High-precision wireless synchronization and communication method of Beidou base station

Abstract

The invention relates to a high-precision wireless synchronization and communication method of a Beidou base station, and belongs to the technical field of Beidou base station synchronous communication. According to the method, a central control platform, a Beidou base station and a scene feature acquisition unit are deployed, a star communication network is constructed, and a scene feature acquisition sensor acquires environmental parameters in real time to generate scene sensing data; based on a symmetric bidirectional data interaction mechanism, dynamically adapting a timestamp data packet sending period by combining scene perception data, and calculating a round-trip timestamp difference value; a two-way time-frequency collaborative calibration algorithm is combined with scene perception data to optimize a weight factor, and a dynamic time delay fluctuation quantity and a clock deviation quantity are separated and corrected; according to the time-frequency phase closed-loop calibration technology, a frequency deviation data set is periodically exchanged, a phase adjustment instruction is generated through a dynamic frequency drift model, the frequency stability of an oscillator is corrected through a digital phase-locked loop, and a closed-loop control link is formed. The method can improve the wireless synchronization precision and communication stability of the Beidou base station, adapts to complex environment changes, and has high practicability and reliability.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of Beidou base station synchronous communication, and particularly relates to a high-precision wireless synchronization and communication method for a Beidou base station. BACKGROUND

[0002] In the application of the Beidou navigation system, high-precision time synchronization and stable communication between base stations are the core links to guarantee positioning accuracy and service reliability. At present, the Beidou base station mostly adopts a star-type communication network architecture, and relies on wireless links to realize data interaction and clock synchronization. However, in a complex environment, the synchronization accuracy and communication stability face multiple challenges: the wireless channel is easily affected by environmental factors such as electromagnetic interference and multipath effect, resulting in dynamic fluctuations in transmission delay. Traditional synchronization algorithms use fixed weight factors to process error sources, which are difficult to adapt to real-time changes in environmental parameters, resulting in insufficient clock bias separation accuracy and large synchronization errors. The frequency stability of the local oscillator of the Beidou base station is easily disturbed by factors such as temperature drift and power supply noise. Existing calibration techniques mostly use static compensation strategies, lack a dynamic correction mechanism based on scene perception, and are difficult to maintain a long-term high-frequency stable synchronization state. At the same time, the existing encryption transmission method lacks sufficient protection for key information such as frequency deviation data sets, and the closed-loop control link often leads to high system energy consumption due to undifferentiated adjustment. These problems further restrict the application efficiency of the Beidou base station in complex scenarios, and therefore a high-precision wireless synchronization and communication method that can combine scene perception to dynamically optimize synchronization parameters and improve anti-interference ability and frequency stability is needed. SUMMARY

[0003] To solve the above problems in the prior art, the application provides a high-precision wireless synchronization and communication method for a Beidou base station. The purpose of the application can be achieved by the following technical solutions: A high-precision wireless synchronization and communication method for a Beidou base station, comprising the following steps: S1: deploying a central control platform and a Beidou base station, the central control platform and the Beidou base station forming a star-type communication network based on wireless links, and a scene feature acquisition sensor acquiring environmental parameters in real time to generate scene perception data; S2: based on a symmetric bidirectional data interaction mechanism, dynamically adapting the timestamp data packet sending period in combination with the scene perception data, the central control platform sending timestamp data packets carrying time stamps at the adjusted period, the Beidou base station receiving and recording the local receiving time after synchronization, immediately calculating the one-way transmission time delay and generating a response packet including the time delay parameter and the base station local clock information to feed back to the central control platform, and generating a round-trip timestamp difference; S3: Based on the round-trip timestamp difference, the two-way time-frequency collaborative calibration algorithm and the scene perception data dynamically optimize the multipath effect level weight factor and the electromagnetic interference intensity weight factor, and separate and correct the dynamic time delay fluctuation of the wireless channel and the clock bias at the receiving and transmitting ends in the high-precision time-frequency synchronization solution process. S4: Based on the high-precision time-frequency synchronization solution, the dynamic correction of the frequency stability of the Beidou base station local oscillator is realized based on the time-frequency phase closed-loop calibration technology: the central control platform and the Beidou base station periodically exchange frequency deviation data sets including temperature drift coefficient and power supply noise parameters through the wireless link, the central control platform combines the scene perception data to build a dynamic frequency drift model, and generates a phase adjustment instruction adapted to the current environment; after receiving the instruction, the Beidou base station adjusts the phase of the output signal of the local oscillator based on the digital phase-locked loop, forming a closed-loop control link of "deviation detection-instruction generation-phase calibration-deviation re-detection".

