A remote data communication platform for exosome therapy of neurodegenerative diseases

Abstract

The present application relates to the field of data communication, and discloses a remote data communication platform for neurodegenerative disease exosome treatment, which is used for guaranteeing reliable transmission of key data in neurodegenerative disease remote monitoring. The exosome concentration data collected in the patient's body in real time is converted into dynamic parameters of a chaotic system through a nonlinear mapping rule, a parameterized chaotic system is constructed, a chaotic state signal carrying concentration information is generated, a composite transmission signal is formed by superposition, and the composite transmission signal is sent to a remote receiving end through a wireless channel. The state synchronization between the sending end and the copy chaotic system is achieved through a synchronization algorithm containing an error feedback term, the dynamic parameter estimation value is extracted, the exosome concentration data is restored through inverse nonlinear mapping operation, and the output data is output to a medical monitoring terminal after sliding window cumulative averaging. The present application effectively solves the problems of insufficient safety, weak anti-fading ability, low parameter estimation accuracy and large output data fluctuation of traditional medical monitoring in a wireless channel.

Description

Technical Field

[0001] This invention relates to the field of data communication, and more particularly to a remote data communication platform for the treatment of exosomes in neurodegenerative diseases. Background Technology

[0002] Neurodegenerative diseases are a major threat to the health of the elderly worldwide, characterized by complex pathogenesis and slow, irreversible progression. In recent years, exosomes have become a research hotspot in the treatment of neurodegenerative diseases due to their crucial roles in intercellular communication, immune regulation, and neuroprotection. Exosome-based treatment regimens typically require real-time, continuous monitoring of exosome concentrations in patients, with the data remotely transmitted to medical monitoring terminals or treatment decision-making systems so that physicians can dynamically adjust treatment parameters. Therefore, achieving reliable and secure remote communication of exosome concentration data under wireless channel conditions is one of the key technical bottlenecks for this treatment model to achieve clinical application.

[0003] In existing technologies, the following solutions are mainly used for remote data communication of medical biosignals, but all of them have obvious shortcomings: Wireless transmission schemes based on traditional digital modulation are widely used in medical settings, but they essentially encode and transmit biological data as ordinary bit streams. These schemes suffer from two core problems: first, insufficient transmission security. Traditional modulation signals are easily intercepted and cracked, while exosome concentration data involves patient privacy and core treatment parameters, requiring high security. Second, limited resistance to channel impairment. Under real-world wireless channel conditions such as multipath fading and frequency shift, traditional schemes require complex channel coding and retransmission mechanisms, leading to increased communication latency and making it difficult to meet the low-latency requirements of real-time exosome concentration monitoring.

[0004] Pilot-assisted channel estimation techniques are relatively mature in traditional communication systems, but their direct application to chaotic communication presents compatibility issues. Chaotic signals are characterized by wide bandwidth and continuous spectrum. If the spectrum of the pilot signal overlaps with that of the chaotic signal, it will severely interfere with the dynamic characteristics of the chaotic signal and compromise the convergence of the synchronization algorithm. Conversely, if the spectrum of the pilot signal is too far removed from that of the chaotic signal, it will be impossible to effectively estimate the true state of the channel in the frequency band where the chaotic signal resides. Currently, there is a lack of pilot design methods that are compatible with the spectral characteristics of chaotic signals.

[0005] Biological signals are inherently characterized by low signal-to-noise ratios and slow time-varying properties, meaning that exosome concentration data obtained from a single measurement often contains significant random fluctuations. Existing remote communication solutions typically transmit single measurement values ​​directly, lacking further data smoothing mechanisms at the receiving end. This results in drastic fluctuations in the data received by medical monitoring terminals, affecting doctors' accurate assessment of patient conditions.

[0006] Therefore, we propose a remote data communication platform for exosome therapy of neurodegenerative diseases to address the above problems. Summary of the Invention

[0007] This invention provides a remote data communication platform for exosome therapy of neurodegenerative diseases, which ensures the reliable transmission of key data in remote monitoring of neurodegenerative diseases.

[0008] The first aspect of this invention provides a remote data communication platform for exosome therapy of neurodegenerative diseases. The platform comprises: a data conversion module for acquiring exosome data from a patient and converting the exosome data into dynamic parameters of a chaotic system using mapping rules to obtain a parameterized chaotic system; a signal acquisition module for running the parameterized chaotic system to generate a chaotic signal carrying the exosome data; a signal transmission module for combining the chaotic signal with a reference signal to form a transmission signal and transmitting it; a channel compensation module for receiving the transmission signal, estimating and compensating the channel based on the reference signal in the transmission signal to obtain a compensated chaotic signal; and a synchronization and restoration module for using the compensated chaotic signal to drive a local chaotic system for synchronization, extracting estimated values ​​of the dynamic parameters from the synchronized local chaotic system, and restoring the exosome data based on the inverse operation of the mapping rules.

[0009] Optionally, in a first implementation of the first aspect of the present invention, the method includes: The exosome data are subjected to linear transformation based on preset baseline parameter values ​​and scaling factors; When the result of the linear transformation operation exceeds the preset range, boundary truncation is performed, and the processed result is used as a dynamic parameter.

[0010] Optionally, in a second implementation of the first aspect of the present invention, the method includes: The reference signal is output independently at the beginning of the data frame; After the reference signal ends, the chaotic signal is superimposed with a continuous sine wave to obtain the transmission signal.

[0011] Optionally, in a third implementation of the first aspect of the present invention, the reference signal is a pseudo-random binary sequence; the frequency of the continuous sine wave is the same as the carrier frequency of the reference signal, and the amplitude of the continuous sine wave is a fixed ratio of the root mean square amplitude of the chaotic signal.

[0012] Optionally, in a fourth implementation of the first aspect of the present invention, the chaotic signal is superimposed with a continuous sine wave, and the instantaneous voltage of the output transmitted signal satisfies the following formula: ; In the formula, The instantaneous voltage of the transmitted signal; The instantaneous voltage of the chaotic signal; It is a continuous sine wave with a fixed amplitude, and satisfies... , For a fixed ratio $f_p$ represents the root mean square amplitude of the chaotic signal; $f_p$ represents the frequency of the continuous sine wave. t represents the initial phase; t is the time variable.

[0013] Optionally, in a fifth implementation of the first aspect of the present invention, the method includes: The reference signal is extracted from the transmitted signal using a bandpass filter; The extracted reference signal is compared with the local reference signal to obtain the channel gain estimate and frequency offset estimate. The transmitted signal is inversely adjusted for gain and phase corrected using the channel gain estimate and the frequency offset estimate to obtain a compensated chaotic signal.

