Intelligent monitoring system and method for anesthesia status

Abstract

The present application provides an intelligent monitoring system and method for anesthesia status, which relates to the field of intelligent monitoring. The method includes: collecting physiological data of anesthetized patients through a sensor device; encrypting the physiological data to generate encrypted data, and transmitting the encrypted data to a central monitoring platform through a routing transmission medium; decrypting the encrypted data through the central monitoring platform to obtain physiological data, and extracting characteristic vectors corresponding to each physiological dimension from the physiological data; obtaining characteristic indicators corresponding to each physiological dimension, comparing the characteristic vectors with the characteristic indicators, and determining the current anesthesia status of the anesthetized patient; and displaying the anesthesia status on a display. The above process realizes the real-time collection, encrypted transmission, accurate decryption and feature analysis of the physiological data of anesthetized patients by integrating sensor devices, data encryption technology, a central monitoring platform and feature extraction algorithms, thereby ensuring the efficiency, security and privacy protection of anesthesia data transmission.

Description

Technical Field

[0001] The present application relates to the field of intelligent monitoring, and more specifically, to an intelligent monitoring system and method for anesthesia status. Background Art

[0002] With the continuous advancement of medical technology, the requirements for anesthesia management during surgery are becoming increasingly higher. Accurate anesthesia management is not only conducive to improving the safety of surgery, but also reduces the side effects of anesthetic drugs, reduces surgical risks, and improves patient comfort and satisfaction. In traditional anesthesia, doctors mainly rely on the patient's vital signs (such as heart rate, blood pressure, breathing, etc.) and the patient's response to stimulation to judge the depth of anesthesia. However, this method has many limitations.

[0003] In the prior art, anesthesia status monitoring is achieved by monitoring physiological data. However, due to limited medical staff, it is generally necessary to upload data from multiple monitoring devices to a host computer for unified monitoring. When transmitting data from multiple monitoring devices to the host computer, it is easy for the efficiency of monitoring data transmission and data processing to be low, which may also easily lead to the problem of patient privacy leakage. Summary of the invention

[0004] The present application provides an intelligent monitoring system and method for anesthesia status, thereby at least to a certain extent being able to solve the problem of low transmission efficiency during monitoring data transmission.

[0005] Other features and advantages of the present application will become apparent from the following detailed description, or may be learned in part by the practice of the present application.

[0006] According to one aspect of the present application, there is provided an intelligent monitoring method for anesthesia status, comprising: collecting physiological data of an anesthetized patient through a sensing device; performing hashing and linear transformation processing on the physiological data and its corresponding tag information to generate an encryption key, encrypting the physiological data through the encryption key to generate encrypted data, and transmitting the encrypted data to a central monitoring platform through a routing transmission medium; decrypting the encrypted data through the central monitoring platform to obtain physiological data, and extracting characteristic vectors corresponding to each physiological dimension from the physiological data; obtaining characteristic indicators corresponding to each physiological dimension, comparing the characteristic vectors with the characteristic indicators to determine the current anesthesia status of the anesthetized patient; and displaying the anesthesia status on a display.

[0007] In the present application, based on the above scheme, the physiological data and its corresponding tag information are hashed and linearly transformed to generate an encryption key, the physiological data is encrypted by the encryption key to generate encrypted data, and the encrypted data is transmitted to the central monitoring platform through a routing transmission medium, including: traversing all sensor devices to form a transmission network, selecting a transmission node from the transmission network; specifically, traversing the information of each sensor device in the transmission network, calculating the sensor device Corresponding transmission parameters for:

[0008] ;

[0009] in, P represents a preset transmission probability, which is used as the probability of each sensor device becoming a transmission node; r Indicates the number of rounds of transmission and selection of transmission nodes; mod indicates the remainder operation; represents the power of the sensor device, represents the data processing efficiency of the sensor device, represents the amount of data to be transmitted; the transmission parameter is obtained through the above calculation, and the node corresponding to the highest transmission parameter is selected from the sensor device as the transmission node;

[0010] The transmission node combines the data collected by each sensor device, performs hash and linear transformation processing on the physiological data obtained after the combination and its corresponding label information to generate an encryption key, and encrypts the physiological data using the encryption key to generate encrypted data; the transmission node transmits the encrypted data to the central monitoring platform via the routing transmission medium.

[0011] In the present application, based on the aforementioned scheme, the transmission node combines the data collected by each sensor device, performs hash and linear transformation processing on the physiological data obtained after the combination and its corresponding label information to generate an encryption key, and encrypts the physiological data through the encryption key to generate encrypted data, including: each sensor device transmits the collected data to the transmission node; the transmission node labels the collected data, and then combines them to generate physiological data; obtains patient information of an anesthetized patient and label information of the physiological data, hashes the patient information and the label information to generate an encryption key; performs hash and linear transformation processing on the physiological data and its corresponding label information to generate an encryption key, and encrypts the physiological data through the encryption key to generate encrypted data.

