timestamp of each data packet. The reconstructed path data finally generated is stored in the routing management module of the Internet of Things platform. ,

[0123] Preferably, step S4 comprises the following steps:

[0124] Step S41: performing information dimension disturbance simulation on the reconstructed path data to obtain simulated disturbance data;

[0125] Step S42: fragmenting the simulated disturbance data to generate disturbance fragmented data;

[0126] Step S43: cross-coding and reorganizing the disturbed fragmented data to obtain reorganized coded data; performing information offset replacement on the reorganized coded data to generate information replacement code;

[0127] Step S44: performing multiple encryption stacking on the random enhanced data based on information replacement coding to obtain encrypted superposition data; performing information fingerprint embedding processing on the encrypted superposition data to generate fingerprint embedding data.

[0128] In this embodiment, when performing information dimension disturbance simulation on the reconstructed path data, the path sequence, data packet allocation table and timestamp are first extracted from the reconstructed path data, and a multidimensional Gaussian noise model is used to perform disturbance processing on these data. The mean of the Gaussian noise is set to 0, and the standard deviation is set to 5% of the path delay. The node order change is randomly introduced into the path sequence, and the transmission timestamp in the data packet allocation table is increased or decreased by a random offset within the range of ±10 milliseconds. At the same time, the path identifier is randomly replaced, and the replacement range is limited to the node ID within the same network topology. The perturbed data is subjected to orthogonal experimental design (Orthogonal Experiment Design). Design) to ensure the diversity of data dimensions. The final generated simulated disturbance data is stored in JSON format. The fields include the path sequence after disturbance, the data packet allocation table and the corresponding offset. The data is stored in the simulation analysis module of the Internet of Things platform. When the simulated disturbance data is fragmented, a combination of fixed-length segmentation and variable-length random segmentation is adopted. First, each field of the simulated disturbance data is preliminarily segmented according to 512 bytes to generate basic data blocks. Then, a random segmentation algorithm is used for each basic data block to randomly generate small data fragments ranging from 64 to 256 bytes. The seed of random segmentation is generated based on the timestamp of the disturbance data to ensure the randomness and unpredictability of each segmentation. The fragmented data after segmentation records the original order through the index table. The index table includes the fragment number, the original data position and the check code. The check code is generated using the CRC32 algorithm to ensure data integrity. The generated disturbance fragmented data is stored in binary format. The index table and the fragment data are respectively stored in the fragment management module of the Internet of Things platform. When cross-coding and reorganizing the disturbance fragmented data, cross-coding (Cross-Interleaving The fragmented data is rearranged and combined using the Bitwise Coding algorithm. First, the odd-numbered fragments in the fragmented data are selected as the main sequence, and the even-numbered fragments are selected as the auxiliary sequence. The main and auxiliary sequences are merged in a fixed alternating order. The merging rule is to insert 2 auxiliary sequence fragments after inserting 3 main sequence fragments. In the merging process, the XOR operation is used to encode adjacent fragments to ensure the confusion of data at the logical level. The encoded data records the index and encoding position of the original fragment through the position mapping table. The position mapping table is saved using Base64 encoding to ensure the security of data transmission. The generated recombined encoded data is stored in the encoding processing module of the Internet of Things platform in the form of a byte stream. When performing information offset replacement on the recombined encoded data, a combination of cyclic shift (Cyclic Shift) and bitwise permutation (Bitwise Permutation) is used. First, the recombined encoded data is divided into 128-byte blocks. The data in each block performs an 8-bit cyclic shift. The offset direction is random, and the offset step is determined based on the difference in the number of parity bits in the data block.Then, the shifted data block is displaced and replaced. The replacement rule is executed according to the preset replacement matrix. The replacement matrix is ​​an 8×8 binary matrix. The matrix is ​​generated based on the hash value of the initial key. After the replacement is completed, the information replacement code is generated. The replacement code is saved in hexadecimal form and stored in the replacement module of the Internet of Things platform. When multiple encryption layers are stacked on the random enhanced data based on the information replacement code, a layered encryption (Layered Encryption) strategy is adopted. First, the Advanced Encryption Standard (AES-256) is used to perform the first layer of encryption on the random enhanced data. The encryption key is generated based on the information replacement code. Then, the AES key is encrypted using RSA-2048 public key encryption to form the second layer of protection. Finally, the national secret SM4 algorithm is used to encrypt the entire data packet to ensure the security and complexity of multiple encryption. The corresponding encryption algorithm identifier is marked in the header of the data packet after each layer of encryption. The generated encrypted superposition data is stored in the encryption module of the Internet of Things platform in the form of an encrypted data stream. When the encrypted superposition data is processed for information fingerprint implantation, a digital watermark (Digital Watermark) is used. Watermarking) technology is used to embed a unique information fingerprint into the encrypted data. The information fingerprint is generated based on the SHA-512 algorithm. The input data is the device identifier, timestamp, and path redirection data. The generated fingerprint is 512-bit binary data. The fingerprint implantation adopts the least significant bit (LSB) embedding technology to embed the fingerprint data into the non-sensitive bit of the encrypted data. Every 128 bytes of data is embedded with 4 bits of fingerprint information. The embedding position is determined based on the pseudo-random number generator (PRNG) to ensure the concealment and anti-tampering ability of the fingerprint information. The implanted fingerprint embedded data is saved in an encrypted format and stored in the fingerprint management module of the IoT platform.

