Living body monitoring information processing method and system based on implantable medical regulation and control instrument

Abstract

The invention provides a living body monitoring information processing method and system based on an implantable medical regulation and control instrument, and relates to the field of data processing. Performing distance calculation on the abnormal data array representation of the reference living body monitoring information and the monitoring information array representation of the living body monitoring information to be processed based on the abnormal data distribution result of the reference living body monitoring information to obtain a similarity score; and adjusting a preset abnormal data identification model through the distribution position reasoning mark of the abnormal data to obtain a convergence abnormal data identification model. Distribution position reasoning marks of abnormal data in the to-be-processed living body monitoring information are obtained through distance calculation, so that distribution positions of a small amount of the to-be-processed living body monitoring information can be obtained based on reference living body monitoring information reasoning marks annotated by a small amount of experts, and a large number of prior knowledge samples can be obtained only through a small amount of pre-annotated samples. And completing sample augmentation of model training. In addition, the accuracy of marking the distribution position of the abnormal data by the convergence abnormal data identification model can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of data processing, in particular to a living body monitoring information processing method and system based on implantable medical control apparatus. BACKGROUND

[0002] In the field of modern medicine, especially in the aspect of living body monitoring involving implantable medical control apparatus, accurate identification and positioning of abnormal data in monitoring information is crucial. With the continuous development of medical technology, implantable medical control apparatus can collect a large amount of living body monitoring information, such as continuous monitoring data of human physiological parameters (such as heart rate, blood pressure, blood glucose, etc.). Traditional methods often rely on a large number of expert annotated samples for model training. This means that a large amount of manpower, material resources and time cost is needed to obtain sufficient annotated samples. For example, when analyzing the long-term monitoring data of heart function for abnormalities, experts need to label each data point that may have abnormalities and its distribution in detail. This process not only requires experts to have high professional knowledge, but also has a huge workload, making it a difficult task to obtain sufficient annotated samples. Due to the difficulty in obtaining sufficient annotated samples, the model may not be able to fully learn various feature patterns in the data, resulting in low accuracy when identifying the location of abnormal data distribution in new living body monitoring information to be processed. At the same time, limited samples also limit the efficiency of model training, making it difficult for the model to quickly converge to an ideal state, thereby affecting the running efficiency of the entire abnormal data identification system. In addition, traditional processing methods may lack effective sample augmentation means. When dealing with complex living body monitoring information, if the number and diversity of samples cannot be effectively increased, the model may overfit to limited samples, resulting in poor generalization ability in actual application, and being unable to accurately identify the location of abnormal data distribution under different conditions. SUMMARY

[0003] The purpose of the present application is to provide a living body monitoring information processing method and system based on implantable medical control apparatus. The present application is implemented as follows: In a first aspect, the present application provides a living body monitoring information processing method based on implantable medical control apparatus, the method comprising: obtaining a reference living body monitoring information library and a to-be-processed living body monitoring information library, wherein the reference living body monitoring information library comprises one or more reference living body monitoring information and abnormal data distribution results of the reference living body monitoring information, and the to-be-processed living body monitoring information library comprises a plurality of to-be-processed living body monitoring information; the living body monitoring information in the reference living body monitoring information library and the to-be-processed living body monitoring information library is collected based on implantable medical control apparatus; According to the abnormal data distribution result, distance calculation is performed on the abnormal data array representation of the reference living body monitoring information and the monitoring information array representation of the to-be-processed living body monitoring information, to obtain a similarity score of the to-be-processed living body monitoring information; According to the similarity score, inference is performed to obtain a distribution position inference mark of abnormal data in the to-be-processed living body monitoring information; According to the distribution position inference mark, a first prior distribution heat map of the abnormal data distribution position in the to-be-processed living body monitoring information is obtained; According to the first prior distribution heat map and the to-be-processed living body monitoring information, a preset abnormal data recognition model is adjusted to obtain a converged abnormal data recognition model, wherein the converged abnormal data recognition model is used for performing distribution position recognition marking of abnormal data on the to-be-processed living body monitoring information.

[0004] Optionally, the inference according to the similarity score to obtain the distribution position inference mark of the abnormal data in the to-be-processed living body monitoring information comprises: According to the similarity score, abnormal data distribution information inference is performed to obtain a first abnormal data distribution information probability density of the to-be-processed living body monitoring information; Stepwise scanning is performed on the first abnormal data distribution information probability density to obtain a regional peak response intensity of each first scanning region of the first abnormal data distribution information probability density; If the regional peak response intensity of the first scanning region is greater than a first set threshold, the first distribution information corresponding to the regional peak response intensity of the first scanning region is determined as the distribution position inference mark.

[0005] Optionally, the method further comprises: If the regional peak response intensity of the first scanning region is not greater than the first set threshold, the to-be-processed living body monitoring information is taken as to-be-processed living body monitoring information; Based on the converged abnormal data recognition model, inference is performed on the to-be-processed living body monitoring information to obtain a marking result of the abnormal data distribution position in the to-be-processed living body monitoring information.

[0006] Optionally, the inference based on the converged abnormal data recognition model on the to-be-processed living body monitoring information to obtain the marking result of the abnormal data distribution position in the to-be-processed living body monitoring information comprises: Based on the converged abnormal data recognition model, inference is performed on the to-be-processed living body monitoring information to obtain a second abnormal data distribution information probability density of the to-be-processed living body monitoring information; perform step scanning on the second abnormal data distribution information probability density to determine a region peak response intensity of each second scanning region of the second abnormal data distribution information probability density; If the region peak response intensity of the second scanning region is greater than a second set threshold, the second distribution information corresponding to the region peak response intensity of the second scanning region is determined as the marking result of the abnormal data distribution position in the to-be-processed living body monitoring information.

[0007] Optionally, the inference on the to-be-processed living body monitoring information based on the converged abnormal data recognition model to obtain the marking result of the abnormal data distribution position in the to-be-processed living body monitoring information comprises: inference on the to-be-processed living body monitoring information based on the converged abnormal data recognition model to obtain second abnormal data distribution information probability density of the to-be-processed living body monitoring information; obtaining distribution information with the maximum abnormal data distribution information probability according to the second abnormal data distribution information probability density, and determining the distribution information as the marking result of the abnormal data distribution position in the to-be-processed living body monitoring information.

[0008] Optionally, the reference living body monitoring information library comprises first reference living body monitoring information and second reference living body monitoring information, and the distance calculation on the abnormal data array representation of the reference living body monitoring information and the monitoring information array representation of the to-be-processed living body monitoring information according to the abnormal data distribution result of the to-be-processed living body monitoring information to obtain the similarity score of the to-be-processed living body monitoring information comprises: obtaining the abnormal data array representation of the first reference living body monitoring information according to the abnormal data distribution result of the first reference living body monitoring information, and obtaining the abnormal data array representation of the second reference living body monitoring information according to the abnormal data distribution result of the second reference living body monitoring information; extracting a feature array from the to-be-processed living body monitoring information to obtain the monitoring information array representation of the to-be-processed living body monitoring information; performing distance calculation on the abnormal data array representation of the first reference living body monitoring information and the monitoring information array representation to obtain a first similarity measurement value between the first reference living body monitoring information and the to-be-processed living body monitoring information; performing distance calculation on the abnormal data array representation of the second reference living body monitoring information and the monitoring information array representation to obtain a second similarity measurement value between the second reference living body monitoring information and the to-be-processed living body monitoring information; integrating the first similarity measurement value and the second similarity measurement value to obtain the similarity score.

[0009] Optionally, the preset abnormal data identification model is adjusted according to the first prior distribution heat map and the to-be-processed living body monitoring information to obtain a converged abnormal data identification model, including: The to-be-processed living body monitoring information is inferred based on the preset abnormal data identification model to obtain a first inference distribution heat map of an abnormal data distribution position in the to-be-processed living body monitoring information; The first error result of the preset abnormal data identification model is obtained according to the first inference distribution heat map and the first prior distribution heat map; The preset abnormal data identification model is adjusted according to the first error result to obtain a converged abnormal data identification model.

[0010] Optionally, the to-be-processed living body monitoring information is inferred based on the preset abnormal data identification model to obtain a first inference distribution heat map of an abnormal data distribution position in the to-be-processed living body monitoring information, including: The to-be-processed living body monitoring information is represented based on the preset abnormal data identification model to obtain an initial array representation of the to-be-processed living body monitoring information; The initial array representation of the to-be-processed living body monitoring information is subjected to a convolution operation based on the preset abnormal data identification model to obtain a convolution array representation of the to-be-processed living body monitoring information; The first inference distribution heat map of the to-be-processed living body monitoring information is obtained according to the residual connection operation of the initial array representation and the convolution array representation.

[0011] Optionally, the preset abnormal data identification model is adjusted according to the first error result to obtain a converged abnormal data identification model, including: A second prior distribution heat map of an abnormal data distribution position in the reference living body monitoring information is obtained according to the abnormal data distribution result of the reference living body monitoring information; The reference living body monitoring information is inferred based on the preset abnormal data identification model to obtain a second inference distribution heat map of an abnormal data distribution position in the reference living body monitoring information; The second error result of the preset abnormal data identification model is obtained according to the second inference distribution heat map and the second prior distribution heat map; The preset abnormal data identification model is adjusted according to the first error result and the second error result to obtain a converged abnormal data identification model.

[0012] Optionally, the first prior distribution heat map of an abnormal data distribution position in the to-be-processed living body monitoring information is obtained according to the distribution position inference label, including: According to the distribution position inference label, the to-be-processed living body monitoring information is binary coded to obtain coded monitoring information of the to-be-processed living body monitoring information. The coded monitoring information is subjected to smoothing filtering processing to obtain a first prior distribution heat map of the abnormal data distribution position in the to-be-processed living body monitoring information.

[0013] In a second aspect, the present application provides a computer system, comprising: one or more processors; a memory; one or more computer programs; wherein the one or more computer programs are stored in the memory and configured to be executed by the one or more processors, and when the one or more computer programs are executed by the processors, the method described above is implemented.

[0014] The present application has the following beneficial effects: based on the abnormal data distribution result of the reference living body monitoring information, the present application calculates the distance between the abnormal data array representation of the reference living body monitoring information and the monitoring information array representation of the to-be-processed living body monitoring information to obtain the similarity score of the to-be-processed living body monitoring information, which is used to infer the distribution position inference label of the abnormal data in the to-be-processed living body monitoring information; in this way, the preset abnormal data recognition model is adjusted based on the distribution position inference label of the to-be-processed living body monitoring information to obtain the converged abnormal data recognition model. First, because the distance between the abnormal data array representation of the reference living body monitoring information and the monitoring information array representation of the to-be-processed living body monitoring information is calculated, the distribution position inference label of the abnormal data in the to-be-processed living body monitoring information can be inferred, so that a small amount of expert-annotated reference living body monitoring information inference labels are used to obtain the distribution position of a small amount of to-be-processed living body monitoring information, and a large number of prior knowledge samples can be obtained only by using a small amount of prior annotation samples to complete sample augmentation for model training. In addition, because the sample can be augmented based on vector distance calculation to complete the label generation of a large number of to-be-processed living body monitoring information, the preset abnormal data recognition model is adjusted based on the to-be-processed living body monitoring information, which can improve the accuracy of the converged abnormal data recognition model in labeling the distribution position of the abnormal data. In summary, the present application can reduce the cost, improve the efficiency and accuracy when processing the living body monitoring information. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 is a flowchart of a living body monitoring information processing method based on an implantable medical control device provided by an embodiment of the present application.

[0016] Figure 2 is a composition schematic diagram of a computer system provided by an embodiment of the present application. DETAILED DESCRIPTION

[0017] The execution subject of the living body monitoring information processing method based on the implantable medical regulation and control device in the embodiments of the present application is a computer system, including but not limited to a server, a personal computer, a notebook computer, a tablet computer, a smart phone and the like. As shown in FIG. 1, Figure 1 The method includes the following steps. Step S100: obtaining a reference living body monitoring information library and a to-be-processed living body monitoring information library, wherein the reference living body monitoring information library includes one or more reference living body monitoring information and abnormal data distribution results of the reference living body monitoring information. The to-be-processed living body monitoring information library includes a plurality of to-be-processed living body monitoring information. The living body monitoring information in the reference living body monitoring information library and the to-be-processed living body monitoring information library is collected based on an implantable medical regulation and control device.

[0018] In step S100, the reference living body monitoring information library contains one or more reference living body monitoring information. These information are obtained by the computer system from a specific data source, which is related to the implantable medical regulation and control device. For example, in the heart disease monitoring scenario, the implantable medical regulation and control device may be a monitoring chip attached to a cardiac pacemaker. The reference living body monitoring information can be various data about the heart function, such as heart rate, systolic and diastolic pressure, electrocardiogram and the like. Taking the heart rate as an example, it may be the number of heartbeats per minute within a period of time (such as 24 hours), forming a time series data such as [70, 72, 75, 73, …]. For the electrocardiogram, it may be the amplitude value of the electrical signal collected every millisecond, forming a complex waveform data sequence. These data reflect the physiological characteristics of the living body in normal or specific state, and are an important basis for subsequent abnormal data judgment and processing.