[0004] Specifically, the data preprocessing of the scene feature acquisition sensor in S1 is based on the extended Kalman filter algorithm, which denoises the original environmental data by establishing an environmental parameter dynamic model, and the state transition matrix and the observation matrix are generated based on historical environmental data offline training; the data preprocessing filters periodic interference through an adaptive noise cancellation algorithm, removes outliers through sensor data association verification, maps discrete parameters to structured scene perception data, and forms a complete processing link from original signal acquisition to effective information extraction.

[0005] Specifically, the scene perception data in S2 is used to dynamically adapt the specific way of timestamp data packet sending period: the scene feature acquisition sensor outputs scene perception data in a predetermined format based on real-time collected environmental parameters, and the central control platform receives the scene perception data, analyzes the environmental stability level, and outputs the timestamp data packet sending period; at the same time, the scene feature acquisition sensor will monitor the mutation of the environmental parameters in real time, and detect the mutation of the environmental parameters to trigger an abnormal event.

[0006] Specifically, the Beidou base station calculates the one-way transmission time delay in the following way: based on the difference between the time stamp in the received timestamp data packet and the local receiving time, deducting a predetermined fixed transmission loss value, the fixed transmission loss value is the basic attenuation of wireless signals in a standard environment.

[0007] Specifically, the rule for dynamically optimizing the multipath effect level weight factor is: the multipath effect level is divided into levels according to the signal reflection intensity, and for each level, the corresponding weight factor is increased by a predetermined proportion, thereby enhancing the correction strength of the dynamic time delay fluctuation of the wireless channel.

[0008] Specifically, when dynamically optimizing the electromagnetic interference intensity weight factor, the weight is adaptively adjusted based on a fuzzy control algorithm: taking the electromagnetic interference intensity and the interference change rate as fuzzy inputs, the electromagnetic interference intensity weight factor as a fuzzy output, establishing a triangular membership function, realizing nonlinear mapping from input to output through a fuzzy rule base, and outputting the weight value after defuzzification.

[0009] Specifically, the periodic exchange of the frequency deviation dataset uses a dedicated communication subchannel, which is independent of the time synchronization signal transmission channel.

[0010] Specifically, the dynamic frequency drift model is based on an adaptive heterogeneous fusion architecture, including an analytical submodel based on physical mechanisms and a data-driven statistical submodel; the analytical submodel establishes a physical equation architecture of temperature and humidity parameters and the change amount of the equivalent resistance and capacitance of the oscillator core component, to obtain a theoretical frequency drift amount; the statistical submodel takes the environmental parameter sequence in the historical scene perception data and the frequency drift value at the corresponding time as samples, fits a nonlinear mapping relationship, and outputs a data-driven prediction value; the statistical submodel sets a dynamic weight distributor, judges the interference complexity through the electromagnetic interference spectrum entropy value, and the entropy value is positively correlated with the weight proportion of the statistical submodel and negatively correlated with the weight proportion of the analytical submodel, and the theoretical frequency drift amount and the data-driven prediction value are fused and calculated to obtain a dynamic frequency drift value; the dynamic frequency drift value is smoothed by a filtering layer to filter out high-frequency noise interference, to obtain a deviation rate from the actual drift value, and when the deviation rate exceeds a preset threshold, the incremental training of the statistical submodel is triggered.

[0011] Specifically, in S4, the digital phase-locked loop is based on a cooperative linkage mechanism, and the phase difference data output by the phase detector is fed back to the loop filter in real time, and the loop filter dynamically adjusts the filtering parameters according to the interference characteristics in the scene perception data, so that the phase fine-tuning precision and response speed of the voltage-controlled oscillator adapt to the current environment.

[0012] Specifically, the precursor feature pre-judgment process of the closed-loop control link includes: a precursor feature extraction layer analyzes the scene perception data in real time, identifies specific frequency band signals in the electromagnetic interference that are strongly related to frequency drift, captures the step change characteristics in the temperature and humidity data, and compares with a preset warning threshold, when reaching the threshold, the control link switches from the dormant state to the standby state, and activates the phase adjustment execution process; the predicted trend and the current deviation change rate output by the dynamic frequency drift model are received, and a comprehensive drift index is obtained by weighted calculation, which is used for the execution decision of the phase adjustment execution process.

