Data security management system and method of intelligent nursing cloud platform

Abstract

The invention relates to the technical field of nursing data security management, and discloses a data security management system and method for an intelligent nursing cloud platform, and the system comprises an edge gateway module, a multi-track transmission module, and a management reconstruction module. The method comprises the following steps that: an edge gateway module acquires a nursing data stream, splits the nursing data stream into data slices, and analyzes the data slices into a data load, a time sequence index and an identity label vector; extracting physiological rhythm characteristics, calculating a sending interval, and sending data through an independent channel; the governance reconstruction module records the actual arrival time interval and restores the physiological rhythm features; and calculating the correlation between the two, and reconstructing the data by using the identity label and the time sequence index if a threshold value is met. According to the invention, by associating the physiological rhythm and the transmission behavior of the nursing data and utilizing a correlation verification mechanism to identify counterfeiting and replay attacks, the authenticity and integrity of the multi-source heterogeneous nursing data converged at the cloud end are ensured.

Description

Technical Field

[0001] This invention relates to the field of nursing data security governance technology, specifically to a data security governance system and method for a smart nursing cloud platform. Background Technology

[0002] The deepening of digital transformation in healthcare has driven the application of smart nursing cloud platforms, which integrate monitoring data from electronic medical records, bedside monitoring devices, and wearable sensors. This multi-source, heterogeneous nursing data is of practical value in improving nursing efficiency, assisting clinical analysis, and developing personalized care plans. Because nursing data involves patient privacy and identity information, ensuring data security and governance is a crucial aspect of system construction during cross-institutional transfer and cloud aggregation.

[0003] Existing nursing data security governance solutions typically employ encrypted transmission protocols and boundary access control technologies. Conventional techniques involve encrypting the data payload at the acquisition end, establishing a transmission channel through an authentication mechanism to protect the confidentiality of data during network transmission, and using an access control system to control access permissions to nursing data, preventing unauthorized reading or manipulation.

[0004] However, the aforementioned technical methods primarily focus on algorithmic encryption of data content, failing to correlate the physiological characteristics at the time of data generation with the physical behavior of data transmission. Traditional encryption mechanisms struggle to verify the consistency between the data stream's generation logic and actual transmission characteristics. If the terminal device's key is illegally obtained, the system cannot distinguish between real-time collected data streams and forged or replayed data streams based on the decryption results. Furthermore, the existing system lacks joint verification capabilities based on physiological rhythms and network transmission timing. When facing network fluctuations, it struggles to differentiate between malicious traffic tampering and normal network scheduling delays, resulting in technical blind spots in verifying the authenticity of data sources.

[0005] Therefore, this invention proposes a data security governance system and method for a smart nursing cloud platform to address the shortcomings of existing technologies. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a data security governance system and method for a smart nursing cloud platform. This system solves the problems of existing technologies, such as difficulty in verifying the consistency between the logic of nursing data generation and transmission behavior, making it difficult to effectively identify forgery and replay attacks targeting nursing data, and the reduced accuracy of determining the authenticity of nursing data sources due to network scheduling fluctuations.

[0007] To achieve the above objectives, the present invention provides the following technical solution: The first aspect of the present invention provides a data security governance system for a smart nursing cloud platform, comprising: an edge gateway module, a multi-track transmission module, and a governance reconstruction module; the edge gateway module is communicatively connected to the multi-track transmission module, and the multi-track transmission module is communicatively connected to the governance reconstruction module. The edge gateway module is used to collect nursing data streams and segment them into data slice unit sequences, and parse the slice units into data payload vectors, time sequence index vectors, and identity vectors; the edge gateway module extracts physiological rhythm feature values ​​from the data payload vectors and calculates the target physical transmission interval, and transmits the data payload vectors according to the target physical transmission interval through the independent transmission channel provided by the multi-track transmission module. The governance and reconstruction module is used to record the actual arrival time interval sequence of the data payload vector to the cloud and restore the physiological rhythm feature sequence; the governance and reconstruction module calculates the statistical correlation between the actual arrival time interval sequence and the physiological rhythm feature sequence. If the statistical correlation meets the preset threshold condition, the identity vector and the time sequence index vector are called to reconstruct the data payload vector.

[0008] Preferably, the step of the edge gateway module acquiring nursing data streams and segmenting them into data slice unit sequences includes: The edge gateway module sets a time window and divides the continuously input nursing data stream into a data slice unit sequence in the time dimension according to the time window. The data slice unit sequence includes multiple data slice units.

