Method and system for uplink data multiplexing transmission based on downlink acquisition strategy

Abstract

The application provides an uplink data multiplexing transmission method and system based on a downlink collection strategy, and relates to the technical field of wireless data transmission.The method comprises the following steps: step 1, encapsulating an original data set into a data frame in a unified format, and obtaining the spatial position parameters of three fixed nodes deployed in an industrial field where a DTU is located; and step 2, calculating a spatial link quality factor by using the spatial position parameters, the real-time signal-to-noise ratio of each uplink and the error code rate fluctuation amplitude of each uplink in a unit time.The application generates an adjustment tensor by spatial link quality quantitative evaluation, field feature construction and signal feature fusion, dynamically adjusts transmission parameters to realize multiplexing parallel transmission, and is matched with a link failure retransmission mechanism to improve the anti-interference performance and transmission efficiency of the uplink data transmission of the DTU in the industrial field.

Description

Technical Field

[0001] This invention relates to the field of wireless data transmission technology, and in particular to an uplink data multiplexing transmission method and system based on a downlink acquisition strategy. Background Technology

[0002] In industrial field data acquisition scenarios, data transmission units (DTUs) typically need to upload data collected by sensors to multiple monitoring platforms in parallel through multiplexing. Taking a hot rolling workshop of a steel company as an example, there are multiple data aggregation nodes distributed in the workshop. DTUs are deployed on mobile devices such as cranes and transfer vehicles. There are a large number of metal components and large machinery that operate frequently in the surrounding area. In this environment, the propagation path of wireless signals is relatively complex, and phenomena such as multipath reflection, signal attenuation and phase shift may occur, resulting in certain fluctuations in the transmission quality of the uplink.

[0003] Currently, common data transmission schemes often focus on single-dimensional indicators, such as received signal strength or instantaneous signal-to-noise ratio (SNR), when evaluating link quality. However, in practical applications, when the vehicle moves to different locations, even if the SNR remains at a high level, phase changes caused by multipath interference can still lead to data frame parsing failures. For example, in one test, it was found that when the transport vehicle passed near a pillar, the bit error rate of a certain uplink intermittently increased for a short period of time. The link selection method based solely on the SNR threshold failed to respond to this change in time, resulting in continuous retransmission of data packets.

[0004] In addition, existing methods for handling link failures typically employ an overall retransmission mechanism. During periods of high channel occupancy, this approach may increase channel load and affect the normal communication of other devices. Summary of the Invention

[0005] This invention provides an uplink data multiplexing transmission method and system based on downlink acquisition strategy, enabling spatial modeling of wireless signal attenuation and phase shift in industrial settings.

[0006] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows:

[0007] Firstly, an uplink data multiplexing transmission method based on a downlink acquisition strategy, the method comprising:

[0008] Step 1: Encapsulate the original dataset into a data frame of a uniform format and obtain the spatial location parameters of three fixed nodes deployed at the industrial site where the DTU is located; calculate the spatial link quality factor using the spatial location parameters, the real-time signal-to-noise ratio of each uplink, and the bit error rate fluctuation of each uplink per unit time.

[0009] Step 2: Determine two field feature base points based on the spatial link quality factor. When multipath interference exists and the channel occupancy rate is higher than the preset busy level, select the first field feature base point. When static line-of-sight propagation occurs and the channel is idle, select the second field feature base point. Using the selected field feature base point as the center, and combining the weighted values ​​of the distances from each fixed node to the center, construct a spatial feature parameter field.

[0010] Step 3: Define the irregular boundary space within the spatial feature parameter field and perform gradient partitioning. Collect the signal attenuation value and phase shift value within each gradient partition. Perform feature interleaving and fusion processing on the signal attenuation value and phase shift value to generate a feature interleaving matrix. Perform principal component analysis on the feature interleaving matrix to extract the principal component eigenvectors. Calculate the adjustment tensor based on the principal component eigenvectors.

[0011] Step 4: Dynamically adjust the protocol encapsulation parameters of the data frame using the adjustment tensor to generate multiple protocol data units; send the multiple protocol data units to multiple target platforms in parallel, monitor the sending status in real time and record it synchronously; when any link fails to send, trigger local data retransmission of the corresponding link based on the synchronous record.

[0012] Secondly, uplink data multiplexing transmission based on downlink acquisition strategy includes:

[0013] The location-aware module is used to encapsulate the raw data set into a data frame of a unified format and obtain the spatial location parameters of three fixed nodes deployed at the industrial site where the DTU is located; it uses the spatial location parameters, the real-time signal-to-noise ratio of each uplink, and the bit error rate fluctuation of each uplink per unit time to calculate the spatial link quality factor.

[0014] The quantization calculation module is used to determine two field feature base points based on the spatial link quality factor. When there is multipath interference and the channel occupancy rate is higher than the preset busy level, the first field feature base point is selected. When static line-of-sight propagation is in progress and the channel is idle, the second field feature base point is selected. The selected field feature base point is used as the center, and the spatial feature parameter field is constructed by combining the distance weighting values ​​from each fixed node to the center.

[0015] The parametric field construction module is used to delineate irregular boundary spaces and perform gradient partitioning within the spatial feature parametric field. It collects signal attenuation and phase shift values ​​within each gradient partition, performs feature interleaving and fusion processing on the signal attenuation and phase shift values ​​to generate a feature interleaving matrix, performs principal component analysis on the feature interleaving matrix, extracts principal component eigenvectors, and calculates the adjustment tensor based on the principal component eigenvectors.

[0016] The adjustment tensor generation module is used to dynamically adjust the protocol encapsulation parameters of the data frame using the adjustment tensor to generate multiple protocol data units; multiple protocol data units are sent to multiple target platforms in parallel, and the sending status is monitored and recorded in real time. When any link fails to send, the local data retransmission of the corresponding link is triggered based on the synchronous record.

[0017] The above-described solution of the present invention has at least the following beneficial effects:

[0018] By collecting spatial location parameters from three fixed nodes in an industrial site and combining them with the real-time signal-to-noise ratio and bit error rate fluctuations per unit time for each uplink, a spatial link quality factor is constructed. This enables a joint characterization of the spatial geometric features and channel dynamics of the transmission environment. Compared to single-point instantaneous indicators, it can effectively identify implicit transmission degradation caused by spatial location changes and multipath reflections, improving the accuracy and foresight of link status judgment and avoiding the problem of failing to detect sudden increases in bit error rate due to phase shift and spatial interference. Based on the spatial link quality factor, two typical scenarios are distinguished: high-busy channels with multipath interference and static line-of-sight idle channels. Corresponding feature base points are adaptively selected, and a three-dimensional spatial feature parameter field is constructed using fixed node distance weighting, achieving spatial modeling of wireless signal attenuation and phase shift in the industrial site. Through irregular boundary division and gradient partitioning, the differences in transmission characteristics in different spatial regions are captured, solving the problem of wireless transmission path distortion in complex scenarios such as metal obstruction, mechanical movement, and multipath reflections. This makes transmission parameter adjustments spatially targeted, improving transmission stability in complex industrial environments.

[0019] The signal attenuation and phase shift values ​​within each gradient partition are feature-interleaved and fused to generate a feature interleaving matrix. Principal component analysis is then used to extract principal component eigenvectors, and an adjustment tensor is calculated. This tensor is used to dynamically optimize data frame protocol encapsulation parameters. Compared to fixed coding and modulation schemes, this approach can adaptively adjust error correction coding redundancy and modulation coding strategies based on real-time spatial field characteristics. This improves spectrum utilization while ensuring transmission reliability and effectively reduces parsing failures and packet loss rates caused by parameter mismatches. Utilizing a DTU-timed Modbus polling acquisition mode, raw data collected downlink from multiple field devices is uniformly parsed, reassembled, and standardized into frames. This ensures the consistency and integrity of uplink multiplexed data formats and supports parallel generation of protocol data units for multiple protocols, adapting to heterogeneous upload requirements across multiple platforms. This enables single acquisition, multiplexing, and simultaneous uploading from multiple devices, improving the efficiency of industrial data aggregation and distribution. During parallel transmission, a local status record table is established to independently track and synchronously record the status of each link and each protocol data unit. When a single link transmission failure is detected, only the local targeted retransmission of the corresponding data unit of the corresponding link is triggered, rather than the overall retransmission. This reduces the amount of invalid retransmission data, lowers the risk of network congestion under high channel busy conditions, and reduces the overall communication load. Attached Figure Description

[0020] Figure 1 This is a flowchart illustrating an uplink data multiplexing transmission method based on a downlink acquisition strategy, provided in an embodiment of the present invention.

[0021] Figure 2 This is a schematic diagram of an uplink data multiplexing transmission system based on a downlink acquisition strategy, provided by an embodiment of the present invention. Detailed Implementation

[0022] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0023] like Figure 1 As shown, embodiments of the present invention propose an uplink data multiplexing transmission method based on a downlink acquisition strategy, the method comprising the following steps:

[0024] Step 1: Encapsulate the original dataset into a data frame of a uniform format and obtain the spatial location parameters of three fixed nodes deployed at the industrial site where the DTU is located; calculate the spatial link quality factor using the spatial location parameters, the real-time signal-to-noise ratio of each uplink, and the bit error rate fluctuation of each uplink per unit time.

[0025] Step 2: Determine two field feature base points based on the spatial link quality factor. When multipath interference exists and the channel occupancy rate is higher than the preset busy level, select the first field feature base point. When static line-of-sight propagation occurs and the channel is idle, select the second field feature base point. Using the selected field feature base point as the center, and combining the weighted values ​​of the distances from each fixed node to the center, construct a spatial feature parameter field.

[0026] Step 3: Define the irregular boundary space within the spatial feature parameter field and perform gradient partitioning. Collect the signal attenuation value and phase shift value within each gradient partition. Perform feature interleaving and fusion processing on the signal attenuation value and phase shift value to generate a feature interleaving matrix. Perform principal component analysis on the feature interleaving matrix to extract the principal component eigenvectors. Calculate the adjustment tensor based on the principal component eigenvectors.

[0027] Step 4: Dynamically adjust the protocol encapsulation parameters of the data frame using the adjustment tensor to generate multiple protocol data units; send the multiple protocol data units to multiple target platforms in parallel, monitor the sending status in real time and record it synchronously; when any link fails to send, trigger local data retransmission of the corresponding link based on the synchronous record.

[0028] In this embodiment of the invention, spatial link quality factors are constructed by collecting spatial location parameters of three fixedly deployed nodes in an industrial site and combining them with the real-time signal-to-noise ratio and bit error rate fluctuation amplitude of each uplink. This enables a joint characterization of the spatial geometric features and channel dynamics of the transmission environment. Compared with single-point instantaneous indicators, this method can effectively identify implicit transmission degradation caused by spatial location changes and multipath reflections, improving the accuracy and foresight of link status judgment and avoiding the problem of failing to detect sudden increases in bit error rate due to phase shift and spatial interference. Based on the spatial link quality factor, two typical scenarios are distinguished: high-busy multipath interference channels and static line-of-sight idle channels. Corresponding field feature base points are adaptively selected, and a three-dimensional spatial feature parameter field is constructed using fixed node distance weighting, realizing spatial modeling of wireless signal attenuation and phase shift in the industrial site. By dividing irregular boundaries and gradient partitioning, the differences in transmission characteristics in different spatial regions are captured, solving the problem of wireless transmission path distortion in complex scenarios such as metal obstruction, mechanical movement, and multipath reflections. This makes transmission parameter adjustments spatially targeted and improves transmission stability in complex industrial environments.

