A wireless anti-interference data acquisition method and system for gas field stations under multiple working conditions

Abstract

The application discloses a kind of wireless anti-interference data acquisition methods and systems under multi-working condition of gas station, it is related to wireless anti-interference data acquisition technical field, the method includes: access the operating state signal of station equipment, establish anti-interference atlas, atlas search and load target communication parameter;Determine dynamic control parameter;The data sampling and transmission management of gas station are executed, and stored to station database;Using frequency energy feature clustering mode, concurrent collision signal separation and verification are carried out to superimposed signal, and independent acquisition signal is determined.The application solves the technical problems that existing wireless acquisition scheme of gas station is difficult to adapt to dynamic noise interference caused by working condition change, signal separation effect is poor when multi-path data packet concurrent conflict, and data acquisition stability is insufficient, realizes multi-working condition adaptive communication control and conflict superimposed signal separation of gas station, and improves the technical effects of anti-interference ability and data transmission reliability of wireless data acquisition under complex working condition.

Description

Technical Field

[0001] This invention relates to the field of wireless anti-interference data acquisition technology, specifically to a method and system for wireless anti-interference data acquisition under multiple operating conditions in gas stations. Background Technology

[0002] Gas stations operate under various alternating conditions, such as starting, stopping, regulating, and boosting, which generate different spectral noise interference. Existing wireless data acquisition methods mostly use fixed communication parameters for data transmission, which cannot adapt to dynamic changes in operating conditions and have limited anti-interference capabilities. At the same time, when multiple acquisition terminals report data concurrently, data packet signal superposition and conflict are very likely to occur. Conventional communication mechanisms lack sophisticated conflict signal separation methods, which can easily lead to data distortion and packet loss, making it difficult to meet the continuous and stable data acquisition requirements of gas stations in complex scenarios.

[0003] Existing wireless data acquisition solutions for gas stations are ill-suited to adapt to dynamic noise interference caused by changes in operating conditions, suffer from poor signal separation when multiple data packets collide, and exhibit insufficient data acquisition stability. Summary of the Invention

[0004] This application provides a wireless anti-interference data acquisition method and system for gas stations under multiple operating conditions, which is used to address the technical problems of existing wireless acquisition schemes for gas stations being unable to adapt to dynamic noise interference caused by changes in operating conditions, poor signal separation effect when multiple data packets conflict concurrently, and insufficient data acquisition stability.

[0005] In view of the above problems, this application provides a wireless anti-interference data acquisition method and system for gas stations under multiple operating conditions.

[0006] The first aspect of this application provides a method for wireless anti-interference data acquisition under multiple operating conditions in a gas station, the method comprising: The system accesses the operating status signals of the station equipment via the serial communication interface of the data acquisition unit. Based on the signal combination status, it identifies the operating condition type and establishes an anti-interference map relating the operating condition to the noise spectrum characteristics. When a precursor to an operating condition switch is detected, the map is retrieved using the precursor characteristics as an index, and the target communication parameter combination is loaded into the communication channel. Using the target communication parameter combination as the initial configuration, the system monitors the channel received signal strength and signal-to-noise ratio to assess the interference level, activates the automatic adaptation module, and dynamically adjusts the spreading factor and transmit power of the communication channel according to the determined dynamic control parameters and the interference level. After completing the dynamic configuration of the communication physical layer of the dynamic control parameters, the system performs data sampling and transmission management of the gas station and stores the data in the station database. When multiple data packets arrive and cause signal conflicts, the system uses a frequency domain energy feature clustering method, based on the signal separation component deployed at the signal receiver, to perform concurrent conflict signal separation and verification on the superimposed signals, determining the independently acquired signal.

[0007] A second aspect of this application provides a wireless anti-interference data acquisition system for gas stations under multiple operating conditions, the system comprising: The target communication parameter loading module is used to access the operating status signals of the station equipment through the serial communication interface of the data acquisition unit, identify the operating condition type based on the signal combination status, and establish an anti-interference spectrum that associates the operating condition with the noise spectrum characteristics. When an indication of an operating condition switch is detected, the spectrum is retrieved using the indication characteristics as an index, and the target communication parameter combination is loaded into the communication channel. The dynamic control parameter determination module is used to monitor the channel received signal strength and signal-to-noise ratio to evaluate the interference level with the target communication parameter combination as the initial configuration, activate the automatic adaptation module, and dynamically adjust the spreading factor and transmit power of the communication channel according to the interference level based on the determined dynamic control parameters. The station database storage module is used to perform data sampling and transmission management of the gas station after completing the dynamic configuration of the communication physical layer of the dynamic control parameters, and store the data in the station database. The independent acquisition signal determination module is used to determine the independent acquisition signal when multiple data packets arrive and cause signal conflicts, based on the signal separation component deployed at the signal receiving end, and using frequency domain energy feature clustering, to perform concurrent conflict signal separation and verification on the superimposed signals.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages: The system accesses the operating status signals of the gas station equipment via the serial communication interface of the data acquisition unit, establishing an anti-interference map that correlates operating conditions with noise spectrum characteristics. When a precursor to an operating condition switch is detected, a map search is performed, and the target communication parameter combination is loaded into the communication channel. The system monitors the received signal strength and signal-to-noise ratio of the channel to assess the interference level. Based on the determined dynamic control parameters, the spreading factor and transmit power of the communication channel are dynamically adjusted according to the interference level. Data sampling and transmission management of the gas station are performed, and the data is stored in the station database. When multiple data packets arrive and cause signal conflicts, a frequency domain energy feature clustering method is used to separate and verify the superimposed signals, determining the independently acquired signal. This achieves the technical effect of realizing multi-condition adaptive communication control and conflict superimposed signal separation in gas stations, improving the anti-interference capability and data transmission reliability of wireless data acquisition under complex operating conditions. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1This is a schematic flowchart of a wireless anti-interference data acquisition method for gas stations under multiple operating conditions, provided in an embodiment of this application.

[0011] Figure 2 A bar chart comparing the data packet loss rate of the present invention's scheme and the traditional fixed parameter scheme under different gas station operating conditions provided in the embodiments of this application.

[0012] Figure 3 This is a schematic diagram of a wireless anti-interference data acquisition system for a gas station under multiple operating conditions, provided in an embodiment of this application.

[0013] Explanation of reference numerals in the attached diagram: Target communication parameter loading module 10, dynamic control parameter determination module 20, station database storage module 30, and independent acquisition signal determination module 40. Detailed Implementation

[0014] This application provides a wireless anti-interference data acquisition method and system for gas stations under multiple operating conditions, which addresses the technical problems of existing wireless acquisition schemes for gas stations being unable to adapt to dynamic noise interference caused by changes in operating conditions, poor signal separation effect when multiple data packets conflict concurrently, and insufficient data acquisition stability.

