Health data processing system for household health Internet of Things equipment

Abstract

The invention provides a health data processing system for home health Internet of Things equipment, and the system comprises a data cleaning module which is configured to be used for receiving a data stream transmitted by the home health Internet of Things equipment, cleaning non-health related data, and retaining health related data; the data conversion module is connected with the data cleaning module and is configured to be used for converting the health-related data into health data codes through a preset symptom conversion rule; the data encryption module is connected with the data conversion module and is configured to be used for encrypting the health data codes by using an improved AES encryption method and then transmitting the health data codes; 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 system for home health Internet of Things devices. Background Art

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

[0003] 1. Smart home device data volumes are enormous, communication protocols vary widely, and health data requires high real-time performance. Transmitting all device data to a cloud platform for processing and analysis would consume significant network bandwidth and result in low transmission efficiency.

[0004] 2. The data generated by smart home devices cannot be directly interpreted as health data. Further processing is required. That is, the device data collected by the devices is processed according to certain rules to become understandable and further identifiable health data. Existing solutions rely on large pre-established databases to achieve this. By pre-setting keyword mapping relationships between data and then obtaining health data through queries, this approach is inefficient and relies on the detailed and accurate level of the database, making it unsuitable for large-scale data acquisition and processing.

[0005] 3. Home health data is highly private, and ensuring privacy and security during data transmission is crucial. IoT devices are often equipped with resource-constrained embedded systems with low processor frequencies and limited memory capacity. Ensuring encryption efficiency within resource-constrained embedded systems while ensuring data transmission security is a pressing issue. Summary of the Invention

[0006] In order to solve the above technical problems, the present invention provides a health data processing system for home health IoT devices, specifically comprising:

[0007] Health data processing system for home health IoT devices, including:

[0008] a data cleaning module configured to 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;

[0009] a data conversion module, connected to the data cleaning module, and configured to convert the health-related data into health data codes according to preset symptom conversion rules;

[0010] a data encryption module, connected to the data conversion module, and configured to encrypt the health data code using an improved AES encryption method before transmission;

[0011] The data conversion module includes:

[0012] A first storage unit, wherein the first storage unit is configured to have a built-in symptom conversion database, wherein the symptom conversion database is composed of symptom conversion rules, wherein the symptom conversion rules include: a current device ID, a data content determination condition, a next device ID, a duration threshold, a repetition count flag, a condition satisfaction flag, a condition satisfaction count, and a condition satisfaction count threshold;

[0013] a health-related data acquisition unit, connected to the data cleaning module and the first storage unit, respectively, and configured to acquire health-related data uploaded by health devices in chronological order, the health-related data including health device ID and data content;

[0014] A rule determination unit is connected to the first storage unit and the health-related data acquisition unit, and is configured to determine whether the health-related data satisfies the symptom conversion rule, wherein the determination method includes:

[0015] S11. Obtain the latest health-related data uploaded by the health device, query the symptom conversion database based on the health device ID to see whether there is a symptom conversion rule, if so, extract the symptom conversion rule corresponding to the health device ID, determine whether the data content meets the determination condition, and if so, record the health-related data of the current health device, set a condition satisfaction flag, and increment the number of times the condition is satisfied;

[0016] S22. Obtain the latest health-related data uploaded by the health device. 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 increment 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, increment the condition satisfaction number, and repeat step S22.

[0017] The conversion unit is connected to the rule judgment unit and the data encryption module respectively, and is configured to encode the current symptom conversion rule and the health-related data of each health device as health data together with the symptom name when the number of times the condition is met is greater than the condition satisfaction number threshold in the symptom conversion rule, and pass it to the encryption module as health data code.

[0018] 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.

[0019] Preferably, the method by which the rule determination unit determines whether the data content satisfies the determination condition includes:

[0020] 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.

[0021] Preferably, the first storage unit further includes a scan buffer subunit for temporarily storing health-related data uploaded by health devices previously acquired.

[0022] Preferably, the data conversion module further includes a cleaning unit, which is connected to the conversion unit and the first storage unit respectively, and clears the scan buffer subunit of the first storage unit after the conversion unit encodes and reports the health data.

