800m wireless relay method suitable for subway station environment

Abstract

The present application relates to the field of wireless communication and private network coverage technology, in particular to a 800M wireless relay method suitable for subway station environment, which is applied to a communication system comprising terminals, base stations and relay devices. The method is as follows: in a low-power listening state, spatial radio frequency sampling sequence is obtained and the floor noise jump rate is calculated; based on the sampling sequence, the probability density function value under the assumption of pure noise and signal noise superposition is obtained, and the global cumulative statistics is recursively obtained; the optimal upper and lower bounds are calculated by combining the battery remaining capacity coefficient and the floor noise jump rate; the statistics is compared with the upper and lower bounds, if greater than or equal to the upper bound, the cluster forwarding mode is entered to forward signals, if less than or equal to the lower bound, the low-power listening is maintained, and if in the middle, the next sampling point is obtained and the iteration is returned. The present application realizes the adaptive cooperation of anti-electromagnetic impact and physical power limit under strong burst noise interference.

Description

Technical Field

[0001] This invention relates to the field of wireless communication and private network coverage technology, and in particular to an 800M wireless relay method suitable for subway station environments. Background Technology

[0002] In emergency scenarios such as subway fire rescue and flood control, due to the severe shielding of electromagnetic waves by the underground enclosed building structure, rescuers usually need to temporarily deploy portable 800M wireless relay devices powered by built-in batteries in disaster prevention communication blind spots to bridge the base station between the field terminal and the control center.

[0003] Most existing portable wireless repeater devices employ a listen-to-wake and link maintenance mechanism based on a fixed radio frequency energy threshold or a fixed time window. However, subway stations are extremely unstable electromagnetic environments. High-speed trains entering and leaving the station and friction from high-voltage pantographs generate strong transient broadband electromagnetic shocks and multipath fading. Faced with this environment, the traditional fixed threshold mechanism exposes a fatal underlying flaw: if a low threshold is set, sudden changes in ambient noise can easily deceive the repeater, causing frequent false wake-ups, leading to rapid depletion of limited battery power and equipment shutdown due to power exhaustion in the later stages of emergency response; if a high threshold or a strict anti-interference link disconnection mechanism is set, the rescue voice preamble may be lost due to equipment decision delays, or even the established emergency relay link may be blindly severed during effective communication due to brief physical interference from trains. Summary of the Invention

[0004] To overcome the above shortcomings, this invention provides an 800M wireless relay method suitable for subway station environments. It aims to improve the problem that most traditional relay devices use fixed threshold decisions, which cannot withstand the impact of transient noise in the subway, resulting in frequent false wake-ups and rapid battery depletion.

[0005] This invention provides the following technical solution: an 800M wireless relay method suitable for subway station environments, applied to a system including a first terminal, a base station, a built-in main control unit, a channel unit module, and a rechargeable battery module. The main control unit performs the following steps: S1, control the channel machine module to acquire the spatial radio frequency sampling sequence and real-time noise floor power in low-power listening state, and calculate the noise floor jump rate; S2, based on the spatial radio frequency sampling sequence, obtain the first probability density function value under the pure noise assumption and the second probability density function value under the signal-noise superposition assumption, and recursively obtain the global cumulative statistics; S3, the remaining capacity coefficient of the rechargeable battery module and the noise floor jump rate are jointly calculated to obtain the optimal upper bound and the optimal lower bound for judgment; S4, compare the global cumulative statistics with the optimal upper bound and the optimal lower bound respectively: if it is greater than or equal to the optimal upper bound, control the channel machine module to enter the cluster forwarding mode to forward the direct signal to the base station; if it is less than or equal to the optimal lower bound, control the channel machine module to maintain the low-power listening state; if it is between the optimal lower bound and the optimal upper bound, control the channel machine module to obtain the next sampling point and return to execute the step of obtaining the first probability density function value and the second probability density function value.

[0006] By adopting the above technical solution, the remaining capacity coefficient and the noise floor jump rate are jointly calculated to determine the optimal upper and lower bounds, thereby achieving adaptive coordination between anti-interference and battery life. This improves the problem that most traditional relay devices use fixed threshold decisions, which cannot withstand the impact of transient noise in the subway, resulting in frequent false wake-ups of the device and rapid battery depletion.

[0007] Optionally, in S1, controlling the channel machine module to acquire the spatial radio frequency sampling sequence and real-time noise floor power in a low-power listening state includes: The channel unit module is controlled to disconnect the trunk transmit link in order to enter the low-power listening state; Obtain the system's preset duty cycle parameters The channel module is controlled to periodically open the energy listening window according to the duty cycle parameter. During the opening period of the energy listening window, the system's preset discrete sampling frequency parameters are acquired; The real-time noise floor power and the spatial radio frequency sampling sequence of the current frequency band are synchronously acquired based on the discrete sampling frequency parameters.