[0019] The reference living body monitoring information library also contains abnormal data distribution results of the reference living body monitoring information, i.e. the distribution position of the abnormal data in the reference living body monitoring information. For example, continuing with the heart monitoring example, if in the above heart rate data, it is found that the heart rate fluctuates abnormally in certain time period. Assuming that during the 10th-15th minute, the heart rate suddenly rises to 120-130 times per minute, while the normal range should be 60-100 times per minute, the computer system will mark this time period as the distribution position of the abnormal data. For the electrocardiogram, if the amplitude or shape of some waveforms is abnormal, such as the occurrence of sharp peak or irregular waveform lasting for a certain time, the computer system will accurately record the start position, duration and other information of this abnormal waveform in the entire electrocardiogram sequence, which is the abnormal data distribution result.

[0020] In more complex physiological monitoring scenarios, such as monitoring brain neural activity. Implantable medical control devices may be brain-implanted electrodes used to monitor neuron discharge activity. The reference living body monitoring information is the frequency, intensity, etc. of these neuron discharges. If the neuron discharge frequency of a certain brain area suddenly increases or decreases abnormally within a certain period of time, the computer system will record the location information of this abnormal situation within the entire monitoring period as an abnormal data distribution result. This has important significance for studying brain diseases or nervous system dysfunction.

[0021] The to-be-processed living body monitoring information library contains a plurality of to-be-processed living body monitoring information. These information is also collected by implantable medical control devices. For example, in the blood glucose monitoring scenario of diabetic patients, the implantable blood glucose monitor will continuously collect the blood glucose values of the patient. These blood glucose values change with the patient's diet, exercise, medication, etc. The to-be-processed living body monitoring information obtained by the computer system is a sequence of these blood glucose values, such as [5.0, 6.5, 4.8, 7.2, …], where each value represents the patient's blood glucose concentration at a certain time. For example, in kidney function monitoring, implantable medical control devices may monitor the content of various components in urine, such as creatinine, urea nitrogen, etc. The to-be-processed living body monitoring information is a data sequence of the content of these components changing over time.

[0022] In muscle activity monitoring, if the implantable medical control device is a sensor that can monitor muscle electrical signals, the to-be-processed living body monitoring information is the electrical signal intensity, frequency, etc. of the muscle in different activity states (such as rest, exercise, force, etc.). These data can help doctors understand the functional status of the muscle and whether there are potential muscle diseases or injuries.

[0023] The computer system obtains these to-be-processed living body monitoring information library in order to compare and analyze with the reference living body monitoring information later, so as to find out the abnormal data distribution situation that may exist in it, and perform corresponding processing such as abnormal data identification, marking and analysis. This is an important basis for the entire living body monitoring information processing method.

[0024] Step S200: According to the abnormal data distribution result, the distance between the abnormal data array representation of the reference living body monitoring information and the monitoring information array representation of the to-be-processed living body monitoring information is calculated to obtain the similarity score of the to-be-processed living body monitoring information.

[0025] In the embodiments of the present application, step S200 calculates the distance between the abnormal data array representation of the reference living body monitoring information and the monitoring information array representation of the living body monitoring information to be processed according to the abnormal data distribution result in the reference living body monitoring information library obtained in step S100, so as to obtain the similarity score of the living body monitoring information to be processed. The reference living body monitoring information is obtained based on the implantable medical control device, and the information contains rich physiological data. Taking heart monitoring as an example, it is assumed that the reference living body monitoring information collected by the implantable device includes heartbeat frequency, blood pressure, electrocardiogram signal and other data. Among these data, there are some data that have been marked as abnormal, for example, the abnormal value of heartbeat frequency. If the values of heartbeat frequency within a period of time are regarded as a sequence, such as [70, 72, 150, 73, 75] (where 150 is an abnormal value), the sequence can be converted into an array representation form convenient for computer processing. The array can not only contain original values, but also can contain time marks or other auxiliary information related to the values, for example, [(70, t1), (72, t2), (150, t3), (73, t4), (75, t5)], where ti represents the corresponding time point. This is one possible form of the abnormal data array representation of the reference living body monitoring information.

[0026] Similarly, for the monitoring information array representation of the living body monitoring information to be processed, the computer system needs to perform similar processing on the living body monitoring information to be processed. Continuing to take heart monitoring as an example, if the living body monitoring information to be processed contains newly collected heartbeat frequency data, such as [71, 73, 74, 72, 76], the computer system will convert it into a monitoring information array representation, which can be [(71, t1'), (73, t2'), (74, t3'), (72, t4'), (76, t5')]. This array representation form can accurately reflect the characteristics of the living body monitoring information to be processed, and has the same structure as the abnormal data array representation of the reference living body monitoring information, so as to perform subsequent distance calculation.

[0027] The distance calculation performed by the computer system is based on the above two array representations. There are many methods for distance calculation, for example, Euclidean distance calculation. Taking a simple two-dimensional array as an example, it is assumed that there are an abnormal data array A of the reference living body monitoring information [(1, 2), (3, 4)] and a monitoring information array B of the living body monitoring information to be processed [(2, 3), (4, 5)]. The Euclidean distance formula is For the first element (1, 2) in array A and the first element (2, 3) in array B, the distance is calculated as For the second element in array A (3, 4) and the second element in array B (4, 5), the distance is These distances are then aggregated (e.g., summed, averaged, etc.) to obtain a value representing the distance between the two.

[0028] In more complex vital monitoring information scenarios, such as when considering multi-dimensional data like heart rate, blood pressure, and ECG signals, the dimensionality of the arrays increases. Suppose the array of abnormal data of the reference vital monitoring information is represented as A = [(70, 120, waveform1), (72, 118, waveform2), (150, 130, waveform3)] (where 70 is the heart rate, 120 is the blood pressure, and waveform1 is the ECG signal waveform, etc.), and the array of monitoring information of the vital monitoring information to be processed is represented as B = [(71, 119, waveform4), (73, 121, waveform5), (74, 120, waveform6)]. The computer system will use a distance calculation method suitable for high-dimensional data, such as Manhattan distance or cosine similarity, etc. Taking cosine similarity as an example, it measures the cosine value of the angle between two vectors. For high-dimensional vectors A and B, by calculating their cosine similarity, a value between -1 and 1 can be obtained, representing the degree of similarity between the two. The closer this value is to 1, the more similar the information represented by the two arrays; the closer to -1, the less similar; close to 0 indicates that there is almost no linear relationship between the two.

[0029] Through such distance calculation, the computer system obtains a distance measurement value for each vital monitoring information to be processed with the reference vital monitoring information. Then, according to this distance measurement value, the computer system further calculates the similarity score of the vital monitoring information to be processed. The similarity score is an index that comprehensively reflects the degree of similarity between the vital monitoring information to be processed and the reference vital monitoring information. For example, if the distance measurement value is smaller, the similarity score may be higher. The computer system may use a mapping function to convert the distance measurement value to the similarity score. Suppose the range of distance measurement value d is [0, +∞), and the similarity score s can be calculated by the function s = 1 / (1+d) (this is only a simple example function). In this way, when d = 0, s = 1, indicating complete similarity; when d tends to +∞, s tends to 0, indicating complete dissimilarity.

[0030] The similarity score provides a critical input for subsequent steps, such as reasoning based on the similarity score in step S300. By calculating the similarity score, the computer system can establish a quantitative relationship between the reference vital monitoring information and the to-be-processed vital monitoring information, enabling reasoning and prediction of abnormal data distribution in the to-be-processed vital monitoring information based on the known abnormal data distribution in the reference vital monitoring information. This helps improve the accuracy and efficiency of medical monitoring, especially when dealing with a large amount of vital monitoring information, enabling quick screening of potentially abnormal information to provide strong support for further diagnosis and treatment.

[0031] Step S300: Reasoning based on the similarity score to obtain a distribution position reasoning mark of abnormal data in the to-be-processed vital monitoring information.

[0032] In the embodiment of the present application, the computer system reasons based on the similarity score obtained in step S200 in this step, thereby obtaining a distribution position reasoning mark of abnormal data in the to-be-processed vital monitoring information. The similarity score reflects the degree of similarity between the to-be-processed vital monitoring information and the reference vital monitoring information. The reference vital monitoring information contains known abnormal data distribution, so the similarity score can be used to infer the possible distribution position of abnormal data in the to-be-processed vital monitoring information. For example, in the vital monitoring of blood glucose level, the reference vital monitoring information is from multiple cases of typical blood glucose fluctuation (including abnormal fluctuation), and these data have obtained abnormal data distribution results through previous processing, and the similarity score is calculated with the new patient's blood glucose monitoring information in step S200. The computer system can use a classifier (such as softmax) to reason based on the similarity score.

[0033] Step S400: Obtaining a first prior distribution heat map of the abnormal data distribution position in the to-be-processed vital monitoring information based on the distribution position reasoning mark.

[0034] In the embodiment of the present application, the computer system obtains a distribution position reasoning mark of abnormal data in the to-be-processed vital monitoring information in step S300. This reasoning mark is based on the similarity score reasoning, which represents a preliminary judgment of the possible distribution position of abnormal data. In step S400, the computer system obtains a first prior distribution heat map of the abnormal data distribution position in the to-be-processed vital monitoring information based on this distribution position reasoning mark.

[0035] For example, assume that in the previous steps, the computer system has obtained a distribution location inference label by processing the reference living body monitoring information (containing heartbeat frequency, electrocardiogram signal data, etc.) and the to-be-processed living body monitoring information. This label can indicate that in the to-be-processed living body monitoring information, heartbeat abnormalities can occur in a specific time period, for example, in a certain two-hour monitoring period, the 30th-40th minute interval is the most likely position of heartbeat abnormal data. This label is a preliminary judgment based on the similarity between data and the understanding of the overall physiological law.

[0036] The computer system encodes the to-be-processed living body monitoring information according to the distribution location inference label. Continuing with the example of heart monitoring, assume that the entire monitoring period is divided into small time units, such as 1 minute per unit. For each time unit, if it is located in the possible abnormal interval (30th-40th minute) indicated by the distribution location inference label, it is encoded as 1, indicating that this unit can have abnormalities; if it is not in this interval, it is encoded as 0. In this way, the entire to-be-processed living body monitoring information is converted into a coded monitoring information sequence composed of 0 and 1. For example, for a 60-minute monitoring information, the coded monitoring information can be [0, 0, 0, …, 0, 1, 1, 1, …, 1, 0, 0, 0, …, 0], where the 30th-40th minute corresponds to 1.

[0037] The significance of this binary encoding is to convert the inference label into a form that the computer system can further process to generate a prior distribution heat map. It simplifies the complex inference results about the distribution location of abnormal data into a binary state representation, highlighting the areas where abnormalities can exist.

[0038] Next, the computer system performs a smoothing filtering process on the coded monitoring information to obtain a first prior distribution heat map. The smoothing filtering process is to make the obtained prior distribution heat map more consistent with the actual physiological data variation law and reduce the discrete and noise effects that may be caused by binary encoding. For example, in the smoothing filtering process, the computer system can use a weighted average method. For each time unit encoded as 1, it not only affects its own heat value, but also affects its adjacent time units according to a certain weight. Assume that in the above example of heart monitoring, for the 30th minute time unit encoded as 1, it affects the heat values of the 29th and 31st minutes with a weight of 0.5, affects the heat values of the 28th and 32nd minutes with a weight of 0.3, and so on.

[0039] Through such a smoothing filtering process, the computer system obtains a first prior distribution heat map. This heat map can be regarded as a probability distribution, which represents the prior probability distribution of abnormal data appearing at different positions in the to-be-processed living body monitoring information. In the heat map, the higher the value of a region, the greater the possibility of abnormal data appearing at this position. For example, in the example of heart monitoring, the interval of 30-40 minutes may be displayed as a region with a higher value in the heat map, and other intervals are displayed as a region with a lower value, indicating that the prior probability of abnormal heartbeat data appearing in this interval is higher.

[0040] Taking the monitoring of the concentration of a specific substance in blood as an example again. It is assumed that in the previous step, the distribution position inference label points to the possibility of abnormal concentration of the specific substance in a certain intermediate period during blood collection. The computer system performs binary coding on the blood monitoring data corresponding to this period, and then performs a smoothing filtering process. If there is a certain continuity between different stages during blood collection, for example, the concentration of the substance is related to each other in the absorption, metabolism and excretion process of the substance, then the smoothing filtering process can better reflect the influence of this continuity on the distribution of abnormal data. The obtained first prior distribution heat map can accurately show the prior probability distribution of the abnormal concentration of the specific substance at different times during the entire blood collection process. The first prior distribution heat map provides a key input for the subsequent steps, such as step S500, in which the preset abnormal data recognition model is adjusted according to the heat map and the to-be-processed living body monitoring information. This heat map is not only a simple result presentation, but also further integrates the inference and judgment in the previous steps into a form that can be quantitatively and intuitively analyzed, which helps to improve the accuracy and effectiveness of the entire living body monitoring information processing method. In this way, the computer system can more deeply mine the distribution rules of abnormal data in the living body monitoring information, and provide more powerful support for medical diagnosis and regulation.