[0015] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0016] Example 1, as Figure 1 As shown, this application provides a wireless anti-interference data acquisition method for gas stations under multiple operating conditions, the method comprising: Step S100: Access the operating status signal of the station equipment through the serial communication interface of the data acquisition unit, identify the operating condition type based on the signal combination status, and establish an anti-interference spectrum that associates the operating condition with the noise spectrum characteristics. When an early sign of operating condition switching is detected, perform spectrum retrieval using the early sign characteristics as an index and load the target communication parameter combination into the communication channel.

[0017] Specifically, the serial communication interface on the data acquisition unit is used to access various station equipment operation status signals in real time, such as pump and valve start / stop signals, pressure regulator action signals, and venting valve status signals. Real-time monitoring of signal logic level changes is conducted, and different operating conditions are distinguished based on different signal combinations: normal transmission and distribution, peak shaving switching, venting operations, and equipment maintenance. Electromagnetic noise spectrum data corresponding to each operating condition is collected synchronously, and noise fingerprint features are extracted through operating condition clustering. Based on the one-to-one correspondence between operating condition types and noise fingerprint features, an anti-interference correlation map is constructed, including multiple parameter combination strategies such as channel, spreading factor, transmit power, and bandwidth. The system continuously monitors the accessed operating status signals in real time, identifying precursor features of operating condition switching caused by sudden level changes. Once a precursor feature is captured, an operating condition switching command is generated. Using this precursor feature as a retrieval index, the anti-interference correlation map is traversed to match the optimal communication parameter combination suitable for the current operating condition. This target communication parameter combination is then synchronously loaded into the wireless communication channel to complete the initial channel configuration, providing a basic communication parameter benchmark for subsequent dynamic channel interference control.

[0018] Step S200: Using the target communication parameter combination as the initial configuration, monitor the channel received signal strength and signal-to-noise ratio to assess the interference level, activate the automatic adaptation module, and dynamically adjust the spreading factor and transmit power of the communication channel according to the determined dynamic control parameters and the interference level.

[0019] Specifically, the loaded target communication parameter combination is used as the initial configuration of the wireless communication physical layer. The real-time received signal strength indication value and signal-to-noise ratio of the communication channel are continuously collected. The channel interference level is comprehensively classified in combination with the current site operating conditions. The built-in automatic adaptation module is called. This module pre-calibrates the mapping relationship between the spreading factor, transmit power and communication success rate under different interference levels. The interference level determined in real time is used as the input. The minimum available spreading factor and the optimal transmit power are output as dynamic adjustment parameters. Then, the physical layer parameters of the communication channel are rewritten in real time according to this set of dynamic adjustment parameters. The channel spreading factor and the device transmit power are dynamically adjusted synchronously according to the current interference level to realize the adaptive matching of the communication channel transmission performance with the interference intensity.

[0020] Step S300: After completing the dynamic configuration of the communication physical layer of the dynamic control parameters, perform data sampling and transmission management of the gas station and store it in the station database.

[0021] Specifically, after the dynamic control parameters such as the communication physical layer spreading factor and transmission power are issued and the hardware configuration takes effect, the data sampling and transmission scheduling management process of the entire gas station equipment is initiated. According to the preset sampling cycle, core station operation data such as pump and valve operating parameters, pipeline pressure, flow, pressure regulator status, and vent valve monitoring data are collected synchronously. Data packets are encapsulated, verified, and uploaded in an orderly manner through a wireless communication channel that has completed anti-interference adaptive optimization. During the transmission process, the integrity of the data packets and the check code are verified frame by frame, and distorted data is filtered out. The valid collected data that passes the verification is uniformly written into the station database for persistent storage, providing complete and reliable underlying data support for subsequent operating condition analysis, noise characteristic updates, and station operation status assessment.

[0022] Step S400: When multiple data packets arrive and cause signal collisions, the signal separation component deployed at the signal receiver is used to perform concurrent collision signal separation and verification on the superimposed signals using frequency domain energy feature clustering to determine the independently acquired signals.

[0023] Specifically, in scenarios where multiple devices simultaneously upload data packets, causing signal superposition conflicts at the receiving end, the pre-deployed signal separation component at the signal receiver is invoked to process the mixed superimposed signals. First, the mixed signals are despread and converted from the time domain to the frequency domain using a fast Fourier transform. The frequency position, energy amplitude, and time window segmentation ratio corresponding to each frequency domain peak are extracted to form a multi-dimensional feature vector. Using this multi-dimensional feature vector as the clustering basis, cluster analysis is performed on the frequency domain peaks of adjacent Chirp signals to obtain the corresponding peak sequence. After eliminating time and frequency offset interference, multiple independent signals are initially separated. Subsequently, the separated signals are subjected to dual verification. On the one hand, the signal-to-noise ratio of a single independent signal is calculated and compared with a preset threshold. For signals with insufficient signal-to-noise ratio, adaptive filtering or frequency modulation retransmission is initiated to eliminate residual interference. On the other hand, the superimposed waveform is reconstructed using the separated independent signals and compared with the original mixed superimposed signal. If the matching degree is lower than the preset threshold, a second clustering separation is triggered until the separation effect meets the standard. Finally, an interference-free, waveform-matched independent acquisition signal is output for system storage and use.

[0024] In one possible implementation, step S100 further includes: Step S110: Based on the operating status signals of the data acquisition device connected to the station equipment, monitor the changes in the logic level of the signals in real time. The operating status signals include pump and valve start / stop signals, pressure regulator action signals, and venting valve status signals.

[0025] Step S120: Based on the change in logic level, determine whether there are any precursor features of operating condition switching. If so, generate an operating condition switching command.

[0026] Step S130: According to the operating condition switching instruction, using the precursor features as an index, retrieve and load the target communication parameter combination into the communication channel from the anti-interference correlation map.

[0027] Specifically, the data acquisition unit continuously receives digital operating status signals from various field devices at the gas station through its built-in serial communication interface. These signals include pump and valve start / stop feedback signals, pressure regulator action trigger signals, and vent valve switch status signals. The acquisition unit's hardware circuit samples the levels of these multiple input signals in real time, continuously capturing the instantaneous transitions, durations, and timing combinations of high and low logic levels of each signal. It fully records the level switching time and stable level value of each status signal and synchronously caches the level timing data of all signals at each moment, providing original level characteristics for subsequent identification of early warning signs of operating condition switching and differentiation of different operating conditions at the gas station.

