Environment backscatter communication enhancement method and system based on biphase detection

Abstract

The invention relates to the technical field of wireless communication, and discloses an environment backscatter communication enhancement method and system based on biphase detection, and the method comprises the steps: obtaining an environment radio frequency signal received by an antenna, and generating an in-phase component I-path signal and an orthogonal component Q-path signal through two-path orthogonal demodulation, generating a binary state identifier representing an antenna matching state; a historical state identifier sequence is formed based on the identifier, an energy-communication alternating period is identified, the average duration is counted, and a phase switching prediction signal is generated in combination with the identifier; receiving a prediction signal to generate a switching control sequence, analyzing a target state type to generate a modulation control instruction, and executing the modulation control instruction to form closed-loop optimization; according to the invention, bit synchronization drift and judgment threshold offset caused by carrier multipath attenuation in the shadow area of the large-span metal structure can be effectively relieved, and the communication stability is guaranteed.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and more specifically, to a method and system for enhancing environmental backscatter communication based on biphasic detection. Background Technology

[0002] With the deep penetration of IoT technology into fields such as smart transportation, smart municipal administration (including smart streetlights / smart poles), and smart warehousing, the demand for deployment of various low-power sensor nodes is exploding. In smart transportation scenarios, roadside sensors and road condition monitoring equipment of vehicle-road cooperative systems need to work around steel structure bridges and metal guardrails of elevated roads for extended periods. They need to achieve real-time transmission of data such as traffic flow and road surface slippage, while avoiding high maintenance costs caused by frequent battery replacements. As the core carrier of urban sensing networks, smart streetlights (smart poles) integrate environmental monitoring, video surveillance, emergency call, and other functional modules, which rely on stable wireless communication. However, they often face the risk of communication interruption due to signal shadows formed by the metal structure of the light pole and reflections from surrounding buildings. In smart warehousing scenarios, cargo positioning tags and temperature and humidity monitoring nodes need to operate in enclosed spaces with dense metal shelves. Environmental radio frequency signals are prone to multipath interference after multiple reflections from the shelves, leading to an increased data transmission error rate. Against this backdrop, environmental backscatter communication technology has become a key direction for solving the communication challenges of low-power sensor nodes in the aforementioned scenarios. This technology does not require devices to actively transmit radio frequency signals, but only uses existing radio frequency signals in the environment, such as reflected base stations, radar, or radio frequency sources inside warehouses, to achieve data transmission. This technology can significantly reduce device energy consumption and extend the life cycle of sensor networks. However, its signal stability and state determination accuracy in complex metal structure scenarios (such as under the elevated guardrails of smart transportation, around the metal poles of smart streetlights, and in the metal shelving area of ​​smart warehouses) still face many technical bottlenecks that need to be overcome.

[0003] In the prior art, existing patents have researched the system construction and interference suppression of environmental backscatter communication. For example, Chinese patent CN114845339B discloses an environmental backscatter communication system that uses a radio frequency switch to control the antenna matching state to carry data through "reflection / absorption" signals. The receiving end improves the transmission distance through detection and threshold calculation, but it cannot cope with the irregular fluctuations in signal amplitude and phase caused by multipath propagation under smart transportation overpasses and smart warehouse metal rack areas. Chinese patent application CN120498614A discloses an interference suppression method for environmental IoT transmission using backscatter, focusing on multi-tag interference suppression and ensuring the accuracy of multiple nodes through subcarrier allocation, but it does not solve the problems of bit synchronization drift and decision threshold offset in the shadow area of ​​smart street light pole metal structure and under smart transportation steel structure bridges.

[0004] In these scenarios, the multiple reflections of the environmental carrier by the metal surface can cause the bit synchronization reference point (a synchronization benchmark established based on the sampling time of the time-domain signal) of the biphase detection module to drift. This leads to the high-low level switching decision threshold deviating from the actual signal characteristics. For example, in intelligent transportation scenarios, a lagging bit synchronization reference point may mistakenly identify a "low-amplitude signal in impedance matching state" as a "high-amplitude signal in impedance mismatch state," triggering unnecessary communication switching. At this time, the energy storage capacitor may not have sufficient energy, which can easily cause the traffic data transmitted back by the roadside sensor to be disconnected. In intelligent warehousing scenarios, an advanced bit synchronization reference point may mistakenly identify a "high-amplitude signal in impedance mismatch state" as a "low-amplitude signal in impedance matching state," extending the energy collection phase and wasting the communication window of the cargo positioning tag. In intelligent street light scenarios, this misjudgment can also cause the light pole integrated module to frequently enter sleep mode due to insufficient energy, failing to continuously provide environmental monitoring data. Ultimately, this results in sensor nodes in various scenarios being unable to stably achieve "energy collection-communication transmission" coordination, making it difficult to meet the high requirements for communication continuity and reliability in intelligent transportation vehicle-road collaboration, intelligent street light municipal perception, and intelligent warehouse precision management. Summary of the Invention

[0005] To overcome the aforementioned shortcomings of existing technologies, this invention provides an environmental backscatter communication enhancement method and system based on dual-phase detection. By using dual-path quadrature demodulation, the environmental radio frequency signal is converted into an in-phase component (I-path) and a quadrature component (Q-path) signal. A historical sequence is constructed using binary state identifiers to predict phase switching and form a closed-loop optimization, accurately characterizing the antenna impedance state. This invention transforms the "time-domain bit synchronization drift" problem within the shadow region of a large-span metal structure into a "phase-domain state determination" problem. Even if carrier multipath attenuation causes a shift in the decision time, the impedance state can still be accurately identified through the I / Q two-dimensional plane vector amplitude, avoiding switching decision threshold shifts and significantly improving communication stability and energy utilization efficiency.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] Environmental backscatter communication enhancement methods based on two-phase detection include:

[0008] The system acquires the ambient radio frequency signal received by the antenna, and generates an in-phase component I signal and a quadrature component Q signal through dual-channel quadrature demodulation processing. Based on the in-phase component I signal and the quadrature component Q signal, a binary state identifier representing the antenna matching state is generated.

[0009] Based on binary state identifiers, a historical state identifier sequence is formed; multiple energy-communication alternation cycles in the historical state identifier sequence are identified, and the average energy harvesting phase duration and average communication phase duration are calculated by statistically analyzing the multiple energy-communication alternation cycles; based on the average energy harvesting phase duration, average communication phase duration, and binary state identifiers, a phase switching prediction signal is generated.

[0010] The system receives the phase switching prediction signal, generates a switching control sequence to be executed, parses the target state type in the switching control sequence, generates corresponding modulation control commands based on different target state types, executes the switching control sequence and modulation control commands, and forms a closed-loop optimization.

[0011] The method for generating the binary status identifier includes:

[0012] A three-dimensional temperature and humidity compensation lookup table is established, ambient temperature and humidity values ​​are collected, impedance offset compensation values ​​are obtained by searching the three-dimensional temperature and humidity compensation lookup table, and impedance detection thresholds are generated after real-time calibration by combining them with pre-calibrated reference detection thresholds.

[0013] The in-phase component I signal and the quadrature component Q signal are combined to obtain the amplitude value of the combined signal. The amplitude value of the combined signal is compared with the impedance detection threshold after real-time calibration to generate a binary status identifier that represents the antenna matching state.

[0014] The method for calculating the amplitude value of the synthesized signal is as follows:

[0015] Calculate the squared amplitude values ​​of the in-phase component I signal and the quadrature component Q signal respectively; add the squared amplitude values ​​of the in-phase component I signal and the quadrature component Q signal and then perform a square root operation to generate the composite signal amplitude value that reflects the antenna reflection intensity.

[0016] The method for comparing the synthesized signal amplitude value with the impedance detection threshold after real-time calibration to generate a binary state identifier characterizing the antenna matching state includes:

[0017] When the amplitude of the synthesized signal is less than the impedance detection threshold after real-time calibration, a binary status identifier "0" representing the impedance matching state is generated; otherwise, a binary status identifier "1" representing the impedance mismatch state is generated.

[0018] The method for identifying the energy-communication alternation cycle includes:

[0019] Set a sliding window for the historical state identifier sequence, and count the length of the consecutive "0" sequence and the length of the consecutive "1" sequence within the sliding window;

[0020] When the length of the consecutive "0" sequence and the length of the immediately following consecutive "1" sequence both exceed the preset minimum period, the "0"-"1" combination formed by the "0" sequence and the "1" sequence is marked as a complete energy-communication alternation cycle.

[0021] The average energy harvesting phase duration is the average of the lengths of all “0” sequences, and the average communication phase duration is the average of the lengths of all “1” sequences.

[0022] The method for generating the phase-switching prediction signal includes:

[0023] A handover decision threshold is set based on the average energy harvesting phase duration and the average communication phase duration; the current operating phase is determined based on the latest binary state identifier in the historical state identifier sequence; and a phase handover prediction signal is generated based on the current operating phase and the handover decision threshold.

[0024] The switching decision threshold includes a minimum energy harvesting threshold and a communication integrity threshold; the operating phase includes an energy harvesting phase and a communication phase; the phase switching prediction signal includes a phase switching prediction signal for switching to the communication phase and a phase switching prediction signal for switching to the energy harvesting phase.

[0025] The method for generating the phase switching prediction signal for switching to the communication phase includes:

[0026] The duration of the current working phase is recorded, and the real-time voltage value of the energy storage capacitor integrated in the dual-phase detection power management submodule is monitored.

[0027] When the current working phase is the energy harvesting phase, if the duration of the current working phase is greater than or equal to the minimum energy harvesting threshold, the real-time voltage value of the energy storage capacitor is continuously detected to be greater than or equal to the preset communication start-up voltage threshold. If so, a phase switching prediction signal for switching to the communication phase is generated.