[0041] Step S500: adjusting a preset abnormal data recognition model according to the first prior distribution heat map and the to-be-processed living body monitoring information to obtain a converged abnormal data recognition model, and the converged abnormal data recognition model is used for distribution position identification labeling of abnormal data in the to-be-processed living body monitoring information.

[0042] In step S500, the computer system adjusts the preset abnormal data identification model based on the first prior distribution heat map and the to-be-processed living body monitoring information to obtain a converged abnormal data identification model. The preset abnormal data identification model is a model previously constructed by the computer system for identifying abnormal data in the living body monitoring information. It can be based on various machine learning algorithms, such as neural network algorithms. Taking a simple neural network model as an example, it includes an input layer, a hidden layer, and an output layer. The input layer receives the living body monitoring information, which can be pre-processed numerical values, such as heart rate, blood pressure, and electrocardiogram signals in heart monitoring. The hidden layer performs complex nonlinear transformation on the input data, and the output layer outputs the judgment result of the abnormal data, such as a probability value indicating whether there is abnormal data or directly marking the position of the abnormal data.

[0043] In step S500, the computer system infers the to-be-processed living body monitoring information based on the preset abnormal data identification model to obtain a first inference distribution heat map of the abnormal data distribution position in the to-be-processed living body monitoring information. This process involves feature extraction and analysis of the input to-be-processed living body monitoring information by the model. Taking blood glucose monitoring as an example, assume that the to-be-processed living body monitoring information received by the preset abnormal data identification model is a sequence of blood glucose values measured at certain time intervals (e.g., 15 minutes) within a certain period of time (e.g., one day). The model first extracts features from these blood glucose values, which may include calculating statistical features such as mean, variance, and fluctuation amplitude of the blood glucose values, and may also include analyzing the trend of the blood glucose values over time, such as whether the blood glucose values are rising, falling, or remaining stable. Then, the model infers based on these features to generate the first inference distribution heat map. This heat map represents the model's judgment of the likelihood of abnormal blood glucose data at each time point or time period. For example, in the period after breakfast, due to the digestion and absorption of food, the blood glucose value usually rises, but if the rising amplitude exceeds the normal range, the model may display a higher value at the corresponding heat map position of this time period, indicating that there is a higher likelihood of abnormal high blood glucose data in this time period.

[0044] Then, the computer system obtains a first error result of the preset abnormal data recognition model according to the first inferred distribution heat map and the first prior distribution heat map. The first prior distribution heat map is obtained in step S400 according to the distribution position inference label, which reflects the prior probability distribution of the abnormal data distribution position inferred in the previous steps. The first inferred distribution heat map is the result of the preset abnormal data recognition model inferring the data based on its own algorithm and structure. The computer system determines the error of the model by comparing the two heat maps. For example, the mean square error (MSE) method can be used to calculate the error. Assuming that the heat value of a certain time point t in the first prior distribution heat map is h1, and the heat value of the corresponding time point t in the first inferred distribution heat map is h2, then the mean square error is calculated as where n is the number of time points in the heat map. This error result reflects the difference between the current inference result of the preset abnormal data recognition model and the prior distribution heat map obtained based on the previous steps.

[0045] Then, the computer system adjusts the preset abnormal data recognition model according to the first error result to obtain a converged abnormal data recognition model. The adjustment process is to make the output of the model closer to the true abnormal data distribution. In neural network models, adjustment usually involves adjusting the weight parameters of the model. For example, if it is found that the error of a certain area is large when calculating the first error result, it means that the model's judgment of abnormal data in this area is not accurate. The computer system will propagate the error from the output layer to the input layer according to the error back propagation algorithm, and adjust the connection weights between the intermediate hidden layers and the input layer to the hidden layer. Continuing with the blood sugar monitoring example, if it is found that the model has a large error in judging the blood sugar fluctuation after lunch, the model will adjust the weight parameters related to processing blood sugar data after lunch during the adjustment process. If a neuron has too high a weight for judging the rising trend of blood sugar after lunch, resulting in a too high probability of predicting abnormal blood sugar, the weight value will be reduced during the adjustment process.

[0046] As another example, the entire step S500 is described. In the living body monitoring of kidney function, the preset abnormal data recognition model receives the data of urine composition content, creatinine and urea nitrogen level in blood, etc. collected from the implanted medical control device as the living body monitoring information to be processed. The model first extracts and analyzes the complex physiological data to generate a first inference distribution heat map about the abnormal data distribution position of kidney function. This heat map may show that the excretion function of the kidney may be abnormal in some time period, for example, the reabsorption function of the kidney for certain substances may be abnormal at night, and the corresponding night period on the heat map will show a higher abnormal probability value. Then, the computer system compares the first inference distribution heat map with the first prior distribution heat map obtained based on the previous steps (this heat map may be obtained based on the similarity analysis of a large number of reference data of normal and abnormal cases of kidney function and the living body monitoring information to be processed), and calculates the first error result. If it is found that the judgment of the abnormal reabsorption function of the kidney at night in the first inference distribution heat map is greatly different from the first prior distribution heat map, for example, the abnormal probability shown in the first inference distribution heat map is too high, while the probability shown in the first prior distribution heat map is lower, then the computer system will adjust the weight parameter in the preset abnormal data recognition model according to the error result. The weight of the input feature related to the kidney function at night may be adjusted, such as reducing the weight of a certain feature related to the change of urine composition at night, because this feature may be overvalued by the model to cause the error. Through multiple such adjustment processes, the preset abnormal data recognition model gradually converges, and finally a converged abnormal data recognition model is obtained. This converged model can more accurately identify the abnormal data distribution position in the living body monitoring information to be processed, thereby improving the accuracy and reliability of the entire living body monitoring information processing method.

[0047] As an implementation, the reference living body monitoring information library includes first reference living body monitoring information and second reference living body monitoring information, step S200, and the distance calculation of the abnormal data array representation of the reference living body monitoring information and the monitoring information array representation of the living body monitoring information to be processed according to the abnormal data distribution result to obtain the similarity score of the living body monitoring information to be processed can include: Step S210: obtaining the abnormal data array representation of the first reference living body monitoring information according to the abnormal data distribution result of the first reference living body monitoring information; and obtaining the abnormal data array representation of the second reference living body monitoring information according to the abnormal data distribution result of the second reference living body monitoring information; Step S220: extracting the monitoring information array representation of the living body monitoring information to be processed according to the living body monitoring information to be processed; Step S230: distance calculation is performed between the abnormal data array representation of the first reference living body monitoring information and the monitoring information array representation, to obtain a first similarity measurement value between the first reference living body monitoring information and the living body monitoring information to be processed; Step S240: distance calculation is performed between the abnormal data array representation of the second reference living body monitoring information and the monitoring information array representation, to obtain a second similarity measurement value between the second reference living body monitoring information and the living body monitoring information to be processed; Step S250: the first similarity measurement value and the second similarity measurement value are integrated to obtain a similarity score.

[0048] In the embodiments of the present application, the specific implementation of step S200 includes steps S210-S250, which are executed by the computer system, and are aimed at obtaining the similarity score of the living body monitoring information to be processed, by performing a series of operations on the abnormal data array representation of the reference living body monitoring information and the monitoring information array representation of the living body monitoring information to be processed, according to the abnormal data distribution result in the reference living body monitoring information library.

[0049] In step S210, the computer system first performs operations on the first reference living body monitoring information. In the medical monitoring scenario, taking the monitoring of the cardiovascular system as an example, it is assumed that the first reference living body monitoring information contains data from an implanted heart monitoring device, which covers heartbeat frequency, blood pressure, and electrocardiogram signals, etc. The heartbeat frequency data may be measurement values within 24 hours at certain time intervals (for example, recorded once every 5 minutes), the blood pressure data includes systolic pressure and diastolic pressure, and the electrocardiogram signal is more complex continuous waveform data.

[0050] In this reference living body monitoring information, there are already known abnormal data distribution results. For example, after annotation by professional medical personnel or preliminary data analysis, it is determined that the heartbeat frequency has abnormal fluctuations in a certain time period (such as 10:00-11:00 am), the blood pressure has values exceeding the normal range between 3:00-4:00 pm, and the electrocardiogram signal has irregular waveforms at night.

[0051] The computer system constructs abnormal data array representation based on these abnormal data distribution results. For the abnormal heartbeat frequency data, assuming the heartbeat frequency values during 10:00-11:00 am are [105, 110, 108, 112, 106] (normal range is assumed to be 60-100 beats / min), these abnormal heartbeat frequency values are combined with corresponding time markers to form array elements, such as [(105, 10:00), (110, 10:05), (108, 10:10), (112, 10:15), (106, 10:20)]. For the abnormal blood pressure data, the systolic and diastolic pressure values during 3:00-4:00 pm are [(150, 90), (148, 88), (152, 92)] (normal systolic pressure range is assumed to be 90-139 mmHg, diastolic pressure range is 60-89 mmHg), combined with time markers, can be represented as [((150, 90), 15:00), ((148, 88), 15:05), ((152, 92), 15:10)]. Due to the complexity of the electrocardiogram signal, it can be represented by extracting specific feature values (such as peak values of waveforms, intervals, etc.) of abnormal data, assuming that the feature values extracted at a certain period of the night are [5, 3, 4] (these values are quantized features), the corresponding array elements can be [(5, 0:30), (3, 0:35), (4, 0:40)] (0:30-0:40 is the abnormal period at night). Combining these abnormal data array elements of heartbeat frequency, blood pressure and electrocardiogram signal in a certain order forms the abnormal data array representation of the first reference living body monitoring information. Similarly, for the second reference living body monitoring information, the computer system performs similar operations. Taking the nervous system monitoring as an example, assuming that the implanted medical control device monitors the discharge frequency of brain neurons, neurotransmitter concentration and other data as the second reference living body monitoring information.

[0052] The known abnormal data distribution results show that during the brain performing a specific cognitive task (such as a memory recall task), the neuron firing frequency appears abnormal fluctuations, and the neurotransmitter concentration in a specific brain region deviates from the normal range. For example, within 3-5 minutes of the memory recall task, the abnormal values of neuron firing frequency are [150, 160, 155, 148, 152] (assuming the normal range is 100-130 times / s), and the corresponding task time composition array elements are [(150, 3:00), (160, 3:05), (155, 3:10), (148, 3:15), (152, 3:20)]. At the same time, during the same task, the abnormal values of neurotransmitter concentration (assuming dopamine concentration) in a certain brain region are [0.8, 0.9, 0.75] (assuming the normal range is 0.5-0.7), combined with time markers [(0.8, 3:00), (0.9, 3:05), (0.75, 3:10)]. Combining the abnormal data array elements of neuron firing frequency and neurotransmitter concentration, the second reference living body monitoring information abnormal data array representation is obtained.

[0053] In step S220, the computer system extracts the characterization array according to the to-be-processed living body monitoring information, and obtains the monitoring information array representation of the to-be-processed living body monitoring information.

[0054] Taking liver function monitoring as an example, it is assumed that the to-be-processed living body monitoring information is the data of various liver function related indicators (such as glutamic-pyruvic transaminase, glutamic-oxalacetic transaminase, bilirubin, etc.) in blood changing with time collected by an implantable liver monitoring device.

[0055] For the glutamic-pyruvic transaminase, assume that the measurements are taken every 2 hours within 12 hours, the data obtained are [40, 45, 50, 55, 60, 65] (normal range is assumed to be 0-40 U / L). The computer system extracts the feature array from these data, which can not only record the values, but also combine the measurement time, for example, [(40, 0:00), (45, 2:00), (50, 4:00), (55, 6:00), (60, 8:00), (65, 10:00)]. For the glutamic-oxalacetic transaminase, the measurement values are [35, 40, 42, 48, 50, 52] (normal range is assumed to be 0-40 U / L), and the extracted feature array can be [(35, 0:00), (40, 2:00), (42, 4:00), (48, 6:00), (50, 8:00), (52, 10:00)]. The measurement values of bilirubin are [1.2, 1.5, 1.8, 2.0, 2.2, 2.5] (normal range is assumed to be 0-1.0 mg / dL), and the extracted array is [(1.2, 0:00), (1.5, 2:00), (1.8, 4:00), (2.0, 6:00), (2.2, 8:00), (2.5, 10:00)].

[0056] The feature arrays of these liver function related indicators are combined in a certain order to obtain the monitoring information array representation of the to-be-processed living body monitoring information.

[0057] In step S230, the computer system calculates the distance between the abnormal data array representation of the first reference living body monitoring information and the monitoring information array representation, to obtain a first similarity measurement value between the first reference living body monitoring information and the to-be-processed living body monitoring information.