[0023] Preferably, the data encryption module includes: a second storage unit and an encryption unit;

[0024] The second storage unit includes an S-box storage unit and a column hybrid lookup table storage unit;

[0025] The S-box storage unit stores m chaotic S-boxes generated after logical mapping according to the initial key; the column mixing query table storage unit stores 2m column mixing query tables generated according to the X-multiplication results of the m chaotic S-boxes and the column mixing matrix elements 02 and 03;

[0026] The encryption unit is connected to the second storage unit and is configured to receive the health data code and perform one round of initial change, m-1 rounds of cyclic encryption and one round of final encryption on the health data to obtain encrypted information;

[0027] 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 operation in the column mixing step in the cyclic encryption step is replaced by a table lookup operation in the column mixing lookup table.

[0028] Preferably, the method for generating the m chaotic S-boxes includes:

[0029] Obtain the initial key and perform k iterations using the logical mapping to obtain the encryption parameters;

[0030] The encryption parameters are iterated continuously using logical mapping, and the result of each iteration is quantized into a binary sequence. After 16 iterations, the sequence is converted into a hexadecimal value. The operation is repeated until a 16*16 chaotic S-box is formed. This step is repeated to generate m chaotic S-boxes.

[0031] Preferably, the method of performing several iterations of the initial key using logical mapping includes: normalizing the initial key to retain 3 decimal places and recording it 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.

[0032] 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.

[0033] Beneficial effects

[0034] 1. The present invention realizes the conversion of smart home device data into health data on the edge side through the data conversion module. Through the 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 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 a home environment with limited resources.

[0035] 2. The data encryption module optimizes the "column mixing" step in the AES algorithm, replacing the resource-intensive matrix multiplication operation with a pre-calculated and stored table lookup. This significantly reduces computation time in the embedded system and improves the overall efficiency of the encryption process. Chaotic mapping technology is introduced. By generating multiple chaotic S-boxes and applying them to the byte substitution and key expansion steps of the AES algorithm, this increases the randomness and complexity of the key, expands the key space, and reduces the correlation between keys in different rounds. This improves the AES algorithm's anti-attack capabilities, enhances data transmission security, and reduces embedded system resource usage. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 This is a schematic diagram of the structure of a health data processing system for home health Internet of Things devices provided in a preferred embodiment of the present invention;

[0037] Figure 2 This is a structural diagram of a data conversion module provided in another preferred embodiment of the present invention;

[0038] Figure 3 This is a structural diagram of a data encryption module provided in a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0039] In order to make the objectives, technical solutions and advantages of the present invention more clear, the present invention is further described below with reference to 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", "inner", "outer", etc., indicating directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings and are only for the convenience of describing the present invention and simplifying the description. They do not indicate or imply that the devices or components referred to must have a specific direction, be constructed and operate in a specific direction. Therefore, they should not be understood as limiting the present invention.

[0040] Example 1

[0041] like Figure 1 As shown, the present invention provides a health data processing system for home health Internet of Things devices, comprising:

[0042] The data cleaning module is configured to 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.

[0043] 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 the Internet of Things can be adopted. In some preferred embodiments, a specific example of cleaning non-health-related data is given, which specifically includes:

[0044] S101. Read cleaning rule configuration information. This cleaning rule configuration information is pre-stored on a local server or cloud platform and is read via a command or remote embedding method. This cleaning rule configuration information includes device information and corresponding cleaning methods. Device information includes device category, device ID, filter identifier, and data identifier. Cleaning methods include data filtering, data verification and deduplication, and data aggregation.

[0045] S102: Receive the data stream transmitted by the home health IoT device and perform three-level data cleaning according to the configured rules. Specifically, the device category is used to determine whether it is a non-health-related device. If so, the data of the device is filtered. At this time, the remaining data may still be redundant. Then, based on the data type identifier, the data that needs to be checked and cleaned is temporarily stored in the buffer array buf. For continuous data, a two-step process is adopted. The first step is intra-device outlier verification, that is, the mean μ and standard deviation σ of the data in the array are first calculated, and the verification threshold is set to (μ-3σ, μ+3σ). Data in the array outside (μ-3σ, μ+3σ) is eliminated. The second step is inter-device outlier verification. The difference between similar data with matching timestamps is compared pairwise. Similarly, based on the set verification threshold, data pairs with a difference greater than the verification threshold are eliminated. The retention ratio is then calculated and compared, and the device data with a small retention ratio is eliminated. Finally, based on the cleaning rules, it is determined whether to continue data aggregation and whether the aggregation method is mean, mode, or median aggregation, further reducing the amount of data sent to the cloud and improving data transmission efficiency.

[0046] S103: Upload or forward the retained health-related data after cleaning to the processing module of the next step.

