Signal acquisition and processing system and method for multi-reaction zone implantable sensors

Abstract

This invention relates to the field of medical testing device technology, and particularly to a signal acquisition and processing system and method for a multi-reaction zone implantable sensor. The signal acquisition and processing method for a multi-reaction zone implantable sensor is characterized by the following steps: S10: Retrieving stored clinical sample data, extracting subcutaneous fat layer depth range data for different users from the clinical sample data, and performing fusion analysis on the subcutaneous fat layer depth range data for all users. This invention employs a dual adaptation principle of constructing a universal range using clinical data and real-time detection to lock onto individual fat layer depth. Combined with a stable sensor fixation design, it automatically filters effective electrical signals from the target area and converts them into detection results. This solves the problems of high operational difficulty, cumbersome steps, and low user experience when relying on users to independently adjust the sensor implantation depth. Simultaneously, it adapts to individual differences and acquisition location variations, meets the needs of special populations, and improves detection accuracy and convenience.

Description

Technical Field

[0001] This invention relates to the field of medical testing device technology, and in particular to an adaptive implantable sensor. Background Technology

[0002] An implantable glucose sensor (CGMS) is a minimally invasive medical device used for continuous monitoring of blood glucose levels. It is implanted subcutaneously into the fat layer, allowing the sensor to fully contact the interstitial fluid. Dissolved oxygen and glucose in the interstitial fluid penetrate the sensor's outer membrane and enter the enzyme layer. Glucose reacts with enzymes to produce products such as hydrogen peroxide. The hydrogen peroxide diffuses to the electrode surface, triggering an electrode reaction and generating an electric current. Since this current has an approximately linear relationship with glucose concentration, it can be converted into a blood glucose value through signal processing, thus enabling the detection of blood glucose levels.

[0003] Traditional continuous glucose monitoring systems (CGMS) typically use fixed-length sensor probes and guide pins, with a preset implantation depth of approximately 5mm as the detection depth for the glucose response zone. However, the thickness of human skin and subcutaneous fat layers varies significantly depending on race, age, gender, and implantation site. This fixed detection depth cannot suit all users, easily leading to the sensor failing to be implanted in the ideal monitoring area rich in capillaries. This results in large deviations in blood glucose monitoring values, making it difficult to accurately reflect the true fluctuations in blood glucose levels.

[0004] To address this issue, Chinese patent application CN115919300A proposes an analyte level monitoring system. This system first acquires the user's skin thickness data through a thickness detection unit. The user then manually rotates an adjustment knob to pre-adjust the guide needle's insertion depth for the sensor. Simultaneously, the sensor supports an automatic adjustment mode. For fine-tuning the depth, the user simply presses the elastic button to unlock the locking unit, then slides the adjustment knob and refers to the scale area to complete the calibration. This interconnected and precise operation allows the sensor to accurately land in the target fat layer area, effectively improving the accuracy of blood glucose monitoring data and significantly reducing monitoring errors caused by mismatched implantation depths.

[0005] However, in practical applications, there are significant individual differences among CGMS users. Some users find it difficult to successfully complete the entire skin thickness detection and depth adjustment process. The rigorous steps originally designed for accurate monitoring become an operational burden for users, significantly lowering the user experience. For example, elderly diabetic patients who are overweight often have problems such as decreased vision and reduced limb flexibility. When performing skin thickness detection, the implantation sites such as the abdomen and upper arm have many skin folds and uneven fat distribution, making it difficult to accurately locate the detection points and prone to data deviation. When adjusting the guide pin length and sensor depth, they need to bend over or use a mirror to identify the scale and accurately operate the adjustment knobs and sliders, which is extremely difficult for them, resulting in a poor user experience. Summary of the Invention

[0006] This invention provides a signal acquisition and processing system and method for multi-reaction zone implantable sensors to solve the problem that when users rely on themselves to adjust the sensor implantation depth, the operation is difficult and the operation steps are cumbersome, resulting in a poor user experience.

[0007] To solve the above-mentioned technical problems, this application provides the following technical solution: A signal acquisition and processing system and method for a multi-reaction zone implantable sensor includes the following steps: S10: Retrieve stored clinical sample data, extract subcutaneous fat layer depth range data of different users from the clinical sample data, perform fusion analysis on the subcutaneous fat layer depth range data of all users, and construct a unified depth detection range covering the subcutaneous fat layer depth of all users as the reaction zone setting range. S20: On the component that comes into contact with each tissue layer of the human body after the sensor is implanted, multiple reaction zones are arranged continuously along the implantation depth direction. Each reaction zone is equipped with a biological enzyme that can undergo biochemical reactions with human tissue fluid. After the sensor is implanted in the human body, the actual implantation depth range of the multiple reaction zones in the human body must completely cover the range of the reaction zone settings. S30: The sensor is implanted into the preset collection site of the human body. After implantation, the sensor is fixedly fixed to the human tissue at the implantation site. The electrical signals generated by the reaction between each reaction zone and the tissue fluid are collected in real time as the initial detection signal. The initial detection signal is associated with and stored with the preset depth range of the corresponding reaction zone. S40: When the initial detection signal is acquired for the first time, a detection electrical signal is emitted from the skin surface around the implantation site into the human body according to the preset current frequency and power parameters, and feedback electrical signals formed after reflection or conduction through different tissue layers of the human body are received at the same time; the preset current frequency and power parameters are combined with the feedback electrical signals for processing, and the depth range of the subcutaneous fat layer at the implantation site is obtained by the signal analysis module and used as the target detection range. S50: Based on the initial detection signal and the preset depth range of the associated stored reaction zone, the initial detection signal corresponding to each reaction zone within the target detection range is selected as the target detection signal, and the concentration analysis module is called to process the target detection signal into the analyte detection result.

[0008] And, a signal acquisition and processing system for a multi-reaction zone implantable sensor, which uses a signal acquisition and processing method for a multi-reaction zone implantable sensor, includes: The acquisition unit includes a probe of preset length and a power supply; it is used to retrieve stored clinical sample data, extract subcutaneous fat layer depth range data for different users from the clinical sample data, perform fusion analysis on the subcutaneous fat layer depth range data of all users, and construct a unified depth detection range covering the subcutaneous fat layer depth of all users as the reaction zone setting range; combined with the implantation depth of the probe in various parts of the human body, multiple continuously arranged reaction zones are arranged on the probe along the implantation depth direction, and the outer side of the probe in each reaction zone is equipped with a biological enzyme that can undergo biochemical reactions with human tissue fluid; and after the probe is implanted in the human body, the actual implantation depth range of the multiple reaction zones in the human body must completely cover the reaction zone setting range. The detection unit is electrically connected to the probes in the reaction zone; it acquires and stores the preset depth range of each reaction zone; when the probe is implanted into the preset collection site of the human body and the probe is firmly fixed to the human tissue at the implantation site, it collects the electrical signals generated by the reaction between each reaction zone and the tissue fluid in real time as the initial detection signal, and stores the initial detection signal in association with the preset depth range of the corresponding reaction zone. The control module acquires the initial detection signal and the associated stored preset depth range. When the initial detection signal is acquired for the first time, it generates a detection command based on the preset current frequency and power parameters. The detection module includes an induction antenna electrically connected to a power source; it is used to acquire detection commands, and after acquiring the detection commands, it transmits detection electrical signals from the skin surface around the implantation site into the human body according to the current frequency and power parameters in the detection commands, and at the same time receives feedback electrical signals formed after reflection or conduction through different tissue layers of the human body, and sends the feedback electrical signals as feedback information of the detection commands to the control module. After receiving the feedback electrical signal, the control module acquires the current frequency and power parameters from the corresponding detection command. It then combines these parameters with the feedback electrical signal and analyzes the signal to determine the depth range of the subcutaneous fat layer at the implantation site, using this as the target detection range. Based on the initial detection signal and the associated stored preset depth range of the reaction zone, the control module selects the initial detection signals corresponding to each reaction zone within the target detection range as target detection signals. Finally, it calls the concentration analysis module to process these target detection signals into analyte detection results.

[0009] The basic principle and beneficial effects of this invention are as follows: First, stored clinical sample data is retrieved, and subcutaneous fat layer depth data from different users is extracted and fused for analysis. This constructs a unified detection range covering the fat layer depth of most users. Based on this, continuous reaction zones are deployed on the sensor to ensure that the reaction zones completely cover this unified detection range after sensor implantation, thereby guaranteeing comprehensive and accurate acquisition of tissue fluid electrical signals for most users. After the sensor is implanted and fixed at a preset location in the human body, when the initial detection signal generated by the reaction zone is first acquired, a detection electrical signal with preset parameters is emitted from the skin surface surrounding the implantation site into the body. Simultaneously, feedback electrical signals reflected or conducted through different tissue layers such as skin, fat, and muscle are received. By analyzing the feedback electrical signals, the specific fat layer depth range of the current implantation site is accurately determined, thus locking in the exclusive detection range suitable for that site. Based on this exclusive detection range, effective electrical signals acquired from the corresponding reaction zone are selected, avoiding interference from invalid signals and ensuring accurate acquisition of electrical signals from the target fat layer. Finally, the accurate electrical signals are processed into precise detection results through a concentration analysis module. Throughout the process, users do not need to manually read the detection depth or adjust the implantation depth after the sensor is implanted. The sensor will directly extract the electrical signal within the corresponding range after automatically acquiring the detection depth, without requiring any confirmation or adjustment from the user. This greatly saves operation steps, reduces operation difficulty, and effectively improves the user experience.

[0010] Sensors need to be implanted in a fat layer rich in capillaries to ensure monitoring accuracy. However, the depth of the fat layer and the distribution of capillaries vary among different users, and even among the same user, the depth of the fat layer and the distribution of capillaries can vary depending on the implantation site. Traditional fixed-depth solutions and solutions requiring manual adjustment cannot achieve accurate matching. This solution adopts a dual adaptation principle of constructing a general range through clinical data fusion and obtaining the specific range of the individual through real-time detection. It can cover the fat layer depth range of most users through continuous reaction zones, and accurately pinpoint the specific fat layer depth of the current user through the analysis of detection electrical signals. It then selects reaction zone signals within this range for detection, avoiding situations where the reaction zone exceeds or does not cover the target fat layer area. Compared with solutions that only rely on manual adjustment of the adaptation depth, this further improves the stability and accuracy of monitoring data and reduces monitoring deviations caused by individual differences and implantation site variations.

