Traditional Chinese medicine chronic disease data anomaly detection method and system based on body area network

Abstract

The invention relates to the field of data processing, and provides a traditional Chinese medicine chronic disease data anomaly detection method and system based on a body area network, and the method comprises the steps: carrying out the data collection of a sensing node in the body area network, transmitting the collected data to a centralized node, carrying out the preprocessing of the collected data in the centralized node, obtaining first data, and carrying out the detection of the first data; and processing the first data by adopting a first matching algorithm, and identifying abnormal data. According to the method, abnormal identification can be carried out on the data collected by the sensing nodes in real time, meanwhile, timely processing is carried out, the real-time detection speed and accuracy of abnormal data are greatly improved, unnecessary data retransmission is avoided, the transmission bandwidth and computing resources are saved, and a stable and reliable transmission and communication basis is provided for chronic disease data.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and particularly to a method for detecting abnormal traditional Chinese medicine chronic disease data based on a body area network. Background Art

[0002] With the development of modern society and the improvement of people's health awareness, the management and monitoring of chronic diseases have become increasingly important. The application of body area network (BAN) technology provides a new solution for the management of traditional Chinese medicine chronic diseases. By arranging various sensors and devices in a local area of the human body, BAN can collect and transmit in real time a variety of health data, including physiological data such as heart rate, blood pressure, blood sugar, etc., and exercise data such as steps and calorie consumption, so as to realize continuous monitoring of patients' health data.

[0003] However, in the actual application of body area network, the data transmission process is often interfered by various factors, resulting in unstable signal strength. Such as signal interference caused by base stations and other electronic devices, congestion of wireless channels, and mutual interference between devices. The fluctuation of signal strength will not only affect the stability and reliability of data transmission, but also lead to data loss, delay or repetition, thus affecting the accuracy and timeliness of data in the monitoring process.

[0004] Therefore, considering the signal interference and strength problems in the data transmission process, how to improve the stability and reliability of data transmission in combination with abnormal detection is still a technical challenge that needs to be solved. Summary of the Invention

[0005] The purpose of the present invention is to propose a method and system for detecting abnormal traditional Chinese medicine chronic disease data based on a body area network, so as to solve one or more technical problems existing in the prior art, and at least provide a beneficial choice or create conditions.

[0006] The present invention provides a method and system for detecting abnormal traditional Chinese medicine chronic disease data based on a body area network, which collects data from sensor nodes in the body area network and sends the collected data to a central node. In the central node, the collected data is preprocessed to obtain first data, and a first matching algorithm is used to process the first data to identify abnormal data. The method can identify abnormalities in the data collected by sensor nodes in real time and process them in a timely manner, greatly improving the real-time detection speed and accuracy of abnormal data, avoiding unnecessary data retransmission, saving transmission bandwidth and computing resources, and providing a stable and reliable transmission and communication foundation for chronic disease data.

[0007] To achieve the above object, according to one aspect of the present invention, there is provided a method for detecting abnormal traditional Chinese medicine chronic disease data based on a body area network, the method comprising the following steps: S100, Collect data from the sensor nodes in the body area network and send the collected data to the central node; S200, In the central node, preprocess the collected data to obtain the first data; S300, Use the first matching algorithm to process the first data and identify abnormal data.

[0008] Furthermore, the body area network refers to a network composed of wearable or embeddable devices on the patient's body (these devices can collect and send the patient's health data). Each wearable or embeddable device serves as a sensor node in the body area network, so the body area network consists of multiple sensor nodes.

[0009] Furthermore, in step S100, the method of collecting data from the sensor nodes in the body area network and sending the collected data to the central node is specifically as follows: Denote Node(x) as any sensor node in the body area network, store the data collected by Node(x) within the time period T, the length of the time period T is N seconds. Let t(i) be the i-th second within the time period T, t(i) is a moment, and n(i) represents the data collected by Node(x) at the moment t(i). i is the serial number, and the value of i is i = 1, 2,..., N. Then the data collected by Node(x) within the time period T is expressed as n(1), n(2),..., n(N). Combine n(1), n(2),..., n(N) into an array n[], and send the array n[] to the central node.

[0010] Furthermore, the time period T is any time period during the patient's active period, and the length of the time period T is set to [1000, 1500] seconds, that is, N is an integer within the interval [1000, 1500].

