A mine intelligent plate data automatic uploading system and method

Abstract

The application discloses a kind of mine intelligent plaque data automatic uploading system and method, acquisition underground environment data, recognize damaged data frame by signal integrity detection, utilize error feature analysis to generate error correction seed sequence;Based on error correction seed sequence reconstruction complete data stream, extract frame boundary identifier and trigger protocol switching mechanism, establish multiple protocol concurrent transmission channel;To transmission channel is carried out collaborative frequency analysis and generates path coordination parameter, implements multiple path coordination enhancement processing and forms collaborative transmission domain, generates multiple path parallel transmission stream by data fragment injection;Monitoring transmission error identifies error energy distribution characteristics, triggers energy feedback collection mechanism to generate feedback energy parameters, and enhances compensation to transmission power;Carry out network state monitoring analysis and generate network load distribution curve, optimize transmission timing selection to determine the best sending time, execute data batch upload to complete intelligent data transmission, improve the reliability and efficiency of data transmission.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of downhole communication, in particular to a mine intelligent board data automatic uploading system and method. BACKGROUND

[0002] Downhole environment data transmission, as an important technical support for mine safety monitoring and production management, carries out real-time transmission tasks of key safety parameters such as temperature, humidity and gas concentration. However, the complex geological environment and electromagnetic interference conditions in the downhole environment lead to frequent problems such as signal attenuation, data packet loss and transmission errors in the data transmission process, which seriously affect the integrity and real-time performance of the monitoring data.

[0003] The existing downhole data transmission technology mainly adopts a single protocol and a fixed transmission strategy, lacks intelligent processing capability and energy recovery mechanism for transmission errors, and cannot dynamically adjust transmission parameters and protocol configuration according to network load. The traditional method has poor adaptability when facing sudden network congestion and channel quality deterioration, and the error energy generated in the transmission process cannot be effectively utilized, resulting in low overall transmission efficiency and high energy consumption, which is difficult to meet the real needs of downhole environment monitoring for high reliability, high efficiency and intelligentization of data transmission. SUMMARY

[0004] The present application provides a mine intelligent board data automatic uploading system and method, which collects downhole environment data through board monitoring and performs signal integrity detection, generates an error correction seed sequence by analyzing the error characteristics of damaged data frames, reconstructs a complete data stream based on the error correction seed sequence and extracts a frame boundary identifier, establishes a multi-protocol concurrent transmission channel through a protocol switching signal, performs collaborative frequency analysis on the transmission channel to form a collaborative transmission domain, realizes the generation of a multi-path parallel transmission stream, simultaneously monitors transmission errors and triggers an energy feedback collection mechanism, and finally optimizes the transmission opportunity through a network load distribution curve to complete the intelligent and efficient transmission of downhole environment data.

[0005] The present application provides a mine intelligent board data automatic uploading system and method, which collects downhole environment data through board monitoring and performs signal integrity detection, generates an error correction seed sequence by analyzing the error characteristics of damaged data frames, reconstructs a complete data stream based on the error correction seed sequence and extracts a frame boundary identifier, establishes a multi-protocol concurrent transmission channel through a protocol switching signal, performs collaborative frequency analysis on the transmission channel to form a collaborative transmission domain, realizes the generation of a multi-path parallel transmission stream, simultaneously monitors transmission errors and triggers an energy feedback collection mechanism, and finally optimizes the transmission opportunity through a network load distribution curve to complete the intelligent and efficient transmission of downhole environment data.

[0006] Collecting downhole environment data monitored by the board, performing signal integrity detection on the downhole environment data to identify damaged data frames, and generating an error correction seed sequence by analyzing the error characteristics of the damaged data frames;

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

[0008] The multi-protocol concurrent transmission channel is subjected to cooperative frequency analysis to generate path cooperation parameters, multi-path cooperation enhancement processing is implemented based on the path cooperation parameters to form a cooperative transmission domain, and data fragmentation injection is performed on the cooperative transmission domain to generate a multi-path parallel transmission stream;

[0009] Transmission error monitoring is performed on the multi-path parallel transmission stream to identify error energy distribution characteristics, an energy feedback collection mechanism is triggered based on the error energy distribution characteristics to generate feedback energy parameters, and the transmission power is enhanced and compensated using the feedback energy parameters to construct an enhanced transmission configuration;

[0010] Network state monitoring analysis is performed based on the enhanced transmission configuration to generate a network load distribution curve, transmission timing optimization selection is implemented on the network load distribution curve to determine an optimal sending time, and data batch uploading is performed at the optimal sending time to complete intelligent data transmission.

[0011] The second aspect of the present application proposes a mine intelligent plate data automatic uploading system, comprising:

[0012] The data acquisition module is used for acquiring underground environment data monitored by the plate, performing signal integrity detection on the underground environment data to identify damaged data frames, and generating an error correction seed sequence using the damaged data frames for error feature analysis;

[0013] The protocol control module is used for performing data packet reconstruction processing 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 trigger a transmission protocol switching mechanism to generate a protocol switching signal, and establishing a multi-protocol concurrent transmission channel through the protocol switching signal;

[0014] The cooperative transmission module is used for cooperative frequency analysis on the multi-protocol concurrent transmission channel to generate path cooperation parameters, multi-path cooperation enhancement processing is implemented based on the path cooperation parameters to form a cooperative transmission domain, and data fragmentation injection is performed on the cooperative transmission domain to generate a multi-path parallel transmission stream;

[0015] The energy feedback module is used for transmission error monitoring on the multi-path parallel transmission stream to identify error energy distribution characteristics, an energy feedback collection mechanism is triggered based on the error energy distribution characteristics to generate feedback energy parameters, and the transmission power is enhanced and compensated using the feedback energy parameters to construct an enhanced transmission configuration;

[0016] The transmission scheduling module is used for network state monitoring analysis based on the enhanced transmission configuration to generate a network load distribution curve, transmission timing optimization selection is implemented on the network load distribution curve to determine an optimal sending time, and data batch uploading is performed at the optimal 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, for purposes of explanation and not limitation, specific details are set forth such as particular architectures, techniques, etc. in order to provide 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 can be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known methods, devices, and circuits are omitted so as not to obscure the description of the present application with unnecessary detail.

[0024] It is to be understood that the terminology "including", "comprising", "consisting" and "consisting essentially of" used in the specification and the appended claims, are used in the sense of open ended inclusion, that is, to mean including, but not limited to, in order to allow for the inclusion of additional steps, elements, features, and / or components without reciting every combination of those additional steps, elements, features, and / or components.

[0025] Reference throughout this specification to "one embodiment", "some embodiments", or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the application. Thus, the appearances of the phrases "in one embodiment", "in some embodiments", "in other embodiments", "in additional embodiments", and the like in various places throughout this specification are not necessarily referring to the same embodiment, unless otherwise specified. The terms "including", "containing", "comprising", "having" and variations thereof mean "including, but not limited to", unless expressly specified otherwise.

[0026] The technical solutions of the embodiments of the present application are introduced as follows.

[0027] As shown in Figure 1 The embodiment of the present application provides a mine intelligent board data automatic uploading method, which comprises the following steps S110-S150:

[0028] In step S110, underground environment data monitored by the board is collected, damaged data frames are identified by performing signal integrity detection on the underground environment data, and error characteristic analysis is performed on the damaged data frames to generate an error correction seed sequence.

