Mining intelligent board data automatic uploading system and method

Abstract

The invention discloses a mining intelligent board data automatic uploading system and method, and the method comprises the steps: collecting underground environment data, recognizing a damaged data frame through signal integrity detection, and generating an error correction seed sequence through error feature analysis; reconstructing a complete data stream based on the error correction seed sequence, extracting a frame boundary identifier, triggering a protocol switching mechanism, and establishing a multi-protocol concurrent transmission channel; performing cooperative frequency analysis on the transmission channel to generate path cooperative parameters, implementing multi-path cooperative enhancement processing to form a cooperative transmission domain, and generating a multi-path parallel transmission stream through data fragmentation injection; monitoring transmission errors, identifying error energy distribution characteristics, triggering an energy feedback collection mechanism to generate feedback energy parameters, and performing enhanced compensation on transmission power; network state monitoring analysis is carried out to generate a network load distribution curve, the transmission opportunity is optimized to select and determine the optimal sending moment, data batch uploading is executed to complete intelligent data transmission, and the reliability and efficiency of data transmission are improved.

Description

Technical Field

[0001] The present invention relates to the field of underground communication technology, and in particular to a system and method for automatically uploading data of a mining intelligent signboard. Background Art

[0002] Underground environmental data transmission, a crucial technical support for mine safety monitoring and production management, carries the real-time transmission of critical safety parameters such as temperature, humidity, and gas concentration. However, the complex underground geological environment and electromagnetic interference conditions lead to frequent signal attenuation, packet loss, and transmission errors during data transmission, seriously impacting the integrity and real-time performance of monitoring data.

[0003] Existing downhole data transmission technologies primarily rely on a single protocol and fixed transmission strategy. These technologies lack intelligent error handling capabilities and energy recovery mechanisms, and are unable to dynamically adjust transmission parameters and protocol configurations based on network load. Traditional methods are inadequately adaptable to sudden network congestion and channel quality degradation, and the error energy generated during transmission is not effectively utilized. This results in low overall transmission efficiency and high energy consumption, making it difficult to meet the real-world requirements for highly reliable, efficient, and intelligent data transmission in downhole environmental monitoring. Summary of the Invention

[0004] The present invention provides a system and method for automatically uploading data from an intelligent mining signboard. The system collects underground environmental data through signboard monitoring and performs signal integrity detection. Error feature analysis of damaged data frames is used to generate an error correction seed sequence. Based on the error correction seed sequence, a complete data stream is reconstructed and a frame boundary identifier is extracted. A multi-protocol concurrent transmission channel is established through a protocol switching signal. A collaborative frequency analysis is performed on the transmission channel to form a collaborative transmission domain, thereby realizing the generation of multi-path parallel transmission streams. Transmission errors are monitored and an energy feedback collection mechanism is triggered. Finally, the transmission timing is optimized through a network load distribution curve to complete the intelligent and efficient transmission of underground environmental data.

[0005] The first aspect of the present invention provides a method for automatically uploading data of a mining smart signboard, comprising the following steps:

[0006] Collecting downhole environmental data monitored by a cardboard, performing signal integrity detection on the downhole environmental data to identify damaged data frames, and performing error feature analysis on the damaged data frames to generate an error correction seed sequence;

[0007] Performing data packet reconstruction based on the error correction seed sequence to generate a complete data stream, performing link layer frame synchronization analysis on the complete data stream to extract a frame boundary identifier, using the frame boundary identifier to stimulate a transmission protocol switching mechanism to generate a protocol switching signal, and establishing a multi-protocol concurrent transmission channel through the protocol switching signal;

[0008] Performing collaborative frequency analysis on the multi-protocol concurrent transmission channel to generate path collaboration parameters, performing multi-path collaboration enhancement processing based on the path collaboration parameters to form a collaborative transmission domain, and performing data slicing injection on the collaborative transmission domain to generate a multi-path parallel transmission stream;

[0009] Performing transmission error monitoring on the multipath parallel transmission stream to identify error energy distribution characteristics, triggering an energy feedback collection mechanism to generate feedback energy parameters based on the error energy distribution characteristics, and using the feedback energy parameters to enhance transmission power compensation to construct an enhanced transmission configuration;

[0010] Based on the enhanced transmission configuration, network status monitoring and analysis are performed to generate a network load distribution curve, and transmission timing optimization is performed on the network load distribution curve to determine the best sending time. At the best sending time, data batch upload is performed to complete intelligent data transmission.

[0011] The second aspect of the present invention provides a system for automatically uploading data of a mining smart signboard, comprising:

[0012] A data acquisition module is used to collect downhole environmental data monitored by a cardboard, perform signal integrity detection on the downhole environmental data to identify damaged data frames, and perform error feature analysis on the damaged data frames to generate an error correction seed sequence;

[0013] a protocol control module configured to perform data packet reconstruction processing based on the error correction seed sequence to generate a complete data stream, perform link layer frame synchronization analysis on the complete data stream to extract a frame boundary identifier, utilize the frame boundary identifier to activate a transmission protocol switching mechanism to generate a protocol switching signal, and establish a multi-protocol concurrent transmission channel via the protocol switching signal;

[0014] a collaborative transmission module, configured to perform collaborative frequency analysis on the multi-protocol concurrent transmission channels to generate path collaboration parameters, perform multi-path collaboration enhancement processing based on the path collaboration parameters to form a collaborative transmission domain, and perform data slicing injection on the collaborative transmission domain to generate a multi-path parallel transmission stream;

[0015] An energy feedback module is configured to perform transmission error monitoring on the multipath parallel transmission stream to identify error energy distribution characteristics, trigger an energy feedback collection mechanism based on the error energy distribution characteristics to generate feedback energy parameters, and utilize the feedback energy parameters to enhance transmission power compensation and construct an enhanced transmission configuration;

[0016] The transmission scheduling module is used to perform network status monitoring and analysis based on the enhanced transmission configuration to generate a network load distribution curve, optimize the transmission timing of the network load distribution curve to determine the best sending time, and perform batch data upload at the best sending time to complete intelligent data transmission.

[0017] The beneficial effects of the present application are embodied in the following points: first, by signal integrity detection and error feature analysis of damaged data frames of downhole environment data, an efficient error correction seed sequence is generated, realizing intelligent conversion of transmission errors to repair resources. The data packet reconstruction technology based on the error correction seed sequence restores the complete data stream, extracts the frame boundary identifier through link layer frame synchronization analysis, and stimulates the transmission protocol switching mechanism to dynamically establish a multi-protocol concurrent transmission channel, significantly improving the data transmission adaptability and reliability in complex downhole environments. Second, the multi-protocol concurrent transmission channel is subjected to coordinated frequency analysis to generate path coordination parameters, and through synchronous tuning and hierarchical modulation of frequency characteristic information, a coordinated field is constructed to form a unified coordinated transmission domain. Based on the data slicing injection and diffusion coefficient analysis of the coordinated transmission domain, a multi-level slicing propagation link is established to generate a multi-path parallel transmission stream, realizing maximum utilization of transmission bandwidth and significant improvement of transmission efficiency. Finally, through error monitoring of the multi-path parallel transmission stream, the error energy distribution characteristics are identified, the transmission loss is converted into a source of system enhanced energy, the energy feedback collection mechanism is triggered and feedback energy parameters are generated, the transmission power is compensated to build an enhanced transmission configuration. Combined with the analysis of network load distribution curve, the best sending time is accurately determined by distinguishing the real-time transmission layer from the delay buffer layer and establishing the time reversal anchor point, realizing intelligent scheduling of data batch uploading, improving transmission performance while reducing system energy consumption.

[0018] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present application. BRIEF DESCRIPTION OF DRAWINGS

[0019] The drawings herein show specific examples of the technical solutions described in the present application, and constitute part of the specification together with the specific embodiments, for explaining the technical solutions, principles and effects of the present application.

[0020] Unless specifically stated or otherwise, the same reference signs in different drawings represent the same or similar technical features, and different reference signs may also be used to represent the same or similar technical features.

[0021] Figure 1 is a flowchart of a mine intelligent plate data automatic uploading method of the present application.

[0022] Figure 2 is a structural block diagram of a mine intelligent plate data automatic uploading system of the present application. DETAILED DESCRIPTION

[0023] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.

[0024] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0025] References to "one embodiment" or "some embodiments" in this specification mean that a particular feature, structure, or characteristic described in conjunction with that embodiment is included in one or more embodiments of the present application. Thus, phrases such as "in one embodiment," "in some embodiments," "in other embodiments," and "in other embodiments" appearing in various places in this specification do not necessarily refer to the same embodiment, but rather mean "one or more but not all embodiments," unless otherwise specifically emphasized. The terms "including," "comprising," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0026] The technical solutions of the embodiments of this application are introduced below.

