An Internet of Things information platform and implementation method thereof

Abstract

The present invention relates to the technical field of communication platform construction, and in particular to an Internet of Things information platform and its implementation method. The method comprises the following steps: collecting telegraph basic signals and filtering independent source signals to generate filtered telegraph signals, then reshaping the communication path topology to form a communication path network, based on the network, performing intensity compression fingerprinting on the telegraph signal to obtain a signal feature fingerprint, and generating random enhancement data through dynamic descrambling technology, performing network topology transition encryption on the random enhancement data to obtain a communication encryption path, and reconstructing the communication path based on this path to generate reconstructed path data, performing information disturbance simulation on the reconstructed path data to generate disturbance fragmentation data, and implanting encrypted fingerprints on the reconstructed path data, reconstructing the channel, and then performing progressive optimization of the platform to form an Internet of Things information platform. The present invention realizes a safer and more efficient method for implementing an Internet of Things information platform.

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 (IoT) technology and the widespread use of various devices and sensors, data collection and processing have become increasingly complex. Traditional information processing platforms often exhibit insufficient performance when faced with massive amounts of data. In particular, data loss or distortion is prone to occur during the actual acquisition, filtering, and reshaping of signals, 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, posing 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 fail to fully utilize information from the communication path network, resulting in unsatisfactory signal feature fingerprint extraction results. The application of signal 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 addressed. In addition, traditional data processing methods lack effective reconstruction and optimization strategies when faced with 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 its implementation method to solve at least one of the above technical problems.

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

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

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

[0007] Step S3: Performing network topology jump encryption on the random enhanced data 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 perturbation simulation on the reconstructed path data to generate perturbation fragmentation data; performing encrypted fingerprint implantation processing on the random enhancement data based on the perturbation fragmentation data to generate fingerprint embedding data;

[0009] Step S5: reconstruct the channel of the fingerprint embedded data to generate protocol conversion data; perform platform progressive 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 de-jamming of signal situations, 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 encryption fingerprint implantation processing, guarantees the adaptation of data transmission protocols by channel reconstruction, promotes data compatibility and interoperability between different platforms, and promotes the overall performance and response speed of the Internet of Things information platform by platform progressive optimization, thus promoting the advancement and application development of Internet of Things technology as a whole.

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

[0012] Step S11: collecting a basic telegraph signal; performing a fast Fourier transform on the basic telegraph signal 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. 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. 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. Spectral 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 overall improves the performance and reliability of Internet of Things information platform in data processing and communication management.

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

[0018] Perform grid division on the spectrum deconstruction data to obtain grid unit data; perform node feature extraction on 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] Perform connectivity analysis based on node association data to obtain a node connectivity graph; perform shortest path determination on the node connectivity graph to generate a communication path set;

[0021] Identify bottlenecks on a set of communication paths to obtain bottleneck nodes; perform load balancing based on the 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 spectral 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. Cutting based on similarity threshold realizes the effective screening of node association 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 communication path network, which improves the performance and adaptability of Internet of Things information platform in network management and optimization as a whole.

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

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

[0026] Step S22: compressing the filtered telegraph signal in terms of signal dimension based on the strength marker 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. 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. The generation of compressed mapping data improves the efficiency of signal processing. The application of signal feature recognition enhances the understanding of signal characteristics. The generation of signal feature fingerprints provides a basis for subsequent situation analysis. The execution of signal situation tomography realizes a comprehensive analysis of signal status. The situation analysis data lays the 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. 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. 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. The generation of random enhancement data lays the foundation for the overall performance optimization and stable operation of the Internet of Things information platform.

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

[0032] Step S31: performing path dynamic encoding on the random enhancement data to generate path redirection data;

[0033] Step S32: encrypt the path redirection data using the random enhancement data to obtain a communication encrypted path;

[0034] Step S33: performing heterogeneous network mapping on the communication encryption path to obtain network mapping data; performing reliability assessment on the communication encryption path based on 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; and reconstruct the communication encryption path based on 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 dynamic path 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 during data transmission. The generation of communication encryption paths provides protection for data confidentiality. The process of heterogeneous network mapping realizes 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 overall optimizes the data transmission capability and reliability of the Internet of Things information platform in complex environments.

[0037] Preferably, step S4 includes 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: performing cross-coding reorganization on the disturbed fragmented data to obtain recombined coded data; performing information offset replacement on the recombined 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. The generation of fingerprint embedded data provides important support for the overall security and reliability improvement of the Internet of Things information platform.