[0009] Preferably, the step of the edge gateway module parsing the data slice unit into a data payload vector, a time-series index vector, and an identity vector includes: The edge gateway module reads the header and payload information of the original data frame; The edge gateway module removes the device serial number, patient name, check digit and original timestamp from the original data frame, and retains the clinical observation values ​​to generate the data payload vector. The edge gateway module records the absolute timestamp of the current clock, generates an incrementing sequence number of the data slicing unit within the time window, and combines the absolute timestamp and the incrementing sequence number to form the time-series index vector. The edge gateway module reads the patient's electronic medical record ID bound to the monitoring device, performs a hash calculation on the patient's electronic medical record ID using a one-way hash function, and generates a hash string as the identity vector.

[0010] Preferably, the step of the edge gateway module transmitting the data payload vector according to the target physical transmission interval through the independent transmission channel provided by the multi-track transmission module includes: The edge gateway module stores linear mapping parameters, which include a time scaling factor and a reference transmission delay constant. The edge gateway module calculates the product of the physiological rhythm feature value and the time scaling factor, and adds the product to the reference transmission delay constant to obtain the target physical transmission interval; The edge gateway module calculates the difference between the actual time interval to be sent and the target physical sending interval to obtain the internal scheduling error, and encapsulates the encrypted internal scheduling error into the data payload vector of the next data slice unit; The edge gateway module writes the encapsulated data payload vector into the data channel of the multitrack transmission module according to the target physical transmission interval.

[0011] Preferably, the step of the governance and reconstruction module recording the actual arrival time interval sequence of the data payload vector to the cloud and restoring the physiological rhythm feature sequence includes: The governance and reconstruction module reads the arrival timestamp of the data packet and calculates the actual arrival time interval between the current data packet and the arrival timestamp of the previous data packet; The governance and reconstruction module stores the continuously calculated actual arrival time intervals sequentially into a memory buffer to form the actual arrival time interval sequence. The governance and reconstruction module uses a symmetric key to decrypt the data payload vector to restore the vital sign data, and extracts the physiological rhythm feature sequence from the vital sign data.

[0012] Preferably, the governance and reconstruction module performs a backtracking matching operation to extract the internal scheduling error belonging to the currently processed data slice unit from the subsequently received data payload vector; The governance and reconstruction module subtracts the internal scheduling error from the actual arrival time interval to obtain the corrected network transmission characteristic value, and uses the corrected network transmission characteristic value to update the actual arrival time interval sequence.

[0013] Preferably, the step of the governance reconstruction module calculating the statistical correlation between the actual arrival time interval sequence and the physiological rhythm feature sequence includes: The governance and reconstruction module sets a sliding window to obtain the actual arrival time interval sequence and the physiological rhythm feature sequence within the sliding window; The governance and reconstruction module uses the Pearson correlation coefficient algorithm to calculate the statistical correlation between the actual arrival time interval sequence and the physiological rhythm feature sequence.

[0014] Preferably, the step of reconstructing the data payload vector by calling the identity vector and the time-series index vector if the statistical correlation meets a preset threshold condition includes: The governance reconstruction module uses the identity vector to retrieve the patient's medical record index ID and parses the absolute timestamp and sequence number in the time-series index vector; The governance and reconstruction module uses the sequence number to sort the data payload vector, associates the sorted data payload vector with the patient medical record index ID, and writes it into the cloud storage system of the smart nursing cloud platform.

[0015] Preferably, the preset threshold condition is: the statistical correlation is greater than or equal to a preset correlation threshold.

[0016] A second aspect of this invention provides a data security governance method for a smart nursing cloud platform, comprising the following steps: The edge gateway module collects nursing data streams and segments them into a sequence of data slice units, then parses the data slice units into data payload vectors, time-series index vectors, and identity vectors. The edge gateway module extracts physiological rhythm feature values ​​from the data payload vector and calculates the target physical transmission interval, and transmits the data payload vector according to the target physical transmission interval through the independent transmission channel provided by the multi-track transmission module. The governance and reconstruction module records the actual arrival time sequence of the data payload vector to the cloud and restores the physiological rhythm feature sequence; The governance reconstruction module calculates the statistical correlation between the actual arrival time interval sequence and the physiological rhythm feature sequence. If the statistical correlation meets a preset threshold condition, the identity vector and the time sequence index vector are called to reconstruct the data payload vector.

[0017] This invention provides a data security governance system and method for a smart nursing cloud platform. It has the following beneficial effects: 1. This invention decouples nursing data streams into data payloads, time-series indexes, and identity vectors through an edge gateway module. It also adjusts the physical transmission interval of the data payloads using physiological rhythm characteristics, establishing a correlation between data content and transmission behavior. The multi-track transmission and rhythm modulation mechanism prevents attackers from reconstructing original medical record information when intercepting data from a single channel, thus preventing privacy leaks. This design addresses the security risks in cross-institutional data sharing within smart nursing cloud platforms, ensuring the confidentiality of multi-source heterogeneous nursing data during its circulation.