[0058] There are various distance calculation methods, and here the Euclidean distance is taken as an example for illustration. Assume that an element in the abnormal data array representation of the first reference living body monitoring information is the heartbeat frequency abnormal data [(105, 10:00), (110, 10:05), (108, 10:10), (112, 10:15), (106, 10:20)], and the heartbeat frequency data in the monitoring information array representation of the to-be-processed living body monitoring information is [(90, 10:00), (95, 10:05), (92, 10:10), (98, 10:15), (96, 10:20)].

[0059] For each corresponding time point heartbeat frequency value, the Euclidean distance is calculated. For example, at the time point of 10:00, the distance is ; at 10:05, the distance is ; and so on. Then these distance values are processed synthetically, for example, averaged to obtain a first similarity measure value. Suppose that after calculating the distance for all corresponding time points and averaging, the first similarity measure value is 12 (here is just an example value).

[0060] If the array representation of multiple physiological indicators (such as simultaneously containing heartbeat frequency, blood pressure, and electrocardiogram signals) is considered, the calculation process will be more complex. The distance is calculated for each array element of each indicator, and then these distances are integrated according to certain weights (the weights can be pre-set according to the importance of the indicators) to obtain a first similarity measure value.

[0061] In step S240, the computer system calculates the distance between the array representation of the abnormal data of the second reference living body monitoring information and the array representation of the monitoring information, to obtain a second similarity measure value between the second reference living body monitoring information and the living body monitoring information to be processed.

[0062] Take the previously mentioned nervous system monitoring and liver function monitoring as an example. Suppose that the neuron firing frequency in the array representation of the abnormal data of the second reference living body monitoring information is [(150, 3:00), (160, 3:05), (155, 3:10), (148, 3:15), (152, 3:20)], and the data of a certain indicator related to the nervous system (suppose it is the neural response time) in the array representation of the monitoring information of the living body monitoring information to be processed is [(130, 3:00), (140, 3:05), (135, 3:10), (138, 3:15), (142, 3:20)].

[0063] Similarly, using the Euclidean distance calculation method, at 3:00, the distance is ; at 3:05, the distance is ; the distance at each time point is calculated and processed synthetically.

[0064] For the comparison between neurotransmitter concentration and liver function indicators, if a certain conversion method is used to map them to a comparable space (for example, through normalization processing), similar distance calculation can also be performed. Suppose that after calculation and synthetic processing, the second similarity measure value is 15 (also an example value).

[0065] In step S250, the computer system integrates the first similarity measure value and the second similarity measure value to obtain a similarity score. The method of integration can be various, such as weighted average method. Suppose the first similarity measure value is 12, the second similarity measure value is 15, and according to the importance of the first reference living body monitoring information and the second reference living body monitoring information (the importance can be determined based on medical experience or previous data analysis), the weight of the first reference living body monitoring information is set to 0.4, and the weight of the second reference living body monitoring information is set to 0.6. Then the similarity score is .

[0066] Another integration method can be a combination based on a certain nonlinear function. For example, suppose where a and b are parameters determined in advance according to data characteristics and processing requirements.

[0067] Through this integration method, the computer system obtains a similarity score that comprehensively reflects the similarity between the to-be-processed living body monitoring information and the reference living body monitoring information (including the first reference living body monitoring information and the second reference living body monitoring information), which will be an important basis for subsequent steps (such as the inference operation in step S300).

[0068] As an implementation, step S300, according to the similarity score, performs inference to obtain a distribution position inference marker of abnormal data in the to-be-processed living body monitoring information, which can include: Step S310: According to the similarity score, abnormal data distribution information inference is performed to obtain a first abnormal data distribution information probability density of the to-be-processed living body monitoring information. Step S320: Stepwise scanning is performed on the first abnormal data distribution information probability density to obtain a regional peak response intensity of each first scanning region of the first abnormal data distribution information probability density. Step S330: If the regional peak response intensity of the first scanning region is greater than a first set threshold, the first distribution information corresponding to the regional peak response intensity of the first scanning region is determined as the distribution position inference marker.

[0069] In step S310, according to the similarity score, abnormal data distribution information inference is performed to obtain a first abnormal data distribution information probability density of the to-be-processed living body monitoring information. The similarity score is a value obtained by performing complex distance calculation and integration on the reference living body monitoring information and the to-be-processed living body monitoring information in step S200, which reflects the similarity between the to-be-processed living body monitoring information and the reference living body monitoring information.

[0070] For example, according to the similarity score, abnormal data distribution information inference is performed to obtain a first abnormal data distribution information probability density of the to-be-processed living body monitoring information, which includes, for example: The computer system first normalizes the obtained similarity scores. Suppose there are n similarity scores , the normalized value of each similarity score is calculated as . For example, if there are three similarity scores , their normalized values are , respectively.

[0071] Then, the weight vector is determined according to the normalized similarity scores, where . These weights will be used later to construct a probability density function, representing the relative importance of each similarity score in inferring the distribution information of abnormal data.

[0072] Next, the computer system selects a suitable form of probability density function, such as Gaussian Mixture Model (GMM). GMM is a probability model composed of multiple Gaussian distributions, suitable for modeling complex distribution data. Suppose k Gaussian distributions are selected to construct the GMM, and the parameters of each Gaussian distribution are , where is the mean, is the standard deviation, .

[0073] For the determination of the parameters of each Gaussian distribution, it can be based on the data characteristics in the reference living body monitoring information and the weights of the similarity scores. For example, for the mean , it can be determined according to the statistical characteristics of the data in the reference living body monitoring information that has a high similarity score (i.e., a larger weight) with the living body monitoring information to be processed. If the reference living body monitoring information contains physiological data of different time periods, and the data of a certain time period has a high similarity score with the living body monitoring information to be processed, the mean of the data of that time period may have a greater contribution to . For the standard deviation , it can also be similarly determined according to the dispersion of the data and the weight of the similarity score.

[0074] Next, according to the determined Gaussian distribution parameters and weight vector, the mixed probability density function is constructed, where x represents the possible distribution position of abnormal data. This probability density function represents the probability distribution of abnormal data in the living body monitoring information to be processed at different positions, i.e., the first abnormal data distribution information probability density.

[0075] Considering the prior distribution of the reference living body monitoring information, the computer system obtains the prior distribution information of the abnormal data from the reference living body monitoring information. For example, if the reference living body monitoring information is monitoring data about blood glucose level, and the probability distribution of abnormal increase or decrease of blood glucose in certain specific situations (such as a certain period of time after eating, a certain exercise state) is known, these prior distribution information can be expressed as a probability density function .

[0076] The probability density function constructed based on the similarity score is fused with the prior distribution probability density function of the reference living body monitoring information. A simple fusion method is weighted summation, and the final first abnormal data distribution information probability density is obtained, where a is a weight coefficient, and the value range is between [0, 1], which is used to balance the importance of the inference result based on the similarity score and the prior distribution information. For example, when a = 0.8, it means that the inference result based on the similarity score is preferred, but a certain proportion of the prior distribution information is also considered.

[0077] Taking cardiovascular system monitoring as an example, it is assumed that the reference living body monitoring information contains multi-dimensional data such as heart rate, blood pressure, and electrocardiogram signal, which presents a specific pattern in a period of time, and the abnormal data distribution of the reference living body monitoring information is known. The living body monitoring information to be processed is also similar data about the cardiovascular system. If the similarity score is high, it means that the living body monitoring information to be processed is more similar to the reference living body monitoring information in data pattern. The computer system will infer the abnormal data distribution information in the living body monitoring information to be processed according to this similarity score, combined with the abnormal data distribution pattern in the reference living body monitoring information. For heart rate, it is assumed that within 30 minutes after getting up in the morning, according to the similarity score and the reference information, the computer system infers that the probability density function of the abnormal data occurrence is a normal distribution form, such as , where t represents time (within 30 minutes), may be the most likely abnormal time point (for example, 10 minutes) determined according to the reference information and the similarity score, is the standard deviation representing the probability density distribution (determined according to data characteristics and similarity).

[0078] In this way, the computer system constructs the first abnormal data distribution information probability density of different physiological indicators in the living body monitoring information to be processed according to the similarity score, considering the abnormal data distribution pattern in the reference living body monitoring information comprehensively. These probability density functions comprehensively describe the possible distribution of abnormal data in the living body monitoring information to be processed.

[0079] ​In step S320, the computer system performs stepwise scanning on the first abnormal data distribution information probability density. Taking the above cardiovascular system monitoring as an example, for the heartbeat frequency probability density function , assume that scanning is performed with a step of 1 minute. Within 30 minutes after getting up in the morning, starting from the 1st minute, the computer system calculates the probability density value corresponding to each time point t .

[0080] When t = 1 minute, calculate ; when t = 2 minutes, calculate , and so on until t = 30 minutes.

[0081] For the blood pressure probability density function , also perform scanning with a step of 1 minute within 1-2 hours (60-120 minutes) after lunch, and calculate the probability density value corresponding to each time point.

[0082] In the example of respiratory system monitoring, for the respiratory frequency probability density function , within 10-20 minutes, the blood oxygen saturation probability density function , within 5-10 minutes, also perform scanning according to the set step (e.g., a step of 30 seconds), and calculate the probability density value corresponding to each step.

[0083] During the stepwise scanning, the computer system determines the regional peak response intensity of each first scanning region. Taking the heartbeat frequency as an example, assume that the 30 minutes after getting up in the morning is divided into three regions, 0-10 minutes as the first region, 11-20 minutes as the second region, and 21-30 minutes as the third region.

[0084] Within the first region, the computer system finds the maximum value of the probability density during scanning , and the intensity corresponding to this maximum value is the regional peak response intensity of the first region. Assume that within this region, when t = 5 minutes, reaches the maximum value .

[0085] For the blood pressure probability density function, different regions within 1-2 hours after lunch are divided, such as 30-60 minutes as one region, 61-90 minutes as another region, and 91-120 minutes as the third region. Similarly, find the maximum value of the probability density within each region, and determine it as the regional peak response intensity of the region.

[0086] In the respiratory system, the probability density functions of the respiratory frequency and the blood oxygen saturation are also determined in this way to determine the regional peak response intensity in the respective scanning region. For example, the respiratory frequency is a region from 10 to 13 minutes, and the maximum value of the probability density in the region is found as the regional peak response intensity; the blood oxygen saturation is a region from 5 to 7 minutes, and the regional peak response intensity of the region is determined.

[0087] In step S330, the computer system compares the regional peak response intensity of the first scanning region with the first set threshold value. Assuming that in the cardiovascular system monitoring, the first regional peak response intensity of the heartbeat frequency , and the first set threshold value is .

[0088] If , the computer system determines the first distribution information corresponding to the first regional peak response intensity as the distribution position inference mark. For example, if corresponds to the time t = 5 minutes, then the heartbeat frequency related information (such as the heartbeat frequency value, the relationship with the surrounding heartbeat frequency, etc.) at this 5 minutes is determined as the distribution position inference mark, which means that there may be an abnormal data distribution near this time point. For physiological indicators such as blood pressure, respiratory frequency and blood oxygen saturation in the respiratory system, the same comparison operation is also performed. If the regional peak response intensity of a region is greater than the corresponding first set threshold value, the distribution information (such as time, numerical range, etc.) corresponding to the regional peak response intensity is determined as the distribution position inference mark.

[0089] In this way, in steps S310-S330, the computer system gradually constructs the probability density of the abnormal data distribution information according to the similarity score, performs step-by-step scanning to determine the regional peak response intensity, and determines the distribution position inference mark according to the comparison with the set threshold value, providing a preliminary judgment basis for the subsequent processing steps about the distribution position of the abnormal data in the to-be-processed living body monitoring information.

[0090] As an implementation manner, the method can further include: Step S301: If none of the regional peak response intensities of the first scanning region is greater than the first set threshold value, the to-be-processed living body monitoring information is taken as the to-be-processed living body monitoring information; Step S302: performing inference on the to-be-processed living body monitoring information based on the converged abnormal data recognition model to obtain a mark result of the distribution position of the abnormal data in the to-be-processed living body monitoring information.

[0091] In the embodiments of the present application, the derived implementation of step S300 includes steps S301-S302, which are a series of steps performed by the computer system and are supplementary operations for reasoning and marking the position of abnormal data distribution under certain conditions.

[0092] In step S301, the regional peak response intensity of the first scanning region is first compared with the first set threshold value. This comparison is based on the regional peak response intensity obtained in the previous step (such as step S320) and the pre-set threshold value.

[0093] Taking kidney function monitoring as an example, suppose that the living body monitoring information to be processed includes data such as creatinine, urea nitrogen concentration, and glomerular filtration rate in the blood. In the previous steps, the computer system has obtained the first abnormal data distribution information probability density about these data according to the similarity score for reasoning abnormal data distribution information. For example, for the creatinine concentration, the probability density function is constructed at different time periods in a day, and scanning is performed according to a certain step size to determine the regional peak response intensity of each scanning region.