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

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

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

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

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

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

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

[0050] The present invention improves data consistency and operability through the implementation of length regularization processing. The generation of regularized fragmented data provides a basis for subsequent processing. The application of segmentation tags enhances the organization and manageability of data. The generation of paragraph tag data provides a clear structure for data analysis. The implementation of parity separation processing helps to improve data redundancy and reliability. The generation of parity sequences provides support for data error detection and recovery. The process of position rearrangement improves the degree of data obfuscation. The generation of rearranged data sequences lays the foundation for subsequent security processing. The implementation of group merging processing enhances data integration. The generation of cross-grouped data enhances the flexibility of data processing. The application of code mapping ensures data security and unpredictability. The generation of recombined coded 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 data obfuscation and protection. The generation of information permutation matrix provides a guarantee for secure data storage. The implementation of information rearrangement ensures the security and integrity of data during transmission. The information permutation code 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-validation 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 overlay data to obtain an encrypted feature vector; perform repeated pattern recognition on the encrypted feature vector to generate an encrypted pattern sequence;

[0056] Perform unique identification code screening based on the encryption pattern 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 check 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 recursive merging process 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 of 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 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 capability of data. The generated encrypted watermark data provides an additional layer of protection for information security. The implementation of fusion mapping improves the integration and processing efficiency of data. 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 data. The fingerprint embedded data finally generated improves the overall security and reliability of the Internet of Things information platform.

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

[0060] Step S51: performing channel mapping on the fingerprint embedded data to generate mapping channel features; performing spectrum dynamic reconstruction on 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 based on 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 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 profile 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. The application of platform closed-loop adjustment enhances the adaptability and intelligence level of the Internet of Things information platform, and ultimately 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 basic telegraph signals; perform independent source signal filtering on the basic telegraph signals to obtain filtered telegraph signals; and perform communication path topology reshaping on the filtered telegraph signals to generate a communication path network;

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

[0068] The communication encryption reconstruction module is used to perform network topology jump encryption on the random enhanced data to obtain a communication encryption path; and 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 information disturbance on 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 based on 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 reconstruction module, improves the signal quality by filtering the independent source signal, and provides a clear structure for subsequent data processing by reshaping the communication path topology. The signal feature enhancement module performs intensity compression fingerprint processing based on the communication path network, thereby enhancing the identifiability of the signal features, and dynamic de-jamming provides support for the randomness and security of the data. The communication encryption reconstruction module improves the security of the data transmission process 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 the 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 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 overall progress and application development of the Internet of Things technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 A flowchart of a method for implementing an Internet of Things information platform;

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

[0074] Figure 3 Detailed implementation flow chart of step S3;

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

[0076] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. It is obvious that the embodiments described are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts are within the scope of protection of the present invention.

[0077] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0078] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as 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 a basic telegraph signal; performing independent source signal filtering on the basic telegraph signal to obtain a filtered telegraph signal; reshaping the communication path topology of the filtered telegraph signal to generate a communication path network;

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

[0082] Step S3: Performing network topology jump encryption on the random enhanced data 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 perturbation simulation on the reconstructed path data to generate perturbation fragmentation data; performing encrypted fingerprint implantation processing on the random enhancement data based on the perturbation fragmentation data to generate fingerprint embedding data;

[0084] Step S5: reconstruct the channel of the fingerprint embedded data to generate protocol conversion data; perform platform progressive 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 de-jamming of signal situations, 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 encryption fingerprint implantation processing, guarantees the adaptation of data transmission protocols by channel reconstruction, promotes data compatibility and interoperability between different platforms, and promotes the overall performance and response speed of the Internet of Things information platform by platform progressive optimization, thus promoting the advancement 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 includes the following steps:

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

[0088] In this embodiment, when collecting telegraph basic signals, a high-precision radio frequency signal receiving device is used to perform full spectrum scanning of telegraph signals of different frequency bands, and a sampling rate is set between 500kHz and 5MHz through an adaptive sampling mechanism. 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. 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. A multi-stage orthogonal transform is used for time-frequency domain decomposition to extract basic features of the signal such as instantaneous amplitude, phase, and frequency. 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 performing independent source signal filtering on the telegraph basic signal, blind source separation (Blind Source Separation) is used. The independent components of the mixed signal are extracted using the Fast Independent Component Analysis (FastICA) algorithm. The maximum number of iterations is set to 500. The initial weight matrix is initialized using Cholesky decomposition. During the iteration process, the independent components are calculated using the non-Gaussian maximization criterion, background noise, interference signals, and redundant signals are filtered out. After normalization of the signal, the double threshold method is used to eliminate abnormal signals, and the final result is the denoised filtered telegraph signal. When reshaping the communication path topology of the filtered telegraph signal, the signal propagation path is calculated using the multilateration method based on the time delay information of the received signal and the geographic coordinates of the transmitting source. A weighted topology graph is constructed. The nodes in the graph represent signal relay stations, and the edge weights are calculated based on the signal strength attenuation and delay parameters. The optimal communication path is calculated using the Dijkstra shortest path algorithm, 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 signal quality, where the signal quality is evaluated by the signal-to-noise ratio (SNR). Finally, an optimized communication path network is generated.