[0028] The method for generating the phase switching prediction signal for switching to the energy harvesting phase includes:

[0029] When the current working phase is the communication phase, if the duration of the current working phase is greater than or equal to the communication integrity threshold, or the real-time voltage value of the energy storage capacitor is less than the preset safe working voltage lower limit, a phase switching prediction signal for switching to the energy harvesting phase is generated.

[0030] A two-phase detection-based environment backscatter communication enhancement system is used to implement the above-mentioned two-phase detection-based environment backscatter communication enhancement method. The system includes:

[0031] Antenna matching status identification module: used to acquire the ambient radio frequency signal received by the antenna, generate in-phase component I signal and quadrature component Q signal through dual-channel quadrature demodulation processing; based on the in-phase component I signal and quadrature component Q signal, generate a binary status identifier representing the antenna matching status;

[0032] Phase switching prediction module: Based on binary state identifiers, a historical state identifier sequence is formed; multiple energy-communication alternation cycles in the historical state identifier sequence are identified, and the average energy harvesting phase duration and average communication phase duration are calculated by statistically analyzing the multiple energy-communication alternation cycles; based on the average energy harvesting phase duration, average communication phase duration, and binary state identifiers, a phase switching prediction signal is generated.

[0033] Phase switching execution module: It is used to receive the phase switching prediction signal, generate the switching control sequence to be executed, parse the target state type in the switching control sequence, generate the corresponding modulation control command according to different target state types, execute the switching control sequence and modulation control command, and form a closed loop optimization.

[0034] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0035] This invention utilizes dual-path quadrature demodulation to process environmental radio frequency signals, generating in-phase (I) and quadrature (Q) components. Compared to traditional single-signal detection methods, this approach more comprehensively captures the phase and amplitude characteristics of antenna impedance. Even in scenarios with severe multipath interference, such as under elevated guardrails in smart transportation systems or metal shelving areas in smart warehouses, it accurately identifies impedance states through I / Q two-dimensional plane vector amplitude, providing reliable data support for precise antenna matching and reducing feature loss under weak carrier signals. For example, in smart warehousing, this technology can prevent signal distortion caused by metal shelving reflections from cargo positioning tags, ensuring accurate data transmission for positioning. By constructing a historical state identifier sequence and statistically analyzing the average duration of energy-communication alternation cycles, it combines historical patterns with real-time states, such as the voltage of smart street light energy storage capacitors and the remaining energy of smart transportation roadside sensors, to generate phase switching prediction signals. This avoids the misjudgment problems that easily occur when relying solely on instantaneous signals, improving the accuracy of switching timing judgment. For instance, in smart transportation scenarios, the system can adjust communication demand patterns in advance based on historical traffic peak periods. The switching rhythm of energy harvesting and communication ensures continuous data transmission for vehicle-road cooperative systems. Through a complete process of switching control sequence generation, target state analysis, and closed-loop optimization, modulation control and phase switching can be precisely executed according to the actual communication needs of different scenarios. In smart streetlight scenarios, it can dynamically adapt to signal changes in the shadow areas of the metal structure of the light pole, avoiding communication interruptions caused by decision threshold shifts and ensuring continuous uploading of environmental monitoring data. In smart warehousing scenarios, it can optimize modulation parameters to reduce the bit error rate for the multipath attenuation characteristics of densely packed metal shelving areas, ensuring reliable interaction of cargo temperature and humidity data. Ultimately, it effectively alleviates the environmental carrier multipath attenuation and bit synchronization drift caused by complex metal structures in smart transportation, smart streetlights, and smart warehousing scenarios, reduces communication interference caused by high-low level switching decision threshold shifts, and significantly improves the operational stability, data transmission reliability, and energy utilization efficiency of environmental backscatter communication in complex environments with weak carrier attenuation. This meets the long-term stable operation requirements of low-power sensor nodes for smart transportation vehicle-road cooperation, smart municipal sensing, and smart warehouse precision management. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 A flowchart of the environmental backscatter communication enhancement method based on dual-phase detection provided in this embodiment of the invention;

[0038] Figure 2 A flowchart illustrating the principle of generating binary status identifiers provided in an embodiment of the present invention;

[0039] Figure 3 A flowchart illustrating a method for calculating the average energy harvesting phase duration and the average communication phase duration, provided in an embodiment of the present invention;

[0040] Figure 4 A state switching control flowchart is provided for an embodiment of the present invention;

[0041] Figure 5 A functional block diagram of an environmental backscatter communication enhancement system based on dual-phase detection provided in an embodiment of the present invention. Detailed Implementation

[0042] The technical solutions of 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.

[0043] Example 1

[0044] Please see Figure 1 As shown, this embodiment provides an environmental backscatter communication enhancement method based on dual-phase detection, including:

[0045] Step S10: Acquire the ambient radio frequency signal received by the antenna, and generate in-phase component I signal and quadrature component Q signal through dual-channel quadrature demodulation processing; based on the in-phase component I signal and quadrature component Q signal, generate a binary state identifier representing the antenna matching state.

[0046] Step S10 focuses on achieving accurate antenna matching status determination in environmental backscatter communication using dual-phase detection technology. The core of this step is to address the detection inaccuracy problem caused by impedance drift in the RF front-end circuit under high temperature and humidity conditions. Traditional single-phase detection relies on only a single signal component, which is prone to phase ambiguity due to changes in component parasitic capacitance and inductance with temperature and humidity. This makes it impossible to distinguish between normal signal fluctuations and impedance mismatch, leading to a sharp drop in energy harvesting efficiency and an increase in communication error rate. This step upgrades passive signal detection to active environmental adaptive determination through a three-level technical system of "dual-path quadrature demodulation - environmental compensation threshold - state quantization identification," providing reliable state input for subsequent energy-communication coordinated scheduling.

[0047] Further, step S10 includes:

[0048] Step S11: Obtain the ambient radio frequency signal received by the antenna, and generate the in-phase component I signal and the quadrature component Q signal by the dual-phase detection power management submodule through dual-channel quadrature demodulation processing;

[0049] The dual-phase detection power management submodule is a core hardware unit integrating RF signal processing, demodulation, threshold calibration, and status determination. Dual-channel orthogonal demodulation, by generating local carrier pairs with a 90-degree phase difference, projects the RF signal into two orthogonal dimensions, eliminating phase ambiguity in a single dimension. Environmental RF signals received by the antenna, such as broadband RF signals radiated by base stations and radar in smart cities, first enter the RF front-end circuit within the dual-phase detection power management submodule. The RF front-end circuit consists of a low-noise amplifier and a bandpass filter. The low-noise amplifier amplifies weak environmental RF signals to prevent signal attenuation and subsequent demodulation distortion. The bandpass filter filters out interference signals outside the frequency band, such as noise generated by industrial equipment, ultimately generating a pre-processed RF signal with a signal-to-noise ratio that meets demodulation requirements. The local oscillator within the dual-phase detection power management submodule generates a pair of local carrier signals with a strict 90-degree phase difference, namely an in-phase carrier signal and a quadrature carrier signal, based on the center frequency of the ambient radio frequency signal, such as 2.4GHz or 5.8GHz commonly used in intelligent transportation scenarios. The in-phase carrier signal is in phase with the pre-processed radio frequency signal, while the quadrature carrier signal lags behind the in-phase carrier signal by 90 degrees. This 90-degree phase difference is precisely controlled by the phase-shifting circuit inside the oscillator to ensure the orthogonality of subsequent demodulation. The pre-processed radio frequency signal is input to two parallel mixers. The first mixer receives the in-phase carrier signal, and the second receives the quadrature carrier signal. Through mixing operations, the radio frequency signal is down-converted to a baseband signal. Subsequently, the two baseband signals enter low-pass filters to filter out high-frequency harmonic components generated by mixing, ultimately outputting a stable, clutter-free in-phase component I signal and a quadrature component Q signal. The in-phase component I signal and the quadrature component Q signal together constitute a two-dimensional signal plane, which can uniquely characterize the phase and amplitude state of the antenna impedance.

[0050] Traditional single-phase detection suffers from phase ambiguity. In high-temperature and high-humidity environments, the parameters of components such as capacitors and inductors in the RF front-end circuit drift, causing distortion of the phase information of a single signal component and making it impossible to accurately deduce the antenna impedance state. Dual-path quadrature demodulation, however, projects the signal onto a two-dimensional I / Q plane. Any impedance state (matched or mismatched) corresponds to a unique vector point in the plane determined by the amplitudes of the I and Q signals. Even if component parameters drift, the positional change of the vector point can still be accurately captured, thus achieving accurate impedance state identification. For example, when the antenna is in an impedance-matched state, the reflected signal is weakest, and the amplitudes of both the I and Q signals are small, corresponding to a vector point near the origin of the I / Q plane. When the antenna is in an impedance-mismatched state, the reflected signal is enhanced, and the amplitudes of either the I or Q signal increase significantly, corresponding to a vector point far from the origin in the plane. The distinction between the two states is unaffected by environmental parameter drift.

[0051] Step S12: Establish a three-dimensional temperature and humidity compensation lookup table, collect ambient temperature and humidity values, obtain impedance offset compensation values ​​by searching the three-dimensional temperature and humidity compensation lookup table, and generate a real-time calibrated impedance detection threshold by combining it with a pre-calibrated reference detection threshold.

[0052] The core of step S12 is to establish a three-dimensional temperature and humidity compensation lookup table to generate a real-time calibrated impedance detection threshold, solving the problem that traditional fixed thresholds cannot adapt to changes in temperature and humidity. The three-dimensional temperature and humidity compensation lookup table is a data table storing the correspondence between temperature, humidity, and impedance offset compensation values. The three dimensions are temperature, humidity, and impedance offset compensation value. The temperature dimension covers the extreme ambient temperatures the sensor may face, for example, from -20℃ to 80℃, in 5℃ increments. The humidity dimension covers common humidity ranges, for example, from 10%RH to 95%RH, in 5%RH increments. The impedance offset compensation value dimension corresponds to the amount of compensation for the antenna impedance deviating from the reference value under different temperatures and humidity conditions, in ohms.