[0029] Specifically, the downhole environment data is collected by the card board monitoring. In the downhole environment monitoring system, multi-dimensional data information of the downhole environment is collected in real time by the multi-path card board monitoring equipment. The temperature sensor card board is configured to monitor the temperature change of the downhole environment, and the measurement range covers extreme low temperature to high temperature environment. The humidity sensor card board monitors the humidity distribution of the downhole environment, and realizes full-range humidity monitoring. The gas sensor card board monitors the concentration of harmful gases such as methane, carbon monoxide and hydrogen sulfide in the downhole environment, and the detection accuracy reaches the micro level. The pressure sensor card board monitors the atmospheric pressure change of the downhole environment, and adopts a high-precision pressure sensor. A distributed data acquisition network is established for card board monitoring, and industrial standard bus is used to connect each sensor card board to support multi-point concurrent acquisition. The card board monitoring system is configured with local data cache to prevent loss of downhole environment data when communication is interrupted. Through the hardware clock synchronization mechanism of the card board monitoring system, the downhole environment data collected at different positions has a unified time reference. The sampling frequency of the card board monitoring is set, and the sampling rate is dynamically adjusted according to the change characteristics of the downhole environment data. A self-checking mechanism is established for the card board monitoring equipment, and sensor calibration is performed regularly to ensure the accuracy of downhole environment data acquisition.

[0030] The downhole environment data is subjected to signal integrity detection to identify damaged data frames. The collected downhole environment data needs to undergo strict signal integrity detection to systematically identify damaged data frames generated during data transmission. A cyclic redundancy check is performed on each data frame of the downhole environment data using the standard CRC-16 polynomial x^16+x^15+x^2+1, where x represents a data bit. A frame structure checking mechanism is established for the downhole environment data to verify the integrity of the frame header identifier, data length field, device address and frame tail identifier. The timestamps of the downhole environment data frames are checked for continuity, and when the time interval between adjacent data frames is abnormal, it is determined to be a timing error. Range checking is performed on the downhole environment data, and when the data exceeds the reasonable range of the physical quantity, it is marked as a content error. Single-bit flip error is identified through parity check of the downhole environment data. The sequence number of the downhole environment data frame is verified to detect sequence number jumps, repetitions or backtracking, and to identify data frame loss or retransmission. The type distribution of damaged data frames in the downhole environment data is counted, including the proportion of each type of error. A feature vector of damaged data frames is established to record key information such as error type, error position and error time.

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

[0032] The data packet coupling features are generated based on the corrupted data frames. The correlation between the corrupted data frames and the adjacent normal data frames is analyzed, and the coupling characteristics between the data packets are extracted. The correlation coefficient matrix of multiple data frames before and after the corrupted data frame is calculated, and the matrix element r_ij represents the correlation degree of the i-th frame and the j-th frame, with a value range of [-1, 1]. The error pattern vector is extracted from the bit sequence of the corrupted data frame, and the error position is marked as 1 and the normal position is marked as 0. The error diffusion characteristics of the corrupted data frame are analyzed, the influence probability of a single error bit on adjacent bit positions is counted, and an error diffusion probability matrix is formed. Through frequency domain analysis of the corrupted data frame, the frequency spectrum features are extracted using discrete Fourier transform to identify the distribution rule of errors in the frequency domain. The Hamming distance d_H between the corrupted data frame and the normal data frame is calculated, d_H = Σ(b_i⊕b_j), where b_i and b_j are the bit values of the corresponding positions of the two frames, ⊕ represents the exclusive or operation, and Σ represents the summation. A comprehensive data packet coupling feature vector is established, which contains multiple-dimensional information such as correlation, distance metric, diffusion probability and frequency spectrum features. The coupling features of multiple corrupted data frames are clustered and analyzed to identify similar coupling modes.

[0033] The mutation point of the data packet coupling features is captured to identify the error propagation boundary. From the generated data packet coupling feature sequence, a mutation detection algorithm is used to identify the key turning point of error propagation. The first-order difference of the data packet coupling feature sequence is calculated to detect the sharp change of the feature value. The mutation judgment threshold is set to the mean value plus three times the standard deviation, and when the difference value exceeds the threshold, it is marked as a mutation point. The sliding window detection method is applied to calculate the local statistics of the coupling features in the window. The cumulative sum algorithm is used to detect the persistent change of the data packet coupling features to identify the slow accumulation of error propagation process. The detected mutation points are time clustered, and adjacent mutation points are classified into the same error propagation event. The spatial distribution of the mutation points of the data packet coupling features is analyzed to determine the propagation path and impact range of the error in the data stream. The structured description of the error propagation boundary is established to record the time, position and intensity information of the boundary. Through the boundary feature analysis, the dominant direction and propagation speed of the error propagation are identified.

[0034] The error interference suppression curve is constructed by the error propagation boundary. The identified error propagation boundary information is used to design a targeted interference suppression strategy. At each position point of the error propagation boundary, the local error density is calculated to quantify the concentration of errors. According to the time distribution of the error propagation boundary, a time-varying suppression function S(t) = A·exp(-λ|t-t_b|) is constructed, where S(t) is the suppression intensity at time t, A is the suppression amplitude parameter, λ is the decay coefficient, t_b is the boundary occurrence time, and exp represents the natural exponential function. Along the spatial path of the error propagation boundary, the spatial distribution of the suppression intensity is set, and the suppression intensity in the boundary core area is the largest. The time and spatial dimensions are integrated to form a two-dimensional error interference suppression curve. The error interference suppression curve is smoothed to eliminate discontinuous points. According to the intensity distribution of the error propagation boundary, the shape parameters of the suppression curve are adaptively adjusted. The parameterized representation of the suppression curve is established, including key parameters such as peak position, width, and decay rate. Through the superposition operation of the suppression curve, the intersection of multiple error propagation boundaries is handled.

[0035] The error interference suppression curve is used to establish the error correction seed sequence. The constructed error interference suppression curve contains rich error suppression strategy information, which is used to generate the error correction seed sequence through feature extraction and encoding conversion. A set of peak points is extracted from the error interference suppression curve, each peak point corresponding to a key error correction position. 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. The error interference suppression curve is subjected to spectral analysis to extract the main frequency component and harmonic component, and the frequency information is encoded to represent the periodicity of error correction. The total suppression energy is calculated by integrating the suppression curve, and the energy value determines the error correction intensity. The suppression curve features are mapped to a binary sequence using a segmented encoding method, where different curve features correspond to different binary encodings. Multi-dimensional feature information is combined to generate a structured error correction seed sequence, which contains position information, gradient information, frequency pattern, and energy level. The error correction seed sequence is subjected to error protection encoding to increase the redundancy and improve the reliability of the seed sequence itself. An appropriate length of the seed sequence is set to ensure that it contains enough error correction information while controlling the storage overhead.

[0036] In step S120, the data packet reconstruction process is performed based on the error correction seed sequence to generate a complete data stream. Frame boundary identifiers are extracted by implementing link layer frame synchronization analysis on the complete data stream. The frame boundary identifiers are used to trigger a 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, the data packet reconstruction process is performed based on the 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. According to the position information in the error correction seed sequence, the specific bit positions that need to be repaired in the damaged data packet are located. The error correction rules encoded by the error correction seed sequence are applied to perform flip or replace operations on the damaged bits to restore the original data values. The redundant information contained in the error correction seed sequence is used to reconstruct the lost data packet header and tail structure. Through the timing information of the error correction seed sequence, the correct order relationship between data packets is restored, and the out-of-order and duplication problems are corrected. The reconstructed data packets are subjected to integrity verification to ensure the correctness of the error correction operation. The data packets after error correction are rearranged in timestamp order to form a continuous data stream. Necessary synchronization markers and separators are inserted in the data stream to ensure the parseability of the data stream. An index structure of the complete data stream is established to record the position and length information of each data packet in the stream. The success rate of data packet reconstruction is counted to evaluate the effectiveness of the error correction seed sequence. The generated complete data stream contains all successfully reconstructed data packets.

