Health data processing method for household health Internet of Things equipment

Abstract

The invention discloses a health data processing method for home health Internet of Things equipment, which comprises the following steps: receiving a data stream transmitted by the home health Internet of Things equipment, cleaning non-health related data, and retaining health related data which comprises a health equipment ID and data content; converting the health-related data into health data codes through a preset symptom conversion rule; encrypting the health data code by using an improved AES encryption method and then transmitting the health data code; according to the invention, the conversion from the smart home equipment data to the health data is realized, and the association between the collected equipment data and the health data is established. According to the invention, the operation time in the embedded system is greatly reduced, the overall efficiency of the encryption process is improved, the security of data transmission is enhanced, and the resource occupation of the embedded system is reduced.

Description

Technical Field

[0001] The present invention relates to the field of home health technology, and in particular to a health data processing method for a home health Internet of Things device. Background Art

[0002] With the continuous development of science and technology, smart home devices have gradually entered people's daily lives, such as smart air purifiers, smart bracelets and smart sleep pillows. These devices can not only improve the convenience of life, but also generate a large amount of data. If these data are deeply mined, cleaned and analyzed, they can be used to establish a mapping relationship between smart home device data and user health data, which is of great significance for early screening of diseases and analysis of human health status. However, existing technologies face the following major problems when processing these data:

[0003] 1. Smart home device data is huge, communication protocols vary greatly, and health data has high real-time requirements. If all device data is transmitted to the cloud platform for processing and analysis, it will occupy a large network bandwidth and have low transmission efficiency;

[0004] 2. The data generated by smart home devices cannot be directly interpreted as health data. Further processing is required between the two. That is, the device data collected by the device is processed according to certain rules into health data that can be understood and further identified. The existing solution relies on a large pre-established database to obtain health data by pre-setting the keyword mapping relationship between data and then obtaining health data through query. This method is inefficient on the one hand, and relies on the detail and accuracy of the database on the other hand, and is not suitable for the acquisition and processing of large-scale data;

[0005] 3. Home health data is highly private, and it is crucial to ensure privacy security during data transmission. The IoT device layer is usually equipped with resource-limited embedded systems, which have low processor frequency and limited memory capacity. How to ensure encryption efficiency in resource-limited embedded systems while ensuring data transmission security is an urgent problem to be solved. Summary of the invention

[0006] In order to solve the above technical problems, the present invention provides a health data processing method for a home health IoT device, and the specific steps include:

[0007] S1. Receive the data stream transmitted by the home health IoT device, clean non-health-related data, and retain health-related data, where the health-related data includes the health device ID and data content;

[0008] S2. Converting the health-related data into health data codes according to preset symptom conversion rules;

[0009] S3, encrypting the health data code using an improved AES encryption method and then transmitting it;

[0010] Step S2 includes:

[0011] S201, construct symptom conversion rules and store them as a symptom conversion database; the symptom conversion rules include: current device ID, data content determination condition, next device ID, duration threshold, repetition number flag, condition satisfaction flag, condition satisfaction number of times and condition satisfaction number threshold;

[0012] S202, querying whether there is a symptom conversion rule in the symptom conversion database according to the health device ID, and if so, executing the next step;

[0013] S203, extracting the symptom conversion rule corresponding to the health device ID, determining whether the data content meets the determination condition, and if so, recording the health-related data of the current health device, setting a condition satisfaction flag and incrementing the number of condition satisfaction times;

[0014] S204, continue to obtain the latest health-related data. If the current health device ID meets the next device ID in the symptom conversion rule, and the current health-related data acquisition time meets the duration threshold in the symptom conversion rule, then increase the number of repetitions of this step. When the number of repetitions is greater than the number of repetitions flag in the symptom conversion rule, record the health-related data of the current health device, set the condition satisfaction flag and increase the number of condition satisfaction times, otherwise repeat this step;

[0015] S205. Repeat the above step S204. When the number of times the condition is met is greater than the threshold number of times the condition is met in the symptom conversion rule, the current symptom conversion rule and the health-related data of each health device are taken as health data and encoded together with the symptom name to obtain the health data code.

[0016] Preferably, the symptom conversion rule also includes a data identifier; the data content determination condition includes a data content determination threshold and a Boolean determination condition.