[0053] The establishment of the three-dimensional temperature and humidity compensation lookup table needs to be completed before the product leaves the factory. A test environment with different temperature and humidity combinations is constructed using an environmental simulation chamber. Under each environmental combination, the actual offset of the antenna impedance is measured. The antenna reference impedance under standard conditions, such as 50 ohms, is compared with the actual impedance under the current environment to calculate the impedance offset compensation value. All temperature and humidity combinations and their corresponding impedance offset compensation values ​​are stored in a three-dimensional structure in the non-volatile memory of the dual-phase detection power management submodule, such as in EEPROM, forming a complete three-dimensional temperature and humidity compensation lookup table. Real-time threshold calibration is performed during equipment operation: the temperature and humidity sensors integrated within the dual-phase detection power management submodule collect the current ambient temperature and humidity values ​​in real time. The processor calls an interpolation algorithm, such as trilinear interpolation, to ensure accurate compensation values ​​are obtained even for non-integer step temperature and humidity values. The corresponding impedance offset compensation value is read from the three-dimensional temperature and humidity compensation lookup table. Subsequently, this impedance offset compensation value is added to a preset reference detection threshold to generate the real-time calibrated impedance detection threshold Th. If the current temperature and humidity cause the antenna impedance to increase, such as high temperature causing the capacitance value to decrease and the impedance to increase, the compensation value will be positive, and the real-time threshold will increase accordingly to ensure that the impedance matching and mismatch states can still be accurately distinguished.

[0054] The benchmark detection threshold is the initial critical value used to clearly distinguish between the antenna's "impedance matching state" and "impedance mismatch state" under a standard environment (for example, a standard environment is an electromagnetic shielding environment with a temperature of 25℃±2℃, a relative humidity of 50%RH±5%RH, and no electromagnetic interference). The core function of the benchmark detection threshold is reflected in three dimensions: First, it serves as a reference for real-time calibration. The "impedance offset compensation value" stored in the three-dimensional temperature and humidity compensation lookup table is essentially the offset of the antenna impedance relative to the standard environment under different environments. It needs to be superimposed based on the benchmark detection threshold to obtain a real-time calibration threshold adapted to the current environment. If this benchmark is missing, the compensation value will lose its reference object, causing the compensation logic to fail. Second, it defines the state boundary under the standard environment to ensure accurate switching between energy harvesting and communication states under the standard environment. Third, it unifies the system's judgment benchmark. In a network composed of multiple sensor nodes, such as a smart pole sensor network, the benchmark detection threshold of all nodes is calibrated based on the same standard environment, avoiding coordination and scheduling chaos caused by differences in thresholds between nodes, and ensuring that the judgment logic of different nodes is consistent under the same environmental conditions.

[0055] The method for setting the benchmark detection threshold is as follows: First, based on the principle of environmental backscatter communication, the amplitude boundary of the synthesized signal under antenna impedance matching and mismatch states is defined. When the antenna impedance is matched, the amplitude value of the synthesized signal is the minimum, denoted as A. min When the antenna impedance is mismatched, the amplitude of the synthesized signal is at its maximum, denoted as A.max To initially balance energy harvesting efficiency and communication signal strength, the initial reference range for the baseline detection threshold is limited to A. min With A max The range of 20% to 30% between these values ​​can prevent the threshold from being too close to A. min This could lead to misjudgment, mismatch, or being too close to A. max (This could lead to mismatches). Subsequently, under standard conditions, experiments were conducted to determine the final critical value: with the antenna impedance fixed in a matched state, the amplitude of the synthesized signal was measured multiple times (≥30 times) to ensure that the energy harvesting efficiency was ≥80% at this threshold; then, with the antenna impedance fixed in a mismatched state, multiple measurements were taken again to ensure that the communication bit error rate was <5% at this threshold. Finally, the amplitude of the synthesized signal that simultaneously met both performance targets was taken as the benchmark detection threshold. This value falls within the initial reference range and, through experimental verification, becomes a practically effective state-distinguishing critical value, avoiding a disconnect between theoretical range and actual performance.

[0056] Traditional fixed threshold sensors have poor environmental adaptability and are only effective in standard environments. When the smart pole sensor is exposed to high outdoor temperatures in summer (e.g., 60°C) or high humidity in underground pipe networks (e.g., 90% RH), temperature and humidity changes can cause the decision benchmark of the detection circuit to drift. A matching state may be misjudged as a mismatch, triggering unnecessary communication switching, or a mismatch may be misjudged as a match, leading to a decrease in energy harvesting efficiency. In contrast, the three-dimensional temperature and humidity compensation lookup table pre-quantifies the impact of temperature and humidity on impedance, transforming passive threshold error correction into active compensation adjustment. This ensures that the threshold after real-time calibration always matches the impedance characteristics of the current environment.

[0057] Step S13: Perform a synthesis operation on the in-phase component I-channel signal and the quadrature component Q-channel signal to obtain the synthesized signal amplitude value. Compare the synthesized signal amplitude value with the impedance detection threshold after real-time calibration to generate a binary state identifier characterizing the antenna matching state.

[0058] Please see Figure 2 As shown, step S13 further includes:

[0059] Step S131: Calculate the squared amplitude values ​​of the in-phase component I signal and the quadrature component Q signal, respectively.

[0060] Step S132: Add the squared amplitude value of the in-phase component I signal and the squared amplitude value of the quadrature component Q signal, and then perform a square root operation to generate a composite signal amplitude value that reflects the antenna reflection intensity.

[0061] Step S133: Compare the amplitude value of the synthesized signal with the impedance detection threshold after real-time calibration. When the amplitude value of the synthesized signal is less than the impedance detection threshold after real-time calibration, generate a binary status identifier "0" representing the impedance matching state; otherwise, generate a binary status identifier "1" representing the impedance mismatch state.

[0062] The core of Step S13 is to generate a binary status identifier characterizing the antenna matching state through the comparison between the amplitude value of the synthesized signal and the real-time calibration threshold, realizing the quantitative output of the impedance state. The amplitude value of the synthesized signal is a physical quantity reflecting the intensity of the antenna reflection signal, which is obtained by synthesizing the amplitudes of the I-channel and Q-channel signals and is directly related to the antenna impedance state; the binary status identifier is a digital identifier for distinguishing the energy harvesting and communication phases. "0" represents the antenna impedance matching state, at which time the RF signal is efficiently absorbed and suitable for energy harvesting. "1" represents the antenna impedance mismatch state, at which time the RF signal is efficiently reflected and suitable for backscatter communication.

[0063] The implementation process of Step S13 is as follows: The processor reads the instantaneous amplitude value I of the in-phase component I-channel signal amp and the instantaneous amplitude value Q of the quadrature component Q-channel signal amp , and respectively calculates the squared amplitude values I amp 2 and Q amp 2 through a hardware multiplier. This operation can amplify the amplitude difference between the two channels of signals and improve the discrimination of the subsequent synthesis result; calculate the sum value of I amp 2 and Q amp 2 through an adder, and then perform a square root operation on the sum value through a square rooter to obtain the amplitude value A of the synthesized signal. The amplitude value A of the synthesized signal converts the vector points in the I / Q two-dimensional plane into scalar amplitudes, intuitively reflecting the intensity of the reflection signal. When the impedance is matched, the reflection is weak and the A value is small; when the impedance is mismatched, the reflection is strong and the A value is large; finally, perform threshold comparison and status identifier generation. The processor compares the amplitude value A of the synthesized signal with the impedance detection threshold Th after real-time calibration: If A < Th, it is determined that the antenna is in the impedance matching state and a binary status identifier "0" is generated; if A ≥ Th, it is determined that the antenna is in the impedance mismatch state and a binary status identifier "1" is generated. This identifier is stored in the register of the dual-phase detection power management sub-module for subsequent steps to read and call.

[0064] Traditional analog signal output suffers from state ambiguity and processing delays. Specifically, traditional technologies directly output analog signals, such as voltage or current, requiring subsequent control units to perform complex analog-to-digital conversion and signal analysis. This is susceptible to noise interference, leading to processing delays, and the critical states of analog signals are difficult to define; for example, signals approaching a threshold cannot be determined as either a match or a mismatch. In contrast, synthesized signal amplitude values ​​quantize analog signals into clear digital identifiers, with no ambiguity in the distinction between "0" and "1." Subsequent software state machines can directly read these identifiers for state switching, significantly reducing processing delays. For example, when the synthesized signal amplitude is 15 ohms and the real-time calibration threshold is 20 ohms, a "0" identifier is generated, and the system immediately enters the energy harvesting phase; when the amplitude rises to 25 ohms, a "1" identifier is generated, and the system switches to the communication phase. The switching between these two states requires no additional signal analysis, resulting in improved response speed.

[0065] The binary state identifier generated in step S10 is the sole input for constructing the historical state identifier sequence in step S20. If the detection in step S10 is inaccurate, the phase switching prediction in step S20 will be based on incorrect state data, leading to energy-communication timing conflicts. For example, if a matching state is mistakenly determined to be mismatched, prioritizing communication will result in insufficient energy. On the other hand, accurate detection in step S10 ensures that the prediction logic in step S20 is based on reliable data, achieving coordinated scheduling of energy and communication. The state determination result in step S10 directly affects the generation of the switching control sequence in step S30. If the detection in step S10 is incorrect, step S30 will execute incorrect switching actions, such as forcing communication when energy is insufficient, leading to sensor disconnection. On the other hand, accurate determination in step S10 ensures that the switching command in step S30 matches the actual impedance state, avoiding invalid actions. Without step S10, the entire environmental backscatter communication system would revert to the traditional detection mode: impedance drift under high temperature and humidity would lead to misjudgment of the state, frequent conflicts between energy harvesting and communication, and sensor nodes would either be unable to communicate due to insufficient energy or exhaust energy due to excessive communication, failing to meet the "full node, full connection" requirements of smart cities and smart transportation; while step S10 provides a reliable state basis for subsequent steps through accurate detection, making the coordination of "energy harvesting-communication modulation" possible.