[0094] Suppose that the first set threshold value is set according to a large amount of clinical experience and normal physiological data range. For the scanning region of the creatinine concentration in the morning period (such as 0:00-6:00), the regional peak response intensity is , and the first set threshold value is . If , it indicates that in this scanning region, although there is a certain possibility of abnormal data distribution, according to the set standard, this possibility is not sufficient to be identified as a high-risk region.

[0095] Similarly, for data such as urea nitrogen concentration and glomerular filtration rate, such a comparison is also performed in the respective scanning regions. For example, the regional peak response intensity of the urea nitrogen concentration in the afternoon period (12:00-18:00) is , and the corresponding first set threshold value is . If ; the regional peak response intensity of the glomerular filtration rate in the evening period (18:00-24:00) is , and the corresponding first set threshold value is . If .

[0096] When the regional peak response intensity of all first scanning regions is not greater than the first set threshold, the computer system regards the to-be-processed living body monitoring information as to-be-processed living body monitoring information, which means that further processing is needed to determine the distribution position of abnormal data therein. Because in the previous step, although preliminary reasoning and scanning are performed through the similarity score, no region that obviously meets the condition (regional peak response intensity greater than the threshold) is found, so the distribution position reasoning mark of abnormal data cannot be directly determined, and additional operations such as processing based on the converged abnormal data recognition model (step S302) need to be performed.

[0097] Continuing to take kidney function monitoring as an example, if the concentrations of creatinine, urea nitrogen, and glomerular filtration rate in the respective scanning regions are not greater than the corresponding thresholds, the computer system considers that the current reasoning and scanning results cannot accurately determine the distribution position of abnormal data, and thus retains the complete to-be-processed living body monitoring information for subsequent in-depth analysis based on the converged abnormal data recognition model. This is because there may be complex and not yet obviously identified abnormal data distribution patterns that need a more powerful model to mine.

[0098] In step S302, the computer system performs reasoning on the to-be-processed living body monitoring information based on the converged abnormal data recognition model to obtain a marked result of the distribution position of abnormal data in the to-be-processed living body monitoring information. The converged abnormal data recognition model is obtained through a series of calibrations (such as calibration in step S500), and it can perform more in-depth and accurate analysis on the living body monitoring information.

[0099] Taking nervous system monitoring as an example, suppose that the to-be-processed living body monitoring information contains data such as the discharge frequency of brain neurons, neurotransmitter concentration, and brain wave signals. After receiving these data, the converged abnormal data recognition model first performs feature extraction and analysis on them.

[0100] For neuron firing frequency, the model can analyze its average firing frequency, fluctuation amplitude, periodicity, etc. in different brain regions. For example, in the frontal lobe region of the brain, the normal neuron firing frequency is between 10-30 Hz, with small fluctuation amplitude and certain periodicity. If the neuron firing frequency in the processed living body monitoring information shows an average firing frequency of 40 Hz, a large fluctuation amplitude, and a disorderly periodicity in the frontal lobe region, the model will record these characteristics as an abnormality. For neurotransmitter concentration, such as dopamine, serotonin, etc., the model will analyze whether its concentration is within the normal range and the ratio with other neurotransmitter concentrations. Assuming that the normal concentration ratio of dopamine to serotonin is 1:2, if this ratio changes to 1:3 in the processed living body monitoring information, the model will identify this change as a possible abnormality.

[0101] For brain wave signals, the model will extract their frequency, amplitude, phase, etc. For example, the normal alpha brain wave frequency is between 8-13 Hz, with relatively stable amplitude. If the alpha brain wave frequency in the processed living body monitoring information exceeds this range or the amplitude fluctuates greatly, the model will use these characteristics as the basis for analyzing abnormal data. Through comprehensive analysis of these data, the converged abnormal data recognition model constructs a probability density function or other forms of representation about the distribution location of abnormal data in the processed living body monitoring information, such as constructing a matrix about the possibility of abnormal data distribution in different regions of the brain, each element representing the probability of abnormal data in the corresponding region.

[0102] In this process, the model can be based on a large amount of training data and knowledge accumulated in the previous calibration process. For example, it may have learned that in certain disease states (such as Parkinson's disease, which is related to abnormal neuron activity in the substantia nigra region of the brain), specific neuron firing frequency, neurotransmitter concentration, and brain wave signal change patterns, so it can match and reason according to the characteristics in the processed living body monitoring information to determine the possible distribution location of abnormal data.

[0103] In this way, the computer system comprehensively and deeply reasons the processed living body monitoring information based on the converged abnormal data recognition model, providing the basis for the final determination of the labeling result of the distribution location of abnormal data.

[0104] As an implementation, step S302, based on the converged abnormal data recognition model, inferring the processed living body monitoring information to obtain the labeling result of the distribution location of abnormal data in the processed living body monitoring information, can include: Step S3021: Based on the convergent abnormal data recognition model, the to-be-processed living body monitoring information is inferred to obtain the second abnormal data distribution information probability density of the to-be-processed living body monitoring information. Step S3022: Stepwise scanning is performed on the second abnormal data distribution information probability density to determine the regional peak response intensity of each second scanning region of the second abnormal data distribution information probability density. Step S3023: If the regional peak response intensity of the second scanning region is greater than the second set critical value, the second distribution information corresponding to the regional peak response intensity of the second scanning region is determined as the marking result of the abnormal data distribution position in the to-be-processed living body monitoring information.

[0105] In step S3021, the to-be-processed living body monitoring information is inferred based on the convergent abnormal data recognition model to obtain the second abnormal data distribution information probability density of the to-be-processed living body monitoring information. The convergent abnormal data recognition model is obtained through the previous steps (such as the calibration process in step S500), and it has the ability to analyze the abnormal data distribution in the living body monitoring information.

[0106] Taking cardiovascular system monitoring as an example, suppose the to-be-processed living body monitoring information contains heartbeat frequency, blood pressure, and electrocardiogram signals, etc. After receiving these data, the convergent abnormal data recognition model will process them according to its internal algorithms and parameters.

[0107] For heartbeat frequency data, the model may consider the long-term trend, short-term fluctuations, and differences from normal physiological patterns. For example, the resting heartbeat frequency of a normal adult is usually between 60-100 times / minute. The model will analyze whether the heartbeat frequency in the to-be-processed living body monitoring information is within this range and whether its fluctuation pattern conforms to the normal physiological rhythm. If the heartbeat frequency is consistently higher than 100 times / minute or lower than 60 times / minute, and this situation does not conform to normal physiological fluctuations (such as fluctuations caused by exercise, emotional excitement, etc.), the model will consider these data features as the basis for constructing the second abnormal data distribution information probability density.

[0108] Assume the heart rate data over a period of time (e.g., 24 hours) is [70, 72, 80, 90, 105, 110, 100, 95, 90, 85, 80, 75, 70, 65, 60, 55, 50, 55, 60, 65, 70, 75, 80, 85] (unit: beats / minute). The model can identify that the heart rate in the range of 105-110 beats / minute is likely abnormal. Based on the overall distribution of heart rate, the degree of deviation from the normal range, and similar patterns in historical data, the model can construct a probability density function for the abnormal data distribution of heart rate, for example, using a Gaussian Mixture Model (GMM) to describe. Let where x represents the value of heart rate, k is the number of Gaussian distributions (assume k = 2), is the weight of the i-th Gaussian distribution, and are the mean and variance of the i-th Gaussian distribution, respectively. For this example, one Gaussian distribution can describe the normal heart rate range ( ), and the other Gaussian distribution describes the abnormal heart rate range ( ), with weights indicating that the probability of normal heart rate is higher, but there is also a certain possibility of abnormal heart rate.

[0109] For blood pressure data, assume that systolic and diastolic blood pressures are analyzed separately. The normal systolic blood pressure range is assumed to be 90-139 mmHg, and the diastolic blood pressure range is 60-89 mmHg. If the blood pressure data in the live monitoring information to be processed is [(120, 80), (130, 85), (140, 90), (150, 95), (145, 92), (135, 88), (130, 85), (125, 82), (120, 80)] (unit: mmHg). The model can identify that the systolic blood pressure in the range of 140-150 mmHg is likely abnormal. Similarly, a probability density function for the abnormal data distribution of blood pressure is constructed using a similar method, such as where y represents the blood pressure value (here it can be systolic or diastolic blood pressure), m is the number of Gaussian distributions, and are the weight, mean, and variance of the i-th Gaussian distribution, respectively.

[0110] For ECG signals, due to its complexity, the model can extract features of the ECG signal, such as the width of QRS complex, the offset of ST segment, the morphology of T wave, etc. For example, the normal QRS complex width is between 0.06-0.10 seconds. If the QRS complex width in the ECG signal to be processed is 0.12 seconds, the model will take this feature into account in constructing the probability density of the second abnormal data distribution information. Through the comprehensive analysis of these ECG signal features, a probability density function of the abnormal data distribution of the ECG signal is constructed , and its construction method can also be based on Gaussian mixture model or other suitable probability model.

[0111] Through the analysis of heartbeat frequency, blood pressure and ECG signal data, the computer system constructs the probability density of the second abnormal data distribution information of the living body monitoring information based on the convergent abnormal data recognition model, which comprehensively describes the distribution possibility of abnormal data under different data features.

[0112] In step S3022, after obtaining the probability density of the second abnormal data distribution information, the computer system needs to perform step-by-step scanning. Taking the above cardiovascular system monitoring as an example, for the probability density of the second abnormal data distribution information of the heartbeat frequency , it is assumed that the scanning is performed with a step size of 5 times / minute. If , the value of is calculated when x=50 times / minute, and the value of is calculated when x=55 times / minute, and so on. For example, scanning from 40 times / minute to 120 times / minute, the probability density value corresponding to each step size is calculated.

[0113] For the probability density of the second abnormal data distribution information of the blood pressure , it is assumed that the scanning is performed with a step size of 5 mmHg. If , for the systolic pressure, scanning is performed from 80 mmHg to 160 mmHg, and the probability density value corresponding to each step size is calculated; for the diastolic pressure, scanning is performed from 50 mmHg to 100 mmHg, and the probability density value corresponding to each step size is calculated.

[0114] For the probability density of the second abnormal data distribution information of the ECG signal , due to the complexity of the ECG signal features, the definition of the step size may be adjusted according to the specific features. For example, for the QRS complex width, scanning is performed with a step size of 0.01 seconds, from 0.05 seconds to 0.15 seconds, and the probability density value corresponding to each step size is calculated; for the ST segment offset, scanning is performed with a step size of 0.1 mV, and the probability density value corresponding to each step size is calculated within a certain voltage range.

[0115] In the stepwise scanning process, the computer system determines the regional peak response intensity of each second scanning region. Taking the heart rate as an example, assume that 40-60 beats / minute is divided into the first region, 61-80 beats / minute is divided into the second region, 81-100 beats / minute is divided into the third region, and 101-120 beats / minute is divided into the fourth region.

[0116] In the first region, the computer system finds the maximum value of the probability density in the scanning process , and the intensity corresponding to this maximum value is the regional peak response intensity of the first region. For example, in this region, when x = 50 beats / minute, P_{hf}(50) reaches the maximum value .

[0117] For blood pressure data, assume that for systolic blood pressure, 80-90 mmHg is divided into the first region, 91-110 mmHg is divided into the second region, 111-130 mmHg is divided into the third region, and 131-160 mmHg is divided into the fourth region. The maximum value of the probability density in each region is found to determine the regional peak response intensity of the region. For example, in the second region, when y = 100 mmHg, P_{bp}(100) reaches the maximum value, which is the regional peak response intensity of the region.

[0118] For an electrocardiogram signal, taking the QRS complex width as an example, 0.05-0.07 seconds is divided into the first region, 0.071-0.09 seconds is divided into the second region, 0.091-0.11 seconds is divided into the third region, 0.111-0.13 seconds is divided into the fourth region, and 0.131-0.15 seconds is divided into the fifth region. The maximum value of the probability density in each region is found to determine the regional peak response intensity of the region.

[0119] In step S3023, the computer system compares the regional peak response intensity of the second scanning region with the second set threshold value. Assume that in the cardiovascular system monitoring, the regional peak response intensity of the fourth region (101-120 beats / minute) of the heart rate , and the second set threshold value is .

[0120] If > , then the computer system determines the second distribution information corresponding to the fourth region peak response intensity as the marking result of the abnormal data distribution position in the to-be-processed living body monitoring information. For example, if If the corresponding heartbeat frequency value is 110 beats per minute, then the heartbeat frequency related information (such as the relationship with the previous and subsequent heartbeat frequency, the time of occurrence, etc.) at 110 beats per minute is determined as the marking result, which means that there may be an abnormal data distribution near this heartbeat frequency.

[0121] For blood pressure data, if the regional peak response intensity of a certain region (such as the fourth region 131-160 mmHg of systolic pressure) is greater than the corresponding second set threshold, then the distribution information (such as blood pressure value, time of occurrence, etc.) corresponding to the regional peak response intensity is determined as the marking result of the abnormal data distribution position in the to-be-processed living body monitoring information.