[0011] Existing adjustable solutions often suffer from cumbersome operation and difficulty in accurately reading scales, hindering depth adjustment and resulting in a poor user experience. This new solution utilizes automated signal acquisition, depth analysis, and signal filtering. The entire process eliminates the need for manual rotation of adjustment knobs, unlocking / locking units, or reading scales for depth calibration. Only sensor implantation and fixation are required for automated depth adaptation and signal processing. Even elderly diabetic patients with limited mobility can easily use the device, effectively meeting the needs of special populations, overcoming the limitations imposed by existing solutions on user capabilities, and further optimizing the user experience.

[0012] When the implanted component (probe) of the sensor shifts, the bio-enzyme originally located in the adipose layer may detach from its fixed position, or be carried into other tissue layers, bringing substances from other tissue layers into the adipose layer. The longer the shift and adjustment time, the more significant the shift in the bio-enzyme's position, introducing more interference signals and ultimately distorting the initial detection signal, resulting in inaccurate detection results. This solution reduces the positional changes of the bio-enzyme caused by sensor shift and external interference factors by firmly fixing the sensor to human tissue, ensuring that the bio-enzyme in the reaction zone always acts stably on the target area, thereby significantly improving the accuracy of the detection results.

[0013] In summary, this invention employs a dual adaptation principle that combines clinical data to construct a universal range and real-time detection to lock onto the depth of the individual fat layer. Coupled with a stable sensor fixation design, it automatically filters effective electrical signals from the target area and converts them into detection results. This solves the problems of high operational difficulty, cumbersome steps, and poor user experience when relying on users to adjust the sensor implantation depth independently. At the same time, it adapts to individual differences and collection location differences, meets the needs of special populations, and improves detection accuracy and convenience.

[0014] Furthermore, in step S40, four induction antennas are arranged in four directions at a cross intersection on the skin surface around the implantation site. The four induction antennas serve as the transmitting end for detecting electrical signals and the receiving end for receiving feedback electrical signals.

[0015] Four inductive antennas are arranged in a cross shape around the implantation site, achieving omnidirectional coverage of the detected electrical signals and avoiding detection blind spots caused by signal blockage or attenuation in a single direction. They also simultaneously receive feedback signals from different directions, providing data support for multi-angle verification. For example, for patients with uneven distribution of abdominal fat, the four-way inductive antennas can simultaneously transmit or receive feedback signals from different directions, ensuring no feedback signals are missed. This solves the problem of traditional single antennas being unable to fully cover the target area due to directional limitations, laying the foundation for accurate analysis of fat layer depth and structure.

[0016] Furthermore, when transmitting the detection signal and receiving the feedback signal, at the same time, at least one of the four induction antennas transmits the detection signal into the human body, and at least one induction antenna receives the feedback signal; the feedback characteristics of the same tissue layer to feedback signals from induction antennas of different frequencies, different powers and from different directions are compared and analyzed, and the depth range and layered structure of the subcutaneous fat layer are analyzed based on the feedback characteristics.

[0017] By varying the transmission and reception combinations of four antennas, multi-angle signal acquisition and verification of the same subcutaneous fat layer can be performed, effectively identifying complex structures with multiple overlapping fat layers. For example, in cases where a patient's waist contains multiple layers of thin fat and fascia alternating, the feedback signals from different antenna combinations can corroborate each other, eliminating signal distortion from a single angle and accurately pinpointing the boundaries and thickness of each fat layer. Compared to fixed transmission and reception signals, this significantly reduces the target detection range deviation caused by interlayer interference, thus significantly improving detection accuracy.

[0018] Furthermore, the subcutaneous fat layer thickness is calculated based on the difference between the upper and lower limits of the target detection range. When the subcutaneous fat layer thickness is less than the preset thin layer thickness, the subcutaneous fat layer is treated as a thin layer structure; when the subcutaneous fat layer thickness is greater than the preset thick layer thickness, the subcutaneous fat layer is treated as a thick layer structure; when the number of subcutaneous fat layers obtained is greater than 1, the layered structure is treated as a multi-layered structure.

[0019] Abandoning traditional, fixed methods of structural determination, this invention dynamically distinguishes between thin, thick, and multi-layered structures based on the thickness and number of fat layers, adapting to different scenarios for different users and implantation sites. For example, thinner individuals have thinner upper arm fat layers, obese individuals have thicker abdominal fat layers, and elderly individuals may have multiple layers of fat and muscle intertwined on their backs. This invention can specifically determine the structural type for each case, avoiding detection bias caused by uniform standards, making the detection more closely reflect actual physiological conditions, and improving the accuracy and adaptability of the results.

[0020] Furthermore, when the layered structure is a thin layer, other tissue layers adjacent to the subcutaneous fat layer are used as signal interference layers. The initial detection signals collected from the signal interference layer and the subcutaneous fat layer are compared, and the temporal differences in the comparison results are analyzed. Based on the differences obtained from the analysis, the initial detection signals collected from the subcutaneous fat layer are filtered and stripped. The initial detection signals obtained after filtering and stripping are used as target detection signals. The average current magnitude of the target detection signals in each reaction zone is obtained, and the analyte concentration data is calculated based on the average current magnitude.

[0021] Thin adipose layers are susceptible to signal interference from adjacent tissue layers. By comparing the initial detection signals of the subcutaneous adipose layer and the interfering layer, the interfering components can be accurately identified by utilizing temporal differences. For example, due to the different bioenzymatic reaction sequences and signal attenuation patterns between the interfering layer and the adipose layer, filtering and stripping can completely remove irrelevant signals. In this way, the present invention can focus on the effective signals of the adipose layer, avoid the dilution of real data by interfering signals, and make the analyte concentration calculation more accurate; at the same time, it can quickly screen signals by time-series comparison alone, shortening the data processing flow and improving the calculation speed.

[0022] Furthermore, target detection signals collected when the layered structure is a thick layer are obtained from clinical sample data, along with the preset depth range corresponding to the target detection signals. The obtained target detection signals are combined with the corresponding preset depth ranges to establish a signal intensity depth distribution model. The signal intensity depth distribution model takes the depth range as input and the signal intensity of the target detection signals as output. When the current layered structure is a thick layer, target detection signals within the preset depth range of the reaction area in the current subcutaneous fat layer are obtained. The preset depth range corresponding to the currently obtained target detection signals is input into the signal intensity depth distribution model, and the signal intensity output by the signal intensity depth distribution model is used as the reference intensity. The reference intensity of all reaction areas in the current subcutaneous fat layer is obtained and compared with the intensity of all corresponding target detection signals. Target detection signals with a similarity lower than the preset reference similarity are removed. For the remaining target detection signals, weights are assigned according to the position of the preset depth range corresponding to the target detection signal in the current subcutaneous fat layer. A weighted average is calculated based on the weights to obtain the analyte concentration data.

[0023] Thick fat layers have a wide depth range, and signal intensity attenuation varies greatly with depth. A signal intensity depth distribution model constructed using clinical data can provide reference intensities at each depth, quickly eliminating abnormal signals that deviate from the standard. Simultaneously, by assigning weights based on depth location, the dominant role of the signal in the core region can be highlighted, while the error influence of peripheral regions can be weakened. For example, in the detection of thick abdominal fat layers, signal deviations at different depths can be accurately corrected, avoiding concentration calculation distortions caused by uneven signal distribution within the thick layer. Compared to uniform weighting methods, this significantly improves the detection accuracy of analyte concentrations in thick structures.

[0024] Furthermore, analyte concentration data, the target detection range of the subcutaneous fat layer corresponding to the analyte concentration data, and the interval distance with adjacent subcutaneous fat layers are obtained from clinical sample data when the layered structure is multi-layered. A mapping relationship model is established based on the obtained data to clarify the gradient diffusion relationship of analyte concentration data under different target detection ranges and different interlayer interval distances. When the current layered structure is multi-layered, the analyte concentration data of the corresponding layer is calculated based on the thickness of each subcutaneous fat layer. The target detection range and interlayer interval distance of each layer are obtained simultaneously, and the corresponding gradient diffusion relationship in the mapping relationship model is retrieved as the gradient reference relationship. If the current analyte concentration data of each subcutaneous fat layer does not match the gradient reference relationship, the detection signal is re-emitted into the human body to update the target detection range and re-collect and calculate the analyte concentration data of each layer. If the current analyte concentration data of each subcutaneous fat layer conforms to the gradient reference relationship, the gradient diffusion relationship of the current analyte concentration data of each layer is used as the control relationship. Combining the control relationship and the analyte concentration data of each layer, the final analyte concentration data is calculated.

[0025] For scenarios where multiple layers of fat of varying thicknesses may overlap in different parts of the human body, such as the abdomen where superficial thin fat layers, middle fat layers, and deep thick fat layers coexist, this invention first calculates the analyte concentration data for each layer based on its actual thickness, ensuring that the concentration calculation for each layer is not affected by other layers. Simultaneously, it acquires the target detection range and interlayer spacing for each fat layer, retrieves a mapping model based on clinical sample data, and extracts the corresponding gradient diffusion relationship as a gradient reference. Since the analytes between adjacent fat layers diffuse according to inherent physiological laws, this diffusion relationship is stable and predictable. If the analyte concentration data obtained from the current layered calculation conforms to this gradient reference relationship, it indicates that the layered concentration calculation results are logically consistent and the data is reliable. In this case, the current gradient diffusion relationship of each layer is used as a control relationship, and the final result is obtained by combining the control relationship with the concentration data of each layer. If the concentration data does not match the gradient reference relationship, it indicates that there may be detection errors or interlayer signal interference, requiring a re-transmission of the detection signal, an update of the target detection range, and a re-collection and calculation of the concentration data for each layer. The entire process requires no additional computing resources, and the verification and computation stages proceed simultaneously. This effectively solves the problem of interlayer concentration interference and difficulty in accurate differentiation when multiple fat layers are stacked, while avoiding the extended detection cycle caused by setting up separate verification steps. It significantly improves the accuracy of analyte concentration detection in multilayer structures while taking into account detection efficiency, making the implantable sensor more practical and reliable in detection scenarios with complex fat layer structures.

[0026] Furthermore, multiple preset current frequency and power combinations corresponding to typical electrical characteristic parameters of different human tissue layers are pre-stored, and theoretical response models corresponding to each combination are established. After the feedback electrical signal is acquired for the first time, the spectral characteristics and attenuation rate of the feedback electrical signal are extracted. The extracted spectral characteristics and attenuation rate are matched one by one with the theoretical response models of all preset combinations, and the combination with the highest matching degree is selected as the currently applicable initial current frequency and power. When the probe electrical signal is transmitted, the amplitude change and signal-to-noise ratio change of the feedback electrical signal are monitored. When the amplitude change and signal-to-noise ratio change are lower than the preset stability threshold, the human tissue impedance change trend is inverted based on the current probe electrical signal and the corresponding feedback electrical signal. Based on the inverted human tissue impedance change trend, the combination with the highest matching degree and the best signal quality is iteratively selected as the current basic detection parameters. At the same time, the power in the current basic detection parameters is gradually adjusted based on the preset adjustment step size.