[0038] Link layer frame synchronization analysis is performed on the complete data stream to extract frame boundary identifiers. In the complete data stream, search for the characteristic synchronization pattern defined by the link layer protocol to identify the starting position of the frame. Use the sliding window method to scan the complete data stream, the window size is equal to the minimum frame length, detect the synchronization field byte by byte. When the frame preamble 0x7E or other protocol-specific starting mark is found in the complete data stream, it is marked as a potential frame boundary. Verify the validity of the detected boundary identifier through the bit padding rules of the complete data stream. Analyze the distribution law of the frame interval in the complete data stream to establish a statistical model of the frame length. Use the characteristics of the forward error correction code to identify the implicit frame boundary information in the complete data stream. Perform protocol state machine analysis on the complete data stream to track the protocol state transition to determine the frame boundary position. The extracted frame boundary identifier contains key information such as frame start position, frame end position, and frame type marker. Establish a mapping table between the frame boundary identifier and the position of the complete data stream to support fast positioning and access. Perform consistency check on the extracted frame boundary identifier to eliminate incorrect boundary markers. Generate a structured frame boundary identifier sequence, each identifier corresponds to a complete frame in the complete data stream.

[0039] In some embodiments, the use of the frame boundary identifier to trigger a transmission protocol switching mechanism generates a protocol switching signal, including: obtaining the protocol energy convergence point of the frame boundary identifier; deriving a cross-protocol cascade effect based on the protocol energy convergence point; generating a switching preferred point through reverse positioning by the cascade effect; determining a protocol switching signal based on the switching preferred point.

[0040] The protocol energy sink points are obtained by acquiring frame boundary identifiers. The distribution characteristics of the frame boundary identifier sequence are analyzed, and the region where the identifiers appear densely is identified as a potential energy sink point. The spatial density function of the frame boundary identifier is calculated 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 region, and ΔL is the length of the statistical interval. By analyzing the time correlation of the frame boundary identifiers, periodically appearing identifier patterns are identified, which correspond to the fixed overhead positions of the protocol. The protocol type information carried by the frame boundary identifiers is counted to determine the distribution proportion of different protocols in the data stream. The frame boundary identifiers are spatially clustered using a clustering algorithm, and the cluster centers are the protocol energy sink points. The intensity value of each protocol energy sink point is calculated, which reflects the activity level of the protocol at this position. The feature vector of the protocol energy sink point is established, including attributes such as position, intensity, protocol type, and duration. The energy concentration characteristics in the frequency domain are identified through spectral analysis of the frame boundary identifiers. The spatial distribution map of the protocol energy sink points is drawn to visually display the energy concentration area.

[0041] The cross-protocol layer cascade effect is derived based on the protocol energy sink points. The mutual influence relationship of the protocol energy sink points between different protocol layers is analyzed, and a cross-layer propagation model is constructed. The coupling strength C_ij between the sink points of adjacent protocol layers is calculated as C_ij=E_i·E_j·exp(-d_ij / λ), where C_ij is the coupling strength between layer i and layer 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 the exponential function. The propagation delay of energy in the protocol stack is determined through time sequence analysis of the protocol energy sink points. The propagation equation of the cascade effect is established to describe the diffusion process of energy from one protocol layer to other layers. The chain reaction triggered by the protocol energy sink points is identified, including buffer overflow, queue blocking, and other phenomena. The influence of the cascade effect on system performance is quantified, and performance indicators such as throughput reduction and delay increase are calculated. The synchronization and asynchronous behavior between different protocol layers is identified through phase relationship analysis of the protocol energy sink points. The influence matrix of the cascade effect is constructed, and the matrix elements represent the influence degree of one sink point on other positions. The development trend of the cascade effect is predicted to provide decision basis for protocol switching.

[0042] The reverse positioning generates switching preferred points through cascading effects. Starting from the observed results of cascading effects, the optimal switching positions causing these effects are traced back. The reverse propagation algorithm is applied to find the switching points that maximize the target function of the impact strength of cascading effects. The gradient field ∇F(x, t) of cascading effects is calculated, where F is the cascading effect strength function, and the gradient points to the direction of effect enhancement. Searching along the opposite direction of the gradient, the source position of cascading effects is found as a candidate switching point. The feasibility analysis of the candidate switching point is performed to evaluate the technical feasibility of executing the protocol switching at this position. Through the sensitivity analysis of cascading effects, the switching position with the greatest impact on system performance is identified. A scoring mechanism for switching preferred points is established, considering the switching effect, implementation difficulty, and resource consumption. The simulated annealing algorithm is used to optimize the switching point position, seeking a balance between effect and cost. A priority list of switching preferred points is generated to provide the execution order for actual switching operations. The attribute information of each switching preferred point is recorded, including position, expected effect, required resources, etc.

[0043] The protocol switching signal is determined based on the switching preferred points. The position and attribute information of the switching preferred points are encoded into specific protocol switching control signals. The data structure of the protocol switching signal is designed, including fields such as switching time, source protocol identifier, target protocol identifier, and switching parameters. According to the priority of the switching preferred points, the execution order and urgency of the protocol switching signal are set. The trigger conditions for protocol switching are calculated, and the switching signal is generated when the system state meets the conditions. Performance constraint parameters are embedded in the protocol switching signal to ensure that the switching process does not severely affect the quality of service. The encoding format of the switching signal is designed, using compact binary encoding to reduce control overhead. Sequence numbers and timestamps are added to the protocol switching signal to support tracking and auditing of switching operations. A confirmation mechanism for the switching signal is established, and the receiving end returns a confirmation message indicating that the switching instruction has been accepted. The generated protocol switching signal is distributed to the relevant protocol processing modules through the control channel. The switching signal is encrypted and authenticated to prevent malicious protocol switching attacks.

[0044] The multi-protocol concurrent transmission channel is established through a protocol switching signal. Channel configuration parameters in the protocol switching signal are parsed to determine the number and type of concurrent channels to be established. According to the protocol type specified in the switching signal, a corresponding protocol stack instance is allocated to each channel. Resources such as bandwidth and buffer are allocated to each concurrent channel using resource allocation information in the switching signal. A channel initialization process based on the switching signal is implemented to establish protocol state machines and connection parameters. A coordination mechanism is established between the multi-protocol concurrent transmission channels to prevent resource competition and conflict. Through the load balancing strategy in the switching signal, data streams are dynamically allocated among the concurrent channels. The running state of each concurrent channel is monitored, and dynamic adjustment is performed according to the indication of the switching signal. A data synchronization mechanism is established between channels to ensure the consistency of concurrent data transmission. A fault switching function is implemented to migrate traffic to other channels when a channel fails. Performance data of the multi-protocol concurrent transmission channel is recorded, and the stable operation of the concurrent transmission channel is maintained through the continuous guidance of the protocol switching signal.

[0045] In step S130, path coordination parameters are generated by performing coordinated frequency analysis on the multi-protocol concurrent transmission channels, and multi-path coordination enhancement processing is performed based on the path coordination parameters to form a coordinated transmission domain. Data fragmentation injection is performed on the coordinated transmission domain to generate a multi-path parallel transmission stream.