[0011] Furthermore, the central node is responsible for receiving, processing, storing, and forwarding data from each sensor node in the body area network. The central node is built-in with a processor, a storage device, and a wireless communication module. All sensor nodes communicate with the central node wirelessly for data.

[0012] Furthermore, in step S200, in the central node, the method of preprocessing the collected data to obtain the first data is specifically as follows: Perform data cleaning on the array n[], and save the cleaned array n[] and denote it as the first data.

[0013] Furthermore, in step S300, the method of using the first matching algorithm to process the first data and identify abnormal data is specifically as follows: S301. Create empty arrays S1[] and S2[] respectively. Sort the array n[] in ascending order (i.e., rearrange all the data in the array n[] from smallest to largest), and denote the sorted array in ascending order as An[]. Then the length of An[] is N. Denote the j-th value in the array An[] as An(j). Set variables k1 and k2, with the initial value of k1 set to 1 and the initial value of k2 set to N. Starting from k1 = 1 and k2 = N, go to S302; S302. Add An(k1) to the array S1[], add An(k2) to the array S2[]. Denote the sum of the array S1[] as Sum1 (the sum of the array S1[], that is, the total sum of all the values in the array S1[]), and denote the sum of the array S2[] as Sum2. Go to S303; S303. If Sum2÷Sum1 > D1, then increment the variable k1 by 1, decrement the value of the variable k2 by 1, and go to S302; if Sum2÷Sum1 ≤ D2, then denote the (current) value of An(k1) as r1, denote the (current) value of An(k2) as r2, and go to S304; where D1 = O(R1)÷R1, R1 represents the mean value of the array n[] (the mean value of the array n[] is the average value of all the values in the array n[]), and O(R1) represents the total sum of all the values in the array n[] that are greater than R1; S304. Screen out abnormal data based on the values of r1 and r2.

[0014] The beneficial effects of this step are as follows: Since each micro-sensing device (sensing node) that makes up the body area network needs to continuously send or exchange various data within a working cycle, data delay, distortion, or even loss may occur. And it is difficult for these data anomalies to be discovered manually in real time. Therefore, a specific anomaly recognition mode needs to be set to quickly and accurately locate the abnormal data and immediately request retransmission. The method of this step matches the position where the abnormal data is located in the first data by setting the first matching algorithm. The principle is that since healthy data usually does not have large data fluctuations, by sorting the array n[] in ascending order and starting from both ends of the array, searching for the truncation position D1 along the middle of the array, D1 reflects the proportion of data with larger values in the array. When the proportion of larger values is relatively large, the truncation speed will be faster, that is, the truncation judgment formula Sum2÷Sum1≤D1 stops earlier, and the number of traversals is reduced. When the proportion of smaller values is relatively small, the truncation speed will be slower. When the distribution of healthy data is normal, that is, there are no large data fluctuations in the data n[], the abnormal data is empty, indicating that the healthy data in the current time period T is normal and there is no need to initiate retransmission. Through the anomaly matching implemented by this method, anomalies can be quickly located during the real-time transmission of data, significantly reducing the time cost and resource consumption for discovering data errors, and fully improving the stability of the body area network during monitoring. While ensuring the quality of transmitted data, it provides reliable data support for the design and management of chronic disease solutions.

[0015] Further, the method for screening out abnormal data based on the values of r1 and r2 is specifically as follows: S3041, form a set G = {r1, r2} with r1 and r2, set a variable p, and set the initial value of p to 1. Denote the p-th value corresponding to the variable p in the array n[] as n(p). Starting from p = 1, go to S3042; S3042, determine whether n(p) is equal to any number in the set G. If so, go to S3043; if not, go to S3044; S3043, if n(p) is equal to r1 in the set G, then delete r1 from the set G (that is, only the value r2 remains in the set G at this time). If n(p) is equal to r2 in the set G, then delete r2 from the set G; Record the value of (the current) p as b0, and set the value of p to N, then go to S3045; S3044, increase the value of the variable p by 1, then go to S3042; S3045, determine whether n(p) is equal to any number in the set G. If so, then record the value of (the current) p as b1, and at the same time go to S3047; if not, go to S3046; S3046, decrease the value of the variable p by 1, then go to S3045; S3047, mark n(b0) and n(b1) as abnormal data.