[0014] Optionally, in a sixth implementation of the first aspect of the present invention, obtaining the channel gain estimate and the frequency offset estimate includes: The channel gain estimate is obtained by calculating the amplitude ratio of the extracted reference signal to the local reference signal. The frequency offset estimate is obtained by measuring and tracking the phase difference between adjacent symbols in the extracted reference signal using a phase-locked loop.

[0015] Optionally, in a seventh implementation of the first aspect of the present invention, the method includes: An error feedback term is introduced into the state equation of the local chaotic system. The error feedback term is based on the difference between the compensated chaotic signal and the state variable of the local chaotic system. Based on the error feedback term, the values ​​of the state variables and dynamic parameters of the local chaotic system are updated in real time during the numerical iteration process until the error feedback term is lower than a preset threshold in multiple consecutive iteration steps, at which point the system is determined to have reached synchronization.

[0016] Optionally, in an eighth implementation of the first aspect of the present invention, a smoothing module is further included: After reconstructing the exosome data, a moving average is calculated on multiple consecutively acquired exosome data based on a preset time window, and the calculated moving average is output as the measurement result.

[0017] The mechanism of this invention is as follows: It achieves highly sensitive and interference-resistant remote communication for weak biomarker concentration information. Its essence is to use the inherent deterministic chaotic state of the chaotic system as an information carrier to encode the biological signal into the evolutionary trajectory of the chaotic attractor, thereby ensuring the reliable transmission and high-fidelity recovery of key data in the remote monitoring of neurodegenerative diseases without the need for high-precision synchronization and complex channel coding. Beneficial effects: Exosome concentration data is transformed into dynamic parameters of a chaotic system through nonlinear mapping, so that the data is hidden in the chaotic evolution process. The signal power spectral density is close to white noise, which naturally has low interception probability and high confidentiality. It can ensure the transmission security of sensitive medical data without relying on upper-layer encryption. The pilot signal is deliberately placed in the concave region of the chaotic signal spectrum to achieve frequency domain orthogonality. This not only completes the accurate estimation of channel gain and frequency offset, but also avoids the interference of the pilot signal on the chaotic trajectory. This enables the receiver to replicate the chaotic system to track the state of the transmitter with high precision and decode synchronously, eliminating the error accumulation caused by the traditional two-stage processing. The synchronous algorithm embeds a dynamic parameter real-time update law and uses error feedback to drive parameter convergence. The estimation accuracy of exosome concentration is significantly higher than that of the symbol-by-symbol hard decision scheme, and it has a natural iterative averaging suppression effect on instantaneous fading in wireless channels. The restored data is averaged by equal weighting through a fixed-length sliding window, which effectively smooths out the fluctuations in a single measurement caused by biological sampling noise. This ensures that the medical monitoring terminal outputs a trend of concentration change rather than random jitter, with extremely low computational overhead, making it suitable for resource-constrained embedded medical terminals. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of an embodiment of a remote data communication platform for exosome therapy of neurodegenerative diseases according to an embodiment of the present invention; Figure 2 This is a schematic diagram showing how exosome concentration data is nonlinearly mapped to the dynamic parameters of a chaotic system. Figure 3 This is a schematic diagram of another embodiment of the remote data communication platform for exosome therapy of neurodegenerative diseases in this invention; Figure 4 This is a schematic diagram of an embodiment of a remote data communication device for exosome therapy of neurodegenerative diseases according to an embodiment of the present invention. Detailed Implementation

[0019] This invention provides a remote data communication platform for exosome therapy of neurodegenerative diseases, ensuring reliable transmission of key data in remote monitoring of neurodegenerative diseases. The terms first, second, third, fourth, etc. (if present) in the specification, claims, and accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms include or have, and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatuses.

[0020] For ease of understanding, the specific process of the embodiments of the present invention is described below. Please refer to [link / reference]. Figure 1 One embodiment of the remote data communication platform for exosome therapy of neurodegenerative diseases in this invention includes: 101. Data conversion module, used to take the real-time exosome concentration data collected in the patient's body as raw data and convert it into the dynamic parameters of the chaotic system through a pre-set nonlinear mapping rule to obtain a parameterized chaotic system.

[0021] It is understood that the executing entity of this invention can be a remote data communication device for exosome therapy of neurodegenerative diseases, or it can be a terminal or a server; the specific implementation is not limited here. This embodiment of the invention will be described using a server as an example.

[0022] It should be noted that the execution entity is a medical data server deployed in the hospital's data center or edge computing node. It acquires raw data on the concentration of neurogenic exosomes collected by front-end biosensors in real time through a low-level interface. The raw monitoring data returned by the sensors within the current monitoring period is set at 3.5 billion exosomes carrying specific amyloid proteins per mL of blood sample. The server's central processing unit (CPU) allocates a dedicated data processing thread in memory to perform normalization preprocessing on this raw physical quantity. The system internally sets a physiological monitoring baseline of 1 billion exosomes / mL as the lower limit and 5 billion exosomes / mL as the upper limit. The CPU calculates the current acquired value by subtracting the lower limit and then dividing by the range between the upper and lower limits, converting the raw concentration into a dimensionless normalized value. After linear calculation, the normalized value corresponding to this concentration data is 0.625.

[0023] The normalized value is processed by invoking a nonlinear mapping rule stored in non-volatile memory. This embodiment uses a sigmoid nonlinear activation function as the mapping rule, the expression of which is as follows: ,in The gain coefficient (used to adjust the slope) is used as a mapping rule to improve the parameter sensitivity in the intermediate concentration range. The processor substitutes the value 0.625 into this nonlinear rule for numerical transformation, outputting a mapping coefficient between 0 and 1. The calculated mapping coefficient is 0.710. The server initializes a one-dimensional logistic mapping system in memory as the underlying chaotic signal generation carrier. According to the dynamic principle, for this carrier to maintain a stable chaotic evolution state, its dynamic control parameters must be limited to the range of 3.800 to 4.000, with a numerical span of 0.200. The central processing unit multiplies the calculated mapping coefficient 0.710 by the range span of 0.200, obtaining a parameter increment of 0.142. Finally, the lower limit of the chaotic range, 3.800, is added to this increment, determining the system dynamic parameter to be 3.942. The server assigns this dynamic parameter to the underlying mathematical model, thus completing the parameterized chaotic system controlled by the patient's real-time vital signs at the software level.

[0024] 102. The signal acquisition module is used to run the parameterized chaotic system for numerical evolution and generate a chaotic state signal carrying exosome concentration information.