[0027] Based on iterative selection of frequency and power combinations based on tissue impedance change trends, it can quickly adapt to the local tissue characteristics of the implantation site. For example, different sites have different proportions of fat and muscle, resulting in differences in impedance characteristics. This setting method can accurately match the local state and can also quickly adjust the frequency and power of the probe electrical signal to the optimal detection parameters, achieving efficient and accurate acquisition. At the same time, the power fine-tuning strictly adheres to the safety threshold of the local tissue, avoiding excessive power stimulation. Compared with fixed parameters or whole-body adaptation adjustments, it is more in line with local detection needs, improving safety and detection accuracy.

[0028] Furthermore, during the transmission of the detection signal, the signal-to-noise ratio of the feedback signal and the resolution sharpness index of the reflection interface of each tissue layer are monitored in real time. The resolution sharpness index is obtained by comprehensively and weighting the full width at half maximum (FWHM), inter-peak distance, peak signal-to-noise ratio, and the matching degree between the reflection peak sequence and the standard template of the feedback signal reflection peak. The reflection or conduction depth corresponding to the feedback signal in the human body is taken as the target detection depth. When the feedback signal obtained at the target detection depth does not reach the preset resolution threshold, the spectral characteristics of the feedback signal during the monitoring process are extracted, and the adjustment step size is dynamically reduced according to the spectral characteristics, thereby adjusting the frequency and power of the transmitted detection signal. The detection signal is continuously transmitted to the target detection depth that has not reached the preset resolution threshold until the feedback signal obtained at the target detection depth reaches the preset resolution threshold. The target detection depth corresponding to the feedback signal that has reached the preset resolution threshold is taken as the clear detection depth, and the transmission power or frequency of the detection signal transmitted to the clear detection depth is reduced.

[0029] During the transmission of the detection electrical signal, this invention monitors the signal-to-noise ratio of the feedback electrical signal and the resolution of the tissue layer reflection interface in real time. It dynamically adjusts the frequency and power parameters based on the actual detection situation, ensuring that the detection parameters better match the actual tissue state at the implantation site. For clearly resolved detection depths, reducing the transmission power or frequency minimizes unnecessary power consumption, saves sensor power resources, and extends usage time. Simultaneously, it reduces the electrical stimulation intensity in that area (the area corresponding to the clearly resolved detection depth), especially beneficial for patients with sensitive skin or those requiring long-term monitoring, reducing discomfort such as tingling and numbness caused by electrical stimulation. This precise adjustment method avoids resource waste and overstimulation caused by fixed parameters while ensuring detection accuracy in unclear areas, improving detection reliability while balancing power economy and patient tolerability. Attached Figure Description

[0030] Figure 1 This is a flowchart of the signal acquisition and processing method for a multi-reaction zone implantable sensor in Example 1; Figure 2 This is a functional block diagram of the signal acquisition and processing system for the multi-reaction zone implanted sensor in Example 4. Detailed Implementation

[0031] The following will describe the concept and technical effects of the present invention clearly and completely with reference to embodiments, so as to fully understand the purpose, features and effects of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are all within the scope of protection of the present invention. Example 1 like Figure 1 As shown, the signal acquisition and processing method for a multi-reaction zone implantable sensor includes the following steps: S10: Retrieve stored clinical sample data (pre-stored by medical staff, containing the depth range of subcutaneous fat layer for different users), extract the subcutaneous fat layer depth range data for different users from the clinical sample data, compare and fuse the subcutaneous fat layer depth range data of all users, and construct a unified depth detection range covering the subcutaneous fat layer depth of all users as the reaction zone setting range.

[0032] When performing fusion analysis on the subcutaneous fat layer depth range data of all users, the data of the subcutaneous fat layer depth range of all users is integrated. By overlaying the data, the minimum value of the fat layer depth of all users is used as the lower limit and the maximum value is used as the upper limit to construct the widest unified interval covering the depth range of all individual users. This ensures that the interval includes the subcutaneous fat layer depth of every user without omission, and provides a comprehensive range parameter for the deployment of reaction zones.

[0033] S20: On the component that contacts various tissue layers of the human body after the sensor is implanted (on the probe implanted in the human body), multiple continuously arranged reaction zones are arranged along the implantation depth direction (multiple continuous, mutually insulated reaction zones are set on the probe). Each reaction zone is equipped with a biological enzyme that can undergo a biochemical reaction with human tissue fluid (the biological enzyme is attached to the outside of the probe). After the sensor is implanted in the human body, the actual implantation depth range of the multiple reaction zones in the human body must completely cover the range of the reaction zones. The number of reaction zones is reasonably set by technicians after comprehensively weighing the effective contact reaction range between the biological enzyme and human tissue fluid, and the detection accuracy requirements of the current generated by the reaction collected from each reaction zone.

[0034] S30: Implant the sensor into the preset collection site on the human body. After implantation, fix the sensor firmly to the human tissue at the implantation site (after the probe is implanted into the sensor, use medical tape to fix and adhere the sensor to the human skin surface); collect the electrical signals generated by the reaction between each reaction zone and the tissue fluid in real time as the initial detection signal, and store the initial detection signal in association with the preset depth range of the corresponding reaction zone.

[0035] S40: Upon initial acquisition of the detection signal, a detection electrical signal is emitted from the skin surface surrounding the implantation site into the human body according to the preset current frequency and power parameters (based on clinical sample data, the technician extracts the detection power and current frequency parameters corresponding to the shallowest detectable subcutaneous fat layer, and uses these as the preset current frequency and power parameters). Simultaneously, feedback electrical signals formed after reflection or conduction through different tissue layers of the human body are received. The preset current frequency and power parameters are combined with the feedback electrical signals for processing, and the depth range of the subcutaneous fat layer at the implantation site is obtained through signal analysis module analysis, which is used as the target detection range.

[0036] S50: Based on the initial detection signal and the preset depth range of the associated stored reaction zone, the initial detection signal corresponding to each reaction zone within the target detection range is selected as the target detection signal, and the concentration analysis module is called to process the target detection signal into the analyte detection result.

[0037] In step S40, four induction antennas are set in four directions at a cross intersection on the skin surface around the implantation site. The four induction antennas (using medical-grade oxygen-free copper or silver-plated copper wire with high conductivity to ensure low-loss transmission of detection electrical signals and efficient reception of feedback electrical signals; their good conductivity can reduce signal attenuation and ensure detection accuracy; in this embodiment, oxygen-free copper is selected by default) serve as the transmitting end of detection electrical signals and the receiving end of feedback electrical signals.

[0038] When transmitting detection signals and receiving feedback signals, at the same time, at least one of the four induction antennas transmits detection signals into the human body and at least one induction antenna receives feedback signals. The feedback characteristics of the same tissue layer to feedback signals from induction antennas of different frequencies, different powers and from different directions are compared and analyzed, and the depth range and layered structure of the subcutaneous fat layer are determined based on the feedback characteristics.

[0039] The conduction speed of the probe electrical signal in human tissue is stable and is not affected by tissue depth. However, the path length of the reflection or conduction process of the probe electrical signal varies in tissue layers of different depths. This results in a significant time difference in the reception time of the corresponding feedback electrical signal, and this time difference is positively correlated with the tissue layer depth. This provides a core basis for subsequently dividing the tissue layer depth range by time dimension.

[0040] Based on statistical results of different tissue layer depths in clinical sample data, technicians first calculate the range of difference between the transmission time of the detection electrical signal and the reception time of the feedback electrical signal. Then, combining this time difference with the correspondence between tissue layer depth, they scientifically set the unit test time (e.g., 50ms / cycle) to ensure that the unit test time can completely cover the signal reflection and conduction cycle of the target tissue layer. According to the detection requirements corresponding to different tissue layer depths of patients, the transmission power level and current frequency range of each induction antenna are determined (e.g., preset current frequency and power parameters), and the signal acquisition trigger conditions and feedback signal storage format are set. Using the unit test time as the cycle, one induction antenna is activated as the transmitting end in a clockwise or preset order. For example, the four induction antennas are designated as antennas 1-4. In the first round, only antenna 1 is activated to transmit a detection signal, while the other three induction antennas act as receivers, continuously receiving feedback signals reflected / conducted through human tissue until the unit test time of that round ends. Subsequently, antenna 2 is switched to the transmitter, and the remaining three induction antennas receive signals. This process of single-end transmission and multi-end reception by antennas 3 and 4 is repeated to form a complete four-way test cycle.

[0041] In each round of testing, the feedback electrical signal is bound and stored with the transmitter identifier (antenna number), transmission parameters (frequency, power), and receiver identifier (numbers 1-4) to ensure that each signal can be traced back to its source location and test conditions.

[0042] Extract feedback signal characteristics (including spectral peak, attenuation rate, and phase difference) of the same tissue layer under different emission orientations, frequencies, or powers. By comparing the characteristics of multiple sets of signals, eliminate signal distortion in a single orientation (such as deviations caused by occlusion or local tissue abnormalities).

[0043] First, all feedback electrical signals undergo unified preprocessing, including extracting core features such as spectral peaks, attenuation rates, and phase differences of the same tissue layer under different transmission orientations, frequencies, or powers. All feature values ​​are then normalized using a formula. Mapped to the interval [0,1] (where The normalized value of the k-th feature. The original value of the k-th feature. It is the minimum value of the same type of feature in all directions. The maximum value of the same type of feature in all directions is used to eliminate the differences in the dimensions of different features through a normalization formula, ensuring that the comparison benchmark is consistent.

[0044] Then, construct a feature similarity matrix with the emission azimuth as the row and column, and use the cosine similarity formula. Calculate the feature similarity of signals from different azimuth directions one by one (where Let be the feature similarity between the i-th and j-th emission azimuth directions, with a value range of [0,1]. The closer to 1, the more consistent the features are. Let be the normalized value of the k-th type feature at the i-th orientation. Let be the normalized value of the k-th type feature at the j-th orientation. The numerator is the dot product of the two types of feature vectors, reflecting the similarity of the features, and the denominator is the product of the magnitudes of the two types of feature vectors, used for normalization to avoid the influence of feature magnitude differences on similarity judgment. Count the number of similarities between each orientation and all other orientations that reach the preset acceptable threshold, and compare the total acceptable ratio using the consistency rate formula. Calculate the consistency rate (where The signal consistency rate at the i-th azimuth is qualified. The total number of comparisons is the number of times the similarity between this location and other locations is greater than or equal to a preset threshold. The total number of times the target was compared to the target in that azimuth (i.e., the total number of launch azimuths minus 1). If the consistency rate of a certain azimuth... If the similarity is lower than the preset similarity (set by technicians, the default value in this implementation is 50%), it is determined to be a signal distortion, and all feedback electrical signals corresponding to that direction are removed from the dataset, thereby achieving accurate removal of signal distortion in a single direction.