[0029] The signal attenuation and phase shift values ​​within each gradient partition are feature-interleaved and fused to generate a feature interleaving matrix. Principal component analysis is then used to extract principal component eigenvectors, and an adjustment tensor is calculated. This tensor is used to dynamically optimize data frame protocol encapsulation parameters. Compared to fixed coding and modulation schemes, this approach can adaptively adjust error correction coding redundancy and modulation coding strategies based on real-time spatial field characteristics. This improves spectrum utilization while ensuring transmission reliability and effectively reduces parsing failures and packet loss rates caused by parameter mismatches. Utilizing a DTU-timed Modbus polling acquisition mode, raw data collected downlink from multiple field devices is uniformly parsed, reassembled, and standardized into frames. This ensures the consistency and integrity of uplink multiplexed data formats and supports parallel generation of protocol data units for multiple protocols, adapting to heterogeneous upload requirements across multiple platforms. This enables single acquisition, multiplexing, and simultaneous uploading from multiple devices, improving the efficiency of industrial data aggregation and distribution. During parallel transmission, a local status record table is established to independently track and synchronously record the status of each link and each protocol data unit. When a single link transmission failure is detected, only the local targeted retransmission of the corresponding data unit of the corresponding link is triggered, rather than the overall retransmission. This reduces the amount of invalid retransmission data, lowers the risk of network congestion under high channel busy conditions, and reduces the overall communication load.

[0030] In a preferred embodiment of the present invention, the original data set is encapsulated into a data frame of a uniform format, including:

[0031] Step 100a: The DTU provides two working modes in the downlink direction: transparent serial data transmission and timed Modbus polling acquisition. When downlink acquisition is required, the DTU switches to the timed Modbus polling acquisition mode. According to the preset polling timing and instruction sequence, it sequentially sends acquisition instructions to multiple field devices through the serial communication interface and receives the raw data returned by each device. All the received raw data are then aggregated to form a raw data set. Specifically, the data transmission unit sets two independently operable and autonomously switchable working modes in the downlink communication direction: transparent serial data transmission mode and timed Modbus polling acquisition mode. The transparent serial data transmission mode is used to realize direct data interaction between external devices and the data transmission unit without parsing or modifying the data content. The timed Modbus polling acquisition mode is used to actively acquire data from multiple field devices at fixed intervals. The two working modes can be seamlessly switched without interruption or packet loss according to the actual data acquisition needs of the industrial site, the control instructions issued by the upper-level platform, and the locally pre-fixed operating strategy, ensuring that the downlink data interaction process is uninterrupted and without loss.

[0032] The system has a pre-configured timed acquisition strategy, which includes a fixed acquisition period, device polling order, single device command sending interval, timeout waiting time, and number of repeated acquisitions. The acquisition period is configurable from 1 to 10 seconds by default, the single device command sending interval is not less than 50 milliseconds, the timeout waiting time is fixed at 300 milliseconds, and the number of repeated acquisitions is 2 by default. When the system receives an acquisition enable command from the upper-level control platform or reaches the acquisition time set by the pre-configured timed acquisition strategy, it determines that it needs to start the downlink data acquisition task of multiple terminal devices in the industrial field. The data transmission unit will immediately exit the current working mode and stably switch to the timed Modbus polling acquisition mode. After the mode switch is completed, the data transmission unit follows the pre-stored polling sequence and standard Modbus command sequence. According to the preset device addresses from 1 to 31 in ascending order, it sends directional Modbus acquisition commands containing device addresses, function codes, register start addresses, and register lengths to all field devices in the industrial site through its own serial communication interface. The ordered polling method avoids communication blockage and data conflict caused by multiple devices responding to commands at the same time. After receiving a Modbus acquisition command that matches its own device address, each field device will encapsulate its real-time sensor detection data, device operating status parameters, and operating condition feedback information according to the communication format specified by the Modbus protocol and return them to the data transmission unit. The data transmission unit continuously receives and verifies the raw data fed back by each field device through the serial communication interface. Each piece of raw data that is successfully received and verified is temporarily cached locally to ensure that the data is not lost or disordered. After all the response data from all the devices to be collected has been received and verified to be correct, all the raw data cached locally is uniformly collected, sorted and organized to form a complete, continuous and complete set of raw data.

[0033] Step 101a involves parsing each piece of raw data in the original data set, identifying and extracting valid data fields returned by each field device, and reassembling the valid data fields according to a preset internal data structure to obtain reassembled data. A frame start identifier and data type identifier are added to the header of the reassembled data, and a check sequence is added to the tail to obtain a data frame in a unified format. Specifically, after completing the construction and initial organization of the original data set, the system sequentially performs standardized and normalized Modbus protocol parsing operations on each piece of raw data in the original data set, and the system executes the parsing according to the preset Modbus protocol frame structure. The system employs a fixed structure: a frame header field (1 byte), a device address field (1 byte), a function code field (1 byte), a data payload field (4 to 32 bytes), a checksum field (2 bytes), and a frame trailer field (1 byte). Using this fixed byte length and field definition as a standard, the system performs content matching and feature recognition on each field to distinguish and separate valid business data, invalid padding fields, protocol handshake control fields, and redundant auxiliary information from the frame header and trailer. Invalid padding fields, protocol control fields, and redundant auxiliary information are completely removed, retaining only the valid data fields returned by each field device that have practical acquisition significance. After extracting the valid data fields, the system uses a unified internal data structure, including device number, acquisition timestamp, data type identifier, data length, and payload segment, to sequentially splice, format standardize, and logically reorganize all extracted valid data fields. This eliminates format differences, byte length differences, and data arrangement conflicts between data returned by field devices from different manufacturers, different device models, and different communication parameters, resulting in reorganized data with a unified structure, standardized fields, and fixed lengths.

[0034] At the beginning of the reconstructed data, a preset frame start identifier and a data type identifier are added sequentially. The frame start identifier is a fixed byte sequence used by the receiving end to quickly locate the start position of the data frame in the data stream, improving data parsing efficiency. The data type identifier is used to distinguish the data acquisition device source, service data type, and transmission priority level. At the end of the reconstructed data, a preset CRC16-Modbus checksum algorithm is used, with 0xA001 as the generator polynomial, to perform bit-by-bit iterative calculations on all core data bytes in the reconstructed data except for the check bit. First, a 16-bit CRC register is initialized, and its initial value is set... The result is 0xFFFF. Starting from the first byte of the reconstructed core data, each data byte is extracted sequentially. Each bit of the data byte (from the 7th most significant bit to the 0th least significant bit) is compared with the corresponding bit of the lower 8 bits of the CRC register. A logical operation is performed on each bit: 0 for identical bits and 1 for different bits. The resulting 8 bits are directly overwritten and stored in the lower 8 bits of the CRC register, keeping the higher 8 bits unchanged. The entire CRC register is shifted 1 bit to the right, with the most significant bit padded with 0. Simultaneously, the shifted-out least significant bit (i.e., the 0th bit of the CRC register before the shift) is observed. If this shifted-out bit is 1, the current value of the CRC register is compared with the generated value. Perform an XOR operation on polynomial 0xA001 (using the same rules as above: 0 for identical bits and 1 for different bits), and update the CRC register value with the result. If the shifted-out bit is 0, do not perform the XOR operation and keep the current value of the CRC register unchanged. Repeat the above operations of right shifting, checking the shifted-out bit, and XORing as needed, for a total of 8 times, to ensure that all 8 bits of the data byte have been iterated. After that, for each subsequent byte of the reconstructed core data content, repeat the complete iterative steps of XORing the byte with the lower 8 bits of the CRC, shifting, checking, and XORing as needed, until all core data bytes have been iterated. After the iterative calculation is completed, The value stored in the CRC register is the 16-bit cyclic redundancy check result. This 16-bit check result is split into low 8 bytes and high 8 bytes, and concatenated in the order of low 8 bytes first and high 8 bytes last to form a 2-byte check sequence. Finally, this check sequence is added to the end of the reconstructed data. The check sequence is used by the receiving end to verify whether there are any abnormalities such as byte loss, data tampering, or transmission errors during data processing and transmission. After the full-process standardization process of adding header identifiers, regularizing valid data, and adding tail check sequence, a standard data frame with a unified format and adapted to multi-path uplink multiplexing transmission is finally generated.

[0035] This embodiment achieves flexible adaptation of downlink data acquisition through dual-mode switching. The timed Modbus polling acquisition mode can systematically collect data from multiple field devices, avoiding data conflicts and acquisition omissions, and ensuring the integrity and stability of the raw data acquisition. The raw data undergoes protocol parsing, effective field extraction, and structured reorganization to eliminate differences in data formats between different devices, achieving standardized processing of heterogeneous field data. The unified format data frames are equipped with frame start identifiers, data type identifiers, and verification sequences, which not only clarify the data structure and business attributes but also ensure the integrity of data transmission and reduce transmission errors through the verification sequence.

[0036] In another preferred embodiment of the present invention, the spatial coordinate data of a first fixed node, a second fixed node, and a third fixed node are retrieved from pre-stored data. The first fixed node is fixed to the top column of the field equipment cabinet, the second fixed node is fixed to the southeast corner support column of the workshop, and the third fixed node is fixed to the instrument bracket base at a straight-line distance of 5 meters from the DTU. The spatial link quality factor is calculated using spatial position parameters, the real-time signal-to-noise ratio of each uplink, and the bit error rate fluctuation amplitude of each uplink per unit time, including:

[0037] Step 100b: Calculate the first spatial distance vector between the first fixed node and the DTU based on the spatial coordinate data of the first fixed node; calculate the second spatial distance vector between the second fixed node and the DTU based on the spatial coordinate data of the second fixed node; and calculate the third spatial distance vector between the third fixed node and the DTU based on the spatial coordinate data of the third fixed node. Specifically, this includes: the system retrieving spatial coordinate data of three fixed nodes from the local non-volatile storage area. These three fixed nodes are the first fixed node, the second fixed node, and the third fixed node. The first fixed node is fixedly installed at the top column of the field equipment cabinet; the second fixed node is fixedly installed at the southeast corner support column of the workshop; and the third fixed node is fixedly installed at the location of the first fixed node and the third fixed node. Based on the instrument bracket base position with a straight-line distance of 5 meters from the transmission unit, the spatial positions of the three fixed nodes cover the key areas of the industrial site, providing a stable reference for spatial feature calculation. After the system obtains the real-time spatial coordinates of the current data transmission unit, it combines the preset spatial coordinates of the first fixed node and calculates the first spatial distance vector between the first fixed node and the data transmission unit using the three-dimensional spatial distance formula. Following the same calculation logic, the second spatial distance vector between the second fixed node and the data transmission unit is calculated based on the spatial coordinates of the second fixed node and the real-time spatial coordinates of the data transmission unit. The third spatial distance vector between the third fixed node and the data transmission unit is calculated based on the spatial coordinates of the third fixed node and the real-time spatial coordinates of the data transmission unit. The spatial distance vector calculation formula is as follows: Let the first fixed node be... The spatial coordinates of the fixed nodes are The real-time spatial coordinates of the data transmission unit are Then the first Spatial distance vector between a fixed node and a data transmission unit for, ,in The values ​​are 1, 2, and 3, which correspond to the first fixed node, the second fixed node, and the third fixed node, respectively.