[0025] It should be noted that, to ensure a consistent pseudo-random sequence at both ends of the communication system, the server pre-distributes and establishes an initial state value via a secure channel; in this embodiment, this initial value is set to 0.450. The processor substitutes the initial value 0.450 and the control parameter 3.942 into the mapping rule to perform single-step closed-loop iterative calculation. The output of each iteration is within the dimensionless range of 0 to 1 and will be used as the input value for the next iteration.

[0026] To meet the requirements of the physical frame format for long-distance wireless communication and ensure that the obtained sequence is highly uncorrelated, the server control processor continuously executes 1000 iterative calculations. Since the state trajectory of a chaotic system in the initial evolution stage often exhibits regular transient responses and lacks broadband spectral characteristics, the server program performs a truncation operation in the registers, actively discarding the transient state values ​​generated in the first 100 iterations based on a predetermined number of discard points according to the decay time of the system's autocorrelation function.

[0027] The processor extracts 900 discrete values ​​from the 101st to the 1000th iteration, forming a discrete-time sequence in a steady state. The server's internal digital signal processing module converts this dimensionless mathematical sequence into a baseband signal suitable for transmission through a physical channel. The system uses a fixed linear level conversion rule: multiplying the discrete state value by 2 and subtracting 1 maps it to an analog voltage amplitude in mV. Under this rule, state value 0 corresponds to -1mV, and state value 1 corresponds to +1mV. Through a digital-to-analog converter, the aforementioned 900 discrete values, jumping between 0 and 1, are converted into an analog baseband waveform with a voltage amplitude exhibiting irregular, noise-like jumps between -1mV and +1mV. This baseband waveform, composed of these 900 sampling points, is the chaotic state signal, and its internal jumping pattern is uniquely locked by parameter 3.942.

[0028] 103. Signal transmission module, used to superimpose the chaotic state signal with the known pilot signal to form a composite transmission signal, and transmit it to the remote receiving end through a wireless channel.

[0029] It should be noted that after obtaining the 900 chaotic state signals with voltage amplitudes between -1mV and +1mV, the transmitting device needs to introduce reference markers for channel quality assessment. The baseband receiver inside the transmitter reads a pseudo-random binary sequence pre-programmed into the read-only memory; this sequence also has a length of 900 sampling points. The digital-to-analog converter converts this binary sequence into a fixed analog voltage, distributing its amplitude as an alternating waveform of -0.5mV and +0.5mV, using this as a known pilot signal. The sequence structure of this pilot signal is known at both the transmitting and remote receiving ends.

[0030] The adder circuit at the transmitting end performs a linear superposition of the two baseband signals in the time domain. The hardware clock-controlled adder performs an arithmetic summation of the sampled voltages of 900 chaotic state signals and the sampled voltages of 900 pilot signals, clock cycle by clock cycle.

[0031] In the 150th sampling period, if the instantaneous output voltage of the chaotic signal is +0.7mV and the instantaneous voltage of the synchronously arriving pilot signal is -0.5mV, then the superimposed output voltage for that period is +0.2mV. After 900 consecutive synchronous point-by-point additions, the system obtains a structurally complete composite baseband transmission signal.

[0032] Due to the physical voltage boundaries of the two signals, the dynamic range of the analog voltage of the superimposed composite signal is limited to between -1.5mV and +1.5mV. This composite baseband waveform is sent to the RF modulation link. The transmitter selects 403MHz as the RF communication carrier frequency, and the mixer upconverts the low-frequency baseband signal (from -1.5mV to +1.5mV) to the 403MHz carrier. To comply with the electromagnetic radiation ratio regulations for medical implant communication, the RF power amplifier limits the output power of the transmitting antenna port to 25µW (compliant with the MICS medical implant communication system frequency band standard, and meeting the signal-to-noise ratio threshold requirements of the receiver at typical penetration depths after in vivo-to-outside wireless link budget analysis). The miniature antenna radiates the electromagnetic waves carrying the composite signal into the external physical space, completing the wireless transmission operation.

[0033] 104. Channel compensation module, used by the remote receiver to receive the composite transmission signal, separate the known pilot signal from it, estimate the channel gain and frequency offset based on the pilot signal, and use this to perform channel compensation on the composite transmission signal to obtain the compensated chaotic state signal.

[0034] It should be noted that the remotely deployed receiving equipment captures weak radio electromagnetic waves at a frequency of 403MHz in the space link via an RF receiving antenna. A low-noise amplifier at the receiving end initially amplifies the received signal, followed by a mixer combined with a local oscillator down-converting it to baseband, and then an analog-to-digital converter converting it into a discrete digital signal at the same sampling rate. Due to absorption by human tissue, multipath effects, and the physical tolerances of the transceiver crystal oscillators, the baseband signal entering the processor at this point is distorted in both amplitude and phase.

[0035] The receiver's digital signal processor initiates the channel estimation program. The processor retrieves a 900-point pilot sequence from its local memory, identical to the transmitter's sequence, with amplitude distributions of -0.5mV and +0.5mV. The processor performs a sliding cross-correlation operation between the received noisy, distorted signal and the local pilot sequence. Since broadband chaotic signals are statistically uncorrelated with pseudo-random pilot sequences, the correlation operation filters out the chaotic background and extracts the distortion characteristics of the pilot signal.

[0036] The processor compares the extracted average amplitude of the received pilot with the standard amplitude of the local pilot, and calculates the amplitude attenuation coefficient of the current wireless channel as 0.4 by using the least squares (LS) channel estimation algorithm and considering the effect of additive white noise.

[0037] By calculating the phase rotation slope of the relevant peak values, the carrier frequency offset was estimated to be 20Hz. After obtaining these two core channel parameters, the processor began to perform reverse compensation on the entire composite signal. The processor applied reverse phase rotation to each of the 900 sampling points in the time domain to eliminate the accumulated phase difference caused by the 20Hz frequency offset; then, the voltage values ​​of all sampling points were uniformly divided by an attenuation coefficient of 0.4. After compensation, the voltage range of the composite signal was restored to the original amplitude range of the composite signal at the transmitting end (i.e., -1.5mV to +1.5mV).

[0038] The processor performs a synchronous subtraction operation, subtracting the locally stored pilot sequence voltage values ​​point by point from the compensated composite signal sequence. Through 900 consecutive subtraction operations, the pilot components are stripped away, and the processor outputs a pure discrete baseband waveform with a voltage amplitude between -1mV and +1mV. This waveform is the compensated chaotic state signal.

[0039] 105. Synchronization and Restoration Module: This module is used to remotely replicate a chaotic system identical to the one sent from the receiving end. It uses the compensated chaotic state signal as a driving signal and employs a synchronization algorithm to ensure that the state of the replicated chaotic system matches that of the sending end's chaotic system. It then extracts the estimated value of the dynamic parameter from the synchronized chaotic system and restores the exosome concentration data through the inverse operation of the nonlinear mapping rule, thus completing remote data communication.