[0027] like Figure 1 As shown, an embodiment of the present invention provides a method for automatically uploading data of a mining smart signboard, comprising the following steps S110 to S150:

[0028] Step S110 , collecting downhole environmental data monitored by the board, performing signal integrity detection on the downhole environmental data to identify damaged data frames, and performing error feature analysis using the damaged data frames to generate an error correction seed sequence.

[0029] Specifically, data from downhole environmental monitoring systems is collected using monitoring panels. In the downhole environmental monitoring system, multiple monitoring panels collect multi-dimensional data about the downhole environment in real time. Temperature sensor panels monitor downhole temperature changes, covering temperatures from extremely low to high temperatures. Humidity sensor panels monitor humidity distribution, enabling full-range humidity monitoring. Gas sensor panels monitor the concentrations of harmful gases such as methane, carbon monoxide, and hydrogen sulfide in the downhole environment, with detection accuracy down to trace levels. Pressure sensor panels monitor atmospheric pressure changes using high-precision pressure sensors. A distributed data acquisition network for monitoring panels is established, connecting each sensor panel using an industrial standard bus to support multi-point concurrent data collection. The panel monitoring system is equipped with a local data cache to prevent data loss in the event of communication interruptions. The hardware clock synchronization mechanism of the panel monitoring system ensures that downhole environmental data collected at different locations has a unified time base. The sampling frequency for panel monitoring is set, and the sampling rate is dynamically adjusted based on the changing characteristics of downhole environmental data. A self-check mechanism is established for the panel monitoring equipment, and sensor calibration is performed regularly to ensure the accuracy of downhole environmental data collection.

[0030] Perform signal integrity checks on downhole environmental data to identify corrupted data frames. Collected downhole environmental data must undergo rigorous signal integrity checks to systematically identify corrupted data frames generated during data transmission. A cyclic redundancy check (CRC) is performed on each frame of downhole environmental data using the standard CRC-16 polynomial x^16+x^15+x^2+1, where x represents the data bit. A frame structure check mechanism for downhole environmental data is established to verify the integrity of the frame header, data length field, device address, and frame trailer. A continuity check is performed on downhole environmental data frames. Abnormal time intervals between adjacent frames are considered a timing error. A range check is performed on downhole environmental data, marking data outside the acceptable range of physical quantities as a content error. Parity checks on downhole environmental data are used to identify single-bit flip errors. Sequence number verification is performed on downhole environmental data frames to detect sequence number jumps, duplications, or rollbacks, identifying frame loss or retransmission. The distribution of corrupted data frames in the downhole environmental data is analyzed, including the percentage of each error type. Establish the feature vector of the damaged data frame and record key information such as error type, error location, and error time.

[0031] In some embodiments, the use of the damaged data frame to perform error feature analysis to generate an error correction seed sequence includes: generating a data packet coupling feature based on the damaged data frame; capturing mutation points on the data packet coupling feature to identify error propagation boundaries; constructing an error interference suppression curve through the error propagation boundaries; and establishing an error correction seed sequence using the error interference suppression curve.

[0032] Generate packet coupling features based on damaged data frames. Analyze the correlation between the damaged data frame and adjacent normal data frames to extract the coupling characteristics between packets. Calculate the correlation coefficient matrix for multiple frames preceding and following the damaged data frame. The matrix element r_ij represents the correlation between frame i and frame j, and its value range is [-1, 1]. Extract the error pattern vector from the bit sequence of the damaged data frame, marking error positions as 1 and normal positions as 0. Analyze the error diffusion characteristics of the damaged data frame and calculate the probability of a single error bit affecting adjacent bits to form an error diffusion probability matrix. Through frequency domain analysis of the damaged data frame, use discrete Fourier transform to extract spectral features and identify the distribution of errors in the frequency domain. Calculate the Hamming distance d_H = Σ(b_i ⊕ b_j) between the damaged and normal data frames, where b_i and b_j are the bit values ​​at corresponding positions in the two frames, ⊕ represents an exclusive-or operation, and Σ represents a sum. Build a comprehensive packet coupling feature vector that incorporates multi-dimensional information such as correlation, distance metric, diffusion probability, and spectral features. Cluster analysis is performed on the coupling features of multiple damaged data frames to identify similar coupling patterns.

[0033] The packet coupling signature is used to capture mutation points and identify error propagation boundaries. A mutation detection algorithm is used to identify key turning points in error propagation within the generated packet coupling signature sequence. First-order differences are calculated for the packet coupling signature sequence to detect sharp changes in the signature value. A mutation threshold is set at the mean plus three standard deviations, and mutation points are marked when the difference exceeds the threshold. A sliding window detection method is used to calculate local statistics of the coupling signature within the window. A cumulative sum algorithm is used to detect persistent changes in the packet coupling signature and identify slowly accumulating error propagation processes. Detected mutation points are temporally clustered, and adjacent mutation points are grouped as belonging to the same error propagation event. The spatial distribution of mutation points in the packet coupling signature is analyzed to determine the error propagation path and impact range within the data stream. A structured description of the error propagation boundary is established, recording the time, location, and intensity of the boundary. Boundary feature analysis allows the dominant direction and speed of error propagation to be identified.

[0034] Construct an error interference suppression curve based on the error propagation boundary. Utilize the identified error propagation boundary information to design targeted interference suppression strategies. At each location on the error propagation boundary, calculate the local error density to quantify the error concentration. Based on the temporal distribution of the error propagation boundary, construct a time-varying suppression function S(t) = A·exp(-λ|t-t_b|), where S(t) is the suppression strength at time t, A is the suppression amplitude parameter, λ is the decay coefficient, t_b is the time when the boundary appears, and exp represents the natural exponential function. A spatial distribution of the suppression strength is set along the spatial path of the error propagation boundary, with the maximum suppression strength occurring in the core region of the boundary. Combining the temporal and spatial dimensions, a two-dimensional error interference suppression curve is formed. The error interference suppression curve is smoothed to eliminate discontinuities. Based on the strength distribution of the error propagation boundary, the shape parameters of the suppression curve are adaptively adjusted. A parametric representation of the suppression curve is established, including key parameters such as peak position, width, and decay rate. Suppression curves are superimposed to handle the intersection of multiple error propagation boundaries.

[0035] An error correction seed sequence is constructed using the error interference suppression curve. The constructed error interference suppression curve contains rich information about the error suppression strategy. The error correction seed sequence is generated through feature extraction and code conversion. A set of peak points is extracted from the error interference suppression curve, with each peak point corresponding to a key error correction location. The gradient field ∇S(x, t) of the suppression curve is calculated, where ∇ is the gradient operator and S(x, t) is the two-dimensional suppression curve function. The gradient direction indicates the priority direction of error correction. Spectral analysis is performed on the error interference suppression curve to extract the main frequency and harmonic components. The frequency information encodes the periodicity of error correction. The total suppression energy is calculated by integrating the suppression curve, and the energy value determines the error correction strength. The suppression curve features are mapped into a binary sequence, using a segmented encoding method, with different curve features corresponding to different binary codes. The multi-dimensional feature information is combined to generate a structured error correction seed sequence containing location information, gradient information, frequency pattern, and energy level. Error protection coding is performed on the error correction seed sequence, and redundant bits are added to improve the reliability of the seed sequence. The seed sequence length is appropriately set to ensure sufficient error correction information while controlling storage overhead.

[0036] Step S120, based on the error correction seed sequence, data packet reconstruction processing is performed to generate a complete data stream, link layer frame synchronization analysis is performed on the complete data stream to extract the frame boundary identifier, the frame boundary identifier is used to stimulate the transmission protocol switching mechanism to generate a protocol switching signal, and a multi-protocol concurrent transmission channel is established through the protocol switching signal.

[0037] Specifically, data packets are reconstructed based on an error correction seed sequence to generate a complete data stream. The error correction seed sequence is decoded into a specific set of error correction instructions, each corresponding to a specific data repair operation. Based on the position information in the error correction seed sequence, the specific bits that need to be repaired in the damaged data packet are located. The error correction rules encoded in the error correction seed sequence are applied to flip or replace the damaged bits to restore the original data value. The redundant information contained in the error correction seed sequence is used to reconstruct the lost packet header and trailer structures. The timing information in the error correction seed sequence is used to restore the correct order between packets, correcting out-of-order and duplication issues. Integrity verification is performed on the reconstructed packets to ensure the correctness of the error correction operation. The corrected packets are rearranged in timestamp order to form a continuous data stream. Necessary synchronization markers and delimiters are inserted into the data stream to ensure parsability. An index structure for the complete data stream is established, recording the position and length of each packet in the stream. The packet reconstruction success rate is statistically calculated to evaluate the effectiveness of the error correction seed sequence. The resulting complete data stream contains all successfully reconstructed packets.