[0017] Preferably, the method for determining whether the data content satisfies the determination condition includes:

[0018] The category of the data content is identified according to the data identifier. If it is continuous data, whether it meets the conditions is determined according to the data content determination threshold; if it is Boolean data, whether it meets the conditions is determined according to the Boolean determination condition.

[0019] Preferably, the step S2 further includes: temporarily storing the health-related data uploaded by the health devices acquired previously in a scan buffer, and clearing the scan buffer after the health data is encoded and reported.

[0020] Preferably, step S3 comprises:

[0021] S301, generating m chaotic S-boxes after logical mapping according to the initial key;

[0022] S302, respectively calculating the X-multiplication results of the m chaotic S-boxes and the column mixing matrix elements 02 and 03 to form 2m column mixing lookup tables;

[0023] S303, performing one round of initial change, m-1 rounds of cyclic encryption and one round of final encryption on the health data encoding to obtain encrypted information;

[0024] Among them, the chaotic S box corresponding to the current round is used when performing byte substitution in the initial change and cyclic encryption steps; the X-multiplication calculation operation in the column mixing step in the cyclic encryption step is replaced by a table lookup operation in the column mixing lookup table.

[0025] Preferably, the method for generating m chaotic S-boxes after performing logical mapping according to the initial key comprises:

[0026] Obtain the initial key and use the logical mapping for k iterations to obtain the encryption parameters;

[0027] Continue to iterate the encryption parameter using logical mapping, and quantize each iteration result into a binary sequence. After 16 iterations, convert the sequence into a hexadecimal value, and repeat the operation until a 16*16 chaotic S-box is formed; repeat this step to generate m chaotic S-boxes.

[0028] Preferably, the method of performing several iterations of the initial key using logical mapping includes: normalizing the initial key and retaining three decimal places as x; performing k rounds of chaotic mapping on x according to the following formula: k+1 =μx k (1-x k ), where μ is the chaos parameter.

[0029] Preferably, the method of quantizing each iteration result into a binary sequence includes: quantizing the iteration result into a binary sequence with 0.5 as a threshold.

[0030] Beneficial Effects

[0031] 1. The present invention realizes the conversion of smart home device data into health data on the edge side. Through preset symptom conversion rules and symptom conversion database, the collected device data is associated with the health data. Through the cleaning and conversion processing on the edge side, the amount of data that needs to be transmitted is reduced, the network bandwidth occupancy is greatly reduced, and the overall network load capacity and response speed are improved. It is suitable for application in home environments with limited resources.

[0032] 2. By optimizing the "column mixing" step in the AES algorithm, the matrix multiplication operation that originally consumed a lot of computing resources was changed to a pre-calculated and stored table lookup operation, which greatly reduced the computing time in the embedded system and improved the overall efficiency of the encryption process. Chaotic mapping technology was introduced. By generating multiple chaotic S-boxes and applying them to the byte substitution and key expansion steps of the AES algorithm, the randomness and complexity of the key were increased, the key space was expanded, and the correlation between keys in different rounds was reduced, thereby improving the anti-attack capability of the AES algorithm, enhancing the security of data transmission, and reducing the resource usage of the embedded system. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 A schematic flow chart of a method for processing health data for a home health IoT device provided in a preferred embodiment of the present invention;

[0034] Figure 2 A schematic flow chart of a method for converting the health-related data into health data codes provided in another preferred embodiment of the present invention;

[0035] Figure 3 The figure is a flow chart of an improved AES encryption method provided in a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0036] In order to make the purpose, technical solution and advantages of the present invention clearer, the present invention is further described below in conjunction with the accompanying drawings. In the description of the present invention, it should be understood that the terms "upper", "lower", "front", "back", "left", "right", "top", "bottom", "inside", "outside" and the like indicate directions or positional relationships based on the directions or positional relationships shown in the accompanying drawings, which are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention.