[0040] It should be noted that after obtaining the compensated chaotic state signal (voltage range of -1mV to +1mV) from 900 sampling points, the receiving server instantiates a one-dimensional logistic mapping model with the same structure in memory. Since the transmitting end discarded the first 100 states in step 102, the first sampling point received actually corresponds to the 101st evolution state of the transmitting end. The receiving end cannot predict the current true starting state, so it assigns a random reasonable value of 0.500 to the initial state of the local system and temporarily sets the control parameter to the lower limit of the interval, 3.800.

[0041] The receiver processor invokes the Extended Kalman Filter (EKF) synchronization algorithm. Specifically, the algorithm constructs a Jacobian matrix for the state transition and observation model based on the one-dimensional logistic mapping to achieve recursive estimation of the nonlinear state. To match the physical signal with the mathematical model, the processor performs an inverse level conversion on the received chaotic state voltage signal (adding 1 to the voltage value and dividing by 2) to restore the dimensionless ideal state reference value. The algorithm uses these reference values ​​as a driving source, continuously comparing the error with the single-step predicted state of the local model, and dynamically correcting the internal state and parameter estimates of the local model. As the sampling points advance, the synchronization error gradually converges.

[0042] The specific evolution node data is shown in Table 1 below: Table 1

[0043] When the evolution reaches the 600th sampling point, the local model and the transmitting model achieve complete dynamic synchronization. At this point, the processor extracts a stable and invariant control parameter estimate of 3.942.

[0044] The server initiates the data restoration pipeline. The processor subtracts the baseline value of 3.800 from the parameter estimate of 3.942, obtaining 0.142, and then divides it by the interval span of 0.200 to restore the mapping coefficient to 0.710. Next, it calls the inverse mapping module of the sigmoid nonlinear activation function to restore 0.710 to a normalized value of 0.625. Finally, the processor performs an inverse normalization calculation, multiplying 0.625 by the preset upper and lower limit span of 4 billion exosomes / mL, obtaining an increment of 2.5 billion exosomes / mL. Adding this to the baseline lower limit of 1 billion exosomes / mL, the original exosome concentration data is accurately restored to 3.5 billion exosomes / mL. The receiving device completes the reverse analysis from the underlying physical channel to the upper-level biological indicators.

[0045] Please see Figure 2 and Figure 3 Another embodiment of the remote data communication platform for exosome therapy of neurodegenerative diseases in this invention includes: 201. Data conversion module, used to take the real-time exosome concentration data collected in the patient's body as raw data and convert it into the dynamic parameters of the chaotic system through a pre-set nonlinear mapping rule to obtain a parameterized chaotic system.

[0046] Specifically, the method for converting exosome concentration data into dynamic parameters of a chaotic system is as follows: a baseline parameter value and a scaling factor are set. The real-time collected exosome concentration value is multiplied by the scaling factor and then added to the baseline parameter value. The result is used as a key parameter of the chaotic system. At the same time, the effective range of the parameter is set. When the calculation result exceeds the range, it is restricted to the boundary value by truncation, thereby obtaining a parameterized chaotic system that is always in a chaotic state.

[0047] It should be noted that the server deployed on the edge computing node in the neurology ward is responsible for receiving data transmitted from the front-end microfluidic biosensors. The current monitoring scenario is the concentration of exosomes carrying specific neurogenic proteins in blood samples from Parkinson's disease patients. At the sampling trigger moment of the current communication cycle, after biochemical fluorescence quantitative analysis, the sensor uploads a raw value of 2.5 for the exosome concentration (this parameter is based on publicly available clinical datasets of exosomes in Parkinson's disease), and the standard unit for this physical detection value is defined as one billion exosomes / mL.

[0048] To ensure seamless integration of this biometric data into subsequent broadband secure communication protocols, the server's central processing unit invokes pre-defined linear mapping rules and boundary security clamping programs in memory, transforming them into the driving source of the underlying mathematical model. Considering the computational efficiency of the baseband at the transmitting end, this embodiment selects a Lorenz continuous-time three-dimensional chaotic system as the underlying dynamics generator. To ensure that the Lorenz system continuously exhibits singular attractor properties on the time axis and does not degenerate into a periodic limiting cycle, the system sets a dynamically valid range for the key state parameter (i.e., the Rayleigh number) that determines its chaotic characteristics. In the local non-volatile memory, the lower boundary value of this range is securely set to 28.0, and the upper boundary value is set to 40.0.

[0049] The specific execution process of the mapping rule is as follows: Two core constants are pre-configured within the system. The baseline parameter value is fixed at the lower limit of the effective range, i.e., 28.0, and the system dynamic scaling factor is set to 1.5. The arithmetic logic unit of the central processing unit extracts the current exosome concentration value of 2.5, performs a high-precision floating-point multiplication operation, multiplies the concentration value of 2.5 by the scaling factor 1.5, and calculates the parameter increment as 3.75. Next, the processor performs an addition operation, algebraically summing the parameter increment 3.75 with the baseline parameter value of 28.0, and calculates the preliminary parameter mapping result as 31.75.

[0050] Before the parameters are sent to the dynamic model, the system must enter the boundary condition determination stage to prevent model divergence. The register performs a hardware-level comparison operation between the calculated preliminary parameter result of 31.75 and the preset valid value range (28.0 to 40.0). The logic gate circuit determines that 31.75 is greater than the lower boundary value of 28.0 and less than the upper boundary value of 40.0, confirming that the calculation result is within the safe range of system stability, and no software or hardware truncation processing is required.

[0051] As a supplementary explanation of the fault tolerance mechanism, if the exosome concentration value collected by the sensor surges to 10.0 under an extreme metabolic abnormality physiological state, the preliminary parameter result obtained by the system through multiplication and addition operations will reach 43.0. The comparator will determine that 43.0 exceeds the set upper limit boundary value of 40.0, and then trigger a forced truncation interruption. The system will output the upper limit boundary value of 40.0, sacrificing the measurement extreme value to safeguard the bottom line of the chaotic state of the communication link.

[0052] Under the current normal conditions, the server successfully converted the real-time exosome concentration value of 2.5 into a specific dynamic parameter of 31.75, and wrote this parameter into the underlying constant register of the Lorentz model, thus completing the construction of the parameterized chaotic system.

[0053] 202. Signal acquisition module, used to run the parameterized chaotic system for numerical evolution and generate chaotic state signals carrying exosome concentration information.