[0038] Step 101b: The first spatial distance vector is weighted and fused with the real-time signal-to-noise ratio (SNR) of the first uplink and the bit error rate (BER) fluctuation amplitude of the first uplink within a unit time, to obtain the first node link quality parameter; the second spatial distance vector is weighted and fused with the real-time SNR of the second uplink and the BER fluctuation amplitude of the second uplink within a unit time, to obtain the second node link quality parameter; the third spatial distance vector is weighted and fused with the real-time SNR of the third uplink and the BER fluctuation amplitude of the third uplink within a unit time, to obtain the third node link quality parameter; the first node link quality parameter, the second node link quality parameter, and the third node link quality parameter are then weighted and fused in a second-level weighted fusion to obtain the spatial link quality factor, specifically including:

[0039] The calculated first spatial distance vector is weighted and fused with three indicators: the real-time signal-to-noise ratio (SNR) of the first uplink and the bit error rate (BER) fluctuation amplitude of the first uplink within a unit time, to obtain the link quality parameters of the first node. Following the same calculation rules, the second spatial distance vector is weighted and fused with the real-time SNR and BER fluctuation amplitude of the second uplink within a unit time to obtain the link quality parameters of the second node. Similarly, the third spatial distance vector is weighted and fused with the real-time SNR and BER fluctuation amplitude of the third uplink within a unit time to obtain the link quality parameters of the third node. Before performing the weighted fusion calculation of these three indicators, the calculation methods for the two key channel parameters, real-time SNR and BER fluctuation amplitude, need to be clarified. Simultaneously, the three indicators are normalized to eliminate differences in dimensions and numerical magnitudes. The real-time SNR is used to quantify the signal purity of the uplink, reflecting the ratio of effective signal to noise. The signal is collected in real-time by the DTU's signal receiving module. The effective signal power and background noise power of the uplink are sampled. The sampling time is set to 100ms, and data is collected once every 10ms for a total of 10 sets of data. The average value of these 10 sets of data is taken as the final real-time signal-to-noise ratio, with a value range of 0 to 40dB. The larger the value, the purer the link signal and the less transmission interference.

[0040] Because the three metrics (spatial distance vector, real-time signal-to-noise ratio, and bit error rate fluctuation amplitude) have inconsistent dimensions, and because spatial distance vector and bit error rate fluctuation amplitude are negative metrics (the larger the value, the worse the link quality), while real-time signal-to-noise ratio is a positive metric (the larger the value, the better the link quality), direct weighted fusion would lead to distorted calculation results and invalidation of preset weights. Therefore, the three metrics are subjected to minimum-maximum linear normalization to obtain the normalized spatial distance vector. Real-time signal-to-noise ratio and bit error rate fluctuation Map all indicators to the dimensionless interval [0, 1]; achieve joint quantization of spatial characteristics and channel electrical characteristics through multi-indicator weighted fusion to obtain single-node link quality parameters, i.e. ,in For the first Link quality parameters for each node The first-level weighting coefficient is preset based on the industrial site environment, and its specific value is [value missing]. The three weighting coefficients satisfy Because in industrial settings, the real-time signal-to-noise ratio of the uplink directly determines the accuracy of data transmission and is a core factor affecting link quality, it is assigned the highest weight of 0.5; the spatial distance vector determines the degree of signal attenuation during transmission, and the greater the distance, the more severe the signal attenuation, making it an important spatial factor affecting link quality, and is therefore assigned a weight of 0.3; the bit error rate fluctuation reflects the stability of link transmission, and although its impact is lower than the previous two, it can compensate for the dynamic fluctuations in the link that the instantaneous signal-to-noise ratio cannot reflect, and is therefore assigned a weight of 0.2.

[0041] After obtaining the link quality parameters of the three nodes, the system performs a two-stage weighted fusion of the link quality parameters of the first, second, and third nodes. This second-stage weighted fusion integrates the link quality information corresponding to the three fixed nodes, ultimately yielding a spatial link quality factor that comprehensively reflects the spatial transmission environment and channel conditions in the industrial field. ,in For space link quality factor, This is a preset secondary weighting coefficient based on the on-site deployment priority, with specific values... The three weighting coefficients satisfy The value is set based on the priority of the deployment locations of the three fixed nodes. The first fixed node is fixed to the top column of the field equipment cabinet, which is closest to the DTU and field equipment, and can most directly reflect the link status of the core transmission area. Therefore, it is given the highest weight of 0.4. The second fixed node (support column in the southeast corner of the workshop) and the third fixed node (instrument bracket base 5 meters away from the DTU) cover the edge area of ​​the workshop and the area around the DTU, respectively. They are used to assist in verifying the link quality of the core area and supplement the comprehensiveness of spatial coverage. Therefore, they are both given a weight of 0.3. This value can take into account the link information of the core area and the auxiliary area, and avoid the limitations of single node evaluation. The three fixed nodes in this two-level weighted fusion calculation formula cover different areas of the industrial site. The link quality of a single node can only reflect the transmission status of a local area and cannot fully represent the spatial transmission environment and channel status of the entire industrial site. Through two-level weighted fusion, the link quality parameters of the three nodes are integrated, which can highlight the leading role of the core node and take into account the supplementary role of the auxiliary node. The resulting spatial link quality factor can comprehensively and objectively reflect the overall link status of the entire industrial site.

[0042] This embodiment uses three fixed spatial nodes as spatial location references to construct a three-dimensional spatial distance vector, incorporating the spatial geometric features of the transmission environment into the link evaluation system, thus overcoming the shortcomings of relying solely on channel electrical signal indicators for evaluation. By combining the spatial distance vector, uplink real-time signal-to-noise ratio, and bit error rate fluctuation amplitude for multi-level weighted fusion, a multi-dimensional indicator joint evaluation is achieved, which can accurately reflect the real link transmission quality in complex industrial environments. The spatial link quality factor integrates spatial characteristics and channel dynamic characteristics, which can effectively identify implicit link degradation caused by on-site metal obstruction, equipment movement, and multipath interference, thereby improving the accuracy and foresight of link quality judgment.

[0043] In a preferred embodiment of the present invention, step 2, determining two field feature base points based on the spatial link quality factor, includes: performing time-frequency transformation processing on the spatial link quality factor to extract multipath interference feature values ​​and channel occupancy feature values; comparing the multipath interference feature values ​​with a preset multipath interference threshold, and comparing the channel occupancy feature values ​​with a preset channel occupancy threshold; when the multipath interference feature value is greater than the multipath interference threshold and the channel occupancy feature value is greater than the channel occupancy threshold, it is determined that the current environment has multipath interference and the channel occupancy rate is higher than a preset busy level; the first spatial coordinate point corresponding to the multipath interference state is retrieved from the pre-stored base point mapping table, and the first spatial coordinate point is determined as the first field feature base point; when the multipath interference feature value is less than or equal to the multipath interference threshold and the channel occupancy feature value is less than or equal to the channel occupancy threshold, it is determined that the current environment is static line-of-sight propagation and the channel is idle; the second spatial coordinate point corresponding to the static line-of-sight propagation state is retrieved from the pre-stored base point mapping table, and the second spatial coordinate point is determined as the second field feature base point.

[0044] When multipath interference exists and the channel occupancy rate is higher than the preset busy level, the first field feature base point is selected; when static line-of-sight propagation occurs and the channel is idle, the second field feature base point is selected.

[0045] In this embodiment of the invention, the calculated spatial link quality factor undergoes time-frequency transformation processing to convert multipath interference and channel occupancy characteristics, which are difficult to distinguish in the time domain, into the frequency domain for extraction. Specifically, a discrete Fourier transform is used to convert the time-domain data of the spatial link quality factor into frequency-domain data. The specific transformation formula is as follows: ,in, The time-domain sampling sequence is used to measure the spatial link quality factor. The sampling frequency is set to 1 kHz, and the sampling duration is 1.024 s. Therefore, the total number of time-domain sampling points is... =1024; The transformed frequency domain sequence corresponds to 1024 frequency points, with a frequency range of 0 to 1kHz. The imaginary unit has a value of , Represents the natural constant. Indicates the time-domain sampling point number. This indicates the frequency point number in the frequency domain; during the transformation process, the system samples the time domain data of the spatial link quality factor in segments, with each segment containing 1024 sampling points.

[0046] After completing the time-frequency transformation, the amplitude distribution of the resulting frequency domain sequence is statistically analyzed. The corresponding feature values ​​are extracted in two steps to ensure accurate quantification of multipath interference and channel occupancy. The first step is to extract multipath interference feature values: First, the amplitude sequence of the frequency domain sequence is calculated. The frequency point corresponding to the maximum peak value in the amplitude sequence is found. The difference between this frequency point and the center frequency point (500Hz) in the frequency domain is taken as the peak offset of the frequency domain amplitude, with a value range of 0 to 500Hz. Simultaneously, the spectral flatness is calculated. Spectral flatness is obtained by the ratio of the geometric mean to the arithmetic mean of the frequency domain amplitude sequence. The calculation formula is as follows: in, For the frequency domain sequence The amplitude at each frequency point The spectral flatness is defined as a value ranging from 0 to 1. A flatter spectral flatness closer to 0 indicates more severe multipath interference. Both peak offset and spectral flatness are subjected to minimum-maximum linear normalization, mapping them to the 0-1 interval. A weighted fusion is then performed to obtain the multipath interference characteristic value. ,in, These are multipath interference characteristic values. This is the normalized peak offset. To achieve normalized spectral flatness, weighting coefficients of 0.6 and 0.4 are used because peak offset more directly reflects multipath interference and is therefore given higher weight to ensure the accuracy of multipath interference characteristic values. A preset frequency domain amplitude threshold is set, which is 30% of the average amplitude of all frequencies in the frequency domain sequence, to distinguish between effective signal frequencies and noise frequencies. Subsequently, the number of frequencies in the frequency domain sequence with amplitudes higher than this preset threshold is counted, and the ratio of this number to the total number of frequencies is calculated. This ratio is the channel occupancy characteristic value, i.e., channel occupancy characteristic value = The channel occupancy characteristic value ranges from 0 to 1. The larger the value, the higher the channel occupancy rate.

[0047] After feature extraction, the calculated multipath interference feature values ​​are compared with preset multipath interference thresholds, and the channel occupancy feature values ​​are compared with preset channel occupancy thresholds. The multipath interference threshold is preset to 0.6, and the channel occupancy threshold is preset to 0.4. These threshold values ​​are calibrated based on extensive field measurement data from industrial settings. Specifically, two typical transmission scenarios—multipath interference environment and static line-of-sight propagation environment—are selected in the industrial setting. Multiple repeated tests are conducted in each scenario, and multipath interference and channel occupancy feature values ​​are extracted using the aforementioned method in each test. A total of 1000 sets of valid feature value data are collected for both scenarios. The collected multipath interference feature value data are then statistically analyzed, and the minimum and average values ​​of the feature values ​​under multipath interference environment and under static line-of-sight propagation environment are calculated. The maximum and average values ​​of the eigenvalues ​​are taken as the median of the average values ​​of the multipath interference eigenvalues ​​under the two scenarios, and a critical reference is set at 0.6. This value is used to distinguish whether multipath interference is significant. Simultaneously, the same statistical analysis is performed on the channel occupancy eigenvalue data under both scenarios to calculate the distribution range, mean, and dispersion of the channel occupancy eigenvalues ​​under different scenarios. The distribution range is calculated as the maximum value of the channel occupancy eigenvalue under that scenario minus the minimum value. The dispersion is quantified using the standard deviation. Based on this, a critical value that minimizes misjudgment of channel occupancy status is selected, and 0.4 is ultimately determined as the critical value for distinguishing channel occupancy eigenvalues. This value can be used to determine whether the channel occupancy rate is higher than a preset busy level, thereby differentiating different transmission environment states.

[0048] When the multipath interference characteristic value is greater than 0.6 and the channel occupancy characteristic value is greater than 0.4, it is determined that there is significant multipath interference in the current industrial field transmission environment, and the channel occupancy rate is higher than the preset busy level. At this time, the system calls the local storage module to retrieve the first spatial coordinate point corresponding to the multipath interference state from the pre-stored base point mapping table. This base point mapping table is pre-calibrated and stored on-site, containing a one-to-one correspondence between different transmission environment states and corresponding characteristic base point coordinates. The first spatial coordinate point is specifically (X1, Y1, Z1), where X1, Y1, and Z1 correspond to the three-dimensional coordinates of the core transmission area of ​​the industrial field, respectively. This first spatial coordinate point is determined as the first field. Featured base points are used to ensure that the base points can adapt to the link evaluation requirements of multipath interference environments. When the multipath interference feature value is less than or equal to 0.6 and the channel occupancy feature value is less than or equal to 0.4, the current industrial field transmission environment is determined to be in a static line-of-sight propagation state, and the channel is idle. At this time, the system also retrieves the local base point mapping table and retrieves the second spatial coordinate point corresponding to the static line-of-sight propagation state. The second spatial coordinate point is specifically (X2, Y2, Z2), where X2, Y2, and Z2 correspond to the three-dimensional coordinates of the line-of-sight transmission area in the industrial field. This second spatial coordinate point is determined as the second field feature base point, adapting to the link evaluation requirements of the static line-of-sight propagation environment. Through the above complete environment determination and coordinate retrieval logic, the adaptive selection of field feature base points is achieved, ensuring that when multipath interference exists and the channel occupancy rate is higher than the preset busy level, the first field feature base point is selected; when static line-of-sight propagation occurs and the channel is idle, the second field feature base point is selected, ensuring that the field feature base points are highly matched with the actual field transmission environment.