[0046] Specifically, path coordination parameters are generated by performing coordinated frequency analysis on the multi-protocol concurrent transmission channels. Comprehensive frequency characteristic measurements are performed on the established multi-protocol concurrent transmission channels to obtain the frequency response curve and phase response characteristics of each channel. In-depth spectral analysis is performed on the data transmission rate of the multi-protocol concurrent transmission channels, and the main frequency component, harmonic component and sideband frequency of each channel are extracted using fast Fourier transform. The frequency correlation matrix R_f between the multi-protocol concurrent transmission channels is calculated, and the matrix element r_ij represents the frequency correlation coefficient between channel i and channel j, with a value range of [-1, 1]. Through phase spectrum analysis of the multi-protocol concurrent transmission channels, the phase difference distribution and phase drift rate between different channels are determined. Resonant frequency points and anti-resonant frequency points in the multi-protocol concurrent transmission channels are identified, which correspond to strong coupling or decoupling regions between channels. The bandwidth utilization time-varying curve of each concurrent transmission channel is extracted, and the periodicity and burst characteristics of bandwidth occupation are analyzed. The group delay characteristics of the multi-protocol concurrent transmission channels are measured, and the propagation delay difference of different frequency components is evaluated. A comprehensive path coordination parameter vector is established, including center frequency, effective bandwidth, phase difference, quality factor, group delay and other multi-dimensional features. 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 cooperative parameter-based implementation of the cooperative transmission domain includes: extracting frequency characteristic information based on the path cooperation parameter; using the frequency characteristic information to generate a synchronous frequency signal by frequency synchronization tuning; modulating the synchronous frequency signal to form a cooperative field; and constructing a cooperative transmission domain by amplitude superposition of the cooperative field.

[0048] The frequency characteristic information is extracted based on the path cooperation parameter. The frequency components of each channel are systematically separated from the path cooperation parameter matrix to construct a multi-dimensional frequency characteristic space, each dimension corresponding to a specific frequency attribute. The distribution law of the center frequency in the path cooperation parameter is analyzed in depth to identify the spatial distribution characteristics of the frequency aggregation area, the frequency hollow area and the transition area. The frequency dispersion degree σ_f = sqrt(Σ(f_i-f_mean)² / N) of the path cooperation parameter is calculated, 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 index reflects the concentration degree of frequency distribution. The frequency spectrum range, frequency spectrum overlap area and frequency spectrum gap that can be used for cooperation are accurately determined through the bandwidth information in the path cooperation parameter. The frequency modulation mode implied in the path cooperation parameter is extracted, including key feature parameters such as frequency modulation depth, frequency modulation rate and modulation index. A multi-level structure of frequency characteristic information is established, including a fundamental frequency characteristic layer, a harmonic characteristic layer, an intermodulation characteristic layer and a noise characteristic layer. Principal component analysis and independent component analysis are performed on the path cooperation parameter to extract the most representative and independent frequency characteristic vectors. A detailed frequency characteristic information map is generated, and a three-dimensional visualization is used to display the relationship between frequency, power and time. The extracted frequency characteristic information is standardized and coded to generate a standardized feature sequence, ensuring the consistency of subsequent processing.

[0049] The frequency synchronization tuning is performed by using the frequency characteristic information to generate a synchronization frequency signal. According to the center frequency distribution and the frequency interval in the frequency characteristic information, an optimization scheme of multi-channel frequency synchronization is designed to ensure the fast convergence of the synchronization process. The optimal target synchronization frequency f sync =∑(w i·f i) is calculated, where w i is the weight coefficient of the i th channel determined by the signal quality and stability of the channel, and f i is the characteristic frequency of the channel. Precise phase alignment processing is performed on the frequency characteristic information, and digital phase compensation technology is used to eliminate the phase offset and phase jitter between different channels. A high-precision frequency synchronization algorithm based on digital phase-locked loop is implemented to lock the frequencies of the channels to the target synchronization frequency, and the locking precision reaches the level of Hz. Through cross-correlation and cross-spectrum analysis of the frequency characteristic information, the optimal synchronization opportunity, synchronization step and synchronization convergence criterion are accurately determined. A complete synchronization control signal sequence is generated, including parameters such as frequency adjustment amount, phase correction value, synchronization trigger time and synchronization holding time. The frequency jitter, phase noise and synchronization sliding phenomenon in the synchronization process are monitored in real time, and adaptive filtering is used to suppress the synchronization error. An accurate time-domain representation of the synchronization frequency signal is established, including mathematical descriptions of the amplitude envelope, instantaneous frequency and instantaneous phase. The generated synchronization frequency signal is comprehensively evaluated in terms of key indicators such as spectral purity, phase noise and spurious suppression. The performance parameters of the synchronization tuning process are recorded in detail, including synchronization establishment time, synchronization accuracy, frequency stability and long-term drift characteristics.

[0050] For example, the modulation processing of the synchronization frequency signal forms a synergistic field, including: performing injection response analysis on the synchronization frequency signal to obtain a frequency matching degree; generating a hierarchical modulation signal based on the frequency matching degree, wherein when the matching degree is greater than a preset matching threshold, an enhanced modulation mode is used to improve the synergistic strength; when the matching degree is less than the preset matching threshold, a compensation modulation mode is used to improve the synergistic effect; and superimposing and synthesizing the hierarchical modulation signal to form a synergistic field.

[0051] The frequency matching degree is obtained by performing injection response analysis on the synchronous frequency signal. The calibrated synchronous frequency signal is accurately injected into each transmission channel, and the amplitude-frequency response and phase-frequency response characteristics of the channel are measured using a network analyzer. The complex cross-correlation function of the injected signal and the channel response is calculated, and the amplitude and phase of the correlation peak are analyzed to comprehensively evaluate the frequency matching degree. The normalized frequency matching degree M = |H(f_sync)| / |H_max| is defined, where H(f_sync) is the complex transmission function value at the synchronous frequency, |H_max| is the maximum value of the transmission function amplitude, and M has a value range of [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. The frequency response curves of each channel within the synchronous frequency ±10% range are obtained through precise sweep frequency testing, and the flatness and transition band characteristics of the response are identified. Various factors affecting the frequency matching degree are studied in depth, including channel bandwidth limitation, noise power spectral density, nonlinear distortion coefficient, and multipath effect. The probability distribution of the frequency matching degree is established, and the statistical characteristics of the matching degree are fitted using a beta distribution to determine the distribution parameters α and β. The frequency matching degree is subjected to multi-level quantization processing, and the continuous matching degree value is mapped to discrete levels to facilitate subsequent hierarchical modulation decision-making.

[0052] The hierarchical modulation signal is generated based on the frequency matching degree. According to the comparison result of the frequency matching degree and the preset matching threshold, the transmission channel is divided into two modulation strategies. The matching threshold M_th = 0.6 is set, when the frequency matching degree is greater than the preset matching threshold, it is determined as a high matching degree channel, and an enhanced modulation method is used to improve the cooperation strength. The enhanced modulation selects a high-order modulation scheme such as 64-QAM or 256-QAM, which increases the information capacity per symbol by increasing the constellation point density, fully utilizing the cooperative transmission capability under good channel conditions. When the frequency matching degree is less than the preset matching threshold, it is determined as a low matching degree channel, and a compensation modulation method is used to improve the cooperation effect. The compensation modulation uses a low-order modulation such as QPSK or BPSK, combined with forward error correction coding and interleaving technology, to compensate for the lack of channel quality by increasing the redundancy to ensure the basic reliability of cooperative transmission. In the enhanced modulation, more power is allocated to high-quality subcarriers through the power injection principle to maximize the improvement effect of the cooperative strength. In the compensation modulation, diversity transmission and repetition coding mechanism are introduced to improve the cooperative effect through time and space redundancy to resist the adverse effects of channel fading. A hysteresis mechanism is designed for modulation switching, which sets upper and lower thresholds to avoid frequent switching when the frequency matching degree fluctuates around the threshold. The generated hierarchical modulation signal contains modulation type identification, enabling the receiving end to identify whether enhanced modulation or compensation modulation is currently used. Through this threshold-based two-part modulation strategy, the adaptive optimization of cooperative strength is realized.