[0122] For ECG signals, the regional peak response intensity of each region is also compared with the corresponding second set threshold. For example, for the regional peak response intensity of the third region (0.091-0.11 seconds) of QRS complex width If , then the QRS complex width information (such as the specific width value, the relationship with other ECG signal features, etc.) corresponding to the regional peak response intensity is determined as the marking result, indicating that there may be an abnormal data distribution within this QRS complex width range.

[0123] In this way, in steps S3021-S3023, the computer system performs reasoning, scanning and comparison on the to-be-processed living body monitoring information based on the converged abnormal data identification model, and finally determines the marking result of the abnormal data distribution position in the to-be-processed living body monitoring information, providing an important basis for further medical diagnosis and decision-making.

[0124] As another embodiment of step S302, step S302, based on the converged abnormal data identification model, performs reasoning on the to-be-processed living body monitoring information to obtain the marking result of the abnormal data distribution position in the to-be-processed living body monitoring information, which can include: Step S302A: based on the converged abnormal data identification model, performing reasoning on the to-be-processed living body monitoring information to obtain the second abnormal data distribution information probability density of the to-be-processed living body monitoring information; Step S302B: obtaining the distribution information with the maximum abnormal data distribution information probability according to the second abnormal data distribution information probability density, and determining it as the marking result of the abnormal data distribution position in the to-be-processed living body monitoring information.

[0125] ​In the embodiments of the present application, another implementation of step S302 includes steps S302A-S302B, which are executed by the computer system and are aimed at processing the to-be-processed living body monitoring information based on the converged abnormal data identification model, so as to obtain the marking result of the abnormal data distribution position in the to-be-processed living body monitoring information.

[0126] In step S302A, the to-be-processed living body monitoring information is inferred based on the converged abnormal data identification model to obtain the second abnormal data distribution information probability density of the to-be-processed living body monitoring information. The converged abnormal data identification model is obtained through the previous calibration process (such as the calibration operation in step S500), and it has been optimized to accurately identify the abnormal data distribution in the living body monitoring information.

[0127] Taking liver function monitoring as an example, it is assumed that the to-be-processed living body monitoring information contains data of indicators such as alanine aminotransferase (ALT), aspartate aminotransferase (AST), and bilirubin in blood. After receiving these data, the converged abnormal data identification model will analyze them according to its internal algorithm and pre-learned knowledge.

[0128] For example, for the alanine aminotransferase (ALT) data, the normal range is assumed to be 0-40 U / L. If the ALT value in the to-be-processed living body monitoring information is [30, 35, 45, 50, 55, 60, 58, 52, 48, 42] (unit: U / L). The model will consider the degree of deviation of these values from the normal range and the trend of the values. For example, it gradually rises from 30-35 U / L to 60 U / L, which may imply an abnormal situation. The model may use a certain probability distribution model to describe the possibility of such abnormal data, assuming that a lognormal distribution is used to construct the probability density function , where x represents the value of ALT. According to the characteristics of the data, the parameters of the lognormal distribution are determined, such as the mean and the standard deviation . For example, through learning a large amount of normal and abnormal liver function data, the model determines that for the ALT data, = 35, = 10, so is constructed.

[0129] For the AST data, the normal range is assumed to be 0-40 U / L. The AST values in the processed living body monitoring information are assumed to be [32, 38, 42, 48, 50, 52, 55, 50, 45, 40] (unit: U / L). The model will also analyze the deviation from the normal range and the trend of change. Since AST and ALT have some correlation in liver function, the model can consider the relationship between the two. For example, if ALT increases while AST also increases, this synergistic change can increase the likelihood of abnormality. The model can construct a joint probability density function where x represents the value of ALT and y represents the value of AST. This joint probability density function can be based on a multivariate normal distribution or other distribution model suitable for describing the relationship between the two.

[0130] For the bilirubin data, the normal range is assumed to be 0-1.0 mg / dL, and the bilirubin values in the processed living body monitoring information are [0.8, 0.9, 1.1, 1.2, 1.3, 1.5, 1.4, 1.3, 1.2, 1.1] (unit: mg / dL). The model will consider the increase in bilirubin and its interaction with other liver indicators. For example, an increase in bilirubin can occur simultaneously with an increase in ALT and AST, which can imply a problem with the liver's bile excretion function. The model can construct a probability density function for bilirubin where z represents the value of bilirubin. It is assumed that a gamma distribution is used, and the parameters and are determined through analysis of the data, such as = 2, = 0.5.

[0131] Through analysis of data such as ALT, AST, and bilirubin, the computer system constructs a second abnormal data distribution information probability density for the processed living body monitoring information based on the convergent abnormal data identification model. This probability density comprehensively describes the distribution likelihood of abnormal data under different liver function indicators.

[0132] In step S302B, after obtaining the second abnormal data distribution information probability density, the computer system obtains the distribution information with the maximum abnormal data distribution information probability based on this probability density. Continuing with the above example of liver function monitoring, for the constructed probability density function of ALT , the computer system will find the value of x that maximizes in the entire possible range of ALT values.

[0133] Suppose that through computation, it is found that P_{ALT}(x) attains its maximum value when x = 55 U / L. This x = 55 U / L is the value of ALT data that has the highest probability of abnormal data distribution information.

[0134] For the joint probability density function of aspartate aminotransferase (AST) and alanine aminotransferase (ALT) , the computer system will find the (x, y) combination in two-dimensional space (x represents the value of ALT, and y represents the value of AST) that makes attains its maximum value. Suppose that through computation, it is found that attains its maximum value when x = 52 U / L (ALT value) and y = 50 U / L (AST value). This (x, y) combination is the distribution information with the highest probability of abnormal data distribution information when considering the relationship between ALT and AST.

[0135] For the probability density function of bilirubin , suppose that through computation, it is found that attains its maximum value when z = 1.3 mg / dL. This z = 1.3 mg / dL is the value of bilirubin data that has the highest probability of abnormal data distribution information.

[0136] The computer system determines the distribution information found above with the highest probability of abnormal data distribution information as the marking result of the abnormal data distribution position in the to-be-processed living body monitoring information. For liver function monitoring, this marking result contains the values of each liver function indicator (ALT, AST, bilirubin, etc.) at the time when abnormalities are most likely to occur.

[0137] For example, the marking result can be expressed as: ALT = 55 U / L, AST = 50 U / L, bilirubin = 1.3 mg / dL. This marking result provides medical personnel with key information about the abnormal data distribution position in the to-be-processed living body monitoring information, which helps them further analyze possible problems with liver function, such as whether there is liver cell damage, bile excretion disorder, etc.

[0138] In this way, in steps S302A-S302B, the computer system infers the to-be-processed living body monitoring information based on the converged abnormal data recognition model and obtains the distribution information with the highest probability of abnormal data distribution information, and finally determines the marking result of the abnormal data distribution position in the to-be-processed living body monitoring information, providing an important basis for the diagnosis and subsequent treatment of liver diseases. This implementation can effectively utilize the capabilities of the converged abnormal data recognition model when processing living body monitoring information, accurately locate the distribution position of abnormal data, and improve the accuracy and reliability of the entire medical monitoring system.

[0139] As an implementation, in step S400, acquiring the first prior distribution heat map of the abnormal data distribution position in the to-be-processed living body monitoring information according to the distribution position inference label can include: Step S410: performing binary coding on the to-be-processed living body monitoring information according to the distribution position inference label to obtain coded monitoring information of the to-be-processed living body monitoring information. Step S420: performing smoothing filtering processing on the coded monitoring information to obtain the first prior distribution heat map of the abnormal data distribution position in the to-be-processed living body monitoring information.

[0140] In the embodiments of the present application, the specific implementation of step S400 includes steps S410-S420, which are executed by a computer system and are aimed at acquiring the first prior distribution heat map of the abnormal data distribution position in the to-be-processed living body monitoring information according to the distribution position inference label.

[0141] In step S410, the to-be-processed living body monitoring information is binary coded according to the distribution position inference label obtained in step S300. The distribution position inference label is an inference result of the possible abnormal data distribution position, which provides a basis for binary coding.

[0142] Taking kidney function monitoring as an example, it is assumed that the to-be-processed living body monitoring information includes data such as creatinine concentration, urea nitrogen concentration, and glomerular filtration rate in blood, which are collected at certain time intervals (such as every hour) within a period of time (such as 24 hours).

[0143] It is assumed that the distribution position inference label indicates that there may be abnormal data distribution of creatinine concentration in the time period from 9 am to 11 am. The computer system will binary code the creatinine concentration data in the entire 24 hours. For the creatinine concentration data corresponding to each time point in the time period from 9 am to 11 am, it is coded as 1, indicating that there may be abnormalities; and for the creatinine concentration data in other time periods (such as 0-8 am, 12-24 am), it is coded as 0, indicating that there is no abnormality according to the inference label in these time periods.

[0144] For example, the creatinine concentration data sequence is [C1, C2, C3, …, C9, C10, C11, …, C24] (wherein Ci represents the creatinine concentration value corresponding to each hour), if 9 am corresponds to C9, 10 am corresponds to C10, and 11 am corresponds to C11, then the coded sequence can be [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0].

[0145] Through this binary coding manner, the computer system converts the complex to-be-processed living body monitoring information into a simple binary state representation according to the distribution position inference label, highlights the area where the abnormality may exist, and lays a foundation for subsequent generation of a prior distribution heat map.

[0146] In step S420, after the binary coding is completed, the computer system performs a smoothing filtering process on the obtained coded monitoring information to obtain a first prior distribution heat map of the abnormal data distribution position in the to-be-processed living body monitoring information. The purpose of the smoothing filtering process is to reduce the discreteness caused by the binary coding, so that the obtained heat map is more in line with the actual abnormal data distribution rule.

[0147] Taking kidney function monitoring as an example, for the coded monitoring information [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] of creatinine concentration, the computer system adopts a weighted average smoothing filtering method. Assuming that a simple 3-point weighted average filter is adopted, for each point in the coded sequence, its value will be updated to the weighted average value of itself and the adjacent points before and after it. For example, for the 9th point (coded as 1), its new value will be calculated as (here, it is assumed that the weights of the adjacent points before and after it are 0.2, and the weight of itself is 0.6), and 0.8 is obtained. For the 10th point (coded as 1), the new value is calculated as , and for the 11th point (coded as 1), the new value is .

[0148] After such a smoothing filtering process, these updated coded values constitute the first prior distribution heat map. In this heat map, the higher the value in a region, the greater the possibility of abnormal data appearing in this position. For example, for creatinine concentration, the heat values corresponding to the time period from 9 to 11 o'clock in the morning are relatively high, indicating that the prior probability of abnormal creatinine concentration data appearing in this time period is relatively large; for urea nitrogen concentration, the heat values corresponding to the time period from 3 to 5 o'clock in the afternoon are relatively high; and for glomerular filtration rate, the heat values corresponding to the time period from 7 to 9 o'clock in the evening are relatively high.

[0149] This first prior distribution heat map provides an important input for the subsequent steps (such as the adjustment of the preset abnormal data recognition model in step S500), which intuitively represents the prior probability distribution of the abnormal data distribution position, and helps to improve the accuracy and effectiveness of the entire living body monitoring information processing method.

[0150] As an implementation manner, in step S500, the preset abnormal data recognition model is adjusted according to the first prior distribution heat map and the to-be-processed living body monitoring information to obtain a converged abnormal data recognition model, which can include: Step S510: based on the preset abnormal data recognition model, the inference of the to-be-processed living body monitoring information is performed to obtain a first inference distribution heat map of the abnormal data distribution position in the to-be-processed living body monitoring information. Step S520: according to the first inference distribution heat map and the first prior distribution heat map, a first error result of the preset abnormal data recognition model is obtained. Step S530: according to the first error result, the preset abnormal data recognition model is adjusted to obtain a converged abnormal data recognition model.

[0151] In the embodiments of the present application, the specific implementation of step S500 includes steps S510-S530, which are executed by a computer system, and the purpose is to adjust the preset abnormal data recognition model according to the first prior distribution heat map and the to-be-processed living body monitoring information to obtain a converged abnormal data recognition model.

[0152] In step S510, the first inference distribution heat map is obtained by inferring the to-be-processed living body monitoring information based on the preset abnormal data recognition model. The preset abnormal data recognition model is a pre-constructed model designed to identify the abnormal data distribution position in the living body monitoring information. This model can be based on various machine learning algorithms, such as neural networks.

[0153] Taking cardiovascular system monitoring as an example, suppose the to-be-processed living body monitoring information includes heartbeat frequency, blood pressure, and electrocardiogram signals, etc. After receiving these data, the preset abnormal data recognition model first preprocesses the data. For heartbeat frequency data, normalization processing may be performed to map the heartbeat frequency value to a specific interval, such as [0, 1]. Suppose the original heartbeat frequency data is [60, 65, 70, 80, 90, 100, 110, 120, 110, 100, 90, 80, 75, 70, 65] (unit: times / minute), and after normalization processing, it may become [0.2, 0.25, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.7, 0.6, 0.5, 0.4, 0.35, 0.3, 0.25].