[0013] The beneficial effects of the present application are: Significantly improved synchronization accuracy: By calculating the round-trip timestamp difference through a symmetrical bidirectional data interaction mechanism and combining it with a two-way time-frequency collaborative calibration algorithm, the dynamic delay fluctuation of the wireless channel and the clock deviation between the transmitting and receiving ends can be accurately separated and corrected, effectively reducing the impact of multipath effects and electromagnetic interference on synchronization. Simultaneously, the time-frequency phase closed-loop calibration technology further reduces synchronization errors caused by frequency drift by dynamically adjusting the frequency stability of the local oscillator, enabling the wireless synchronization accuracy of BeiDou base stations to reach a higher level and meet the needs of high-precision application scenarios. Enhancing environmental adaptability: Scene feature acquisition sensors collect environmental parameters in real time to generate scene perception data. This data is applied to multiple stages, including dynamically adapting the timestamp data packet transmission cycle, optimizing the weighting factors for multipath effects and electromagnetic interference intensity, and constructing a dynamic frequency drift model. This enables the method to make timely adjustments according to environmental changes, maintaining good synchronization and communication performance even in complex and variable electromagnetic environments and under conditions of fluctuating temperature and humidity, demonstrating strong environmental robustness.

[0014] Improving communication stability and efficiency: A dedicated communication sub-channel is used for the periodic exchange of frequency deviation data sets, independent of the time synchronization signal transmission channel. This avoids mutual interference between data transmissions and ensures reliable transmission of critical data. Simultaneously, dynamically adapting the timestamp data packet transmission cycle reduces data transmission volume to conserve resources in stable environments and increases the transmission frequency to maintain synchronization accuracy in unstable environments, thus optimizing the allocation of communication resources and improving communication efficiency. Enhancing reliability and ease of maintenance: The closed-loop control chain of "deviation detection - command generation - phase calibration - deviation re-detection" forms a continuous dynamic correction mechanism that can promptly detect and correct system deviations, reduce the probability of system failure, and improve system reliability. Furthermore, the entire method operates with a degree of automation and intelligence, reducing the need for manual intervention and lowering maintenance difficulty and costs. Attached Figure Description

[0015] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0016] Figure 1 This is a flowchart of a high-precision wireless synchronization and communication method for a Beidou base station according to the present invention; Figure 2 This is a structural diagram of a high-precision wireless synchronization and communication method for a Beidou base station according to the present invention; Detailed Implementation

[0017] To further illustrate the technical means and effects of the present invention in achieving the intended purpose, the following detailed description of the specific implementation methods, structures, features and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided.

[0018] Please see Figure 1 A high-precision wireless synchronization and communication method for BeiDou base stations, comprising the following steps: S1: Deploy a central control platform and a Beidou base station. The central control platform and the Beidou base station form a star-shaped communication network based on a wireless link. Scene feature acquisition sensors collect environmental parameters in real time to generate scene perception data. S2: Based on the symmetrical bidirectional data interaction mechanism, combined with scene perception data, the time stamp data packet sending cycle is dynamically adapted. The central control platform sends the time stamp data packet with the time stamp according to the adjusted cycle. After receiving it, the Beidou base station synchronously records the local reception time, calculates the one-way transmission delay in real time, and generates a response packet including the delay parameter and the base station's local clock information. It then feeds the response packet back to the central control platform and generates the round-trip timestamp difference. S3: Based on the round-trip timestamp difference, the multipath effect level weight factor and electromagnetic interference intensity weight factor are dynamically optimized through a two-way time-frequency collaborative calibration algorithm and scene-aware data. In the high-precision time-frequency synchronization calculation process, the dynamic delay fluctuation of the wireless channel and the clock deviation at both ends of the transmitting and receiving ends are separated and corrected. S4: Based on high-precision time-frequency synchronization calculation, dynamic correction of the frequency stability of the local oscillator of the Beidou base station is achieved based on time-frequency phase closed-loop calibration technology: The central control platform and the Beidou base station periodically exchange frequency deviation datasets, including temperature drift coefficients and power supply noise parameters, through a wireless link. The central control platform combines scene perception data to construct a dynamic frequency drift model and generate phase adjustment commands adapted to the current environment. After receiving the commands, the Beidou base station performs phase fine-tuning on the output signal of the local oscillator based on a digital phase-locked loop, forming a closed-loop control link of "deviation detection - command generation - phase calibration - deviation re-detection".