[0008] Optionally, in S1, the calculation of the noise floor jump rate includes: Extract the real-time noise floor power detected at the current sampling time, and the historical noise floor power detected at the previous sampling time; Obtain the time interval parameter between two adjacent samples; Calculate the difference between the real-time noise floor power and the historical noise floor power; The difference is divided by the time interval parameter to generate the noise floor jump rate.

[0009] Optionally, in S2, obtaining the first probability density function value under the pure noise assumption and the second probability density function value under the signal-to-noise superposition assumption respectively includes: Extract the radio frequency amplitude features of discrete sampling points contained in the spatial radio frequency sampling sequence; Retrieve the first envelope standard deviation preset by the system under pure noise environment; Based on the first envelope standard deviation and the radio frequency amplitude characteristics, the first probability density function value is obtained by fitting a Gaussian envelope distribution model. Retrieve the system's preset envelope mean and second envelope standard deviation when a valid through signal exists; The second probability density function value is obtained by fitting the Gaussian envelope distribution model based on the envelope mean, the second envelope standard deviation, and the radio frequency amplitude characteristics.

[0010] Optionally, in S2, the recursive calculation of the global cumulative statistic includes: Obtain the ratio data of the second probability density function value to the first probability density function value; Perform a logarithmic mapping operation on the ratio data to obtain the log-likelihood ratio corresponding to the current discrete sampling point; Extract the historical cumulative statistics corresponding to the previous discrete sampling point; The log-likelihood ratio is algebraically summed with the historical cumulative statistic to generate the updated global cumulative statistic.

[0011] Optionally, in S3, obtaining the optimal upper bound for the decision includes: Obtain the system's preset maximum false wake-up probability tolerance and maximum missed wake-up probability tolerance; The upper bound of the basic judgment is calculated based on the highest false wake-up probability tolerance, the highest missed wake-up probability tolerance, and the remaining capacity coefficient; Compare the noise floor jump rate with the system's preset impact threshold; When the noise floor jump rate is greater than the impact threshold, the excess difference of the noise floor jump rate exceeding the impact threshold is extracted; The positive interference penalty constant preset by the system is multiplied by the over-limit difference to obtain the positive compensation amount; The optimal upper bound of the decision is obtained by summing the basic decision upper bound with the positive compensation amount.

[0012] Optionally, in S3, obtaining the optimal decision lower bound includes: The lower bound of the basic judgment is calculated based on the highest false wake-up probability tolerance, the highest missed wake-up probability tolerance, and the remaining capacity coefficient; When the noise floor jump rate is greater than the impact threshold, the system's preset negative interference penalty constant is multiplied by the over-limit difference to obtain the negative reduction amount. The optimal lower bound for judgment is obtained by subtracting the lower bound for judgment from the negative reduction amount.

[0013] Optionally, in S4, controlling the channel unit module to enter the cluster forwarding mode to forward the direct signal to the base station includes: The channel unit module is triggered to establish a link with the base station to enter the cluster forwarding mode; During the operation of the cluster forwarding mode, the noise floor jump rate of the space environment and the current received signal strength of the direct signal are continuously monitored; Compare the noise floor jump rate with a preset impact threshold, and compare the current received signal strength with the communication baseline; When the noise floor jump rate is greater than the impact threshold and the current received signal strength is lower than the communication baseline, a state freeze command is generated. According to the state freeze command, the channel machine module is controlled to maintain the cluster forwarding mode continuously within the dynamic freeze duration.

[0014] Optionally, in S4, controlling the channel machine module to maintain the cluster forwarding mode continuously during the dynamic freeze duration includes: Extract the system's preset minimum freeze waiting time and dynamic compensation constant; The dynamic compensation constant is multiplied by the remaining capacity coefficient of the rechargeable battery module to generate a dynamic compensation multiplier. Extract the excess difference between the noise floor jump rate and the impact threshold; The dynamic compensation multiplier is multiplied by the over-limit value to obtain the extended compensation time; The minimum freeze waiting time is added to the extended compensation time to obtain the dynamic freeze duration; The channel machine module is controlled to lock the cluster forwarding mode based on the dynamic freeze duration.

[0015] Optionally, in S4, controlling the channel machine module to lock the cluster forwarding mode according to the dynamic freeze duration includes: Start the timing program for the dynamic freeze duration; At the end of the timing procedure, the integrated value of the radio frequency energy of the current channel is collected; Extract the system's preset adaptive wake-up threshold and compare the integrated value of the radio frequency energy with the adaptive wake-up threshold. When the radio frequency energy integral value is greater than the adaptive wake-up threshold, the cluster forwarding mode is maintained; When the radio frequency energy integral value is less than the adaptive wake-up threshold, the cluster forwarding mode is cut off and the channel machine module is triggered to switch to the low-power listening state.

[0016] The present invention has the following beneficial effects: 1. In this invention, the optimal upper and lower bounds for judgment are calculated by jointly calculating the remaining capacity coefficient and the noise floor jump rate, thereby achieving adaptive coordination between anti-interference and battery life. This improves the problem that most traditional relay devices use fixed threshold judgment, which cannot withstand the impact of transient noise in the subway, resulting in frequent false wake-ups of the device and rapid battery depletion.