[0045] Technicians extracted core features such as spectral peaks, attenuation rates, and phase differences of signals within each depth interval based on time difference-divided depth intervals and the effective feedback signals after distortion removal. They then compared the signal features of each depth interval with templates established from clinical samples (the signal characteristics of the fat layer differ inherently from those of skin, muscle, and fascia) to identify the corresponding tissue type. For depth intervals identified as fat layers, they further analyzed the continuity and spacing of signal reflection peaks (sorting reflection peaks by detection depth, verifying peak continuity and inter-peak distance stability, and combining this with the fat layer signal attenuation pattern to determine whether the intervals belong to the same or a new fat layer). Based on the continuity and spacing between fat layers, they determined the relationships between them. Finally, by analyzing all depth intervals identified as fat layers, the location of reflection interfaces, and interlayer spacing, they clarified the total number of fat layers, the depth range and thickness of each layer, and obtained a complete layered structure of multiple fat layers.

[0046] After completing the tissue type labeling for each depth interval (identifying which intervals are fat layers), all depth intervals labeled as fat layers are arranged sequentially from shallowest to deepest. The characteristics and spacing of the feedback electrical signal reflection peaks of adjacent fat layer intervals are analyzed one by one. Specifically, the core features (spectral peak value, phase difference) of the reflection peaks of two adjacent fat layer intervals are extracted first. The rate of change of the spectral peak value and the abrupt change in the phase difference are calculated. If the rate of change of the spectral peak value is ≤10% and the abrupt change in the phase difference is ≤5° (the values ​​of 5° and 10% are set by technicians based on the electrical characteristics of the transmitted electrical signal in each tissue layer and fat layer; this is just an example), it indicates that there is no significant abrupt change in the signal reflection peaks between intervals, which conforms to the stable signal attenuation law caused by the uniformity of tissue within the fat layer. Furthermore, the interval spacing is within a reasonable range of continuous distribution within the fat layer (based on the normal spacing range of adjacent intervals within the fat layer according to clinical data statistics). Therefore, these two adjacent intervals are merged into one. A fat layer; if a certain depth interval labeled as a fat layer shows a significant abrupt change in the reflection peak characteristics compared with the previous fat layer interval (e.g., a spectral peak change rate ≥30% and a phase difference abrupt change value ≥15°), and the signal characteristics of this interval are completely matched after comparison with the fat layer template established by clinical samples (a fat layer template constructed based on the depth corresponding to the fat layers of all patients in clinical data), and this feature abrupt change originates from the difference in electrical properties of different tissues (or different fat layers), forming a clearly identifiable reflection interface, and the interval spacing exceeds the normal spacing range within the same fat layer, then this interval is determined to be a new fat layer, clearly distinguishable from the previous fat layer.

[0047] The subcutaneous fat layer thickness is calculated based on the difference between the upper and lower limits of the target detection range. When the subcutaneous fat layer thickness is less than the preset thin layer thickness (set by technicians based on statistical data of fat thickness in clinical data), the subcutaneous fat layer is treated as a thin layer structure; when the subcutaneous fat layer thickness is greater than the preset thick layer thickness (set by technicians based on statistical data of fat thickness in clinical data), the subcutaneous fat layer is treated as a thick layer structure; when the number of subcutaneous fat layers obtained is greater than 1, the layered structure is treated as a multi-layered structure.

[0048] When the layered structure is a thin layer, other tissue layers adjacent to the subcutaneous fat layer are used as signal interference layers. The initial detection signals collected from the interference signal layer and the subcutaneous fat layer are compared, and the temporal differences in the comparison results are analyzed. Based on the differences obtained from the analysis, the initial detection signals collected from the subcutaneous fat layer are filtered and stripped. The initial detection signals obtained after filtering and stripping are used as target detection signals. The average current magnitude of the target detection signals in each reaction zone is obtained, and the analyte concentration data is calculated based on the average current magnitude.

[0049] When the subcutaneous fat layer is a thin layer, the initial detection signal is easily interfered with by the superposition of signals from adjacent tissues such as skin and fascia. Furthermore, the thin layer's own signal differs from the interference signal in terms of timing. Therefore, it is necessary to first extract the timing difference of the initial detection signal, and then perform adaptive timing filtering and stripping to verify the initial detection signal, and finally determine the target detection signal.

[0050] When extracting temporal differences, the initial detection signal is first divided into continuous temporal signal segments according to timestamps (corresponding to tissue depths), in order from shallow to deep depth. Each initial detection signal segment corresponds to a fixed depth interval. The core features (spectral peak values) of each temporal signal segment are then extracted. Phase attenuation coefficient , (As time-series sampling points), compare the characteristic differences between the target time-series interval corresponding to the thin fat layer and the time-series interval of adjacent tissues to clarify the time-series distribution pattern of interference signals (e.g., skin signals are concentrated in the superficial time-series segment, and fascia signals are concentrated in the deep time-series segment).

[0051] During adaptive temporal filtering, an adaptive filtering algorithm based on temporal characteristics is used to specifically remove interference components that do not conform to the temporal characteristics of the thin fat layer signal. The formula is as follows: , : The filtered intermediate signal; : The initial detection signal at time; Number of interference signal types (e.g., two types of interference: skin and fascia) ); : No. The weighting coefficients of interference signals are determined by the degree of difference in time-series characteristics (the greater the difference, the higher the weighting coefficient). The closer to 1, the more thorough the peeling. : No. Interference signals in time offset The signal value at that location, The time delay between the interference signal and the target signal is calculated from the propagation time difference corresponding to the depth difference.

[0052] During the initial detection signal stripping verification, the filtered signal variables were calculated based on the temporal feature template of a thin fat layer (trained from clinical thin-layer sample data). The temporal similarity to the template is calculated using the following formula: , Filtered signal variables Temporal similarity, value range The closer to 1, the higher the signal matching degree; , : The start and end times of the time interval corresponding to the thin fat layer; : Timing template of thin-layer fat layer signal at a given time.

[0053] When determining the target detection signal, if (A pre-set pass threshold for technicians, calibrated based on clinical data), then That is, the target detection signal after removing interference; if Then adjust the interference signal weighting coefficient. With timing delay The adaptive timing filtering and initial detection signal stripping verification steps are repeated until the signal meets the similarity requirements, and finally the target detection signal is output.

[0054] The target detection signal segment is divided into m consecutive sampling segments according to a preset time interval (e.g., 10ms / segment). Each sampling segment contains n current sampling points, and the average instantaneous current value of each sampling segment is calculated. , The average current (in μA) of the j-th sampling segment in the i-th reaction zone. This represents the number of current sampling points in a single sampling segment; This represents the instantaneous current value (unit: μA) at the k-th sampling point in the j-th sampling segment of the i-th reaction zone.

[0055] Calculate the average magnitude of the overall current in the reaction zone. , The average current magnitude of the target detection signal in the i-th reaction zone (unit: μA); This represents the total number of sampling segments in the effective signal segment of the reaction zone; This represents the average current value of the j-th sampling segment in the i-th reaction zone.

[0056] A linear regression model based on clinical sample calibration was used to establish the mapping relationship between the mean current and the analyte concentration. , This represents the analyte concentration (unit: mmol / L, adjusted according to the analyte type) corresponding to the i-th reaction zone. Calibration factor (unit: mmol·L) -1 ·μA -1 The value is obtained by fitting clinical sample data and reflects the linear proportional relationship between the mean current and the concentration. Let be the average current magnitude in the i-th reaction zone; The calibration intercept (unit: mmol / L) was obtained from the mean current of the blank sample (without target analyte) and was used to correct for baseline interference.

[0057] The average concentration data from all reaction zones was taken as the final analyte concentration result. , For the final analyte concentration data; The total number of effective reaction zones (excluding reaction zones with distorted signals); Let be the analyte concentration in the i-th reaction zone.

[0058] The target detection signal and the corresponding preset depth range are obtained from the clinical sample data when the layered structure is a thick layer. The obtained target detection signal and the corresponding preset depth range are combined to establish a signal intensity depth distribution model. The signal intensity depth distribution model takes the depth range as input and the signal intensity of the target detection signal as output. When the current layered structure is a thick layer, the target detection signal of the reaction area in the current subcutaneous fat layer within the preset depth range is obtained. The preset depth range corresponding to the currently obtained target detection signal is input into the signal intensity depth distribution model, and the signal intensity output by the signal intensity depth distribution model is used as the reference intensity.

[0059] Signal strength depth distribution model: , It is suitable for thick fat structures (e.g., single layer thickness ≥ 5 mm, signal intensity exhibits segmented attenuation characteristics with depth); according to the signal attenuation law inside thick fat, it is fitted in two segments to take into account the different attenuation rates of surface and deep signals.

[0060] Depth is The target detection signal intensity at the location (scaled to the [0,1] interval by the extreme value method, corresponding to the normalized value of the current signal amplitude); Input variable: Depth value within the fat layer (unit: mm), with a value range defined by the preset depth range for clinical samples. ; Thick fat layer ( The initial signal intensity at the location (obtained from statistical analysis of surface signals of clinical samples, serving as model fitting parameters); Superficial area of ​​the fat layer ( Signal attenuation coefficient (unit: mm) -1 Fitting parameters reflect the shallow signal attenuation rate. : Deep fat layer region ( The signal strength baseline value (fitting parameter, determined by the connection characteristics of shallow and deep signals) at the location); Signal attenuation coefficient in the deep fat layer region (unit: mm) -1 Fitting parameters, due to the slightly higher density of deep tissues, are usually... ); : The boundary depth between the superficial and deep regions (unit: mm, fitting parameter, determined by the statistical analysis of the signal attenuation inflection point of clinical samples, usually 1 / 3 to 1 / 2 of the total thickness of the thick layer); : Unit step function, when hour (Enable deep attenuation term), when hour (Only shallow attenuation terms are retained); Model error term (value ≤ 0.05, obtained from the statistical analysis of the fitting residuals of clinical sample data, reflecting the model fitting accuracy).