[0038] Link-layer frame synchronization analysis is performed on the complete data stream to extract frame boundary identifiers. The complete data stream is searched for characteristic synchronization patterns defined by the link-layer protocol to identify the start of the frame. A sliding window approach is used to scan the complete data stream, with a window size equal to the minimum frame length, to detect the synchronization field byte by byte. When a frame preamble 0x7E or other protocol-specific start marker is found in the complete data stream, it is marked as a potential frame boundary. The validity of the detected boundary identifier is verified by verifying the bit-stuffing rules of the complete data stream. The distribution of interframe intervals in the complete data stream is analyzed to establish a statistical model for frame length. The characteristics of forward error correction codes are leveraged to identify implicit frame boundary information in the complete data stream. Protocol state machine analysis is performed on the complete data stream, tracking protocol state transitions to determine the location of frame boundaries. The extracted frame boundary identifier contains key information such as the start and end positions of the frame, and the frame type flag. A mapping table is established between frame boundary identifiers and locations in the complete data stream to enable fast location and access. Consistency checks are performed on the extracted frame boundary identifiers to eliminate incorrect boundary markers. A structured sequence of frame boundary identifiers is generated, with each identifier corresponding to a complete frame in the complete data stream.

[0039] In some embodiments, the use of the frame boundary identifier to stimulate the transmission protocol switching mechanism to generate a protocol switching signal includes: obtaining the protocol energy convergence point of the frame boundary identifier; deriving the cross-protocol layer cascade effect based on the protocol energy convergence point; performing reverse positioning through the cascade effect to generate a switching preferred point; and determining the protocol switching signal based on the switching preferred point.

[0040] Obtain the protocol energy convergence points of frame boundary identifiers. Analyze the distribution characteristics of the frame boundary identifier sequence and identify areas with dense identifiers as potential energy convergence points. Calculate the spatial density function of frame boundary identifiers as ρ(x) = N(x) / ΔL, where ρ(x) is the identifier density at position x, N(x) is the number of identifiers in the local area, and ΔL is the length of the statistical interval. Through temporal correlation analysis of frame boundary identifiers, identify periodic identifier patterns. These patterns correspond to fixed overhead locations of the protocol. Statistically analyze the protocol type information carried by frame boundary identifiers to determine the distribution ratio of different protocols in the data stream. Use a clustering algorithm to spatially cluster frame boundary identifiers, with the cluster center being the protocol energy convergence point. Calculate the intensity of each protocol energy convergence point, which reflects the activity of the protocol at that location. Construct a feature vector for the protocol energy convergence point, containing attributes such as location, intensity, protocol type, and duration. Spectral analysis of the frame boundary identifiers identifies the energy convergence characteristics in the frequency domain. Draw a spatial distribution map of the protocol energy convergence points to visually display the energy concentration areas.

[0041] Based on protocol energy converging points, we derive cross-protocol layer cascading effects. We analyze the mutual influence of protocol energy converging points across different protocol layers and construct a cross-layer propagation model. We calculate the coupling strength C_ij = E_i·E_j·exp(-d_ij / λ) between converging points in adjacent protocol layers, where C_ij is the coupling strength between layers i and j, E_i and E_j are the energy values ​​of the two layers, d_ij is the inter-layer distance, λ is the decay length, and exp is an exponential function. Through timing analysis of protocol energy converging points, we determine the energy propagation delay in the protocol stack. We establish a propagation equation for the cascading effect, describing the energy diffusion process from one protocol layer to other layers. We identify the chain reactions triggered by protocol energy converging points, including phenomena such as buffer overflows and queue congestion. We quantify the impact of the cascading effect on system performance, calculating performance metrics such as throughput degradation and latency increase. Through phase relationship analysis of protocol energy converging points, we identify synchronous and asynchronous behavior between different protocol layers. We construct an impact matrix for the cascading effect, where the matrix elements represent the degree of influence of a converging point on other locations. We predict the development trend of the cascading effect and provide a basis for decision-making on protocol switching.

[0042] Cascading effects are used to reversely locate optimal switching points. Starting from the observed cascading effects, the optimal switching locations that induce these effects are traced back. A backpropagation algorithm is applied, using the intensity of the cascading effect as the objective function to find the switching point that maximizes the objective function. The gradient field ∇F(x, t) of the cascading effect is calculated, where F is the cascading effect intensity function and the gradient points in the direction of increasing effect. A search is performed in the reverse direction of the gradient to locate the source of the cascading effect as a candidate switching point. A feasibility analysis is conducted on the candidate switching points to assess the technical feasibility of performing protocol switching at these locations. A sensitivity analysis of the cascading effects is performed to identify the switching locations with the greatest impact on system performance. A scoring mechanism for optimal switching points is established, comprehensively considering switching effectiveness, implementation difficulty, and resource consumption. A simulated annealing algorithm is used to optimize switching point locations, finding a balance between effectiveness and cost. A prioritized list of optimal switching points is generated to provide an execution order for the actual switching operation. Attributes of each optimal switching point are recorded, including location, expected effectiveness, and required resources.

[0043] Determine the protocol switching signal based on the preferred switching point. Encode the location and attribute information of the preferred switching point into a specific protocol switching control signal. Design the data structure of the protocol switching signal, including fields such as the switching time, source protocol identifier, target protocol identifier, and switching parameters. Set the execution order and urgency of the protocol switching signal based on the priority of the preferred switching point. Calculate the trigger conditions for protocol switching and generate a switching signal when the system status meets the conditions. Embed performance constraint parameters in the protocol switching signal to ensure that the switching process does not seriously affect the quality of service. Design the encoding format of the switching signal, using compact binary encoding to reduce control overhead. Add a sequence number and timestamp to the protocol switching signal to support tracking and auditing of switching operations. Establish a confirmation mechanism for the switching signal, and the receiving end returns a confirmation message to indicate that the switching instruction has been accepted. The generated protocol switching signal is distributed to the relevant protocol processing modules through the control channel. Encrypt and authenticate the switching signal to prevent malicious protocol switching attacks.

[0044] Establish multi-protocol concurrent transmission channels using protocol switching signals. Parse the channel configuration parameters in the protocol switching signals to determine the number and types of concurrent channels to be established. Allocate the corresponding protocol stack instance to each channel based on the protocol type specified by the switching signal. Leverage the resource allocation information in the protocol switching signals to allocate bandwidth, buffers, and other resources to each concurrent channel. Implement a channel initialization process based on the switching signals to establish the protocol state machine and connection parameters. Establish a coordination mechanism between multi-protocol concurrent transmission channels to prevent resource contention and conflicts. Dynamically allocate data flows between concurrent channels using the load balancing policy in the switching signals. Monitor the operating status of each concurrent channel and dynamically adjust based on the switching signals. Establish a data synchronization mechanism between channels to ensure data consistency during concurrent transmission. Implement failover functionality. When a channel fails, traffic is migrated to another channel based on the switching signals. Record performance data for the multi-protocol concurrent transmission channels and maintain stable operation of the concurrent transmission channels through continuous guidance from the protocol switching signals.

[0045] Step S130 , performing collaborative frequency analysis on the multi-protocol concurrent transmission channels to generate path collaborative parameters, performing multi-path collaborative enhancement processing based on the path collaborative parameters to form a collaborative transmission domain, and performing data slicing injection on the collaborative transmission domain to generate a multi-path parallel transmission stream.

[0046] Specifically, a coordinated frequency analysis is performed on the multiprotocol concurrent transmission channels to generate path coordination parameters. Comprehensive frequency characteristic measurements are performed on the established multiprotocol concurrent transmission channels to obtain the frequency response curve and phase response characteristics of each channel. In-depth spectrum analysis is performed on the data transmission rate of the multiprotocol concurrent transmission channels, using fast Fourier transform to extract the main frequency component, harmonic components, and sideband frequencies of each channel. The frequency correlation matrix R_f between the multiprotocol concurrent transmission channels is calculated, where the matrix element r_ij represents the frequency correlation coefficient between channel i and channel j, with a value range of [-1, 1]. Phase spectrum analysis of the multiprotocol concurrent transmission channels is used to determine the phase difference distribution and phase drift rate between different channels. Resonant and antiresonant frequency points in the multiprotocol concurrent transmission channels are identified. These special frequency points correspond to regions of strong coupling or decoupling between channels. A time-varying bandwidth utilization curve is extracted for each concurrent transmission channel to analyze the periodicity and burst characteristics of bandwidth occupancy. The group delay characteristics of the multiprotocol concurrent transmission channels are measured to evaluate the propagation delay differences of different frequency components. A comprehensive path coordination parameter vector is established, including multi-dimensional characteristics such as center frequency, effective bandwidth, phase difference, quality factor, group delay, etc. The analyzed path coordination parameters are organized into a structured parameter matrix to support subsequent coordination processing and optimization.

[0047] In some embodiments, the multi-path collaborative enhancement processing based on the path collaborative parameters is implemented to form a collaborative transmission domain, including: extracting frequency characteristic information based on the path collaborative parameters; using the frequency characteristic information to perform frequency synchronization tuning to generate a synchronous frequency signal; modulating the synchronous frequency signal to form a collaborative field; and amplitude superimposing the collaborative field to construct a collaborative transmission domain.