[0016] The beneficial effect of this step is that since there are often multiple duplicate data in the health data, after locating the abnormal data, duplicate values of these abnormal data will also appear in the array n[]. If any of these duplicate values are randomly selected, it will lead to inaccurate retransmitted data, that is, some abnormal data are not selected for retransmission. Therefore, r1 and r2 need to be selected from both ends of the array n[] to maximize the coverage range of the abnormal data and ensure that all possible abnormal data can be correctly screened out.

[0017] Further, the first matching algorithm is used to process the first data to identify the abnormal data. It also includes data retransmission according to the abnormal data. Specifically, record the time when the sensing node Node(x) collects the data n(b0) as T0 and the time when it collects the data n(b1) as T1. Send a retransmission instruction to the sensing node Node(x) through the central node. After Node(x) receives this instruction, it will send all the data collected from time T0 to time T1 to the central node.

[0018] The present invention also provides a traditional Chinese medicine chronic disease data abnormal detection system based on a body area network. The traditional Chinese medicine chronic disease data abnormal detection system based on a body area network includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps in a traditional Chinese medicine chronic disease data abnormal detection method based on a body area network. The traditional Chinese medicine chronic disease data abnormal detection system based on a body area network can run on computing devices such as a desktop computer, a laptop computer, a mobile phone, a hand-held phone, a tablet computer, a palm computer, and a cloud data center. The operable system may include, but is not limited to, a processor, a memory, and a server cluster. The processor executes the computer program and runs in the following system units: A data collection unit, used to collect data from the sensing nodes in the body area network and send the collected data to the central node; A data processing unit, used to preprocess the collected data in the central node to obtain the first data; A data matching unit, used to process the first data by using the first matching algorithm to identify the abnormal data.

[0019] The beneficial effect of the present invention is that the method can identify and process abnormal data in real time for the data collected by the sensing nodes, greatly improving the real-time detection speed and accuracy of abnormal data, avoiding unnecessary data retransmission, saving transmission bandwidth and computing resources, and providing a stable and reliable transmission and communication foundation for chronic disease data. Brief Description of the Drawings

[0020] By describing in detail the embodiments shown in the accompanying drawings, the above and other features of the present invention will become more apparent. The same reference numerals in the drawings of the present invention denote the same or similar elements. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings. In the drawings: Figure 1 Shown is a flowchart of a method for detecting abnormal traditional Chinese medicine chronic disease data based on a body area network; Figure 2 Shown is a system structure diagram of a system for detecting abnormal traditional Chinese medicine chronic disease data based on a body area network. Detailed implementation manners

[0021] The following will clearly and completely describe the concept, specific structure and technical effects of the present invention in combination with embodiments and drawings to fully understand the purpose, solution and effects of the present invention. It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments can be combined with each other.

[0022] In the description of the present invention, the meaning of several is one or more, the meaning of multiple is two or more, greater than, less than, exceeding, etc. are understood as not including the present number, above, below, within, etc. are understood as including the present number. If the first and second are described only for the purpose of distinguishing technical features, they cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features or implicitly indicating the sequence relationship of the indicated technical features.

[0023] As Figure 1 Shown is a flowchart of a method for detecting abnormal traditional Chinese medicine chronic disease data based on a body area network according to the present invention. The following will describe a method for detecting abnormal traditional Chinese medicine chronic disease data based on a body area network according to the embodiments of the present invention in combination with Figure 1 to elaborate.

[0024] S100, collect data from the sensor nodes in the body area network and send the collected data to the central node; S200, in the central node, preprocess the collected data to obtain first data; S300, process the first data using a first matching algorithm to identify abnormal data.

[0025] Furthermore, the body area network refers to a network composed of wearable or embeddable devices on the patient's body (these devices can collect the patient's health data and send it). Each wearable or embeddable device serves as a sensor node in the body area network, and the body area network is composed of multiple sensor nodes.