[0089] Step S2: Performing intensity compression fingerprinting on the telegraph basic signal based on the communication path network to obtain a signal feature fingerprint; performing signal situation dynamic descrambling on the signal fingerprint feature to obtain random enhancement 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 with a quantization step size of 0.05, and the quantized signal features are mapped to a 128-dimensional fingerprint feature vector. Subsequently, the local sensitive hashing (LSH) algorithm is used to construct a fingerprint index, and representative signal features are screened out using the Hamming distance to ensure the uniqueness and stability of the fingerprint features. The signal fingerprint is finally obtained. When the signal fingerprint features are 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: Performing network topology jump encryption on the random enhanced data 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 performing network topology transition encryption on random enhanced data, AES-256 (Advanced Encryption Standard) is used to perform symmetric encryption on the data. CBC (Cipher Block Chaining) 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 (an asymmetric encryption algorithm) is used to encrypt the AES key to ensure the security of key transmission. The encrypted data is fragmented according to the network topology path, with each data fragment being 512 bytes in length. The data is sent to different network nodes through a multi-path transmission mechanism to form a communication encryption path. When reconstructing the communication path based on the communication encryption path, the Border Gateway Protocol (BGP) is used to obtain the current network topology structure, collect the delay, packet loss rate, and bandwidth information of each path, and apply the entropy weight method to comprehensively score the stability and security of each path. 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 perturbation simulation on the reconstructed path data to generate perturbation fragmentation data; performing encrypted fingerprint implantation processing on the random enhancement data based on the perturbation fragmentation data to generate fingerprint embedding data;

[0094] In this embodiment, when simulating information perturbation on the reconstructed path data, a Poisson noise model is used to perturb the data, the signal-to-noise ratio (SNR) is set to 40 dB, and a pseudo-random number generator (PRNG) is used to generate perturbation parameters. The perturbation parameters are added to the reconstructed path data to simulate random interference during transmission. The data is then fragmented using a fixed block method, with the length of each fragment set to 256 bytes. The fragmented data is randomly disrupted in storage order, ultimately generating perturbed fragmented data. When performing encrypted fingerprint embedding on the random enhanced data based on the perturbed fragmented data, a 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 using 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 embedding, the data is restored to a time domain signal using an inverse DCT, ultimately generating fingerprint-embedded data.

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

[0096] In this embodiment, when channel reconstruction is performed on the fingerprint embedded data, 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 spacing 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 performing platform progressive optimization based on the protocol conversion data, Protobuf (protocol buffer) is used to serialize the data structure to ensure data compatibility 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 includes the following steps:

[0098] Step S11: collecting a basic telegraph signal; performing a fast Fourier transform on the basic telegraph signal 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 basic telegraph signals, a high-sensitivity wide-band receiving antenna is used to receive telegraph signals 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 down-conversion processing with a first intermediate frequency of 10.7 MHz. The intermediate frequency signal is filtered out of irrelevant frequency band interference through an analog bandpass filter with a bandwidth of 2 MHz. 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. Signal preprocessing is performed inside the FPGA, 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 within each time window is windowed using the [window] function to reduce spectral leakage. The windowed data is then transformed into the frequency domain using a 64-bit FFT algorithm to obtain telegraph spectrum data within the corresponding frequency range. The frequency resolution is determined by the sampling rate and the number of sampling points; in this case, the resolution is 2.44 kHz. The resulting spectrum data contains amplitude and phase information, which is used to analyze the signal's intensity distribution. The spectrum data is then saved to a solid-state drive (SSD). When blind source separation of the telegraph signal is performed based on the telegraph spectrum data, the independent component analysis (ICA) algorithm is used to separate multiple sources. The telegraph spectrum data is organized into a matrix X. Centering is first performed to remove the mean, followed by whitening to ensure that the frequency components are orthogonal and have a variance of 1. Independent component extraction is performed on the whitened data using the FastICA algorithm. The non-Gaussianity measure function is set to negentropy, 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), background noise and irrelevant signals are removed, and the target signal component, i.e., the filtered telegraph signal, is finally 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 Hilbert transform. Its envelope is calculated as the amplitude spectrum. The amplitude spectrum data is represented in a logarithmic compression form to improve the visualization effect of the dynamic range. The amplitude spectrum data is then input into the 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 telegraph signal amplitude spectrum is generated. When the telegraph signal amplitude spectrum 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. 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 discretization process maps continuous eigenvalues to discrete values within a fixed range to generate structured spectrum deconstruction data. When performing signal node mapping on the spectrum deconstruction data, 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, an edge is established between the nodes to form a preliminary node graph structure. The PageRank algorithm is then used to sort the importance of the nodes. Nodes with high weights represent the main propagation paths of the signal. All node information and their mutual relationships generate node associations. Data, which includes the frequency characteristics, similarity weights, and topological structure information of each node, is used to reshape the communication path topology based on node association data. A weighted graph model is used to construct a communication path network. This node association data is imported into a graph analysis tool, and the Dijkstra algorithm is used to calculate the shortest path. The Bellman-Ford algorithm is also used to handle negatively weighted edges to ensure the accuracy of path calculation. During the path calculation process, node weights and edge weights are comprehensively considered, with the weight parameters set at a ratio of 0.6:0.4 to represent the relative importance of node characteristics and signal strength. This ultimately results in an optimized communication path network.