[0066] Step S20: Based on the binary state identifier, a historical state identifier sequence is formed; multiple energy-communication alternation cycles in the historical state identifier sequence are identified, and the multiple energy-communication alternation cycles are statistically analyzed to calculate the average energy harvesting phase duration and the average communication phase duration; based on the average energy harvesting phase duration, the average communication phase duration, and the binary state identifier, a phase switching prediction signal is generated.

[0067] Further, step S20 includes:

[0068] Step S21: Store the binary status identifier into a circular buffer to form a dynamically updated sequence of historical status identifiers;

[0069] The circular buffer is a fixed-capacity data storage area within the RAM of the two-phase detection power management submodule. It features a first-in, first-out (FIFO) characteristic to ensure the latest state data is always retained. The circular buffer capacity is set to N storage units, with the value of N determined based on the system sampling period and the required analysis time window. It must cover at least three potential energy-communication alternation cycles to ensure pattern extraction. For example, if the sampling period is 100 microseconds and a typical cycle has 100 sampling points, then N is set to 128 storage units, covering the complete cycle while reserving redundant space. Write and read pointers are configured; the write pointer is used to locate the storage position of new data, and the read pointer is used to retrieve data. The two-phase detection power management submodule executes step S13 with a fixed sampling period Ts. Each time a binary state identifier is generated, it is immediately written to the current write pointer position in the circular buffer. After writing, the write pointer automatically moves one position to the right. When the pointer reaches the end of the buffer, it automatically jumps to the beginning position, overwriting the oldest stored state identifier. This FIFO mechanism ensures that the buffer always retains the state data of the most recent N sampling cycles, forming a dynamically updated historical state identifier sequence. After each new data is written, the dual-phase detection power management submodule verifies the integrity of the data through CRC check to avoid data bit flipping due to electromagnetic interference. If the check fails, the output result of step S13 is reread and rewritten to ensure the accuracy of the historical sequence.

[0070] In traditional technologies, state determination relies solely on a single-moment binary state identifier. This instantaneous value is susceptible to environmental noise, such as electromagnetic interference in the shadow area of ​​a large-span metal structure or transient signal fluctuations, leading to misjudgments. For example, a brief interference signal might be misinterpreted as impedance mismatch, causing unnecessary communication switching, or the true state change might be missed, resulting in untimely energy harvesting. Step S21 addresses this deficiency by storing historical sequences in a circular buffer. The historical sequence contains multiple consecutive state data, reflecting trends in state changes rather than instantaneous values. For instance, a sequence of five consecutive "0"s more reliably characterizes the energy harvesting phase than a single "0" which might indicate noise interference. Furthermore, the historical data in the circular buffer can be used for later fault tracing. For example, frequent alternation of "0" and "1" within a certain period can be traced back to channel quality deterioration during that time, such as increased metal multipath attenuation, providing a basis for system optimization. Simultaneously, the first-in-first-out mechanism of the circular buffer eliminates the need for additional expansion, reducing RAM usage and adapting to the low-power, small-size hardware requirements of sensor nodes.

[0071] Step S21 receives the binary status identifier generated in step S13 to ensure the accuracy of the source of the sequence data. If there is an error in the identifier in S13, S21 will mitigate the impact of a single error through the trend of continuous data. It also provides a complete historical sequence for step S22. Without step S21, step S22 will lack data to support periodic identification and will have to rely solely on subjective experience to determine the switching timing, reverting to the blindness of traditional techniques. Without S21, the entire "statistics-period identification-switching prediction" link in step S20 will be broken, making it impossible to extract the system's operating rhythm from historical data. This results in the phase switching prediction in S23 losing its quantitative basis, leading to timing conflicts between energy and communication.

[0072] Step S22: Identify multiple energy-communication alternation cycles in the historical state identifier sequence, perform statistics on multiple energy-communication alternation cycles, and calculate the average energy harvesting phase duration and the average communication phase duration.

[0073] Please see Figure 3 As shown, step S22 further includes:

[0074] Step S221: Set a sliding window for the historical state identifier sequence, and count the length of the consecutive "0" sequence and the length of the consecutive "1" sequence within the sliding window;

[0075] Step S222: When the length of the consecutive "0" sequence and the length of the immediately following consecutive "1" sequence both exceed the preset shortest period, the "0"-"1" combination formed by the "0" sequence and the "1" sequence is marked as a complete energy-communication alternation cycle.

[0076] Step S223: Extract multiple energy-communication alternation cycles identified in the historical state identifier sequence, calculate the average length of all "0" sequences as the average energy harvesting phase duration, and calculate the average length of all "1" sequences as the average communication phase duration.

[0077] Specifically, step S22 addresses the problem in traditional technologies that fail to identify energy-communication alternation patterns, leading to blind switching timing and an inability to adapt to changes in system operating rhythm. The core of step S221 is to use a sliding window to statistically analyze the length of consecutive "0" and "1" sequences. The implementation process is as follows: First, the width W of the sliding window is set. The value of W is determined based on the length N of the historical state identifier sequence and the potential period length. It needs to cover at least one complete period's possible length while avoiding excessive width that could cause computational delays. For example, if N=128, and a typical period has 100 sampling points, then W is set to 32 sampling points, which covers the period segment while ensuring that local patterns can be analyzed with each slide. Then, the sliding window starts from the beginning of the historical state identifier sequence, with one sampling point... The step size slides backward point by point. At each window position, the processor of the two-phase detection power management submodule calls the sequence statistics subroutine to traverse all binary state identifiers within the window, and calculates the maximum length L0 of consecutive "0"s and the maximum length L1 of consecutive "1"s. This calculation is achieved through state transition detection; that is, when an identifier changes from "0" to "1" or from "1" to "0", the length of the previous state is recorded. For example, if the data within the window is "0,0,0,1,1", then L0=3 and L1=2. The statistical results are stored in real-time in the submodule's temporary data area for subsequent steps. Traditional technologies lack effective local data analysis methods, making it impossible to extract the length characteristics of consecutive states from long sequences. This leads to an inability to distinguish between "effective phase" and "instantaneous interference," such as misjudging a single "1" as a communication phase or missing a sequence of consecutive "0"s within a short period. Step S221 achieves local data focusing by using a sliding window to solve the problem: the statistics within the window can filter out the instantaneous interference of a single sampling point. For example, when the window width is 32, the proportion of a single "1" interference within the window is extremely low and will not be counted as a valid L1. At the same time, the sliding mechanism ensures coverage of the entire historical sequence and avoids the omission of local data due to a fixed window. For example, the periodic patterns in the latter half of the sequence can also be accurately captured.

[0078] The core of step S222 is to determine the complete energy-communication alternation cycle, which is achieved by first setting a preset minimum period T. min T min The value of T is determined based on the minimum operating period of the system and must be greater than the maximum possible duration of the instantaneous interference. For example, if the sampling period Ts = 100 microseconds and the instantaneous interference usually does not exceed 5 sampling points, then T... min The sampling points are set to 10 to avoid misjudging short interference as valid phase; then the submodule processor reads L0 and L1 of each window position stored in S221 and determines whether "L0≥T" exists. min And L1≥T minIf the window data exists, further verify whether the sequence of "1"s within the window directly follows the sequence of "0", that is, the state jumps from "0" to "1" without any other state being inserted. If this is satisfied, then the "0"-"1" combination within the window is marked as a complete energy-communication alternation cycle. To avoid duplicate marking, the processor records the start and end sampling points of the marked cycle, and the judgment is not repeated when the subsequent window slides to this interval. Traditional techniques cannot define "effective cycle" and "invalid interference". For example, it may misjudge the combination of short "0" sequences and short "1" sequences as a cycle, leading to distorted cycle-based decision-making. Step S222 uses a preset minimum period T. min Verify using the "0"-"1" sequence to resolve the defect: T min Short-term interference is filtered out to ensure that only phase combinations with sufficient duration are identified as periods; sequential verification ensures compliance with the natural workflow of "energy harvesting → communication" and avoids misjudging "1"-"0" combinations (communication → energy harvesting) as new periods, thus ensuring the logical consistency of period identification.

[0079] Extract all complete energy-communication alternation cycles marked in step S222, traverse each cycle, and record the actual length of the "0" sequence, denoted as L0. i , where i is the period index and the actual length of the "1" sequence is denoted as L1. i Then the arithmetic mean subroutine is called to calculate all L0 values. i The average value of L1 is the average energy harvesting phase duration, Tave0; similarly, all L1 values ​​are calculated. i The average value of Tave0 and Tave1 is the average communication phase duration, Tave1. After calculation, Tave0 and Tave1 are stored in the threshold storage area of ​​the submodule for subsequent steps. Traditional technology lacks a mechanism for calculating the average duration, causing the switching timing to rely solely on fixed empirical values, which cannot adapt to changes in the system's operating rhythm. For example, when environmental carrier attenuation shortens the communication time, the fixed empirical value still controls communication according to the original duration, easily leading to energy depletion. Step S223 solves this deficiency by statistically averaging the duration of multiple cycles: Tave0 and Tave1 reflect the actual operating rhythm of the system, rather than subjective settings. For example, when the shadow of a metal structure shortens the communication time, Tave1 will automatically decrease, providing a basis for switching in step S23 to adapt to the current environment.