[0154] For blood pressure data, let's assume that systolic and diastolic pressures are treated separately. The systolic pressure raw data is [100, 110, 120, 130, 140, 150, 160, 150, 140, 130, 120, 110, 105, 100, 95] (unit: mmHg), which, after similar normalization, becomes [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.15, 0.1, 0.05]. The diastolic pressure raw data is [60, 65, 70, 75, 80, 85, 90, 85, 80, 75, 70, 65, 62, 60, 58], which, after normalization, becomes [0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.35, 0.3, 0.25, 0.2, 0.15, 0.12, 0.1, 0.08]. For the ECG signal, due to its complexity, the model might extract some key features such as the width of the QRS complex, the offset of the ST segment, etc. Let's assume that the extracted QRS complex width feature values are [0.08, 0.09, 0.1, 0.11, 0.12, 0.11, 0.1, 0.09, 0.08, 0.09, 0.1, 0.11, 0.1, 0.09, 0.08] (unit: seconds), which, after normalization, might become [0.2, 0.3, 0.4, 0.5, 0.6, 0.5, 0.4, 0.3, 0.2, 0.3, 0.4, 0.5, 0.4, 0.3, 0.2].

[0155] Then, the model inputs these preprocessed data into its internal neural network structure. The input layer of the neural network receives these normalized heartbeat frequency, blood pressure, and ECG signal feature values. The hidden layers perform complex non-linear transformations on these input data. For example, a simple hidden layer might contain multiple neurons, each neuron outputs after a weighted sum of inputs and adding a bias term, followed by an activation function (such as ReLU function). Let's assume that the hidden layer has 5 neurons. For the input value of heartbeat frequency, the calculation through the first neuron might be where is the weight of the input data to the first neuron, is the normalized value of the heartbeat frequency, is the bias term.

[0156] After the processing of the hidden layer, the output layer outputs a result about the location of the abnormal data distribution. The result is presented in the form of a first inference distribution heat map. For example, in a heat map representing the likelihood of abnormal data distribution at different time points within 24 hours, if the number of neurons in the output layer corresponds to 24 time points, then each neuron outputs a value representing the probability of abnormal data at that time point. Assuming that the output first inference distribution heat map has the values [0.1, 0.12, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.35, 0.3, 0.25, 0.2, 0.15, 0.12, 0.1, 0.08, 0.05, 0.03, 0.02, 0.01, 0.01, 0.02, 0.03, 0.05], the time points with higher values represent a higher probability of abnormal data (such as abnormal heart rate, blood pressure, or ECG signal) at that time point.

[0157] In step S520, after obtaining the first inference distribution heat map and the first prior distribution heat map, the computer system needs to calculate the error between the two to obtain the first error result of the preset abnormal data recognition model. The first prior distribution heat map is obtained in step S400 based on the distribution location inference label, which reflects the prior probability distribution of the abnormal data distribution location inferred in the previous steps; while the first inference distribution heat map is the result obtained by the preset abnormal data recognition model based on its own algorithm to infer the data.

[0158] Continuing with the example of cardiovascular system monitoring, assume that the first prior distribution heat map has the values [0.15, 0.18, 0.2, 0.22, 0.25, 0.32, 0.38, 0.42, 0.38, 0.32, 0.25, 0.22, 0.18, 0.15, 0.12, 0.1, 0.08, 0.06, 0.04, 0.03, 0.02, 0.02, 0.03, 0.04].

[0159] The method of calculating the error can use mean squared error (MSE). The formula for calculating mean squared error is where n is the number of elements in the heat map (24 in this example, representing 24 time points), is the i-th element in the first prior distribution heat map, is the i-th element in the first inference distribution heat map.

[0160] For the first time point, ; for the second time point, and so on, the sum of the squares of the errors of all 24 time points is calculated, and then divided by 24 to obtain the mean squared error.

[0161] Besides mean squared error, other error metrics can also be used, such as mean absolute error (MAE), which is calculated as The computer system calculates these error metrics to obtain a first error result of the preset anomaly data recognition model, which reflects the difference between the current inference result of the model and the prior distribution heat map obtained based on the previous steps.

[0162] In step S530, after obtaining the first error result, the computer system adjusts the preset anomaly data recognition model based on this result to obtain a converged anomaly data recognition model. The adjustment process aims to reduce the error of the model and make the output of the model closer to the true anomaly data distribution.

[0163] Suppose the preset anomaly data recognition model is a neural network model, the adjustment process mainly involves adjusting the weights and bias terms of the neural network. Taking a simple neural network structure as an example, suppose the input layer has 3 nodes (corresponding to the normalized data of heart rate, blood pressure, and ECG signal), the hidden layer has 5 neurons, and the output layer has 24 nodes (corresponding to the anomaly data distribution probability of 24 time points).

[0164] In the backpropagation algorithm, the gradient of each weight and bias term is calculated based on the first error result. For example, for the connection weight from the input layer to the hidden layer , its gradient is calculated as (if mean squared error is used as the first error result). This gradient represents the direction and size of the impact of a small change in the weight on the error.

[0165] Suppose during the calculation process, it is found that for the weight from the heart rate input to the first hidden layer neuron, its gradient is positive, which means increasing the value of will cause the error to increase. Therefore, during the adjustment process, the value of will be reduced by a certain learning rate (such as ), and the new value of is .

[0166] Similarly, for the weights within the hidden layer, the weights from the hidden layer to the output layer, and the bias terms of each neuron, they will be adjusted according to their corresponding gradients. Through multiple iterations of this adjustment process, the weights and bias terms of the model are constantly adjusted based on the new first error result until the error of the model reaches an acceptable range or no longer decreases significantly.

[0167] For example, after multiple iterations, the first error result (such as mean square error) of the model is reduced from the initial 0.05 to 0.01. At this time, the preset abnormal data identification model is adjusted and converged to obtain a converged abnormal data identification model. This converged model can more accurately identify the abnormal data distribution position in the to-be-processed living body monitoring information, thereby improving the accuracy and reliability of the entire living body monitoring information processing method. Steps S510-S530 gradually optimize the model through the reasoning, error calculation and adjustment process of the preset abnormal data identification model, and finally obtain the converged abnormal data identification model.

[0168] As an implementation form, step S510, based on the preset abnormal data identification model, reasoning the to-be-processed living body monitoring information to obtain a first reasoning distribution heat map of the abnormal data distribution position in the to-be-processed living body monitoring information, can include: Step S511: based on the preset abnormal data identification model, extracting a feature array of the to-be-processed living body monitoring information to obtain an initial array representation of the to-be-processed living body monitoring information; Step S512: based on the preset abnormal data identification model, performing convolution operation on the initial array representation of the to-be-processed living body monitoring information to obtain a convolution array representation of the to-be-processed living body monitoring information; Step S513: performing residual connection operation according to the initial array representation and the convolution array representation to obtain the first reasoning distribution heat map of the to-be-processed living body monitoring information.

[0169] In step S511, when the computer system performs inference on the to-be-processed living body monitoring information based on the preset abnormal data identification model, it first needs to perform feature array extraction. Feature array extraction is a feature extraction process. When processing living body monitoring information, the information is often complex and contains multiple features. The computer system needs to extract an array representation that can represent the essential features from the complex data. For example, assuming that the to-be-processed living body monitoring information is monitoring data about heart activity, the data may contain heart rate, electrocardiogram waveform, heart contraction and diastolic pressure, and the changes of multiple parameters over time. The computer system analyzes these parameters, selects the key features, and combines them into an initial array representation. If the heart rate, the peak and valley of the electrocardiogram waveform, and the values of the heart contraction and diastolic pressure at specific times are used as key features, these feature values will be organized into an initial array. This initial array can reflect the core features of the to-be-processed living body monitoring information to some extent and provide a basis for subsequent model inference operations. For the preset abnormal data identification model, this initial array representation is an important part of the model input. The model is based on learning a large amount of data to identify the position of abnormal data distribution, and this initial array representation is to present the to-be-processed living body monitoring information in a form suitable for the model to process. It is like a translation process that "translates" the original, complex living body monitoring information into an array form that the model can understand, so that the model can perform subsequent operations. If the model is a neural network model, each element of the initial array is equivalent to the input value of a neuron, and the neural network will process these input values according to its internal weights and connection relationships.

[0170] In step S512, after obtaining the initial array representation of the to-be-processed living body monitoring information, the computer system performs convolution operation. Convolution operation is a mathematical operation widely used in image processing, signal processing, and other fields, and has important significance in this living body monitoring information processing. Taking the previously mentioned heart activity monitoring data as an example, assuming that the elements in the initial array representation represent the values of different time points and different features to some extent. Convolution operation can be regarded as a process of local feature extraction of these data. For example, a specific convolution kernel (which can be a small array such as a 3x3 array, and the elements in it are pre-set weight values) is used to slide and calculate on the initial array. This convolution kernel is like a filter that can extract the feature combination of different local regions in the initial array. For example, the convolution kernel may emphasize the correlation of certain specific heart rate and electrocardiogram waveform combination features, and through sliding calculation on the initial array, these correlations are embodied in the form of new numerical values in the convolution array representation.

[0171] This convolution operation will transform the initial array representation into a convolution array representation. In this process, the features of the data are further refined and transformed. Some subtle, local features in the initial array representation can be amplified or suppressed after the convolution operation. For the heart activity monitoring data, some subtle changes in heart rate and ECG waveform that are not obvious and related to anomalies can not be easily identified in the initial array, but after the convolution operation, these features can become more obvious in the convolution array representation. This helps the model to better capture the key information in the data, because the convolution array representation after the convolution operation focuses more on the specific patterns and relationships in the data, which can be closely related to the distribution of abnormal data.

[0172] In step S513, after obtaining the initial array representation and the convolution array representation, the computer system will perform a residual connection operation on the two to obtain a first inference distribution heat map of the to-be-processed living body monitoring information. The basic principle of the residual connection operation is to add or combine the input information (here, the initial array representation) and the processed information (the convolution array representation) in a specific way. Taking the previous example, assuming that the initial array representation contains the basic features of the original heart activity monitoring data, and the convolution array representation contains the local features extracted by the convolution operation. The residual connection operation is like fusing these two different levels of feature information. For example, for a specific time point and feature value, the heart rate value in the initial array representation and the local feature value related to the heart rate value emphasized by the convolution operation in the convolution array representation are added or combined. The purpose of this operation is to enable the model to better retain the information in the original data, while also utilizing the processed feature information.

[0173] The first inference distribution heat map obtained by the residual connection operation is the final output of the entire step S510. This heat map reflects the preliminary inference result of the computer system on the distribution of abnormal data in the to-be-processed living body monitoring information based on the preset anomaly data recognition model. In the example of heart activity monitoring, this heat map may show higher heat values in some areas, indicating a higher possibility of abnormal data distribution in these areas (corresponding to specific heart rate ranges, ECG waveform intervals, or heart pressure intervals, etc.). This inference result is based on the comprehensive effect of the previous feature array extraction, convolution operation, and residual connection operation. Feature array extraction provides basic data features, convolution operation further extracts local features, and residual connection operation fuses the two, thereby obtaining this heat map that reflects the preliminary inference of the distribution of abnormal data, providing an important basis for subsequent steps such as calculating error results and adjusting the model.

[0174] As an implementation, step S530, calibrating the preset abnormal data identification model according to the first error result to obtain a converged abnormal data identification model, can include: Step S531: obtaining a second prior distribution heat map of abnormal data distribution positions in the reference living body monitoring information according to the abnormal data distribution result of the reference living body monitoring information; Step S532: performing inference on the reference living body monitoring information based on the preset abnormal data identification model to obtain a second inference distribution heat map of abnormal data distribution positions in the reference living body monitoring information; Step S533: obtaining a second error result of the preset abnormal data identification model according to the second inference distribution heat map and the second prior distribution heat map; Step S534: calibrating the preset abnormal data identification model according to the first error result and the second error result to obtain a converged abnormal data identification model.

[0175] The computer system performs step S531 to obtain a second prior distribution heat map of abnormal data distribution positions in the reference living body monitoring information according to the abnormal data distribution result of the reference living body monitoring information. This second prior distribution heat map is a probabilistic description of the abnormal data distribution in the reference living body monitoring information, which reflects the prior information about the positions where the abnormal data may appear in the reference data. This prior information is crucial for calibrating the preset abnormal data identification model, because it provides the model with the expectation of abnormal data distribution under normal circumstances (based on the reference data), which can help the model adjust its parameters to better adapt to new data (the living body monitoring information to be processed). Assuming that the reference living body monitoring information is monitoring data about human blood glucose levels, which are collected by an implantable medical control device over a period of time. The computer system first obtains the abnormal data distribution result in the reference living body monitoring information, for example, the data distribution of abnormal increase or decrease in blood glucose level under certain specific time periods (such as within two hours after a meal, in the early morning, etc.) or under certain body states (such as after exercise, during emotional fluctuations, etc.). Then, the computer system constructs a second prior distribution heat map according to these abnormal data distribution results. For example, if the frequency of abnormal increase in blood glucose within two hours after a meal is relatively high, then the region corresponding to this time period in the heat map will show a relatively high heat value (indicating a relatively high probability); and if the abnormality of blood glucose during the sleep process at night is less, then the heat value of the corresponding region will be lower. This heat map can be in the form of a two-dimensional array, where one dimension represents time and the other dimension represents body state and other related factors, and each element in the array represents the probability value of abnormal data distribution under the corresponding conditions.