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

[0104] Perform grid division on the spectrum deconstruction data to obtain grid unit data; perform node feature extraction on 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] Perform connectivity analysis based on node association data to obtain a node connectivity graph; perform shortest path determination on the node connectivity graph to generate a communication path set;

[0107] Identify bottlenecks on a set of communication paths to obtain path bottleneck nodes; perform load balancing 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 grid-divided, the spectrum deconstruction data is first mapped to a two-dimensional coordinate plane, where the horizontal axis represents frequency and 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. 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. 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 forms a complete grid unit data set. When node features are extracted from the grid unit data, the four characteristic values of average amplitude, maximum amplitude, frequency change rate and amplitude fluctuation 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 as the mean, and amplitude fluctuation is expressed as the standard deviation. These four eigenvalues are combined to form the eigenvector of each grid cell. The eigenvector dimension is 4, and the eigenvector is normalized using the Z-score normalization method. When calculating the similarity of node feature data, the cosine similarity algorithm is used to measure the similarity of eigenvectors between nodes. All node feature vectors are combined pairwise and their cosine similarity is calculated. The cosine similarity calculation formula is the vector dot product divided by the product of the vector modulus. The similarity value ranges from 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, and the elements in the matrix represent the similarity between the corresponding node pairs. Finally, a complete node similarity matrix is formed. When the node similarity matrix is thresholded 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, the node similarity matrix is cut.When the node number is 85, the corresponding node pair is marked as mutually associated, otherwise it is marked as unassociated. The information of all mutually associated node pairs is extracted to form a node association list. Each node association contains the identifiers of the 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 construct 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 are visited to form a connected subgraph. This process is repeated until all nodes are 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, the source node is added to the priority queue. The neighboring nodes directly connected to the current node are visited in sequence, and the shortest distances to the neighboring nodes are updated. 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 and corresponding path lengths on the path. When bottleneck identification is performed on the communication path set, a method based on traffic analysis is used to determine the path bottleneck node. First, the access frequency of each node on each shortest path is counted. The higher the access frequency, the greater the communication load 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 traffic processed by 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 it is located on. When load balancing is performed based on the path bottleneck node, a 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 / 3 of the original path.The A* algorithm searches for optimal backup paths in the node connectivity graph, redistributing some or all of the traffic on the original paths to the backup paths. The adjusted path set is called balanced path data, which includes the adjusted node sequence, path length, and load distribution ratio. When reconstructing the communication path set based on the balanced path data, a weighted directed graph model is used to represent the adjusted communication network. Nodes represent communication devices, and edges represent communication links between devices. Edge weights are set based on the load distribution ratio in the balanced path data. A minimum spanning tree is constructed using the Kruskal algorithm to ensure network connectivity while minimizing total communication overhead. The resulting communication path network includes all device nodes and the optimized communication links between them.