[0054] Specifically, the numerical evolution of the parameterized chaotic system is carried out by using the fourth-order Runge-Kutta method with a fixed step size to discretize and iteratively solve the state equation of the parameterized chaotic system. In each iteration, the chaotic state variables at the current moment are used to calculate the state variables at the next moment. The iteration continues until a chaotic state sequence of a preset time length is generated. The values ​​of the state variables at each moment in the sequence change dynamically with the exosome concentration data, forming a chaotic state signal that carries the exosome concentration information.

[0055] Furthermore, after obtaining a chaotic state sequence of a preset time length, the value of each state variable in the sequence is linearly scaled so that the maximum absolute value of the scaled sequence matches the full-scale input range of the digital-to-analog converter at the transmitting end, thereby obtaining an amplitude-normalized chaotic state signal, which is then used as the chaotic state signal in subsequent steps.

[0056] It should be noted that the server processor assigns initial state values ​​(x, y, z) to the three spatial dimensions of the Lorentz system's state variables, uniformly set to 1.0 in this embodiment. To ensure that the obtained digital chaotic signal band can legally and without distortion carry the subsequent steps' up to 125kHz RF pilot and sine wave (meeting the physical requirements of the Nyquist sampling theorem), the system drastically compresses the fixed physical time step of a single iteration to 2 microseconds (i.e., 0.000002 seconds). This step size is equivalent to a hardware sampling rate of up to 500kHz in a digital communication system.

[0057] The processor initiates a fourth-order Runge-Kutta (RK4) discretization iterative evaluation program based on a fixed step size. Within each 2-microsecond iteration cycle, the processor's floating-point unit uses the three state variables at the current moment to calculate the tangent slopes of the system's evolution trajectory at four different tiny time points within the integration interval. These four slopes are then precisely weighted and averaged to deduce the state variable values ​​for the next 2-microsecond interval. Since the protocol specifies a fixed physical time length of 500ms for each data frame, the processor needs to continuously execute this complex fourth-order Runge-Kutta iterative calculation 250,000 times (500ms / 0.002ms) within a single frame.

[0058] After continuous high-speed simulations, the memory buffer contained a sequence of original chaotic states containing 250,000 three-dimensional data points. Following a predetermined communication protocol, the system extracted 250,000 consecutive values ​​from the first spatial dimension (x-axis component) as waveform carriers to carry the information. These 250,000 purely mathematical sequence points, controlled by parameter 31.75, had already undergone disordered transitions, making it difficult to directly extract the concentration information of 2.5.

[0059] The processor performs physical linear scaling for the digital-to-analog converter (DAC). It uses a global scan instruction to iterate through the 250,000 extracted raw state variable values, finding the maximum absolute value of the sequence within the current data frame. Assuming the pure values ​​in this evolved sequence fluctuate wildly between -18.5 and +17.2, the maximum absolute value of the extracted mathematical sequence is 18.5. After reading the hardware registers of the transmitting DAC, the system learns that its full-scale input voltage physical range is fixed at -1000mV to +1000mV. To maximize the dynamic range of the digital signal, the processor performs a division operation, dividing the DAC's maximum absolute voltage of 1000mV by the maximum absolute value of the sequence, 18.5, calculating a high-precision linear scaling factor of approximately 54.05mV / unit.

[0060] Using a digital multiplier, each of the 250,000 original state variable values ​​is multiplied by a scaling factor of 54.05. This mapping transforms the original dimensionless sequence, originally ranging from -18.5 to +17.2, into a discrete digital baseband signal with voltage amplitudes fluctuating between -999.9 mV and +929.7 mV. This amplitude-normalized signal sequence is then written into the high-speed direct memory access (DMA) buffer register at the transmitting end.

[0061] 203. Signal transmission module, used to superimpose the chaotic state signal with the known pilot signal to form a composite transmission signal, and transmit it to the remote receiving end through a wireless channel.

[0062] Specifically, the method for superimposing the chaotic state signal with a known pilot signal to form a composite transmission signal is as follows: At the transmitting end, a pilot sequence of fixed duration is independently output during the initial period of each frame of data. This pilot sequence consists of a set of predefined binary phase keying symbols with constant amplitude and frequency located in the concave region of the chaotic state signal spectrum. After the pilot sequence ends, the chaotic state signal generated in subsequent periods is linearly superimposed with a continuous sine wave of fixed amplitude and the same frequency. The amplitude of this sine wave is less than the average amplitude of the chaotic state signal. The superposition forms a composite transmission signal, which is then transmitted to the remote receiving end via a wireless channel according to a preset frame format.

[0063] Furthermore, the pilot sequence adopts a pseudo-random binary sequence, whose start time is aligned with the frame start time of the chaotic state signal, and its length is one-thousandth of the frame length of the chaotic state signal; the frequency of the continuous sine wave is the same as the carrier frequency of the pilot sequence, and its amplitude is a fixed ratio of the root mean square amplitude of the chaotic state signal, which ranges from 0.3 to 0.5.

[0064] It should be noted that after preparing an amplitude-normalized chaotic state signal with a total length of 500ms and a sampling rate of 500kHz (i.e., containing 250,000 sampling points) in the high-speed buffer at the transmitting end, the system enters the physical baseband frame assembly stage. This stage requires precise mixing of the time-domain signal with specific pilot signals to facilitate the detection and alignment of the wireless channel.

[0065] The digital signal processor independently obtains and outputs a pilot sequence specifically for channel estimation during the period from 0 to 0.5 ms at the beginning of the current data frame.

[0066] To resolve the physical inconsistency of the original clock division and ensure symbol alignment, the pilot sequence is constructed using a 32-bit binary sequence (including 31 pseudo-random data bits and 1 stop parity bit). This sequence can be transmitted within exactly 0.5ms, with a calculated symbol rate of 64 kilobits per second (64 kbps). The pilot sequence employs binary phase-keying (BPSK) modulation, with a carrier frequency set to 125 kHz, falling within the spectral dip region outside the low-frequency main lobe of the aforementioned Lorentz chaotic signal. Within the 0 to 0.5ms interval, the transmitter controls the physical output voltage amplitude of the pilot to remain constant at 200mV, and its starting pulse edge is locked and aligned with the frame start flag of the system's master clock.

[0067] After the 0.5ms pilot sequence is sent, the system clock switches to the effective data transmission interval from 0.5ms to 500.5ms. During this phase, two signals must be output in parallel. One is a chaotic state signal, continuously read from the DMA buffer at clock ticks, between -999.9mV and +929.7mV. Simultaneously, the local waveform generator begins outputting a continuous reference sine wave with the exact same frequency as the pilot (both at 125kHz).