[0080] The Tave0 and Tave1 output in step S22 are the direct basis for setting the switching decision threshold in step S23. The collaboration of the two steps makes the switching prediction based on historical patterns and real-time status, such as the duration and energy storage voltage. Compared with relying solely on real-time status, the reliability of the decision is improved. For example, if step S23 only relies on real-time voltage, it may make a misjudgment due to voltage fluctuations. However, by combining Tave0, it can be verified whether energy collection has reached a statistically sufficient state.

[0081] Step S23: Set a handover decision threshold based on the average energy harvesting phase duration and the average communication phase duration; determine the current operating phase based on the latest binary state identifier in the historical state identifier sequence; generate a phase handover prediction signal based on the current operating phase and the handover decision threshold; the handover decision threshold includes a minimum energy harvesting threshold and a communication integrity threshold; the operating phase includes an energy harvesting phase and a communication phase; the phase handover prediction signal includes a phase handover prediction signal for switching to the communication phase and a phase handover prediction signal for switching to the energy harvesting phase.

[0082] Further, step S23 includes:

[0083] Step S231: Calculate the duration of the current working phase and monitor the real-time voltage value of the energy storage capacitor integrated in the dual-phase detection power management submodule.

[0084] Step S232: When the current working phase is the energy harvesting phase, if the duration of the current working phase is greater than or equal to the minimum energy harvesting threshold, then continue to detect whether the real-time voltage value of the energy storage capacitor is greater than or equal to the preset communication start voltage threshold. If so, then generate a phase switching prediction signal to switch to the communication phase.

[0085] Step S233: When the current working phase is the communication phase, if the duration of the current working phase is greater than or equal to the communication integrity threshold, or the real-time voltage value of the energy storage capacitor is less than the preset safe working voltage lower limit, then a phase switching prediction signal for switching to the energy harvesting phase is generated.

[0086] Specifically, the core of step S23 is to establish a dynamic and adaptive switching decision mechanism. By integrating historical statistical patterns with real-time status monitoring, an accurate phase switching prediction signal is generated to solve the problem of energy supply and data transmission timing conflicts caused by the lack of intelligent scheduling in traditional environmental backscatter communication systems.

[0087] The handover decision thresholds are set using a dynamic generation mechanism based on historical statistics. The minimum energy harvesting threshold is set as the average energy harvesting phase duration (Tave0) multiplied by the energy guarantee coefficient α, where α ranges from 0.7 to 0.85. This coefficient is determined based on the priority of system energy demand and the stability of the environmental radio frequency signal: when the environmental radio frequency signal is stable and energy demand is high priority, α is set to a larger value to ensure sufficient energy accumulation; when signal fluctuations are frequent or communication needs are urgent, α is set to a smaller value to accelerate the phase switching frequency. The communication integrity threshold is set as the average communication phase duration (Tave1) multiplied by the data guarantee coefficient β, where β ranges from 0.85 to 0.95. This coefficient is determined considering data packet integrity requirements and communication protocol characteristics: for protocols that support breakpoint resumption, β can be set to a smaller value; for protocols that require single complete transmission, β needs to be set to a larger value to ensure that data packets are not truncated. The dynamic nature of the two thresholds is reflected in their automatic adjustment as Tave0 and Tave1 are updated in real time. When changes in the system operating environment cause changes in the average duration, the thresholds adapt accordingly, ensuring that the handover decision always conforms to the current system rhythm.

[0088] The current operating phase is determined by parsing the latest binary state identifier in the historical state identifier sequence. If the latest identifier is "0", the system determines that it is currently in the energy harvesting phase. In this phase, the antenna impedance is matched, and ambient radio frequency signals are efficiently absorbed and converted into electrical energy stored in the energy storage capacitor. If the latest identifier is "1", the system determines that it is currently in the communication phase. In this phase, the antenna impedance is mismatched, and radio frequency signals are efficiently reflected to achieve backscatter communication. The clear distinction between the energy harvesting phase and the communication phase provides a state basis for subsequent differentiated switching strategies, avoiding the switching logic confusion caused by state ambiguity in traditional technologies.

[0089] In step S231, the duration of the current working phase is counted by traversing the historical state identifier sequence in reverse. Starting from the latest binary state identifier, the traversal proceeds backward, counting the number N of consecutive identical binary state identifiers. dur N continues until a different binary status identifier is encountered or the start position of the sequence is reached. dur Multiplying by the sampling period Ts gives the duration T of the current working phase. current For example, if the sampling period Ts = 100 microseconds and 20 consecutive "0"s appear, then T... current=20 × 100 microseconds = 2 milliseconds. The real-time voltage value of the energy storage capacitor is detected through a voltage monitoring circuit integrated within the dual-phase detection power management submodule, consisting of a voltage divider resistor and an ADC module. The voltage divider resistor reduces the high voltage of the energy storage capacitor (for example, 3.3V-5V) to the input range of the ADC module, such as 0-3.3V. The ADC module samples the divided voltage at a fixed frequency, converting the analog voltage signal into a digital signal to obtain the real-time voltage value V of the energy storage capacitor. cap This monitoring process needs to be continuous, with sampled data stored in real-time in the submodule's temporary register to ensure that the latest voltage data is used when making a switching decision. Traditional technologies have the drawback of only looking at the real-time status without historical reference, making it impossible to determine whether the duration of the current phase conforms to the system's pattern. Step S231, however, correlates the real-time status with historical patterns by statistically analyzing the duration, avoiding misjudgments such as "switching after a short period of time." At the same time, voltage monitoring ensures that the energy has actual supporting capabilities, rather than relying solely on the theoretical sufficiency of time. The combination of these two aspects provides comprehensive status data for subsequent decisions.

[0090] The switching logic in step S232 embodies a balance strategy between energy priority and communication readiness. When the system is in the energy harvesting phase and has been in this phase for a duration that reaches the minimum energy harvesting threshold, it indicates that energy harvesting, based on historical patterns, is nearly sufficient. At this point, the real-time voltage value of the energy storage capacitor is further checked to see if it reaches the communication start-up voltage threshold. The satisfaction of these two conditions ensures that there is sufficient energy support when switching to the communication phase, without wasting energy due to premature switching. The method for setting the communication start-up voltage threshold is as follows: parameters are extracted from the hardware manual, and the maximum operating current of the ASK modulator is added to the minimum operating current of the data transmission unit to obtain the minimum operating current I of the communication module. comm,min Simultaneously, the sum of the load resistance of the ASK modulator and the resistance of the data transmission unit is calculated as the equivalent load resistance R. comm Based on I comm,min and R comm Calculate the base starting voltage V according to Ohm's law. start,base A fluctuation margin coefficient k with a value range of 1.1 to 1.3 is introduced. start To address the voltage drop during capacitor discharge, the final communication start-up voltage threshold is V. start =V start,base ×k startFinally, the system was validated through 100 communication tests under standard conditions to ensure a communication success rate of ≥98%, thus confirming the setting was effective. If the energy harvesting minimum threshold has been reached for a certain duration but the real-time voltage value of the energy storage capacitor has not met the standard, the system continues to maintain the energy harvesting phase until the voltage condition is met. This delayed switching mechanism avoids the problem of insufficient energy caused by temporary attenuation of the environmental radio frequency signal. If the real-time voltage value of the energy storage capacitor has met the standard but the energy harvesting minimum threshold has not been reached for a certain duration, the current phase is also maintained to ensure that the continuity of energy harvesting is not interrupted by frequent switching. After the prediction signal for switching to the communication phase is generated, the system reserves a switching preparation period Tprep, during which the ASK modulator performs parameter pre-configuration and the data buffer loads the data to be sent, ensuring that efficient communication can begin immediately upon switching.

[0091] The switching logic in step S233 prioritizes communication integrity and system safety. When the system is in the communication phase and the duration has reached the communication integrity threshold, it indicates that data transmission based on historical patterns is nearing completion. A predictive signal for switching to the energy harvesting phase is generated to avoid energy depletion due to excessively prolonged communication time. When the real-time voltage value of the energy storage capacitor drops to the lower limit of the safe operating voltage, a switching signal is immediately generated regardless of whether communication is complete. This forced switching mechanism ensures that the system will not completely fail due to energy depletion. The method for setting the lower limit of the safe operating voltage is as follows: obtain the minimum allowable discharge voltage V to avoid capacitor damage from the energy storage capacitor datasheet. cap,min Obtain its minimum startup voltage V from the manual of the dual-phase detection power management submodule. eh,start The maximum value of the two conditions is taken as the lower limit of the safe operating voltage. Then, calibrating through 100 discharge tests ensures that the capacitor capacity decay is ≤5% and the module startup success rate is ≥99%, thus completing the setup. The two switching conditions use an "OR" logic relationship; switching is triggered when either condition is met, reflecting the system's safety-first design philosophy. The switching prediction signal includes a switching type identifier and the expected switching time. The switching type identifier distinguishes between "normal switching" and "emergency switching." Normal switching is triggered based on a time threshold, while emergency switching is triggered based on the lower voltage limit. Subsequent control logic can adopt different processing strategies based on the switching type; for example, during an emergency switch, critical status data is prioritized for saving.

[0092] Step S23 implements predictive handover decision-making through a cyclic threshold method. Compared to traditional reactive mechanisms, predictive decision-making provides a time window for the system to prepare for handover actions, significantly reducing handover latency. The adaptive nature of the dynamic threshold allows the system to adapt to environmental changes. For example, when the shadow area of ​​the metal structure causes carrier attenuation, Tave1 automatically decreases, and the communication integrity threshold is adjusted accordingly, ensuring that the handover timing matches the actual communication capability. The dual-condition judgment mechanism ensures energy sufficiency in the energy harvesting phase and data integrity in the communication phase. The differentiated strategy for the two phases reflects precise control over both energy and communication objectives. Real-time monitoring of the energy storage voltage introduces dynamic feedback in the energy dimension, making the handover decision not only based on time patterns but also combined with the actual energy state, avoiding energy imbalances that may result from purely time-based control. The emergency handover mechanism serves as a safety guarantee for the system, ensuring that the system can maintain basic operation even in extreme environments, improving overall robustness.