[0037] Example 1

[0038] like Figure 1 As shown, this embodiment provides a health data processing method for a home health IoT device, comprising the steps of:

[0039] S1. Receive the data stream transmitted by the home health IoT device, clean non-health-related data, and retain health-related data, wherein the health-related data includes the health device ID and data content. The cleaning of non-health-related data refers to eliminating non-health-related data according to the preset cleaning rule configuration information. Specifically, conventional data cleaning and filtering methods in the field of IoT can be used. In some preferred embodiments, a specific example of cleaning non-health-related data is given, which specifically includes:

[0040] S101, read the cleaning rule configuration information. The cleaning rule configuration information is pre-stored in a local server or cloud platform and is read by issuing instructions or remote embedding. The cleaning rule configuration information includes device information and corresponding cleaning methods. The device information includes device category, device ID, filter identifier, and data identifier. The cleaning methods include data filtering, data verification deduplication, and data aggregation.

[0041] S102, receiving the data stream transmitted by the home health IoT device and performing three-level data cleaning according to the configuration rules. Specifically, the device category is used to determine whether it belongs to a non-health-related device. If so, the data of the device is filtered. At this time, the remaining data may still be redundant, and then the data that needs to be checked and cleaned is temporarily stored in the buffer buf array according to the data type identification bit. For continuous data, two steps are used. The first step is the abnormal value check within the device, that is, the mean μ and standard deviation σ of the data in the array are first calculated, and the check threshold is set to (μ-3σ, μ+3σ). Eliminate the data outside the array (μ-3σ, μ+3σ). The second step is the abnormal value check between devices. According to the timestamp, the difference of the same type of data matching the timestamp is compared two by two. According to the set check threshold, the data pairs with a difference greater than the check threshold are eliminated, and then the retention ratio is calculated and compared, and the device data with a smaller retention ratio is eliminated. Finally, according to the cleaning rules, it is determined whether to continue to aggregate the data, and the aggregation method is determined to be mean, mode, and median aggregation, so as to further reduce the amount of data sent to the cloud and improve data transmission efficiency.

[0042] S103, uploading or forwarding the health-related data retained after cleaning to the processing module of the next step.

[0043] S2. Convert the health-related data into health data code according to the preset symptom conversion rules. Figure 2 As shown, this step specifically includes:

[0044] S201. Construct symptom conversion rules and store them as a symptom conversion database; the symptom conversion rules include: current device ID, data content judgment conditions, next step device ID, duration threshold, repetition number flag, condition satisfaction flag, condition satisfaction times and condition satisfaction times threshold.

[0045] It should be understood that the symptoms refer to health problems or abnormal manifestations that may occur in the user's daily life, which can be monitored and identified through the data of smart home devices. For example, symptoms such as frequent urination at night, insomnia, and qi deficiency that Chinese medicine pays attention to can be monitored and converted into health-related data through the data of smart home devices. The identification and monitoring of these symptoms helps to evaluate the user's health status and detect potential health problems at an early stage.

[0046] The data storage order of the symptom conversion rules will be set in advance according to the placement of the sensors in the room. By recording the physical position and triggering order of the sensors, it is possible to verify whether certain symptoms are triggered by the same user within a specific time, thereby ensuring the accuracy and consistency of the data, that is, the design of the symptom conversion database has an individual verification mechanism. The symptom conversion rules of the present invention can be constructed by technicians in this field according to actual needs, or they can be customized by technicians in cooperation with medical staff. The present invention does not further limit the construction method of this part.

[0047] In actual application scenarios, there is at least one next-step device. If there are multiple next-step devices, the number of next-step device IDs here should also be multiple, and the subsequent corresponding data and / or labels should also be multiple.

[0048] The duration threshold refers to the time stamp difference between two adjacent devices.

[0049] S202, querying whether there is a symptom conversion rule in the symptom conversion database according to the health device ID, and if so, executing the next step.

[0050] It should be understood that the health device acquires raw device data, which needs to be converted into health-related data through cleaning and screening. On the one hand, it reduces the amount of data transmission, and on the other hand, the data processed in the subsequent steps is more relevant to the target content (i.e., health-related content). Then, after further processing in the subsequent steps of the present invention, the health-related data is converted into the final health data.

[0051] The data content refers to the specific data information uploaded by the smart home device. These data usually include but are not limited to various sensor data detected by the device, status information, timestamps, etc. For example, the data content of a smart pillow includes bed leaving status and sleep quality data, the data content of a smart toilet includes usage status and urination status data, and the data content of a smart water cup includes water intake, water intake frequency, and location data. These data contents are collected and uploaded by smart home devices and can be used for health monitoring and analysis through specific processing and conversion rules.