[0068] To determine the specific driving voltage of the sine wave, the processor performed root mean square (RMS) statistics on the data in the chaotic buffer. After integration, the measured RMS amplitude of this segment of chaotic signal was 410mV. The system read a fixed scaling factor of 0.4 from the register (satisfying the legal protocol range of 0.3 to 0.5), multiplied the RMS amplitude of 410mV by 0.4, and set the physical output amplitude of this continuous sine wave to 164mV.

[0069] In the hardware adder at the transmitting end, the chaotic signal and the continuous sine wave are subjected to rigorous point-by-point voltage superposition calculation, and its instantaneous voltage output logic is characterized by the following physical calculation formula:

[0070] In the formula, the output composite voltage With chaotic input voltage The physical dimensions of all of them are voltage, and the unit is mV; It is a continuous sine wave with a fixed amplitude, and satisfies... , For a fixed ratio; chaotic root mean square The physical quantity is voltage, and the unit is mV (value 410); the frequency of a continuous sine wave. The physical dimension is frequency, and the unit is Hz (value 125000); initial phase The physical dimension is plane angle, and the unit is rad; the physical dimension of the clock variable t is time, and the unit is s.

[0071] After obtaining the 500.5ms baseband composite signal, the RF modulator upconverts it to a carrier frequency of 403MHz, and the micro-power amplifier clamps its power to 25µW and transmits it to the receiver via the antenna.

[0072] 204. Channel compensation module, used by the remote receiver to receive the composite transmission signal, separate the known pilot signal from it, estimate the channel gain and frequency offset based on the pilot signal, and use this to perform channel compensation on the composite transmission signal to obtain the compensated chaotic state signal.

[0073] Specifically, the method for separating a known pilot signal from a composite transmission signal and estimating the channel gain and frequency offset based on the pilot signal is as follows: During the initial period of each frame of data, the remote receiver uses a bandpass filter to extract the pilot sequence from the received composite transmission signal. The center frequency and bandwidth of the bandpass filter match the spectral parameters of the transmitting pilot signal. The extracted pilot sequence is compared symbol by symbol with the locally stored reference pilot sequence. By calculating the ratio of the received pilot amplitude to the reference pilot amplitude, a channel gain estimate is obtained. By measuring the phase difference between adjacent symbols in the received pilot sequence, a first-order phase-locked loop is used to integrate and track the phase difference to obtain a frequency offset estimate. The channel gain estimate is used to perform inverse gain adjustment on the overall amplitude of the received composite transmission signal, and the frequency offset estimate is used to perform rotation correction on the carrier phase of the received composite transmission signal to obtain a compensated chaotic state signal with amplitude normalization and frequency offset elimination.

[0074] Furthermore, the loop filter of the first-order phase-locked loop adopts a fixed-gain proportional-integral structure, with a proportional gain to integral gain ratio of 4:1. This phase-locked loop takes the phase difference between adjacent symbols in the received pilot sequence as input and outputs a frequency offset estimate. The bandpass filter is a second-order infinite impulse response digital filter, whose passband center frequency is consistent with the carrier frequency of the transmitted pilot signal, and its passband bandwidth is 0.1 times the pilot symbol rate. The coefficients of this filter are preset at the factory and stored in the receiver's memory.

[0075] It should be noted that the remote receiving equipment deployed in the hospital's central computer room captures a 403MHz composite electromagnetic wave signal that has undergone spatial attenuation and interference through a medical band radio frequency antenna array. This weak high-frequency analog waveform is input into the radio frequency front-end receiver, amplified by a low-noise amplifier, down-converted and demodulated by a voltage-controlled oscillator, and then digitized into a discrete baseband composite sequence by a high-speed analog-to-digital converter with the sampling rate also set to 500kHz.

[0076] The processor immediately initiates the time-domain frame synchronization and pilot separation algorithm. Within the first 0.5ms of each captured 500.5ms data frame, the processor inputs the digital sequence consisting of these 250 sampling points into a second-order infinite impulse response (IIR) digital bandpass filter. The passband center frequency of this filter is preset to 125kHz in the receiver's memory, matching the pilot carrier at the transmitter. Since the corrected pilot symbol rate at the transmitter has been confirmed to be 64 kilobits per second, the processor configures the filter's passband bandwidth to cover the core main lobe bandwidth of the BPSK signal. Using this narrowband filtering window, broadband chaotic background noise and channel white noise are effectively blocked, and the system successfully extracts the BPSK pilot sequence after channel physical distortion.

[0077] After entering the channel parameter estimation process, the processor performs symbol-by-symbol detection comparison on the amplitude branch with the extracted received pilot voltage waveform sequence and the standard reference pilot (with a known physical amplitude of 200mV) stored in the local read-only memory. Through mean integration of 32 symbols, the processor measures the average amplitude attenuation of the current received pilot to 50mV. By dividing the received amplitude of 50mV by the reference amplitude of 200mV, the system calculates the current wireless physical channel gain estimate to be 0.25.

[0078] In the phase branch, the processor detects the continuous phase drift between adjacent pilot symbols and inputs this phase difference sequence into a digital first-order phase-locked loop (PLL). The proportional-integral (PI) loop filter configured inside the PLL has its proportional control gain to integral control gain ratio fixed in hardware at 4:1. After closed-loop feedback tracking, the PLL converges to the inherent physical frequency difference between the transceiver crystal oscillators, and its output register outputs a steady-state estimate of the current carrier frequency offset as 20Hz.

[0079] After acquiring the channel parameters, the system performs inverse physical compensation on the entire 500.5ms composite signal. Inverse gain boosting is applied to the overall amplitude, meaning the instantaneous voltages of over 250,000 sampling points are uniformly divided by the channel gain of 0.25 (equivalent to multiplying by 4), ensuring the complete restoration of the physical voltage envelope of all sequence points. Subsequently, an inverse rotation operator is obtained using the 20Hz frequency offset value, and point-by-point digital mixing is performed on the amplitude-restored signal to correct phase distortion.

[0080] The key cleanup step before dynamic synchronization is as follows: The processor uses a locally recovered 125kHz carrier reference to obtain a local sinusoidal waveform sequence that is in phase and frequency with the sinusoidal component in the compensated signal, with a constant physical amplitude of 164mV. In the digital subtractor, this local sine wave is subtracted point by point from the compensated composite signal, thereby removing the strong sinusoidal interference artificially introduced for mixing. The receiver outputs a compensated chaotic state voltage signal that eliminates all channel distortion and artificial carrier interference.