[0048] Frequency feature information is extracted based on path coordination parameters. The frequency components of each channel are systematically separated from the path coordination parameter matrix to construct a multidimensional frequency feature space, with each dimension corresponding to a specific frequency attribute. The central frequency distribution patterns within the path coordination parameters are thoroughly analyzed to identify the spatial distribution characteristics of frequency clusters, frequency holes, and transition regions. The frequency dispersion of the path coordination parameters is calculated as σ_f = sqrt(Σ(f_i - f_mean)² / N), where f_i is the characteristic frequency of the i-th channel, f_mean is the average frequency of all channels, and N is the total number of channels. This metric reflects the degree of frequency concentration. The bandwidth information within the path coordination parameters is used to accurately determine the spectrum range, spectrum overlap, and spectrum gaps available for coordination. The frequency modulation patterns implicit in the path coordination parameters are extracted, including key characteristic parameters such as frequency modulation depth, frequency modulation rate, and modulation index. A multi-level structure of the frequency feature information is established, consisting of fundamental frequency, harmonic, intermodulation, and noise layers. Principal component analysis and independent component analysis are performed on the path coordination parameters to extract the most representative and independent frequency feature vectors. Generate detailed frequency feature information maps, using 3D visualization to display the relationship between frequency, power, and time. Normalize and encode the extracted frequency feature information to generate a standardized feature sequence to ensure consistency in subsequent processing.

[0049] Frequency signature information is used for frequency synchronization tuning to generate a synchronization frequency signal. Based on the center frequency distribution and frequency spacing in the frequency signature information, an optimization scheme for multi-channel frequency synchronization is designed to ensure rapid convergence of the synchronization process. The optimal target synchronization frequency f_sync = Σ(wi·fi), where wi is the weight coefficient of the i-th channel, determined by the signal quality and stability of that channel, and fi is the characteristic frequency of that channel, is calculated. Precise phase alignment is performed on the frequency signature information, using digital phase compensation to eliminate phase offset and phase jitter between channels. A high-precision frequency synchronization algorithm based on a digital phase-locked loop (DPLL) is implemented to lock each channel frequency to the target synchronization frequency with Hz-level locking accuracy. Cross-correlation and cross-spectral analysis of the frequency signature information are used to precisely determine the optimal synchronization timing, synchronization step size, and synchronization convergence criteria. A complete synchronization control signal sequence is generated, including parameters such as frequency adjustment, phase correction, synchronization trigger time, and synchronization hold time. Frequency jitter, phase noise, and synchronization slip are monitored in real time during the synchronization process, and adaptive filtering is used to suppress synchronization errors. Establish an accurate time-domain representation of the synchronized frequency signal, including a mathematical description of its amplitude envelope, instantaneous frequency, and instantaneous phase. Perform a comprehensive quality assessment of the generated synchronized frequency signal, including key metrics such as spectral purity, phase noise, and spurious suppression. Detailed documentation of the performance parameters of the synchronized tuning process, including synchronization setup time, synchronization accuracy, frequency stability, and long-term drift characteristics.

[0050] Exemplarily, the modulation processing of the synchronous frequency signal to form a collaborative field includes: performing injection response analysis on the synchronous frequency signal to obtain a frequency matching degree; performing hierarchical modulation based on the frequency matching degree to generate a hierarchical modulation signal, wherein, when the matching degree is greater than a preset matching threshold, an enhanced modulation method is used to enhance the collaborative strength; when the matching degree is less than the preset matching threshold, a compensatory modulation method is used to improve the collaborative effect; and the hierarchical modulation signals are superimposed and synthesized to form a collaborative field.

[0051] Frequency matching is determined by analyzing the injection response of the synchronous frequency signal. A calibrated synchronous frequency signal is precisely injected into each transmission channel, and the channel's amplitude-frequency response and phase-frequency response characteristics are measured using a network analyzer. The complex cross-correlation function between the injected signal and the channel response is calculated, and the amplitude and phase of the correlation peak are analyzed to comprehensively assess the degree of frequency matching. A standardized frequency matching degree, M, is defined as |H(f_sync)| / |H_max|, where H(f_sync) is the complex transfer function value at the synchronous frequency, |H_max| is the maximum transfer function amplitude, and M ranges from [0, 1]. The propagation characteristics of the synchronous frequency signal in different channels are analyzed in detail, including group delay, phase delay, amplitude attenuation, and nonlinear distortion. Precision swept frequency testing is used to obtain the frequency response curves of each channel within a range of ±10% of the synchronous frequency, identifying the response flatness and transition band characteristics. Factors influencing frequency matching are thoroughly investigated, including channel bandwidth limitations, noise power spectral density, nonlinear distortion coefficient, and multipath effects. The probability distribution of frequency matching is established, and the statistical characteristics of matching are fitted using the beta distribution to determine the distribution parameters α and β. The frequency matching is quantized at multiple levels, mapping the continuous matching values ​​to discrete levels to facilitate subsequent hierarchical modulation decisions.

[0052] Hierarchical modulation is performed based on frequency matching to generate a hierarchically modulated signal. The transmission channel is divided into two modulation strategies based on the comparison of the frequency matching with a preset matching threshold. A matching threshold, M_th, is set at 0.6. When the frequency matching exceeds the preset matching threshold, the channel is identified as highly matched and enhanced modulation is used to improve cooperative transmission strength. Enhanced modulation uses higher-order modulation schemes such as 64-QAM or 256-QAM. By increasing the constellation density, the channel carries more information per symbol, fully leveraging the cooperative transmission capabilities under favorable channel conditions. When the frequency matching falls below the preset matching threshold, the channel is identified as poorly matched and compensation modulation is used to improve cooperative transmission. Compensation modulation uses lower-order modulation schemes such as QPSK or BPSK, combined with forward error correction coding and interleaving techniques, to compensate for channel quality deficiencies by increasing redundancy and ensure basic cooperative transmission reliability. In enhanced modulation, power injection is used to allocate more power to high-quality subcarriers, maximizing the improvement in cooperative strength. Compensation modulation introduces diversity transmission and repetition coding mechanisms to improve cooperative effects through temporal and spatial redundancy, counteracting the adverse effects of channel fading. A hysteresis mechanism for modulation switching is designed. When the frequency matching fluctuates around a threshold, upper and lower thresholds are set to prevent frequent switching. The generated hierarchical modulation signal includes a modulation type identifier, allowing the receiver to identify whether enhancement or compensation modulation is currently being used. This threshold-based binary modulation strategy achieves adaptive optimization of synergy strength.

[0053] Hierarchically modulated signals are superimposed and synthesized to form a synergistic field. Precise time and frequency alignment is performed on all hierarchically modulated signals, using high-precision clock synchronization to ensure nanosecond-level alignment accuracy. The optimized superposition weight coefficient α_i = Mi_i / Σ(M_j) is calculated, where Mi_i represents the frequency matching of the i-th channel and Σ(M_j) represents the sum of the frequency matching of all channels j, ensuring weight normalization. A weighted superposition operation is performed in the complex domain, taking both amplitude and phase information into account to achieve maximum coherent superposition gain. During the superposition process, the signal coherence is monitored in real time, and phase compensation is used to ensure constructive interference and avoid destructive cancellation. The spectral characteristics of the superimposed signals are thoroughly analyzed, and the power spectral density is estimated using the Welch method to identify frequency regions and energy concentration points where synergistic enhancement occurs. The three-dimensional spatial distribution function E(x, y, z, t) of the synergistic field is calculated, where E represents the synergistic field intensity, x, y, and z are the three-dimensional spatial coordinates, and t is the time variable. This function describes the spatial and temporal variation of the synergistic field. Establish a complete mathematical description of the synergistic field, including near-field and far-field characteristics, the principle of field superposition, and the influence of boundary conditions. Through energy density analysis of the synergistic field, use the Poynting vector to calculate the energy flow direction, determine the energy concentration area and radiation pattern. Generate a high-resolution visualization of the synergistic field, using vector field plots and isosurface plots to intuitively display the field's three-dimensional structure. Detailed documentation of key characteristic parameters of the synergistic field is required, including peak field intensity, 3dB beamwidth, field uniformity index, and temporal stability.