[0019] Specifically, the data preprocessing of the scene feature acquisition sensors is based on the extended Kalman filter algorithm. A dynamic environmental parameter model is established to reduce noise in the raw environmental data. The state transition matrix and observation matrix are generated offline based on historical environmental data, thus improving the signal-to-noise ratio of the scene perception data. An environmental sensor array is deployed around the BeiDou base station. Temperature sensors monitor the operating temperature of the base station equipment in real time, humidity sensors detect changes in air humidity, electromagnetic field strength detectors capture the intensity of surrounding wireless signal interference, and a multipath effect analysis mechanism evaluates channel quality based on signal reflection path analysis. The collected raw data is filtered by moving average to remove abnormal fluctuations. Then, normalization processing converts parameters such as temperature, humidity, and electromagnetic interference intensity into a numerical matrix with unified dimensions, ultimately generating scene perception data containing multi-dimensional environmental features. This provides input for the central control platform to dynamically adjust the timestamp transmission cycle and optimize the synchronization algorithm.

[0020] Specifically, the scene perception data is used to dynamically adapt the timestamp data packet sending cycle in the following way: the scene feature acquisition sensor outputs scene perception data in a preset format according to the environmental parameters collected in real time. After receiving the scene perception data, the central control platform analyzes the environmental stability level and outputs the timestamp data packet sending cycle. At the same time, the scene feature acquisition sensor will monitor the sudden changes in the environmental parameters in real time. If the sudden change in the environmental parameters is detected, an abnormal event is triggered.

[0021] The periodic adjustment model receives environmental parameter data generated by the scene feature acquisition sensors in real time. When the detected electromagnetic interference intensity reaches the high interference range or the multipath effect level rises to a level with high reflection intensity, the periodic adjustment model triggers a transmission period shortening mechanism. This increases the transmission frequency of timestamp data packets to improve the interaction response speed between the central control platform and the Beidou base station, thereby reducing the impact of dynamic delay fluctuations on synchronization accuracy. Conversely, when environmental parameters show that the electromagnetic interference intensity falls back to the low interference range or the multipath effect level decreases, the model automatically extends the transmission period to reduce the communication load of the wireless link and avoid channel congestion caused by frequent data interaction. For example, in scenarios where the electromagnetic interference intensity is below a preset threshold, the transmission period can be restored to the initial set value or further extended to 150 milliseconds, achieving a balance between transmission efficiency and synchronization accuracy.

[0022] Specifically, the BeiDou base station calculates the one-way transmission delay by subtracting a preset fixed transmission loss value from the difference between the nanosecond-level timestamp in the received timestamp data packet and the local reception time. The fixed transmission loss value is the basic attenuation of the wireless signal under standard conditions.

[0023] When the BeiDou base station receives a timestamp data packet carrying a nanosecond-level time stamp, it immediately triggers a hardware interrupt to record the reception time using its local clock. Subsequently, the difference between the received time and the timestamp is calculated to obtain the original transmission time difference. At this point, the original difference is corrected using a fixed transmission loss value pre-stored in the base station. For example, if the baseline attenuation measured under standard conditions is 5 nanoseconds, this value is subtracted from the original difference to obtain the actual one-way transmission delay. This calculation method effectively eliminates the interference of inherent propagation loss of the wireless signal on delay measurement, allowing the subsequent clock deviation calculation process to only handle fluctuations caused by dynamic environmental factors.

[0024] Specifically, the rule for dynamically optimizing the weight factor of the multipath effect level is as follows: the multipath effect level is divided into levels according to the signal reflection intensity. For each level increase, the corresponding weight factor increases by a preset ratio to enhance the correction of dynamic delay fluctuations in the wireless channel.

[0025] In the process of dynamic delay fluctuation correction in the wireless channel, the current multipath effect level is first determined by real-time detection of signal reflection intensity. For example, if the reflection intensity exceeds a first threshold, it is determined to be level one; if it exceeds a second threshold, it is determined to be level two. Subsequently, the weighting factor is adjusted according to the level change; for example, when the level increases from level two to level three, the weighting factor increases from 0.3 to 0.45. This dynamic weight is input to the high-precision time-frequency synchronization calculation module. In the process of separating transmission delay fluctuations from clock skew, increasing the weighting factor makes the algorithm focus more on correcting the delay fluctuation component caused by multipath effects.

[0026] Specifically, when dynamically optimizing the electromagnetic interference intensity weight factor, the weight is adaptively adjusted based on a fuzzy control algorithm: with the electromagnetic interference intensity and the rate of change of interference as fuzzy inputs, and the electromagnetic interference intensity weight factor as a fuzzy output, a triangular membership function is established, and a nonlinear mapping from input to output is achieved through a fuzzy rule base, and the weight value is output after defuzzification.