[0018] 2. This invention establishes a security verification system based on traffic characteristics by calculating the statistical correlation between the actual arrival time interval sequence of the network and the physiological rhythm characteristic sequence through a governance reconstruction module. The system only performs data reconstruction when the statistical correlation meets a preset threshold condition, which can identify tampering or replay behavior during data transmission. This verification mode makes up for the shortcomings of traditional data governance methods in tracing the legitimacy of the source, ensuring the authenticity and integrity of nursing data when it is aggregated in the cloud, and providing a reliable basis for clinical decision-making.

[0019] 3. This invention eliminates the impact of terminal hardware performance differences and task scheduling delays on transmission characteristics by calculating internal scheduling errors at the edge and encapsulating them into subsequent data slices, in conjunction with cloud-based error backtracking matching and correction mechanisms. This mechanism improves the accuracy of correlation verification in complex network environments, ensuring that the governance and reconstruction module can extract effective transmission characteristics from network traffic. The system thus adapts to the access needs of various heterogeneous terminals in the digital transformation of healthcare, achieving efficient governance and storage of nursing data. Attached Figure Description

[0020] Figure 1 This is a data security governance system architecture diagram of a smart nursing cloud platform according to the present invention; Figure 2 This is a flowchart of a data security governance method for a smart nursing cloud platform according to the present invention; Figure 3 This is a schematic diagram illustrating the statistical correlation verification between the physiological rhythm feature sequence and the actual arrival time interval sequence of the network in a smart nursing scenario, according to the present invention.

[0021] Among them, 10 is the edge gateway module; 20 is the multi-track transmission module; and 30 is the governance and reconstruction module. Detailed Implementation

[0022] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] See attached document Figure 1 This invention provides a data security governance system for a smart nursing cloud platform. The system includes an edge gateway module 10, a multi-track transmission module 20, and a governance reconstruction module 30. The edge gateway module 10 is communicatively connected to the multi-track transmission module 20, and the multi-track transmission module 20 is communicatively connected to the governance reconstruction module 30.

[0024] Edge gateway module 10 is deployed at the data acquisition front end of the nursing unit, physically connected or wirelessly coupled to various vital sign monitoring devices. Edge gateway module 10 is used to acquire continuously input nursing data streams. For the acquired nursing data stream, edge gateway module 10 performs discretization processing, dividing the continuous data stream into a sequence of data slice units along the time dimension. For each data slice unit, edge gateway module 10 performs decoupling operations. Edge gateway module 10 parses and splits the data slice unit into three logically independent data structures: a data payload vector, a time-series index vector, and an identity vector.

[0025] The edge gateway module 10 is also used to extract physiological rhythm feature values ​​from the data payload vector and calculate the target physical transmission interval based on the physiological rhythm feature values; The multi-track transmission module 20 is configured to provide three logically isolated independent transmission channels. The edge gateway module 10 sends data payload vectors, timing index vectors, and identity vectors to the governance and reconstruction module 30 through these independent transmission channels. Specifically, when sending the data payload vector, the edge gateway module 10 executes the transmission action according to the target physical transmission interval, ensuring that the transmission rhythm of the data payload vector in the multi-track transmission module 20 is consistent with physiological rhythm characteristics. For the timing index vector and identity vector, the edge gateway module 10 sends them through the multi-track transmission module 20 according to different preset strategies.

[0026] The governance reconstruction module 30 is deployed in the trusted execution environment of the cloud server of the smart nursing cloud platform. The governance reconstruction module 30 receives data transmitted by the multi-track transmission module 20, records the actual arrival time interval sequence of the data payload vector to the cloud, and pre-decrypts the data payload vector to reconstruct the physiological rhythm feature sequence. The governance reconstruction module 30 calculates the statistical correlation between the actual arrival time interval sequence and the physiological rhythm feature sequence. If the statistical correlation meets a preset threshold condition, it calls the identity vector and time sequence index vector to reconstruct the data payload vector.

[0027] See attached document Figure 2 The present invention also provides a data security governance method for a smart nursing cloud platform, which is applied to the above-mentioned system and includes the following steps: S1. Edge gateway module 10 collects nursing data streams, divides the nursing data streams into data slice units, and parses out data payload vectors, time-series index vectors, and identity vectors. S2, the edge gateway module 10 extracts physiological rhythm feature values ​​from the data payload vector, calculates the target physical transmission interval based on the physiological rhythm feature values, and sends the data payload vector, time sequence index vector and identity identification vector to the governance and reconstruction module 30 independently through the multi-track transmission module according to the target physical transmission interval; S3, the governance and reconstruction module 30 records the actual arrival time interval sequence of the data payload vector to the cloud, and pre-decrypts the data payload vector to restore the physiological rhythm feature sequence; S4. The governance and reconstruction module 30 calculates the statistical correlation between the actual arrival time interval sequence and the physiological rhythm feature sequence. If the statistical correlation meets the preset threshold condition, the identity vector and time sequence index vector are called to reconstruct the data payload vector.