[0129] Preferably, step S43 includes the following steps:

[0130] Performing length regularization processing on the disturbed fragmented data to obtain regularized fragmented data; segmenting and marking the regularized fragmented data to generate paragraph marking data;

[0131] Performing parity separation processing on the paragraph mark data to generate a parity sequence; rearranging the parity sequence to obtain a rearranged data sequence;

[0132] Perform grouping and merging processing on the rearranged data sequence to generate cross-grouped data; perform coding mapping on the cross-grouped data to obtain recombined coded data;

[0133] Performing reference point positioning on the reorganized coded data to obtain an initial reference point; performing offset calculation on the reorganized coded data according to the initial reference point to generate an offset vector;

[0134] Performing substitution rule mapping based on the offset vector to obtain the offset substitution rule; performing matrix transformation processing on the reorganized coded data according to the offset substitution rule to generate an information substitution matrix;

[0135] The information permutation matrix is ​​rearranged to generate information permutation code.

[0136] In this embodiment, when the disturbed fragmented data is length-regularized, all the fragmented data are first standardized into groups of 128 bytes. Data fragments less than 128 bytes are padded by filling randomly generated binary noise at the end of the data. Data fragments exceeding 128 bytes are truncated according to multiples of 128 bytes, and the truncated parts are stored as independent fragments. The filling noise is generated by a pseudo-random number generator (Pseudo-Random Number Generator). NumberGenerator, PRNG), the generated seed is based on the original timestamp and node ID of the fragment. When the regular fragment data is segmented and marked, a unique paragraph identifier is first assigned to each regular fragment. The identifier is generated by the hash value of the original position of the fragment and the current regular sequence number. The hash algorithm uses the SHA-256 algorithm. The generated paragraph mark data includes the paragraph number, the original position index and the data length information. All marked data are packaged and stored in JSON format. The fields include "segment_id" (paragraph ID), "original_index" (original index) and "length" (data length). When the paragraph mark data is processed for parity separation, the data is divided into odd sequences and even sequences according to the parity of the paragraph number. The paragraph number is extracted through the "segment_id" field in the paragraph tag, and the odd-numbered paragraph number is The data is stored in the odd sequence buffer, and the even-numbered segment data is stored in the even sequence buffer. When the odd and even sequences are rearranged, a random position rearrangement method based on the Fisher-Yates shuffle algorithm is used. First, random rearrangement is performed independently within the odd and even sequences. The random seeds for the rearrangement are generated based on the device ID and the current timestamp to ensure the unpredictability of each rearrangement. After the rearrangement, the odd and even sequences are alternately merged to generate the final rearranged data sequence. The merging order is 2 odd segments followed by 1 even segment. When the rearranged data sequence is grouped and merged, the data sequence is divided into groups of 256 bytes, and each 4 groups of data are cross-merged as a unit. The cross-merging rule is that the first group and the third group are cross-linked at the byte level, and the second group and the fourth group are cross-linked at the bit level. The cross-merging uses a bit exclusive OR (XOR) operation to enhance the data confusion. When encoding and mapping the cross-grouped data, Hamming Code is used for error detection and correction encoding. Each group of data passes through 7,4 Hamming coding, the generated redundant check bits are attached to the original data to form a complete coding block. The coded data block is secondary mapped through a hash mapping table. The mapping rule is determined based on the hash function generated by the initial device ID. When locating the reference point of the recombined coded data, the sliding window method is used. Window) searches for a specific reference pattern in the encoded data. The reference pattern is a predefined binary sequence "10101010". Each time a reference pattern is detected, its starting position in the data stream is recorded. All reference point positions are stored in the reference point index table. The index table is saved in JSON format. The fields include "base_point" (reference point position) and "pattern" (reference pattern). When calculating the offset of the reorganized encoded data based on the initial reference point, the byte offset between each reference point and the starting point of the data stream is calculated. The offset calculation uses a simple byte accumulation method. All offsets are stored in the offset vector table. The offset vector table format is CSV, including the fields "offset" (offset) and "base_point" (corresponding to the reference point). When performing permutation rule mapping based on the offset vector, the offset vector is input into the permutation rule generator. The permutation rule generator is based on the LinearCongruential Generator (LinearCongruential Generator). Generator (LCG) generates a set of permutation matrices with a matrix dimension of 8×8. Each element in the matrix represents the new position of the data in the reorganized coded data. When the reorganized coded data is subjected to matrix transformation according to the offset permutation rule, the reorganized coded data is divided into 8×8 byte blocks. Each data block undergoes byte position transformation according to the permutation matrix rule. The transformed data blocks are reassembled into an information permutation matrix. Each row in the matrix represents a permuted data sequence. When the information permutation matrix is ​​rearranged, the column-major order is used to rearrange the data in the matrix. The rearrangement order is determined based on the random sequence generated by the initial offset vector. The rearranged data stream is the final information permutation code.

[0137] Preferably, step S44 includes the following steps:

[0138] Performing hierarchical mapping processing on the information replacement code to obtain hierarchical mapping data;

[0139] The random enhanced data is divided into blocks to obtain a data block sequence; the data block sequence is cross-validated encoded to generate cross-validation code data;

[0140] Recursively merge the hierarchical mapping data and the cross-verification code data to generate a data merge sequence; perform multi-level encryption on the data merge sequence to obtain encrypted superposition data;

[0141] Extract features from the encrypted overlay data to obtain encrypted feature vectors; perform repeated pattern recognition on the encrypted feature vectors to generate encrypted pattern sequences;

[0142] Perform unique identification code screening based on the encryption mode sequence to obtain identification code data; perform watermark encoding processing on the identification code data to generate encrypted watermark data;

[0143] The encrypted overlay data and the encrypted watermark data are fused and mapped to generate mapped fused data; a verification code fingerprint is constructed based on the mapped fused data to generate fingerprint embedded data.