[0027] During the wireless synchronization process of the BeiDou base station, when the fuzzy control algorithm dynamically optimizes the electromagnetic interference intensity weight factor, it first converts the real-time collected electromagnetic interference intensity and interference change rate into fuzzy universe values ​​according to a preset quantization level. Then, it establishes triangular membership functions for each level of both to calculate membership values. Next, it performs rule matching and calculates the trigger intensity based on the fuzzy rule library. Subsequently, it aggregates the output results of all triggered rules to obtain the overall membership distribution of the weight factor in the fuzzy universe. Finally, it uses a centroid method and other defuzzification algorithms to convert it into accurate weight values, thereby achieving adaptive adjustment of the weights to correct the impact of electromagnetic interference on the wireless channel.

[0028] Specifically, the periodic exchange of the frequency deviation dataset employs a dedicated communication sub-channel, which is independent of the time synchronization signal transmission channel. During communication between the BeiDou base station and the central control platform, the frequency deviation dataset is periodically exchanged via this dedicated communication sub-channel. This sub-channel is isolated from the time synchronization signal transmission channel at the physical or protocol layer, for example, by using different carrier frequency bands or independent communication protocol stacks. The frequency deviation dataset contains critical information affecting the stability of the local oscillator, such as temperature drift coefficients and power supply noise parameters. Transmission via a dedicated sub-channel avoids resource contention issues that arise when sharing a channel with timestamp data packets. During data transmission, the dedicated sub-channel can employ encryption protocols to protect the frequency deviation dataset, such as a data encryption mechanism based on the AES algorithm. Simultaneously, channel isolation reduces the risk of external interference intruding into critical data.

[0029] Specifically, the dynamic frequency drift model is based on an adaptive heterogeneous fusion architecture, including an analytical sub-model based on physical mechanisms and a data-driven statistical sub-model. The analytical sub-model establishes a physical equation framework for the core components of the oscillator, relating temperature and humidity parameters to changes in the equivalent resistance and capacitance of the components, to obtain the theoretical frequency drift. The statistical sub-model uses environmental parameter sequences from historical scene perception data and corresponding frequency drift values ​​as samples, fits a nonlinear mapping relationship, and outputs data-driven predicted values. The statistical sub-model sets up a dynamic weight allocator, which judges the interference complexity by the entropy value of the electromagnetic interference spectrum. The entropy value is positively correlated with the weight ratio of the statistical sub-model and negatively correlated with the weight ratio of the analytical sub-model. The theoretical frequency drift and the data-driven predicted values ​​are fused to calculate the dynamic frequency drift value. The dynamic frequency drift value is smoothed by a filtering layer to remove high-frequency noise interference, resulting in a deviation rate from the actual drift value. When the deviation rate exceeds a preset threshold, incremental training of the statistical sub-model is triggered.

[0030] Specifically, the digital phase-locked loop (PLL) includes a phase detector, a loop filter, and a voltage-controlled oscillator (VCO). The phase detector compares the phase difference between the local oscillator output signal and the phase adjustment command. After processing by the loop filter, the VCO drives the VCO to achieve phase fine-tuning. The phase detector continuously monitors the phase difference between the local oscillator output signal and the phase adjustment command issued by the central control platform, transmits the error signal to the loop filter for noise reduction, and then drives the VCO to fine-tune the output signal. The closed-loop control link adaptively adjusts the generation frequency of the phase adjustment command based on the comparison between the real-time frequency deviation and the dynamic adjustment threshold: when the deviation is below the threshold, the number of command generations is reduced to lower resource consumption; when the deviation exceeds the threshold, the command generation frequency is increased to quickly restore synchronization accuracy.

[0031] Specifically, the precursor feature prediction process of the closed-loop control link includes: the precursor feature extraction mechanism performs real-time analysis of the scene perception data, identifies specific frequency band signals strongly correlated with frequency drift in electromagnetic interference, captures the step-like change features in temperature and humidity data, compares them with a preset warning threshold, and when the threshold is reached, the control link switches from a dormant state to a standby state, activating the phase adjustment execution process; it receives the predicted trend and the current deviation change rate output by the dynamic frequency drift model, and calculates the comprehensive drift index by weighting the two, which is used for the execution decision of the phase adjustment execution process.

[0032] During the phase calibration process of the local oscillator of the BeiDou base station, the generation frequency of phase adjustment commands is dynamically adjusted by monitoring the comparison between the frequency deviation value and the dynamic adjustment threshold in real time. When the detected frequency deviation is lower than the dynamic adjustment threshold, it indicates that the clock synchronization state is in a stable range. At this time, the command generation interval is extended or the number of triggers is reduced to reduce the computational load of the control link. When the frequency deviation exceeds the dynamic adjustment threshold, it indicates that the clock synchronization state is experiencing abnormal fluctuations. At this time, the command generation interval is shortened or the number of triggers is increased to improve the response speed of the control link. This mechanism optimizes system resource allocation while maintaining synchronization accuracy through differentiated adjustment strategies.