[0028] The system retrieves real-time cached logic level timing data of multiple signals, including pump and valve start / stop, pressure regulator operation, and vent valve status. It pre-configures level timing templates corresponding to various operating conditions as a precursor judgment benchmark. The system compares the real-time acquired level change timing with the preset templates frame by frame to identify precursor features that can indicate operating condition changes, such as sudden changes in single-channel signal level and simultaneous jumps in timing of multiple channels. When the real-time level timing matches any type of operating condition switching precursor template, it determines that the station is about to undergo an operating condition switch. It then generates an operating condition switch command carrying the current precursor feature code and the target operating condition type identifier and sends it to the parameter retrieval unit. If no precursor template is matched, it continuously monitors the logic level changes of each status signal in a loop.

[0029] The system receives and outputs a condition switching command carrying precursor feature codes and target condition identifiers. It extracts the precursor features stored in the command as a retrieval index, retrieves a pre-constructed anti-interference correlation map, and traverses various operating conditions, noise fingerprint features, and communication parameter matching entries stored in the map. It accurately matches the target communication parameter combination corresponding to the precursor feature, which consists of the preferred channel, spreading factor, transmit power, and bandwidth. After completing the parameter retrieval, the entire set of target communication parameter combinations is sent to the wireless communication hardware module to write the parameters overwrite to the current working communication channel, completing the initial parameter loading and configuration of the communication channel. This provides the reference communication parameters for subsequent channel interference level monitoring and physical layer adaptive control.

[0030] In one possible implementation, step S130 further includes: Step S131: Identify the operating condition type based on the status of each signal combination. The operating condition type includes normal transmission and distribution operating condition, peak shaving switching operating condition, venting operation operating condition, and equipment maintenance operating condition.

[0031] Step S132: Traverse the operating condition types, obtain electromagnetic noise spectrum data and perform operating condition clustering processing to obtain noise fingerprint features.

[0032] Step S133: Based on the operating condition type and noise fingerprint characteristics, configure a combination strategy to determine the anti-interference correlation spectrum between the operating condition and noise, wherein each combination strategy is a parameter combination based on the preferred channel, spreading factor, transmit power and bandwidth combination.

[0033] Specifically, the system reads the real-time logic level combination status of multiple status signals from pump and valve start / stop, pressure regulator operation, and venting valve, which are collected in real time by the data acquisition device. It then retrieves the standard level combination discrimination rules corresponding to various operating conditions pre-stored in the system and compares the current synchronous level status of multiple signals with the discrimination rules one by one: when the pumps and valves are running stably and the pressure regulator and venting valve have no change in operation level, it is determined to be a normal transmission and distribution condition; when the pressure regulator frequently generates level jumps and the matching pumps and valves synchronously adjust the start / stop level signals, it is determined to be a peak-shaving switching condition; when the venting valve generates a high-level opening action signal, it is determined to be a venting operation condition; when most pumps, valves, and pressure regulators in the entire station are in a low-level shutdown state, it is determined to be an equipment maintenance condition. By accurately classifying the current operating condition type of the station through the combination status of multiple equipment signal linkages, it provides a basis for subsequent acquisition of corresponding operating condition noise spectrum and construction of anti-interference correlation map.

[0034] The system sequentially traverses all operating conditions, including normal power transmission and distribution, peak shaving and switching, venting operations, and equipment maintenance. During the stable operation of each condition, it continuously collects raw electromagnetic noise spectrum data within the wireless communication frequency band, fully recording the noise energy amplitude, noise duration, and periodic interference fluctuation patterns corresponding to different frequency points. For each type of operating condition, multiple sets of noise spectrum samples are clustered to remove discrete and abnormal noise samples. Fixed spectral features such as unique frequency point distribution, energy peak range, and interference period are extracted for each type of operating condition. These spectral features that uniquely identify the noise of the corresponding operating condition are integrated and solidified into a noise fingerprint feature specific to that operating condition, which is used to subsequently build an anti-interference correlation map that corresponds one-to-one between operating conditions and noise spectra.

[0035] First, a one-to-one mapping and binding relationship is established between four operating condition types: normal transmission and distribution, peak shaving switching, venting operation, and equipment maintenance, and the unique noise fingerprint features obtained by matching each operating condition. For each set of bound operating conditions and noise fingerprints, multiple sets of different channel, spreading factor, transmit power, and bandwidth combinations are iterated to conduct communication performance simulations and field tests. Data transmission packet loss rate, channel signal-to-noise ratio, and data transmission delay are used as quantitative evaluation indicators. From these, the set of parameters with the best overall transmission performance is selected as the corresponding operating condition. A dedicated parameter combination strategy is implemented; then, using the operating condition identifier and noise fingerprint characteristics as the joint index primary key, the primary key is structured and associated with the corresponding preferred channel, spreading factor, transmit power, and bandwidth parameter combination strategy, and each entry is entered into the spectrum storage unit. After all operating condition pairing data is entered, an operating condition-noise anti-interference association spectrum that can be quickly retrieved and matched through operating condition precursor characteristics is generated. Each index entry in the spectrum completely saves the operating condition category, noise fingerprint characteristics, and a complete set of communication parameter combination information, enabling one-click retrieval of the appropriate communication configuration based on the operating condition characteristics.

[0036] In one possible implementation, step S200 further includes: Step S210: Using the target communication parameter combination as the initial configuration of the communication physical layer, monitor the received signal strength indication value and signal-to-noise ratio of the communication channel after the initial configuration, and evaluate the channel interference level in combination with the operating condition type.

[0037] Step S220: Based on the automatic adaptation module, evaluate the channel interference level and determine the dynamic adjustment parameters.

[0038] Step S230: Dynamically configure the communication physical layer of the communication channel according to the dynamic adjustment parameters.

[0039] Specifically, the target communication parameters, including preferred channel, spreading factor, transmit power, and bandwidth, obtained through anti-interference correlation map retrieval, are written into the wireless communication chip to complete the initial configuration of the communication physical layer. During the data acquisition cycle of the current site operating condition, two sets of quantized channel quality data—the real-time received signal strength indicator (RSSI) and the channel signal-to-noise ratio (SNR)—are continuously acquired. Simultaneously, the site operating condition type identified by the system in real time is retrieved, and the preset noise reference weighting coefficient α for that condition is retrieved; the stronger the interference, the larger the value of α. The original RSSI and SNR are first normalized. The signal strength value R and the signal-to-noise ratio value S are obtained by mapping the signal strength value to the 0-1 interval. Then, the comprehensive interference scoring formula is constructed: comprehensive interference value I = α × (1 − R) + (1 − α) × (1 − S). The higher the value of I, the more severe the channel interference. Multiple threshold intervals are pre-set and correspond one-to-one with the interference level. The standardized channel interference level is output by matching the comprehensive interference value I with the corresponding interval. This quantitatively characterizes the severity of electromagnetic noise interference on the current wireless channel and provides reliable quantitative input data for the automatic adaptation module to calculate adaptive dynamic control parameters.