[0093] The synergy between steps S23 and S22 is reflected in the integration of historical statistics and real-time decision-making. The average duration provided by S22 provides a quantitative basis for setting the dynamic threshold in S23, ensuring that the switching decision follows historical patterns while maintaining flexibility. The synergy with step S31 is reflected in the seamless integration of the prediction signal and the state machine. The prediction signal generated by S23 directly triggers the state transition of the software state machine in S31. The prediction lead time reserves execution time for the state machine's preparation actions, ensuring a smooth switching process. The closed-loop optimization with step S33 forms a feedback loop. The energy accumulation and data transmission volume statistics of S33 are fed back to S23 to fine-tune the energy guarantee coefficient α and data guarantee coefficient β, gradually converging the switching decision threshold setting to the optimal value. Without step S23, the system would degenerate into a passive response mode, only switching after energy depletion or communication interruption, leading to frequent service interruptions. The lack of a prediction mechanism prevents subsequent steps from being prepared in advance, significantly increasing switching latency and impacting overall communication efficiency.

[0094] Step S20 fundamentally solves the timing coordination problem of energy harvesting and communication modulation in environmental backscatter communication systems by constructing a statistical analysis and prediction mechanism based on historical data. The establishment of historical state identifier sequences transforms discrete instantaneous states into continuous time series, providing a data foundation for extracting the system's operating patterns. The sliding window sequence analysis method effectively filters out instantaneous interference, extracting the true energy-communication alternation cycle and avoiding the impact of noise on cycle identification. The calculation of the average duration quantifies the statistical characteristics of multiple cycles, reflecting the stable operating rhythm of the system under the current environment and providing a reliable basis for predicting switching timing. The cyclic threshold method combines historical patterns with real-time states, achieving intelligent decision-making that is both experience-based and adaptable to the current situation, exhibiting stronger environmental adaptability compared to fixed threshold methods. The generation of phase switching prediction signals reserves preparation time for subsequent switching control, transforming the original hard switching into soft switching, significantly improving the smoothness and success rate of switching. Step S20 establishes a complete prediction chain from data acquisition, pattern extraction, parameter calculation to decision generation, upgrading the traditional blind switching to data-driven intelligent scheduling, and providing decision support for the stable operation of the environmental backscatter communication system in complex environments.

[0095] Step S30: Receive the phase switching prediction signal to generate a switching control sequence to be executed, parse the target state type in the switching control sequence, generate corresponding modulation control commands according to different target state types, execute the switching control sequence and modulation control commands, and form a closed-loop optimization.

[0096] In the shadowed areas of long-span metal structures, multipath attenuation of the environmental carrier causes bit synchronization drift. Traditional hardware handover typically involves high signal transmission delays, easily missing brief communication windows under weak carriers. Static modulation parameters can further lead to bit error rates significantly exceeding acceptable limits. Step S30, through a three-level architecture of "software state machine prediction and preparation - dynamic modulation parameter adaptation - closed-loop feedback optimization," compresses the handover response from a high level at the hardware level to a lower level, reducing the communication bit error rate. Simultaneously, it enables the system to self-adapt to long-term environmental changes, providing stable communication support for scenarios such as smart city smart pole integration and smart transportation dynamic perception.

[0097] Further, step S30 includes:

[0098] Step S31: Receive the phase switching prediction signal, configure the state transition conditions in the software state machine containing "energy harvesting state", "communication preparation state" and "communication execution state", and generate the switching control sequence to be executed.

[0099] In step S31, the software state machine is constructed using a finite state machine architecture at the microcontroller firmware layer. It includes three core states: energy harvesting, communication preparation, and communication execution. Each state corresponds to a specific operating mode of the environmental backscatter communication system. The energy harvesting state corresponds to the antenna impedance matching mode, where the antenna load impedance equals the antenna characteristic impedance, and the environmental radio frequency signal is efficiently absorbed and converted into electrical energy. The communication preparation state is a transitional state used to complete the ASK modulator parameter configuration and data buffer loading before the actual switching. The communication execution state corresponds to the antenna impedance mismatch mode, where the antenna load impedance deviates from the characteristic impedance, and the radio frequency signal is efficiently reflected to carry modulation information. The configuration of state transition conditions follows strict triggering logic, such as... Figure 4 As shown, the transition from the energy harvesting state to the communication preparation state is triggered by the phase switching prediction signal generated in step S23, which contains the predicted switching time information. Upon receiving the signal, the software state machine immediately enters the communication preparation state. The transition from the communication preparation state to the communication execution state is triggered by the system clock. When the clock counter reaches the predicted switching time, the state machine automatically completes the transition, i.e., automatically switches to the communication execution state. The transition condition from the communication execution state back to the energy harvesting state is the detection that data transmission has been completed or the receipt of the phase switching prediction signal for switching to the energy harvesting phase. The process of generating the switching control sequence is as follows: the software state machine generates a structured data packet containing the target state type, the expected switching time, and the switching priority based on the current state and the transition conditions. The target state type is represented by an enumerated variable, with a value range of one of the three states: energy harvesting state, communication preparation state, and communication execution state. The expected switching time is represented by the system clock count value, with an accuracy of microseconds. The switching priority distinguishes between normal switching and emergency switching; emergency switching has the authority to interrupt the current operation.

[0100] Traditional hardware-triggered handover suffers from inherent signal propagation delays. From the generation of the handover signal by the detection circuit to the response of the execution circuit, signal transmission requires multiple stages of analog circuitry. The charging and discharging processes of each stage accumulate to create millisecond-level delays. In scenarios with rapidly changing environmental carrier waves, the actual handover moment may have missed the expected window. Software state machines, by digitizing the handover logic, eliminate the accumulated delays from analog signal propagation. State transitions are completed within a single instruction cycle, improving response speed to the microsecond level. The introduction of a communication preparation state is a key innovation. Traditional technologies directly switch from energy harvesting to communication, requiring multiple preparatory tasks to be completed simultaneously at the handover moment, prolonging the handover process. The communication preparation state utilizes the time difference between the predicted signal and the actual handover to pre-complete time-consuming operations such as ASK modulator initialization, data packet assembly, and transmit power setting. This allows the actual handover moment to only require changing the antenna load impedance, compressing the handover time from milliseconds to microseconds. The structured design of the handover control sequence makes handover actions traceable and schedulable. Each handover action has a clear timestamp and priority, facilitating resource scheduling in multi-tasking environments and avoiding handover conflicts.

[0101] Step S32: Analyze the target state type in the switching control sequence and generate corresponding modulation control commands according to different target state types;

[0102] In step S32, the generation of the modulation control instruction adopts a differential strategy based on the target state type, and corresponding ASK modulation parameters are configured for different target state types. When the target state type is the communication execution state, according to the synthesized signal amplitude value and the historical state identifier sequence, the modulation depth and symbol rate of the ASK modulator are dynamically adjusted to generate a modulation control instruction corresponding to the communication execution state; the determination of the modulation depth is adaptively adjusted according to the synthesized signal amplitude value. The synthesized signal amplitude value reflects the reflection ability of the current antenna. The larger the amplitude value, the higher the impedance mismatch degree and the stronger the reflection ability. At this time, increasing the modulation depth can make full use of the reflection ability to improve the communication signal strength; on the contrary, when the amplitude value is small, the modulation depth is appropriately reduced, but it is necessary to ensure that the minimum recognizable signal strength is maintained. The calculation of the modulation depth M uses a piecewise function to ensure physical rationality: when A≥Ath, M = Mbase×(1 + k×(A - Ath) / Ath), at this time the antenna reflection ability is higher than the standard level, and the additional reflection ability is fully utilized by increasing the modulation depth; when A < Ath, M = Mbase×exp(-k×(Ath - A) / Ath), an exponential decay function is used to ensure that the modulation depth decreases smoothly but always remains positive. exp() represents the natural exponential function. The characteristics of the exponential function enable M to still maintain the minimum value of Mbase×exp(-k) when A approaches 0, avoiding the situation that the signal is too weak to be recognized by the receiving end. Among them, Mbase is the basic modulation depth, and its value range is 0.3 to 0.5, which is set based on the conventional effective range of ASK modulation in backscatter communication that takes into account signal recognition and energy consumption, ensuring the basic communication requirements in the absence of extreme environmental interference; k is the adjustment coefficient, and its value range is 0.2 to 0.4, which is used to control the sensitivity of the modulation depth to the change of A, control the growth amplitude in the A≥Ath segment to avoid overmodulation, and control the attenuation speed in the A < Ath segment to ensure that the signal does not weaken too quickly; A is the synthesized signal amplitude value generated in step S13; Ath is the synthesized signal amplitude threshold, which is equal to the impedance detection threshold after real-time calibration generated in step S12. The physical basis of the piecewise function design is that when the antenna reflection ability is higher than or equal to the threshold, that is, A≥Ath, the system is in good communication conditions, and the communication performance can be improved by linearly increasing the modulation depth; when the reflection ability is lower than the threshold, that is, A < Ath, the system is in a restricted condition, and conservative adjustment through exponential decay is required to adapt to the weak reflection environment and ensure the basic communication ability. The lower bound characteristic of the exponential function naturally avoids the non-physical situation of too small or negative modulation depth.