[0061] When training a signal intensity depth distribution model based on clinical sample data, clinical samples with thick fat structures are selected, and the target detection signal intensity of each sample is extracted. and corresponding depth Remove outlier data (such as samples with signal distortion or incorrect depth calibration); divide the dataset into a training set (for model fitting) and a validation set (for verifying the accuracy of the signal intensity depth distribution model) in a ratio (e.g., 7:3); then divide the training set... Substituting the data into the signal intensity depth distribution model expression, the least squares method is used to minimize the fitting residual for actual prediction. Solve , , , , The optimal value; the depth of the validation set. Substitute the trained signal strength depth distribution model into the data to calculate the predicted signal strength. Measured values coefficient of determination ,Require (Ensure the model fits well); if the validation passes, finalize the signal intensity depth distribution model parameters; if it fails, supplement with clinical samples and refit until the accuracy requirements are met.

[0062] Preset depth range for thick fat layers any depth value within Substituting the values ​​into the model allows for the direct output of the corresponding target detection signal intensity. Conversely, the corresponding depth can also be inferred from the measured signal intensity, providing data support for the accurate analysis of the depth range of thick fat layers.

[0063] The reference intensity of all reaction zones in the current subcutaneous fat layer is obtained and compared with the intensity of all corresponding target detection signals. Target detection signals with a similarity lower than the preset reference similarity are removed. Weights are assigned according to the position of the corresponding depth of the retained target detection signals in the fat layer, and the analyte concentration data is calculated by weighted average.

[0064] Specifically, first, the reference intensity set of each reaction area in the clinical sample that is consistent with the current subcutaneous fat layer structure, the preset reference similarity threshold, and the weight level divided by depth position (the deeper the signal stability, the larger the weight coefficient) are; then, the target detection signal intensity of all reaction areas in the current subcutaneous fat layer is obtained after filtering and stripping; and finally, the target detection signal intensity of each reaction area is calculated one by one using a simplified cosine similarity formula. and corresponding reference strength Calculate similarity: , : No. The similarity between the target detection signal and the reference intensity in each reaction zone, with a value range of... ; : No. Target detection signal intensity (normalized value) in each reaction zone; : No. The clinical reference strength (standardized value) corresponding to each reaction zone.

[0065] Invalid signals with similarity below a preset threshold are removed, while valid signals and their corresponding reaction zone numbers and signal strengths are retained. Based on the depth position, the corresponding weight level is matched and weights are assigned according to the preset depth position of the reaction zone corresponding to each valid signal. Calculate the sum of the products of the effective signal strength and the weights: , : No. The strength value of each valid signal; : No. The weighting coefficients corresponding to each valid signal; The number of reaction zones corresponding to valid signals ( , (Total number of reaction zones).

[0066] Simultaneously calculate the weighted sum: , Dividing the two yields the weighted average signal strength: , : The weighted average strength value (standardized value) of the effective signal.

[0067] Finally, the weighted average signal intensity was substituted into the clinically calibrated linear regression model to calculate the final analyte concentration data: , Final analyte concentration (unit: mmol / L, can be adjusted according to analyte type); Calibration coefficient (unit: mmol·L) -1 Standardized values -1 (), obtained by fitting clinical samples; Calibration intercept (unit: mmol / L) is used to correct for baseline interference.

[0068] If the number of valid signals The coefficient of variation of the concentration results can be calculated. Verify reliability; if it is not met, readjust the parameters and repeat the above process.

[0069] In practice, type 2 diabetes patients from the middle-aged population were selected as clinical samples. Data on the depth range of the subcutaneous fat layer of all samples were retrieved and fused to construct a unified reaction zone setting range covering all samples. Multiple continuous and mutually insulated reaction zones were arranged on the medical probe along the implantation depth direction. Each reaction zone was attached with a biological enzyme that could react with glucose in the tissue fluid. After the probe was implanted subcutaneously in the patient's abdomen, the sensor was fixed to the skin surface with medical tape to ensure that the actual implantation depth of the reaction zone completely covered the preset range.

[0070] During the initial acquisition of detection signals, four medical-grade oxygen-free copper induction antennas were deployed in four intersecting directions on the skin surface surrounding the implantation site. Following preset current frequency and power parameters, each antenna was activated in turn to transmit detection signals at fixed unit test intervals. The remaining antennas simultaneously received feedback signals and bound relevant identifiers and parameters to the signals. After preprocessing the feedback signals, core features such as spectral peak value, attenuation rate, and phase difference were extracted. A feature similarity matrix was constructed through normalization, and distorted signals were eliminated through cosine similarity calculation and consistency rate statistics. Combined with clinical tissue feature templates, the tissue type was identified, and the characteristics of the reflection peaks were analyzed, determining that the patient's subcutaneous fat layer was a thin layer structure.

[0071] To address the issue of thin-layer structures being susceptible to interference from adjacent tissues, the initial detection signal was divided into time-series signal segments based on timestamps. Features of each segment were extracted, and the differences between the target interval and adjacent tissue intervals were compared to clarify the interference distribution pattern. An adaptive temporal filtering algorithm was employed to remove interference, and signal similarity was calculated based on a template trained using clinical thin-layer samples. After optimization and adjustment, a clean target detection signal was obtained.

[0072] Target detection signals from each reaction zone are extracted, and sampling segments are divided at fixed time intervals. The mean current value of each reaction zone is calculated, and a mapping relationship between current and blood glucose concentration is established using a clinically calibrated linear regression model. A clinical reference intensity set is retrieved, and the similarity between the current signal intensity and the reference intensity is calculated, eliminating invalid signals. Weights are assigned based on the depth location of the reaction zone corresponding to the valid signal; intervals with higher signal stability have larger weights. The weighted average signal intensity is calculated, substituted into the calibration model, and the final blood glucose concentration data is output. Verification using the coefficient of variation shows that the results meet the accuracy requirements for clinical monitoring.

[0073] Example 2 The only difference between this embodiment and Embodiment 1 is that the analyte concentration data, the target detection range of the subcutaneous fat layer corresponding to the analyte concentration data, and the interval distance with adjacent subcutaneous fat layers are obtained from the clinical sample data when the layered structure is multi-layered. A mapping relationship model is established based on the obtained data, and the gradient diffusion relationship of the analyte concentration data under different target detection ranges and different interlayer interval distances is clarified through the mapping relationship model.

[0074] Data on the subcutaneous fat layer of a large number of clinical samples (covering subjects of different ages and body types) were collected, including: the layered structure of the fat layer of each sample (number of layers, thickness of each layer), the target detection range (depth boundary) of each layer, the interlayer spacing (distance between adjacent fat layers), and the analyte concentration data of each layer calculated according to the method in Example 1.

[0075] The input variables of the mapping relationship model are the target detection range identifier (divided into three categories according to depth: superficial, middle and deep, corresponding to different fat layer locations) and the interlayer spacing distance level (divided into three levels: near, middle and far according to the spacing range of clinical data statistics); the output variable is the concentration gradient value (the concentration difference between adjacent fat layers, reflecting the gradient diffusion direction and amplitude).

[0076] The effective dataset was divided into training and validation sets in a 7:3 ratio. With the input variable as the independent variable and the concentration gradient value as the dependent variable, a multiple linear regression algorithm was used to fit the mapping relationship model. The formula is as follows: (Where G is the concentration gradient value, R is the quantized value corresponding to the target detection range identifier, and D is the quantized value corresponding to the interlayer spacing level.) , (where b is the regression coefficient and b is the intercept). The accuracy of the mapping relationship model is verified using validation set data. The similarity between the predicted gradient value and the measured value is required to be ≥0.9 (cosine similarity). If this is not met, additional samples are added and the model is refitted. The final mapping relationship model is then finalized and stored as the core basis for subsequent gradient reference.

[0077] When the current layered structure is a multi-layered structure, the analyte concentration data of the corresponding layer is first calculated based on the thickness of each subcutaneous fat layer. The target detection range and interlayer spacing of each layer are obtained simultaneously, and the corresponding gradient diffusion relationship in the mapping relationship model is retrieved as the gradient reference relationship.

[0078] For the current test subjects, the fat layer layer identification (number of layers, thickness of each layer) was completed according to the method in Example 1. For each fat layer, the analyte concentration data of each layer was accurately obtained through signal acquisition, filtering and stripping, weight calculation and other processes to ensure that the concentration data of each layer has been distorted and validated.

[0079] The key spatial parameters of the current sample are recorded synchronously, including the target detection range corresponding to each fat layer (clearly defining the depth start and end boundaries of the layer and matching the "target detection range identifier" in the model), and the interlayer spacing distance (measuring the actual distance between adjacent fat layers, classifying them according to the level standards set by the model, and matching the "interlayer spacing distance level").

[0080] The extracted target detection range identifier and interlayer spacing level are input into the established mapping relationship model, and the corresponding concentration gradient value is output as the gradient reference relationship of the current sample (i.e. the gradient diffusion law that should theoretically be followed).

[0081] If the current analyte concentration data of each subcutaneous fat layer does not match the gradient reference relationship, the detection electrical signal is re-emitted into the human body to update the target detection range and re-collect and calculate the analyte concentration data of each layer; if the current analyte concentration data of each subcutaneous fat layer matches the gradient reference relationship, the gradient diffusion relationship of the current analyte concentration data of each layer is used as the control relationship, and the final analyte concentration data is obtained by combining the control relationship with the analyte concentration data of each layer.

[0082] Calculate the actual concentration gradient value of the current sample (the concentration difference between two adjacent fat layers) and compare it with the gradient reference relationship output by the model. Set the matching threshold to ±10% (this is the default value and can be adjusted according to clinical accuracy requirements): if the actual gradient value is within the threshold range of the reference relationship, it is determined to meet the gradient reference relationship; if it exceeds the threshold range, it is determined to be a mismatch.

[0083] If a mismatch is determined, it indicates that there may be a deviation in the target detection range or insufficient signal acquisition. The detection signal transmission process needs to be re-executed, and the four-directional cyclic detection of the four induction antennas should be restarted according to the preset parameters. The depth range and layer structure of the subcutaneous fat layer should be re-analyzed to update the target detection range. Based on the new target detection range, the initial detection signals of each reaction zone should be re-acquired, and the steps of filtering and stripping, concentration calculation, etc. should be repeated to update the analyte concentration data of each layer until the actual gradient value matches the gradient reference relationship.

[0084] If the gradient reference relationship is determined to be met, the actual concentration gradient diffusion relationship of each layer is used as the reference relationship, and a weighted average method is used to calculate the final concentration: the thickness of each fat layer is used as the weight (the greater the thickness, the larger the weight coefficient), and the weighted average of the concentration data of all layers is calculated using the following formula: (in For the final analyte concentration, For the first The thickness of the fat layer, For the first Analyte concentration in the layer (This represents the total number of fat layers). After calculation, the final concentration data is output, and the control relationship and spatial parameters are recorded for subsequent optimization of the mapping relationship model.