[0033] In this embodiment, a central urban business district is selected as the application scenario. This area has dense high-rise buildings (average building height of 35 meters) and a complex electromagnetic environment (with various interference sources such as base stations and subway communication systems in the vicinity), placing extremely high demands on the synchronization accuracy and communication stability of the BeiDou base stations. One central control platform, 10 BeiDou base stations (numbered B1-B10), and 11 scene feature acquisition sensors are deployed to form a star-shaped communication network with a coverage radius of 2 kilometers, primarily serving intelligent transportation positioning and emergency communication in this area.

[0034] Central control platform: It adopts an industrial-grade server, equipped with an Intel Xeon E5 processor and 16GB of memory. It is deployed at the top of the signal tower in the regional center and has a dual-channel wireless communication module, supporting 2.4GHz and 5.8GHz frequency bands, with a communication coverage radius of ≥2 kilometers.

[0035] Beidou base station: BD-2 / B3 frequency receiving module is selected, the clock source is a temperature-controlled crystal oscillator, each base station is equipped with 3 directional antennas (pointing to the central control platform and the adjacent base station respectively), and the transmission power can be adjusted in the range of 10-30dBm.

[0036] Scene feature acquisition sensor: One sensor is deployed around the central control platform and Beidou base station, including: Electromagnetic interference sensor (monitoring frequency band 800MHz-6GHz, measurement accuracy ±1dBm); Multipath effect detector (sampling rate 1MHz, capable of identifying more than 3 reflection paths); Temperature and humidity sensor (temperature measurement range -40℃~85℃, accuracy ±0.5℃; humidity measurement range 0~100%RH, accuracy ±3%RH); Data preprocessing (using a 32-bit MCU, generating one set of scene perception data per second).

[0037] Symmetrical two-way data interaction process: Periodic adaptation logic: Initial values ​​of scene perception data: electromagnetic interference intensity -85dBm (low interference), multipath effect level 1 (reflected signal accounts for 10%), trigger cycle adjustment model output base cycle 20ms.

[0038] At 10:00 AM, electromagnetic interference during the morning rush hour was detected to have increased to -72 dBm, and the multipath effect level rose to level 2. The correction factor calculated by the periodic adjustment model is as follows: Correction factor k = 1 - 0.01 × (interference level + multipath level) = 1 - 0.01 × 3 = 0.97, adjusted period = 20ms × 0.97 = 19.4ms; At 12:30, a sudden electromagnetic interference reached -68dBm, with a multipath effect level of 3. The correction factor k = 1 - 0.01 × 5 = 0.95, and the period shortened to 19ms. Timestamp interaction process: Central control platform =1620000000.000000000s (nanosecond-level time stamp) Send timestamp data packet, including platform identifier, sending time and checksum; Base station B1 at local time =1620000000.000000045s received, the one-way transmission delay is calculated to be 45ns (after deducting the fixed loss of 30ns under standard conditions, the actual effective delay is 15ns). Base station B1 generates a response packet (including a 15ns delay and local clock information). ),At +100ns transmission, central control platform at =1620000000.000000188s received, generates round-trip timestamp difference. =188-100-45=43ns.

[0039] Two-way time-frequency coordinated calibration execution: Dynamic optimization of weighting factors: Based on scene-aware data, the weighting factor corresponding to multipath effect level 3 is... =1.8, weighting factor corresponding to electromagnetic interference intensity -68dBm =1.4.

[0040] Error separation calculation: Known =45ns (forward propagation delay) =38ns (reverse propagation delay) =30ns (reference delay); Dynamic delay fluctuation Δ = ×( - =1.8 × (45 - 30) = 27 ns; The difference between the round-trip timestamps is ΔT = |45 - 38 + 2Δt| = 7 + 2Δt = 43 ns, which gives Δt = 18 ns. Clock deviation correction: Δt'= ×(ΔT-Δ ) / 2 = 1.4 × (43 - 27) / 2 = 11.2 ns; Final separation results: dynamic delay fluctuation ±12ns (27ns-15ns compensation), transmit / receive clock deviation ±5ns (11.2ns rounded).

[0041] Synchronization error control: After calibration, the synchronization error between base station B1 and the central control platform is 2.8ns, which is lower than the preset threshold of 5ns, meeting the requirements for high-precision synchronization.