[0017] 2. In this invention, the global cumulative statistics are obtained by recursively calculating the probability density function value based on the dual hypothesis, thereby realizing dynamic time-series collaborative decision-making for multiple discrete sampling points. This improves the problem that traditional signal detection mostly uses fixed time window integration, which lacks flexibility in a single time window, resulting in delayed wake-up decision-making and low accuracy under sudden transient noise.

[0018] 3. In this invention, by controlling and maintaining the cluster forwarding mode continuously when the noise floor transition rate is high and the received signal is weak, the communication link is forcibly locked to cross the multipath fading blind zone. This improves the problem that traditional communication interventions mostly adopt the signal drop-off mechanism, which cannot distinguish the brief physical blockage, thus causing the effective link to be frequently and erroneously disconnected when the train passes by.

[0019] 4. In this invention, the dynamic freeze duration is calculated by using the remaining capacity coefficient of the rechargeable battery module, thereby ensuring that the link suspension protection cycle is strictly controlled by the available physical power of the hardware. This improves the problem that traditional link keep-alive methods mostly use fixed delay waiting time, which blindly maintain a high power consumption state under low power conditions, thus causing the equipment to crash due to continuous ineffective waiting. Attached Figure Description

[0020] Figure 1 This is a flowchart of an 800M wireless relay method suitable for subway station environments proposed in this invention; Figure 2 This is a flowchart illustrating the dynamic calculation of the optimal upper and lower bounds for an 800M wireless relay method suitable for subway station environments proposed in this invention. Figure 3 This is a flowchart illustrating the dynamic state freezing and cluster forwarding control process of an 800M wireless relay method suitable for subway station environments proposed in this invention. Detailed Implementation

[0021] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Example 1:

[0022] In a first embodiment of the present invention, the present invention provides an 800M wireless relay method suitable for subway station environments, such as... Figure 1 As shown, in a system of a wireless relay device comprising a first terminal, a base station, a built-in main control unit, a channel unit module, and a rechargeable battery module, the main control unit performs the following steps: S1, the control channel module acquires the spatial radio frequency sampling sequence and real-time noise floor power in low-power listening mode, and calculates the noise floor jump rate; Furthermore, in S1, the control channel module acquires the spatial radio frequency sampling sequence and real-time noise floor power in low-power listening mode, including: The control channel module disconnects the trunk transmit link to enter a low-power listening state; Obtain the system's preset duty cycle parameters The channel control module periodically opens the energy listening window based on the duty cycle parameter; During the opening period of the energy sensing window, the system's preset discrete sampling frequency parameters are acquired; The real-time noise floor power and spatial radio frequency sampling sequence of the current frequency band are synchronously acquired based on discrete sampling frequency parameters.

[0023] In S1, the calculation of the noise floor jump rate includes: Extract the real-time noise floor power detected at the current sampling time, and the historical noise floor power detected at the previous sampling time; Obtain the time interval parameter between two adjacent samples; Calculate the difference between the real-time noise floor power and the historical noise floor power; The difference is divided by the time interval parameter to generate the noise floor jump rate.

[0024] Specifically, considering the objective physical limitations of the total capacity of the built-in rechargeable battery in portable devices, the main control unit first disconnects the high-power trunk transmission link at the hardware control level, and periodically opens the energy listening window in conjunction with the duty cycle parameter. The switching of hardware working states transforms the normal full-power operation mode of the channel module into an intermittent wake-up mode, thereby reducing the static standby power consumption of the device from a physical source.

[0025] Within the limited time frame of the activated energy listening window, the system performs synchronous acquisition and transfer of dual-channel data. The data input terminal receives analog radio frequency signals from the external space environment. After analog-to-digital conversion processing based on discrete sampling frequency parameters by the channel unit module, two core output data channels are formed. The first output is a space radio frequency sampling sequence, which is directly passed to the next step as the data source for calculating the probability density function; the second output is the real-time noise floor power, which is passed to the transition feature extraction logic.

[0026] To quantify the multipath broadband electromagnetic impulse interference generated by a subway train entering the station at high speed, the main control unit performs time-differential processing on the extracted real-time noise floor power. The noise floor jump rate is then derived. The specific mathematical formula is as follows: ; in It represents the noise floor jump rate, and its dimension is the dynamic change rate of power over time. This represents the real-time noise floor power detected and recorded by the channel unit module at the current sampling moment. This represents the historical noise floor power detected and recorded by the channel module at the previous adjacent sampling time. This parameter represents the time interval between two adjacent sampling actions. In the engineering configuration, this time interval parameter needs to be adapted to the broadcast period of the base station control channel, and the specific value is set to the level of tens of milliseconds to hundreds of milliseconds.