[0052] Obviously, the same health device ID may correspond to multiple symptom conversion rules at the same time, and multiple symptom conversion rules need to be processed in parallel.

[0053] S203, extracting the symptom conversion rule corresponding to the health device ID, determining whether the data content satisfies the determination condition, and if so, recording the health-related data of the current health device, setting a condition satisfaction flag, and incrementing the number of times the condition is satisfied.

[0054] This step is to screen and filter data. Specifically, by setting judgment conditions, data that meets health monitoring needs can be screened out and irrelevant or abnormal data can be filtered out. For example, only when the data content meets a specific threshold or Boolean condition will it be recorded and further processed, which can avoid the interference of redundant data and improve the accuracy of data processing. Furthermore, the judgment condition helps to establish the association between smart home device data and health-related data. For example, if the heart rate data uploaded by the smart bracelet meets the set threshold, the data can be marked as health-related data and further used for health status assessment and monitoring. In some preferred embodiments, the symptom conversion rule also includes a data identifier; the data content judgment condition includes a data content judgment threshold and a Boolean judgment condition. At this time, the method for judging whether the data content meets the judgment condition includes: identifying the category of the data content according to the data identifier, if it is continuous data, judging whether it meets the condition according to the data content judgment threshold; if it is Boolean data, judging whether it meets the condition according to the Boolean judgment condition.

[0055] It should be understood that the setting of the condition satisfaction flag includes two operations. On the one hand, when the data content meets the judgment condition, the condition satisfaction flag is set and the number of times the condition is satisfied is incremented. On the other hand, after the increment operation of the number of times the condition is satisfied is completed, the condition satisfaction flag needs to be set to zero, that is, the flag bit is cleared, to facilitate the setting operation when the judgment condition is met next time.

[0056] S204. Continue to obtain the latest health-related data. If the current health device ID meets the next device ID in the symptom conversion rule, and the current health-related data acquisition time meets the duration threshold in the symptom conversion rule, then increase the number of repetitions of this step. When the number of repetitions is greater than the repetition number flag in the symptom conversion rule, record the health-related data of the current health device, set the condition satisfaction flag and increase the number of condition satisfaction times, otherwise repeat this step.

[0057] It should be noted that when the present invention acquires health-related data uploaded by health devices, it can be acquired sequentially in real time based on the time flow, or it can be acquired sequentially according to the order of timestamps in the stored historical data. The specific acquisition method should be set by technical personnel in this field according to the actual needs of the site.

[0058] The technical considerations for setting this step are as follows:

[0059] 1. Timeliness and order of data: The timeliness and order of health-related data are crucial for accurate identification of health symptoms. By obtaining the latest uploaded health-related data, it can be ensured that the system processes the latest and most relevant data.

[0060] 2. Multi-step judgment of symptom conversion rules: Symptom conversion rules contain multi-step judgments to ensure that a health symptom is identified only when multiple conditions are met. By judging conditions such as device ID, acquisition time, and number of repetitions, data related to health symptoms can be accurately screened out.

[0061] 3. Determine the number of times a single device is used: The setting of the number of times a single device is used can avoid data anomalies caused by accidental factors. By accumulating the number of times a single device is used, the reliability and consistency of the data can be ensured.

[0062] The following is a specific example of the collection of symptoms of frequent nocturia in traditional Chinese medicine:

[0063] 1. Get the latest data: Get the latest data uploaded by single health devices, such as smart pillows, bathroom door magnets, and smart toilets.

[0064] 2. Device ID judgment: judge whether the currently acquired health device ID meets the next device ID in the symptom conversion rule. For example, after the smart pillow triggers the bed leaving state, the next device ID may be the ID of the bathroom door magnet or smart toilet.

[0065] 3. Time threshold judgment: judge whether the acquisition time of the current health-related data is within the specified time threshold. For example, whether the time interval from leaving the bed to the change of the bathroom door magnetic state is within the set time range.

[0066] 4. Determine the number of repetitions of a single device: accumulate the number of repetitions of a single healthy device in the current step. When the number of times a single healthy device is used is greater than the repetition number flag in the symptom conversion rule, record the data of the current healthy device, set the condition satisfaction flag and increment the number of times the condition is satisfied.