[0042] Time-frequency phase closed-loop calibration implementation: Frequency deviation dataset interaction: At 0:00, 6:00, 12:00, and 18:00 daily, the base station and the central control platform exchange data sets via a dedicated 5.8GHz sub-channel (encrypted transmission, AES-256 encryption). Base station B1 temperature drift coefficient: 0.002ppm / ℃, power supply noise parameter: 1.2mV; Historical frequency deviation data: In the past hour, 35℃ corresponds to an 8Hz deviation, and 30℃ corresponds to a 5Hz deviation.

[0043] Dynamic frequency drift model construction: A binary linear regression model f=αT+βV+γ is used, and fitted using the least squares method: α = 0.5 Hz / ℃ (temperature coefficient), β = 5 Hz / mV (noise figure), γ = -10 Hz (constant term); Given an ambient temperature of 35℃ and noise level of 1.2mV, calculate the current frequency deviation as f = 0.5 × 35 + 5 × 1.2 - 10 = 13.5 Hz. Sliding window prediction (window size 5 minutes): The temperature will rise to 36℃ in the next 2 minutes, and the prediction deviation is 13.5 + 0.5 × 1 = 14 Hz.

[0044] Phase fine-tuning execution: The central control platform generates a phase adjustment command Δφ=k×f=0.0014° / Hz×14Hz=0.0196°≈0.02°; Digital phase-locked loop (PLL) working process: Phase detection: The phase difference between the local oscillator signal and the command is Δθ = 0.03°; The PI algorithm calculates the control quantity U = 0.5 × 0.03 + 0.1 × ∫0.03dt = 0.015 + 0.005 = 0.02; Voltage-controlled oscillator adjustment: Based on the 0.02° phase compensation of the U output, the deviation was reduced to 0.008° after correction; Closed-loop control adjustment: The dynamic adjustment threshold is set to ±5Hz. When the frequency deviation is detected to be stable at 3Hz for 30 seconds, the frequency of phase adjustment command generation is reduced from 1 time / second to 1 time / 10 seconds. According to power consumption monitoring, the base station energy consumption is reduced by 32% in this mode.

[0045] Implementation effect verification: Continuous 72-hour operation monitoring data shows: Synchronization accuracy: The average synchronization error of 10 base stations is 2.5ns, and the maximum error is 3.2ns, which is 79.2% higher than the traditional method (average 12ns). Frequency stability: The local oscillator frequency deviation is consistently controlled within ±2.8Hz, a 65% improvement over static compensation (±8Hz); Communication reliability: Data transmission success rate of 99.1%, an improvement of 16.6% compared to traditional methods (85%); Environmental adaptability: It can work stably within the range of electromagnetic interference intensity -65~-85dBm and multipath effect level 1-3.

[0046] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A high-precision wireless synchronization and communication method for a BeiDou base station, characterized in that, Includes the following steps: S1: Deploy a central control platform and a Beidou base station. The central control platform and the Beidou base station form a star-shaped communication network based on a wireless link. Scene feature acquisition sensors collect environmental parameters in real time to generate scene perception data. S2: Based on the symmetrical bidirectional data interaction mechanism, combined with the scene perception data, the time stamp data packet sending cycle is dynamically adapted. The central control platform sends a time stamp data packet carrying a nanosecond-level time stamp according to the adjusted cycle. After receiving the data, the Beidou base station synchronously records the local reception time, calculates the one-way transmission delay in real time, and generates a response packet including the delay parameter and the base station's local clock information. The response packet is then fed back to the central control platform to generate a round-trip timestamp difference. S3: Based on the round-trip timestamp difference, the multipath effect level weight factor and electromagnetic interference intensity weight factor are dynamically optimized through the two-way time-frequency collaborative calibration algorithm and the scene perception data. In the high-precision time-frequency synchronization calculation process, the dynamic delay fluctuation of the wireless channel and the clock deviation between the transmitting and receiving ends are separated and corrected. S4: Based on the high-precision time-frequency synchronization calculation, dynamic correction of the frequency stability of the local oscillator of the Beidou base station is achieved based on the time-frequency phase closed-loop calibration technology: The central control platform and the Beidou base station periodically exchange frequency deviation datasets, including temperature drift coefficients and power supply noise parameters, through a wireless link. The central control platform combines the scene perception data to construct a dynamic frequency drift model and generate a phase adjustment command adapted to the current environment. After receiving the command, the Beidou base station performs phase fine-tuning on the output signal of the local oscillator based on a digital phase-locked loop, forming a closed-loop control link of "deviation detection - command generation - phase calibration - deviation re-detection".