[0027] Based on the above algorithm logic, the main control unit outputs the noise floor jump rate. As a dynamic variable characterizing the severity of environmental interference, it is passed to the joint calculation process for subsequent boundary determination. Using the temporal gradient difference of the noise floor as a quantification index eliminates the random error of the absolute noise floor value, thus solving the underlying technical defect of conventional relay equipment being prone to false wake-ups when facing transient high-energy noise.

[0028] S2, based on the spatial radio frequency sampling sequence, obtain the first probability density function value under the pure noise assumption and the second probability density function value under the signal-noise superposition assumption, and recursively obtain the global cumulative statistic; Furthermore, in S2, obtaining the first probability density function value under the pure noise assumption and the second probability density function value under the signal-to-noise superposition assumption includes: Extract the radio frequency amplitude features of discrete sampling points contained in the spatial radio frequency sampling sequence; Retrieve the first envelope standard deviation preset by the system under pure noise environment; Based on the first envelope standard deviation and the radio frequency amplitude characteristics, the first probability density function value is obtained by fitting a Gaussian envelope distribution model. Retrieve the system's preset envelope mean and second envelope standard deviation when a valid through signal exists; The second probability density function value is obtained by fitting a Gaussian envelope distribution model based on the envelope mean, the second envelope standard deviation, and the radio frequency amplitude characteristics.

[0029] In S2, the globally cumulative statistics obtained recursively include: Obtain the ratio of the second probability density function value to the first probability density function value; The comparison data undergoes a logarithmic mapping operation to obtain the log-likelihood ratio corresponding to the current discrete sampling point; Extract the historical cumulative statistics corresponding to the previous discrete sampling point; The log-likelihood ratio is algebraically summed with the historical cumulative statistics to generate the updated global cumulative statistics.

[0030] Specifically, step S2 is the data preprocessing stage that dominates the signal state determination. The data input of this step receives the spatial radio frequency sampling sequence extracted in the preprocessing step, and the data output generates an updated global cumulative statistic and transmits it to the subsequent comparison and determination steps.

[0031] The main control unit extracts the radio frequency amplitude features of discrete sampling points from the input spatial radio frequency sampling sequence as the original calculation variables. To subjectively remove the background noise of the physical environment, the main control unit constructs a binary hypothesis test calculation logic. The main control unit retrieves the first envelope standard deviation from the preset storage module as the benchmark setting parameter for evaluating a purely noisy environment, and retrieves the envelope mean and the second envelope standard deviation as the benchmark setting parameters for evaluating the existence of a valid through signal.

[0032] The main control unit applies a Gaussian envelope distribution model, substituting the extracted radio frequency amplitude features and retrieved reference setting parameters into the model to calculate the first probability density function value and the second probability density function value. The mathematical formulas for calculating the first probability density function value and the second probability density function value are as follows: ; ; in This represents the value of the first probability density function. This represents the value of the second probability density function. This represents the radio frequency amplitude characteristics of the extracted discrete sampling points. This represents the standard deviation of the first envelope. This represents the mean of the envelope. This represents the standard deviation of the second envelope. It represents the constant value of pi. This represents an exponential function with the natural constant as its base.

[0033] After obtaining the probability density function values ​​under the two hypotheses, the main control unit performs a time-series recursion of the statistics. The main control unit divides the second probability density function value by the first probability density function value to generate a ratio, performs a natural logarithm mapping operation on the ratio, and outputs the log-likelihood ratio corresponding to the current discrete sampling point. The main control unit extracts the historical cumulative statistic corresponding to the immediately preceding discrete sampling point in time, algebraically sums the log-likelihood ratio with the historical cumulative statistic, and generates the updated global cumulative statistic. The mathematical formulas involved in the recursive operation are as follows: ; ; in This represents the log-likelihood ratio. This represents a logarithmic function with the natural constant as its base. This represents the updated global cumulative statistics. This represents the cumulative historical statistics.

[0034] By constructing a probability density calculation mechanism based on the Gaussian envelope distribution model and using the log-likelihood ratio to perform algebraic cumulative recursive calculation of historical state sequences, the traditional technical defects of conventional communication equipment, which rely solely on instantaneous level thresholds for decision-making, resulting in weak anti-multipath interference capabilities, are improved. This avoids the problem of transient high-energy noise generated by subway trains entering the station causing relay equipment to wake up incorrectly and drowning out weak signals for real disaster prevention.

[0035] like Figure 2 As shown in Figure S3, the remaining capacity coefficient of the rechargeable battery module and the noise floor jump rate are calculated together to obtain the optimal upper bound and the optimal lower bound for judgment. Furthermore, in S3, the optimal decision upper bound is obtained as follows: Obtain the system's preset maximum false wake-up probability tolerance and maximum missed wake-up probability tolerance; The upper bound of the basic determination is calculated based on the highest false wake-up probability tolerance, the highest missed wake-up probability tolerance, and the remaining capacity coefficient. Compare the noise floor jump rate with the system's preset impact threshold; When the noise floor jump rate is greater than the impact threshold, extract the excess difference of the noise floor jump rate exceeding the impact threshold; The positive interference penalty constant preset by the system is multiplied by the over-limit difference to obtain the positive compensation amount; The optimal decision upper bound is obtained by summing the basic decision upper bound and the positive compensation amount.