[0176] In step S532, the computer system aims to obtain a second inference distribution heat map of the abnormal data distribution position in the reference living body monitoring information when inferring the reference living body monitoring information based on the preset abnormal data identification model. The preset abnormal data identification model is a pre-constructed model that contains certain algorithm and parameter structure, which is used to analyze the input living body monitoring information and infer the distribution position of abnormal data. When inferring the reference living body monitoring information, the model will process the data according to its internal operation logic.

[0177] Continuing with the blood glucose monitoring data as an example, assume that the preset abnormal data identification model is a neural network-based model. The computer system inputs the reference living body monitoring information (such as the sequence of blood glucose values changing over time and related body state indicators, etc.) into the model. The neurons in the model will process these input data layer by layer according to the pre-learned weights and activation functions. For example, the first layer of neurons may perform preliminary feature extraction on the input blood glucose values and body state indicators, such as identifying the trend of blood glucose value changes, the preliminary association between body state and blood glucose fluctuations, etc. As the data propagates in the neural network, subsequent neurons will further analyze the complex relationships between these features, and finally obtain an inference result about the abnormal data distribution position in the reference living body monitoring information, which is presented in the form of a second inference distribution heat map. Similar to the second prior distribution heat map in step S531, this heat map is also a two-dimensional array, and the heat values in it represent the probabilities of abnormal data distribution under different conditions inferred by the model.

[0178] The computer system performs step S533 to obtain a second error result of the preset abnormal data identification model based on the second inference distribution heat map and the second prior distribution heat map. This error result reflects the difference between the model's inference result (second inference distribution heat map) and the prior expectation based on the reference living body monitoring information (second prior distribution heat map). By calculating this error, the computer system can evaluate the performance of the model on the reference living body monitoring information, and then determine the direction and magnitude of the model's adjustment.

[0179] For calculating the error, the computer system can use various methods, such as the Mean Squared Error (MSE) method. Assume that an element in the second prior distribution heat map (corresponding to the probability of abnormal data distribution under a certain time and body state) is and the corresponding element in the second inference distribution heat map is . Then the mean squared error is , where n and m are the number of elements in the heat map in two dimensions. Taking blood glucose monitoring data as an example, if in a certain time period (such as 10-11 am), the probability of blood glucose abnormal rise in the second prior distribution heat map is and the corresponding probability in the second inference distribution heat map inferred by the model is These differences will be reflected in the calculation of the entire mean square error. This error result reflects the deviation of the model's inference of the abnormal data distribution location on the reference vital monitoring information from the actual prior situation.

[0180] In step S534, the computer system adjusts the preset abnormal data identification model based on the first error result and the second error result to obtain a converged abnormal data identification model. The first error result is calculated based on the to-be-processed vital monitoring information, and the second error result is calculated based on the reference vital monitoring information. These two error results reflect the performance of the model from different aspects. The purpose of adjusting the model is to make the model better adapt to the data and reduce the error in identifying the abnormal data distribution location. Assuming that the preset abnormal data identification model is a model based on a gradient descent algorithm. The computer system adjusts the parameters of the model according to the first error result and the second error result. For example, for a certain weight parameter w in the model, if the first error result indicates that the model has a large error in identifying the abnormal data distribution location of a certain feature (such as a certain electrocardiogram feature in heart monitoring) on the to-be-processed vital monitoring information, and the second error result also shows that there is a deviation in processing a similar feature in the reference vital monitoring information, then the computer system adjusts the weight w according to the direction (is the predicted probability too high or too low) and size of the error. If the error indicates that the predicted probability is too high, the computer system will decrease the value of w according to a certain learning rate a (for example where E is the error function). Through such adjustment of multiple parameters in the model, the model gradually optimizes in the direction of reducing the error, and finally obtains a converged abnormal data identification model. This converged model can be more accurate in identifying the distribution location of abnormal data in the to-be-processed vital monitoring information, because it has considered the error information in the to-be-processed data and the reference data and made corresponding adjustments.

[0181] It should be noted that the vital monitoring information processing method based on the implanted medical control instrument provided in the embodiments of the present application only performs data labeling to serve as training data for the subsequent converged abnormal data identification model, and does not involve specific diagnosis of diseases and generation of treatment suggestions.

[0182] The embodiments of the present application provide a computer system, as shown in Figure 2 The computer system 100 includes a processor 101 and a memory 103. The processor 101 and the memory 103 are connected, such as through a bus 102. Optionally, the computer system 100 can also include a transceiver 104. It should be noted that in actual applications, the transceiver 104 is not limited to one, and the structure of the computer system 100 does not constitute a limitation on the embodiments of the present application.

[0183] The computer system in the embodiments of the present application comprises one or more processors, a memory, and one or more computer programs, wherein the one or more computer programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs are executed by the processors to implement the method provided in the embodiments of the present application.

[0184] The above only describes some embodiments of the present application. It should be noted that, for those skilled in the art, without departing from the principles of the present application, some improvements and refinements can be made, and these improvements and refinements should also be considered as the protection scope of the present application.

Claims

1. A living body monitoring information processing method based on an implanted medical control device, characterized by, The method comprises: obtaining a reference living body monitoring information library and a to-be-processed living body monitoring information library, wherein the reference living body monitoring information library comprises one or more reference living body monitoring information and abnormal data distribution results of the reference living body monitoring information, and the to-be-processed living body monitoring information library comprises a plurality of to-be-processed living body monitoring information; the living body monitoring information in the reference living body monitoring information library and the to-be-processed living body monitoring information library is collected based on an implantable medical regulation and control device; performing distance calculation on abnormal data array representation of the reference living body monitoring information and monitoring information array representation of the to-be-processed living body monitoring information according to the abnormal data distribution results, to obtain a similarity score of the to-be-processed living body monitoring information; performing reasoning according to the similarity score to obtain a distribution position reasoning mark of abnormal data in the to-be-processed living body monitoring information; obtaining a first prior distribution heat map of the abnormal data distribution position in the to-be-processed living body monitoring information according to the distribution position reasoning mark; performing adjustment on a preset abnormal data recognition model according to the first prior distribution heat map and the to-be-processed living body monitoring information to obtain a converged abnormal data recognition model, wherein the converged abnormal data recognition model is used for performing distribution position recognition marking of abnormal data on to-be-processed living body monitoring information.

2. The living body monitoring information processing method based on the implantable medical regulation apparatus according to claim 1, wherein The method further comprises: if the region peak response intensity of each first scanning region of the first abnormal data distribution information probability density is not greater than the first set threshold, the to-be-processed living body monitoring information is taken as to-be-processed living body monitoring information; performing reasoning on the to-be-processed living body monitoring information based on the converged abnormal data recognition model to obtain a marking result of the abnormal data distribution position in the to-be-processed living body monitoring information. The method further comprises:

3. The living body monitoring information processing method based on the implantable medical regulation apparatus according to claim 2, characterized by, if the region peak response intensity of each first scanning region of the first abnormal data distribution information probability density is not greater than the first set threshold, the to-be-processed living body monitoring information is taken as to-be-processed living body monitoring information; performing reasoning on the to-be-processed living body monitoring information based on the converged abnormal data recognition model to obtain a marking result of the abnormal data distribution position in the to-be-processed living body monitoring information. The method further comprises:

4. The living body monitoring information processing method based on the implantable medical regulation apparatus according to claim 3, wherein if the region peak response intensity of each first scanning region of the first abnormal data distribution information probability density is not greater than the first set threshold, the to-be-processed living body monitoring information is taken as to-be-processed living body monitoring information; performing reasoning on the to-be-processed living body monitoring information based on the converged abnormal data recognition model to obtain a marking result of the abnormal data distribution position in the to-be-processed living body monitoring information. The method further comprises: if the region peak response intensity of each first scanning region of the first abnormal data distribution information probability density is not greater than the first set threshold, the to-be-processed living body monitoring information is taken as to-be-processed living body monitoring information; performing reasoning on the to-be-processed living body monitoring information based on the converged abnormal data recognition model to obtain a marking result of the abnormal data distribution position in the to-be-processed living body monitoring information. If the area peak response intensity of the second scanning area is greater than a second set threshold, the second distribution information corresponding to the area peak response intensity of the second scanning area is determined as the marking result of the abnormal data distribution position in the to-be-processed living body monitoring information.

5. The living body monitoring information processing method based on the implantable medical regulation apparatus according to claim 3, wherein The inference of the to-be-processed living body monitoring information based on the converged abnormal data identification model includes: Inference of the to-be-processed living body monitoring information based on the converged abnormal data identification model to obtain the second abnormal data distribution information probability density of the to-be-processed living body monitoring information; According to the second abnormal data distribution information probability density, the distribution information with the maximum abnormal data distribution information probability is obtained as the marking result of the abnormal data distribution position in the to-be-processed living body monitoring information.

6. The living body monitoring information processing method based on an implantable medical regulation apparatus according to Claim 1, wherein The reference living body monitoring information library includes first reference living body monitoring information and second reference living body monitoring information. The distance calculation of the abnormal data array representation of the reference living body monitoring information and the monitoring information array representation of the to-be-processed living body monitoring information based on the abnormal data distribution result of the to-be-processed living body monitoring information obtains the similarity score of the to-be-processed living body monitoring information, including: According to the abnormal data distribution result of the first reference living body monitoring information, the abnormal data array representation of the first reference living body monitoring information is obtained. According to the abnormal data distribution result of the second reference living body monitoring information, the abnormal data array representation of the second reference living body monitoring information is obtained. According to the to-be-processed living body monitoring information, the monitoring information array representation of the to-be-processed living body monitoring information is obtained. The distance calculation of the abnormal data array representation of the first reference living body monitoring information and the monitoring information array representation obtains the first similarity measurement value between the first reference living body monitoring information and the to-be-processed living body monitoring information. The distance calculation of the abnormal data array representation of the second reference living body monitoring information and the monitoring information array representation obtains the second similarity measurement value between the second reference living body monitoring information and the to-be-processed living body monitoring information. The first similarity measurement value and the second similarity measurement value are integrated to obtain the similarity score.

7. The living body monitoring information processing method based on an implantable medical regulation apparatus according to Claim 6, wherein The calibration of the preset abnormal data identification model to obtain the converged abnormal data identification model based on the first prior distribution heat map and the to-be-processed living body monitoring information includes: Inference of the to-be-processed living body monitoring information based on the preset abnormal data identification model to obtain the first inference distribution heat map of the abnormal data distribution position in the to-be-processed living body monitoring information. According to the first inference distribution heat map and the first prior distribution heat map, the first error result of the preset abnormal data identification model is obtained. According to the first error result, the preset abnormal data identification model is calibrated to obtain the converged abnormal data identification model.

8. The living body monitoring information processing method based on the implantable medical regulation apparatus according to Claim 7, wherein The preset abnormal data identification model is used to infer the to-be-processed living body monitoring information, to obtain a first inference distribution heat map of an abnormal data distribution position in the to-be-processed living body monitoring information, including: Based on the preset abnormal data identification model, the to-be-processed living body monitoring information is characterized by array extraction, to obtain an initial array representation of the to-be-processed living body monitoring information; Based on the preset abnormal data identification model, the initial array representation of the to-be-processed living body monitoring information is subjected to convolution operation, to obtain a convolution array representation of the to-be-processed living body monitoring information; According to the initial array representation and the convolution array representation, a residual connection operation is performed, to obtain a first inference distribution heat map of the to-be-processed living body monitoring information.

9. The living body monitoring information processing method based on the implantable medical regulation apparatus according to Claim 7, wherein The preset abnormal data identification model is adjusted according to the first error result to obtain a converged abnormal data identification model, including: According to the abnormal data distribution result of the reference living body monitoring information, a second prior distribution heat map of an abnormal data distribution position in the reference living body monitoring information is obtained; Based on the preset abnormal data identification model, the reference living body monitoring information is inferred, to obtain a second inference distribution heat map of an abnormal data distribution position in the reference living body monitoring information; According to the second inference distribution heat map and the second prior distribution heat map, a second error result of the preset abnormal data identification model is obtained; The preset abnormal data identification model is adjusted according to the first error result and the second error result to obtain a converged abnormal data identification model.

10. A computer system, characterized by including: one or more processors; a memory; one or more computer programs; wherein the one or more computer programs are stored in the memory and configured to be executed by the one or more processors, and when the one or more computer programs are executed by the processor, the method of any one of claims 1-9 is implemented.

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