[0040] The standardized channel interference level output is input into the pre-calibrated automatic adaptation module. The module pre-stores a mapping data table that corresponds one-to-one with the multi-level interference level, spreading factor level, and transmit power level. At the same time, it enters the low power consumption constraint threshold and the communication loss tolerance threshold as evaluation constraints. The module first reads the current interference level, matches the corresponding reference spreading factor and reference transmit power in the data table, and then performs a secondary evaluation and correction by combining the preset balancing logic: if the interference level is in the high interference range, the evaluation determines that the anti-interference capability needs to be enhanced, and a larger value of spreading factor and a higher level of transmit power are selected accordingly; if the interference level is in the low interference range, the evaluation determines that the transmission power consumption can be reduced and the air transmission time can be shortened, and a smaller value of spreading factor and a lower level of transmit power are selected accordingly. During the evaluation process, the parameter combination is simultaneously checked to see if it exceeds the device hardware limit and power consumption threshold, and out-of-limit combinations are eliminated. Finally, a set of spreading factor and transmit power that balances communication transmission reliability and terminal power consumption is output as dynamic adjustment parameters and sent to the communication physical layer configuration unit.

[0041] The system receives two sets of dynamically adjustable parameters—spreading factor and transmit power—and performs online real-time overwriting of the communication physical layer registers of the wireless communication chip during the continuous data acquisition cycle under the current operating conditions. This simultaneously updates the channel spread spectrum modulation parameters and the RF transmit power register values. If the dynamic adjustment parameters are set to a high spread spectrum factor and high transmit power, the signal spread spectrum gain is increased, and the RF output power is raised to enhance the wireless signal's anti-noise interference capability and prevent data packet loss caused by electromagnetic noise from the site. If the dynamic adjustment parameters are set to a low spread spectrum factor and low transmit power, the spread spectrum bandwidth is reduced, and the RF output power is lowered to shorten the signal transmission time and reduce the power consumption of the acquisition terminal's RF module. After the parameters are configured, the physical layer register values ​​are immediately read back to complete the configuration verification. Once the parameters are confirmed to be effective, site data sampling and transmission continue. Throughout the entire acquisition cycle without changing operating conditions, the physical layer parameters are iteratively updated according to the interference level, continuously maintaining a dynamic balance between wireless transmission anti-interference performance and low-power terminal operation.

[0042] In one possible implementation, step S220 further includes: Step S221: Calibrate the mapping relationship between the combination of spreading factors and transmit power and the communication success rate under different interference levels.

[0043] Step S222: Based on the mapping relationship, with the interference level as input and the minimum available spreading factor and optimal transmit power as output, deploy the automatic adaptation module.

[0044] Step S223: Wherein, the interference level is classified according to the combined threshold of the received signal strength indication value and the signal-to-noise ratio.

[0045] Specifically, for all channel interference levels defined by the system, a joint calibration process combining on-site testing and simulation was established. This involved iterating through all spread spectrum factor and transmit power levels supported by the equipment hardware and generating all parameter combinations. For a single fixed interference level, after selecting a set of spread spectrum factor and transmit power combinations, the acquisition terminal was controlled to continuously send a fixed number of test data packets, and the total number of data packets sent was counted. Number of valid received data packets verified by the receiving end Communication success rate according to formula Complete the quantitative calculation; repeat multiple rounds of tests for the same parameter combination under the current interference level and calculate the average value of multiple sets of success rates as the final calibration value. After traversing all parameter combinations under this interference level, switch to the next interference level and repeat all the test and calculation processes; finally, the four-element data of interference level number, spreading factor value, transmit power gear, and corresponding average communication success rate are entered item by item into the structured mapping data table and solidified and stored in the local storage unit of the automatic adaptation module, forming a quickly retrievable quantitative mapping relationship, which provides a complete calculation calibration benchmark for subsequently selecting the optimal regulation parameters according to the real-time interference level.

[0046] Call the calibrated mapping data table that stores the correspondence between interference levels, spreading factors, transmit power, and communication success rates, take the real-time output channel interference level as the only retrieval input condition, first filter the set of spreading factor and transmit power combinations that meet the preset minimum communication success rate threshold under this interference level, then preferentially match the available spreading factor with the smallest value from the filtered valid combinations, lock multiple sets of transmit power corresponding to the minimum spreading factor, and select the transmit power with the highest communication success rate as the optimal transmit power; encapsulate the entire logic of input interference level - filtering qualified parameter combinations - optimally matching the minimum spreading factor and the optimal transmit power into a program processing unit and solidify it for deployment in the automatic adaptation module. When the module is running, it can automatically execute the above filtering and optimization calculation process according to the real-time input interference level, and directly output a set of dynamic regulation parameters of spreading factor and transmit power that take into account transmission reliability, low latency and low power consumption, for the communication physical layer to complete adaptive configuration.

[0047] First, perform linear normalization processing on the collected Received Signal Strength Indication (RSSI) value and Signal-to-Noise Ratio (SNR) respectively, map the two types of indicators uniformly to the standard interval of 0 to 1 to obtain the normalized signal strength R and normalized signal-to-noise ratio S, then combine the noise weighting coefficient α corresponding to the current working condition and substitute it into the comprehensive interference calculation formula I=α×(1−R)+(1−α)×(1−S) to solve the comprehensive interference value I; the system presets multiple groups of continuous non-overlapping comprehensive interference threshold intervals, and each threshold interval uniquely corresponds to one interference level. For example, the first interval 0≤I<I1 is set as level 1 slight interference, I1≤I<I2 as level 2 mild interference, I2≤I<I3 as level 3 moderate interference, and I3≤I≤1 as level 4 severe interference. After calculating the real-time comprehensive interference value I, compare it with each segment threshold one by one, and match the threshold interval where I is located to determine the standardized interference level corresponding to the current channel. The whole process relies on the comprehensive quantitative thresholds of the two channel indicators RSSI and SNR to complete the classification judgment, so as to provide standardized classification input for the automatic adaptation module to optimize parameter regulation.

[0048] In one possible implementation, step S400 further includes: Step S410: Deploy a signal separation component at the signal receiving end of the data collector.

[0049] Step S420: When multiple acquisition data packets arrive at the signal receiving end and cause signal collisions, the signal separation component is activated to perform concurrent collision signal separation driven by frequency domain energy feature clustering to obtain independent acquisition signals.