[0144] In this embodiment, when performing hierarchical mapping processing on the information replacement code, the information replacement code is first segmented according to a fixed length of 256 bytes, and each segment of data is used as a hierarchical unit. The mapping rule is determined by the hash function generated by the device ID and the timestamp. The MD5 hash algorithm is used to generate a hash value for each data segment and use it as the mapping index. The mapping result is stored as hierarchical mapping data in JSON format. The fields include "layer_index" (hierarchical index), "hash_value" (hash value) and "data_segment" (data segment). When the random enhanced data is divided into blocks, the random enhanced data is first cut into 512 bytes as a basic unit. A fixed offset is used in the cutting process to ensure that the length of each data block is consistent. The data block number after cutting is generated according to the original order of the data. The numbering information is stored together with the data block as a data block sequence. The data block is stored in binary format, and the numbering information is recorded in CSV format, including the fields "block_id" (data block ID) and "offset" (offset). When cross-validation encoding is performed on the data block sequence, CRC (CyclicRedundancy The cyclic redundancy check (CRC) algorithm generates a check code for each data block. The check polynomial selects the CRC-32 standard polynomial. The check code is attached to the end of the corresponding data block to generate cross-validation coded data. To further enhance data integrity, triple redundancy (Triple Modular Redundancy,The data block is copied using the TMR method, and the three sets of data are compared and verified. When the hierarchical mapping data and the cross-verification code data are recursively merged, the hierarchical mapping data and the cross-verification code data are first aligned according to the paragraph number, and the data segments with the same paragraph number are merged at the byte level. The divide and conquer algorithm (Divide and Conquer) is used for recursive processing. The new data generated after each level of merging is merged with the next layer of data again. The recursive depth is set to 3 layers. The merged data is stored as a data merge sequence. The sequence format is a binary stream. The merge index is recorded in XML format, including "merge_level" (merge level) and "segment_id" (segment ID). When the data merge sequence is encrypted at multiple levels, AES-256 is first used to perform the first layer of encryption on the data merge sequence. The encryption key is generated based on the device ID and timestamp. The second layer of encryption uses RSA-2048 public key encryption. The public key is obtained from the key management system of the IoT platform. The third layer of encryption uses a hash chain (Hash Chain) encryption algorithm, the seed of the chain is generated by the first two layers of encrypted data. When extracting features from the encrypted superimposed data, the principal component analysis (PCA) algorithm is used to extract the main feature vectors from the encrypted data. The feature extraction dimension is set to 128 dimensions, and each feature vector is represented as a 128-dimensional floating-point array. Data standardization is used in the extraction process to ensure that the numerical distribution of each feature dimension is consistent. The feature vector is stored as an encrypted feature vector in CSV format, containing the fields "feature_vector" (feature vector) and "segment_id" (segment ID). When performing repeated pattern recognition on the encrypted feature vector, dynamic time warping (Dynamic Time Warping) is used. The DTW algorithm is used to detect repeated patterns in feature vectors. First, the distance matrix between feature vectors is calculated. Similar sequences are identified by minimizing path matching. The identified repeated patterns are stored as encrypted pattern sequences in JSON format, including the fields "pattern_id" (pattern ID), "start_index" (starting index) and "length" (pattern length). When unique identification codes are screened based on the encrypted pattern sequence, the pattern ID is extracted from the encrypted pattern sequence and a unique identification code is generated. The SHA-512 hash algorithm is used to hash the pattern ID. The generated hash value is the identification code data. Duplicate identification codes are removed during the screening process to ensure the uniqueness of each identification code. The identification code data is stored in CSV format, including the fields "unique_id" (unique identification code) and "pattern_reference" (pattern reference). When watermark encoding is performed on the identification code data, Discrete Cosine Transform (Discrete Cosine Transform,The DCT algorithm is used to encode the identification code watermark. First, the identification code is converted into binary format, and then embedded in a specific frequency domain position through the DCT algorithm. The encoded data is used as the encrypted watermark data. The watermark data is stored in the watermark encoding module in binary format. The encoding parameters are recorded in JSON format, including the fields "frequency_band" and "embedding_strength". When the encrypted superposition data and the encrypted watermark data are fused and mapped, the encrypted watermark data is embedded into the multi-scale representation of the encrypted superposition data by wavelet transform. First, the encrypted superposition data is decomposed into three layers of wavelets, and the detail coefficients are selected to partially embed the watermark data. The embedded data is restored to the mapped fused data through inverse wavelet transform. The mapping parameters are recorded in the fusion mapping table in XML format, including the fields "wavelet_level" and "embedding_position". When the checksum fingerprint is constructed according to the mapped fusion data, fuzzy hashing is used. Hashing) algorithm to generate data fingerprints. The fuzzy hash algorithm is implemented using the SSDEEP library. The mapped fusion data is divided into blocks and the hash value of each block is calculated. The generated hash values ​​are combined into a verification code fingerprint. The fingerprint data is compared with the original data to ensure consistency. The final generated fingerprint embedded data is stored in the fingerprint management module in binary format. The verification results are recorded in CSV format, including the fields "fingerprint_hash" (fingerprint hash) and "verification_status" (verification status). ,