[0026] Further, in step S100, the method for collecting data from the sensor nodes in the body area network and sending the collected data to the central node is as follows: Denote Node(x) as any sensor node in the body area network. Store the data collected by Node(x) within the time period T, where the length of the time period T is N seconds. Let t(i) be the i-th second within the time period T, t(i) is a moment, and let n(i) represent the data collected by Node(x) at the moment t(i), where i is the serial number and the value range of i is i = 1, 2, …, N. Then the data collected by Node(x) within the time period T is represented as n(1), n(2), …, n(N). Form an array n[] with n(1), n(2), …, n(N). The length of the array n[] is N, and n(i) represents the i-th value within the array n[]. Send the array n[] to the central node.

[0027] Specifically, the length of the time period T is set to 1200.

[0028] Further, the central node is responsible for receiving, processing, storing, and forwarding the data from each sensor node in the body area network. The central node is built-in with a processor, a storage device, and a wireless communication module. All sensor nodes communicate with the central node wirelessly for data communication.

[0029] Further, in step S200, in the central node, the method for preprocessing the collected data to obtain the first data is as follows: Perform data cleaning on the array n[], and save the array n[] after the cleaning is completed and denote it as the first data.

[0030] Specifically, performing data cleaning on the array n[] includes removing data noise and handling missing values.

[0031] Further, in step S300, the method for processing the first data using the first matching algorithm to identify abnormal data is as follows: S301, respectively create blank arrays S1[] and S2[]. Sort the array n[] in ascending order (that is, rearrange all the data in the array n[] from small to large), and denote the sorted array in ascending order as An[]. The length of An[] is N. Let An(j) represent the j-th value within the array An[], where j is the serial number and j = 1, 2, …, N. Set variables k1 and k2, set the initial value of k1 to 1, and the initial value of k2 to N. Let An(k1) represent the k1-th value corresponding to the variable k1 in the array An[], and let An(k2) represent the k2-th value corresponding to the variable k2 in the array An[]; Starting from k1 = 1 and k2 = N, go to S302; S302, Add An(k1) to the array S1[], add An(k2) to the array S2[], denote the sum of the array S1[] as Sum1 (the sum of the array S1[], that is, the total sum of all values in the array S1[]), denote the sum of the array S2[] as Sum2, and go to S303; S303, If Sum2 ÷ Sum1 > D1, then increment the value of the variable k1 by 1, decrement the value of the variable k2 by 1, and go to S302; If Sum2 ÷ Sum1 ≤ D2, then denote the value of (the current) An(k1) as r1, denote the value of (the current) An(k2) as r2, and go to S304; where, D1 = O(R1) ÷ R1, R1 represents the mean value of the array n[] (the mean value of the array n[] is the average value of all values in the array n[]), and O(R1) represents the total sum of all values in the array n[] that are greater than R1; S304, Screen out abnormal data based on the values of r1 and r2.

[0032] Furthermore, the method for screening out abnormal data based on the values of r1 and r2 is specifically as follows: S3041, Combine r1 and r2 to form a set G = {r1, r2}, set a variable p, set the initial value of p to 1, denote the p-th value corresponding to the variable p in the array n[] as n(p), start from p = 1, and go to S3042; S3042, Determine whether n(p) is equal to any number in the set G. If so, go to S3043; If not, go to S3044; S3043, If n(p) is equal to r1 in the set G, then delete r1 from the set G (that is, at this time, only the value r2 remains in the set G). If n(p) is equal to r2 in the set G, then delete r2 from the set G; Denote the value of (the current) p as b0, and set the value of p to N, then go to S3045; S3044, Increment the value of the variable p by 1, and go to S3042; S3045, Determine whether n(p) is equal to any number in the set G. If so, then denote the value of (the current) p as b1, and at the same time go to S3047; If not, go to S3046; S3046, Decrement the value of the variable p by 1, and go to S3045; S3047, Mark n(b0) and n(b1) as abnormal data.

[0033] Specifically, the first matching algorithm is used to process the first data to identify abnormal data. It also includes data retransmission based on the abnormal data. Specifically: Denote the time when the sensing node Node(x) collects data n(b0) as T0 and the time when it collects data n(b1) as T1. A retransmission instruction is sent from the centralized node to the sensing node Node(x). After receiving this instruction, Node(x) sends all the data collected from time T0 to time T1 to the centralized node.