[0054] A cooperative transmission domain is constructed by amplitude superposition of the cooperative field. The resulting cooperative field is systematically expanded in three-dimensional space, and the field distribution covering all transmission paths is calculated using the field propagation equation. The amplitude envelope of the cooperative field, |E(x,y,z)|, is calculated, where |E| represents the field strength amplitude. The effective region is defined when |E| > E_th, and E_th is a preset field strength threshold. This criterion is used to determine the spatial boundary of the cooperative transmission domain. A vector amplitude superposition operation is used to accumulate the field strength components contributed by each transmission path, accounting for the influence of polarization direction and phase relationship. The level set method is used to accurately identify the three-dimensional boundary of the cooperative transmission domain, defined as the isosurface where the field strength drops to 10% of the peak value. The field strength distribution characteristics within the cooperative transmission domain are comprehensively analyzed, and statistical parameters such as mean, variance, skewness, and kurtosis are calculated to assess the uniformity of the field distribution. A three-dimensional grid of monitoring points is established within the cooperative transmission domain, with spacing less than a quarter wavelength, to track the spatiotemporal changes of the field strength within the domain in real time. Construct the topology of the cooperative transmission domain, using graph theory to describe the connectivity, reachability, and path redundancy of different regions within the domain. Quantitatively evaluate the coverage performance of the cooperative transmission domain, calculating the effective coverage volume, surface area, and shape factor, and optimizing the domain's geometric properties. Optimize the shape of the cooperative transmission domain through parametric methods, using ellipsoids or hyperellipsoids to fit the constraints of the actual transmission environment. Generate a detailed three-dimensional representation of the cooperative transmission domain, including various visualizations such as field strength distribution, isosurfaces, and streamline plots, providing a precise spatial reference for data transmission planning.

[0055] In some embodiments, the performing of data shard injection on the collaborative transmission domain to generate a multi-path parallel transmission stream includes: constructing an initial shard influence area based on the collaborative transmission domain; performing a deep diffusion analysis on the initial shard influence area to obtain a diffusion coefficient; obtaining a multi-level shard propagation link based on the diffusion coefficient; and generating a multi-path parallel transmission stream based on the multi-level shard propagation link.

[0056] The initial fragment influence area is constructed based on the cooperative transmission domain. Within the three-dimensional space of the cooperative transmission domain, field strength analysis is used to identify optimal fragment injection locations. These locations correspond to local field strength maxima and offer optimal signal propagation conditions. Based on the field strength gradient distribution within the cooperative transmission domain, a watershed algorithm is used to divide the influence areas into different levels, forming a hierarchical regional structure. The channel capacity upper limit C_max = BW·log2(1+SNR) is accurately calculated for each influence area, where BW is the available bandwidth and SNR is the average signal-to-noise ratio (SNR) of the area. This provides a theoretical basis for fragment size optimization. An adaptive initial fragment size is set, ranging from 64 bytes to 1500 bytes, based on regional capacity and latency requirements, to balance transmission efficiency and processing overhead. The three-dimensional location coordinates, spatial extent, and boundary features of each fragment influence area are precisely marked on the topology of the cooperative transmission domain. The spatial overlap and coupling between adjacent influence areas are thoroughly analyzed to design an intelligent fragment allocation strategy for boundary areas to avoid conflicts and interference. A multi-level management structure for influence areas is established, with core areas receiving the highest fragment processing priority and resource allocation weight. Comprehensively assess the transmission performance potential of each impact zone, measuring key metrics such as throughput, round-trip latency, jitter, and packet loss rate. Generate a detailed initial shard impact zone configuration table, including zone identifiers, spatial coordinates, capacity parameters, performance metrics, and priority settings. Establish a management framework for impact zones, adjusting zone divisions and parameter configurations based on network status and service needs.

[0057] A deep diffusion analysis of the initial fragmentation's impact region is performed to obtain the diffusion coefficient. An accurate description of data fragmentation diffusion is established, analyzing the fragmentation's propagation within the initial impact region, taking into account the influence of network topology, link bandwidth, and node processing capacity. The governing diffusion equation ∂ρ / ∂t=D∇²ρ+S is constructed, where ρ is the fragmentation density function, D is the diffusion coefficient tensor, ∇² is the Laplace operator, and S is the source term, describing the fragmentation injection rate. The diffusion equation is numerically solved using the finite element method, with adaptive mesh refinement techniques improving computational accuracy in key areas and obtaining the spatiotemporal distribution of the fragmentation density. The boundary conditions within the initial fragmentation's impact region are thoroughly analyzed, including the effects of reflecting boundaries (fragment return), absorbing boundaries (fragment disappearance), and mixed boundary conditions. Multiple characteristic parameters of fragmentation diffusion are precisely measured, including the diffusion time constant τ=L² / D, where L is the characteristic length and D is the diffusion coefficient; the diffusion length L_d=√(D·t), where D is the diffusion coefficient and t is the diffusion time and the diffusion wavefront velocity. The system identifies multiple factors influencing the diffusion coefficient, including the combined effects of network congestion, queue length, link bit error rate, protocol overhead, and processing delay. It performs three-dimensional spatial interpolation of the diffusion coefficient using kriging or radial basis function interpolation to obtain a continuous distribution field across the entire impact area. It establishes a quantitative correlation between the diffusion coefficient and actual transmission performance, and uses regression analysis to determine the relationship parameters for performance evaluation. It generates high-resolution diffusion coefficient distribution maps, using heat maps and contour plots to visualize the spatial variation of diffusion capacity. It also documents the complete results of the diffusion analysis, including the numerical solution of the diffusion equation, characteristic parameters, and performance correlations.

[0058] The multi-level fragmented propagation link is obtained according to the diffusion coefficient. Based on the three-dimensional spatial distribution of the diffusion coefficient, an intelligent path planning algorithm is used to design the optimal propagation path of the fragment, and high-diffusion-coefficient areas are preferentially selected to improve transmission efficiency. A weighted directed graph G=(V, E, W) is constructed for fragmented propagation, where V is a node set representing transmission locations, E is an edge set representing available links, and W is a weight function. The weight value W_ij=1 / D_ij, where W_ij is the edge weight from node i to node j, and D_ij is the diffusion coefficient between node i and node j, which reflects the propagation resistance. The improved Dijkstra algorithm or A* algorithm is applied to calculate the optimal propagation link from any source point to the destination, considering path optimization under multiple constraints. According to the numerical range and distribution characteristics of the diffusion coefficient, the propagation link is intelligently classified into three service levels: high-speed channel (D>0.8), standard channel (0.3<D≤0.8), and guarantee channel (D≤0.3). Intelligent relay nodes are deployed at key locations on each level of the propagation link to implement storage and forwarding, path switching, and traffic shaping functions, enhancing the reliability of end-to-end transmission. The performance indicators of multi-level propagation links are accurately calculated, including theoretical and measured values of end-to-end delay, effective bandwidth, packet loss rate, and delay jitter. The switching strategy between multi-level links is optimized, and a fast switching protocol is designed to reduce the signaling overhead and interruption time of fragment migration between different levels of links. A comprehensive link state monitoring system is established, and distributed probes are deployed to collect real-time state information such as link utilization, queue length, and bit error rate. A detailed topology graph of multi-level fragmented propagation links is generated, and a hierarchical graphical method is used to display link levels, connection relationships, capacity allocation, and real-time status. A link resource reservation and allocation framework is established to ensure the transmission quality of critical services.

[0059] A multi-path parallel transmission stream is generated based on the multi-level fragmented propagation link. An intelligent data fragmentation strategy is implemented, and based on the capacity, delay, and reliability characteristics of the multi-level link, the original data is segmented into fragments of different sizes, each carrying path identification and recombination information. Data fragments are concurrently injected on the multi-level fragmented propagation link, and multi-thread or coroutine technology is used to achieve true parallel sending, fully utilizing the aggregated bandwidth of multiple paths. An independent transmission control block is established for each propagation link, containing sequence number space, congestion window, retransmission queue, and other state information, supporting independent flow control. Advanced multi-path load balancing algorithms are implemented to adjust the allocation proportion of fragments on each path based on real-time measured link performance indicators, achieving global optimization. Precise synchronization markers are embedded in the multi-path parallel transmission stream, using a global clock and sequence number to ensure that the receiving end can correctly process out-of-order arriving fragments. The aggregated performance indicators of multi-path transmission are accurately calculated, including total throughput, average delay, delay variance, and overall transmission efficiency, to evaluate the actual effect of parallel transmission.

[0060] Step S140 , performing transmission error monitoring on the multi-path parallel transmission stream to identify error energy distribution characteristics, triggering an energy feedback collection mechanism based on the error energy distribution characteristics to generate feedback energy parameters, and using the feedback energy parameters to enhance and compensate the transmission power to construct an enhanced transmission configuration.