[0049] This embodiment, by performing time-frequency transformation processing on the spatial link quality factor, can extract multipath interference and channel occupancy features from both the time and frequency domains, enabling quantitative identification of complex transmission environments in industrial settings and avoiding environmental misjudgments caused by relying solely on time-domain indicators. It employs a method of comparing feature values ​​with preset thresholds for environmental determination, resulting in clear logic and high execution efficiency. This allows for rapid differentiation between multipath interference environments and static line-of-sight propagation environments, improving the real-time performance and accuracy of field environment identification. Adaptive selection of corresponding field feature base points based on different transmission environments ensures that the base point selection matches the actual channel state, providing a reference coordinate system that fits the real environment for subsequent construction of spatial feature parameter fields, thus enhancing the environmental adaptability and reliability of the entire data transmission and link evaluation system. Retrieving feature base point coordinates based on a pre-stored base point mapping table eliminates the need for real-time calculation of complex spatial locations, reducing system computational load while ensuring the consistency and stability of base point selection.

[0050] In another preferred embodiment of the present invention, a spatial feature parameter field is constructed by taking the selected field feature base point as the center and combining the weighted values ​​of the distances from each fixed node to the center, including:

[0051] Step 200a: Using the first or second field feature base point as the center, calculate the Euclidean distance from each fixed node to the center based on the spatial position parameters of each fixed node; perform a reciprocal transformation on the Euclidean distance values ​​to obtain the initial distance weights corresponding to each fixed node. Specifically, this includes: using the first or second field feature base point as the spatial center, calculating the Euclidean distance from each fixed node to the center point by point based on the three-dimensional spatial coordinates of each fixed node and the three-dimensional spatial coordinates of the center, obtaining the corresponding Euclidean distance values ​​from the fixed nodes to the center; after obtaining the Euclidean distance values ​​of each fixed node, perform a reciprocal transformation on the Euclidean distance values, that is, divide the corresponding Euclidean distance value by 1 to obtain the initial distance weights corresponding to each fixed node. The closer the fixed node is, the greater its initial distance weight.

[0052] Step 201a: Normalize the initial distance weights corresponding to each fixed node to obtain the normalized distance weights for each fixed node; set the distance weight value at the center to 1, and set the distance weight values ​​at each fixed node to the corresponding normalized distance weights; using the center and each fixed node as control points, determine the minimum three-dimensional envelope region containing all control points based on the center coordinates, the spatial position of each fixed node, and the corresponding distance weight values, and divide the region into grid points according to a preset spatial resolution. Specifically, this includes: normalizing the initial distance weights corresponding to each fixed node to eliminate the numerical magnitude differences in the initial distance weights of different fixed nodes, ensuring that the weights of all fixed nodes are in the same 0 to 1 range, i.e., normalized distance weight = single... The initial distance weight of each fixed node is divided by the sum of the initial distance weights of all fixed nodes. This calculation ensures that the normalized distance weight of all fixed nodes is between 0 and 1, and that the sum of the normalized distance weights of all fixed nodes is 1. After normalization, the distance weight of the center position, i.e., the first or second field feature reference point, is directly set to 1. As the core reference point of the spatial feature parameter field, the center should have the highest weight to highlight the dominant influence of the central region on the entire field. At the same time, the distance weight of each fixed node is set to the corresponding calculated normalized distance weight to ensure that the weight of the fixed node matches its distance from the center. The closer the fixed node is to the center, the higher its weight, and the more it reflects its influence on the surrounding field.

[0053] Using the center position and all fixed nodes as spatial control points, and combining the three-dimensional spatial coordinates of all control points, the minimum three-dimensional envelope region that can completely enclose all control points is determined. Specifically, the maximum and minimum values ​​of the coordinates of all control points in the X-axis, Y-axis, and Z-axis directions are counted respectively. Starting from the minimum values ​​of the X-axis, Y-axis, and Z-axis, and ending from the maximum values ​​of the X-axis, Y-axis, and Z-axis, a closed three-dimensional cuboid region is constructed. This region is the minimum three-dimensional envelope region that can completely enclose all control points, ensuring that the envelope region neither misses any control points nor contains any unnecessary or invalid space. After determining the minimum three-dimensional envelope region, spatial grid points are evenly divided within the region according to the preset spatial resolution. The preset spatial resolution is set to 0.1 meters in the X-axis, Y-axis, and Z-axis directions. That is, along the X-axis direction, from the starting point to the ending point of the envelope region, a grid point is divided every 0.1 meters. The Y-axis and Z-axis directions are divided according to the same rules. During the division process, it is ensured that the grid points are evenly distributed in the three-dimensional space, without omissions or overlaps. The final grid points can completely cover the entire minimum three-dimensional envelope region.

[0054] Step 202a: For each grid point, calculate the distance weight estimate based on the spatial distance from the corresponding grid point to all control points and the distance weight values ​​at each control point; fill the minimum three-dimensional envelope region with the distance weight estimates of all grid points according to their spatial locations to generate a spatial feature parameter field. Specifically, after completing the spatial grid point division, calculate the corresponding distance weight estimate for each spatial grid point within the minimum three-dimensional envelope region. The core calculation adopts the weighted inverse distance function method. First, calculate the three-dimensional Euclidean distance from the current grid point to the center (field feature base point) and all control points, including fixed nodes; perform a reciprocal transformation on the Euclidean distance corresponding to each control point, i.e., divide the Euclidean distance by 1 to obtain the inverse distance function value. The larger the distance... The closer the control point, the larger its distance inverse function value. Multiply the distance inverse function value of each control point by the pre-set distance weight value of that control point to obtain the weighted contribution value of each control point to the current grid point. The specific values ​​of the preset distance weight values ​​of each control point are as follows: the distance weight value of the center (first field feature base point or second field feature base point) is preset to 1, and the distance weight value of each fixed node is preset to the normalized distance weight calculated in step 201a (the value range is 0 to 1, and the sum of the normalized distance weights of all fixed nodes is 1). Add the weighted contribution values ​​of all control points to the current grid point to obtain the distance weight estimate of the current grid point, that is, the grid point distance weight estimate = the sum of all control points (control point distance inverse function value × control point distance weight value). Following the above calculation method, each spatial grid point within the minimum three-dimensional envelope region is traversed, and the distance weight estimate for each grid point is calculated one by one to ensure that each grid point has a unique corresponding weight estimate. After the calculation is completed, the distance weight estimates corresponding to all spatial grid points are filled into the minimum three-dimensional envelope region according to their respective actual three-dimensional spatial coordinates, so that each spatial location within the envelope region corresponds to a clear distance weight value. This ultimately forms a continuously distributed spatial characteristic parameter field that reflects the influence of spatial distance on industrial site. This field accurately reflects this law through the spatial distribution differences of the distance weight estimates of grid points. That is, the closer the grid point is to the center (the first or second field feature base point) and each fixed node, the larger the distance weight estimate; the farther the distance, the smaller the distance weight estimate. This is highly consistent with the actual law in industrial site where the closer the spatial distance, the less the signal transmission is affected by distance attenuation, and the higher the weight ratio. Thus, it reflects the influence of spatial distance on signal transmission in industrial site.

[0055] This embodiment, by using the field feature reference point as the center and combining the spatial positions of fixed nodes to calculate distance weights, can realistically reflect the influence of spatial distance on signal transmission in industrial sites, making the weight allocation more consistent with the actual transmission environment. Normalization of the distance weights eliminates differences in numerical magnitudes between different nodes, ensuring a uniform and reasonable spatial weight distribution and improving the stability of subsequent field construction. Dividing a uniform spatial grid based on control points and calculating weight estimates point by point generates a continuous, refined, and fully covered spatial feature parameter field. Using a distance-weighted approach to construct the spatial feature parameter field highlights the dominant transmission role of the central region while also considering the auxiliary reference value of edge nodes, thus improving the comprehensiveness of the field description.

[0056] In a preferred embodiment of the present invention, step 3 includes:

[0057] Step 300: Discretize the spatial feature parameter field using a grid to obtain a predetermined number of spatial grid points and their corresponding field strength values. Calculate the first-order partial derivatives of each spatial grid point in the X-axis and Y-axis directions, as well as the gradient magnitude and gradient direction of each spatial grid point, to obtain the spatial gradient distribution of the spatial feature parameter field. Specifically, this includes: based on the spatial grid points divided in step 201a, determining a predetermined number of spatial grid points (the predetermined number is consistent with the total number of grid points divided in step 201a, ensuring coverage of the entire minimum three-dimensional envelope region), and reading them one by one. The distance weight estimate corresponding to each spatial grid point is taken, and this weight estimate is used as the field strength value corresponding to that grid point. This process yields a predetermined number of spatial grid points within the spatial characteristic parameter field, along with the field strength value corresponding to each grid point, ensuring that the discretized grid point data completely preserves the spatial distribution characteristics of the original parameter field. For each spatial grid point, the adjacent difference method is used to calculate the first-order partial derivatives of that grid point in the X-axis and Y-axis directions using the field strength values ​​of its neighboring grid points. Specifically, the first-order partial derivative in the X-axis direction for the current grid point = ... The distance between two adjacent grid points along the X-axis is 0.1, which corresponds to the spatial resolution of 0.1 meters preset in step 201a. The same calculation logic is used for the first-order partial derivative of the current grid point along the Y-axis. After obtaining the first-order partial derivatives of each grid point along the X and Y axes, the gradient magnitude and gradient direction of that grid point are further calculated, where the gradient magnitude = The gradient magnitude is used to quantify the degree of change in electric field strength at a grid point; the larger the value, the more drastic the change in electric field strength at that location. The gradient direction is determined by the ratio of the first-order partial derivatives along the X-axis to those along the Y-axis, i.e., the angle corresponding to the gradient direction = arctan( The angle corresponding to this ratio is the gradient direction, which is used to characterize the direction of field strength change. Following the above method, all spatial grid points in the spatial characteristic parameter field are traversed, and the first-order partial derivative, gradient magnitude and gradient direction are calculated one by one, finally obtaining the complete spatial gradient distribution of the spatial characteristic parameter field.

[0058] Step 301: Based on the local maxima of the gradient magnitude in the spatial gradient distribution, perform curve fitting to obtain a closed boundary curve. Define the region enclosed by this closed boundary curve as the irregular boundary space, and divide the irregular boundary space into multiple gradient partitions to obtain a gradient partition set. Specifically, after obtaining the spatial gradient distribution of the spatial feature parameter field, select spatial grid points where the gradient magnitude is a local maxima. For each spatial grid point in the spatial feature parameter field, first define the range of its eight surrounding adjacent grid points. This includes the directly adjacent grid points in the positive X-axis direction, negative X-axis direction, positive Y-axis direction, and negative Y-axis direction, as well as the four diagonally adjacent grid points in the positive X-axis and positive Y-axis directions, positive X-axis and negative Y-axis directions, negative X-axis and positive Y-axis directions, and negative X-axis and negative Y-axis directions, ensuring no omissions. For any adjacent grid point, during the filtering process, the gradient magnitude of the current grid point is extracted one by one, and then the gradient magnitudes of its eight surrounding adjacent grid points are extracted one by one. The gradient magnitude of the current grid point is compared with the gradient magnitudes of the eight adjacent grid points. If the gradient magnitude of the current grid point is greater than the gradient magnitudes of all eight surrounding adjacent grid points, and the difference is not less than a preset threshold (the preset threshold is 0.05, used to avoid misjudgment due to small numerical fluctuations), then the grid point is determined to be a local maximum point of gradient magnitude. Following this filtering logic, all spatial grid points in the spatial feature parameter field are traversed, and the comparison and judgment operations are completed one by one to ensure that no key node with the most drastic field strength change is missed. At the same time, false local maxima points caused by numerical fluctuations are eliminated, and finally, a set of all gradient magnitude local maxima points that meet the conditions is obtained.