[0067] 5. Record and increment: When the above conditions are met, record the data of the current health device, set the condition satisfaction flag, and increment the number of times the condition is satisfied. Otherwise, repeat this step and continue to obtain new data for judgment.

[0068] It should be understood that the number of repetitions in this step refers to the number of times a single device is repeatedly used in a symptom conversion rule, and the initial value of the number should be the target number of uses minus one. For example, in the determination of the symptom of frequent urination at night, after the smart pillow triggers the bed leaving state, the bathroom door magnet or smart toilet is used once, which can be considered that the symptom conversion rule is satisfied once, and the number of repetitions at this time is 0. In the determination of healthy hydration, the smart water cup needs to be used 7 times to be considered that the symptom conversion rule is satisfied once, and the number of repetitions at this time is 6.

[0069] Through the setting and specific operation of the above step S4, the data of smart home devices can be effectively screened and converted to ensure the accuracy and timeliness of the data, and ultimately achieve accurate monitoring and analysis of health symptoms. This not only improves the efficiency of data processing, but also ensures the reliability and practicality of health-related data.

[0070] S205. Repeat the above step S204. When the number of times the condition is met is greater than the threshold number of times the condition is met in the symptom conversion rule, the current symptom conversion rule and the health-related data of each health device are taken as health data and encoded together with the symptom name to obtain the health data code.

[0071] Taking the above-mentioned frequent urination at night as an example, if the above conditions are met 3 times or more at night, it means that the symptom of frequent urination at night is met. At this time, the symptom name of "frequent urination at night" is encoded and reported. It should be understood that at this time, only the symptom satisfaction data is reported instead of all health-related data. On the one hand, it saves the computing power of encoding and decoding, and on the other hand, it also reduces the capacity of reported data and reduces the network load during data transmission.

[0072] In some preferred embodiments, in order to effectively utilize system resources, avoid problems such as memory overflow or data processing delays, and ensure the stability and efficiency of system operation, it is considered to temporarily store the health-related data uploaded by the health devices in the scanning buffer. After the health data is encoded and reported, the scanning buffer is cleared to prepare for subsequent data collection and processing.

[0073] S3. Encrypt the health data encoding using an improved AES encryption method and transmit it. The AES algorithm is widely used in various embedded systems due to its efficient encryption performance, relatively low resource usage and strong security. Since the application of the basic AES algorithm in the IoT device layer still faces problems such as computing resource consumption and limited key space, technicians in this field have proposed many improved AES encryption algorithms. The present invention can be selected and used by those skilled in the art from the existing improved AES algorithms according to actual needs, and the present invention does not make mandatory requirements.

[0074] Example 2

[0075] This embodiment is developed on the basis of the above-mentioned embodiment 1. Figure 3 As shown, this embodiment provides an improved AES encryption method to complete the encryption of the health data encoding. Specifically, it includes the following steps:

[0076] S301, m chaotic S-boxes are generated after logical mapping according to the initial key. S-box (Substitution Box) is an important component in symmetric encryption algorithm (such as AES), which plays the role of byte substitution and is a key nonlinear transformation component in the algorithm. Traditional S-box is realized by a fixed table lookup method, while the chaotic S-box of the present application is an S-box generated based on chaos theory, which is used to replace the S-box in the traditional encryption algorithm, and is generated by chaotic mapping, thereby increasing the unpredictability and randomness of the S-box.

[0077] Logical mapping refers to a mathematical mapping function based on chaos theory, which is used to generate highly random and unpredictable sequences. Commonly used ones include Logistic mapping, Tent mapping, Henon mapping, etc. Its characteristic is that it is highly sensitive to initial conditions. Even if the initial value changes slightly, the final generated sequence will be completely different, which brings higher randomness and security to the encryption algorithm. Those skilled in the art can generate chaotic S-boxes according to the commonly used logical mapping method. In some preferred embodiments, a method for generating m chaotic S-boxes after logical mapping based on the initial key is provided, which specifically includes:

[0078] S3011, obtain the initial key and perform k iterations of logical mapping to obtain encryption parameters. The number of iterations is set to make the system reach a fully chaotic state. In some preferred embodiments, K>=500. The specific operation steps are as follows: normalize the initial key and retain 3 decimal places as x; perform k rounds of chaotic mapping on x according to the following formula: x k+1 =μx k (1-x k); where μ is the chaos parameter. The normalization method can make the value normalized to (0, 1) after logarithmic transformation. The value range of the chaos parameter μ is (3.57, 4], and the preferred μ = 4.