[0061] Specifically, transmission error monitoring is performed on multipath parallel transmission streams to identify error energy distribution characteristics. An error detection mechanism is deployed on each transmission channel of the multipath parallel transmission stream to monitor the transmission status and error occurrence of packets in real time. Cyclic redundancy check and sequence number continuity check are performed on each packet in the multipath parallel transmission stream to identify transmission anomalies such as bit errors, packet loss, and packet reordering. The temporal and spatial distribution of errors in the multipath parallel transmission stream are statistically analyzed to establish a spatiotemporal mapping of error events. The error rate distribution function E(x, t) is calculated for each transmission path, where x represents the location on the transmission path and t represents time. The function value reflects the instantaneous error density at that location. The burst characteristics of errors in the multipath parallel transmission stream are analyzed, and the error state transition process is described using a chain model. Spectral analysis of the error data in the multipath parallel transmission stream is used to identify the concentrated distribution characteristics of error energy in the frequency domain. A spatial heat map of the error energy distribution is constructed to visually display the concentration areas and diffusion patterns of error energy in the multipath parallel transmission stream. Key characteristic parameters of the error energy distribution are extracted, including energy peak location, energy concentration, and energy gradient. Classify error patterns in multipath parallel transmission streams, distinguishing between random errors, burst errors, and correlated errors. Record the time-varying patterns of error energy distribution characteristics to provide precise target positioning for subsequent energy feedback collection.

[0062] In some embodiments, the energy feedback collection mechanism is triggered based on the erroneous energy distribution characteristics to generate feedback energy parameters, including: performing energy density analysis on the erroneous energy distribution characteristics to identify high-energy areas; performing energy distribution analysis based on the high-energy areas to obtain lost energy values; performing power conversion processing on the lost energy values ​​to generate available power signals; and generating feedback energy parameters based on the available power signals.

[0063] Energy density analysis of the error energy distribution signature is performed to identify high-energy regions. The error energy distribution signature data is imported into a three-dimensional energy density analysis system, and a spatial distribution function of energy density, ρ(x, y, z), is constructed, where ρ represents energy density and x, y, and z are three-dimensional spatial coordinates. The local energy density of the error energy distribution signature is calculated, and discrete energy sampling points are smoothed using density estimation methods. An energy density threshold is set, and connected regions with a density greater than the threshold are marked as candidate high-energy regions. Gradient analysis of the error energy distribution signature is performed to identify the direction and rate of change in energy density. Three-dimensional isosurfaces of different energy density levels are plotted through isosurface analysis of the error energy distribution signature, visually displaying the spatial distribution of energy. A clustering algorithm is used to spatially cluster high-density points in the error energy distribution signature, forming several independent high-energy regions. Characteristic parameters of each high-energy region are calculated, including center location, volume, average density, and total energy. The morphological characteristics of the high-energy regions in the error energy distribution signature are analyzed to identify spherical, ellipsoidal, or irregularly shaped energy aggregates. A time evolution sequence of the high-energy regions is established to track the energy accumulation and diffusion process. Generate an identification mapping table of high-energy areas to provide accurate spatial positioning information for subsequent energy analysis.

[0064] Exemplarily, the energy distribution analysis based on the high-energy area to obtain the lost energy value includes: extracting energy density distribution data from the high-energy area; decomposing the energy density distribution data into a core dense area and an edge diffusion area; keeping the core dense area for main energy collection, applying focusing processing to the edge diffusion area to form a converged energy parameter; and reintegrating the converged energy parameter with the core dense area to obtain the lost energy value.

[0065] Energy density distribution data is extracted from high-energy regions. A dense energy sampling grid is deployed within the identified high-energy region, with a grid resolution of 1 / 10 the characteristic energy variation scale to ensure that the fine structure of the energy distribution is captured. The energy density value of each sampling point within the high-energy region is read to form a high-resolution energy density dataset. The extracted energy density distribution data is cleaned, and a filtering algorithm is used to remove outliers and noise interference. The discrete sampling data is continuousized using cubic spline interpolation to obtain a smooth energy density field. The statistical characteristics of the energy density in the high-energy region are calculated, including parameters such as maximum, minimum, mean, standard deviation, kurtosis, and skewness. A probability distribution function of the energy density is established to analyze the energy distribution pattern within the high-energy region. The spatial correlation of the energy density distribution data is extracted to evaluate the energy coupling strength between adjacent regions. A three-dimensional visualization of the energy density distribution is generated, using color coding to represent different density levels, intuitively demonstrating the spatial distribution characteristics of energy.

[0066] The energy density distribution data is decomposed into a core-dense region and an edge-diffuse region. An adaptive threshold segmentation algorithm is applied to the energy density data, classifying regions with densities greater than twice the average as the core-dense region. The radial distribution function of the energy density is calculated, and the density decay pattern is analyzed from the center of the high-energy region outward to determine the characteristic decay length. A watershed algorithm is used to identify local energy density maxima, which serve as seed points for the core-dense region. The boundaries of the core-dense region are expanded using a region growing method based on the density gradient until a significant gradient change occurs. The region outside the core-dense region is defined as the edge-diffuse region, which contains a gradually decaying energy distribution. The energy contributions of the core-dense region and the edge-diffuse region are analyzed. Typically, the core region contains 60-70% of the total energy, while the edge region contains 25-35%. An interface description of the two regions is established, with mathematical equations defining the boundary. The energy transfer mechanism from the core to the edge is analyzed. The geometric and energy parameters of each decomposed region are recorded to provide basic data for subsequent processing.

[0067] Maintaining the core dense area for primary energy collection, focusing processing is applied to the edge diffusion area to establish converged energy parameters. The energy in the core dense area is maintained as is, serving as the primary source of energy collection, to avoid energy loss due to additional processing. An energy focusing scheme is designed for the edge diffusion area, using the principle of a virtual lens to converge scattered energy toward the center, improving energy utilization. The energy flow vector field in the edge diffusion area is calculated to determine the primary energy diffusion direction and velocity, providing a reference for focusing design. Energy reflective surfaces are placed at key locations in the edge diffusion area, using a parabolic design to reflect escaping energy back to the collection area. Phase modulation technology is used to coherently superimpose the edge energy, enhancing the convergence effect and improving energy collection efficiency. Efficiency factors of the focusing process, including geometric focusing efficiency and phase coherence efficiency, are calculated to quantify the focusing effect. A converged energy parameter vector is generated, containing information such as converged power, focal point location, and beamwidth. Focusing parameter configuration is optimized, maximizing energy recovery by adjusting the curvature and phase distribution of the reflective surface.

[0068] Reintegrate the converged energy parameters with the core-dense region to obtain the dissipated energy value. Perform vector superposition of the focused energy from the edge diffusion region with the original energy from the core-dense region, taking into account the amplitude and phase relationships. Calculate the integrated total energy: E_total = E_core + E_focused, where E_total is the total energy, E_core is the core energy, and E_focused is the focused energy. Analyze the energy coupling effects during the integration process, consider the impact of phase matching on the total energy, and optimize the superposition effect. Calculate the dissipated energy value as the difference between the initial total energy and the integrated total energy using the law of conservation of energy. Establish a distribution description of the dissipated energy and identify the main energy loss mechanisms and locations, including diffusion loss and conversion loss. Quantify the contribution of different loss sources and analyze the proportions of transmission loss, conversion loss, leakage loss, and other components. Generate a detailed dissipated energy value report, including the total amount, distribution, and time-varying characteristics. Record key parameters of the energy integration process, including integration efficiency and coupling coefficient, to provide input data for subsequent power conversion.

[0069] The dissipated energy values ​​are converted to power to generate a usable power signal. Based on the magnitude and distribution of the dissipated energy values, a corresponding energy-to-power conversion scheme is designed. Calculate the instantaneous power P(t) = dE / dt, where P(t) is the power at time t and E is the dissipated energy. Obtain the power time series through time differentiation. Apply power factor correction techniques to convert reactive power into active power, improving energy efficiency. A rectifier circuit converts the alternating energy signal into DC power, facilitating subsequent power management and distribution. Implement a power smoothing filter to eliminate high-frequency ripple and transient spikes in the power signal. Establish a power conversion characterization and analyze the response characteristics from energy input to power output. Calculate power conversion efficiency and optimize conversion parameters to improve efficiency. Generate a standardized usable power signal, including attributes such as power amplitude, stability, and usable duration. Evaluate the quality of the power signal to ensure it meets transmission enhancement requirements. Record the performance indicators and operating parameters of the power conversion process.

[0070] Generate feedback energy parameters based on the available power signal. Analyze the time domain characteristics of the available power signal to extract key indicators such as average power, peak power, and power change rate. Perform spectral analysis on the available power signal to identify the frequency components and harmonic content of the power. Establish a power-energy mapping relationship and calculate the total available energy within different time windows. Design a data structure for the feedback energy parameters, including multi-dimensional information such as power level, energy capacity, time constraints, and quality indicators. Based on the stability of the available power signal, classify the feedback energy into three types: constant power, pulsed power, and random power. Calculate the reliability index of the feedback energy to assess the continuity and stability of the energy supply. Generate a coded sequence for the feedback energy parameters to facilitate transmission and parsing within the system. Establish a matching relationship between the feedback energy parameters and transmission requirements to determine the optimal energy allocation strategy. Complete the standardized packaging of the feedback energy parameters to form a control parameter set that can be directly used for power enhancement. Record the complete parameter generation process to provide traceability information for system optimization.