[0085] In practice, technicians collect a large number of clinical samples covering different populations, extracting the layered structure of the adipose layer, the target detection range of each layer, the interlayer spacing, and the corresponding analyte concentration data for each sample. After cleaning, the valid dataset is retained. The model input is set as the target detection range identifier (superficial, middle, deep) and the interlayer spacing level (near, middle, far), and the output is the concentration gradient value between adjacent layers. A multiple linear regression algorithm is used to fit the model, which is then verified by splitting the training set and validation set to ensure that the similarity between the predicted gradient and the measured value meets the standard before being finalized and stored.

[0086] For the current subject, multi-layer fat layer identification and concentration calculation for each layer were performed using the method described above. The target detection range and interlayer spacing of each layer were recorded simultaneously. After matching the corresponding parameters of the model, the gradient reference relationship was retrieved. The actual concentration gradient value of the current sample was calculated and compared with the reference relationship at a ±10% threshold.

[0087] If there is a mismatch, restart the four-way induction antenna detection process, update the target detection range and layer structure, re-acquire signals and calculate the concentration of each layer until the gradient meets the requirements; if there is a match, use the actual gradient as a reference, set the weight according to the thickness of each fat layer, calculate the final analyte concentration by weighted average method, output the results and record the relevant parameters for subsequent model optimization.

[0088] Example 3 The only difference between this embodiment and embodiments 1-2 is that multiple preset current frequency and power combinations corresponding to typical electrical characteristic parameters of different human tissue layers are pre-stored, and theoretical response models corresponding to each combination are established.

[0089] After the feedback signal is acquired for the first time, the spectral characteristics and attenuation rate of the feedback signal are extracted. The extracted spectral characteristics and attenuation rate are matched one by one with the theoretical response models of all preset combinations, and the combination with the highest matching degree is selected as the applicable initial current frequency and power.

[0090] Typical electrical properties of core human tissue layers (skin, subcutaneous fat layer, fascia, muscle, and blood vessels) were collected, including conductivity and dielectric constant (covering clinically measured data from individuals of different ages and body types). These parameters were categorized by tissue type, and the range of values ​​for each tissue layer's electrical properties was clearly defined (e.g., the conductivity of the fat layer is lower than that of the muscle layer, and the dielectric constant varies with frequency differently from that of the skin). For the subcutaneous fat layer, it was further subdivided into thin, thick, and multi-layered structures, recording subtle differences in electrical properties under different structures to provide a precise basis for subsequent combination and matching.

[0091] Based on the frequency response characteristics of electrical parameters of various tissue layers (the characteristics of conductivity and dielectric constant changing with current frequency), multiple sets of current frequency and power combinations are designed: the frequency range covers low, medium, and high ranges (adapting to the signal attenuation characteristics of different tissues), and the power levels are graded according to tissue penetration requirements (low power for superficial tissues, medium to high power for deep tissues). Each tissue layer type and substructure corresponds to at least 3 independent combinations (e.g., low to medium frequency and medium to low power combinations for thin fat layers, and multi-layer fat layers corresponding to multi-frequency gradient and power adaptation combinations), ensuring the specificity and redundancy of the combinations and avoiding signal acquisition failure caused by a single combination.

[0092] For each preset combination of current frequency and power, a theoretical response model is constructed based on electromagnetic propagation theory and biological tissue signal transduction models, taking into account the electrical characteristics of the corresponding tissue layer. The inputs to the theoretical response model are tissue layer thickness, electrical characteristics, and probed electrical signal parameters (frequency and power). The output is the expected range of theoretical feedback electrical signal characteristics (spectral peak value, phase difference, and attenuation rate). The theoretical response model is calibrated using clinical experimental data. The measured feedback signal characteristics for each combination are compared with the theoretical values, and the theoretical response model parameters are corrected (e.g., adjusting the signal attenuation coefficient and phase offset calculation factor) to ensure the fit between the theoretical response model and the actual detection scenario. Finally, a one-to-one correspondence is established between tissue layer type, frequency-power combination, and theoretical response model, which is stored in a database for future use.

[0093] When transmitting a probe signal, the amplitude and signal-to-noise ratio of the feedback signal are monitored. When the amplitude and signal-to-noise ratio changes are lower than a preset stability threshold (set by technicians according to the recognition accuracy of the feedback signal), the impedance change trend of human tissue is inverted based on the current probe signal and the corresponding feedback signal. Based on the inverted impedance change trend of human tissue, the combination with the highest matching degree and the best signal quality is iteratively selected as the current basic detection parameters. At the same time, the power in the current basic detection parameters is gradually adjusted based on a preset adjustment step size (set by technicians according to the adjustment accuracy requirements).

[0094] When retrieving the impedance change trend of human tissue, the basic parameters (frequency) of the current probed electrical signal are obtained. ,power And the corresponding feedback electrical signal, extracting the core feature of the feedback signal: amplitude. Phase attenuation coefficient Furthermore, noise interference is removed by moving average filtering to obtain a stationary feature sequence. ( (Sampling time).

[0095] Based on transmission line theory, a mapping relationship between feedback signal characteristics and tissue impedance is established, with the core formula as follows: Impedance amplitude inversion: , Impedance phase inversion: , Overall impedance: , in, The system calibration coefficient (determined by the sensor hardware parameters) is the system calibration coefficient. To detect the initial amplitude of the electrical signal, The signal propagation path length (obtained from the previous deep coarse classification results). The permeability of human tissue (default value is vacuum permeability) ), The dielectric constant of the structure is set (initial values ​​are based on preset typical parameters). To detect the initial phase of the electrical signal.

[0096] The continuous impedance value obtained by inversion ( (At the sampling time), the least squares method is used to fit the linear trend equation: ,in This represents the rate of change of impedance (reflecting the slope of the trend). This is the initial impedance value. (Through...) The sign and absolute value of k determine whether the impedance is increasing, decreasing, or changing steadily (if k > 0, the impedance increases with time; if k < 0, the impedance decreases with time; if |k| approaches 0, the impedance changes steadily). The impedance values ​​at consecutive time points are then used to determine the impedance values. By plotting it as a curve, the trend of impedance change can be visually presented.

[0097] Retrieve all preset current frequency and power combinations from the database. For each combination, the impedance inversion model described above is substituted, and the inverted impedance variation trend is combined to simulate the theoretical feedback signal characteristics under that combination. .

[0098] An evaluation method combining cosine similarity and Euclidean distance is used to calculate the matching degree between the theoretical features and the current measured features of each combination. : Cosine similarity: , European distance: , Overall match: (The weights are calibrated from clinical data and are the default values ​​used in this embodiment;) The closer to 1, the higher the matching degree.

[0099] Define signal quality metrics Taking into account signal amplitude stability, phase jitter, and attenuation consistency: ;in, Standard deviation This is the average value. The closer the value is to 1, the better the signal quality; 0.3 and 0.4 are the default weights set in this embodiment, and the weights can also be calibrated using clinical data.

[0100] Set matching threshold Signal quality threshold Filter out those that simultaneously meet the requirements and The combinations of these factors form a candidate combination set. For the candidate combination set, the dielectric constant in the impedance inversion model is adjusted. (Based on the theoretical response model correction corresponding to this combination), repeat the above impedance inversion and matching degree, signal quality calculation process, iterating 3 times (specifically set by technical personnel), then... The products are sorted, and the combination with the largest product is selected as the current basic detection parameter. If the optimal combination... and (Specific settings to be determined by technical personnel) If the condition is not met, the iteration terminates; if not, two additional combinations of adjacent frequencies or powers (specific settings to be determined by technical personnel) are added to the candidate set, and the iteration is repeated until the optimal combination that meets the requirements is selected.

[0101] During the transmission of the probe electrical signal, the signal-to-noise ratio of the feedback electrical signal and the resolution and sharpness indicators of the reflection interfaces of each tissue layer are monitored in real time. The resolution and sharpness indicators are obtained by comprehensively and weighted by the full width at half maximum (FWHM), interpeak distance, peak signal-to-noise ratio, and the matching degree between the reflection peak sequence and the standard template.

[0102] Real-time extraction of the effective signal amplitude and noise amplitude of the feedback electrical signal, according to the formula Calculate the signal-to-noise ratio (where The peak amplitude of the reflection peak in the feedback electrical signal. (The effective noise value of the signal baseline) The signal-to-noise ratio must be ≥20dB (the qualified threshold preset by technicians). If it is lower than the threshold, the signal enhancement mechanism will be triggered.

[0103] Based on the comparison results of the reflection peak characteristics of the feedback electrical signal and the standard template, the result is calculated by weighting multiple parameters. The core indicator reflects the identifiability of the reflection interface of each tissue layer. The higher the index value, the clearer the interface distinction.

[0104] Real-time identification of reflection peak sequences in feedback electrical signals, and extraction of key parameters for each reflection peak; Half-width at half-maximum (FWHM): The signal width at half the peak value of the reflection peak, reflecting the sharpness of the reflection peak, denoted as... ( (Numbering the reflection peaks). Interpeak distance (DIP): The time difference between the peak values ​​of two adjacent reflection peaks (corresponding to the interstitial spacing), denoted as... ; Peak Signal-to-Noise Ratio (PSNR): The ratio of the peak value of a single reflection peak to the surrounding noise level, denoted as... ( For the first The peak value of each reflection peak, (This is the average noise level of 10 sampling points around the reflection peak).

[0105] Standard templates for reflectance peaks in various tissue layers were retrieved from clinical samples (including standard reflectance peak morphology and spacing at typical interfaces such as skin and fat, and fat and fascia). The matching degree between the measured reflectance peak sequence and the standard templates was calculated using a dynamic time warping algorithm. , , The closer it is to 1, the higher the morphological fit.

[0106] Set the weighting coefficients for each parameter (calibrated from clinical data) according to the formula: , Calculate the final indicators; where The average of the full width at half maximum (FWHM) of all reflection peaks. This represents the maximum half-width at half-maximum (FWHM) of the reflection peak in the standard template. Standardized to the [0,1] interval, weighted ; This represents the average distance between adjacent reflection peaks. This represents the standard value of the interpeak distance in the standard template. Reflects the degree of conformity between the actual spacing and the standard, weighting ; The weights are the normalized values ​​of the peak-to-peak signal-to-noise ratio of all reflections (divided by the preset maximum peak-to-peak signal-to-noise ratio of 50dB). ; Weights represent the matching degree between the reflection peak sequence and the standard template. Four weights ( , , , The parameters are determined by technicians based on the differences in their contribution to the identification of tissue layer reflective interfaces, combined with clinical measurement data and statistical analysis. The half-width at half-maximum (WHM) of the reflective peak directly reflects the sharpness of the peak shape; the narrower the peak, the clearer the tissue interface boundary, and the greater its impact on resolution, thus it is given the highest weight. The inter-peak distance matching degree determines the accuracy of inter-layer interfacial recognition, while the peak signal-to-noise ratio ensures the extractability of reflective peak features. Both contribute similarly to interface differentiation, with a weight lower than WHM but higher than template matching degree. The matching degree of the standard template is a comprehensive verification indicator, requiring comparison based on the features of the first three parameters; it is an auxiliary verification item and therefore has the lowest weight.