[0028] To further clarify the technical details of the system and method in this embodiment, the specific implementation methods, principles and technical contents of each module and method will be described in detail below.

[0029] See attached document Figure 1 and Figure 2 The edge gateway module 10 is deployed in hospital nursing units, including but not limited to intensive care units (ICUs), general wards, or home care terminals. At the physical layer, the edge gateway module 10 connects to various vital sign monitoring devices, including but not limited to electrocardiogram monitors, pulse oximeters, and ventilators, via wired serial ports (such as RS-232 / 485) or wireless communication protocols (such as Bluetooth and ZigBee). The edge gateway module 10 collects continuous nursing data streams output by the aforementioned monitoring devices in real time.

[0030] The edge gateway module 10 performs discretization processing on the collected nursing data stream. The discretization process has a set length of [length value missing]. A time window (e.g., 1 to 5 seconds) is used to segment the continuously input nursing data stream into a sequence of data slice units along the time dimension. For each data slice unit, the edge gateway module 10 performs a decoupling operation. This decoupling operation specifically includes the following steps: The edge gateway module 10 reads the header and payload information of the original data frame. The header information includes communication protocol control data (such as synchronization word, frame type, checksum), device unique identifier, and original sampling timestamp; the payload information includes actual clinical observation values ​​(such as heart rate, blood oxygen saturation, and body temperature data) or specific nursing record content. The edge gateway module 10 classifies, extracts, and encapsulates these into three logically independent data structures: a data payload vector, a time-series index vector, and an identity vector.

[0031] The data payload vector consists of the vital sign values ​​in the data slice units. When generating the data payload vector, the edge gateway module 10 removes the device serial number, patient name, check digit, and original timestamp from the original data frame, retaining only the clinical observation values, such as the millivolt voltage value of the electrocardiogram waveform or the percentage value of blood oxygen saturation.

[0032] The timing index vector is used to mark the timing position of the data slice unit. While generating the data payload vector, the edge gateway module 10 records the absolute timestamp of the current clock and generates an incrementing sequence number of the data slice unit within the current time window. The absolute timestamp and the sequence number are combined to form the timing index vector.

[0033] The identity vector is used to uniquely identify the patient. The edge gateway module 10 reads the patient's electronic medical record ID bound to the monitoring device, performs a hash calculation on the ID using a one-way hash function (such as the SHA-256 algorithm), and generates a fixed-length hash string as the identity vector. The specific calculation process of the hash algorithm is well-known to those skilled in the art and will not be described in detail here.

[0034] After completing the above decoupling operations, the edge gateway module 10 is also used to extract physiological rhythm feature values ​​from the data payload vector. When the data payload vector is electrocardiogram data, the edge gateway module 10 uses the differential thresholding method to identify the peak value of the R wave and calculates the time difference between two adjacent R wave peaks (i.e., the RR interval) as the physiological rhythm feature value; when the data payload vector is respiratory data, the edge gateway module 10 calculates the duration of the complete respiratory cycle of the respiratory waveform (i.e., the respiratory interval) as the physiological rhythm feature value.

[0035] The edge gateway module 10 calculates the target physical transmission interval based on physiological rhythm characteristic values. The edge gateway module 10 stores preset linear mapping parameters, including time scaling factors. and reference transmission delay constant Let the first... The physiological rhythm feature value corresponding to each data slice unit is The edge gateway module 10 calculates the target physical transmission interval of the data slice unit relative to the next data slice unit according to the following formula. : ; Wherein, time scaling factor The value range is set between 0.5 and 2.0 to adjust the dynamic range of the network transmission interval; the reference transmission delay constant. Set it to a value greater than the minimum packet sending interval of the network hardware (e.g., 10 milliseconds) to prevent the calculation results from exceeding the physical limitations of the hardware.

[0036] In addition, the edge gateway module 10 also calculates scheduling errors generated within the system. When writing the data payload vector into the network interface controller's transmit queue, the edge gateway module 10 reads a hardware timer to obtain the actual time interval before transmission. The internal scheduling error is obtained by calculating the difference between the internal scheduling error and the target physical transmission interval. : ; Edge gateway module 10 addresses the internal scheduling error Encryption is performed, and the data is encapsulated in the header of the data payload vector of the next data slice unit in the time sequence for error correction during subsequent cloud verification.