[0103] The determination of the symbol rate is based on the stability analysis of the historical state identifier sequence: the processor calculates the state transition frequency F change in the historical state identifier sequence, that is, the number of switches between "0" and "1" per unit time. The state transition frequency F change directly reflects the degree of interference of the channel, and Fchange A higher F indicates more frequent state transitions caused by metal multipath attenuation, electromagnetic interference, etc., resulting in poorer channel stability. In this case, it is necessary to reduce the symbol rate to extend the duration of a single symbol and improve anti-interference capability. The energy of a single symbol increases with time, making it easier to resist noise and interference; F change The lower the value, the more stable the channel, which allows for increased speed to maximize data throughput. The formula for calculating the symbol rate R is R = Rmax × exp(-λ × F). change In this context, Rmax is the maximum symbol rate, determined by the hardware processing capabilities of the two-phase detection power management submodule, such as the microcontroller clock frequency and ASK modulator bandwidth settings, to ensure the rate does not exceed hardware limits; λ is the attenuation factor, ranging from 0.1 to 0.3, used to control the rate as it varies with F. change Smoothness of descent, avoiding F change Even a small increase can lead to a sharp drop in the rate, or a significant increase can still result in an excessively high rate; an exponential function can achieve a rate that follows F. change The monotonically smooth attenuation of exponential functions better reflects the nonlinear relationship between channel stability and rate adaptation compared to linear attenuation. In the initial stage of a channel transitioning from stability to instability, the rate does not need to decrease significantly; only minor adjustments are required to maintain communication. However, when stability deteriorates rapidly, the rate needs to decrease quickly to ensure basic communication. Linear attenuation cannot achieve this differentiated adaptation, while exponential functions naturally meet this requirement. When the target state is energy harvesting, an ASK modulator shutdown instruction is generated, setting the modulation depth and symbol rate to 0, and simultaneously placing the modulator's power amplifier into sleep mode to conserve energy. When the target state is communication preparation, the current modulator state remains unchanged, but the modulation parameters for the next stage are preloaded into the register to ensure that the new parameters can be applied immediately during state transitions.

[0104] Traditional static modulation parameter configurations cannot adapt to the dynamic characteristics of backscatter communication. A fixed modulation depth either wastes transmission capacity or results in insufficient signal strength when channel conditions change; a fixed symbol rate either leads to excessively high bit error rates or low transmission efficiency when channel quality fluctuates. Step S32 achieves cross-layer parameter optimization by combining the amplitude value of the synthesized signal at the physical layer with the stability of the historical state identifier sequence at the link layer: the synthesized signal amplitude value provides real-time feedback on the antenna's current reflection capability, ensuring the modulation depth is fully utilized without exceeding the antenna's physical limits; the stability analysis of the historical state identifier sequence provides statistical characteristics of channel quality, enabling a dynamic balance between reliability and efficiency in the symbol rate. The exponential decay adjustment strategy of the modulation parameters ensures the smoothness of parameter changes, avoiding signal abrupt changes and receiver synchronization loss that may result from step adjustments. Differentiated state processing strategies enable the system to achieve local optima in different operating modes: in energy harvesting mode, the modulator is completely shut down to maximize energy harvesting efficiency; in communication execution mode, parameters are dynamically optimized to maximize communication performance; and parameter preloading in communication preparation mode eliminates configuration delays during handover.

[0105] Step S33: Perform antenna load impedance switching according to the switching control sequence and modulation control command to realize the switching between the energy harvesting phase and the communication phase. During the energy harvesting phase, the energy accumulation is counted, and during the communication phase, the data transmission is counted. Adjust the switching decision threshold according to the balance between the energy accumulation and the data transmission.

[0106] In step S33, the antenna load impedance switching is implemented by controlling the RF switch network through the microcontroller's GPIO port. The RF switch network consists of PIN diodes or MEMS switches, with a response time in the nanosecond range. The impedance switching is specifically implemented as follows: when switching from the energy harvesting phase to the communication phase, the microcontroller outputs a high level to the GPIO port, driving the RF switch to connect the antenna to a high-impedance load, changing the antenna impedance from a matched state to a mismatched state; conversely, it outputs a low level to connect the antenna to a matched load, restoring the impedance matching state. During the energy harvesting phase, the energy storage capacitor voltage is sampled every fixed time interval Δt, recording the voltage sequence V1, V2, ..., Vn, where Vn is the nth energy storage capacitor voltage value in the voltage sequence, and the energy accumulation E = 0.5 × C × (Vn) 2 -V1 2 In this context, C represents the capacitance of the energy storage capacitor. Data transmission volume is counted by counting successfully transmitted data frames: during the communication phase, for each successfully transmitted data frame and received acknowledgment, the counter increments by 1, and the data transmission volume D equals the number of successful frames multiplied by the data size per frame. The balance relationship is evaluated using the energy efficiency ratio index: defined as η = D / E, representing the data transmission volume per unit of energy. The ideal value of the energy efficiency ratio η...target The energy supply condition and data transmission requirements of the application scenario are determined accordingly. For example, a smart city sensor node requires at least M bits of data to be transmitted per joule of energy, then η... target =M bits / joule. The mechanism for achieving closed-loop optimization is as follows: if η is less than η for Nc consecutive periods. target A value of Nc between 3 and 5 indicates insufficient data transmission per joule of energy. Possible causes include: a short energy collection time leading to a small energy base E per cycle, or a long but inefficient communication time resulting in slow data growth (D) and rapid energy consumption. The system improves energy efficiency by adjusting the switching strategy: increasing the minimum energy collection threshold by a step adjustment δ1 increases the energy base E per cycle. Even if D remains unchanged, the increase in the denominator of η=D / E is less than the improvement in communication success rate brought about by sufficient energy, ultimately increasing η. Simultaneously, decreasing the communication integrity threshold by a step adjustment δ2 avoids forcibly extending communication under poor channel conditions, reducing ineffective energy consumption, and allowing limited energy to be used for short-term communication with high success rates, thus increasing the effective data transmission per unit of energy. Both δ1 and δ2 are 3% to 7% of the current corresponding switching decision threshold. If η is greater than η target Furthermore, if the energy storage capacitor voltage remains close to its upper limit, indicating excess energy, a reverse adjustment is made: the minimum energy collection threshold is reduced, and the communication integrity threshold is increased to improve data throughput. The adjusted thresholds are transmitted to step S23 via the feedback channel to update the switching decision threshold, forming a closed-loop control.

[0107] Traditional open-loop control cannot perceive the actual execution effect, and the merits of switching strategies can only be judged through offline analysis, making real-time optimization impossible. Step S33 establishes an adaptive optimization mechanism based on performance feedback by quantifying the actual effects of energy accumulation and data transmission: real-time statistics of energy accumulation provide a direct measure of energy collection efficiency, avoiding energy estimation errors that may result from relying solely on time; accurate counting of data transmission reflects the actual communication results, eliminating interference from invalid transmissions such as retransmissions and packet loss; the introduction of the energy efficiency ratio unifies the performance indicators of the two dimensions into a single optimization objective, simplifying the complexity of multi-objective optimization. The closed-loop optimization mechanism enables the system to have environmental adaptability. When the environmental carrier strength decreases, causing energy collection to slow down, the system automatically extends the energy collection time; when channel quality improves, increasing communication efficiency, the system automatically increases the proportion of communication time, ensuring a dynamic balance between energy and communication in changing environments. The step-by-step threshold adjustment strategy avoids system oscillations that may result from aggressive adjustments, with each adjustment controlled within a reasonable range, allowing system performance to gradually converge to the optimal operating point.

[0108] The phase switching prediction signal generated in step S23 provides a clear switching timing and type for the software state machine in step S30, making the switching action targeted. The synthesized signal amplitude value in step S13 provides physical layer feedback for the modulation depth adjustment in step S32, ensuring that the modulation parameters match the actual antenna capabilities. The historical state identifier sequence in step S21 provides channel quality assessment for the symbol rate adjustment in step S32, making the communication rate adapt to channel conditions. Without step S30, the system will lose its execution capability. Even if step S23 generates an accurate prediction signal, it cannot be converted into an actual switching action, and energy harvesting and communication will remain in a disordered state. The lack of rapid response capability at the software level causes the system to revert to a slow mode triggered by hardware, resulting in a sharp decline in performance in rapidly changing channel environments. The lack of a closed-loop optimization mechanism causes the system to lose its self-learning ability and become unable to adapt to long-term environmental changes, ultimately leading to a gradual deviation of performance from the optimal operating point.

[0109] Step S30 constructs a complete intelligent handover control system through the rapid response of the software state machine, dynamic optimization of ASK modulation parameters, and adaptive adjustment of closed-loop feedback, fundamentally solving the execution delay and environmental adaptability problems of the backscatter communication system. The software state machine elevates the handover logic from the hardware layer to the software layer, achieving microsecond-level handover response by leveraging the flexibility and high speed of software, reducing latency by several orders of magnitude compared to traditional hardware triggering. The innovative introduction of the communication preparation state separates handover preparation from handover execution, fully utilizing prediction lead time to complete time-consuming operations, simplifying the actual handover moment and further compressing the handover time. The cross-layer optimization mechanism of ASK modulation parameters breaks down the isolation between the physical layer and the link layer, achieving globally optimal parameter configuration by fusing state information from different layers, ensuring that communication performance remains high in dynamic environments. The closed-loop optimization mechanism endows the system with self-evolution capabilities; through continuous performance monitoring and parameter fine-tuning, the system can autonomously detect and adapt to slow environmental changes, extending the system's effective operating life. Step S30 organically combines the three stages of prediction, execution, and optimization, forming a complete control loop from decision-making to action and then to feedback. This upgrades the environmental backscatter communication system from passive response to active adaptation, providing execution assurance for the stable operation of sensor nodes in smart city and smart transportation scenarios.