[0047] The data conversion module is connected to the data cleaning module and is configured to convert the health-related data into health data codes according to preset symptom conversion rules. Specifically, the data conversion module includes:

[0048] The first storage unit is configured to have a built-in symptom conversion database, and the symptom conversion database is composed of symptom conversion rules, and the symptom conversion rules include: current device ID, data content judgment condition, next step device ID, time threshold, repetition number flag, condition satisfaction flag, condition satisfaction number and condition satisfaction number threshold.

[0049] It should be understood that symptoms refer to health issues or abnormalities that users may experience in their daily lives, which can be monitored and identified using data from smart home devices. For example, symptoms such as frequent urination at night, insomnia, and qi deficiency, which are of concern to Traditional Chinese Medicine, can all be monitored and converted into health data using data from smart home devices. Identifying and monitoring these symptoms helps assess a user's health status and identify potential health issues early on.

[0050] The data storage order of the symptom conversion rules will be pre-set according to the placement of the sensors in the room. By recording the physical location and triggering order of the sensors, it is possible to verify whether certain symptoms are triggered by the same user within a specific time period, 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.

[0051] In actual application scenarios, there is at least one next-step device. If there are multiple next-step devices, the next-step device IDs here should also be multiple, and the subsequent corresponding data and / or tags should also be multiple.

[0052] The duration threshold refers to the time stamp difference between two adjacent devices.

[0053] The health-related data acquisition unit is connected to the data cleaning module and the first storage unit respectively, and is configured to acquire the health-related data uploaded by the health device in a time sequence, wherein the health-related data includes the health device ID and data content.

[0054] A rule determination unit is connected to the first storage unit and the health-related data acquisition unit, and is configured to determine whether the health-related data satisfies the symptom conversion rule, wherein the determination method includes:

[0055] S11. Obtain the latest health-related data uploaded by the health device, and query whether there is a symptom conversion rule in the symptom conversion database based on the health device ID. If so, extract the symptom conversion rule corresponding to the health device ID, and determine whether the data content meets the judgment condition. If so, record the health-related data of the current health device, set the condition satisfaction flag and increment the number of times the condition is satisfied.

[0056] It should be understood that health devices acquire raw device data, which then needs to be cleaned and filtered to be converted into health-related data. This reduces the amount of data transmitted and ensures that the data processed in subsequent steps is more relevant to the target content (i.e., health-related content). Further processing in the subsequent steps of the present invention converts the health-related data into final health data.

[0057] The data content refers to the specific data information uploaded by smart home devices. This data typically includes, but is not limited to, various sensor data detected by the devices, status information, timestamps, and more. For example, a smart pillow's data content includes bed exit status and sleep quality data; a smart toilet's data content includes usage status and urination status data; and a smart water cup's data content includes water consumption, frequency of drinking, and location data. This data content is collected and uploaded by smart home devices and, through specific processing and conversion rules, can be used for health monitoring and analysis.

[0058] 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.

[0059] This step is to screen and filter data. Specifically, by setting judgment conditions, data that meets health monitoring requirements can be selected and irrelevant or abnormal data can be filtered out. For example, data will only be recorded and further processed if its content meets a specific threshold or Boolean condition. This can avoid interference from redundant data and improve data processing accuracy. Furthermore, the judgment conditions help establish associations between smart home device data and health-related data. For example, if heart rate data uploaded by a smart bracelet meets a 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. In this case, the method for determining whether the data content meets the judgment condition includes: identifying the data content category based on the data identifier; if the data is continuous data, determining whether it meets the condition based on the data content judgment threshold; if the data is Boolean data, determining whether it meets the condition based on the Boolean judgment condition.

[0060] 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 incrementing 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 is cleared to facilitate the setting operation when the judgment condition is met next time.

[0061] S22. Obtain the latest health-related data uploaded by the health device. 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, and repeat step S22.

[0062] It should be noted that when the present invention obtains health-related data uploaded by health devices, it can be obtained in real time based on the time flow, or it can be obtained in sequence according to the order of timestamps in the stored historical data. The specific acquisition method should be set by those skilled in the art according to the actual needs of the site.

[0063] The technical considerations for setting this step are as follows:

[0064] 1. Data timeliness and sequentiality: The timeliness and sequentiality of health-related data are crucial for accurately identifying health symptoms. By obtaining the latest uploaded health-related data, the system can ensure that it processes the latest and most relevant data.

[0065] 2. Multi-step judgment of symptom conversion rules: Symptom conversion rules include multi-step judgment, ensuring that a health symptom is only identified 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 filtered.