[0050] Step S430: Transmit the independently acquired signal to the site database.

[0051] Specifically, a signal separation component is deployed in the signal receiving path between the RF demodulation module and the data decoding module of the gas station data acquisition unit. At the hardware level, the signal separation component, which includes a high-speed signal buffer unit, a fast Fourier transform unit, a frequency domain feature extraction unit, and a clustering operation unit, is serially connected to the signal transmission link and configured with an independent data interaction interface to achieve signal interoperability. At the software level, the component driver, signal scheduling logic, and frequency domain clustering separation algorithm are embedded in the acquisition unit's embedded system, and link control rules are set so that all demodulated RF signals must first be sent to the signal separation component for preprocessing, and mixed signals are prohibited from being directly transmitted to the back-end decoding module. This completes the hardware and software collaborative deployment of the signal separation component and establishes a pre-processing channel for concurrent conflict signals.

[0052] The system monitors the transmission status of the data acquisition unit's signal receiver in real time. When multiple terminal data packets arrive simultaneously, causing time-domain signal superposition conflicts, an activation command is immediately triggered to start the signal separation component, completely importing the multi-channel superimposed mixed signal into the component. First, standard despreading processing is performed on the mixed signal to remove spread spectrum modulation redundancy and restore the original baseband waveform. Then, the time-domain baseband signal is accurately converted into a frequency-domain signal using a fast Fourier transform. Based on a sliding time window, the frequency-domain signal is segmented, and the frequency point position, peak energy amplitude, and corresponding signal segment time window segment of each frequency peak are extracted segment by segment. The system generates a proportional three-dimensional multidimensional feature vector. Using this multidimensional feature vector as the core clustering basis, it calculates the Euclidean distance of the feature vector and matches a preset similarity threshold to accurately cluster and aggregate the frequency domain peaks of adjacent Chirp signals belonging to the same data packet. Noise and clutter peaks are filtered out and regularized to form multiple independent and continuous complete peak sequences. Finally, to address the time and frequency domain offset issues caused by window misalignment, timing calibration and frequency offset compensation are performed on each peak sequence to eliminate signal distortion deviations caused by window misalignment. Ultimately, multiple independent terminal acquisition signals that are non-overlapping, offset-free, and pure and complete are restored.

[0053] The multiple independently acquired signals output by the signal separation component are sequentially sent to the data decoding unit. The independent acquired signals are subjected to frame synchronization identification, CRC verification, and payload data parsing. Invalid signals that fail the verification are eliminated, and valid monitoring raw data such as water volume and equipment status are extracted. According to the preset data encapsulation rules, terminal number, acquisition time, operating condition mark, and channel quality label are added to generate standardized data messages. These messages are then pushed to the site database in an orderly manner through local bus transmission or wired / wireless transmission links. The database writes the data to the corresponding storage partition according to the data labels and establishes a time-series index to complete the persistent storage of the acquired data. This facilitates subsequent water resource service flow accounting, operating condition analysis, and ecological compensation calculation.

[0054] In one possible implementation, step S420 further includes: Step S421: Input the superimposed mixed signal into the signal separation component, perform despreading processing, perform fast Fourier transform on the despread signal to convert it to the frequency domain, and extract multidimensional feature vectors, wherein the multidimensional feature vectors are the frequency position of each frequency domain peak, the energy amplitude and the corresponding signal segment time window segmentation ratio.

[0055] Step S422: Using the multidimensional feature vector as the clustering feature, perform clustering analysis on the frequency domain peaks of adjacent Chirp signals of the same data packet to obtain the peak sequence.

[0056] Step S423: For each peak sequence at the separation point, eliminate the time and frequency offsets introduced by the unaligned window to obtain independently acquired signals.

[0057] Specifically, the superimposed mixed signal formed by the superposition of multiple signals is completely sent to the buffer unit of the signal separation component. The built-in despreading operation unit is called to perform relevant operations with the input mixed signal through the local synchronous spreading sequence to perform despreading processing, eliminate the spectral broadening effect caused by spreading modulation, and output the despread time-domain baseband signal. The baseband signal is segmented by a fixed-duration sliding time window. Fast Fourier transform is performed on each segment of the time-domain signal to convert the time-domain signal to the frequency domain space. An energy threshold is set to filter out noise and detect and identify effective frequency-domain peaks. The frequency position and frequency-domain energy amplitude corresponding to each effective peak are extracted one by one. Combined with the window segmentation ratio of the signal segment where the peak is located in the complete signal time sequence, the corresponding multi-dimensional feature vector is constructed by three sets of parameters. The multi-dimensional feature vectors of all effective frequency-domain peaks are summarized and output as the basic feature dataset for subsequent clustering analysis.

[0058] Using the extracted multidimensional feature vectors containing frequency point location, energy amplitude, and time window segmentation ratio as the clustering criteria, for all Chirp signal frequency domain peaks obtained after despreading, the Euclidean distance algorithm is used to calculate the spatial similarity distance between any two peak feature vectors one by one. The calculated feature distance is compared with the system's preset clustering threshold, and adjacent frequency domain peaks with feature distance less than the threshold and feature high matching are selected. They are determined to belong to the continuous Chirp signal components of the same terminal data packet, and the aggregation and classification of similar peaks are completed. At the same time, isolated clutter peaks with large feature dispersion and belonging to environmental interference noise are removed. Finally, all effective frequency domain peaks belonging to the same data packet are arranged in a regular manner according to the signal time sequence to form multiple complete, orderly and independent frequency domain peak sequences.

[0059] The offset calculation and signal correction are performed on each independent frequency domain peak sequence obtained after clustering. First, the time offset and frequency offset caused by the unaligned window truncation are calculated according to the time window segmentation ratio corresponding to each peak. The time offset is calculated as Δt=(1−r)×Tw, where r is the current signal segment window segmentation ratio and Tw is the fixed sliding window duration. The frequency offset is calculated as Δf=Δt×Fs / N, where Fs is the sampling frequency and N is the number of Fourier transform points in a single window. Based on the calculated Δt, the entire peak sequence is time-domain shifted and compensated. The sampling timing deviation caused by window misalignment is corrected, and the position of each peak frequency point and the spectral phase are corrected point by point according to Δf to offset the spectral drift error caused by the misaligned window. After completing the accurate time-frequency dual-dimensional correction, the effective spectral components are completed for each corrected frequency domain peak sequence, and residual noise from window truncation is filtered out. Then, an inverse fast Fourier transform is performed on each normalized frequency domain sequence to completely restore the frequency domain characteristics to a continuous time domain waveform. Finally, multiple independent acquisition signals with no time-frequency offset, no signal superposition, and complete and pure waveforms are output, realizing the accurate separation and restoration of conflicting and superimposed signals.