[0037] See attached document Figure 1 and Figure 2 The multi-track transmission module 20 is configured to provide three logically isolated independent transmission channels between the edge gateway module 10 and the cloud-based governance and reconfiguration module 30. While these three channels may physically reuse the same Ethernet cable, fiber optic link, or wireless RF band, at the network transport layer, the multi-track transmission module 20 achieves logical isolation by configuring different TCP / UDP port numbers or establishing independent socket connections. Specifically, the multi-track transmission module 20 includes a data channel for transmitting data payload vectors, an index channel for transmitting timing index vectors, and an identity channel for transmitting identity vectors. The edge gateway module 10, by calling the communication interface provided by the multi-track transmission module 20, maps the three decoupled vectors to the corresponding independent transmission channels for transmission.

[0038] For the transmission of the data payload vector, the edge gateway module 10 performs a real-time transmission operation based on physiological rhythm characteristics. The edge gateway module 10 reads the target physical transmission interval calculated in the previous processing step, which already includes internal scheduling error compensation parameters. The edge gateway module 10 writes the data payload vector into the data channel of the multi-track transmission module 20 according to this target physical transmission interval. During this process, the data channel is encapsulated using the User Datagram Protocol (UDP) and no additional traffic shaping or delay buffering is performed to maintain the synchronization of the data payload vector's transmission rhythm in the multi-track transmission module 20 with the physiological rhythm characteristics. This allows the instantaneous transmission rate fluctuations of network data packets on the physical link to directly reflect the temporal characteristics of the patient's physiological signals (such as the RR interval).

[0039] For the transmission of the time-series index vector, the edge gateway module 10 transmits it through the index channel of the multi-track transmission module 20 according to a preset strategy of complementary traffic filling. The edge gateway module 10 uses this preset strategy to adjust the timing of the time-series index vector transmission to smooth out traffic fluctuations caused by carrying physiological rhythms in the data channel, preventing third parties from inferring patient vital signs through side-channel traffic analysis. The edge gateway module 10 stores a preset constant bandwidth control constant. This constant bandwidth control constant represents the system's set total outgoing traffic expected interval (e.g., set to a fixed value between 50 milliseconds and 100 milliseconds). For the first... Each data slice unit, the edge gateway module 10, is based on the actual transmission interval of the data payload vector. Calculate the transmission interval of the timing index vector in the index channel. The calculation formula is as follows: ; in, Defined as the minimum physical packet sending interval allowed by the network interface hardware (e.g., 10 microseconds), used to prevent the calculation result from being less than the hardware physical limit; The value of is set to be greater than the maximum possible transmission interval of the data payload vector. According to this formula, when the physiological rhythm characteristics cause the transmission interval of the data payload vector to be large, the edge gateway module 10 reduces the transmission interval of the index channel and increases the packet transmission frequency; when the data payload vector is transmitted quickly, the edge gateway module 10 increases the transmission interval of the index channel. In this way, the edge gateway module 10 makes the total network traffic after the data channel and the index channel are superimposed in the multi-track transmission module 20 exhibit constant rate or irregular pseudo-random sequence characteristics, thereby shielding the physiological rhythm characteristics at the transmission layer.

[0040] For the transmission of the identity vector, the edge gateway module 10 sends it through the identity channel of the multi-track transmission module 20 according to a preset strategy of low-frequency asynchronous transmission. Since the identity vector remains unchanged within a single nursing session or a single patient monitoring cycle, the edge gateway module 10 only activates the identity channel to send the identity vector when it detects a session establishment initialization event, a patient identity change event, or receives an identity re-verification command initiated by the governance reconstruction module 30. During normal data stream transmission, the edge gateway module 10 keeps the identity channel in a silent state. The identity channel employs a transmission control protocol to ensure transmission reliability. This strategy ensures that the identity vector containing sensitive information is separated from the high-frequency transmitted data payload vector in the time domain, and that they are transmitted through different logical ports in the multi-track transmission module 20, achieving physical isolation between the data content and the identity information.

[0041] See attached document Figure 1 and Figure 2The governance and refactoring module 30 is deployed in the trusted execution environment (TEX) of the cloud server. The TEX is an isolated memory region partitioned on the cloud server's processor to ensure the confidentiality of code execution and data processing. The governance and refactoring module 30 receives data packets transmitted by the multitrack transmission module 20 via a network interface. When a data packet containing a data payload vector arrives at the network interface layer of the cloud server, the governance and refactoring module 30 reads the data packet arrival timestamp recorded by the operating system kernel. ,in Indicates the first in the received sequence One data packet. Targeting the sequence. For each data packet, the governance and reconstruction module 30 calculates the difference between the arrival timestamp of the current data packet and the arrival timestamp of the previous data packet to obtain the actual arrival time interval. The governance restructuring module 30 will continuously calculate the... They are stored sequentially into the memory buffer to form a sequence of actual arrival time intervals.