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

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

[0112] Step S22: compressing the filtered telegraph signal in terms of signal dimension based on the strength marker 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. A high-precision radio frequency signal analyzer is used as the measurement tool. The measurement frequency band is set to 2.4 GHz to 2.5 GHz, and the measurement step is 1 MHz. The signal strength of each link is recorded in dBm (decibel milliwatt). The measured signal strength values are divided into different strength levels at intervals of 0.5 dB. Ten gradient intervals are generated from the lowest signal strength to the highest signal strength. A unique coding value is assigned to each gradient interval. The coding value is represented by 8-bit binary. The gradient code corresponding to the signal strength is associated with the communication link and stored. The generated coding data set is the intensity tag data. The intensity tag data format is a key-value pair, where the key is the communication link identifier and the value is the gradient code. When performing signal dimension compression on the filtered telegraph signal based on the intensity tag data, the received telegraph signal is first matched based on the intensity tag data to extract the signal segment corresponding to the intensity tag. The signal segment is processed using a short-time Fourier transform (STFT). The signal is processed and converted into frequency domain representation. After frequency domain representation, the principal component analysis (PCA) algorithm is used for dimension compression, retaining 95% of the signal energy. The dimension of the compressed signal feature vector is reduced from the original 1024 dimensions to 64 dimensions. The compressed mapping data is saved in matrix form, and each row represents the feature vector of a signal segment. When performing signal feature recognition on the compressed mapping data, the convolutional neural network (CNN) model is used to classify and recognize the compressed signal feature vector. The CNN model contains 3 convolution 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 performing signal situation tomography on the signal feature fingerprint, 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 two-dimensional fingerprint data to three-dimensional space. During the reconstruction process, a 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. Hot spots in the situation map are represented by color gradients to indicate intensity differences. The final situation analysis data is stored as a set of three-dimensional coordinate points, each of which contains time, frequency, and signal strength values. When reconstructing the signal pulse of the situation analysis data, the peak points in the situation analysis data are first extracted. The peak points correspond to the key pulse positions of the signal. The peak points are identified using an adaptive threshold algorithm, and the threshold is set to 1 / 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. 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 the 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 pattern. The generated frequency hopping matrix is stored in the form of a two-dimensional array. The rows represent the time windows and the columns represent the 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) is used Filter) to identify and suppress interference on the signal. The filter type is LMS (Least Mean A least mean square (LMS) adaptive filter is used. The initial filter parameter weights are set to 0.01, and the step size is 0.005. The input signal is the frequency channel signal in the frequency hopping matrix, and the reference signal is a historical interference-free signal sample. The filter adjusts the weights in real time to minimize the error between the input and reference signals. The filtered output signal is the signal after interference removal. The generated anti-interference data is stored in matrix form, containing the denoised signal strength value within each time window. When performing signal randomization enhancement on the anti-interference data, time domain perturbation and frequency domain expansion techniques are used to enhance the randomness of the signal. First, a small perturbation is applied to the signal in the time domain, with the perturbation range set to ±2% of the signal strength. The perturbation pattern is generated based on Gaussian white noise. The signal is then expanded in the frequency domain. The short-time Fourier transform (STFT) is used to convert the signal to the frequency domain. Pseudo-random phase shifts are applied to the frequency components, ranging from 0 to π. After the frequency domain expansion is completed, the signal is converted back to the time domain using the inverse Fourier transform (IFFT). The resulting random enhancement data contains both the time domain waveform and frequency domain characteristics of the signal.

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

[0118] Step S31: performing path dynamic encoding on the random enhancement data to generate path redirection data;

[0119] Step S32: encrypt the path redirection data using the random enhancement data to obtain a communication encrypted path;

[0120] Step S33: performing heterogeneous network mapping on the communication encryption path to obtain network mapping data; performing reliability assessment on the communication encryption path based on 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; and reconstruct the communication encryption path based on 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, intensity and timestamp. These parameters are encoded using a hash function. The hash function uses the SHA-256 algorithm. The generated hash value is 256-bit binary data. The hash value is then 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 unique path encoding data. In order to increase the dynamic nature of the path, a timestamp perturbation is introduced before each data transmission. The perturbation range is ±50 milliseconds. The timestamp after perturbation 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 based on 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 result of the three most recent perturbations. 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 ciphertext generated after encryption 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, the different network protocol types in the IoT environment are first identified, including Wi-Fi, Bluetooth, ZigBee and LoRa, etc. The protocol parser is used 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 reason and analyze 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. The final generated path reliability index is in floating point form, retaining two decimal places. When performing dynamic weight allocation based on the path reliability index, the weighted round-robin algorithm (Weighted Round Robin, 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 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 A weight-based path selection algorithm is used to route packets. First, the path weight data is loaded into the routing allocation module. The module schedules the packets according to the weight data. Paths with higher weights are assigned more packets. The priority and size of the packets are considered during the path selection process. Packets with high priority are assigned to high-weight paths first, and packets with a size exceeding 1MB are assigned 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 packet allocation table. The path sequence represents the transmission order of the packets, and the packet allocation table records the transmission path and timestamp of each packet. The reconstructed path data is finally stored in the routing management module of the IoT platform.