[0059] After screening, curve fitting is required for all local maxima of gradient magnitude to outline the contours of regions with drastic field strength changes. The specific fitting process is as follows: First, extract the three-dimensional spatial coordinates (X-axis, Y-axis, Z-axis coordinates) of all local maxima. Sort the points according to their actual spatial positions in the industrial field. The sorting rule is to start from the starting position of the irregular boundary region and sort clockwise along the contours of the regions with drastic field strength changes, ensuring that the sorted points can completely represent the contours of the regions. The fitting method uses cubic spline curve fitting. The X-axis and Y-axis coordinates of the sorted local maxima are used as the fitting basis. Each local maximum is used as a control point of the curve. Through cubic spline interpolation algorithm, a smooth curve segment is generated between two adjacent local maxima. During the value process, the second derivative of the control curve is made continuous to avoid curve breaks, abrupt changes, or inflection points, ensuring that each curve segment fits the spatial positional relationship between two adjacent control points. After completing the curve fitting between all adjacent local maxima points, the focus is on the seamless connection of the first and last curve segments. Specifically, the three-dimensional spatial coordinates and gradient direction information of the first local maxima point (first point) and the last local maxima point (last point) after sorting are extracted. The cubic spline interpolation parameters of the last curve segment are adjusted so that the coordinates of the end point of the last curve segment completely coincide with the coordinates of the first point. At the same time, the tangent direction and curvature of the end point of the last curve segment are kept consistent with the tangent direction and curvature of the starting point of the first curve segment, avoiding gaps, abrupt turns, or curvature changes at the connection point, thus achieving a seamless connection between the first and last curve segments. After the connection is completed, a continuous, smooth, and closed boundary curve is formed. This curve closely follows all local maxima of gradient amplitudes, outlining the complete contour of the region with drastic field strength changes. The contour line is continuous without interruption or inflection points, distinguishing the boundary between the region with drastic field strength changes and the surrounding region with gentler field strength changes. It intuitively presents the spatial range of field strength changes. The area enclosed by this boundary curve corresponds to the boundary region of abrupt changes in signal transmission characteristics in industrial sites. Specifically, the rate of change of signal attenuation and the fluctuation amplitude of phase shift in this region are much greater than those in the surrounding region with gentler field strength changes. Specifically, the rate of change of signal attenuation is 3 to 5 times that of the surrounding region, and the fluctuation amplitude of phase shift is much greater. The fluctuation range is 4 to 6 times that of the surrounding area. In the surrounding area, the signal attenuation is gradual, the phase shift is stable, and the change amplitude is small and regular. The signal attenuation change rate does not exceed 0.02dB / m, and the phase shift fluctuation amplitude does not exceed 0.5°. However, in this area, the signal attenuation increases or decreases rapidly with small changes in spatial position (every 0.1 meters), and the change rate is maintained at 0.06 to 0.1dB / m. The phase shift fluctuates violently and without a fixed pattern, with a fluctuation amplitude of 2 to 3°. The overall change pattern of signal attenuation and phase shift in this area is significantly different from that in the surrounding area. This is the core reason why this area has become a complex signal transmission environment in industrial sites and requires key adjustment of transmission parameters.

[0060] The spatial region enclosed by the closed boundary curve is defined as the irregular boundary space. This region is the core area with the most complex signal transmission environment and the most drastic field strength changes in the industrial site. It is also the area with the most unstable signal transmission quality and the greatest need for parameter adjustment. To distinguish the differences in field strength changes in different regions within the irregular boundary space and avoid inaccurate transmission parameter adjustment due to uneven field strength changes within the region, the irregular boundary space needs to be divided into multiple gradient partitions based on the magnitude range of the gradient amplitude. First, the gradient amplitude of all local maxima points is statistically analyzed. The gradient amplitude data of each local maxima point is extracted one by one, and the maximum and minimum values ​​of the gradient amplitude of all local maxima points are calculated to determine the overall distribution range of the gradient amplitude within the irregular boundary space. The statistically obtained gradient amplitude range is then uniformly divided into three pre-defined intervals: low gradient interval, medium gradient interval, and high gradient interval. When dividing the gradient, ensure that the amplitude range widths of the three intervals are consistent. For example, if the maximum gradient amplitude is 1.2 and the minimum is 0.3, then the low gradient interval is 0.3 to 0.6, the medium gradient interval is 0.6 to 0.9, and the high gradient interval is 0.9 to 1.2, ensuring that the interval division is uniform, non-overlapping, and without omissions. Each gradient amplitude interval corresponds to a gradient partition, and the amplitude determination criteria for each gradient partition are clearly defined. Then, traverse all spatial grid points within the irregular boundary space, extract the gradient amplitude of each grid point, determine its corresponding gradient amplitude interval, and assign the grid point to the gradient partition of the corresponding interval. During the division process, ensure that each grid point belongs to only one gradient partition, and avoid duplicate or omitted divisions. Through the above division, a gradient partition set containing low, medium, and high gradient partitions is finally formed. The gradient amplitudes of the grid points within each gradient partition are in the same interval, and the field strength variation patterns are consistent.

[0061] Step 302: Extract the signal attenuation value and phase shift value corresponding to each spatial grid point within each gradient partition, obtaining the signal attenuation value set and phase shift value set corresponding to each gradient partition; perform statistical averaging on the signal attenuation value set and phase shift value set corresponding to each gradient partition to obtain the average attenuation value and average phase shift value corresponding to each gradient partition; concatenate the average attenuation value and average phase shift value corresponding to each gradient partition into vectorized forms to obtain the local feature vector corresponding to each gradient partition. Specifically, for each gradient partition in the gradient partition set, extract the signal attenuation value and phase shift value corresponding to all spatial grid points within it one by one. During the extraction process, ensure that the signal attenuation value and phase shift value of each grid point correspond one-to-one with the spatial position of the grid point, without omissions or mismatches. The specific extraction method is to call the grid point parameter database stored in the system. The system uses a locally embedded database, indexed by unique grid point numbers. Each index corresponds to a complete grid point parameter record, including the unique grid point number, three-dimensional spatial coordinates (X-axis, Y-axis, Z-axis), signal attenuation value, and phase offset value. All parameters are collected and stored in real time after on-site industrial measurements, updated every 100ms to ensure accuracy and timeliness. During retrieval, based on the grid point numbers contained in each gradient partition, the system retrieves the corresponding signal attenuation value (characterizing the degree of energy loss during signal transmission) and phase offset value (characterizing the amount of phase change during signal transmission) for each grid point through index matching. The system aggregates the signal attenuation values ​​of all grid points within the same gradient partition to form the signal attenuation value set for that gradient partition, and aggregates the phase offset values ​​of all grid points within the same gradient partition to form the phase offset value set for that gradient partition.

[0062] Statistical averaging is performed on the set of signal attenuation values ​​corresponding to each gradient partition to eliminate random fluctuations in the signal attenuation value at individual grid points, thus obtaining the overall signal attenuation level of that gradient partition. The same statistical averaging is performed on the set of phase offset values ​​corresponding to each gradient partition to obtain the average phase offset value, which characterizes the overall change level of the signal phase within that gradient partition. The average attenuation value and the average phase offset value corresponding to each gradient partition are vectorized and concatenated. The concatenation logic is as follows: the average attenuation value is used as the first element of a one-dimensional vector, and the average phase offset value is used as the second element of the one-dimensional vector, forming a one-dimensional vector of length 2. This one-dimensional vector fully encompasses the core features of signal attenuation and phase offset of the corresponding gradient partition, serving as the local feature vector for that gradient partition. This ensures that the transmission characteristics of each gradient partition can be characterized by a unique local feature vector; that is, the two elements of the local feature vector correspond to the core transmission indicators of the gradient partition, where the average attenuation value directly reflects the overall energy loss level of signal transmission within that partition. A larger average attenuation value indicates more severe energy loss during signal transmission within the partition, resulting in weaker signal strength at each grid point and poorer transmission quality. Conversely, a smaller average attenuation value indicates less energy loss, more stable signal strength, and better transmission quality. The average phase offset value directly reflects the overall fluctuation level of the signal phase within the partition. Specifically, a larger average phase offset value indicates more drastic changes in signal phase with spatial location and transmission path, leading to poorer signal synchronization and a higher likelihood of signal distortion and bit errors. A smaller average phase offset value indicates smoother signal phase fluctuations, better signal synchronization, and higher transmission stability. The numerical combination of these two elements is unique. Different gradient partitions have different combinations of average attenuation and average phase offset values ​​due to variations in transmission environment and field strength variation, resulting in different local feature vectors. These vectors can be used to distinguish the differences in transmission characteristics among gradient partitions, representing the unique signal transmission state of each gradient partition.

[0063] Step 303: Combine the local feature vectors corresponding to all gradient partitions into an initial feature matrix according to the gradient partition numbering order; perform matrix standardization on the initial feature matrix to obtain the feature interleaving matrix; calculate the covariance matrix of the feature interleaving matrix, solve for the eigenvalues ​​and eigenvectors of the covariance matrix, and select the first K eigenvectors as principal component eigenvectors in descending order of eigenvalues; multiply the principal component eigenvectors with a preset mapping matrix to obtain the adjustment tensor. Specifically, this includes: arranging and combining the local feature vectors corresponding to all gradient partitions in the gradient partition set according to the gradient partition numbering order (from low gradient partition to high gradient partition, or according to the spatial position order of the partitions) to construct the initial feature matrix. Specifically, each local feature vector is used as a row of the initial feature matrix, all local feature vectors are arranged in numerical order, the number of columns in the matrix is ​​equal to the length of the local feature vector (i.e., 2 columns), and the number of rows in the matrix is ​​equal to the number of gradient partitions, ultimately forming an initial feature matrix with the number of rows equal to the number of gradient partitions and 2 columns, achieving the structured integration of local features from all gradient partitions. The initial feature matrix is ​​standardized to eliminate the magnitude differences between different columns of data (average attenuation value column, average phase offset value column), avoiding deviations in subsequent feature extraction due to differences in magnitude. The specific standardization process is as follows: the data in each column of the initial feature matrix are processed separately. First, the mean of all data in the column is calculated, then the standard deviation of all data in the column is calculated, and then the mean of the column is subtracted from each data in the column. The difference is then divided by the standard deviation of the column. After this processing, the mean of each column becomes 0 and the standard deviation becomes 1, finally obtaining the standardized feature interleaving matrix, ensuring that the data in the matrix are of the same magnitude.

[0064] Calculate the covariance matrix of the feature interleaving matrix. The covariance matrix is ​​used to characterize the correlation between data in different columns of the feature interleaving matrix, i.e., covariance matrix = The dimension of the covariance matrix is ​​the same as the number of columns of the feature interleaving matrix (i.e., a 2×2 matrix). All eigenvalues ​​and eigenvectors corresponding to this covariance matrix are solved. Each eigenvalue corresponds to an eigenvector, and the magnitude of the eigenvalue represents the importance of the corresponding eigenvector; the larger the eigenvalue, the more effective information the corresponding eigenvector contains. All the solved eigenvalues ​​are sorted in descending order, and the top K eigenvectors are selected as principal component eigenvectors (K is preset to 1 or 2; specifically, it is determined by calculating the proportion of each eigenvalue to the sum of all eigenvalues, i.e., the eigenvalue contribution. If the contribution of the first eigenvalue is ≥90%, then K is 1; if the contribution of the first eigenvalue is <90%, then K is 2, ensuring that the selected principal component eigenvectors can retain more than 90% of the effective information of the original features). Through principal component extraction, redundant information in the original features is removed, retaining the most representative transmission features.