[0079] S3012, continue to iterate the encryption parameters using logical mapping, and quantize each iteration result into a binary sequence, convert the sequence into a hexadecimal value after 16 iterations, and repeat the operation until a 16*16 chaotic S-box is formed; repeat this step to generate m chaotic S-boxes. The number of chaotic S-boxes generated is equivalent to the number of subsequent encryption rounds. The binarization of the iteration result can be designed by those skilled in the art according to a commonly used binary conversion method. In some preferred embodiments, a simple binarization method is provided, which specifically includes: quantizing the iteration result into a binary sequence with a threshold of 0.5.

[0080] S302, respectively calculating the X-multiplication results of the m chaotic S-boxes and the column mixing matrix elements 02 and 03 to form 2m column mixing lookup tables.

[0081] Those skilled in the art will know that in the conventional AES encryption algorithm, when performing the column mixing step of the loop operation, it is necessary to replace the matrix with the given matrix after byte substitution and row shift. Left multiplication is performed, and the ordinary multiplication becomes X multiplication, that is, the x-time algorithm, specifically:

[0082] When the elements of a given matrix are 01, the multiplication of it with a number is still that number itself, which in the present invention is the single-use chaotic S-box itself.

[0083] When the element of the given matrix is ​​02, both numbers need to be converted to binary. If a7 is equal to 1, remove a7, shift one bit to the left, add 0 on the right, and then XOR 00011011; if a7 is equal to 0, remove a7 directly, shift one bit to the left, and add 0 on the right; the formula is as follows:

[0084]

[0085] Among them, a7a6a5a4a3a2a1a0 is the target element after conversion to binary.

[0086] When the element of the given matrix is ​​02, the calculation is more complicated. It is necessary to calculate the XOR of 02 and 01 respectively, then multiply them respectively, and finally XOR them again.

[0087] Obviously, such a calculation step consumes a lot of computing resources to perform multiplication and division operations, which increases the running time of the encryption algorithm. Therefore, the present invention considers replacing the X-multiplication calculation operation in the column mixing step in the cyclic encryption step with a table lookup operation in the column mixing lookup table. The specific implementation method includes:

[0088] First, the X multiplication results of the m chaotic S boxes and the column mixing matrix elements 02 and 03 are calculated respectively to form 2m column mixing query tables, which are stored in appropriate storage containers. When performing cyclic encryption later, the X multiplication calculation operation in the column mixing step is replaced by the table lookup operation in the column mixing query table, and then the table lookup result is XORed to obtain the column mixing matrix. In this regard, the present invention performs complex mathematical operations in advance before the encryption program is implemented, and then stores the operation results as a table. When it is needed later, the table lookup can be directly performed, thereby improving the multiplication operation that consumes more than 10 machine cycles to a simple table lookup operation, greatly reducing the running time of the algorithm, and only needs to store 20 additional tables to achieve it. Together with the previous black box, 30 16*16 operation tables are formed. The actual space occupied by this part of the table is about 7.5KB of storage space, which is basically negligible for today's embedded memory and will not cause the problem of increased time complexity in the plaintext encryption process.

[0089] S303, perform 1 round of initial change, m-1 rounds of cyclic encryption and 1 round of final encryption on the health data encoding to obtain encrypted information; wherein, the chaotic S-box corresponding to the current round is used when performing byte substitution in the initial change and cyclic encryption steps; the X-multiplication operation in the column mixing step in the cyclic encryption step is replaced by a table lookup operation in the column mixing lookup table. This step is the same as the conventional AES encryption algorithm, and the present invention does not make further limitations and explanations on this. It should be noted that when the present invention performs byte substitution in the key expansion step, the chaotic S-box of the corresponding round is used to replace the fixed S-box in the prior art, thereby increasing the randomness and complexity of the key, expanding the key space, and reducing the correlation between keys of different rounds, thereby improving the anti-attack capability of the AES algorithm and enhancing the security of data transmission.