[0123] Preferably, step S4 includes 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: performing cross-coding reorganization on the disturbed fragmented data to obtain recombined coded data; performing information offset replacement on the recombined 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 perturbation simulation on the reconstructed path data, the path sequence, data packet allocation table, and timestamp are first extracted from the reconstructed path data. These data are perturbed using a multidimensional Gaussian noise model. The mean of the Gaussian noise is set to 0, and the standard deviation is 5% of the path delay. Node order changes are randomly introduced into the path sequence, and the transmission timestamps in the data packet allocation table are increased or decreased by random offsets within the range of ±10 milliseconds. At the same time, the path identifiers are randomly replaced, and the replacement range is limited to node IDs within the same network topology. The perturbed data are then subjected to an orthogonal experimental design. Design) is combined 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 fragmenting the simulated disturbance data, a combination of fixed-length segmentation and variable-length random segmentation is adopted. First, each field of the simulated disturbance data is preliminarily segmented by 512 bytes to generate a basic data block. 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 coding algorithm is used to rearrange and combine the fragmented data. 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 every 3 main sequence fragments. During the merging process, the adjacent fragments are encoded using the exclusive OR operation (XOR) to ensure the confusion of the 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 permutation on the recombined encoded data, a combination of cyclic shift and bitwise permutation is used. First, the recombined encoded data is divided into 128-byte blocks. The data in each block is subjected to an 8-bit cyclic shift. The offset direction is random and the offset step size is determined based on the difference in the number of parity bits in the data block.Then, the shifted data block is shifted 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 the random enhanced data is multi-layered based on the information replacement code, a layered encryption (Layered Encryption) strategy is adopted. First, the random enhanced data is encrypted using the Advanced Encryption Standard (AES-256) for the first layer. 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 uses the least significant bit (LSB) embedding technology to embed the fingerprint data into the non-sensitive bits of the encrypted data. 4 bits of fingerprint information are embedded in every 128 bytes of data. 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 embedded fingerprint 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 on the disturbed fragmented data to obtain regularized fragmented data; segmenting and marking the regularized fragmented data to generate paragraph mark data;

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

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

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

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

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

[0136] In this embodiment, when the length of the disturbed fragmented data is regularized, all the fragmented data are first standardized into groups of 128 bytes. Data fragments less than 128 bytes are padded with randomly generated binary noise at the end of the data to make up for the length. Data fragments exceeding 128 bytes are truncated according to multiples of 128 bytes, and the truncated parts are stored as independent fragments. The padding noise is generated using a pseudo-random number generator (Pseudo-Random Number Generator). NumberGenerator, PRNG), the generated seed is obtained based on the original timestamp and node ID of the fragment. When segmenting the regular fragment data, a unique paragraph identifier is first assigned to each regular fragment. The identifier is generated by combining the hash value of the fragment's original position and the current regular sequence number. The hash algorithm uses the SHA-256 algorithm. The generated paragraph mark data includes the paragraph number, original position index and data length information. All mark data are packaged and stored in JSON format. The fields include "segment_id" (paragraph ID), "original_index" (original index) and "length" (data length). When performing parity separation on the paragraph mark data, 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 seed for the rearrangement is 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 every 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 coding. Each group of data passes through 7,4 Hamming code, the generated redundant check bits are attached to the original data to form a complete code block, the encoded data block is secondary mapped through the hash mapping table, the mapping rule is determined based on the hash function generated by the initial device ID, and the sliding window method is used to locate the reference point of the recombined encoded data. Window) searches for a specific reference pattern in the encoded data. The reference pattern is a predefined binary sequence "10101010". Every 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 the offset of the reorganized encoded data is calculated 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 and contains the fields "offset" (offset) and "base_point" (corresponding reference point). When performing permutation rule mapping based on the offset vector, the offset vector is input to the permutation rule generator. The permutation rule generator is based on the Linear Congruential Generator (LinearCongruential Generator). The generator (LCG) generates a set of permutation matrices with a dimension of 8×8. Each element in the matrix represents the new position of the data in the reorganized coded data. When performing matrix transformation processing on the reorganized coded data according to the offset permutation rule, the reorganized coded data is divided into 8×8 byte blocks. The byte position of each data block is permuted according to the rules of the permutation matrix. The transformed data blocks are reassembled into an information permutation matrix. Each row in the matrix represents a permuted data sequence. When rearranging the information permutation matrix, the column-major order method 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-validation 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 an encrypted feature vector; perform repeated pattern recognition on the encrypted feature vector to generate an encrypted pattern sequence;