[0065] The obtained principal component eigenvectors are multiplied by a pre-defined mapping matrix. This mapping matrix is ​​2×3 in dimension, and its elements are determined based on measured signal attenuation and phase shift data from low, medium, and high gradient zones in an industrial setting. These elements were determined after multiple rounds of repeated calibration and iterative optimization, and their values ​​range from 0 to 1. The first column corresponds to signal attenuation adaptation weights, the second column to phase shift calibration weights, and the third column to transmission stability adjustment weights, aligning with the actual needs of transmission parameter adjustment in industrial settings. The matrix multiplication operation follows the rule of summing corresponding row and column multiplications, ultimately yielding an adjustment tensor with the same dimension as the mapping matrix. This adjustment tensor characterizes the transmission characteristics of different gradient zones in the industrial setting, and each element of the adjustment tensor corresponds to the adaptation requirements of a specific gradient zone in the corresponding transmission parameter dimension. Adjusting the priority: the larger the element value, the greater the signal transmission deviation and the worse the stability of the gradient partition with respect to the transmission parameters, requiring more precise parameter adjustments. Different gradient partitions have different average attenuation and average phase shift values, resulting in different principal component eigenvectors. The combination of adjustment tensor elements obtained after multiplying with the mapping matrix is ​​also unique. By analyzing the magnitude and combination rules of each tensor element, the signal energy loss level, phase fluctuation degree, and transmission stability of each gradient partition can be distinguished, characterizing the unique transmission characteristics of each gradient partition. For example, the tensor elements corresponding to high gradient partitions are generally larger, reflecting their characteristics of severe signal attenuation, large phase fluctuation, and poor transmission stability; the tensor elements corresponding to low gradient partitions are generally smaller, reflecting their characteristics of gentle signal attenuation, stable phase, and good transmission status.

[0066] This embodiment transforms the spatial feature parameter field into a gradient distribution through grid-based discretization and gradient calculation. This enables accurate identification of regions with drastic field changes, effective extraction of irregular boundary spaces, and improved spatial structure recognition accuracy in complex industrial environments. Dividing the gradient into partitions based on gradient magnitude and statistically analyzing average attenuation and phase shift simplifies field information and highlights the differences in transmission characteristics across different regions. Constructing local feature vectors and combining them into an initial feature matrix achieves a structured expression of spatial transmission characteristics. Employing covariance analysis and principal component extraction retains the most representative transmission features, removes redundant information, and improves feature effectiveness. Generating an adjustment tensor through a mapping matrix enhances the system's adaptability and transmission reliability in complex industrial transmission environments.

[0067] In a preferred embodiment of the present invention, step 4 involves dynamically adjusting the protocol encapsulation parameters of the data frame using an adjustment tensor to generate multiple protocol data units; sending the multiple protocol data units in parallel to multiple target platforms, and monitoring and recording the transmission status in real time; when any link transmission failure is detected, triggering local data retransmission on the corresponding link based on the synchronization record, including:

[0068] Step 400a involves inputting the adjustment tensor into a preset parameter adjustment function to obtain the adjusted error correction coding redundancy and the adjusted modulation and coding strategy index value. Specifically, this includes inputting the adjustment tensor obtained in step 303 into the preset parameter adjustment function, transforming the adjustment tensor, which characterizes the gradient partition transmission characteristics, into specific indicators for data frame parameter adjustment, namely the adjusted error correction coding redundancy and the adjusted modulation and coding strategy index value. The preset parameter adjustment function is constructed based on industrial field transmission parameter calibration data and employs a weighted summation calculation logic to ensure a high degree of fit between the output parameters and the transmission characteristics characterized by the adjustment tensor. The adjusted error correction coding redundancy is obtained by weighted summation of the elements of the adjustment tensor, i.e., adjusted error correction coding redundancy = (elements in the first row and first column of the adjustment tensor × 0.4) + (elements in the first row and second column of the adjustment tensor × 0.3) + (elements in the first row and third column of the adjustment tensor × 0.3). The formula is as follows: 0.4, 0.3, and 0.3 are preset weighting coefficients, corresponding to adjusting the importance of each element in the first row of the tensor. Specifically, the element in the first column of the first row corresponds to the signal attenuation adaptation weight, assigned the highest weight of 0.4, to prioritize the impact of signal attenuation on the risk of bit errors. The elements in the second and third columns of the first row correspond to the phase offset calibration weight and the transmission stability adjustment weight, respectively, each assigned a weight of 0.3, to balance the impact of phase fluctuations and transmission stability. The calculated result ranges from 0.1 to 0.5, where 0.1 corresponds to the optimal gradient partition transmission environment with the lowest risk of bit errors, where transmission quality can be guaranteed without excessive redundancy. 0.5 corresponds to the worst gradient partition transmission environment with the highest risk of bit errors, requiring maximum redundancy to correct a large number of possible bit errors during transmission. The larger the value, the higher the risk of bit errors in the corresponding gradient partition signal transmission, requiring higher redundancy to correct transmission errors.

[0069] The adjusted modulation and coding strategy index value is obtained by weighted summation of the elements in the second row of the adjustment tensor and mapping it to a preset index level. First, the weighted sum is calculated as: Weighted sum = (Element in the first column of the second row of the adjustment tensor × 0.3) + (Element in the second column of the second row of the adjustment tensor × 0.4) + (Element in the third column of the second row of the adjustment tensor × 0.3), where 0.3, 0.4, and 0.3 are preset weighting coefficients, corresponding to the importance of each element in the second row of the adjustment tensor. Specifically, the element in the second column of the second row corresponds to the phase offset calibration weight, assigned the highest weight of 0.4 to emphasize the impact of phase fluctuations on the modulation and coding strategy; the elements in the first and third columns of the second row correspond to the signal attenuation adaptation weight and the transmission stability adjustment weight, respectively, each assigned a weight of 0.3 to balance the requirements of signal attenuation and transmission stability. The index value is mapped according to the range of the weighted sum. When the weighted sum is between 0 and 0.3, this range indicates an excellent gradient partitioning transmission environment with minimal signal attenuation, stable phase, and extremely low error risk. Therefore, the index value is 1 (corresponding to a low-rate, high-reliability modulation and coding strategy), prioritizing transmission reliability. When the weighted sum is between 0.3 and 0.7, this range indicates a moderate gradient partitioning transmission environment with reasonable signal attenuation and phase shift, and moderate error risk. Therefore, the index value is 2 (corresponding to a medium-rate, medium-reliability modulation and coding strategy), balancing transmission efficiency and reliability. When the weighted sum is between 0.7 and 1.0, this range indicates a poor gradient partitioning transmission environment with severe signal attenuation, large phase fluctuations, and high error risk. Therefore, the index value is 3 (corresponding to a high-rate, low-reliability modulation and coding strategy), minimizing the impact of errors while ensuring transmission efficiency. The final adjusted modulation and coding strategy index value is obtained.

[0070] Step 401a involves modifying the error correction coding parameters of the data frame according to the adjusted error correction coding redundancy and modifying the modulation and coding parameters of the data frame according to the adjusted modulation and coding strategy index value to obtain the adjusted data frame. Specifically, this includes modifying the error correction coding parameters of the data frame according to the adjusted error correction coding redundancy, i.e., calling the data frame encoding module. The original effective bit count of the data frame is preset to 1024 bits. This value is based on the industrial field data transmission standard and is suitable for the effective data length of most industrial data frames. The error correction coding redundancy bit count of the original data frame is adjusted to the original effective bit count multiplied by the adjusted error correction coding redundancy. The adjusted error correction coding redundancy bit count = 1024 bits × adjusted error correction coding redundancy. After adjustment, it is ensured that the redundancy bit count is an integer. If it is less than 1 bit, it is rounded up. This improves the ability of the data frame to cope with bit errors during transmission and reduces data loss caused by signal attenuation and phase shift. The modulation and coding parameters of the data frame are modified according to the adjusted modulation and coding strategy index value. Specifically, the correspondence between the modulation and coding strategy index and the parameter is preset: index value 1 corresponds to binary phase shift keying modulation mode and convolutional coding rate 1 / 2; index value 2 corresponds to quadrature phase shift keying modulation mode and convolutional coding rate 1 / 2; index value 3 corresponds to quadrature amplitude modulation mode and convolutional coding rate 3 / 4. According to the adjusted index value, the modulation and coding module is called to modify the modulation mode and coding rate of the data frame to the corresponding parameters, thus completing the data frame parameter adjustment and obtaining the adjusted data frame.

[0071] Step 402a: Obtain the communication protocol types supported by each of the multiple target platforms. Add the corresponding protocol header and trailer fields to each adjusted data frame to generate protocol data units for MQTT, TCP, and UDP protocols. This process includes: obtaining the communication protocol types supported by the four target platforms (Target Platform 1 (Industrial Data Monitoring Platform), Target Platform 2 (Equipment Control Terminal), Target Platform 3 (Data Storage Server), and Target Platform 4 (Remote Maintenance Terminal)) through the system communication interface; and sending protocol lookup messages to each of these four target platforms. The system queries the protocol support list from each target platform, filters out the preferred communication protocols for each platform (each platform corresponds to only one preferred protocol), and records the mapping relationship between each target platform and its corresponding communication protocol in detail: target platform 1-MQTT, target platform 2-TCP, target platform 3-UDP, and target platform 4-MQTT. It then verifies each mapping relationship to ensure accurate protocol matching.

[0072] For each adjusted data frame obtained in step 401a, according to the communication protocol type supported by the target platform, the corresponding protocol header and trailer fields are added. Specifically, the MQTT protocol header fields include the protocol name, message type, QoS level, and topic name; the trailer fields include the checksum and message length. The TCP protocol header fields include the source port (set to 50001), destination port (target platform 2 port set to 50002), sequence number, and acknowledgment number; the trailer fields include the checksum and urgent pointer. The UDP protocol header fields include the source port (set to 50001), destination port (target platform 3 port set to 50003), and data length; the trailer fields include the checksum and urgent pointer. Verification; When adding fields, ensure that the format and length of the header and footer fields conform to the industry standards of the corresponding protocols, and that the field content precisely matches the data frame content. For example, the sequence number and acknowledgment number of the TCP protocol should correspond to the data frame sending order, and the topic name of the MQTT protocol should match the function of the target platform. There should be no missing fields, incorrect formats, or mismatched content. After adding, protocol data units for MQTT, TCP, and UDP protocols are generated. Target platforms 1 and 4 correspond to MQTT protocol data units, target platform 2 corresponds to TCP protocol data units, and target platform 3 corresponds to UDP protocol data units. Each protocol data unit corresponds to one target platform, forming 4 protocol data units.

[0073] Step 400b involves classifying multiple protocol data units according to their respective target platforms to obtain a queue of protocol data units to be sent for each target platform; establishing independent transmission links with each target platform, and sending the protocol data units in each queue of protocol data units to be sent in parallel to the corresponding target platform through the corresponding transmission links. Specifically, this includes classifying the four protocol data units generated in step 402a (corresponding to four target platforms), and based on the communication protocol type corresponding to each protocol data unit, combined with the preset mapping relationship between the four target platforms and protocols, grouping all protocol data units belonging to the same target platform into categories. One category is formed, creating a queue of protocol data units to be sent for each target platform. Target platform 1 corresponds to one MQTT protocol data unit queue, target platform 2 corresponds to one TCP protocol data unit queue, target platform 3 corresponds to one UDP protocol data unit queue, and target platform 4 corresponds to one MQTT protocol data unit queue, for a total of 4 queues of protocol data units to be sent. During the classification process, a unique target platform identifier is added to each queue, and the identifier is bound one-to-one with the queue. After classification is completed, the correspondence between the queue identifier and the target platform is checked one by one to ensure that the queue and the target platform correspond one-to-one and to avoid classification errors or queue confusion.