[0053] The hierarchical modulation signals are superimposed to form a synergistic field. Precise time alignment and frequency alignment are performed for all hierarchical modulation signals, and high-precision clock synchronization is used to ensure nanosecond-level alignment accuracy. The optimized superposition weight coefficient a_i = M_i / Σ(M_j) is calculated, where M_i is the frequency matching degree of the i-th channel, and Σ(M_j) represents the sum of the frequency matching degrees of all channels j, to ensure weight normalization. The weighted superposition operation in the complex domain is performed, considering both amplitude and phase information, to achieve the maximum gain of coherent superposition. The coherence index of the signal is monitored in real time during the superposition process, and phase compensation is used to ensure constructive interference and avoid destructive cancellation. The spectral characteristics of the superposed signal are analyzed in depth, and the Welch method is used to estimate the power spectral density to identify the frequency region of synergistic enhancement and the energy concentration point. The three-dimensional spatial distribution function E(x, y, z, t) of the synergistic field is calculated, where E represents the synergistic field strength, x, y, z are three-dimensional spatial coordinates, and t is the time variable, which describes the variation law of the synergistic field in space and time. The complete mathematical description of the synergistic field is established, including the near-field and far-field characteristics, the superposition principle of the field, and the influence of boundary conditions. Through the energy density analysis of the synergistic field, the energy flow direction is calculated using the Poynting vector to determine the energy convergence area and the radiation mode. High-resolution visual representation of the synergistic field is generated, and vector field maps and isosurface maps are used to visually display the three-dimensional structure of the field. Key feature parameters of the synergistic field are recorded in detail, including field strength peak, 3dB beam width, field uniformity index, and time stability.

[0054] The cooperative field is constructed by amplitude superposition to form a cooperative transmission domain. The formed cooperative field is systematically expanded in three-dimensional space, and the field distribution covering all transmission paths is calculated by the field propagation equation. The amplitude envelope |E(x, y, z)| of the cooperative field is calculated, where |E| represents the field strength amplitude. When |E| > E_th, it is defined as an effective area, E_th is a preset field strength threshold, and the spatial boundary of the cooperative transmission domain is determined by this criterion. Through vector amplitude superposition operation, the field strength components contributed by each transmission path are accumulated, considering 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, and the boundary is defined as the isosurface where the field strength drops to 10% of the peak value. The field strength distribution characteristics in the cooperative transmission domain are comprehensively analyzed, and statistical parameters such as mean, variance, skewness and kurtosis are calculated to evaluate the uniformity of the field distribution. A three-dimensional grid monitoring point array is established in the cooperative transmission domain, with a spacing less than one-quarter of the wavelength, to track the spatial and temporal changes of the field strength in the domain in real time. The topological structure of the cooperative transmission domain is constructed, and the connectivity, reachability and path redundancy of different regions in the domain are described using graph theory. The coverage performance of the cooperative transmission domain is quantitatively evaluated, and the effective coverage volume, surface area and shape factor are calculated to optimize the geometric characteristics of the domain. The shape of the cooperative transmission domain is optimized by parameterization method, using ellipsoid or hyper-ellipsoid fitting to adapt to the constraints of the actual transmission environment. A fine three-dimensional representation of the cooperative transmission domain is generated, including field strength distribution, isosurface, streamline diagram and other visualization information, providing accurate spatial reference for data transmission planning.

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

[0056] The initial fragment impact area is constructed based on the cooperative transmission domain. In the three-dimensional space of the cooperative transmission domain, the optimal fragment injection positions are identified through field strength analysis, which correspond to local maximum points of field strength and have the best signal propagation conditions. According to the field strength gradient distribution of the cooperative transmission domain, the watershed algorithm is used to divide different levels of impact areas, forming a hierarchical area structure. The upper limit of the channel capacity C_max = BW·log2(1+SNR) of each impact area is accurately calculated, where BW is the available bandwidth and SNR is the average signal-to-noise ratio of the area, providing a theoretical basis for fragment size optimization. The adaptive initial fragment size is set, which is adjusted between 64 bytes and 1500 bytes according to the area capacity and delay requirement, balancing transmission efficiency and processing overhead. The three-dimensional position coordinates, spatial range and boundary characteristics of each fragment impact area are accurately marked on the topology structure of the cooperative transmission domain. The spatial overlap and coupling relationship between adjacent impact areas is analyzed in depth, and an intelligent boundary area fragment allocation strategy is designed to avoid conflicts and interference. A multi-level management structure of the impact area is established, and the core area has the highest fragment processing priority and resource allocation weight. The transmission performance potential of each impact area is comprehensively evaluated, and key indicators such as throughput, round-trip delay, delay jitter and packet loss rate are measured. A detailed initial fragment impact area configuration table is generated, including area identification, spatial coordinates, capacity parameters, performance indicators and priority settings. The management framework of the impact area is established, and the area division and parameter configuration are adjusted according to the network state and business demand.

[0057] The diffusion coefficient is obtained by deep diffusion analysis of the initial slice impact area. A precise data slice diffusion description is established to analyze the propagation process of the slice in the initial impact area, considering the influence of network topology, link bandwidth and node processing capacity. The diffusion control equation ∂ρ / ∂t=D∇²ρ+S is constructed, where ρ is the slice density function, D is the diffusion coefficient tensor, ∇² is the Laplace operator, S is the source term, and describes the injection rate of the slice. The finite element method is used to numerically solve the diffusion equation, and the adaptive mesh refinement technique is used to improve the calculation accuracy of the key area to obtain the spatiotemporal distribution of the slice density. The boundary conditions of the initial slice impact area are analyzed in depth, including the influence of reflective boundary (slice return), absorbing boundary (slice disappearance) and mixed boundary conditions. Multiple characteristic parameters of slice diffusion are accurately measured, including diffusion time constant τ=L² / D, where L is the characteristic length and D is the diffusion coefficient; diffusion length L_d=√(D·t), where D is the diffusion coefficient and t is the diffusion time; and diffusion wavefront speed. Multiple factors affecting the diffusion coefficient are identified, including network congestion, queue length, link error rate, protocol overhead and processing delay. The diffusion coefficient is interpolated in three-dimensional space using Kriging interpolation or radial basis function interpolation to obtain a continuous distribution field in the entire impact area. A quantitative correlation between the diffusion coefficient and the actual transmission performance is established, and the relationship parameters are determined through regression analysis to achieve performance evaluation. High-resolution diffusion coefficient distribution maps are generated, and heat maps and contour maps are used to visualize the spatial variation characteristics of diffusion capacity. The complete results of the diffusion analysis are recorded, including the numerical solution of the diffusion equation, the characteristic parameters and the performance correlation.

[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] In step S140, error energy distribution characteristics are identified by performing transmission error monitoring on the multi-path parallel transmission stream, and a feedback energy parameter is generated based on the error energy distribution characteristics to trigger an energy feedback collection mechanism to enhance transmission power and construct an enhanced transmission configuration.

[0061] Specifically, error energy distribution characteristics are identified by performing transmission error monitoring on the multi-path parallel transmission stream. An error detection mechanism is deployed on each transmission channel of the multi-path parallel transmission stream to monitor the transmission state and error occurrence of data packets in real time. A cyclic redundancy check and sequence number continuity detection are performed on each data packet in the multi-path parallel transmission stream to identify transmission abnormalities such as bit errors, packet loss, and out-of-order. The temporal and spatial distribution of errors in the multi-path parallel transmission stream is counted to establish a spatiotemporal mapping relationship of error events. An error rate distribution function E(x, t) is calculated for each transmission path, where x represents the position on the transmission path, t represents time, and the function value reflects the instantaneous error density at that position. The burst characteristics of errors in the multi-path parallel transmission stream are analyzed, and a chain model is used to describe the conversion process of error states. The concentrated distribution characteristics of error energy in the frequency domain are identified by performing spectral analysis on error data of the multi-path parallel transmission stream. A spatial heat map of error energy distribution is established to visually display the aggregation area and diffusion pattern of error energy in the multi-path parallel transmission stream. Key feature parameters of error energy distribution are extracted, including energy peak position, energy concentration, energy gradient, and other indicators. The error patterns of the multi-path parallel transmission stream are classified to distinguish the energy distribution differences between random errors, burst errors, and correlated errors. The time-varying law of error energy distribution characteristics is recorded to provide accurate target positioning for subsequent energy feedback collection.