[0142] Perform unique identification code screening based on the encryption pattern 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 check 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 (Cyclic Redundancy) is used. The cyclic redundancy check (CRC) algorithm generates a check code for each data block. The check polynomial uses the CRC-32 standard polynomial. The check code is appended 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 blocks are copied using the TMR method, and the three sets of data are compared and verified. When recursively merging the hierarchical mapping data and the cross-verification code data, 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 again with the next layer of data. 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 multi-level encryption is performed on the data merge sequence, 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 chain seed is generated by the first two layers of encrypted data, when the encrypted superimposed data is subjected to feature extraction, 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. During the extraction process, data standardization is used 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, which contains the fields "feature_vector" (feature vector) and "segment_id" (segment ID). When the encrypted feature vector is subjected to repeated pattern recognition, dynamic time warping (DTW) 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" (start 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 watermarking the identification code data, the discrete cosine transform (Discrete Cosine Transform,The identification code is watermarked using the DCT) algorithm. 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" (frequency band) and "embedding_strength" (embedding strength). When the encrypted overlay data and the encrypted watermark data are fused and mapped, the encrypted watermark data is embedded into the multi-scale representation of the encrypted overlay data using wavelet transform. First, the encrypted overlay data is decomposed into three layers of wavelet, and the detail coefficients are partially embedded in 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" (wavelet level) and "embedding_position" (embedding position). When the check code fingerprint is constructed based on the mapped fused data, fuzzy hashing is used. The data fingerprint is generated using the Fuzzy Hashing algorithm. The fuzzy hashing 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 binary format in the fingerprint management module. The verification results are recorded in CSV format, including the fields "fingerprint_hash" (fingerprint hash) and "verification_status" (verification status).

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

[0146] Step S51: performing channel mapping on the fingerprint embedded data to generate mapping channel features; performing spectrum dynamic reconstruction on 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 based on 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 performing channel mapping on the fingerprint embedded data, the fingerprint embedded data is first mapped to multiple subcarrier channels using OFDM (Orthogonal Frequency-Division Multiplexing) technology. The specific operation includes segmenting the fingerprint embedded data into 128-bit segments, converting each segment of data 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 15 kHz. The mapped channel features generated after the channel mapping is completed are stored in 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 and includes the fields "subcarrier_id" (subcarrier ID) and "signal_strength" (signal strength). When performing dynamic spectrum reconstruction on the mapped channel features, the short-time Fourier transform (STFT) method is used to perform time-frequency analysis on the signal. First, the mapped channel features are segmented according to a time window length of 20 ms, and the window function adopts a Hamming window. The data of each time window is Fourier transformed to obtain spectrum information. To achieve dynamic reconstruction, a dynamic spectrum tracking algorithm based on Kalman filtering is used to smooth the spectrum data. The reconstructed spectrum data is stored in a two-dimensional frequency-time matrix, where the rows represent time segments and the columns represent frequency components. When performing differential channel segmentation on the spectrum reconstruction data, a differential signal processing algorithm is used. First, the difference between adjacent time segments is calculated for the reconstructed spectrum data. The difference threshold is set to 5dB. Spectrum segments exceeding this threshold are divided into independent channels, ultimately forming multi-channel channel data. When performing heterogeneous protocol encoding based on multi-channel channel data, Multi-Protocol Label Switching (MPLS) technology is used to encode the channel data. First, a unique label with a length of 20 bits is assigned to each channel based on the channel index. The 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 and analyze 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 eigenvector 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 METIS partitioning algorithm is used to divide the weighted graph into several subgraphs. Each subgraph corresponds to the computing task of an edge node. When predicting service quality based on task sharding data, the long short-term memory network (LSTM) is used. The prediction model is trained on historical task execution data using a machine feature matrix and task sharding data. The output is service quality parameters, including latency, bandwidth utilization, and packet loss rate. The LSTM model has a time step of 10 and 128 hidden units. After training, service prediction data is generated. A dynamic adjustment algorithm based on reinforcement learning is used to make closed-loop adjustments to the platform based on this service prediction data. The platform's adjustment strategy is driven by the Q-learning algorithm. The state space includes device load, network latency, and channel bandwidth, while the action space includes task reallocation, channel bandwidth adjustment, and data transmission path optimization. The reward function is based on the service quality parameters in the service prediction data. The adjusted platform configuration data is stored in JSON format, containing 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 basic telegraph signals; perform independent source signal filtering on the basic telegraph signals to obtain filtered telegraph signals; and perform communication path topology reshaping on the filtered telegraph signals to generate a communication path network;