[0110] Example 2

[0111] This embodiment, based on Embodiment 1, provides an environmental backscatter communication enhancement system based on dual-phase detection, such as... Figure 5 As shown, it includes:

[0112] Antenna matching status identification module: used to acquire the ambient radio frequency signal received by the antenna, generate in-phase component I signal and quadrature component Q signal through dual-channel quadrature demodulation processing; based on the in-phase component I signal and quadrature component Q signal, generate a binary status identifier representing the antenna matching status;

[0113] Phase switching prediction module: Based on binary state identifiers, a historical state identifier sequence is formed; multiple energy-communication alternation cycles in the historical state identifier sequence are identified, and the average energy harvesting phase duration and average communication phase duration are calculated by statistically analyzing the multiple energy-communication alternation cycles; based on the average energy harvesting phase duration, average communication phase duration, and binary state identifiers, a phase switching prediction signal is generated.

[0114] Phase switching execution module: It is used to receive the phase switching prediction signal, generate the switching control sequence to be executed, parse the target state type in the switching control sequence, generate the corresponding modulation control command according to different target state types, execute the switching control sequence and modulation control command, and form a closed loop optimization.

[0115] Furthermore, in the antenna matching state identification module, the method for generating a binary state identifier representing the antenna matching state based on the in-phase component I-channel signal and the quadrature component Q-channel signal includes:

[0116] A three-dimensional temperature and humidity compensation lookup table is established, and ambient temperature and humidity values ​​are collected. The impedance offset compensation value is obtained by searching the three-dimensional temperature and humidity compensation lookup table, and the impedance detection threshold after real-time calibration is generated by combining it with the pre-calibrated reference detection threshold. The in-phase component I signal and the quadrature component Q signal are synthesized to obtain the amplitude value of the synthesized signal. The amplitude value of the synthesized signal is compared with the impedance detection threshold after real-time calibration to generate a binary status identifier that represents the antenna matching state.

[0117] The method for comparing the synthesized signal amplitude value with the impedance detection threshold after real-time calibration to generate a binary state identifier characterizing the antenna matching state includes:

[0118] Step S131: Calculate the squared amplitude values ​​of the in-phase component I signal and the quadrature component Q signal, respectively.

[0119] Step S132: Add the squared amplitude value of the in-phase component I signal and the squared amplitude value of the quadrature component Q signal, and then perform a square root operation to generate a composite signal amplitude value that reflects the antenna reflection intensity.

[0120] Step S133: Compare the synthesized signal amplitude value with the impedance detection threshold after real-time calibration. When the synthesized signal amplitude value is less than the impedance detection threshold after real-time calibration, generate a binary status identifier "0" to indicate the impedance matching state; otherwise, generate a binary status identifier "1" to indicate the impedance mismatch state.

[0121] Furthermore, in the phase-switching prediction module, the method for generating the phase-switching prediction signal includes:

[0122] Step S231: Calculate the duration of the current working phase and monitor the real-time voltage value of the energy storage capacitor integrated in the dual-phase detection power management submodule.

[0123] Step S232: When the current working phase is the energy harvesting phase, if the duration of the current working phase is greater than or equal to the minimum energy harvesting threshold, then continue to detect whether the real-time voltage value of the energy storage capacitor is greater than or equal to the preset communication start voltage threshold. If so, then generate a phase switching prediction signal to switch to the communication phase.

[0124] Step S233: When the current working phase is the communication phase, if the duration of the current working phase is greater than or equal to the communication integrity threshold, or the real-time voltage value of the energy storage capacitor is less than the preset safe working voltage lower limit, then a phase switching prediction signal for switching to the energy harvesting phase is generated.

[0125] The methods and systems of this application may be implemented in many ways. For example, they may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order of steps for the method is for illustrative purposes only, and the steps of the method of this application are not limited to the order specifically described above, unless otherwise specifically stated.

[0126] In addition, the parts of the technical solutions provided in the embodiments of this application that are consistent with the implementation principles of the corresponding technical solutions in the prior art have not been described in detail, so as to avoid excessive elaboration.

[0127] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the invention. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for enhancing environmental backscatter communication based on two-phase detection, characterized in that, The method includes: The system acquires the ambient radio frequency signal received by the antenna, and generates an in-phase component I signal and a quadrature component Q signal through dual-channel quadrature demodulation processing. Based on the in-phase component I signal and the quadrature component Q signal, a binary state identifier representing the antenna matching state is generated. Based on binary state identifiers, a historical state identifier sequence is formed; multiple energy-communication alternation cycles in the historical state identifier sequence are identified, and the average energy harvesting phase duration and average communication phase duration are calculated by statistically analyzing the multiple energy-communication alternation cycles; based on the average energy harvesting phase duration, average communication phase duration, and binary state identifiers, a phase switching prediction signal is generated. The system receives the phase switching prediction signal, generates a switching control sequence to be executed, parses the target state type in the switching control sequence, generates corresponding modulation control commands based on different target state types, executes the switching control sequence and modulation control commands, and forms a closed-loop optimization.

2. The environmental backscatter communication enhancement method based on dual-phase detection according to claim 1, characterized in that, The method for generating the binary status identifier includes: A three-dimensional temperature and humidity compensation lookup table is established, ambient temperature and humidity values ​​are collected, impedance offset compensation values ​​are obtained by searching the three-dimensional temperature and humidity compensation lookup table, and impedance detection thresholds are generated after real-time calibration by combining them with pre-calibrated reference detection thresholds. The in-phase component I signal and the quadrature component Q signal are combined to obtain the amplitude value of the combined signal. The amplitude value of the combined signal is compared with the impedance detection threshold after real-time calibration to generate a binary status identifier that represents the antenna matching state.

3. The environmental backscatter communication enhancement method based on dual-phase detection according to claim 2, characterized in that, The method for calculating the amplitude value of the synthesized signal is as follows: Calculate the squared amplitude values ​​of the in-phase component I signal and the quadrature component Q signal respectively; add the squared amplitude values ​​of the in-phase component I signal and the quadrature component Q signal and then perform a square root operation to generate the composite signal amplitude value that reflects the antenna reflection intensity.

4. The environmental backscatter communication enhancement method based on dual-phase detection according to claim 3, characterized in that, The method for comparing the synthesized signal amplitude value with the impedance detection threshold after real-time calibration to generate a binary state identifier characterizing the antenna matching state includes: When the amplitude of the synthesized signal is less than the impedance detection threshold after real-time calibration, a binary status identifier "0" representing the impedance matching state is generated; otherwise, a binary status identifier "1" representing the impedance mismatch state is generated.

5. The environmental backscatter communication enhancement method based on dual-phase detection according to claim 4, characterized in that, The method for identifying the energy-communication alternation cycle includes: Set a sliding window for the historical state identifier sequence, and count the length of the consecutive "0" sequence and the length of the consecutive "1" sequence within the sliding window; When the length of the consecutive "0" sequence and the length of the immediately following consecutive "1" sequence both exceed the preset minimum period, the "0"-"1" combination formed by the "0" sequence and the "1" sequence is marked as a complete energy-communication alternation cycle.

6. The environmental backscatter communication enhancement method based on dual-phase detection according to claim 5, characterized in that, The average energy harvesting phase duration is the average of the lengths of all "0" sequences, and the average communication phase duration is the average of the lengths of all "1" sequences.

7. The environmental backscatter communication enhancement method based on dual-phase detection according to claim 6, characterized in that, The method for generating the phase-switching prediction signal includes: A handover decision threshold is set based on the average energy harvesting phase duration and the average communication phase duration; the current operating phase is determined based on the latest binary state identifier in the historical state identifier sequence; and a phase handover prediction signal is generated based on the current operating phase and the handover decision threshold. The switching decision threshold includes a minimum energy harvesting threshold and a communication integrity threshold; the operating phase includes an energy harvesting phase and a communication phase; the phase switching prediction signal includes a phase switching prediction signal for switching to the communication phase and a phase switching prediction signal for switching to the energy harvesting phase.

8. The environmental backscatter communication enhancement method based on dual-phase detection according to claim 7, characterized in that, The method for generating the phase switching prediction signal for switching to the communication phase includes: The duration of the current working phase is recorded, and the real-time voltage value of the energy storage capacitor integrated in the dual-phase detection power management submodule is monitored. When the current working phase is the energy harvesting phase, if the duration of the current working phase is greater than or equal to the minimum energy harvesting threshold, the real-time voltage value of the energy storage capacitor is continuously detected to be greater than or equal to the preset communication start-up voltage threshold. If so, a phase switching prediction signal for switching to the communication phase is generated.

9. The environmental backscatter communication enhancement method based on dual-phase detection according to claim 8, characterized in that, The method for generating the phase switching prediction signal for switching to the energy harvesting phase includes: When the current working phase is the communication phase, if the duration of the current working phase is greater than or equal to the communication integrity threshold, or the real-time voltage value of the energy storage capacitor is less than the preset safe working voltage lower limit, a phase switching prediction signal for switching to the energy harvesting phase is generated.

10. An environmental backscatter communication enhancement system based on two-phase detection, used to implement the environmental backscatter communication enhancement method based on two-phase detection as described in any one of claims 1-9, characterized in that, The system includes: Antenna matching status identification module: used to acquire the ambient radio frequency signal received by the antenna, generate in-phase component I signal and quadrature component Q signal through dual-channel quadrature demodulation processing; based on the in-phase component I signal and quadrature component Q signal, generate a binary status identifier representing the antenna matching status; Phase switching prediction module: Based on binary state identifiers, a historical state identifier sequence is formed; multiple energy-communication alternation cycles in the historical state identifier sequence are identified, and the average energy harvesting phase duration and average communication phase duration are calculated by statistically analyzing the multiple energy-communication alternation cycles; based on the average energy harvesting phase duration, average communication phase duration, and binary state identifiers, a phase switching prediction signal is generated. Phase switching execution module: It is used to receive the phase switching prediction signal, generate the switching control sequence to be executed, parse the target state type in the switching control sequence, generate the corresponding modulation control command according to different target state types, execute the switching control sequence and modulation control command, and form a closed loop optimization.

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