[0145] Preferably, step S5 comprises the following steps:

[0146] Step S51: performing channel mapping on the fingerprint embedded data to generate mapping channel features; dynamically reconstructing the spectrum of the mapping channel features to generate spectrum reconstruction data;

[0147] Step S52: performing differential channel segmentation on the spectrum reconstruction data to obtain multi-channel channel data; performing heterogeneous protocol encoding based on the multi-channel channel data to generate protocol conversion data;

[0148] Step S53: construct a device capability profile according to the protocol conversion data to obtain a device feature matrix; perform edge computing task decomposition on the device feature matrix to generate task slice data;

[0149] Step S54: Perform service quality prediction based on the task slice data to obtain service prediction data; perform platform closed-loop adjustment based on the service prediction data to generate an Internet of Things information platform.

[0150] In this embodiment, when the fingerprint embedded data is matched with the channel mapping, the OFDM (Orthogonal Frequency-Division Multiplexing) technology is first used to map the fingerprint embedded data to multiple subcarrier channels. The specific operation includes segmenting the fingerprint embedded data into 128 bits, converting each data segment into a frequency domain signal through a fast Fourier transform (FFT), and then allocating the frequency domain signal to 64 subcarrier channels. The bandwidth of each subcarrier channel is set to 15kHz. The mapping channel characteristics generated after the channel mapping is completed are stored in a matrix form. The rows of the matrix represent the subcarrier channel numbers, and the columns represent the data strength values ​​on each channel. The data format is CSV, including the fields "subcarrier_id" (subcarrier ID) and "signal_strength" (signal strength). When the spectrum of the mapped channel characteristics is dynamically reconstructed, the short-time Fourier transform (STFT) method is used to perform time-frequency analysis on the signal. First, the mapped channel characteristics are segmented according to the time window length of 20ms, and the window function adopts the Hamming window (Hamming Window), the data of each time window is Fourier transformed to obtain spectrum information. In order to realize dynamic reconstruction, the dynamic spectrum tracking algorithm based on Kalman filter is used to smooth the spectrum data. The reconstructed spectrum data is stored in the form of a frequency-time two-dimensional matrix. The rows of the matrix represent time segments and the columns represent frequency components. When performing differential channel segmentation on the spectrum reconstruction data, the differential signal processing algorithm is used. First, the difference value between adjacent time segments is calculated for the reconstructed spectrum data. The differential threshold is set to 5dB. The spectrum segments exceeding the threshold are divided into independent channels, and finally multi-channel channel data are formed. When performing heterogeneous protocol encoding based on multi-channel channel data, the multi-protocol label switching (MPLS, Multi-Protocol Label Switching) technology is used to encode the channel data. First, a unique label is assigned to each channel according to the channel index. The label length is 20 bits. ASN is used in the encoding process.1 (Abstract Syntax Notation One) standard defines the protocol format. Channel data is embedded in tags and encapsulated as protocol conversion data. When constructing device capability profiles based on protocol conversion data, the K-means clustering algorithm is used to cluster the device behavior characteristics in the protocol data. The feature extraction dimensions include channel bandwidth, data transmission rate and signal strength. The number of clusters is set to 5 categories. The feature vector of the cluster center is used as the core parameter of the device capability profile. The constructed device feature matrix is ​​stored in CSV format. The rows of the matrix represent different devices, and the columns represent the capability feature parameters of the devices. When decomposing the device feature matrix for edge computing tasks, the algorithm based on graph partitioning is used to assign computing tasks to different edge nodes. First, a weighted graph of the device feature matrix is ​​constructed. The nodes represent devices, and the weights of the edges represent the feature similarity between devices. The weighted graph is divided into several subgraphs using the METIS partitioning algorithm. Each subgraph corresponds to the computing task of an edge node. When predicting service quality based on task sharding data, a long short-term memory network (LSTM, Long Short-Term The prediction model is trained on historical task execution data using the device feature matrix and task sharding data. The output is service quality parameters, including latency, bandwidth utilization, and packet loss rate. The time step of the LSTM model is set to 10, and the number of hidden layer units is 128. After the training is completed, service prediction data is generated. When the platform closed-loop adjustment is performed based on the service prediction data, a dynamic adjustment algorithm based on reinforcement learning is adopted. The platform adjustment strategy is driven by the Q-learning algorithm. The state space includes device load, network latency, and channel bandwidth. The action space includes task reallocation, channel bandwidth adjustment, and data transmission path optimization. The reward function is set based on the service quality parameters in the service prediction data. The adjusted configuration data of the platform is stored in JSON format, including the fields "adjustment_id", "adjustment_type", and "adjustment_parameters". .