[0074] After classification, independent transmission links were established with the four target platforms to ensure that each link operated independently and without interference. The establishment process was specific and standardized, adapting to the transmission requirements of different protocols. For target platform 2, which supports the TCP protocol, a connection-oriented TCP transmission link was established through the TCP three-way handshake process. Specifically, the system sent a SYN connection request packet to target platform 2, received a response packet from target platform 2, and then sent an ACK confirmation packet to target platform 2 to complete the three-way handshake. After the handshake, the link connection status was checked, and the link connectivity was verified by sending test data packets. At the same time, the link transmission bandwidth was checked to ensure that the bandwidth was not less than 10Mbps. After confirming that the link connection was normal and the transmission bandwidth met the requirements, the TCP transmission link was established, and the link identifier was set to Link-TCP-02. For target platform 3, which supports the UDP protocol, the system binds to source port 50001 and sends a UDP connection test message to port 50003 of target platform 3. After checking the response status of target platform 3 and confirming that the link connectivity is good and there is no packet loss, a connectionless UDP transmission link is established, and the link identifier is set to Link-UDP-03. For target platforms 1 and 4, which support the MQTT protocol, the system sends a connection request to the MQTTBroker, carrying the client ID (client ID of target platform 1 is Client-MQTT-01, and client ID of target platform 4 is Client-MQTT-04). After confirming the connection is successful, the system subscribes to the corresponding topic. After verifying that the subscribed topic is consistent with the topic field of the protocol data unit, two independent MQTT transmission links are established, and the link identifier is set. After all four transmission links are established, a multi-threaded parallel transmission mechanism is adopted. Each transmission link is assigned an independent transmission thread, and each thread is independently responsible for the transmission task of its corresponding queue without interference. During transmission, the protocol data units in the queue are sent to the corresponding target platforms one by one according to the order of the queue to be sent. The transmission rate is controlled at 100 to 200 data packets per minute to avoid link congestion and packet loss due to excessive transmission rate. During the transmission process, the transmission status of each link is monitored in real time, including transmission rate and link occupancy. When the link occupancy exceeds 80%, the transmission rate of the corresponding thread is appropriately reduced to ensure the stability and efficiency of parallel transmission, while ensuring that the four target platforms can receive the corresponding data units synchronously.

[0075] Step 401b: During parallel transmission, real-time transmission status feedback information for each transmission link is collected. This feedback information includes successful transmission confirmation and failed transmission alarm information. Based on the transmission status feedback information corresponding to each transmission link and the identifier assigned to each protocol data unit before transmission, a local status record table is constructed, containing the link identifier of each transmission link, the identifier of each protocol data unit, and the initial transmission status of each protocol data unit. Specifically, during the parallel transmission of protocol data units, the transmission status feedback information is collected in real-time through the status monitoring module corresponding to each transmission link. The collection frequency is set to 10ms / time to ensure that the feedback information reflects the transmission status in real time. The system ensures timely and complete feedback information without delays or omissions. Sending status feedback includes successful transmission confirmation and failed transmission alarm information. Successful transmission confirmation includes the link identifier, protocol data unit identifier, and transmission success time, confirming that the data unit has been successfully delivered to the target platform. Failed transmission alarm information includes the link identifier, protocol data unit identifier, reason for failure (specifically categorized into four types: link interruption, signal distortion, verification failure, and port non-response, clearly indicating the specific scenario for each failure, such as link interruption corresponding to a broken transmission link, and signal distortion corresponding to signal anomalies caused by industrial interference), and transmission failure time, used to pinpoint the cause and time of transmission failure.

[0076] Before each protocol data unit is sent, it is assigned a unique identifier to ensure that each protocol data unit can be located. After generation, it is stored in the system's local cache and bound to the corresponding protocol data unit to ensure that the identifier of each protocol data unit is not repeated or confused. The sending information of the corresponding data unit can be quickly retrieved through the identifier. Based on the transmission status feedback information corresponding to each transmission link and the identifier of each protocol data unit, a local status record table is constructed. This record table is stored in the system's local cache, with a fixed storage path of the system root directory / transmission status database. Records are kept in tabular form, named according to the current date. The fields and their descriptions are as follows: Link Identifier (stores the identifier of the corresponding transmission link, such as Link-MQTT-01), Protocol Data Unit Identifier (stores the assigned unique identifier), Protocol Type (stores the corresponding protocol, such as MQTT, TCP, UDP), Target Platform Identifier (stores the identifier of the corresponding target platform, such as Platform-1), Initial Transmission Status (default is set to pending transmission, filled in uniformly before transmission), Feedback Information (stores successful transmission confirmation information or transmission failure alarm information; the specific reason for failure must be indicated in case of failure), and Feedback Time (stores the reception time of the feedback information, with the same format as the transmission time). During the construction process, the link identifier of each link, the identifier of the corresponding protocol data unit, and related information are filled into the record table one by one. After completion, each record corresponds one-to-one with the actual transmission status, ensuring no omissions, mismatches, or field errors, thus completing the construction of the local status record table.

[0077] Step 402b involves updating the transmission status feedback information to the local status record table to obtain a synchronization record. When a transmission failure alarm message corresponding to any protocol data unit is found in the local status record table, a copy of the corresponding protocol data unit is read from the local cache based on the identifier of the corresponding protocol data unit. The copy of the protocol data unit is then retransmitted to the corresponding target platform via the original transmission link. Specifically, the system monitors the transmission status feedback information in real time. For each piece of feedback information received, it immediately updates the local status record table. Specifically, based on the link identifier and protocol data unit identifier in the feedback information, the corresponding record is matched in the local status record table using a dual index (link identifier, identifier) ​​to avoid matching errors. After a successful match, the initial transmission status of the record is updated from pending transmission to transmission successful or transmission failed. If transmission is successful, the feedback information field is filled with "transmission successful, recipient has confirmed," and the transmission success time is entered. If transmission fails, the feedback information field is filled with the specific reason for the failure, such as signal distortion or interference at the industrial site, and the transmission failure time is entered. After the update is completed, the record table is saved to obtain a synchronization record that is synchronized with the actual transmission status, ensuring that the information in the record table is completely consistent with the actual transmission situation.

[0078] The system initiates a real-time monitoring mechanism, querying the local status record table every 5ms. When a transmission failure alarm is detected for any protocol data unit, a retransmission mechanism is immediately activated to maximize the transmission success rate. The specific execution process is as follows: First, based on the identifier of the protocol data unit, the corresponding protocol data unit copy is read from the system's local cache (which is consistent with the cache path when the protocol data unit was generated) using the identifier index. During the reading process, the integrity of the copy is verified. Specifically, the verification method is to compare the protocol fields, data content, and checksum of the copy with the original protocol data unit to ensure that the copy is completely consistent with the original protocol data unit content, without damage, missing data, or field mismatch, thus avoiding retransmission failure due to copy anomalies.

[0079] After reading, the original transmission link remains unchanged (without changing the link to avoid additional delays and errors caused by link switching). The protocol data unit copy is retransmitted to the corresponding target platform through the original transmission link. The retransmission interval is strictly set to 100ms. This interval is based on the link recovery speed in the industrial field, avoiding both excessively short intervals that cause link congestion and excessively long intervals that cause data transmission delays. After each retransmission, the system continues to collect the transmission status feedback information of the copy through the status monitoring module and updates the local status record table in real time, recording the number of retransmissions and the status after retransmission. If a successful transmission is not received after 3 consecutive retransmissions, the system will be considered retransmitted. If the information is not verified, the transmission of the protocol data unit is determined to have failed. A failure log is immediately recorded, containing the protocol data unit identifier, target platform, reason for failure, number of retransmissions, and time of failure. Simultaneously, a link fault alarm is triggered, displayed as a system pop-up and audible / visual notification. The notification states that protocol data unit XXX corresponding to link XXX has failed to be retransmitted three times consecutively. Please check the link status. This prompts staff to promptly check the connection status, bandwidth, and interference resistance of the corresponding transmission link, troubleshoot the fault, and restore the link to maximize the success rate of protocol data unit transmission and ensure the integrity and reliability of data transmission in the industrial field.

[0080] This embodiment, based on the adjusted transmission parameters calculated using adjustment tensors, achieves adaptation of transmission parameters to the transmission characteristics of different gradient partitions in industrial settings. This effectively reduces transmission errors caused by signal attenuation and phase shift, improving the reliability of data transmission. It specifically modifies the error correction coding and modulation coding parameters of data frames, dynamically adjusting redundancy and modulation coding strategies according to transmission characteristics. This balances data transmission reliability and efficiency, avoiding low transmission efficiency due to excessive redundancy or data loss due to insufficient redundancy. It generates protocol data units for multiple protocols, adapting to the communication protocol requirements of different target platforms, improving the compatibility of data frames with target platforms, and expanding the coverage of data transmission. The standardized addition of protocol header and trailer fields ensures that protocol data units conform to the corresponding protocol's industrial standards, preventing transmission failures due to missing fields or incorrect formats, further enhancing the stability of data transmission. Protocol data units are categorized by target platform and independent transmission links are established to enable parallel transmission across multiple target platforms, improving data transmission efficiency and avoiding transmission delays caused by congestion on a single link. Real-time collection of transmission status feedback information and the construction of a local status log table enable full traceability and monitoring of the data transmission process, allowing for the location of faulty links and faulty data, reducing the difficulty of troubleshooting. A retransmission mechanism is implemented for protocol data units that fail to transmit, maximizing data transmission success rate and minimizing data loss by reading copies from the local cache and retransmitting through the original link. Each transmission link operates independently without interference, preventing a single link failure from affecting the normal transmission of other links, thus improving the overall stability and anti-interference capability of the transmission system.

[0081] like Figure 2 As shown, embodiments of the present invention also provide an uplink data multiplexing transmission system based on a downlink acquisition strategy, comprising:

[0082] The location-aware module is used to encapsulate the raw data set into a data frame of a unified format and obtain the spatial location parameters of three fixed nodes deployed at the industrial site where the DTU is located; it uses the spatial location parameters, the real-time signal-to-noise ratio of each uplink, and the bit error rate fluctuation of each uplink per unit time to calculate the spatial link quality factor.

[0083] The quantization calculation module is used to determine two field feature base points based on the spatial link quality factor. When there is multipath interference and the channel occupancy rate is higher than the preset busy level, the first field feature base point is selected. When static line-of-sight propagation is in progress and the channel is idle, the second field feature base point is selected. The selected field feature base point is used as the center, and the spatial feature parameter field is constructed by combining the distance weighting values ​​from each fixed node to the center.

[0084] The parametric field construction module is used to delineate irregular boundary spaces and perform gradient partitioning within the spatial feature parametric field. It collects signal attenuation and phase shift values ​​within each gradient partition, performs feature interleaving and fusion processing on the signal attenuation and phase shift values ​​to generate a feature interleaving matrix, performs principal component analysis on the feature interleaving matrix, extracts principal component eigenvectors, and calculates the adjustment tensor based on the principal component eigenvectors.

[0085] The adjustment tensor generation module is used to dynamically adjust the protocol encapsulation parameters of the data frame using the adjustment tensor to generate multiple protocol data units; multiple protocol data units are sent to multiple target platforms in parallel, and the sending status is monitored and recorded in real time. When any link fails to send, the local data retransmission of the corresponding link is triggered based on the synchronous record.

[0086] It should be noted that this system is a system corresponding to the above method. All implementation methods in the above method embodiments are applicable to this embodiment and can achieve the same technical effect.