[0071] Enhanced transmission configurations are constructed by using feedback energy parameters to enhance transmission power compensation. The available power information in the feedback energy parameters is analyzed to calculate the additional power resources available for transmission enhancement. Based on the power level of the feedback energy parameters, a hierarchical power compensation scheme is designed to achieve refined power management. Feedback energy is allocated to transmission paths requiring power enhancement based on priority, prioritizing compensation for weak links with high error rates. The enhanced transmission power is calculated as P_enhanced = P_base + α·P_feedback, where P_enhanced is the enhanced power, P_base is the base power, P_feedback is the feedback power, and α is the utilization factor. A timing strategy for power compensation is developed based on the time window information in the feedback energy parameters to ensure timely compensation. Power enhancement modules are deployed at key transmission nodes to inject feedback energy into the signal amplification and forwarding processes. An adaptive control loop for power enhancement is established to adjust the compensation strength based on real-time transmission quality. The parameter set for the enhanced transmission configuration is designed, including power allocation for each path, modulation and coding scheme, and retransmission strategy.

[0072] Step S150 , based on the enhanced transmission configuration, network status monitoring and analysis are performed to generate a network load distribution curve, transmission timing optimization is performed on the network load distribution curve to determine the best sending time, and data batch upload is performed at the best sending time to complete intelligent data transmission.

[0073] Specifically, network status monitoring and analysis based on the enhanced transmission configuration generate a network load distribution curve. Using the monitoring parameters in the enhanced transmission configuration, status acquisition probes are deployed at key network nodes to obtain real-time performance metrics such as link utilization, queue length, and packet loss rate. Based on the sampling frequency and accuracy requirements defined in the enhanced transmission configuration, network status data is collected at a high density, with sampling intervals down to the second level. Leveraging the multipath information in the enhanced transmission configuration, the load status of all transmission paths is simultaneously monitored, creating a full network load view. The instantaneous network load L(t) is calculated as Σ(B_used / B_total), where B_used is the used bandwidth and B_total is the total bandwidth. Time series analysis is performed on the collected load data to identify periodic patterns and burst characteristics of load fluctuations. Moving average and exponential smoothing techniques are used to process the raw load data, eliminating instantaneous jitter and generating a smoothed load trend. A statistical distribution of the network load is established, calculating the load mean, variance, and peak probability for different time periods. A 24-hour network load distribution curve is plotted, with time on the horizontal axis and load percentage on the vertical axis. The curve reflects the time-varying characteristics of the network load. Mark key characteristic points on the load distribution curve, including load valley period, peak period and transition period.

[0074] In some embodiments, the optimization of transmission timing selection of the network load distribution curve to determine the best sending time includes: using the network load distribution curve to distinguish between the immediate transmission layer and the delay cache layer; establishing a timing reverse anchor point based on the immediate transmission layer; using the timing reverse anchor point to perform compensatory deduction on the delay cache layer to determine the transmission lead time; and using the transmission lead time to construct the best sending time.

[0075] Use the network load distribution curve to distinguish between the immediate transmission layer and the delayed buffer layer. Analyze the value range of the network load distribution curve and set load thresholds to divide the curve into different transmission strategy intervals. When the network load distribution curve shows a load below 30%, it is defined as the immediate transmission layer, which has sufficient network resources to support real-time data transmission. When the network load distribution curve shows a load exceeding 70%, it is defined as the delayed buffer layer, where network resources are limited and data needs to be buffered. A transition layer is set in the 30%-70% load range, allowing flexible selection of transmission strategies based on data priority and network trends. By analyzing the network load distribution curve over a timeline, the distribution ratio of the immediate transmission layer to the delayed buffer layer within a 24-hour period is calculated. Consecutive periods in the network load distribution curve are identified, which are optimal windows for batch transmission. Establish rules for layer switching, triggering corresponding transmission strategy adjustments when the network load exceeds the threshold. Generate a transmission layer partition diagram to visually display the transmission strategy allocation during different time periods. Record characteristic parameters of each layer, including average load, duration, and frequency of occurrence.

[0076] Establish timing-backward anchor points based on the real-time transmission layer. Within the real-time transmission layer identified by the network load distribution curve, select the period with the lowest load and longest duration as the ideal transmission window. Starting from the start time of the real-time transmission layer, consider factors such as data preparation time and system startup delays, and work backward to calculate the time when preparation should begin. Set the timing-backward anchor point T_anchor = T_ideal - T_prep, where T_ideal is the ideal transmission time and T_prep is the preparation time. Evaluate the reliability of the anchor points based on the stability characteristics of the real-time transmission layer. A stable real-time transmission layer corresponds to a highly reliable anchor point. Mark all eligible timing-backward anchor points on the network load distribution curve to form an anchor point sequence. Analyze the time intervals between anchor points to ensure sufficient spacing between adjacent anchor points to avoid transmission conflicts. Assign a priority weight to each anchor point, taking into account load level, duration, and historical success rate. Establish associations between anchor points, grouping and managing anchors belonging to the same real-time transmission layer. Generate an anchor point configuration table containing the anchor point time, associated real-time transmission layer parameters, and priority information.

[0077] Using the timing backcast anchor, a compensatory derivation is performed on the delay buffer layer to determine the transmission lead time. Based on the established timing backcast anchor, the amount of data accumulated in the delay buffer layer and the transmission demand are analyzed. The data clearing time for the delay buffer layer is calculated as T_clear = V_buffer / R_trans, where V_buffer is the amount of buffered data and R_trans is the transmission rate. Starting from the timing backcast anchor, the required buffer processing time is deduced forward to ensure that preparations are completed before the arrival of the immediate transmission layer. Considering the data growth rate of the delay buffer layer, a compensation coefficient α = 1 + r_growth · T_clear is established, where r_growth is the data growth rate. Through compensatory derivation, the transmission lead time T_pre = T_anchor - α · T_clear is determined, at which point preprocessing of the buffered data begins. Preprocessing operations such as data sorting, compression, and grouping are triggered at the transmission lead time to prepare for the upcoming transmission window. The rationality of the lead time is evaluated to ensure that resources are not prematurely occupied and the transmission opportunity is not delayed. Establish a lead time adjustment mechanism to dynamically optimize the lead time based on actual cache status and network changes. Record the calculation process and results of compensatory derivation to form a decision record of transmission lead time.

[0078] The optimal sending time is constructed using the transmission lead time. The optimal time for data transmission is determined by combining the transmission lead time and the start time of the immediate transmission layer. The optimal sending time is calculated using the formula T_best = max(T_pre + T_ready, T_instant), where T_ready is the system ready time and T_instant is the start time of the immediate transmission layer. The competition among multiple transmission lead times is considered, and the final sending order is determined by prioritization. An early warning mechanism is implemented before the optimal sending time to notify relevant modules to prepare for transmission. A fault-tolerant window is established for the sending time, with a certain buffer time reserved before and after the optimal time to cope with emergencies. The optimal sending time is compared with the network load distribution curve to confirm that it is indeed within favorable network conditions. An execution plan for the optimal sending time is generated, including detailed information such as the specific time, transmission tasks, and resource allocation. A sending time trigger mechanism is established to automatically initiate the transmission process when the system time reaches the optimal sending time. The process of determining the optimal sending time, including all influencing factors and decision-making basis, is documented to provide a reference for subsequent optimization. Through precise timing selection, data transmission is ensured to be carried out under the most favorable network conditions.

[0079] Intelligent data transmission is achieved by executing batch uploads at the optimal sending time. When the system time reaches the optimal sending time, the batch upload process is triggered, and the cached data to be transmitted is sent simultaneously. Transmission parameters, including the number of concurrent connections, fragment size, and transmission rate, are dynamically adjusted based on network conditions at the optimal sending time. Intelligent traffic scheduling strategies are implemented to distribute batch data across multiple available transmission channels, fully utilizing network resources. During the batch upload process, transmission performance is continuously monitored, including metrics such as real-time throughput, transmission progress, and error rate. An adaptive rate control algorithm dynamically adjusts the transmission rate based on network feedback to avoid network congestion. A breakpoint-resume mechanism is implemented to resume uploads from the point where a transmission was interrupted, ensuring data integrity. Bulk uploaded data is grouped and managed, with different types of data assigned corresponding transmission policies and priorities. Data integrity checks are performed after the transmission is completed to ensure that all data arrives at its destination correctly. Transmission reports are generated to record actual transmission performance, resource consumption, and abnormal events. Intelligent scheduling and optimized control enable efficient and reliable batch data transmission.