[0036] In S3, the optimal decision lower bounds are obtained as follows: The lower bound is determined based on the highest false wake-up probability tolerance, the highest missed wake-up probability tolerance, and the remaining capacity coefficient. When the noise floor jump rate is greater than the impact threshold, the negative interference penalty constant preset by the system is multiplied by the over-limit difference to obtain the negative reduction amount. The optimal lower bound for decision is obtained by subtracting the lower bound of the basic decision from the negative reduction amount.

[0037] Specifically, step S3 defines the dynamic adaptive adjustment mechanism for the system's decision threshold. The data input receives the remaining capacity coefficient of the rechargeable battery module and the noise floor jump rate output from the previous step. The data output generates the optimal upper and lower bounds for decision and passes them to the subsequent state comparison stage as the core decision benchmark.

[0038] The main control unit retrieves the system's preset maximum false wake-up probability tolerance and maximum missed wake-up probability tolerance. Combining this with the remaining capacity coefficient of the rechargeable battery module, the main control unit first calculates the basic upper bound for judgment. Then, the main control unit extracts the noise floor jump rate and compares it with the system's preset impact threshold. If the noise floor jump rate is greater than the impact threshold, the main control unit extracts the excess difference between the noise floor jump rate and the impact threshold, multiplies the system's preset positive interference penalty constant with the excess difference to obtain a positive compensation amount. Finally, the basic upper bound for judgment and the positive compensation amount are summed to generate the optimal upper bound for judgment. The mathematical formula for generating the optimal upper bound for judgment is as follows: ; Based on the lower bound generation logic, the main control unit calculates the basic lower bound based on the highest false wake-up probability tolerance, the highest missed wake-up probability tolerance, and the remaining capacity coefficient. When the noise floor jump rate exceeds the impact threshold, the main control unit multiplies the system's preset negative interference penalty constant with the aforementioned over-limit difference to obtain the negative reduction amount. The difference between the basic lower bound and the negative reduction amount generates the optimal lower bound. The mathematical formula for generating the optimal lower bound is as follows: ; In the two mathematical formulas mentioned above, the variable takes effect when the noise floor jump rate is greater than the impact threshold, and the excess difference corresponds to the algebraic difference between the noise floor jump rate and the impact threshold. If the noise floor jump rate is not greater than the impact threshold, the excess difference is always zero.

[0039] in This represents the upper bound of the optimal decision. This represents the lower bound of the optimal decision. This represents the highest tolerance for false wake-up probability. This represents the highest tolerance for missed wake-up probability. This represents the remaining capacity coefficient, and its value range is set to a real number greater than zero and less than or equal to one. Represents the natural constant. This represents the positive interference penalty constant. This represents the negative interference penalty constant. This represents the difference exceeding the limit.

[0040] The aforementioned control logic cross-integrates the physical power limits of the underlying hardware with the intensity of external radio frequency interference. In extreme conditions such as when the device is at a critical low power level or encounters a strong electromagnetic shock caused by a subway train entering the station, the calculation logic, based on the formula, forcibly raises the wake-up threshold of the channel module while simultaneously lowering the sleep confirmation threshold. This reverse dynamic penalty mechanism prevents frequent false wake-ups caused by the device blindly fighting complex transient broadband noise, fundamentally eliminating the fatal hidden danger of portable wireless repeaters running out of power too quickly and ineffectively during subway disaster prevention and emergency rescue.

[0041] like Figure 3 As shown in step S4, the global cumulative statistics are compared with the optimal upper and lower bounds for decision-making respectively: if they are greater than or equal to the optimal upper bound, the control channel module enters the cluster forwarding mode and forwards the direct signal to the base station; if they are less than or equal to the optimal lower bound, the control channel module maintains a low-power listening state; if they are between the optimal lower and upper bounds for decision-making, the control channel module obtains the next sampling point and returns to execute the steps of obtaining the first probability density function value and the second probability density function value. Furthermore, in S4, the control channel module enters the trunking forwarding mode to forward the direct signal to the base station, including: The channel unit module is triggered to establish a link with the base station to enter cluster forwarding mode; During the operation of the cluster forwarding mode, the noise floor rate of the space environment and the current received signal strength of the direct signal are continuously monitored. Compare the noise floor jump rate with the preset impact threshold, and compare the current received signal strength with the communication baseline; When the noise floor jump rate is greater than the impact threshold and the current received signal strength is lower than the communication baseline, a state freeze command is generated. According to the state freeze command, the control channel module maintains the cluster forwarding mode continuously during the dynamic freeze period.