[0151] The present invention also provides an Internet of Things information platform implementation platform, characterized in that it is used to execute the Internet of Things information platform implementation method according to claim 1, and the Internet of Things information platform implementation platform includes:

[0152] The signal acquisition and reshaping module is used to acquire the basic telegraph signal; perform independent source signal filtering on the basic telegraph signal to obtain a filtered telegraph signal; perform communication path topology reshaping on the filtered telegraph signal to generate a communication path network;

[0153] The signal feature enhancement module is used to perform strength compression fingerprinting on the telegraph basic signal based on the communication path network to obtain the signal feature fingerprint; and to perform signal situation dynamic descrambling on the signal fingerprint feature to obtain random enhancement data;

[0154] The communication encryption reconstruction module is used to perform network topology transition encryption on the random enhanced data to obtain a communication encryption path; and to reconstruct the communication path based on the communication encryption path to generate reconstructed path data;

[0155] The information disturbance implantation module is used to simulate the information disturbance of the reconstructed path data to generate disturbance fragmentation data; based on the disturbance fragmentation data, the random enhancement data is encrypted and fingerprint implanted to generate fingerprint embedded data;

[0156] The platform dynamic iteration module is used to reconstruct the channel of fingerprint embedded data and generate protocol conversion data; the platform is progressively optimized according to the protocol conversion data to generate an Internet of Things information platform.

[0157] The present invention ensures the diversity and accuracy of data sources through the implementation of the signal acquisition and reshaping module, improves the signal quality by filtering independent source signals, provides a clear structure for subsequent data processing by reshaping the communication path topology and generating a communication path network, performs strength compression fingerprinting based on the communication path network, enhances the identifiability of signal features, and provides support for the randomness and security of data by dynamic de-jamming. The communication encryption and reconstruction module improves the security of data transmission through network topology transition encryption, and the generation of reconstructed path data ensures the validity and reliability of data transmission. The disturbance fragmentation data generated by the information disturbance implantation module provides a new idea for data protection, and the encryption fingerprint implantation process enhances the security and anti-counterfeiting ability of data. The platform dynamic iteration module provides a guarantee for the adaptation of the data transmission protocol through channel reconstruction, and the generation of protocol conversion data promotes the data compatibility and interoperability between different platforms. The platform progressive optimization improves the overall performance and response speed of the Internet of Things information platform, and promotes the progress and application development of the Internet of Things technology as a whole.