[0066] 3. Determine the number of times a single device is used: Setting the number of times 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.

[0067] The following is a specific example of the collection of symptoms of frequent nocturia in Traditional Chinese Medicine:

[0068] 1. Get the latest data: Get the latest data uploaded by health devices, such as smart pillows, bathroom door magnets, and smart toilets.

[0069] 2. Device ID Verification: Determine whether the currently acquired health device ID meets the next device ID in the symptom conversion rule. For example, if a smart pillow triggers the bed exit state, the next device ID might be the ID of a bathroom door sensor or a smart toilet.

[0070] 3. Time threshold judgment: Determine whether the acquisition time of the current health-related data is within the specified time threshold. For example, whether the time interval from getting out of bed to the change of the bathroom door sensor status is within the set time range.

[0071] 4. Determine the number of repetitions of a single device: Accumulate the number of repetitions of a single health device in the current step. When the number of times a single health device is used is greater than the repetition number flag in the symptom conversion rule, record the data of the current health device, set the condition satisfaction flag and increment the number of times the condition is satisfied.

[0072] 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 met. Otherwise, repeat this step and continue to obtain new data for judgment.

[0073] It should be understood that the repetition count in this step refers to the number of times a single device is used repeatedly within a symptom conversion rule. The initial value of this count should be the target number of uses minus one. For example, in the determination of frequent nocturnal urination, after the smart pillow triggers the bed exit state, the bathroom door sensor or smart toilet is used once to determine that the symptom conversion rule is satisfied once, and the repetition count flag is 0. In the determination of healthy hydration, the smart water cup needs to be used 7 times to be considered to have satisfied the symptom conversion rule once, and the repetition count flag is 6.

[0074] The conversion unit is connected to the rule judgment unit and the data encryption module respectively, and is configured to encode the current symptom conversion rule and the health-related data of each health device as health data together with the symptom name when the number of times the condition is met is greater than the condition satisfaction number threshold in the symptom conversion rule, and pass it to the encryption module as health data code.

[0075] Taking the aforementioned frequent nocturnal urination symptom as an example, if the above conditions are met three or more times at night, it indicates that the symptom of frequent nocturnal urination has been met. At this time, the symptom name "frequent nocturnal urination" is encoded and reported. It should be understood that only the symptom fulfillment data is reported at this time, rather than all health-related data. On the one hand, this saves computing power for encoding and decoding, and on the other hand, it reduces the reported data capacity and reduces the network load during data transmission.

[0076] In some preferred embodiments, in order to effectively utilize system resources, avoid problems such as memory overflow or data processing delay, and ensure the stability and efficiency of system operation, the first storage unit also includes a scan buffer subunit for temporarily storing health-related data uploaded by previously acquired health devices. The data conversion module also includes a cleaning unit, which is respectively connected to the conversion unit and the first storage unit. After the conversion unit encodes and reports the health data, the scan buffer subunit of the first storage unit is cleared to prepare for subsequent data collection and processing.

[0077] The data encryption module is connected to the data conversion module and is configured to encrypt the health data encoding using an improved AES encryption method and then 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, those skilled in the art have proposed many improved AES encryption algorithms based on this. 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.

[0078] Example 2

[0079] In the IoT (Internet of Things) field, to which this invention is applied, the device layer typically consists of basic components such as an embedded CPU (central processing unit) and ROM memory. Data encryption is primarily implemented using the computing power of the embedded CPU, while encryption keys and results are stored and managed in the ROM memory. However, due to the generally limited hardware resources at the IoT device layer, embedded CPUs often have low clock speeds and relatively small memory capacities. Therefore, in this resource-constrained environment, the encryption algorithm's computational speed and memory usage become key technical bottlenecks.

[0080] While the existing AES (Advanced Encryption Standard) algorithm excels in computational efficiency and security, it still suffers from several shortcomings in specific application environments. For one thing, the AES algorithm's "column mixing" step involves numerous multiplication and division operations. These operations consume significant computing resources and processing time in resource-constrained embedded systems, thus impacting overall system responsiveness. Furthermore, the AES algorithm's key expansion process exhibits strong context-dependent dependencies, resulting in a high degree of correlation between keys and a limited key space. This limited key space makes it possible for an attacker to infer all other keys by obtaining a single key, posing a threat to the security of the entire encryption system.