[0080] In order to implement the method for automatically uploading data of a mining smart signboard corresponding to the above method embodiment, to achieve the corresponding functions and technical effects. Figure 2 , Figure 2The following is a block diagram of a system 200 for automatically uploading data of a smart signboard for mining provided by an embodiment of the present application. For ease of explanation, only the parts related to the present embodiment are shown. The system 200 for automatically uploading data of a smart signboard for mining provided by an embodiment of the present application includes:

[0081] The data acquisition module 201 is used to collect downhole environmental data monitored by the cardboard, perform signal integrity detection on the downhole environmental data to identify damaged data frames, and use the damaged data frames to perform error feature analysis to generate an error correction seed sequence;

[0082] The protocol control module 202 is configured to perform data packet reconstruction based on the error correction seed sequence to generate a complete data stream, perform link layer frame synchronization analysis on the complete data stream to extract a frame boundary identifier, use the frame boundary identifier to trigger a transmission protocol switching mechanism to generate a protocol switching signal, and establish a multi-protocol concurrent transmission channel through the protocol switching signal;

[0083] The collaborative transmission module 203 is configured to perform collaborative frequency analysis on the multi-protocol concurrent transmission channels to generate path collaboration parameters, perform multi-path collaboration enhancement processing based on the path collaboration parameters to form a collaborative transmission domain, and perform data slicing injection on the collaborative transmission domain to generate a multi-path parallel transmission stream;

[0084] An energy feedback module 204 is configured to perform transmission error monitoring on the multipath parallel transmission stream to identify error energy distribution characteristics, trigger an energy feedback collection mechanism based on the error energy distribution characteristics to generate feedback energy parameters, and use the feedback energy parameters to enhance transmission power compensation and construct an enhanced transmission configuration;

[0085] The transmission scheduling module 205 is used to perform network status monitoring and analysis based on the enhanced transmission configuration to generate a network load distribution curve, optimize the transmission timing of the network load distribution curve to determine the best sending time, and perform batch data upload at the best sending time to complete intelligent data transmission.

[0086] The aforementioned automatic data upload system 200 for intelligent mining signboards can implement the automatic data upload method for intelligent mining signboards described in the aforementioned method embodiment. The optional options in the aforementioned method embodiment also apply to this embodiment and will not be described in detail here. The remaining contents of the embodiments of this application can be referred to the contents of the aforementioned method embodiment and will not be further described in this embodiment.

[0087] The purpose of the above embodiments is to exemplify and deduce the technical solution of the present invention, and to fully describe the technical solution, purpose and effect of the present invention. Its purpose is to enable the public to have a more thorough and comprehensive understanding of the disclosed content of the present invention, and it does not limit the scope of protection of the present invention.

[0088] The above embodiments are not exhaustive and may include many other embodiments not listed above. Any replacements and improvements made without violating the concept of the present invention are within the scope of protection of the present invention.

Claims

1. A method for automatically uploading data of a mining intelligent signboard, characterized in that: include: Collecting downhole environmental data monitored by a cardboard, performing signal integrity detection on the downhole environmental data to identify damaged data frames, and performing error feature analysis on the damaged data frames to generate an error correction seed sequence; Performing data packet reconstruction based on the error correction seed sequence to generate a complete data stream, performing link layer frame synchronization analysis on the complete data stream to extract a frame boundary identifier, using the frame boundary identifier to stimulate a transmission protocol switching mechanism to generate a protocol switching signal, and establishing a multi-protocol concurrent transmission channel through the protocol switching signal; Performing collaborative frequency analysis on the multi-protocol concurrent transmission channel to generate path collaboration parameters, performing multi-path collaboration enhancement processing based on the path collaboration parameters to form a collaborative transmission domain, and performing data slicing injection on the collaborative transmission domain to generate a multi-path parallel transmission stream; Performing transmission error monitoring on the multipath parallel transmission stream to identify error energy distribution characteristics, triggering an energy feedback collection mechanism to generate feedback energy parameters based on the error energy distribution characteristics, and using the feedback energy parameters to enhance transmission power compensation to construct an enhanced transmission configuration; Based on the enhanced transmission configuration, network status monitoring and analysis are performed to generate a network load distribution curve, and transmission timing optimization is performed on the network load distribution curve to determine the best sending time. At the best sending time, data batch upload is performed to complete intelligent data transmission.

2. The method according to claim 1, characterized in that The step of using the damaged data frame to perform error feature analysis to generate an error correction seed sequence includes: generating a data packet coupling signature based on the damaged data frame; Capturing the mutation points of the data packet coupling features to identify error propagation boundaries; constructing an error interference suppression curve through the error propagation boundary; An error correction seed sequence is established using the error interference suppression curve.

3. The method according to claim 1, characterized in that The using the frame boundary identifier to stimulate the transmission protocol switching mechanism to generate a protocol switching signal includes: Obtaining a protocol energy convergence point of the frame boundary identifier; Derivation of cross-protocol layer cascading effects based on the protocol energy convergence points; Perform reverse positioning through the cascade effect to generate a switching optimal point; A protocol switching signal is determined based on the switching preferred point.

4. The method according to claim 1, wherein The performing multipath coordination enhancement processing based on the path coordination parameters to form a coordinated transmission domain includes: extracting frequency characteristic information based on the path coordination parameter; Performing frequency synchronization tuning using the frequency characteristic information to generate a synchronous frequency signal; Modulating the synchronous frequency signal to form a collaborative field; Amplitude superposition is performed on the cooperative fields to construct a cooperative transmission domain.

5. The method according to claim 1, wherein The step of injecting data slices into the cooperative transmission domain to generate a multi-path parallel transmission stream includes: Constructing an initial sharding influence area based on the cooperative transmission domain; Performing deep diffusion analysis on the initial fragmentation impact area to obtain a diffusion coefficient; Acquire a multi-level fragmented propagation link according to the diffusion coefficient; A multi-path parallel transmission stream is generated based on the multi-stage fragment propagation link.

6. The method according to claim 1, characterized in that The triggering of the energy feedback collection mechanism to generate feedback energy parameters based on the error energy distribution characteristics includes: Performing energy density analysis on the erroneous energy distribution characteristics to identify high-energy areas; Performing energy distribution analysis based on the high-energy region to obtain a dissipated energy value; Performing power conversion processing on the dissipated energy value to generate an available power signal; A feedback energy parameter is generated based on the available power signal.

7. The method according to claim 1, characterized in that The performing transmission timing optimization on the network load distribution curve to determine the optimal transmission time includes: Using the network load distribution curve to distinguish between an immediate transmission layer and a delayed buffer layer; Determine the anchor point based on the establishment timing of the real-time transport layer; Using the timing to reverse the anchor point, a compensatory deduction is performed on the delay buffer layer to determine the transmission lead time; The transmission lead time is used to construct an optimal sending time.

8. The method according to claim 4, characterized in that The modulating the synchronous frequency signal to form a collaborative field includes: Performing injection response analysis on the synchronous frequency signal to obtain frequency matching; Performing hierarchical modulation based on the frequency matching degree to generate a hierarchical modulation signal, wherein when the matching degree is greater than a preset matching threshold, an enhanced modulation method is used to improve the synergy strength; when the matching degree is less than the preset matching threshold, a compensatory modulation method is used to improve the synergy effect; The hierarchically modulated signals are superimposed and synthesized to form a synergistic field.

9. The method according to claim 6, characterized in that The obtaining of the lost energy value by performing energy distribution analysis based on the high-energy region includes: extracting energy density distribution data from the high-energy region; Decomposing the energy density distribution data into a core dense area and an edge diffuse area; Maintaining the core dense area for main energy collection, applying focusing processing to the edge diffusion area to form concentrated energy parameters; The converged energy parameter is reintegrated with the core dense area to obtain the lost energy value.

10. A mining intelligent signboard data automatic uploading system, characterized in that: include: A data acquisition module is used to collect downhole environmental data monitored by a cardboard, perform signal integrity detection on the downhole environmental data to identify damaged data frames, and perform error feature analysis on the damaged data frames to generate an error correction seed sequence; a protocol control module configured to perform data packet reconstruction processing based on the error correction seed sequence to generate a complete data stream, perform link layer frame synchronization analysis on the complete data stream to extract a frame boundary identifier, utilize the frame boundary identifier to activate a transmission protocol switching mechanism to generate a protocol switching signal, and establish a multi-protocol concurrent transmission channel via the protocol switching signal; a collaborative transmission module, configured to perform collaborative frequency analysis on the multi-protocol concurrent transmission channels to generate path collaboration parameters, perform multi-path collaboration enhancement processing based on the path collaboration parameters to form a collaborative transmission domain, and perform data slicing injection on the collaborative transmission domain to generate a multi-path parallel transmission stream; An energy feedback module is configured to perform transmission error monitoring on the multipath parallel transmission stream to identify error energy distribution characteristics, trigger an energy feedback collection mechanism based on the error energy distribution characteristics to generate feedback energy parameters, and utilize the feedback energy parameters to enhance transmission power compensation and construct an enhanced transmission configuration; The transmission scheduling module is used to perform network status monitoring and analysis based on the enhanced transmission configuration to generate a network load distribution curve, optimize the transmission timing of the network load distribution curve to determine the best sending time, and perform batch data upload at the best sending time to complete intelligent data transmission.

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