[0060] In one possible implementation, step S423 further includes: The signal-to-noise ratio of the independently acquired signal is determined and compared with a preset threshold. If it is less than the preset threshold, residual interference is determined to exist.

[0061] For independently acquired signals with residual interference, residual interference is eliminated by triggering adaptive filtering or frequency modulation retransmission.

[0062] If the value is greater than or equal to a preset threshold, the independently acquired signal is decoded and stored in the site database.

[0063] Specifically, the independently acquired signal is divided into an effective signal range and a noise-free range. The average power of the effective signal range and the average noise floor power of the noise range are extracted separately. The signal-to-noise ratio (SNR) is then quantitatively calculated using the formula SNR=10lg(Ps / Pn), where Ps is the average power of the effective signal and Pn is the average noise floor power. The real-time calculated SNR values ​​of each signal are compared with the system's preset SNR threshold. The threshold discrimination logic is strictly enforced. If the calculated SNR value of the independently acquired signal is less than the preset threshold, it is determined that the separated signal has not completely eliminated superimposed interference and has residual noise and interference problems, and the signal purity does not meet the standard. Conversely, if the SNR is greater than or equal to the preset threshold, it is determined that the signal separation is complete, there is no residual interference, and the signal quality meets the requirements for subsequent decoding and storage.

[0064] For independently acquired signals with residual interference and substandard signal-to-noise ratio (SNR), a dual-layer linkage residual interference elimination mechanism is activated. First, adaptive filtering and noise reduction are prioritized. By real-time acquisition of the spectral characteristics of the current residual interference, the weighting coefficients of the adaptive filter are dynamically updated iteratively to accurately match the distribution pattern of in-band residual noise. This effectively filters out incompletely stripped superimposed residual interference, random background noise, and waveform distortion components, completing noise reduction and repair in both the time and frequency domains. If, after one or more adaptive filtering processes, the signal SNR is still below the preset acceptable threshold, persistent fixed interference is determined to exist in the current fixed frequency band. The frequency modulation retransmission mechanism is immediately triggered, and the receiving end sends a channel switching command to the corresponding acquisition terminal. The terminal switches to a preset low-interference backup frequency to re-complete carrier modulation and data encapsulation, retransmitting the current failed frame data through a new channel. The receiving end then performs signal separation and SNR verification on the retransmitted data packets again, ultimately obtaining a high-quality independently acquired signal without residual interference and meeting the SNR standard, completely resolving the data distortion problem caused by incomplete signal separation.

[0065] If the signal-to-noise ratio calculated from the independently acquired signal is greater than or equal to a preset threshold, it is determined that the signal has no obvious residual interference and the signal quality meets the analysis requirements. The signal decoding process is then initiated, sequentially performing frame synchronization identification, carrier synchronization, and bit synchronization processing. Then, CRC cyclic redundancy check is performed on the signal payload. After the check passes, valid business data such as terminal number, acquisition time, water volume monitoring data, and operating condition information are extracted. Time sequence tags, channel identifiers, and signal quality markers are added according to preset data specifications to generate standardized structured data records. These records are transmitted to the site database via the internal data bus. The database partitions the data according to data type and establishes a time sequence index to complete the persistent storage of the acquired data, which can be used for subsequent water resource service flow calculations, ecological compensation analysis, and other business calls. If the cyclic redundancy check fails during the decoding process, the current signal is discarded and a transmission anomaly is marked.

[0066] In one possible implementation, step S423 further includes: Performing cross-validation on the independently acquired signal and the superimposed mixed signal includes: Based on the independently acquired signals, the superimposed waveform is reconstructed and determined.

[0067] The matching degree of the superimposed waveform and the superimposed mixed signal is analyzed. If the matching degree is lower than the preset value, it is determined that the separation is incomplete, and secondary clustering separation is triggered.

[0068] Specifically, signal cross-validation is performed using independently acquired signals and the original superimposed mixed signal. All independently acquired signals output after time-frequency offset correction are aggregated, and the sampling frequency and sampling duration of the original superimposed mixed signal are used as the benchmark to complete timing alignment and amplitude normalization calibration, eliminating timing misalignment and amplitude gain differences between the independent signals. After calibration, the amplitude of each aligned independent acquired signal is superimposed point by point at the time domain sampling point to obtain the reconstructed superimposed waveform formed by the superposition of each signal. This reconstructed superimposed waveform is a simulated mixed signal formed by the superposition of multiple signals, which is used to perform subsequent matching degree cross-validation with the original superimposed mixed signal at the receiving end.

[0069] The reconstructed superimposed waveform and the original superimposed mixed signal are made to have the same timing reference, sampling frequency, and sampling length. A one-to-one alignment of sampling points is achieved. The waveform matching degree is calculated using the normalized cross-correlation coefficient. The calculation formula is as follows: In the formula, x(n) is the original superimposed mixed signal sampling sequence, and y(n) is the reconstructed superimposed waveform sampling sequence. The values ​​correspond to the mean of two sets of signal sequences, where N is the total number of sampling points. This is the matching degree, and its value range is... The closer the value is to 1, the higher the degree of overlap between the two waveforms; the matching degree... If the matching degree is lower than the preset threshold, it indicates that there is component loss in each independently acquired signal after separation. The signal separation is determined to be incomplete. The system will send the original superimposed mixed signal back into the signal separation component, appropriately optimize the feature clustering distance threshold, adjust the time-frequency offset compensation parameters, and re-execute frequency domain feature extraction, clustering grouping and time-frequency correction to start the secondary clustering separation process.

[0070] The following experimental verification of this method is conducted using a real-world scenario at a city gas station. This gas station is equipped with pressure regulating equipment and booster units, and operates under three typical scenarios: normal pressure stabilization, booster unit startup, and multi-unit coordinated pressure regulation. Synchronous comparative tests were performed using a traditional fixed-parameter data acquisition scheme and the wireless anti-interference data acquisition method for gas stations under various operating conditions according to this invention. The test data are shown in Table 1, and the comparison results of data packet loss rates under different operating conditions are as follows: Figure 2 : Table 1. Performance comparison between this method and traditional fixed parameter acquisition scheme under different site conditions. ; From Table 1 and Figure 2 It can be seen that under the operating conditions of gas stations with different interference levels, this method can adaptively adjust communication parameters based on operating condition identification, and achieve the separation of concurrent superimposed signals by using frequency domain energy feature clustering. Compared with the traditional fixed parameter acquisition scheme, the communication signal-to-noise ratio is significantly improved and the data packet loss rate is significantly reduced. Even in the scenario of concurrent data packet collisions from multiple terminals, it can still maintain a high signal separation success rate, which fully demonstrates that this scheme can adapt to the complex electromagnetic environment of gas stations and has excellent anti-interference performance and data transmission stability.