[0081] Therefore, in order to address these problems, improving the execution efficiency of the AES algorithm in embedded systems and expanding the key space to enhance the security of the algorithm have become important issues that need to be urgently addressed in the field of IoT device-layer data encryption technology.

[0082] like Figure 2 As shown, this embodiment is developed on the basis of the above embodiment 1, and this embodiment provides a specific structural example of the data encryption module, including: a second storage unit and an encryption unit;

[0083] The second storage unit includes an S-box storage unit and a column hybrid lookup table storage unit.

[0084] The S-box storage unit stores m chaotic S-boxes generated through logical mapping based on the initial key; the column mixing lookup table storage unit stores 2m column mixing lookup tables generated based on the results of multiplying the m chaotic S-boxes by the column mixing matrix elements 02 and 03 by X. In this embodiment, the chaotic S-boxes are pre-calculated and stored in the S-box storage unit. They can be burned into the chip's ROM memory during the main program, and then directly called during subsequent encryption processes by specifying the storage address.

[0085] 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. It is generated by chaotic mapping, thereby increasing the unpredictability and randomness of the S-box.

[0086] The S-box (Substitution Box) is an important component of symmetric encryption algorithms such as AES. It performs byte substitution and is a key nonlinear transformation component in the algorithm. Traditional S-boxes are implemented using a fixed lookup table. However, the chaotic S-box described in this application is generated based on chaos theory and is used to replace the S-boxes in traditional encryption algorithms. It is generated using a chaotic map, thereby increasing the unpredictability and randomness of the S-box.

[0087] Logistic mapping refers to a mathematical mapping function based on chaos theory, used to generate highly random and unpredictable sequences. Commonly used methods include logistic mapping, tent mapping, and Henon mapping. Its characteristic is its high sensitivity to initial conditions. Even slight changes in the initial values ​​can result in a completely different final sequence, which provides a high degree of randomness and security for encryption algorithms. Those skilled in the art can generate chaotic S-boxes based on common logical mapping methods. In some preferred embodiments, a method for generating m chaotic S-boxes by performing logical mapping based on an initial key is provided, specifically including:

[0088] Obtain the initial key and perform k iterations of logical mapping to obtain the encryption parameters. The number of iterations is set so that the system reaches a fully chaotic state. In some preferred embodiments, K>=500. The specific operation steps are as follows: normalize the initial key to three decimal places and record it as x; perform 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. The normalization method can be to normalize to (0, 1) after logarithmic transformation. The value range of the chaos parameter μ is (3.57, 4], and the preferred μ = 4.

[0089] The encryption parameters are then iterated using a logical mapping, and the result of each iteration is quantized into a binary sequence. After 16 iterations, the sequence is converted into a hexadecimal value. This process is repeated until a 16x16 chaotic S-box is constructed. This step is repeated to generate m chaotic S-boxes. The number of chaotic S-boxes generated corresponds to the number of subsequent encryption rounds. Binarization of the iterative results can be designed by those skilled in the art using commonly used binary conversion methods. In some preferred embodiments, a simple binarization method is provided, specifically comprising quantizing the iterative results into a binary sequence using a threshold of 0.5.

[0090] Those skilled in the art will know that in the traditional AES encryption algorithm, when performing the column mixing step of the loop operation, it is necessary to compare the matrix after byte substitution and row shift with the given matrix Perform left multiplication, and the ordinary multiplication becomes X multiplication, which is the x-time algorithm. Specifically:

[0091] 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.

[0092] 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 it one bit to the left, add 0 to the right, and then XOR it with 00011011; if a7 is equal to 0, remove a7 directly, shift it one bit to the left, and add 0 to the right. The formula is as follows:

[0093]

[0094] Among them, a7a6a5a4a3a2a1a0 is the target element after conversion to binary.

[0095] 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 separately, then multiply them separately, and finally XOR them again.

[0096] Obviously, such a calculation step consumes a large amount of computing power to perform multiplication and division operations, which increases the runtime of the encryption algorithm. Therefore, the present invention considers replacing the X-multiplication operation in the column mixing step of the cyclic encryption step with a table lookup operation in the column mixing lookup table. Specific implementation methods include:

[0097] First, the X-multiplication results of the m chaotic S-boxes and the column mixing matrix elements O2 and O3 are calculated, forming 2m column mixing lookup tables. These tables are then stored in appropriate storage containers. Later, during cyclic encryption, the X-multiplication operations in the column mixing step are replaced with table lookups in the column mixing lookup tables. The lookup results are then XORed to obtain the column mixing matrix. Similar to the chaotic S-box configuration, the column mixing lookup table in this embodiment is also pre-calculated and designed, then stored in a column mixing lookup table storage unit. This table can be burned into the chip's ROM memory during the main program download process, and then directly called during the subsequent encryption process by specifying the storage address.