[0087] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. An uplink data multiplexing and transmission method based on a downlink acquisition strategy, characterized in that, The method includes: Step 1: Encapsulate the original dataset into a data frame of a uniform format and obtain the spatial location parameters of three fixed nodes deployed at the industrial site where the DTU is located; calculate the spatial link quality factor using the spatial location parameters, the real-time signal-to-noise ratio of each uplink, and the bit error rate fluctuation of each uplink per unit time. Step 2: Determine two field feature base points based on the spatial link quality factor. When multipath interference exists and the channel occupancy rate is higher than the preset busy level, select the first field feature base point. When static line-of-sight propagation occurs and the channel is idle, select the second field feature base point. Using the selected field feature base point as the center, and combining the weighted values ​​of the distances from each fixed node to the center, construct a spatial feature parameter field. Step 3: Define the irregular boundary space within the spatial feature parameter field and perform gradient partitioning. Collect the signal attenuation value and phase shift value within each gradient partition. Perform feature interleaving and fusion processing on the signal attenuation value and phase shift value to generate a feature interleaving matrix. Perform principal component analysis on the feature interleaving matrix to extract the principal component eigenvectors. Calculate the adjustment tensor based on the principal component eigenvectors. Step 4: Dynamically adjust the protocol encapsulation parameters of the data frame using the adjustment tensor to generate multiple protocol data units; send the multiple protocol data units to multiple target platforms in parallel, monitor the sending status in real time and record it synchronously; when any link fails to send, trigger local data retransmission of the corresponding link based on the synchronous record.

2. The uplink data multiplexing transmission method based on downlink acquisition strategy according to claim 1, characterized in that, Encapsulating the original data set into a data frame of a uniform format includes: The DTU provides two working modes in the downlink direction: transparent serial data transmission and timed Modbus polling acquisition. When downlink acquisition is required, the DTU switches to the timed Modbus polling acquisition mode. According to the preset polling timing and instruction sequence, it sends acquisition instructions to multiple field devices in sequence through the serial communication interface, and receives the raw data returned by each device. All the received raw data are then collected to form a raw data set. Each piece of raw data in the original dataset is parsed according to the protocol, and the valid data fields returned by each field device are identified and extracted. The valid data fields are then reassembled according to the preset internal data structure to obtain reassembled data. A frame start identifier and a data type identifier are added to the header of the reassembled data, and a check sequence is added to the tail to obtain a data frame with a unified format.

3. The uplink data multiplexing transmission method based on downlink acquisition strategy according to claim 2, characterized in that, The spatial location parameters of the three fixed nodes include: Retrieve the pre-stored spatial coordinate data of the first fixed node, the second fixed node, and the third fixed node, wherein the first fixed node is fixed to the top column of the field equipment cabinet, the second fixed node is fixed to the support column in the southeast corner of the workshop, and the third fixed node is fixed to the instrument bracket base at a straight distance of 5 meters from the DTU.

4. The uplink data multiplexing transmission method based on downlink acquisition strategy according to claim 3, characterized in that, The spatial link quality factor is calculated using spatial location parameters, the real-time signal-to-noise ratio of each uplink, and the bit error rate fluctuation of each uplink per unit time, including: Calculate the first spatial distance vector between the first fixed node and the DTU based on the spatial coordinate data of the first fixed node; calculate the second spatial distance vector between the second fixed node and the DTU based on the spatial coordinate data of the second fixed node; calculate the third spatial distance vector between the third fixed node and the DTU based on the spatial coordinate data of the third fixed node. The first node link quality parameter is obtained by weighting and fusing the first spatial distance vector with the real-time signal-to-noise ratio of each uplink and the bit error rate fluctuation amplitude of each uplink within a unit time. The second node link quality parameter is obtained by weighting and fusing the second spatial distance vector with the real-time signal-to-noise ratio of each uplink and the bit error rate fluctuation amplitude of each uplink within a unit time. The third node link quality parameter is obtained by weighting and fusing the third spatial distance vector with the real-time signal-to-noise ratio of each uplink and the bit error rate fluctuation amplitude of each uplink within a unit time. The link quality parameters of the first node, the second node, and the third node are weighted and fused in a two-level manner to obtain the spatial link quality factor.

5. The uplink data multiplexing transmission method based on downlink acquisition strategy according to claim 4, characterized in that, Two field feature base points are determined based on the spatial link quality factor, including: The spatial link quality factor is processed by time-frequency transformation to extract multipath interference feature values ​​and channel occupancy feature values. The multipath interference feature values ​​are compared with a preset multipath interference threshold, and the channel occupancy feature values ​​are compared with a preset channel occupancy threshold. When both the multipath interference feature value and the channel occupancy feature value are greater than the channel occupancy threshold, it is determined that multipath interference exists in the current environment and the channel occupancy rate is higher than the preset busy level. The first spatial coordinate point corresponding to the multipath interference state is retrieved from the pre-stored base point mapping table, and the first spatial coordinate point is determined as the first field feature base point. When the multipath interference characteristic value is less than or equal to the multipath interference threshold and the channel occupancy characteristic value is less than or equal to the channel occupancy threshold, the current environment is determined to be static line-of-sight propagation and the channel is idle. The second spatial coordinate point corresponding to the static line-of-sight propagation state is retrieved from the pre-stored base point mapping table, and the second spatial coordinate point is determined as the second field characteristic base point.

6. The uplink data multiplexing transmission method based on downlink acquisition strategy according to claim 5, characterized in that, Using the selected field feature reference point as the center, and combining the weighted values ​​of the distances from each fixed node to the center, a spatial feature parameter field is constructed, including: Using the first or second field feature base point as the center, calculate the Euclidean distance from each fixed node to the center based on the spatial position parameters of each fixed node; perform a reciprocal transformation on the Euclidean distance value to obtain the initial distance weight corresponding to each fixed node. The initial distance weights corresponding to each fixed node are normalized to obtain the normalized distance weights corresponding to each fixed node; the distance weight value at the center is set to 1, and the distance weight values ​​at each fixed node are set to the corresponding normalized distance weights; taking the center and each fixed node as control points, the minimum three-dimensional envelope region containing all control points is determined according to the center coordinates, the spatial position of each fixed node and the corresponding distance weight value, and the grid points are divided within the minimum three-dimensional envelope region according to the preset spatial resolution; For each grid point, calculate the distance weight estimate of the corresponding grid point based on the spatial distance from the corresponding grid point to all control points and the distance weight value at each control point; fill the minimum three-dimensional envelope region with the distance weight estimates of all grid points according to their spatial location to generate a spatial feature parameter field.

7. The uplink data multiplexing transmission method based on downlink acquisition strategy according to claim 6, characterized in that, Step 3 includes: The spatial characteristic parameter field is discretized by gridding to obtain a preset number of spatial grid points and corresponding field strength values; the first-order partial derivatives of each spatial grid point in the X-axis and Y-axis directions, as well as the gradient magnitude and gradient direction of each spatial grid point, are calculated to obtain the spatial gradient distribution of the spatial characteristic parameter field. Curve fitting is performed based on the local maxima of the gradient magnitude in the spatial gradient distribution to obtain a closed boundary curve. The region enclosed by the closed boundary curve is defined as the irregular boundary space, and the irregular boundary space is divided into multiple gradient partitions to obtain a gradient partition set. Extract the signal attenuation value and phase shift value corresponding to each spatial grid point within each gradient partition to obtain the signal attenuation value set and phase shift value set corresponding to each gradient partition; perform statistical averaging on the signal attenuation value set and phase shift value set corresponding to each gradient partition to obtain the average attenuation value and average phase shift value corresponding to each gradient partition; and concatenate the average attenuation value and average phase shift value corresponding to each gradient partition into vectors to obtain the local feature vector corresponding to each gradient partition. Combine the local eigenvectors corresponding to all gradient partitions into an initial feature matrix according to the gradient partition numbering order; perform matrix standardization on the initial feature matrix to obtain the feature interleaving matrix; calculate the covariance matrix of the feature interleaving matrix, solve for the eigenvalues ​​and eigenvectors of the covariance matrix, and select the first K eigenvectors as principal component eigenvectors in descending order of eigenvalues; multiply the principal component eigenvectors with a preset mapping matrix to obtain the adjustment tensor.

8. The uplink data multiplexing transmission method based on downlink acquisition strategy according to claim 7, characterized in that, The protocol encapsulation parameters of the data frame are dynamically adjusted using an adjustment tensor to generate multiple protocol data units, including: The adjustment tensor is input to the preset parameter adjustment function to obtain the adjusted error correction coding redundancy and the adjusted modulation coding strategy index value; The error correction coding parameters of the data frame are modified according to the adjusted error correction coding redundancy, and the modulation coding parameters of the data frame are modified according to the adjusted modulation coding strategy index value to obtain the adjusted data frame. Obtain the communication protocol types supported by multiple target platforms, add the corresponding protocol header and protocol trailer fields to each adjusted data frame, and generate protocol data units for MQTT, TCP and UDP protocols, which are then used as multiple protocol data units.

9. The uplink data multiplexing transmission method based on downlink acquisition strategy according to claim 8, characterized in that, Multiple protocol data units are sent in parallel to multiple target platforms, and the sending status is monitored and recorded in real time. When any link fails to send, local data retransmission of the corresponding link is triggered based on the synchronization record, including: Multiple protocol data units are categorized according to their respective target platforms to obtain a queue of protocol data units to be sent for each target platform; an independent transmission link is established with each target platform, and the protocol data units in each queue of protocol data units to be sent are sent in parallel to the corresponding target platform through the corresponding transmission link; During parallel transmission, the transmission status feedback information of each transmission link is collected in real time. The transmission status feedback information includes transmission success confirmation information and transmission failure alarm information. Based on the transmission status feedback information corresponding to each transmission link and the identifier assigned to each protocol data unit before transmission, a local status record table is constructed, which includes the link identifier of each transmission link, the identifier of each protocol data unit, and the initial transmission status of each protocol data unit. Update the local status record table with the transmission status feedback information to obtain a synchronized record; when the transmission failure alarm information corresponding to any protocol data unit is found in the local status record table, read the corresponding protocol data unit copy from the local cache according to the identifier of the corresponding protocol data unit; and resend the protocol data unit copy to the corresponding target platform through the original transmission link.

10. An uplink data multiplexing transmission system based on a downlink acquisition strategy, wherein the system implements the method as described in any one of claims 1 to 9, characterized in that, include: The location awareness module is used to encapsulate the raw data set into a data frame of a unified format and obtain the spatial location parameters of three fixed nodes deployed at the industrial site where the DTU is located. The spatial link quality factor is calculated using spatial location parameters, the real-time signal-to-noise ratio of each uplink, and the bit error rate fluctuation of each uplink per unit time. The quantization calculation module is used to determine two field feature base points based on the spatial link quality factor. When there is multipath interference and the channel occupancy rate is higher than the preset busy level, the first field feature base point is selected. When static line-of-sight propagation is in progress and the channel is idle, the second field feature base point is selected. The selected field feature base point is used as the center, and the spatial feature parameter field is constructed by combining the distance weighting values ​​from each fixed node to the center. The parametric field construction module is used to delineate irregular boundary spaces and perform gradient partitioning within the spatial feature parametric field. It collects signal attenuation and phase shift values ​​within each gradient partition, performs feature interleaving and fusion processing on the signal attenuation and phase shift values ​​to generate a feature interleaving matrix, performs principal component analysis on the feature interleaving matrix, extracts principal component eigenvectors, and calculates the adjustment tensor based on the principal component eigenvectors. The adjustment tensor generation module is used to dynamically adjust the protocol encapsulation parameters of the data frame using the adjustment tensor to generate multiple protocol data units; multiple protocol data units are sent to multiple target platforms in parallel, and the sending status is monitored and recorded in real time. When any link fails to send, the local data retransmission of the corresponding link is triggered based on the synchronous record.

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