[0090] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A health data processing method for home health IoT devices, characterized in that: Includes steps: S1. Receive the data stream transmitted by the home health IoT device, clean non-health-related data, and retain health-related data, where the health-related data includes the health device ID and data content; S2. Converting the health-related data into health data codes according to preset symptom conversion rules; S3, encrypting the health data code using an improved AES encryption method and then transmitting it; Step S2 includes: S201, construct symptom conversion rules and store them as a symptom conversion database; the symptom conversion rules include: current device ID, data content determination condition, next device ID, duration threshold, repetition number flag, condition satisfaction flag, condition satisfaction number of times and condition satisfaction number threshold; S202, querying whether there is a symptom conversion rule in the symptom conversion database according to the health device ID, and if so, executing the next step; S203, extracting the symptom conversion rule corresponding to the health device ID, determining whether the data content meets the determination condition, and if so, recording the health-related data of the current health device, setting a condition satisfaction flag and incrementing the number of condition satisfaction times; S204, continue to obtain the latest health-related data. If the current health device ID meets the next device ID in the symptom conversion rule, and the current health-related data acquisition time meets the duration threshold in the symptom conversion rule, then increase the number of repetitions of this step. When the number of repetitions is greater than the number of repetitions flag in the symptom conversion rule, record the health-related data of the current health device, set the condition satisfaction flag and increase the number of condition satisfaction times, otherwise repeat this step; S205. Repeat the above step S204. When the number of times the condition is met is greater than the threshold number of times the condition is met in the symptom conversion rule, the current symptom conversion rule and the health-related data of each health device are taken as health data and encoded together with the symptom name to obtain the health data code.

2. The health data processing method for home health IoT device according to claim 1, characterized in that: The symptom conversion rule also includes a data identifier; the data content determination condition includes a data content determination threshold and a Boolean determination condition.

3. The health data processing method for home health IoT device according to claim 2, characterized in that: The method for determining whether the data content meets the determination condition includes: The category of the data content is identified according to the data identifier. If it is continuous data, whether it meets the conditions is determined according to the data content determination threshold; if it is Boolean data, whether it meets the conditions is determined according to the Boolean determination condition.

4. The health data processing method for home health IoT device according to claim 1, characterized in that: The step S2 also includes: temporarily storing the health-related data uploaded by the health devices in the scanning buffer, and clearing the scanning buffer after the health data is encoded and reported.

5. The health data processing method for home health Internet of Things devices according to claim 1, characterized in that: Step S3 includes: S301, generating m chaotic S-boxes after logical mapping according to the initial key; S302, respectively calculating the X-multiplication results of the m chaotic S-boxes and the column mixing matrix elements 02 and 03 to form 2m column mixing lookup tables; S303, performing 1 round of initial change, m-1 rounds of cyclic encryption and 1 round of final encryption on the health data encoding to obtain encrypted information; Among them, the chaotic S box corresponding to the current round is used when performing byte substitution in the initial change and cyclic encryption steps; the X-multiplication calculation operation in the column mixing step in the cyclic encryption step is replaced by a table lookup operation in the column mixing lookup table.

6. The health data processing method for home health IoT device according to claim 5, characterized in that: The method for generating m chaotic S-boxes after performing logical mapping according to the initial key comprises: Obtain the initial key and use the logical mapping to iterate k times to obtain the encryption parameters; Continue to iterate the encryption parameter using logical mapping, and quantize each iteration result into a binary sequence. After 16 iterations, convert the sequence into a hexadecimal value, and repeat the operation until a 16*16 chaotic S-box is formed; repeat this step to generate m chaotic S-boxes.

7. The health data processing method for home health IoT device according to claim 6, characterized in that: The method of performing several iterations of the initial key by using logical mapping comprises: normalizing the initial key and retaining three decimal places as x; performing k rounds of chaotic mapping on x according to the following formula: k+1 =μx k (1-x k ), where μ is the chaos parameter.

8. The health data processing method for home health IoT device according to claim 6, characterized in that: The method of quantizing each iteration result into a binary sequence includes: quantizing the iteration result into a binary sequence with 0.5 as a threshold.

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