[0042] For the received data payload vector, the governance and reconstruction module 30 performs a pre-decryption operation. The governance and reconstruction module 30 uses a symmetric key (e.g., an AES-256 standard session key) pre-negotiated with the edge gateway module 10 to decrypt the data payload vector, restoring the original vital sign numerical data. The governance and reconstruction module 30 then performs the same feature extraction algorithm as the edge gateway module 10 on the decrypted vital sign numerical data. For example, for ECG data, the governance and reconstruction module 30 identifies adjacent R wave peaks and calculates the RR interval value. The governance and reconstruction module 30 arranges the continuously extracted feature values ​​in the receiving order to generate a physiological rhythm feature sequence. .

[0043] To improve the accuracy of the verification, the governance and reconstruction module 30 performs error correction before calculating the correlation. The governance and reconstruction module 30 targets the first [missing information] in the actual arrival time interval sequence. The actual arrival time interval is processed. Because the edge gateway module 10 will process the first... The internal scheduling error of the first data slice unit is encapsulated in the subsequent... In the data slice unit, the governance and reconstruction module 30 performs a backtracking matching operation to extract the data slice belonging to the currently processed data slice from the subsequently received data payload vector. The internal scheduling error of each data slice unit. Let the internal scheduling error after matching be... This internal scheduling error This represents the deviation between the actual network card transmission time and the theoretical transmission time caused by the operating system task scheduling delay after the edge gateway module 10 calculates the target physical transmission interval. actual arrival time interval Subtract this internal scheduling error To obtain the corrected network transmission characteristic values : ; The governance reconstruction module 30 updates the actual arrival time interval sequence using the corrected network transmission characteristic values.

[0044] The governance and reconstruction module 30 calculates the statistical correlation between the actual arrival time interval sequence (specifically, the corrected network transmission characteristic sequence) and the physiological rhythm characteristic sequence. The governance and reconstruction module 30 sets the length to... A sliding window is used to obtain the network transmission feature sequence within the window. With physiological rhythm characteristic sequence The governance restructuring module 30 uses the Pearson correlation coefficient algorithm to calculate the statistical correlation between the two. : ; in, This indicates the preset sliding window length, which is the total number of data slice unit samples participating in a single correlation calculation (e.g., a value of 50 to 100 samples). For sequence The arithmetic mean; For sequence The arithmetic mean of the two. The governance restructuring module 30 calculates the statistical correlation, which quantitatively represents the degree of linear dependence between the two, using this formula. The specific implementation of the Pearson correlation coefficient algorithm is well-known to those skilled in the art and will not be elaborated upon here.

[0045] The governance restructuring module 30 determines the statistical correlation obtained from the calculation. Does it meet the preset threshold condition? This preset threshold condition is set to statistical correlation. Greater than or equal to the preset correlation threshold ,For example Set it to 0.8. If statistical correlation is required... If the statistical correlation meets the preset threshold, the governance and reconstruction module 30 determines that the data source is legitimate and the content has not been tampered with, and then performs the reconstruction operation. If the statistical correlation... If the statistical correlation does not meet the preset threshold condition, the governance and reconstruction module 30 determines that the data is abnormal, discards the data payload vector in the current window, and records the security log.

[0046] When preset threshold conditions are met, the governance reconstruction module 30 calls the identity vector and time-series index vector to reconstruct the data payload vector. Based on the port mapping relationship of the multi-track transmission module 20, the governance reconstruction module 30 reads the corresponding identity vector and uses it to retrieve a unique corresponding patient medical record index ID from the cloud database. Simultaneously, the governance reconstruction module 30 reads the time-series index vector corresponding to the data payload vector and parses its absolute timestamp and relative sequence number. The governance reconstruction module 30 uses the relative sequence number to reorder the out-of-order data payload vectors that arrived during network transmission, restoring their original time sequence. The governance reconstruction module 30 associates the vital sign values ​​in the sorted data payload vectors with the retrieved patient medical record index ID and writes them to the cloud storage system of the smart nursing cloud platform, completing the data reconstruction and storage.

[0047] Example: This embodiment uses a cardiovascular intensive care unit scenario as an example. The edge gateway module 10 is connected to a bedside monitor to collect postoperative vital sign data of the patient. This data stream is an electrocardiogram (ECG) with a sampling rate of 500Hz. Waveform data. The edge gateway module 10 divides the data stream into data slice units in a 2-second time window and parses out the data payload vector (including vital sign values), time sequence index vector (including absolute timestamp), and identity vector (including patient ID hash).