[0158] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0159] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for implementing an Internet of Things information platform, characterized in that: The following steps are involved: Step S1: collecting basic telegraph signals; filtering the basic telegraph signals by independent source signals to obtain filtered telegraph signals; reshaping the communication path topology of the filtered telegraph signals to generate a communication path network; Step S2: Perform intensity compression fingerprinting on the telegraph basic signal based on the communication path network to obtain a signal feature fingerprint; perform signal situation dynamic descrambling on the signal fingerprint feature to obtain random enhanced data; Step S3: encrypting the random enhanced data by network topology transition to obtain a communication encryption path; reconstructing the communication path based on the communication encryption path to generate reconstructed path data; Step S4: performing information disturbance simulation on the reconstructed path data to generate disturbance fragmentation data; Performing encrypted fingerprint implantation processing on random enhanced data based on disturbed fragmented data to generate fingerprint embedded data; Step S5: reconstruct the channel of the fingerprint embedded data to generate protocol conversion data; perform progressive platform optimization based on the protocol conversion data to generate an Internet of Things information platform.

2. The method for implementing the Internet of Things information platform according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: collecting telegraph basic signals; performing fast Fourier transform on the telegraph basic signals to obtain telegraph spectrum data; Step S12: performing blind source separation processing on the telegraph basic signal based on the telegraph spectrum data to obtain a filtered telegraph signal; Step S13: performing amplitude spectrum conversion on the filtered telegraph signal to generate a telegraph signal amplitude spectrum; performing spectrum discretization processing on the telegraph signal amplitude spectrum to obtain spectrum deconstruction data; Step S14: performing signal node mapping on the spectrum deconstruction data to generate node association data; reshaping the communication path topology according to the node association data to generate a communication path network.

3. The method for implementing the Internet of Things information platform according to claim 2, characterized in that: Step S14 includes the following steps: Gridding the spectrum deconstruction data to obtain grid unit data; extracting node features from the grid unit data to obtain node feature data; Calculate the similarity of node feature data to obtain a node similarity matrix; perform threshold cutting on the node similarity matrix based on a preset similarity threshold to generate node association data; Connectivity analysis is performed based on node association data to obtain a node connectivity graph; the shortest path is determined for the node connectivity graph to generate a communication path set; Bottlenecks are identified on a set of communication paths to obtain path bottleneck nodes; load balancing is performed based on the path bottleneck nodes to obtain balanced path data; A path network reconstruction is performed on the communication path set based on the balanced path data to generate a communication path network.

4. The method for implementing the Internet of Things information platform according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: performing signal strength gradient encoding on the communication path network to obtain strength label data; Step S22: compressing the signal dimension of the filtered telegraph signal based on the strength mark data to generate compressed mapping data; performing signal feature recognition on the compressed mapping data to obtain a signal feature fingerprint; Step S23: performing signal situation tomography on the signal feature fingerprint to obtain situation analysis data; performing signal pulse reconstruction on the situation analysis data to generate pulse sequence data; Step S24: performing signal frequency hopping arrangement based on the pulse sequence data to generate a signal frequency hopping matrix; performing signal interference suppression processing according to the signal frequency hopping matrix to generate anti-interference data; Step S25: performing signal randomization enhancement on the anti-interference data to obtain random enhanced data.

5. The method for implementing the Internet of Things information platform according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: dynamically encode the random enhancement data to generate path redirection data; Step S32: encrypt the path redirection data at the communication node according to the random enhancement data to obtain a communication encryption path; Step S33: performing heterogeneous network mapping on the communication encryption path to obtain network mapping data; performing reliability evaluation on the communication encryption path according to the network mapping data to generate a path reliability index; Step S34: Dynamically allocate weights based on the path reliability index to obtain path weight data; reconstruct the communication encryption path according to the path weight data to generate reconstructed path data.

6. The method for implementing the Internet of Things information platform according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: performing information dimension disturbance simulation on the reconstructed path data to obtain simulated disturbance data; Step S42: fragmenting the simulated disturbance data to generate disturbance fragmented data; Step S43: cross-coding and reorganizing the disturbed fragmented data to obtain reorganized coded data; performing information offset replacement on the reorganized coded data to generate information replacement code; Step S44: performing multiple encryption stacking on the random enhanced data based on information replacement coding to obtain encrypted superposition data; performing information fingerprint embedding processing on the encrypted superposition data to generate fingerprint embedding data.