[0062] In some embodiments, generating a feedback energy parameter based on the error energy distribution characteristics includes: performing energy density analysis on the error energy distribution characteristics to identify high-energy areas; performing energy distribution analysis based on the high-energy areas to obtain dissipated energy values; performing power conversion processing on the dissipated energy values to generate available power signals; and generating a feedback energy parameter based on the available power signals.

[0063] The energy density analysis of the error energy distribution feature identifies high-energy regions. The error energy distribution feature data is imported into a three-dimensional energy density analysis system to construct the spatial distribution function of energy density p(x, y, z), where p represents the energy density, and x, y, and z are three-dimensional spatial coordinates. The local energy density of the error energy distribution feature is calculated, and the density estimation method is used to smooth the discrete energy sampling points. Set the energy density threshold, and mark the connected region with a density greater than the threshold as the high-energy region candidate set. Gradient analysis is performed on the error energy distribution feature to identify the direction and rate of change of the energy density. Through the isosurface analysis of the error energy distribution feature, three-dimensional isosurfaces of different energy density levels are drawn to visually display the spatial distribution of energy. The spatial clustering of high-density points in the error energy distribution feature is performed using a clustering algorithm to form several independent high-energy regions. The characteristic parameters of each high-energy region are calculated, including the center position, volume size, average density, total energy, etc. The morphological features of the high-energy regions in the error energy distribution feature are analyzed to identify spherical, ellipsoidal, or irregular energy aggregates. The time evolution sequence of the high-energy region is established to track the process of energy aggregation and diffusion. The identification mapping table of the high-energy region is generated to provide accurate spatial positioning information for subsequent energy analysis.

[0064] For example, the energy distribution analysis based on the high-energy region to obtain the dissipated energy value 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 diffusion area; maintaining the core dense area for main energy collection, and applying a focusing process to the edge diffusion area to form a converging energy parameter; and re-integrating the converging energy parameter with the core dense area to obtain the dissipated energy value.

[0065] Energy density distribution data is extracted from the high-energy region. A dense energy sampling grid is deployed within the identified high-energy region, with a grid resolution of 1 / 10 of the energy variation feature 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 data set. The extracted energy density distribution data is cleaned using a filtering algorithm to remove outliers and noise interference. The discrete sampling data is continuously processed using a cubic spline interpolation method to obtain a smooth energy density field. The energy density statistical features of the high-energy region are calculated, including maximum value, minimum value, mean value, standard deviation, kurtosis, and skewness parameters. The probability distribution function of the energy density is established to analyze the distribution rule of the energy in the high-energy region. The spatial correlation of the energy density distribution data is extracted to evaluate the energy coupling strength of adjacent regions. A three-dimensional visualization image of the energy density distribution is generated, using color coding to represent different density levels, to visually display the spatial distribution features of the energy.

[0066] The energy density distribution data is decomposed into a core dense region and an edge diffusion region. An adaptive threshold segmentation algorithm is applied to the energy density distribution data, dividing the region with a density higher than twice the average value into the core dense region. The radial distribution function of energy density is calculated, and the attenuation law of density is analyzed from the center of the high-energy region outward to determine the characteristic attenuation length. The watershed algorithm is used to identify the local maximum points of energy density, which constitute the seed points of the core dense region. The boundary of the core dense region is extended by the region growing method, and the growth criterion is based on the density gradient until the gradient changes significantly. The part outside the core dense region is defined as the edge diffusion region, which contains the gradually decaying energy distribution. The energy proportion of the core dense region and the edge diffusion region is analyzed, and usually the core region contains 60-70% of the total energy, and the edge region contains 25-35%. The interface description of the two regions is established, and the boundary is defined using mathematical equations to analyze the energy transmission mechanism from the core to the edge. The geometric and energy parameters of each region after decomposition are recorded to provide basic data for subsequent processing.

[0067] The core dense region is maintained for main energy collection, and the edge diffusion region is subjected to focusing treatment to form a convergent energy parameter. The energy of the core dense region is kept as it is, as the main part of energy collection, avoiding energy loss caused by additional processing. An energy focusing scheme is designed for the edge diffusion region, using the principle of virtual lens to converge the dispersed energy to the center, improving energy utilization. The energy flow vector field of the edge diffusion region is calculated to determine the main diffusion direction and speed of energy, providing a reference for focusing design. Energy reflection surfaces are set at key positions in the edge diffusion region, using parabolic design to reflect the escaped energy back to the collection area. The phase modulation technique is used to coherently superimpose the edge energy, enhancing the convergence effect and improving the energy collection efficiency. The efficiency factor of focusing treatment is calculated, including geometric focusing efficiency and phase coherence efficiency, to quantify the focusing effect. The convergent energy parameter vector is generated, containing information such as convergent power, focal point position, beam width, etc. The focusing parameter configuration is optimized by adjusting the curvature of the reflection surface and the phase distribution to maximize the energy recovery rate.

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

[0069] Perform power conversion processing on the dissipated energy values to generate usable power signals. According to the size and distribution characteristics of the dissipated energy values, design corresponding energy-power conversion schemes. Calculate the instantaneous power P(t) = dE / dt, where P(t) is the power at time t, and E is the dissipated energy, to obtain the power time series through time differentiation. Apply power factor correction technology to convert reactive power into active power, improving energy utilization efficiency. Convert alternating energy signals into direct current power through rectifier circuits, facilitating subsequent power management and distribution. Set up power smoothing filters to eliminate high-frequency ripple and transient spikes in the power signal. Establish a characteristic description of power conversion, analyze the response characteristics from energy input to power output. Calculate the power conversion efficiency and optimize the conversion parameters to improve efficiency. Generate standardized usable power signals, including power amplitude, stability, available duration, etc. Perform quality assessment on the power signal to ensure that it meets the requirements of transmission enhancement. Record the performance indicators and working parameters of the power conversion process.

[0070] The feedback energy parameter is generated based on the available power signal. The time domain characteristics of the available power signal are analyzed to extract key indicators such as average power, peak power, and power change rate. The available power signal is subjected to spectral analysis to identify the frequency components and harmonic content of the power. A power-energy mapping relationship is established to calculate the total amount of available energy within different time windows. The data structure of the feedback energy parameter is designed, containing multi-dimensional information such as power level, energy capacity, time constraints, and quality indicators. According to the stability of the available power signal, the feedback energy is divided into three categories: constant power type, pulse power type, and random power type. The reliability indicators of the feedback energy are calculated to evaluate the continuity and stability of energy supply. The encoding sequence of the feedback energy parameter is generated for easy transmission and analysis in the system. The matching relationship between the feedback energy parameter and the transmission demand is established to determine the optimal energy allocation strategy. The standardized packaging of the feedback energy parameter is completed to form a set of control parameters that can be directly used for power enhancement. The complete process of parameter generation is recorded to provide traceability information for system optimization.

[0071] The transmission power is enhanced and compensated using the feedback energy parameter to construct an enhanced transmission configuration. The available power information in the feedback energy parameter is analyzed to calculate the additional power resources available for transmission enhancement. According to the power level of the feedback energy parameter, a hierarchical power compensation scheme is designed to achieve fine-grained power management. The feedback energy is allocated to the transmission paths that need power enhancement according to the priority, and the weak links with high error rates are preferentially compensated. The enhanced transmission power P_enhanced = P_base + α·P_feedback is calculated, where P_enhanced is the enhanced power, P_base is the base power, P_feedback is the feedback power, and α is the utilization coefficient. Through the time window information in the feedback energy parameter, a time sequence strategy for power compensation is developed to ensure the timeliness of compensation. Power enhancement modules are deployed at key transmission nodes to inject feedback energy into the signal amplification and forwarding process. An adaptive control loop for power enhancement is established to adjust the compensation strength according to the real-time transmission quality. The parameter set of the enhanced transmission configuration is designed, including power allocation, modulation and coding scheme, and retransmission strategy for each path.