[0048] The edge gateway module 10 analyzes the data payload vector. For patients exhibiting sinus arrhythmia (RR interval fluctuating between 600ms and 900ms), the edge gateway module 10 extracts the average RR interval within the window as a physiological rhythm feature value. Edge gateway module 10 sets the time scaling factor. and reference transmission delay Milliseconds, used to calculate the target physical transmission interval : ; Edge gateway module 10 according to Control the timing of transmission. Simultaneously, the edge gateway module 10 calculates the operating system scheduling error and assigns the first... The error of the first unit is encapsulated into the first... Each unit header. The edge gateway module 10 independently transmits each vector through the multi-track transmission module 20, wherein the index channel uses a complementary strategy to fill traffic gaps.

[0049] The governance reconstruction module 30 receives data packets and records the actual arrival time interval. The governance and reconstruction module pre-decrypts the data payload vectors (30 pairs) to restore ECG values ​​and extract RR interval sequences. .

[0050] The governance reconstruction module 30 targets the first [item] in the actual arrival time interval sequence. The first element is processed. Because the edge gateway module 10 will process the first... The scheduling error of the first data slice unit is encapsulated in the subsequent... In the data slice unit, the governance and reconstruction module 30 performs a backtracking matching operation to extract data belonging to the data slice unit from the subsequently received data payload vector. The error value of each data slice unit. Let the internal scheduling error value after matching be... The internal scheduling error value This represents the deviation between the actual network interface card (NIC) transmission time and the theoretical transmission time caused by operating system task scheduling delay after the edge gateway module 10 calculates the target physical transmission interval. The governance and reconstruction module 30 uses this internal scheduling error value to correct each element in the actual arrival time interval sequence and calculates the corrected network transmission characteristic value. : ; Governance Restructuring Module 30: Setting a Sliding Window Calculate the Pearson correlation coefficient between the corrected network transmission characteristic sequence and the physiological rhythm characteristic sequence. : ; In this embodiment, the calculation result Meets the preset threshold See attached document. Figure 3 , Figure 3 The graph visually illustrates the data trend of correlation verification in this embodiment. The horizontal axis represents the sample sequence number of the data payload vector. The solid curve represents the reconstructed physiological rhythm sequence (i.e., the ECG RR interval), while the dashed curve represents the corrected network transmission sequence. As shown in the figure, the two curves exhibit a high degree of consistency in their peak and trough fluctuations, visually confirming the correlation coefficient calculated above. The conclusion proves that the edge gateway module 10 successfully modulates the patient's physiological characteristics into the network transmission behavior, and the transmission characteristics after error correction can accurately reflect the physiological rhythm.

[0051] Based on this, the governance reconstruction module 30 determines that the data source is legitimate, calls the identity identifier vector to lock the patient's medical record index ID, uses the time sequence index vector to reconstruct the data, and writes the reconstructed nursing data into the cloud storage system of the smart nursing cloud platform.

[0052] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A data security governance system for a smart nursing cloud platform, characterized in that, include: Edge gateway module, multi-track transmission module, and governance reconstruction module; The edge gateway module is communicatively connected to the multi-track transmission module, and the multi-track transmission module is communicatively connected to the governance and reconstruction module; The edge gateway module is used to collect nursing data streams and segment them into data slice unit sequences, and parse the slice units into data payload vectors, time sequence index vectors, and identity vectors; the edge gateway module extracts physiological rhythm feature values ​​from the data payload vectors and calculates the target physical transmission interval, and transmits the data payload vectors according to the target physical transmission interval through the independent transmission channel provided by the multi-track transmission module. The governance and reconstruction module is used to record the actual arrival time interval sequence of the data payload vector to the cloud and restore the physiological rhythm feature sequence; the governance and reconstruction module calculates the statistical correlation between the actual arrival time interval sequence and the physiological rhythm feature sequence. If the statistical correlation meets the preset threshold condition, the identity vector and the time sequence index vector are called to reconstruct the data payload vector.

2. The data security governance system for a smart nursing cloud platform according to claim 1, characterized in that, The steps of the edge gateway module acquiring nursing data streams and segmenting them into data slice unit sequences include: The edge gateway module sets a time window and divides the continuously input nursing data stream into a data slice unit sequence in the time dimension according to the time window. The data slice unit sequence includes multiple data slice units.