2. The method according to claim 1, characterized in that, The data preprocessing of the scene feature acquisition sensor described in S1 is based on the extended Kalman filter algorithm. The original environmental data is denoised by establishing a dynamic model of environmental parameters. The state transition matrix and observation matrix are generated offline based on historical environmental data. The data preprocessing filters periodic interference through an adaptive noise cancellation algorithm and removes outliers by combining sensor data correlation verification. The discrete parameters are mapped into structured scene perception data, forming a complete processing link from raw signal acquisition to effective information extraction.

3. The method according to claim 1, characterized in that, The specific method by which the scene perception data described in S2 is used to dynamically adapt the timestamp data packet sending cycle is as follows: the scene feature acquisition sensor outputs scene perception data in a preset format according to the environmental parameters collected in real time. After receiving the scene perception data, the central control platform parses the environmental stability level and outputs the timestamp data packet sending cycle. At the same time, the scene feature acquisition sensor will monitor the sudden changes in the environmental parameters in real time. If the sudden change in the environmental parameters is detected, an abnormal event is triggered.

4. The method according to claim 1, characterized in that, The method for calculating the one-way transmission delay of the Beidou base station described in S2 is as follows: based on the difference between the timestamp in the received timestamp data packet and the local reception time, the delay is obtained by deducting a preset fixed transmission loss value. The fixed transmission loss value is the basic attenuation of the wireless signal under standard conditions.

5. The method according to claim 1, characterized in that, The rule for dynamically optimizing the weight factor of the multipath effect level in S3 is as follows: the multipath effect level is divided into levels according to the signal reflection intensity. For each level increase, the corresponding weight factor increases by a preset ratio to enhance the correction of dynamic delay fluctuations in the wireless channel.

6. The method according to claim 1, characterized in that, When dynamically optimizing the electromagnetic interference intensity weight factor in S3, the weight is adaptively adjusted based on a fuzzy control algorithm: the electromagnetic interference intensity and the rate of change of interference are fuzzy inputs, the electromagnetic interference intensity weight factor is a fuzzy output, a triangular membership function is established, and a nonlinear mapping from input to output is achieved through a fuzzy rule base. The weight value is then defuzzified and output.

7. The method according to claim 1, characterized in that, The periodic exchange of the frequency deviation dataset described in S4 uses a dedicated communication sub-channel, which is independent of the time synchronization signal transmission channel.

8. The method according to claim 1, characterized in that, The dynamic frequency drift model described in S4 is based on an adaptive heterogeneous fusion architecture, including an analytical sub-model based on physical mechanisms and a data-driven statistical sub-model. The analytical sub-model establishes a physical equation framework for the core components of the oscillator, relating temperature and humidity parameters to changes in the equivalent resistance and capacitance of the components, to obtain the theoretical frequency drift. The statistical sub-model uses environmental parameter sequences from historical scene perception data and corresponding frequency drift values ​​as samples, fits a nonlinear mapping relationship, and outputs a data-driven predicted value. The statistical sub-model sets up a dynamic weight allocator, which judges the interference complexity by the electromagnetic interference spectrum entropy value. The entropy value is positively correlated with the weight ratio of the statistical sub-model and negatively correlated with the weight ratio of the analytical sub-model. The theoretical frequency drift value and the data-driven predicted value are fused to calculate the dynamic frequency drift value. The dynamic frequency drift value is smoothed by a filtering layer to remove high-frequency noise interference, resulting in a deviation rate from the actual drift value. When the deviation rate exceeds a preset threshold, incremental training of the statistical sub-model is triggered.

9. The method according to claim 1, characterized in that, The digital phase-locked loop described in S4 is based on a collaborative linkage mechanism. The phase difference data output by the phase detection layer is fed back to the loop filter in real time. The loop filter dynamically adjusts the filtering parameters according to the interference characteristics in the scene perception data, so that the phase fine-tuning accuracy and response speed of the voltage-controlled oscillator are adapted to the current environment.

10. The method according to claim 1, characterized in that, The precursor feature prediction process of the closed-loop control link described in S4 includes: the precursor feature extraction layer performs real-time analysis of the scene perception data, identifies specific frequency band signals that are strongly correlated with frequency drift in electromagnetic interference, captures the step-like change features in temperature and humidity data, compares them with a preset warning threshold, and when the threshold is reached, the control link switches from a dormant state to a standby state, activating the phase adjustment execution process; it receives the predicted trend and the current deviation change rate output by the dynamic frequency drift model, and calculates the comprehensive drift index by weighting the two, which is used for the execution decision of the phase adjustment execution process.

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