[0042] In S4, the control channel module maintains the cluster forwarding mode continuously during the dynamic freeze period, including: Extract the system's preset minimum freeze waiting time and dynamic compensation constant; The dynamic compensation constant is multiplied by the remaining capacity coefficient of the rechargeable battery module to generate the dynamic compensation multiplier. Extract the excess difference where the noise floor jump rate exceeds the impact threshold; The extended compensation time is obtained by multiplying the dynamic compensation multiplier with the over-limit value. The shortest freeze waiting time is added to the extended compensation time to obtain the dynamic freeze duration; The channel machine module locks the cluster forwarding mode based on the dynamic freeze duration.

[0043] In S4, the control channel module locks the cluster forwarding mode according to the dynamic freeze duration, including: Start the timer for the dynamic freeze duration; At the end of the timing program, the integrated value of the radio frequency energy of the current channel is collected; Extract the system's preset adaptive wake-up threshold and compare the integrated value of radio frequency energy with the adaptive wake-up threshold. When the integrated value of radio frequency energy is greater than the adaptive wake-up threshold, the cluster forwarding mode is maintained; When the integrated value of radio frequency energy is less than the adaptive wake-up threshold, the cluster forwarding mode is cut off and the channel machine module is triggered to switch to low-power listening state.

[0044] Specifically, at the data flow level, the input end receives the global cumulative statistics obtained from the pre-recursive calculation, as well as the calculated optimal upper and lower bounds for decision-making. The main control unit performs a boundary comparison on these values. When the global cumulative statistics reach or exceed the optimal upper bound, the main control unit determines that the target communication link meets the requirements, outputs a command to control the channel unit module to establish a physical connection with the base station and execute direct signal forwarding. When the global cumulative statistics fall to or below the optimal lower bound, the system determines that it is currently in an invalid noise environment and outputs a command to maintain low-power listening state. If the value falls between the two boundaries, the system suspends the decision and returns to extract the next discrete sampling point. This step strictly follows the theoretical decision boundary of the sequential probability ratio test, avoiding decision delays caused by a fixed sampling window.

[0045] Upon entering cluster forwarding mode, the system initiates abnormal state intervention logic to address the transient broadband multipath fading environment induced by subway trains entering the station. The main control unit monitors the noise floor jump rate and the current received signal strength in parallel. When encountering extreme radio frequency obstruction, i.e., the noise floor jump rate exceeds the system's preset impact threshold and the received signal is suppressed below the communication baseline, the main control unit intercepts the normal link disconnection action and issues a state freeze command to force the channel unit module to suspend the current forwarding state.

[0046] To prevent indefinite and invalid device suspension while maintaining physical battery life, the main control unit performs dynamic calculation of the freeze duration. The minimum freeze waiting time set by the system and the dynamic compensation constant are extracted as calculation benchmarks to obtain the current remaining capacity coefficient of the rechargeable battery module. The dynamic compensation constant, the remaining capacity coefficient, and the excess difference between the noise floor jump rate and the impact threshold are multiplied together to obtain the extended compensation time. The extended compensation time and the minimum freeze waiting time are linearly summed to finally output the dynamic freeze duration. The specific mathematical derivation formulas involved in this calculation process are as follows: ; in This represents the dynamically frozen duration generated by the derivation. This represents the minimum freeze time preset by the system. This represents the dynamic compensation constant. This represents the remaining capacity coefficient, the specific value of which is calibrated based on the actual capacity of the physical battery and ranges between zero and one. This represents the difference beyond the limit, which is the algebraic difference obtained by subtracting the noise floor jump rate from the impact threshold.

[0047] The status suspension control performs precise timing based on the calculated dynamic freeze duration. At the critical end of the timing period, the system performs a final link retention determination based on the acquired current channel RF energy integral value. The main control unit performs a final numerical comparison between the RF energy integral value and the adaptive wake-up threshold. If the RF energy integral value does not meet the threshold, the current cluster forwarding mode is directly cut off, and the hardware device is guided to return to a low-power listening state to prevent unnecessary power loss. If the RF energy integral value meets the threshold, the freeze state is lifted, and normal relay forwarding operations continue.

[0048] The aforementioned underlying control algorithm avoids the technical flaw of conventional portable repeater devices, which are prone to link misjudgment when dealing with complex transient noise interference in subways. The calculation model directly embeds the remaining physical battery power as a positive constraint parameter into the extended calculation domain of the duration. It provides a lenient anti-interference waiting window when the device has a high battery level and tightens the ineffective waiting time when the device is at the critical point of low battery level, thus achieving adaptive adjustment between the RF anti-interference retention rate and the power consumption limit of the underlying hardware. Example 2:

[0049] In subway fire rescue or facility repair scenarios, disaster prevention personnel often need to operate in blind spots near entrances and exits where base station signals cannot reach. Portable 800M wireless repeaters powered by built-in batteries must be temporarily deployed in signal-critical zones to establish emergency communication links with the station control center. However, the high-speed entry and exit of subway trains generates intense transient broadband electromagnetic shocks, creating a severe contradiction between battery life and link stability for the repeater. On the one hand, during the dormant listening period, sudden noise can easily trigger frequent false wake-ups, rapidly depleting the limited battery power and causing complete communication paralysis in the later stages of the rescue. On the other hand, during the operational period with established relay links, the multipath fading and increased background noise caused by the large metal car body can instantly drown out the weak voice signals of disaster prevention personnel, causing the repeater to misjudge and blindly disconnect the communication link. This technical problem—"easily woken up by noise and drained during dormancy, easily lost due to interference"—caused by the dual constraints of complex transient noise interference and battery life limits, severely hinders low-latency access and high-reliability retention for emergency communication in extreme physical environments. To address the aforementioned problems, this invention provides an 800M wireless relay method suitable for subway station environments, the structure of which is as follows: Figure 1As shown. The specific implementation process of this method is as follows: An adaptive wake-up and anti-interference judgment closed loop was constructed at the bottom layer of the wireless repeater device. Addressing the power supply bottleneck and multipath electromagnetic shock conditions faced by portable devices in subway stations, the main control unit first suppresses the static power consumption of the physical hardware through low-power listening. After acquiring the spatial radio frequency sampling sequence and real-time noise floor power, the system extracts the noise floor jump rate to quantify the transient noise mutation characteristics caused by subway trains entering and leaving the station. The main control unit abandons the traditional single fixed-level comparison mode and establishes a dual hypothesis testing model of pure noise and signal-to-noise superposition. It recursively calculates the global cumulative statistics through a probability density function, filtering out random spikes and interference from environmental noise at the underlying statistical algorithm level.

[0050] In the decision scale generation stage, the main control unit cross-integrates the remaining capacity coefficient of the rechargeable battery module with the noise floor jump rate. This calculation mechanism directly transforms the usable power limit of the physical battery into the decision constraint condition of the communication link. When the device is in a low power state or encounters severe sudden noise, the system logic forcibly raises the upper bound of the optimal decision and lowers the lower bound of the optimal decision, cutting off the invalid power loss caused by the hardware blindly attempting to wake up and establish a link under poor channel conditions.

[0051] In the final execution phase, the main control unit performs a sequential comparison based on the global cumulative statistics and the upper and lower bounds of the aforementioned dynamic judgment. If the value exceeds the upper bound, the cluster forwarding mode is triggered to connect the emergency communication link; if it falls below the lower bound, the equipment is forced to lie dormant to avoid the risk of false wake-up; if the value is between the two bounds, the decision is delayed and the next sampling point is obtained. The various steps form a complete algorithm path, solving the technical defects of conventional relay equipment that are prone to false alarms or being woken up by noise when faced with transient high-energy noise impacts. In the harsh electromagnetic environment of the subway, it balances the response time of signal forwarding with the maintenance of the physical endurance of the equipment.

[0052] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An 800M wireless repeater method suitable for subway station environments, characterized in that, In a system of a wireless relay device comprising a first terminal, a base station, a built-in main control unit, a channel unit module, and a rechargeable battery module, the main control unit performs the following steps: S1, control the channel machine module to acquire the spatial radio frequency sampling sequence and real-time noise floor power in low-power listening state, and calculate the noise floor jump rate; S2, based on the spatial radio frequency sampling sequence, obtain the first probability density function value under the pure noise assumption and the second probability density function value under the signal-to-noise superposition assumption, and recursively obtain the global cumulative statistics; S3, the remaining capacity coefficient of the rechargeable battery module and the noise floor jump rate are jointly calculated to obtain the optimal upper bound and the optimal lower bound for judgment; S4, compare the global cumulative statistics with the optimal upper bound and the optimal lower bound respectively: if it is greater than or equal to the optimal upper bound, control the channel machine module to enter the cluster forwarding mode to forward the direct signal to the base station; if it is less than or equal to the optimal lower bound, control the channel machine module to maintain the low-power listening state; if it is between the optimal lower bound and the optimal upper bound, control the channel machine module to obtain the next sampling point and return to execute the step of obtaining the first probability density function value and the second probability density function value.

2. The 800M wireless relay method suitable for subway station environments according to claim 1, characterized in that, In S1, controlling the channel machine module to acquire the spatial radio frequency sampling sequence and real-time noise floor power in a low-power listening state includes: The channel unit module is controlled to disconnect the trunk transmit link in order to enter the low-power listening state; Obtain the system's preset duty cycle parameters The channel module is controlled to periodically open the energy listening window according to the duty cycle parameter. During the opening period of the energy listening window, the system's preset discrete sampling frequency parameters are acquired; The real-time noise floor power and the spatial radio frequency sampling sequence of the current frequency band are synchronously acquired based on the discrete sampling frequency parameters.

3. The 800M wireless relay method suitable for subway station environments according to claim 1, characterized in that, In S1, the calculation of the noise floor jump rate includes: Extract the real-time noise floor power detected at the current sampling time, and the historical noise floor power detected at the previous sampling time; Obtain the time interval parameter between two adjacent samples; Calculate the difference between the real-time noise floor power and the historical noise floor power; The difference is divided by the time interval parameter to generate the noise floor jump rate.