[0107] Resolution and sharpness indicators ≥0.7 (preset threshold set by technicians), when At the same time, based on the signal-to-noise ratio monitoring results, the frequency or power parameters of the detection electrical signal are dynamically adjusted until the indicators meet the requirements, ensuring that the reflection interfaces of each tissue layer are clearly distinguishable.

[0108] The depth of reflection or conduction of the feedback electrical signal within the human body is taken as the target detection depth. When the feedback electrical signal obtained at the target detection depth does not reach the preset resolution threshold, the spectral characteristics of the feedback electrical signal during the monitoring process are extracted. The adjustment step size is dynamically reduced according to the spectral characteristics, thereby adjusting the frequency and power of the transmitted detection electrical signal. The detection electrical signal is continuously transmitted to the target detection depth that has not reached the preset resolution threshold until the feedback electrical signal obtained at the target detection depth reaches the preset resolution threshold.

[0109] During the transmission of detection signals and the acquisition of feedback signals, the resolution and sharpness index corresponding to the target detection depth is calculated in real time and continuously compared with a preset threshold (e.g., 0.7). If the index is lower than the threshold for multiple consecutive sampling periods (e.g., 3), it is determined that the target detection depth feedback signal does not meet the standard, and the parameter dynamic adjustment process is immediately triggered, pausing regular detection and focusing on signal optimization at that depth.

[0110] For feedback signals that do not meet the required depth, a Fast Fourier Transform (FFT) is used to convert the time-domain signal into a frequency-domain signal, allowing for the refined extraction of core parameters such as peak frequency, spectral energy distribution variance, harmonic component proportion, and spectral bandwidth. The extracted spectral features are then compared with a preset standard spectrum established from clinically acceptable signals. If the characteristic frequency deviates from the standard range, it indicates that the current frequency is not matched to the tissue impedance characteristics; if the spectral energy distribution variance is too large, it suggests insufficient power adaptability; if the harmonic component proportion is too high, it indicates interference superposition in the signal. Based on this, the precise direction for parameter adjustment is determined.

[0111] Based on a preset range of current frequency and power combinations, an initial adjustment step size is set (e.g., an initial frequency step size of 10kHz and an initial power step size of 0.05mA, specifically determined based on the variation characteristics of conductivity and dielectric constant of the core tissue layer of the human body over a wide frequency range), ensuring that the first round of adjustment covers a broad parameter range. The adjustment step size is dynamically reduced based on the spectral characteristic analysis results. If the characteristic frequency is close to the standard range, the frequency adjustment step size is reduced to 2kHz (when the characteristic frequency is close to the standard range, the matching degree between tissue impedance and signal frequency enters a highly sensitive range; small step sizes can precisely match subtle differences in tissue electrical characteristics; the 2kHz here is based on the frequency adjustment accuracy setting of the inductive antenna); if the spectral energy distribution tends to be stable, the power adjustment step size is reduced to 0.01mA (when the spectral energy distribution is stable, the impact of power changes on signal quality enters a fine-tuning stage; the specific value is derived from clinical measurements and optimization of signal amplitude stability); if the proportion of harmonic components drops below 10% (set according to the clinically validated signal quality threshold), the current step size is maintained for fine-tuning, following the principle of smaller deviations and finer step sizes to avoid over-adjustment of parameters.

[0112] Following the reduced adjustment step size, parameters are adjusted collaboratively along the direction indicated by the spectral characteristics. If the characteristic frequency is too low, the frequency is increased; if the energy is insufficient, the power is increased; if harmonic interference exists, the frequency is fine-tuned to avoid the interfering frequency band. Each adjustment changes only a single parameter to ensure traceability of the impact. After parameter adjustment, a new parameter probe signal is continuously transmitted to the target detection depth, and feedback signals are simultaneously acquired and resolution and sharpness indicators are calculated in real time. If the indicator meets the standard, adjustment is stopped and the current parameters are used; if it does not meet the standard, the process of spectrum extraction, step size reduction, and parameter adjustment is repeated until the feedback signal reaches the preset resolution threshold, ensuring that the tissue layer reflection interface is clearly distinguishable, providing high-quality signal support for subsequent depth analysis and concentration calculation.

[0113] The target detection depth corresponding to the feedback electrical signal that reaches the preset resolution threshold is obtained as the clear detection depth, and the transmission power or frequency of the detection electrical signal transmitted to the clear detection depth is reduced.

[0114] In practice, multiple combinations of current frequencies and power corresponding to the core tissue layers of the human body (skin, fat, fascia, etc.) and the subdivided structures of the fat layer (thin, thick, multi-layered) are pre-stored. Based on the electromagnetic propagation theory, a theoretical response model for each combination is constructed by combining the electrical characteristic parameters of each tissue. After calibration with clinical data, the model is stored for later use.

[0115] After the initial acquisition of the feedback electrical signal, its spectral characteristics and attenuation rate are extracted and matched one by one with all theoretical response models. The combination with the highest matching degree is selected as the initial detection parameters. When transmitting the probe electrical signal, the stability of the signal amplitude and signal-to-noise ratio is monitored in real time. After reaching the target, the impedance change trend is inverted based on transmission line theory. The combination matching degree is calculated by using a fusion algorithm of cosine similarity and Euclidean distance. The optimal basic detection parameters are iteratively selected by combining signal quality indicators, and the power is fine-tuned according to a preset step size.

[0116] Simultaneously, the signal-to-noise ratio (SNR) of the feedback electrical signal (≥20dB if required) and resolution sharpness indicators (calculated by weighting parameters such as half-width at half-maximum (FWHM) and inter-peak distance, ≥0.7 if required) are monitored in real time. If the target detection depth signal does not meet the standard, spectral features are extracted using Fast Fourier Transform (FFT), compared with the standard spectrum to clarify the adjustment direction, and the adjustment step size is dynamically reduced according to the principle of smaller deviation and finer step size, coordinating the optimization of frequency and power. After each adjustment, detection continues until the signal meets the standard. This depth is then taken as the sharp detection depth, and its detection power or frequency is appropriately reduced to reduce tissue stimulation while ensuring signal quality, providing high-quality data support for subsequent depth analysis and concentration calculation.

[0117] Example 4 The only difference between this embodiment and embodiments 1-3 is that, Figure 2 As shown, a signal acquisition and processing system for a multi-reaction zone implantable sensor includes: The acquisition unit includes a probe of preset length and a power source (i.e., a battery). The probe substrate is made of polyimide (PI), the conductive layer is plated with gold (Au), and the outer layer is coated with a composite semi-permeable membrane of chitosan and polyethylene glycol. PI combines biocompatibility and mechanical strength, gold provides stable conductivity, and the composite membrane provides interference prevention and enzyme activation. These three elements are suitable for subcutaneous implantation, solving the problems of stimulation, signal attenuation, and enzyme inactivation associated with traditional materials, while maintaining both flexibility and stability. The battery uses a Li / CFx primary lithium battery with a nominal voltage of 2.8V, a rated capacity of ≥200mAh, and a self-discharge rate of <1% / year; the volume can be selected as 0.78cm². 3 (e.g., 21.3mm×9.5mm×4.4mm), low-power adaptive sensor, compact and suitable for embedded installation, ensuring safety and comfort.

[0118] This is used to retrieve stored clinical sample data, extract subcutaneous fat layer depth range data for different users from the clinical sample data, perform fusion analysis on the subcutaneous fat layer depth range data of all users, and construct a unified depth detection range covering the subcutaneous fat layer depth of all users as the reaction zone setting range; combined with the implantation depth of the probe in various parts of the human body, multiple continuously arranged reaction zones are arranged on the probe along the implantation depth direction, and the outer side of the probe in each reaction zone is equipped with a biological enzyme that can undergo biochemical reactions with human tissue fluid; and after the probe is implanted in the human body, the actual implantation depth range of the multiple reaction zones in the human body must completely cover the reaction zone setting range.

[0119] The detection unit is electrically connected to the probes in the reaction zone; it acquires and stores the preset depth range of each reaction zone; when the probe is implanted into the preset collection site of the human body and the probe is firmly fixed to the human tissue at the implantation site, it collects the electrical signals generated by the reaction between each reaction zone and the tissue fluid in real time as the initial detection signal, and stores the initial detection signal in association with the preset depth range of the corresponding reaction zone.

[0120] The control module acquires the initial detection signal and the associated stored preset depth range. When the initial detection signal is acquired for the first time, it generates a detection command based on the preset current frequency and power parameters.

[0121] The detection module includes an induction antenna electrically connected to a power source; it is used to acquire detection commands, and after acquiring the detection commands, it transmits detection electrical signals from the skin surface around the implantation site into the human body according to the current frequency and power parameters in the detection commands. At the same time, it receives feedback electrical signals formed after reflection or conduction through different tissue layers of the human body, and sends the feedback electrical signals as feedback information of the detection commands to the control module.

[0122] After receiving the feedback electrical signal, the control module acquires the current frequency and power parameters from the corresponding detection command. It then combines these parameters with the feedback electrical signal and analyzes the signal to determine the depth range of the subcutaneous fat layer at the implantation site, using this as the target detection range. Based on the initial detection signal and the associated stored preset depth range of the reaction zone, the control module selects the initial detection signals corresponding to each reaction zone within the target detection range as target detection signals. Finally, it calls the concentration analysis module to process these target detection signals into analyte detection results.