[0081] 205. Synchronization and Restoration Module: This module is used to remotely replicate the same chaotic system as the sending end. It uses the compensated chaotic state signal as a driving signal and a synchronization algorithm to make the state of the replicated chaotic system consistent with the state of the sending end's chaotic system. It also extracts the estimated value of the dynamic parameter from the synchronized chaotic system and then restores the exosome concentration data through the inverse operation of the nonlinear mapping rule, thus completing remote data communication.

[0082] Specifically, the method for synchronizing the state of the replicated chaotic system with that of the transmitting chaotic system and extracting the estimated value of the dynamic parameters using a synchronization algorithm is as follows: The remote receiver replicates the same chaotic system as the transmitting end and uses the compensated chaotic state signal as the driving signal; an error feedback term is added to the state equation of the replicated chaotic system. This error feedback term consists of the difference between the compensated chaotic state signal and the linear combination of the state variables in the replicated chaotic system. A real-time update law for the dynamic parameters is established, which is obtained by multiplying the error feedback term with a specific component in the state variables of the replicated chaotic system and then adjusting the ratio; after each numerical iteration, the state variables of the replicated chaotic system and the value of the dynamic parameter are updated; the above iterative process is repeated until the absolute value of the error feedback term is lower than a preset threshold in multiple consecutive iterations. At this point, the state of the replicated chaotic system is consistent with that of the transmitting chaotic system, and the current value of the dynamic parameter converges to the estimated value of the dynamic parameter of the transmitting end. The estimated value of the dynamic parameter is substituted into the inverse operation of a pre-set nonlinear mapping rule to calculate the corresponding exosome concentration data, thus completing remote data communication.

[0083] It should be noted that after the remote receiver completes the subtraction cleanup operation in step 204, it obtains a clean, compensated chaotic signal containing 250,000 sampling points in its buffer. It's important to note that at this point, the signal's physical form is still a voltage value, ranging from -999.9mV to +929.7mV. To enable the receiver's pure mathematical model to recognize this signal, the processor performs a hardware inverse scaling operation on the sequence: dividing all 250,000 voltage sampling points by a scaling factor of 54.05 agreed upon by the transmitter. After the division, the physical voltage signal is converted back into a dimensionless mathematical state value sequence (fluctuation range returns to between -18.5 and +17.2), which can then be used as a legitimate driving source for the dynamics model.

[0084] The receiving server initializes a Lorentz chaotic mathematical system in memory, whose internal structure is a complete mirror image of the sending end. The system uniformly presets the initial estimates of the three state variables x, y, and z of the locally replicated chaotic system to a constant of 0.2, and sets the initial point of the key parameter to be estimated (Rayleigh number) to the lower limit of the legal interval, 28.0.

[0085] The synchronization algorithm is then initiated. The processor performs fourth-order Runge-Kutta iterations using a very small integration step size of 2 microseconds. In each microstep of integration, the processor reads the inversely scaled value of the drive signal at the current moment and performs a direct algebraic subtraction with the component x calculated in the local state equation to form a real-time error feedback sequence e(t).

[0086] The current value of the state variable y within the model is extracted and multiplied with the error term e(t) and a preset adjustment coefficient of 0.5 to construct a real-time update law for key parameters. This update law dynamically corrects the memory values ​​of local key parameters at the end of each iteration, forcing the local model's evolution trajectory to converge with the trajectory of the driving source. Key evolution node data during 250,000 high-speed iterations are detailed in Table 2 below: Table 2

[0087] When the processor iterated to the 200,000th step (approximately 0.4 seconds per frame), it detected that the absolute value of the error feedback term had been consistently below the preset computer floating-point convergence threshold of 0.001 for five consecutive iterations. The system thus determined that the local mirror system had achieved both physical and mathematical lock-in with the sending end, and at this point, the estimated stable value of the key parameter in the register was captured as 31.75.

[0088] After obtaining stable parameters, the system switches to the mapping inverse operation pipeline. The processor subtracts the pre-stored baseline parameter value of 28.0 from the captured parameter value of 31.75, obtaining the original increment of 3.75. The arithmetic unit divides the increment 3.75 by a fixed scaling factor of 1.5. Through this final division, the system accurately calculates the exosome concentration data corresponding to the communication frame as 2.5, and the unit parsing module confirms that its physical dimension is one billion exosomes per milliliter, and the data is completely consistent with the source at the sending end.

[0089] 206. The smoothing module is used to calculate the average exosome concentration value within a preset time window after the exosome concentration data is restored, and output the average exosome concentration value as the measurement result to the medical monitoring terminal.

[0090] Furthermore, the cumulative average calculation adopts a fixed-length sliding window method. Each time a reconstructed exosome concentration data is acquired, it is added to the sliding window, while the first data to enter the window is removed. All data in the window are summed with equal weights and then divided by the window length to obtain the moving average concentration value. This moving average concentration value is output as the measurement result to the medical monitoring terminal.

[0091] It should be noted that in a clinical pathology setting, the release of exosomes from the lesion area in a patient's body often has pulsed and irregular clustered distribution characteristics, and the front-end microfluidic sensor inevitably introduces transient baseline spike noise during biochemical reagent reactions.

[0092] To eliminate such high-frequency physical fluctuations, a smooth trend line of vital signs with clinical pathological diagnostic reference value is provided to the medical monitoring terminal of the attending physician in the ward. At the end of the data output level, the server system connects a digital smoothing filter based on a sliding window with a fixed physical time length and executes a cumulative average calculation program.

[0093] During the program initialization phase, the receiver's main processor allocates a dedicated circular queue buffer managed by pointers within the data segment of random access memory (RAM) as the memory carrier for the moving average calculation. The protocol specifies that the length of this moving average time window is fixed at a 5-second observation span. Since the preceding signal reconstruction system can resolve a real-time concentration data point every 0.5 seconds, the system, based on the sampling theorem and time mapping relationship, precisely configures the data sample capacity that the 5-second physical window can accommodate as 10 unit columns.

[0094] Once the system is operational, the processor executes the first-in, first-out (FIFO) queue maintenance logic. Whenever step 205 successfully submits a newly restored single-sample exosome concentration data set, the memory controller pushes this latest value into the highest address of the circular queue via pointer operations. Within the same clock cycle, to ensure the total number of samples in the computation window is locked at 10, the processor forcibly removes the oldest historical data sample from the lowest address of the queue.

[0095] Once the data queue is updated and ready, the arithmetic logic unit immediately traverses the 10 valid sample data currently residing in the window, performs unweighted equal-weight algebraic summation calculation, and thus obtains the total concentration integral within the 5-second window.