[0098] 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 needed later, the table can be directly looked up, thereby improving the multiplication operation that requires more than 10 machine cycles into a simple table lookup operation, greatly reducing the running time of the algorithm. It only requires the storage of 20 additional tables to achieve it, which together with the previous black box constitutes 30 16*16 operation tables. 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.

[0099] The encryption unit is connected to the second storage unit and is configured to receive the health data code and perform 1 round of initial change, m-1 rounds of cyclic encryption and 1 round of final encryption on it to obtain encrypted information; wherein, when performing byte substitution in the initial change and cyclic encryption steps, the chaotic S-box corresponding to the current round is used; and 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. The encryption steps here are 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 performing byte substitution in the key expansion step, the present invention uses the chaotic S-box of the corresponding round 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.

[0100] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the foregoing embodiments. The foregoing embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A health data processing system for home health IoT devices, characterized in that: include: A data cleaning module is configured to 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 a health device ID and data content; a data conversion module, connected to the data cleaning module, configured to convert the health-related data into health data codes according to a preset symptom conversion rule; A data encryption module, connected to the data conversion module, configured to encrypt the health data encoding using an improved AES encryption method before transmission; The data conversion module comprises: A first storage unit, wherein the first storage unit is configured to have a built-in symptom conversion database, wherein the symptom conversion database is composed of symptom conversion rules, and 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; a health-related data acquisition unit, connected to the data cleaning module and the first storage unit respectively, and configured to acquire health-related data uploaded by the health device in a time sequence, wherein the health-related data includes a health device ID and data content; A rule determination unit is connected to the first storage unit and the health-related data acquisition unit respectively, and is configured to determine whether the health-related data satisfies the symptom conversion rule, and the determination method includes: S11, obtaining the latest health-related data uploaded by the health device, querying whether there is a symptom conversion rule in the symptom conversion database according to the health device ID, if so, extracting the symptom conversion rule corresponding to the health device ID, judging whether the data content meets the determination condition, if so, recording the health-related data of the current health device, setting the condition satisfaction flag and incrementing the number of times the condition is satisfied; S22, obtaining the latest health-related data uploaded by the health device. 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 the number of repetitions of this step is incremented. When the number of repetitions is greater than the number of repetitions flag in the symptom conversion rule, the health-related data of the current health device is recorded, the condition satisfaction flag is set, the number of condition satisfaction times is incremented, and step S22 is repeated; The conversion unit is connected to the rule judgment unit and the data encryption module respectively, and is configured to encode the current symptom conversion rule and the health-related data of each health device as health data together with the symptom name when the number of times the condition is met is greater than the condition satisfaction number threshold in the symptom conversion rule, and pass it to the encryption module as the health data code.

2. The health data processing system for home health IoT devices 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 system for home health IoT devices according to claim 2, characterized in that: The method for the rule determination unit to determine 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 system for home health IoT devices according to claim 1, characterized in that: The first storage unit also includes a scan buffer subunit for temporarily storing the health-related data uploaded by the health devices acquired previously.

5. The health data processing system for home health IoT devices according to claim 1, characterized in that: The data conversion module also includes a cleaning unit, which is connected to the conversion unit and the first storage unit respectively. When the conversion unit encodes and reports the health data, the scanning buffer subunit of the first storage unit is cleared.

6. The health data processing system for home health IoT devices according to claim 1, characterized in that: The data encryption module includes: a second storage unit and an encryption unit; The second storage unit includes an S-box storage unit and a column hybrid query table storage unit; The S-box storage unit stores m chaotic S-boxes generated after logical mapping according to the initial key; the column mixing query table storage unit stores 2m column mixing query tables generated according to the X-multiplication results of the m chaotic S-boxes and the column mixing matrix elements 02 and 03; The encryption unit is connected to the second storage unit and is configured to receive the health data code and perform 1 round of initial change, m-1 rounds of cyclic encryption and 1 round of final round encryption on the health data 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.

7. The health data processing system for home health IoT devices according to claim 6, characterized in that: The method for generating the m chaotic S-boxes 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.

8. The health data processing system for home health IoT devices 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.

9. The health data processing system for home health IoT devices 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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