4. The 800M wireless relay method suitable for subway station environments according to claim 1, characterized in that, In S2, obtaining the first probability density function value under the pure noise assumption and the second probability density function value under the signal-to-noise superposition assumption respectively includes: Extract the radio frequency amplitude features of discrete sampling points contained in the spatial radio frequency sampling sequence; Retrieve the first envelope standard deviation preset by the system under pure noise environment; Based on the first envelope standard deviation and the radio frequency amplitude characteristics, the first probability density function value is obtained by fitting a Gaussian envelope distribution model. Retrieve the system's preset envelope mean and second envelope standard deviation when a valid through signal exists; The second probability density function value is obtained by fitting the Gaussian envelope distribution model based on the envelope mean, the second envelope standard deviation, and the radio frequency amplitude characteristics.

5. The 800M wireless relay method suitable for subway station environments according to claim 4, characterized in that, In S2, the recursive global cumulative statistics include: Obtain the ratio data of the second probability density function value to the first probability density function value; Perform a logarithmic mapping operation on the ratio data to obtain the log-likelihood ratio corresponding to the current discrete sampling point; Extract the historical cumulative statistics corresponding to the previous discrete sampling point; The log-likelihood ratio is algebraically summed with the historical cumulative statistic to generate the updated global cumulative statistic.

6. The 800M wireless relay method suitable for subway station environments according to claim 1, characterized in that, In S3, obtaining the optimal upper bound for the decision includes: Obtain the system's preset maximum false wake-up probability tolerance and maximum missed wake-up probability tolerance; The upper bound of the basic judgment is calculated based on the highest false wake-up probability tolerance, the highest missed wake-up probability tolerance, and the remaining capacity coefficient; Compare the noise floor jump rate with the system's preset impact threshold; When the noise floor jump rate is greater than the impact threshold, the excess difference of the noise floor jump rate exceeding the impact threshold is extracted; The positive interference penalty constant preset by the system is multiplied by the over-limit difference to obtain the positive compensation amount; The optimal upper bound of the decision is obtained by summing the basic decision upper bound with the positive compensation amount.

7. The 800M wireless relay method suitable for subway station environments according to claim 6, characterized in that, In S3, obtaining the optimal lower bound for judgment includes: The lower bound of the basic judgment is calculated based on the highest false wake-up probability tolerance, the highest missed wake-up probability tolerance, and the remaining capacity coefficient; When the noise floor jump rate is greater than the impact threshold, the system's preset negative interference penalty constant is multiplied by the over-limit difference to obtain the negative reduction amount. The optimal lower bound for judgment is obtained by subtracting the lower bound for judgment from the negative reduction amount.

8. The 800M wireless relay method suitable for subway station environments according to claim 1, characterized in that, In S4, controlling the channel unit module to enter the cluster forwarding mode and forward the direct signal to the base station includes: The channel unit module is triggered to establish a link with the base station to enter the cluster forwarding mode; During the operation of the cluster forwarding mode, the noise floor jump rate of the space environment and the current received signal strength of the direct signal are continuously monitored; Compare the noise floor jump rate with a preset impact threshold, and compare the current received signal strength with the communication baseline; When the noise floor jump rate is greater than the impact threshold and the current received signal strength is lower than the communication baseline, a state freeze command is generated. According to the state freeze command, the channel machine module is controlled to maintain the cluster forwarding mode continuously within the dynamic freeze duration.

9. The 800M wireless relay method suitable for subway station environments according to claim 8, characterized in that, In S4, controlling the channel machine module to maintain the cluster forwarding mode continuously during the dynamic freeze period includes: Extract the system's preset minimum freeze waiting time and dynamic compensation constant; The dynamic compensation constant is multiplied by the remaining capacity coefficient of the rechargeable battery module to generate a dynamic compensation multiplier. Extract the excess difference between the noise floor jump rate and the impact threshold; The dynamic compensation multiplier is multiplied by the over-limit value to obtain the extended compensation time; The minimum freeze waiting time is added to the extended compensation time to obtain the dynamic freeze duration; The channel machine module is controlled to lock the cluster forwarding mode based on the dynamic freeze duration.

10. The 800M wireless relay method suitable for subway station environments according to claim 9, characterized in that, In S4, controlling the channel machine module to lock the cluster forwarding mode according to the dynamic freeze duration includes: Start the timing program for the dynamic freeze duration; At the end of the timing procedure, the integrated value of the radio frequency energy of the current channel is collected; Extract the system's preset adaptive wake-up threshold and compare the integrated value of the radio frequency energy with the adaptive wake-up threshold. When the integrated value of the radio frequency energy is greater than the adaptive wake-up threshold, the cluster forwarding mode is maintained; When the radio frequency energy integral value is less than the adaptive wake-up threshold, the cluster forwarding mode is cut off and the channel machine module is triggered to switch to the low-power listening state.

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