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

[0154] The communication encryption reconstruction module is used to perform network topology jump encryption on the random enhanced data to obtain a communication encryption path; and 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 information disturbance on 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 based on 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 reconstruction module, improves the signal quality by filtering the independent source signal, and provides a clear structure for subsequent data processing by reshaping the communication path topology. The signal feature enhancement module performs intensity compression fingerprint processing based on the communication path network, thereby enhancing the identifiability of the signal features, and dynamic de-jamming provides support for the randomness and security of the data. The communication encryption reconstruction module improves the security of the data transmission process 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 the 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 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 overall progress and application development of the Internet of Things technology.

[0158] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced therein.

[0159] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily 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 is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A method for implementing an Internet of Things information platform, characterized in that: The following steps are involved: Step S1: collecting a basic telegraph signal; performing independent source signal filtering on the basic telegraph signal to obtain a filtered telegraph signal; reshaping the communication path topology of the filtered telegraph signal to generate a communication path network; Step S2: Performing intensity compression fingerprinting on the telegraph basic signal based on the communication path network to obtain a signal feature fingerprint; performing signal situation dynamic descrambling on the signal fingerprint feature to obtain random enhancement data; Step S3: Perform network topology jump encryption on the random enhanced data to obtain a communication encryption path; reconstruct the communication path based on the communication encryption path to generate reconstructed path data; wherein step S3 includes the following steps: Step S31: performing path dynamic encoding on the random enhancement data to generate path redirection data; Step S32: encrypt the path redirection data using the random enhancement data to obtain a communication encrypted path; Step S33: performing heterogeneous network mapping on the communication encryption path to obtain network mapping data; performing reliability assessment on the communication encryption path based on the network mapping data to generate a path reliability index; Step S34: performing dynamic weight allocation based on the path reliability index to obtain path weight data; performing path allocation reconstruction on the communication encryption path according to the path weight data to generate reconstructed path data; Step S4: performing information perturbation simulation on the reconstructed path data to generate perturbation fragmentation data; performing encrypted fingerprint implantation processing on the random enhancement data based on the perturbation fragmentation data to generate fingerprint embedding data; Step S5: reconstruct the channel of the fingerprint embedded data to generate protocol conversion data; perform platform progressive 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 a basic telegraph signal; performing a fast Fourier transform on the basic telegraph signal 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: Perform grid division on the spectrum deconstruction data to obtain grid unit data; perform node feature extraction on 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; Perform connectivity analysis based on node association data to obtain a node connectivity graph; perform shortest path determination on the node connectivity graph to generate a communication path set; Identify bottlenecks on a set of communication paths to obtain bottleneck nodes; perform load balancing based on the 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 mark data; Step S22: compressing the filtered telegraph signal in terms of signal dimension based on the strength marker 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 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: performing cross-coding reorganization on the disturbed fragmented data to obtain recombined coded data; performing information offset replacement on the recombined 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.

6. The method for implementing the Internet of Things information platform according to claim 5, characterized in that: Step S43 includes the following steps: Performing length regularization on the disturbed fragmented data to obtain regularized fragmented data; segmenting and marking the regularized fragmented data to generate paragraph mark data; Performing parity separation processing on the paragraph mark data to generate an parity sequence; rearranging the parity sequence to obtain a rearranged data sequence; Performing grouping and merging processing on the rearranged data sequence to generate cross-grouped data; performing coding mapping on the cross-grouped data to obtain recombined coded data; Performing reference point positioning on the recombined coded data to obtain an initial reference point; performing offset calculation on the recombined coded data based on the initial reference point to generate an offset vector; Performing substitution rule mapping based on the offset vector to obtain an offset substitution rule; performing matrix transformation processing on the recombined coded data according to the offset substitution rule to generate an information substitution matrix; The information permutation matrix is rearranged to generate an information permutation code.

7. The method for implementing the Internet of Things information platform according to claim 5, 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-validation 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 overlay data to obtain an encrypted feature vector; perform repeated pattern recognition on the encrypted feature vector to generate an encrypted pattern sequence; Perform unique identification code screening based on the encryption pattern 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 check code fingerprint is constructed based on the mapped fused data to generate fingerprint embedded data.

8. 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; performing spectrum dynamic reconstruction on 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 based on 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.

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

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