3. The data security governance system for a smart nursing cloud platform according to claim 2, characterized in that, The steps by which the edge gateway module parses the data slice unit into a data payload vector, a time-series index vector, and an identity vector include: The edge gateway module reads the header and payload information of the original data frame; The edge gateway module removes the device serial number, patient name, check digit and original timestamp from the original data frame, and retains the clinical observation values ​​to generate the data payload vector. The edge gateway module records the absolute timestamp of the current clock, generates an incrementing sequence number of the data slicing unit within the time window, and combines the absolute timestamp and the incrementing sequence number to form the time-series index vector. The edge gateway module reads the patient's electronic medical record ID bound to the monitoring device, performs a hash calculation on the patient's electronic medical record ID using a one-way hash function, and generates a hash string as the identity vector.

4. The data security governance system for a smart nursing cloud platform according to claim 1, characterized in that, The step of the edge gateway module transmitting the data payload vector according to the target physical transmission interval through the independent transmission channel provided by the multi-track transmission module includes: The edge gateway module stores linear mapping parameters, which include a time scaling factor and a reference transmission delay constant. The edge gateway module calculates the product of the physiological rhythm feature value and the time scaling factor, and adds the product to the reference transmission delay constant to obtain the target physical transmission interval; The edge gateway module calculates the difference between the actual time interval to be sent and the target physical sending interval to obtain the internal scheduling error, and encapsulates the encrypted internal scheduling error into the data payload vector of the next data slice unit; The edge gateway module writes the encapsulated data payload vector into the data channel of the multitrack transmission module according to the target physical transmission interval.

5. The data security governance system for a smart nursing cloud platform according to claim 1, characterized in that, The steps of the governance and reconstruction module in recording the actual arrival time interval sequence of the data payload vector to the cloud and restoring the physiological rhythm feature sequence include: The governance and reconstruction module reads the arrival timestamp of the data packet and calculates the actual arrival time interval between the current data packet and the arrival timestamp of the previous data packet; The governance and reconstruction module stores the continuously calculated actual arrival time intervals sequentially into a memory buffer to form the actual arrival time interval sequence. The governance and reconstruction module uses a symmetric key to decrypt the data payload vector to restore the vital sign data, and extracts the physiological rhythm feature sequence from the vital sign data.

6. The data security governance system for a smart nursing cloud platform according to claim 5, characterized in that, The governance and reconstruction module performs a backtracking matching operation to extract the internal scheduling error belonging to the currently processed data slice unit from the subsequently received data payload vector; The governance and reconstruction module subtracts the internal scheduling error from the actual arrival time interval to obtain the corrected network transmission characteristic value, and uses the corrected network transmission characteristic value to update the actual arrival time interval sequence.

7. The data security governance system for a smart nursing cloud platform according to claim 1, characterized in that, The step of the governance reconstruction module in calculating the statistical correlation between the actual arrival time interval sequence and the physiological rhythm feature sequence includes: The governance and reconstruction module sets a sliding window to obtain the actual arrival time interval sequence and the physiological rhythm feature sequence within the sliding window; The governance and reconstruction module uses the Pearson correlation coefficient algorithm to calculate the statistical correlation between the actual arrival time interval sequence and the physiological rhythm feature sequence.

8. The data security governance system for a smart nursing cloud platform according to claim 1, characterized in that, The step of reconstructing the data payload vector by calling the identity vector and the time-series index vector if the statistical correlation meets a preset threshold condition includes: The governance reconstruction module uses the identity vector to retrieve the patient's medical record index ID and parses the absolute timestamp and sequence number in the time-series index vector; The governance and reconstruction module uses the sequence number to sort the data payload vector, associates the sorted data payload vector with the patient medical record index ID, and writes it into the cloud storage system of the smart nursing cloud platform.

9. The data security governance system for a smart nursing cloud platform according to claim 1, characterized in that, The preset threshold condition is: the statistical correlation is greater than or equal to the preset correlation threshold.

10. A data security governance method for a smart nursing cloud platform, applied to a data security governance system for a smart nursing cloud platform as described in any one of claims 1-9, characterized in that, Includes the following steps: The edge gateway module collects nursing data streams and segments them into a sequence of data slice units, then parses the data slice units into data payload vectors, time-series index vectors, and identity vectors. The edge gateway module extracts physiological rhythm feature values ​​from the data payload vector and calculates the target physical transmission interval, and transmits the data payload vector according to the target physical transmission interval through the independent transmission channel provided by the multi-track transmission module. The governance and reconstruction module records the actual arrival time sequence of the data payload vector to the cloud and restores the physiological rhythm feature sequence; The governance reconstruction module calculates the statistical correlation between the actual arrival time interval sequence and the physiological rhythm feature sequence. If the statistical correlation meets a preset threshold condition, the identity vector and the time sequence index vector are called to reconstruct the data payload vector.

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