[0034] The described traditional Chinese medicine chronic disease data anomaly detection system based on a body area network includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps in the above-mentioned embodiment of the traditional Chinese medicine chronic disease data anomaly detection method based on a body area network. The traditional Chinese medicine chronic disease data anomaly detection system based on a body area network can run on computing devices such as desktop computers, laptop computers, mobile phones, handheld phones, tablet computers, palmtop computers, and cloud data centers. The operable system may include, but is not limited to, a processor, a memory, and a server cluster.

[0035] An embodiment of the present invention provides a traditional Chinese medicine chronic disease data anomaly detection system based on a body area network, as Figure 2 shown. The traditional Chinese medicine chronic disease data anomaly detection system in this embodiment includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps in the above-mentioned embodiment of the traditional Chinese medicine chronic disease data anomaly detection method. The processor executes the computer program and runs in the following system units: A data collection unit, configured to collect data from the sensing nodes in the body area network and send the collected data to the centralized node; A data processing unit, configured to preprocess the collected data in the centralized node to obtain first data; A data matching unit, configured to process the first data using the first matching algorithm to identify abnormal data.

[0036] The described traditional Chinese medicine chronic disease data anomaly detection system based on a body area network can run on computing devices such as desktop computers, laptop computers, palmtop computers, and cloud data centers. The described traditional Chinese medicine chronic disease data anomaly detection system based on a body area network includes, but is not limited to, a processor and a memory. Those skilled in the art can understand that the above examples are only examples of a traditional Chinese medicine chronic disease data anomaly detection method and system based on a body area network, and do not constitute a limitation on a traditional Chinese medicine chronic disease data anomaly detection method and system. It may include more or fewer components than the examples, or combine certain components, or different components. For example, the traditional Chinese medicine chronic disease data anomaly detection system based on a body area network may also include input / output devices, network access devices, a bus, etc.

[0037] The so-called processor may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete component gate circuits, or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The processor is the control center of the traditional Chinese medicine chronic disease data anomaly detection system based on a body area network, and connects all sub-regions of the entire traditional Chinese medicine chronic disease data anomaly detection system based on a body area network through various interfaces and lines.

[0038] The memory can be used to store the computer programs and / or modules. The processor realizes various functions of the traditional Chinese medicine chronic disease data anomaly detection method and system by running or executing the computer programs and / or modules stored in the memory, and by calling the data stored in the memory. The memory may mainly include a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created according to the use of the mobile phone (such as audio data, phone book, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage devices.

[0039] A method and system for detecting abnormal traditional Chinese medicine chronic disease data based on a body area network are provided. Data is collected from sensor nodes in the body area network and sent to a central node. In the central node, the collected data is preprocessed to obtain first data, and a first matching algorithm is used to process the first data to identify abnormal data. The method can identify abnormalities in the data collected by sensor nodes in real time and process them in a timely manner, greatly improving the real-time detection speed and accuracy of abnormal data, avoiding unnecessary data retransmission, saving transmission bandwidth and computing resources, and providing a stable and reliable transmission and communication foundation for chronic disease data. Although the description of the present invention has been quite detailed and several of the described embodiments have been described in particular, it is not intended to be limited to any of these details or embodiments or any particular embodiment, so as to effectively cover the intended scope of the present invention. In addition, the present invention has been described above with embodiments foreseeable by the inventors in order to provide a useful description, and non-substantive changes to the present invention that are not currently foreseeable may still represent equivalent changes to the present invention.

Claims

1. A method for detecting abnormal traditional Chinese medicine chronic disease data based on a body area network, characterized in that The method includes the following steps: S100, collect data from the sensor nodes in the body area network and send the collected data to the central node; S200, in the central node, preprocess the collected data to obtain the first data; S300, use the first matching algorithm to process the first data to identify abnormal data; Among them, in step S100, the method of collecting data from the sensor nodes in the body area network and sending the collected data to the central node is specifically as follows: Denote Node(x) as any sensor node in the body area network, store the data collected by Node(x) within the time period T, the length of the time period T is N seconds, use t(i) as the i-th second within the time period T, t(i) is the moment, use n(i) to represent the data collected by Node(x) at the moment t(i), i is the serial number, and the value range of i is i = 1, 2,..., N. Then the data collected by Node(x) within the time period T is expressed as n(1), n(2),..., n(N). Combine n(1), n(2),..., n(N) into an array n[], and send the array n[] to the central node.