7. The method for implementing the Internet of Things information platform according to claim 6, characterized in that: Step S43 includes the following steps: Performing length regularization processing on the disturbed fragmented data to obtain regularized fragmented data; segmenting and marking the regularized fragmented data to generate paragraph marking data; Performing parity separation processing on the paragraph mark data to generate a parity sequence; rearranging the parity sequence to obtain a rearranged data sequence; Perform grouping and merging processing on the rearranged data sequence to generate cross-grouped data; perform coding mapping on the cross-grouped data to obtain recombined coded data; Performing reference point positioning on the reorganized coded data to obtain an initial reference point; performing offset calculation on the reorganized coded data according to the initial reference point to generate an offset vector; Performing substitution rule mapping based on the offset vector to obtain the offset substitution rule; performing matrix transformation processing on the reorganized coded data according to the offset substitution rule to generate an information substitution matrix; The information permutation matrix is ​​rearranged to generate information permutation code.

8. The method for implementing the Internet of Things information platform according to claim 6, characterized in that: Step S44 includes the following steps: Performing hierarchical mapping processing on the information replacement code to obtain hierarchical mapping data; The random enhanced data is divided into blocks to obtain a data block sequence; the data block sequence is cross-validated encoded to generate cross-validation code data; Recursively merge the hierarchical mapping data and the cross-verification code data to generate a data merge sequence; perform multi-level encryption on the data merge sequence to obtain encrypted superposition data; Extract features from the encrypted superposition data to obtain encrypted feature vectors; perform repeated pattern recognition on the encrypted feature vectors to generate encrypted pattern sequences; Perform unique identification code screening based on the encryption mode sequence to obtain identification code data; perform watermark encoding processing on the identification code data to generate encrypted watermark data; The encrypted overlay data and the encrypted watermark data are fused and mapped to generate mapped fused data; a verification code fingerprint is constructed based on the mapped fused data to generate fingerprint embedded data.

9. The method for implementing the Internet of Things information platform according to claim 1, characterized in that: Step S5 includes the following steps: Step S51: performing channel mapping on the fingerprint embedded data to generate mapping channel features; dynamically reconstructing the spectrum of the mapping channel features to generate spectrum reconstruction data; Step S52: performing differential channel segmentation on the spectrum reconstruction data to obtain multi-channel channel data; performing heterogeneous protocol encoding based on the multi-channel channel data to generate protocol conversion data; Step S53: construct a device capability profile according to the protocol conversion data to obtain a device feature matrix; perform edge computing task decomposition on the device feature matrix to generate task slice data; Step S54: Perform service quality prediction based on the task slice data to obtain service prediction data; perform platform closed-loop adjustment based on the service prediction data to generate an Internet of Things information platform.

10. An Internet of Things information platform implementation platform, characterized in that: Used to execute the Internet of Things information platform implementation method according to claim 1, the Internet of Things information platform implementation platform includes: The signal acquisition and reshaping module is used to acquire the basic telegraph signal; perform independent source signal filtering on the basic telegraph signal to obtain a filtered telegraph signal; perform communication path topology reshaping on the filtered telegraph signal to generate a communication path network; The signal feature enhancement module is used to perform strength compression fingerprinting on the telegraph basic signal based on the communication path network to obtain the signal feature fingerprint; and to perform signal situation dynamic descrambling on the signal fingerprint feature to obtain random enhancement data; The communication encryption reconstruction module is used to perform network topology transition encryption on the random enhanced data to obtain a communication encryption path; and to reconstruct the communication path based on the communication encryption path to generate reconstructed path data; The information disturbance implantation module is used to simulate the information disturbance of the reconstructed path data to generate disturbance fragmentation data; based on the disturbance fragmentation data, the random enhancement data is encrypted and fingerprint implanted to generate fingerprint embedded data; The platform dynamic iteration module is used to reconstruct the channel of fingerprint embedded data and generate protocol conversion data; the platform is progressively optimized according to the protocol conversion data to generate an Internet of Things information platform.

Images