[0123] The above are merely embodiments of the present invention. The invention is not limited to the fields covered by these embodiments. Commonly known structures and characteristics in the solutions are not described in detail here. Those skilled in the art are aware of all common technical knowledge in the field prior to the application date or priority date, are able to access all existing technologies in that field, and have the ability to apply conventional experimental methods prior to that date. Those skilled in the art can, under the guidance of this application, improve and implement this solution in combination with their own capabilities. Some typical known structures or methods should not be obstacles for those skilled in the art to implement this application. It should be noted that those skilled in the art can make several modifications and improvements without departing from the structure of the present invention. These should also be considered within the scope of protection of the present invention, and will not affect the effectiveness of the implementation of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. A signal acquisition and processing method for a multi-reaction zone implantable sensor, characterized in that, Includes the following steps: S10: Retrieve stored clinical sample data, extract subcutaneous fat layer depth range data of different users from the clinical sample data, perform fusion analysis on the subcutaneous fat layer depth range data of all users, and construct a unified depth detection range covering the subcutaneous fat layer depth of all users as the reaction zone setting range. S20: On the component that comes into contact with each tissue layer of the human body after the sensor is implanted, multiple reaction zones are arranged continuously along the implantation depth direction. Each reaction zone is equipped with a biological enzyme that can undergo biochemical reactions with human tissue fluid. After the sensor is implanted in the human body, the actual implantation depth range of the multiple reaction zones in the human body must completely cover the range of the reaction zone settings. S30: The sensor is implanted into the preset collection site of the human body. After implantation, the sensor is fixedly fixed to the human tissue at the implantation site. The electrical signals generated by the reaction between each reaction zone and the tissue fluid are collected in real time as the initial detection signal. The initial detection signal is associated with and stored with the preset depth range of the corresponding reaction zone. S40: When the initial detection signal is acquired for the first time, the detection electrical signal is emitted from the skin surface around the implantation site into the human body according to the preset current frequency and power parameters, and the feedback electrical signal formed after being reflected or conducted through different tissue layers of the human body is received at the same time. The preset current frequency and power parameters are combined with the feedback electrical signal for processing. The depth range of the subcutaneous fat layer at the implantation site is obtained through signal analysis module and used as the target detection range. S50: Based on the initial detection signal and the preset depth range of the associated stored reaction zone, the initial detection signal corresponding to each reaction zone within the target detection range is selected as the target detection signal, and the concentration analysis module is called to process the target detection signal into the analyte detection result.

2. The signal acquisition and processing method for a multi-reaction zone implantable sensor according to claim 1, characterized in that: In step S40, four induction antennas are set in four directions at a cross intersection on the skin surface around the implantation site. The four induction antennas serve as the transmitting end for detecting electrical signals and the receiving end for receiving feedback electrical signals.

3. The signal acquisition and processing method for a multi-reaction zone implantable sensor according to claim 2, characterized in that: When transmitting detection signals and receiving feedback signals, at the same time, at least one of the four induction antennas transmits detection signals into the human body and at least one induction antenna receives feedback signals. By comparing and analyzing the feedback characteristics of the same tissue layer to feedback electrical signals of different frequencies, different powers and from induction antennas in different directions, the depth range and layered structure of the subcutaneous fat layer were determined based on the feedback characteristics.

4. The signal acquisition and processing method for a multi-reaction zone implantable sensor according to claim 3, characterized in that: The subcutaneous fat layer thickness is calculated based on the difference between the upper and lower limits of the target detection range. When the subcutaneous fat layer thickness is less than the preset thin layer thickness, the subcutaneous fat layer is treated as a thin layer structure; when the subcutaneous fat layer thickness is greater than the preset thick layer thickness, the subcutaneous fat layer is treated as a thick layer structure; when the number of subcutaneous fat layers obtained is greater than 1, the layered structure is treated as a multi-layer structure.

5. The signal acquisition and processing method for a multi-reaction zone implantable sensor according to claim 4, characterized in that: When the layered structure is a thin layer, other tissue layers adjacent to the subcutaneous fat layer are used as signal interference layers. The initial detection signals collected from the signal interference layer and the subcutaneous fat layer are compared, and the temporal differences in the comparison results are analyzed. Based on the differences obtained from the analysis, the initial detection signals collected from the subcutaneous fat layer are filtered and stripped. The initial detection signals obtained after filtering and stripping are used as target detection signals. The average current magnitude of the target detection signals in each reaction zone is obtained, and the analyte concentration data is calculated based on the average current magnitude.

6. The signal acquisition and processing method for a multi-reaction zone implantable sensor according to claim 4, characterized in that: The target detection signal and the corresponding preset depth range are obtained from the clinical sample data when the layered structure is a thick layer. The obtained target detection signal and the corresponding preset depth range are combined to establish a signal intensity depth distribution model. The signal intensity depth distribution model takes the depth range as input and the signal intensity of the target detection signal as output. When the current layered structure is a thick layer, the target detection signal of the reaction area in the current subcutaneous fat layer within the preset depth range is obtained. The preset depth range corresponding to the currently obtained target detection signal is input into the signal intensity depth distribution model, and the signal intensity output by the signal intensity depth distribution model is used as the reference intensity. The reference intensity of all reaction zones in the current subcutaneous fat layer is obtained and compared with the intensity of all corresponding target detection signals. Target detection signals with a similarity lower than the preset reference similarity are removed. For the remaining target detection signals, weights are set according to the position of the target detection signal in the current subcutaneous fat layer within the preset depth range. The target detection signals are then weighted and averaged to obtain the analyte concentration data.

7. The signal acquisition and processing method for a multi-reaction zone implantable sensor according to claim 5 or 6, characterized in that: The analyte concentration data, the target detection range of the subcutaneous fat layer corresponding to the analyte concentration data, and the interval distance with adjacent subcutaneous fat layers were obtained from the clinical sample data when the layered structure was multi-layered. A mapping relationship model was established based on the obtained data, and the gradient diffusion relationship of the analyte concentration data under different target detection ranges and different interlayer interval distances was clarified through the mapping relationship model. When the current layered structure is a multi-layered structure, the analyte concentration data of the corresponding layer is first calculated based on the thickness of each subcutaneous fat layer. Simultaneously, the target detection range and interlayer spacing of each layer are obtained, and the corresponding gradient diffusion relationship in the mapping relationship model is retrieved as the gradient reference relationship. If the current analyte concentration data of each subcutaneous fat layer does not match the gradient reference relationship, the detection electrical signal is re-emitted into the human body to update the target detection range and re-collect and calculate the analyte concentration data of each layer; if the current analyte concentration data of each subcutaneous fat layer matches the gradient reference relationship, the gradient diffusion relationship of the current analyte concentration data of each layer is used as the control relationship, and the final analyte concentration data is obtained by combining the control relationship with the analyte concentration data of each layer.

8. The signal acquisition and processing method for a multi-reaction zone implantable sensor according to claim 3, characterized in that: Multiple preset current frequency and power combinations corresponding to typical electrical characteristic parameters of different human tissue layers are pre-stored, and theoretical response models corresponding to each combination are established. After the feedback signal is acquired for the first time, the spectral characteristics and attenuation rate of the feedback signal are extracted. The extracted spectral characteristics and attenuation rate are matched one by one with the theoretical response models of all preset combinations, and the combination with the highest matching degree is selected as the applicable initial current frequency and power. When transmitting a probe electrical signal, the amplitude and signal-to-noise ratio of the feedback electrical signal are monitored. When the amplitude and signal-to-noise ratio changes are lower than the preset stable threshold, the trend of human tissue impedance change is inverted based on the current probe electrical signal and the corresponding feedback electrical signal. Based on the inverted trend of human tissue impedance change, the combination with the highest matching degree and the best signal quality is iteratively selected as the current basic detection parameters. At the same time, the power in the current basic detection parameters is gradually adjusted based on the preset adjustment step size.

9. The signal acquisition and processing method for a multi-reaction zone implantable sensor according to claim 8, characterized in that: During the transmission of the detection electrical signal, the signal-to-noise ratio of the feedback electrical signal and the resolution and sharpness index of the reflection interface of each tissue layer are monitored in real time. The resolution and sharpness index is obtained by comprehensively and weighting the full width at half maximum (FWHM), interpeak distance, peak signal-to-noise ratio, and the matching degree between the reflection peak sequence and the standard template of the feedback electrical signal reflection peak. The depth of reflection or conduction of the feedback electrical signal within the human body is taken as the target detection depth. When the feedback electrical signal obtained from the target detection depth does not reach the preset resolution threshold, the spectral characteristics of the feedback electrical signal during the monitoring process are extracted, and the adjustment step size is dynamically reduced according to the spectral characteristics, thereby adjusting the frequency and power of the transmitted detection electrical signal. The detection electrical signal is continuously transmitted to the target detection depth that has not reached the preset resolution threshold until the feedback electrical signal obtained from the target detection depth reaches the preset resolution threshold. The target detection depth corresponding to the feedback electrical signal that reaches the preset resolution threshold is obtained as the clear detection depth, and the transmission power or frequency of the detection electrical signal transmitted to the clear detection depth is reduced.

10. A signal acquisition and processing system for a multi-reaction zone implantable sensor, characterized in that, The signal acquisition and processing method for a multi-reaction zone implantable sensor as described in claim 1 includes: The acquisition unit includes a probe of preset length and a power supply; it is used to retrieve stored clinical sample data, extract subcutaneous fat layer depth range data for different users from the clinical sample data, perform fusion analysis on the subcutaneous fat layer depth range data of all users, and construct a unified depth detection range covering the subcutaneous fat layer depth of all users as the reaction zone setting range; combined with the implantation depth of the probe in various parts of the human body, multiple continuously arranged reaction zones are arranged on the probe along the implantation depth direction, and the outer side of the probe in each reaction zone is equipped with a biological enzyme that can undergo biochemical reactions with human tissue fluid; and after the probe is implanted in the human body, the actual implantation depth range of the multiple reaction zones in the human body must completely cover the reaction zone setting range. The detection unit is electrically connected to the probes in the reaction zone; it acquires and stores the preset depth range of each reaction zone; when the probe is implanted into the preset collection site of the human body and the probe is firmly fixed to the human tissue at the implantation site, it collects the electrical signals generated by the reaction between each reaction zone and the tissue fluid in real time as the initial detection signal, and stores the initial detection signal in association with the preset depth range of the corresponding reaction zone. The control module acquires the initial detection signal and the associated stored preset depth range. When the initial detection signal is acquired for the first time, it generates a detection command based on the preset current frequency and power parameters. The detection module includes an induction antenna electrically connected to a power source; it is used to acquire detection commands, and after acquiring the detection commands, it transmits detection electrical signals from the skin surface around the implantation site into the human body according to the current frequency and power parameters in the detection commands, and at the same time receives feedback electrical signals formed after reflection or conduction through different tissue layers of the human body, and sends the feedback electrical signals as feedback information of the detection commands to the control module. After receiving the feedback electrical signal, the control module acquires the current frequency and power parameters from the corresponding detection command. It then combines these parameters with the feedback electrical signal and analyzes the signal to determine the depth range of the subcutaneous fat layer at the implantation site, using this as the target detection range. Based on the initial detection signal and the associated stored preset depth range of the reaction zone, the control module selects the initial detection signals corresponding to each reaction zone within the target detection range as target detection signals. Finally, it calls the concentration analysis module to process these target detection signals into analyte detection results.

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