[0071] Example 2 is based on the same inventive concept as the wireless anti-interference data acquisition method for gas stations under multiple operating conditions in the foregoing examples, such as... Figure 3 As shown, this application provides a wireless anti-interference data acquisition system for gas stations under multiple operating conditions. The system and method embodiments in this application are based on the same inventive concept. The system includes: The target communication parameter loading module 10 is used to access the operating status signal of the station equipment through the serial communication interface of the data acquisition unit, identify the operating condition type based on the signal combination status and establish an anti-interference spectrum associated with the operating condition and noise spectrum characteristics. When an early sign of operating condition switching is detected, the spectrum is retrieved using the early sign characteristics as an index and the target communication parameter combination is loaded into the communication channel.

[0072] The dynamic control parameter determination module 20 is used to monitor the channel received signal strength and signal-to-noise ratio to evaluate the interference level with the target communication parameter combination as the initial configuration, activate the automatic adaptation module, and dynamically adjust the spreading factor and transmit power of the communication channel according to the determined dynamic control parameters and the interference level.

[0073] The station database storage module 30 is used to perform data sampling and transmission management of the gas station after completing the dynamic configuration of the communication physical layer of the dynamic control parameters, and store the data in the station database.

[0074] The independent acquisition signal determination module 40 is used to determine the independent acquisition signal when multiple data packets arrive and cause signal collisions, based on the signal separation component deployed at the signal receiver, and using frequency domain energy feature clustering, to perform concurrent collision signal separation and verification on the superimposed signals.

[0075] Furthermore, the system is also used to implement the following functions: Based on the operating status signals of the data acquisition device connected to the station equipment, the logic level changes of the signals are monitored in real time. The operating status signals include pump and valve start / stop signals, pressure regulator action signals, and vent valve status signals. Based on the logic level changes, it is determined whether there are any precursor features of operating condition switching. If so, an operating condition switching command is generated. Based on the operating condition switching command, using the precursor features as an index, the target communication parameter combination is retrieved from the anti-interference correlation map and loaded into the communication channel.

[0076] Furthermore, the system is also used to implement the following functions: Based on the state of each signal combination, the operating condition type is identified, which includes normal transmission and distribution, peak shaving switching, venting operation, and equipment maintenance. The operating condition types are traversed to obtain electromagnetic noise spectrum data and perform operating condition clustering to obtain noise fingerprint features. Based on the operating condition type and noise fingerprint features, a combination strategy is configured to determine the anti-interference correlation spectrum between the operating condition and noise. Each combination strategy is a parameter combination based on the preferred channel, spreading factor, transmit power, and bandwidth.

[0077] Furthermore, the system is also used to implement the following functions: Using the target communication parameter combination as the initial configuration of the communication physical layer, the received signal strength indication value and signal-to-noise ratio of the communication channel after the initial configuration are monitored, and the channel interference level is evaluated in combination with the operating condition type; according to the automatic adaptation module, the channel interference level is evaluated, and dynamic adjustment parameters are determined; according to the dynamic adjustment parameters, the communication physical layer of the communication channel is dynamically configured.

[0078] Furthermore, the system is also used to implement the following functions: The mapping relationship between the spread spectrum factor and transmit power combination and the communication success rate under different interference levels is calibrated; based on the mapping relationship, the automatic adaptation module is deployed with the interference level as input and the minimum available spread spectrum factor and the optimal transmit power as output; wherein, the interference level is classified according to the comprehensive threshold of the received signal strength indication value and the signal-to-noise ratio.

[0079] Furthermore, the system is also used to implement the following functions: At the signal receiving end of the data acquisition device, a signal separation component is deployed; when multiple acquisition data packets arrive at the signal receiving end and cause signal conflicts, the signal separation component is activated to perform concurrent conflict signal separation driven by frequency domain energy feature clustering to obtain an independent acquisition signal; the independent acquisition signal is then transmitted to the site database.

[0080] Furthermore, the system is also used to implement the following functions: The superimposed mixed signal is input into the signal separation component for despreading processing. The despread signal is then converted to the frequency domain using a Fast Fourier Transform, and a multidimensional feature vector is extracted. This multidimensional feature vector represents the frequency position, energy amplitude, and corresponding signal segment time window segmentation ratio of each frequency domain peak. Using the multidimensional feature vector as clustering features, clustering analysis is performed on the frequency domain peaks of adjacent Chirp signals of the same data packet to obtain a peak sequence. For each peak sequence at the separation point, the time and frequency offsets introduced by the non-aligned window are eliminated to obtain independently acquired signals.

[0081] Furthermore, the system is also used to implement the following functions: The signal-to-noise ratio (SNR) of the independently acquired signal is determined and compared with a preset threshold. If it is less than the preset threshold, residual interference is determined to exist. For the independently acquired signal with residual interference, residual interference is eliminated by triggering adaptive filtering or frequency modulation retransmission. If it is greater than or equal to the preset threshold, the independently acquired signal is decoded and stored in the site database.

[0082] Furthermore, the system is also used to implement the following functions: Performing cross-validation on the independently acquired signal and the superimposed mixed signal includes: reconstructing and determining the superimposed waveform based on the independently acquired signal; performing a matching degree analysis on the superimposed waveform and the superimposed mixed signal; if the matching degree is lower than a preset value, it is determined that the separation is incomplete, and a secondary clustering separation is triggered.

[0083] It should be noted that the order of the embodiments described above is for descriptive purposes only and does not represent the superiority or inferiority of the embodiments. Specific embodiments of this specification have been described above. Furthermore, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0084] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0085] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application intends to include such modifications and variations.

Claims

1. A method for wireless anti-interference data acquisition under multiple operating conditions in a gas station, characterized in that, The method includes: The system accesses the operating status signals of the station equipment through the serial communication interface of the data acquisition unit, identifies the operating condition type based on the signal combination status, and establishes an anti-interference map that associates the operating condition with the noise spectrum characteristics. When an early sign of operating condition switching is detected, the map is retrieved using the early sign characteristics as an index, and the target communication parameter combination is loaded into the communication channel. Using the target communication parameter combination as the initial configuration, the channel received signal strength and signal-to-noise ratio are monitored to assess the interference level. The automatic adaptation module is activated, and the spreading factor and transmit power of the communication channel are dynamically adjusted according to the interference level based on the determined dynamic control parameters. After completing the dynamic configuration of the communication physical layer of the dynamic control parameters, the data sampling and transmission management of the gas station is performed and stored in the station database; When multiple data packets arrive and cause signal collisions, the signal separation component deployed at the signal receiver is used to perform concurrent collision signal separation and verification on the superimposed signals using frequency domain energy feature clustering to determine the independently acquired signal.