2. The method for detecting abnormal traditional Chinese medicine chronic disease data based on a body area network according to claim 1, wherein The body area network refers to a network composed of wearable or embeddable devices on the patient's body. Each wearable or embeddable device serves as a sensor node in the body area network, and the body area network is composed of multiple sensor nodes.

3. A method for detecting abnormal traditional Chinese medicine chronic disease data based on a body area network according to claim 1, characterized in that, The central node is responsible for receiving, processing, storing, and forwarding data from each sensor node in the body area network. The central node is built-in with a processor, a storage device, and a wireless communication module. All sensor nodes communicate with the central node wirelessly for data.

4. A method for detecting abnormal traditional Chinese medicine chronic disease data based on a body area network according to claim 1, characterized in that In step S200, in the central node, the method of preprocessing the collected data to obtain the first data is specifically as follows: Perform data cleaning on the array n[], save the cleaned array n[] and denote it as the first data.

5. A method for detecting abnormal data of traditional Chinese medicine chronic diseases based on a body area network according to claim 1, characterized in that, In step S300, the method of using the first matching algorithm to process the first data to identify abnormal data is specifically as follows: S301, respectively create blank arrays S1[] and S2[], sort the array n[] in ascending order, and denote the sorted array as An[]. Then the length of An[] is N. Use An(j) to represent the j-th value in the array An[]. Set variables k1 and k2, set the initial value of k1 to 1, and the initial value of k2 to N; start from k1 = 1 and k2 = N, and go to S302; S302, add An(k1) to the array S1[], add An(k2) to the array S2[], denote the sum of the array S1[] as Sum1, denote the sum of the array S2[] as Sum2, and go to S303; S303, if Sum2÷Sum1 > D1, then increase the value of the variable k1 by 1, decrease the value of the variable k2 by 1, and go to S302; if Sum2÷Sum1 ≤ D2, then denote the value of An(k1) as r1, denote the value of An(k2) as r2, and go to S304; Among them, D1 = O(R1)÷R1, where R1 represents the mean value of the array n[], and O(R1) represents the sum of all values in the array n[] that are greater than R1; S304. Screen out abnormal data based on the values of r1 and r2.

6. The method for detecting abnormal traditional Chinese medicine chronic disease data based on a body area network according to claim 5, wherein The method for screening out abnormal data based on the values of r1 and r2 is specifically as follows: S3041. Combine r1 and r2 to form a set G = {r1, r2}, set a variable p, and set the initial value of p to 1. Denote the p-th value corresponding to the variable p in the array n[] as n(p). Starting from p = 1, go to S3042; S3042. Determine whether n(p) is equal to any number in the set G. If it is, go to S3043; if not, go to S3044; S3043. If n(p) is equal to r1 in the set G, delete r1 from the set G. If n(p) is equal to r2 in the set G, delete r2 from the set G; Record the value of p as b0, and set the value of p to N, then go to S3045; S3044. Increase the value of the variable p by 1, and go to S3042; S3045. Determine whether n(p) is equal to any number in the set G. If it is, record the value of p as b1, and at the same time go to S3047; if not, go to S3046; S3046. Decrease the value of the variable p by 1, and go to S3045; S3047. Mark n(b0) and n(b1) as abnormal data.

7. A method for detecting abnormal traditional Chinese medicine chronic disease data based on a body area network according to claim 6, characterized in that, Processing the first data using the first matching algorithm to identify abnormal data further includes data retransmission according to the abnormal data. Specifically: Denote the moment when the sensing node Node(x) collects the data n(b0) as T0, and the moment when it collects the data n(b1) as T1. Send a retransmission instruction to the sensing node Node(x) through the central node. After Node(x) receives this instruction, it sends all the data collected from the moment T0 to the moment T1 to the central node.

8. A traditional Chinese medicine chronic disease data anomaly detection system based on a body area network, characterized in that The described traditional Chinese medicine chronic disease data anomaly detection system based on a body area network includes: a processor, a memory, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps in any one of claims 1 - 7 of the described traditional Chinese medicine chronic disease data anomaly detection method based on a body area network. The described traditional Chinese medicine chronic disease data anomaly detection system runs on computing devices such as a desktop computer, a laptop computer, a palm computer, or a cloud data center.

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