[0096] The system performs fixed-point division, dividing the total integral value obtained from the above summation by a constant window sample size of 10. The quotient represents the moving average concentration value of the patient's current pathophysiological state. Specific window extrapolation and evolution nodes can be found in the following cached table 3: Table 3

[0097] Taking time T in the table as an example, the system has just restored a single transient data point of 2.50. As this data point is added to the queue, the older value of 2.68 at the head of the queue is removed. The processor sums the current data in the new queue (containing 10 values, including 2.50) to obtain 25.10, which, divided by 10, yields the smoothing index of 2.51. The array is then updated every 0.5 seconds, resulting in a continuous data stream.

[0098] The calculated continuous moving average concentration values ​​are encapsulated into messages. These deeply cleaned and smoothed measurement results are continuously pushed to the high-resolution medical monitoring terminal on the attending physician's desk via the standard hospital intranet transmission control protocol (TCP / IP). This data output logic suppresses artifact disturbances and presents the evolution level of biomarkers in the targeted treatment process of degenerative diseases in a stable medical indicator trend chart.

[0099] Figure 4 This is a schematic diagram of a remote data communication device for exosome therapy of neurodegenerative diseases provided by an embodiment of the present invention. The device 300 may include: a processor 301, a receiver 302, a transmitter 303, and a memory 304. The receiver 302, transmitter 303, and memory 304 are respectively connected to the processor 301 via a bus. It should be noted that in some possible implementations, the processor 301 and the memory 304 may be integrated together.

[0100] The processor 301 includes one or more processing cores. The processor 301 executes the methods performed by the base station in the random access method provided in this application embodiment by running software programs and modules. The memory 304 can be used to store software programs and modules. Specifically, the memory 304 can store an operating system 3041 and at least one application module 3042 required for a function. The receiver 302 is used to receive communication data sent by other devices, and the transmitter 303 is used to send communication data to other devices.

[0101] The present invention also provides a remote data communication device for exosome therapy of neurodegenerative diseases. The remote data communication device for exosome therapy of neurodegenerative diseases includes a memory and a processor. The memory stores computer-readable instructions. When the computer-readable instructions are executed by the processor, the processor performs the steps of the remote data communication method for exosome therapy of neurodegenerative diseases described in the above embodiments.

[0102] The present invention also provides a remote data communication device for exosome therapy of neurodegenerative diseases. The remote data communication device for exosome therapy of neurodegenerative diseases includes a memory and a processor. The memory stores computer-readable instructions. When the computer-readable instructions are executed by the processor, the processor performs the steps of the remote data communication platform for exosome therapy of neurodegenerative diseases described in the above embodiments.

[0103] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium, wherein the computer-readable storage medium stores instructions that, when executed on a computer, cause the computer to perform the steps of the remote data communication platform for exosome therapy of neurodegenerative diseases.

[0104] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0105] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0106] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A remote data communication platform for exosome therapy of neurodegenerative diseases, characterized in that, Includes the following steps: The data conversion module is used to acquire the patient's exosome data and convert the exosome data into dynamic parameters of a chaotic system through mapping rules to obtain a parameterized chaotic system. The signal acquisition module is used to run the parameterized chaotic system and generate a chaotic signal carrying the exosome data. The signal transmission module is used to combine the chaotic signal with the reference signal to form a transmission signal and then transmit it. A channel compensation module is used to receive the transmitted signal, estimate and compensate the channel based on the reference signal in the transmitted signal, and obtain a compensated chaotic signal. The synchronization and restoration module is used to drive the local chaotic system to achieve synchronization using the compensated chaotic signal, extract the estimated value of the dynamic parameters from the synchronized local chaotic system, and restore the exosome data based on the inverse operation of the mapping rule.

2. The remote data communication platform for exosome therapy of neurodegenerative diseases according to claim 1, characterized in that, include: The exosome data are subjected to linear transformation based on preset baseline parameter values ​​and scaling factors; When the result of the linear transformation operation exceeds the preset range, boundary truncation is performed, and the processed result is used as a dynamic parameter.

3. The remote data communication platform for exosome therapy of neurodegenerative diseases according to claim 1, characterized in that, include: The reference signal is output independently at the beginning of the data frame; After the reference signal ends, the chaotic signal is superimposed with a continuous sine wave to obtain the transmission signal.

4. The remote data communication platform for exosome therapy of neurodegenerative diseases according to claim 3, characterized in that, The reference signal is a pseudo-random binary sequence; the frequency of the continuous sine wave is the same as the carrier frequency of the reference signal, and the amplitude of the continuous sine wave is a fixed ratio of the root mean square amplitude of the chaotic signal.

5. The remote data communication platform for exosome therapy of neurodegenerative diseases according to claim 4, characterized in that, The instantaneous voltage of the output transmitted signal, obtained by superimposing the chaotic signal with a continuous sine wave, satisfies the following formula: ； In the formula, The instantaneous voltage of the transmitted signal; The instantaneous voltage of the chaotic signal; It is a continuous sine wave with a fixed amplitude, and satisfies... , The ratio is fixed. $f_p$ represents the root mean square amplitude of the chaotic signal; $f_p$ represents the frequency of the continuous sine wave. This is the initial phase; t is a time variable.

6. The remote data communication platform for exosome therapy of neurodegenerative diseases according to claim 1, characterized in that, include: The reference signal is extracted from the transmitted signal using a bandpass filter; The extracted reference signal is compared with the local reference signal to obtain the channel gain estimate and frequency offset estimate. The transmitted signal is inversely adjusted for gain and phase corrected using the channel gain estimate and the frequency offset estimate to obtain a compensated chaotic signal.

7. The remote data communication platform for exosome therapy of neurodegenerative diseases according to claim 6, characterized in that, The acquisition of channel gain estimates and frequency offset estimates includes: The channel gain estimate is obtained by calculating the amplitude ratio of the extracted reference signal to the local reference signal. The frequency offset estimate is obtained by measuring and tracking the phase difference between adjacent symbols in the extracted reference signal using a phase-locked loop.

8. The remote data communication platform for exosome therapy of neurodegenerative diseases according to claim 1, characterized in that, include: An error feedback term is introduced into the state equation of the local chaotic system. The error feedback term is based on the difference between the compensated chaotic signal and the state variable of the local chaotic system. Based on the error feedback term, the values ​​of the state variables and dynamic parameters of the local chaotic system are updated in real time during the numerical iteration process until the error feedback term is lower than a preset threshold in multiple consecutive iteration steps, at which point the system is determined to have reached synchronization.

9. The remote data communication platform for exosome therapy of neurodegenerative diseases according to claim 1, characterized in that, It also includes a smoothing module: After reconstructing the exosome data, a moving average is calculated on multiple consecutively acquired exosome data based on a preset time window, and the calculated moving average is output as the measurement result.

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