[0072] In step S150, network state monitoring analysis is performed based on the enhanced transmission configuration to generate a network load distribution curve. Transmission opportunity optimization selection is implemented on the network load distribution curve to determine the best sending time. Batch data upload is performed at the best sending time to complete intelligent data transmission.

[0073] Specifically, network state monitoring analysis is performed based on the enhanced transmission configuration to generate a network load distribution curve. Using the monitoring parameters in the enhanced transmission configuration, state acquisition probes are deployed at key nodes in the network to obtain performance indicators such as link utilization, queue length, and packet loss rate in real time. According to the sampling frequency and accuracy requirements defined in the enhanced transmission configuration, high-density sampling of network state data is performed, with a sampling interval of seconds. Through the multi-path information in the enhanced transmission configuration, the load status of all transmission paths is monitored simultaneously to form a full-network load view. The instantaneous network load L(t) = Σ(B_used / B_total) is calculated, 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 the periodicity and burst characteristics of load changes. Moving average and exponential smoothing techniques are used to process the original load data, eliminating transient jitter and obtaining a smooth load trend. The statistical distribution of network load is established, and the load mean, variance, and peak occurrence probability of different time periods are calculated. A 24-hour network load distribution curve is drawn, with the horizontal axis representing time and the vertical axis representing load percentage. The curve reflects the time-varying characteristics of network load. Key feature points are marked on the load distribution curve, including load valley periods, peak periods, and transition periods.

[0074] In some embodiments, the transmission opportunity optimization selection on the network load distribution curve determines the optimal sending time, including: using the network load distribution curve to distinguish between the instant transmission layer and the delay buffer layer; establishing an opportunity backtracking anchor point according to the instant transmission layer; using the opportunity backtracking anchor point to perform compensatory derivation on the delay buffer layer to determine the transmission preposition time; and using the transmission preposition time to construct the optimal sending time.

[0075] The network load distribution curve is used to distinguish between the instant transmission layer and the delay buffer layer. The numerical range of the network load distribution curve is analyzed, and a load threshold is set to divide the curve into different transmission strategy intervals. When the network load distribution curve shows that the load is less than 30%, it is defined as the instant transmission layer, which has sufficient network resources to support real-time data transmission. When the network load distribution curve shows that the load exceeds 70%, it is defined as the delay buffer layer, which has limited network resources and needs to buffer data for transmission. A transition layer is set between 30%-70% load interval to flexibly select transmission strategy according to data priority and network trend. Through time axis analysis of the network load distribution curve, the distribution proportion of the instant transmission layer and the delay buffer layer in 24 hours is calculated. The continuous time period of the instant transmission layer in the network load distribution curve is identified, which is the preferred window for batch transmission. The judgment rule of layer switching is established, and the corresponding adjustment of transmission strategy is triggered when the network load crosses the threshold. A transmission layer division diagram is generated to visually display the transmission strategy allocation in different time periods. The characteristic parameters of each layer are recorded, including average load, duration, occurrence frequency, and other statistical information.

[0076] Anchors are established by backtracking the transmission opportunity. In the instant transmission layer identified by the network load distribution curve, the period with the lowest load and the longest duration is selected as the ideal transmission window. Starting from the beginning of the instant transmission layer, considering factors such as data preparation time and system startup delay, the time that needs to be prepared in advance is calculated backward. Set the anchor point T_anchor=T_ideal-T_prep, where T_ideal is the ideal transmission time and T_prep is the preparation time. According to the stability characteristics of the instant transmission layer, the reliability of the anchor point is evaluated, and the stable instant transmission layer corresponds to the anchor point with high reliability. Mark all the anchor points that meet the conditions on the network load distribution curve to form an anchor point sequence. Analyze the time interval between the anchor points to ensure that there is enough interval between adjacent anchor points to avoid transmission conflicts. Assign a priority weight to each anchor point, which considers the load level, duration, and historical success rate. Establish the association relationship of the anchor points, and group manage the anchor points belonging to the same instant transmission layer. Generate an anchor point configuration table containing anchor point time, associated instant transmission layer parameters, and priority information.

[0077] The anchor points are used to compensate for the delay buffer layer and determine the transmission preposition time. Based on the established anchor points, the amount of data accumulated in the delay buffer layer and the transmission demand are analyzed. The data clearing time T_clear=V_buffer / R_trans of the delay buffer layer is calculated, where V_buffer is the buffer data amount and R_trans is the transmission rate. From the anchor point, the reserved buffer processing time is derived forward to ensure that the preparation is completed before the instant transmission layer arrives. Considering the data growth rate of the delay buffer layer, the compensation coefficient α=1+r_growth·T_clear is established, where r_growth is the data growth rate. The transmission preposition time T_pre=T_anchor-α·T_clear is determined by compensatory derivation, and the preprocessing of buffer data starts at this time. At the transmission preposition time, data sorting, compression, grouping, and other preprocessing operations are triggered to prepare for the upcoming transmission window. The rationality of the preposition time is evaluated to ensure that it does not occupy resources too early or delay the transmission opportunity. An adjustment mechanism for the preposition time is established to dynamically optimize the preposition amount according to the actual buffer state and network changes. Record the calculation process and results of the compensatory derivation to form a decision record of the transmission preposition time.

[0078] The optimal sending time is constructed by using the transmission preposition time. The optimal time point of data sending is determined by combining the transmission preposition time and the starting time of the instant transmission layer. The calculation formula of the optimal sending time is T_best = max(T_pre + T_ready, T_instant), wherein T_ready is the system ready time, and T_instant is the starting time of the instant transmission layer. The competition relationship of multiple transmission preposition times is considered, and the final sending order is determined through priority sorting. A warning mechanism is set before the optimal sending time to notify the relevant modules to make transmission preparations in advance. A fault tolerance window of the sending time is constructed, and a certain buffer time is reserved before and after the optimal time to deal with sudden situations. The optimal sending time is compared with the network load distribution curve to confirm that the time is indeed in a favorable network condition. An execution plan of the optimal sending time is generated, including detailed information such as specific time, transmission task, and resource allocation. A triggering mechanism of the sending time is established, and the transmission process is automatically started when the system time reaches the optimal sending time. The determination process of the optimal sending time is recorded, including all influencing factors and decision basis, to provide a reference for subsequent optimization. Through accurate timing selection, data transmission is ensured to be carried out in the most favorable network condition.

[0079] The intelligent data transmission is completed by performing the data batch uploading at the optimal sending time. When the system time reaches the determined optimal sending time, the data batch uploading process is triggered, and the cached data to be transmitted is uniformly sent. According to the network state of the optimal sending time, the transmission parameters are dynamically adjusted, including the number of concurrent connections, the size of the slice, the sending rate, and the like. The intelligent flow scheduling strategy is implemented, and the batch data is distributed to multiple available transmission channels to fully utilize network resources. The transmission performance is continuously monitored during the data batch uploading process, including real-time throughput, transmission progress, error rate, and the like. The adaptive rate control algorithm is adopted, and the sending rate is dynamically adjusted according to network feedback to avoid causing network congestion. The breakpoint resume mechanism is realized, and the data can be continuously uploaded from the breakpoint position when the transmission is interrupted to ensure data integrity. The data uploaded in batches is managed in groups, and different types of data adopt corresponding transmission strategies and priority settings. After the transmission is completed, the data integrity check is performed to ensure that all data correctly reaches the destination. The transmission report is generated to record the actual transmission performance, resource consumption, and abnormal events. Through intelligent scheduling and optimization control, the efficient and reliable batch data transmission is realized.