2. The wireless anti-interference data acquisition method for gas stations under multiple operating conditions as described in claim 1, characterized in that, When a precursor to a change in operating condition is detected, a spectrum retrieval is performed using the precursor features as an index, and the target communication parameter combination is loaded into the communication channel, including: Based on the operating status signals of the station equipment accessed by the data acquisition device, the logic level changes of the signals are monitored in real time. The operating status signals include pump and valve start / stop signals, pressure regulator action signals, and vent valve status signals. Based on the change in the logic level, determine whether there are any precursor features of the operating condition switching. If so, generate an operating condition switching command. According to the operating condition switching instruction, using the precursor features as an index, the target communication parameter combination is retrieved from the anti-interference correlation map and loaded into the communication channel.

3. The wireless anti-interference data acquisition method for gas stations under multiple operating conditions as described in claim 2, characterized in that, The anti-interference correlation map includes: Based on the status of each signal combination, the operating condition type is identified, which includes normal transmission and distribution operating condition, peak shaving switching operating condition, venting operation operating condition and equipment maintenance operating condition; By traversing the aforementioned operating conditions, electromagnetic noise spectrum data is obtained and operating condition clustering is performed to obtain noise fingerprint features. Based on the operating condition type and noise fingerprint characteristics, a combination strategy is configured to determine the anti-interference correlation spectrum between operating condition and noise. Each combination strategy is a parameter combination based on the preferred channel, spreading factor, transmit power and bandwidth.

4. The wireless anti-interference data acquisition method for gas stations under multiple operating conditions as described in claim 1, characterized in that, Dynamically adjusting the spreading factor and transmit power of the communication channel according to the interference level, including: Using the target communication parameter combination as the initial configuration of the communication physical layer, the received signal strength indication value and signal-to-noise ratio of the communication channel after the initial configuration are monitored, and the channel interference level is evaluated in combination with the operating condition type. The channel interference level is evaluated based on the automatic adaptation module, and dynamic adjustment parameters are determined. Based on the aforementioned dynamic adjustment parameters, the communication channel is dynamically configured at the physical layer.

5. The wireless anti-interference data acquisition method for gas stations under multiple operating conditions as described in claim 4, characterized in that, The automatic adaptation module includes: The mapping relationship between different spreading factors and transmit power combinations and communication success rate under different interference levels was determined. Based on the mapping relationship, with the interference level as input and the minimum available spreading factor and optimal transmit power as output, the automatic adaptation module is deployed; The interference level is classified based on a combined threshold of the received signal strength indication value and the signal-to-noise ratio.

6. The wireless anti-interference data acquisition method for gas stations under multiple operating conditions as described in claim 1, characterized in that, Perform concurrent collision signal separation and verification on superimposed signals, including: Deploy a signal separation component at the signal receiving end of the data acquisition unit; When multiple data packets arrive at the signal receiver and cause signal collisions, the signal separation component is activated to perform concurrent collision signal separation driven by frequency domain energy feature clustering, thereby obtaining an independent acquisition signal. The independently acquired signals are transmitted to the site database.

7. The wireless anti-interference data acquisition method for gas stations under multiple operating conditions as described in claim 6, characterized in that, Perform concurrent collision signal separation driven by frequency domain energy feature clustering to obtain independently acquired signals, including: The superimposed mixed signal is input into the signal separation component to perform despreading processing. The despread signal is converted to the frequency domain by fast Fourier transform and multidimensional feature vectors are extracted. The multidimensional feature vectors are the frequency position, energy amplitude and corresponding signal segment time window segmentation ratio of each frequency domain peak. Using the multidimensional feature vector as the clustering feature, clustering analysis is performed on the frequency domain peaks of adjacent Chirp signals in the same data packet to obtain the peak sequence; For each peak sequence at the separation point, the time and frequency offsets introduced by the unaligned window are eliminated to obtain independently acquired signals.

8. The wireless anti-interference data acquisition method for gas stations under multiple operating conditions as described in claim 7, characterized in that, After obtaining the independently acquired signal, the following are included: Determine the signal-to-noise ratio of the independently acquired signal and compare it with a preset threshold. If it is less than the preset threshold, it is determined that there is residual interference. For independently acquired signals with residual interference, residual interference is eliminated by triggering adaptive filtering or frequency modulation retransmission. If the value is greater than or equal to a preset threshold, the independently acquired signal is decoded and stored in the site database.

9. A wireless anti-interference data acquisition method for gas stations under multiple operating conditions as described in claim 7, characterized in that, After obtaining the independently acquired signal, the following are included: Performing cross-validation on the independently acquired signal and the superimposed mixed signal includes: Based on the independently acquired signals, the superimposed waveform is reconstructed and determined; The matching degree of the superimposed waveform and the superimposed mixed signal is analyzed. If the matching degree is lower than the preset value, it is determined that the separation is incomplete, and secondary clustering separation is triggered.

10. A wireless anti-interference data acquisition system for gas stations under multiple operating conditions, characterized in that, The system is used to implement the wireless anti-interference data acquisition method for gas stations under multiple operating conditions as described in any one of claims 1-9, and the system includes: The target communication parameter loading module is used to access the operating status signal of the station equipment through the serial communication interface of the data acquisition unit, identify the operating condition type based on the signal combination status and establish an anti-interference spectrum associated with the operating condition and noise spectrum characteristics. When an early sign of operating condition switching is detected, the spectrum is retrieved using the early sign characteristics as an index and the target communication parameter combination is loaded into the communication channel. The dynamic control parameter determination module is used to monitor the channel received signal strength and signal-to-noise ratio to evaluate the interference level with the target communication parameter combination as the initial configuration, activate the automatic adaptation module, and dynamically adjust the spreading factor and transmit power of the communication channel according to the interference level based on the determined dynamic control parameters. The station database storage module is used to perform data sampling and transmission management of the gas station after completing the dynamic configuration of the communication physical layer of the dynamic control parameters, and store the data in the station database. The independent acquisition signal determination module is used to determine the independent acquisition signal when multiple data packets arrive and cause signal collisions. Based on the signal separation component deployed at the signal receiver, the module uses frequency domain energy feature clustering to perform concurrent collision signal separation and verification on the superimposed signals.

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