[0080] In order to perform the mine intelligent plate data automatic uploading method corresponding to the above-mentioned method embodiment, the corresponding functions and technical effects are realized. Referring to Figure 2 , Figure 2A structural block diagram of a mine intelligent plate data automatic uploading system 200 provided by an embodiment of the application is shown. For ease of illustration, only parts related to the embodiment are shown. The mine intelligent plate data automatic uploading system 200 provided by the embodiment of the application comprises:

[0081] A data acquisition module 201 is configured to acquire downhole environment data monitored by a plate, to perform signal integrity detection on the downhole environment data to identify damaged data frames, and to perform error feature analysis on the damaged data frames to generate an error correction seed sequence;

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

[0083] A cooperative transmission module 203 is configured to perform cooperative frequency analysis on the multi-protocol concurrent transmission channel to generate path cooperation parameters, to perform multi-path cooperative enhancement processing based on the path cooperation parameters to form a cooperative transmission domain, and to perform data fragmentation injection on the cooperative 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 multi-path parallel transmission stream to identify error energy distribution characteristics, to trigger an energy feedback collection mechanism based on the error energy distribution characteristics to generate feedback energy parameters, and to use the feedback energy parameters to enhance and compensate transmission power to build an enhanced transmission configuration;

[0085] A transmission scheduling module 205 is configured to perform network state monitoring analysis based on the enhanced transmission configuration to generate a network load distribution curve, to perform transmission timing optimization selection on the network load distribution curve to determine an optimal sending time, and to perform batch data uploading at the optimal sending time to complete intelligent data transmission.

[0086] The mine intelligent plate data automatic uploading system 200 described above can implement a mine intelligent plate data automatic uploading method of the method embodiment described above. The optional items in the method embodiment described above are also applicable to the embodiment, and will not be described in detail here. The remaining content of the embodiment of the application can be referred to the content of the method embodiment described above, and will not be described in detail in the embodiment.

[0087] The purpose of the above embodiments is to exemplarily reproduce and deduce the technical solutions of the application, and to completely describe the technical solutions, purposes and effects of the application. The purpose is to make the public understand the disclosure of the application more thoroughly and comprehensively, and does not limit the protection scope of the application.

[0088] The above embodiments are also not exhaustive enumeration based on the present application, in addition to which there can be a plurality of other embodiments not listed. Any substitution and improvement made without violating the concept of the present application is within the scope of the present application.

Claims

1. A method for automatically uploading mine intelligent tag data, characterized in that, The application relates to a method for intelligent data transmission. The method comprises the following steps: collecting downhole environment data monitored by a card plate, performing signal integrity detection on the downhole environment 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 processing 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 trigger 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 cooperative frequency analysis on the multi-protocol concurrent transmission channel to generate path cooperation parameters, performing multi-path cooperative enhancement processing based on the path cooperation parameters to form a cooperative transmission domain, and performing data fragmentation injection on the cooperative transmission domain to generate a multi-path parallel transmission stream; 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 transmission power to build an enhanced transmission configuration; 2. The method of claim 1, wherein, performing network state monitoring analysis based on the enhanced transmission configuration to generate a network load distribution curve, performing transmission opportunity optimization selection on the network load distribution curve to determine an optimal sending time, and performing batch data uploading at the optimal sending time to complete intelligent data transmission. The method comprises the following steps: generating data packet coupling characteristics based on the damaged data frames; performing mutation point capture on the data packet coupling characteristics to identify error propagation boundaries; building an error interference suppression curve through the error propagation boundaries; 3. The method of claim 1, wherein, using the error interference suppression curve to establish an error correction seed sequence. The method comprises the following steps: obtaining a protocol energy convergence point of the frame boundary identifier; deriving a cross-protocol cascade effect based on the protocol energy convergence point; generating a switching preferred point through the cascade effect; 4. The method of claim 1, wherein, determining a protocol switching signal based on the switching preferred point. The method comprises the following steps: extracting frequency characteristic information based on the path cooperation parameters; using the frequency characteristic information to generate a synchronous frequency signal through frequency synchronization tuning; performing modulation processing on the synchronous frequency signal to form a cooperative field; 5. The method of claim 1, wherein, performing amplitude superposition on the cooperative field to build a cooperative transmission domain. The method comprises the following steps: building an initial fragmentation influence area based on the cooperative transmission domain; performing deep diffusion analysis on the initial fragmentation influence area to obtain a diffusion coefficient; obtaining a multi-level fragmentation propagation link according to the diffusion coefficient; 6. The method of claim 1, wherein, generating a multi-path parallel transmission stream based on the multi-level fragmentation propagation link. The method comprises the following steps: performing energy density analysis on the error energy distribution characteristics to identify a high-energy area; performing energy distribution analysis on the high-energy area to obtain a dissipated energy value; Converting the dissipated energy value into power generates a usable power signal; Generating a feedback energy parameter based on the usable power signal.

7. The method of claim 1, wherein, The transmission opportunity optimization selection on the network load distribution curve determines the optimal sending time, including: The network load distribution curve is used to distinguish between the instant transmission layer and the delay buffer layer; The instant transmission layer is used to establish an opportunity backtracking anchor point; The delay buffer layer is compensated and derived using the opportunity backtracking anchor point to determine the transmission pre-time; The transmission pre-time is used to build the optimal sending time.

8. The method of claim 4, wherein, The modulation processing of the synchronous frequency signal forms a synergistic field, including: Frequency matching is obtained by injecting response analysis on the synchronous frequency signal; Hierarchical modulation is generated based on the frequency matching, wherein when the matching degree is greater than the 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 compensation modulation method is used to improve the synergy effect; The hierarchical modulation signal is superimposed to form a synergistic field.

9. The method of claim 6, wherein, The energy distribution analysis based on the high-energy area obtains the dissipated energy value, including: Extracting energy density distribution data from the high-energy area; The energy density distribution data is decomposed into a core dense area and an edge diffusion area; The core dense area is used for main energy collection, and the edge diffusion area is subjected to focusing processing to form a converging energy parameter; The converging energy parameter and the core dense area are re-integrated to obtain the dissipated energy value.

10. A mine intelligent board data automatic uploading system, characterized in that, It includes: A data acquisition module is used to acquire downhole environment data monitored by a board, to identify damaged data frames by signal integrity detection on the downhole environment data, and to generate an error correction seed sequence by error feature analysis using the damaged data frames; A protocol control module is used to generate a complete data stream by data packet reconstruction processing based on the error correction seed sequence, to extract a frame boundary identifier by link layer frame synchronization analysis on the complete data stream, to generate a protocol switching signal by exciting a transmission protocol switching mechanism using the frame boundary identifier, and to establish a multi-protocol concurrent transmission channel through the protocol switching signal; A cooperative transmission module is used to generate path synergy parameters by cooperative frequency analysis on the multi-protocol concurrent transmission channel, to form a cooperative transmission domain by multi-path cooperative enhancement processing based on the path synergy parameters, and to generate a multi-path parallel transmission stream by data fragmentation injection on the cooperative transmission domain; An energy feedback module is used to identify error energy distribution characteristics by transmission error monitoring on the multi-path parallel transmission stream, to generate a feedback energy parameter by triggering an energy feedback collection mechanism based on the error energy distribution characteristics, and to build an enhanced transmission configuration by enhancing and compensating transmission power using the feedback energy parameter; A transmission scheduling module is used to generate a network load distribution curve by network state monitoring analysis based on the enhanced transmission configuration, to determine the optimal sending time by transmission opportunity optimization selection on the network load distribution curve, and to complete intelligent data transmission by executing data batch upload at the optimal sending time.

Images