[0012] In the present application, based on the aforementioned scheme, the patient information of an anesthetized patient and the label information of the physiological data are obtained, the patient information and the label information are hashed, and an encryption key is generated, including: obtaining the patient information of an anesthetized patient and the label information of the physiological data, combining the patient information and the label information to obtain a standardized information set; hashing each element in the information set to obtain a hash value set consisting of multiple hash values; performing polynomial operations on multiple hash values ​​in the hash value set to generate key candidate values; performing nonlinear transformation and iterative processing on the key candidate values ​​to generate an encryption key, and encrypting the physiological data with the encryption key to generate encrypted data.

[0013] In the present application, based on the aforementioned scheme, the encrypted data is decrypted by the central monitoring platform to obtain physiological data, and the characteristic vectors corresponding to each physiological dimension are extracted from the physiological data, including: the central monitoring platform obtains the encrypted data, and decrypts the encrypted data to obtain the original physiological data; the physiological data is preprocessed, and then the characteristic vectors corresponding to each physiological dimension are extracted.

[0014] In the present application, based on the aforementioned scheme, the characteristic indicators corresponding to each physiological dimension are obtained, the characteristic vectors are compared with the characteristic indicators, and the current anesthetic state of the anesthetized patient is determined, including: obtaining the characteristic indicators corresponding to each physiological dimension; calculating the difference parameters between the characteristic vectors and the characteristic indicators; and determining the current anesthetic state of the anesthetized patient according to the difference parameters.

[0015] In the present application, based on the aforementioned solution, displaying the corresponding level of the anesthesia state in the display includes: determining the display method corresponding to the anesthesia state according to a preset display method, and displaying it in the display.

[0016] According to one aspect of the present application, there is provided an intelligent monitoring system for anesthesia status, comprising:

[0017] A collection unit, used for collecting physiological data of anesthetized patients through a sensor device;

[0018] An encryption unit, configured to perform hash and linear transformation processing on the physiological data and its corresponding tag information to generate an encryption key, encrypt the physiological data using the encryption key to generate encrypted data, and transmit the encrypted data to a central monitoring platform via a routing transmission medium;

[0019] A feature unit, used to decrypt the encrypted data through a central monitoring platform to obtain physiological data, and extract feature vectors corresponding to each physiological dimension from the physiological data;

[0020] A state unit, used to obtain characteristic indicators corresponding to each physiological dimension, compare the characteristic vector with the characteristic indicator, and determine the current anesthetic state of the anesthetized patient;

[0021] A display unit is used to display the anesthesia state on a display.

[0022] According to one aspect of the present application, a computer-readable medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the intelligent monitoring method of the anesthetic state as described above is implemented.

[0023] According to one aspect of the present application, an electronic device is provided, comprising: one or more processors; a storage device for storing one or more programs, which, when executed by the one or more processors, implements the intelligent monitoring method of the anesthetic state as described in the above embodiments.

[0024] According to one aspect of the present application, a computer program product or computer program is provided, the computer program product or computer program comprising computer instructions, the computer instructions being stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions to implement the above-mentioned intelligent monitoring method for anesthesia state.

[0025] In the technical solution of the present application, the physiological data of an anesthetized patient is collected by a sensor device; the physiological data and its corresponding tag information are hashed and linearly transformed to generate an encryption key, the physiological data is encrypted by the encryption key to generate encrypted data, and the encrypted data is transmitted to a central monitoring platform through a routing transmission medium; the encrypted data is decrypted by the central monitoring platform to obtain physiological data, and characteristic vectors corresponding to each physiological dimension are extracted from the physiological data; characteristic indicators corresponding to each physiological dimension are obtained, and the characteristic vectors are compared with the characteristic indicators to determine the current anesthesia state of the anesthetized patient; and the anesthesia state is displayed on a display. The above process realizes the real-time collection, encrypted transmission, accurate decryption and feature analysis of the physiological data of anesthetized patients by integrating sensor devices, data encryption technology, central monitoring platform and feature extraction algorithm, ensuring the efficiency, security and privacy protection of anesthesia data transmission.

[0026] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The drawings herein are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present application, and together with the specification are used to explain the principles of the present application. Obviously, the drawings described below are only some embodiments of the present application, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0028] Figure 1 The flowchart of the intelligent monitoring method of the anesthesia state in one embodiment of the present application is schematically shown.

[0029] Figure 2 A flowchart for transmitting encrypted data to a central monitoring platform in one embodiment of the present application is schematically shown.

[0030] Figure 3 A schematic diagram of an intelligent monitoring system for anesthesia status in one embodiment of the present application is schematically shown.

[0031] Figure 4 A schematic diagram of the structure of a computer system suitable for implementing an electronic device of an embodiment of the present application is shown. DETAILED DESCRIPTION

[0032] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this application will be more comprehensive and complete and fully convey the concept of the example embodiments to those skilled in the art.

[0033] In addition, described feature, structure or characteristic can be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present application. However, those skilled in the art will appreciate that the technical scheme of the present application can be put into practice without one or more of the specific details, or other methods, components, devices, steps, etc. can be adopted. In other cases, known methods, devices, realizations or operations are not shown or described in detail to avoid blurring the various aspects of the application.

[0034] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0035] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps can be decomposed, and some operations / steps can be combined or partially combined, so the actual execution order may change according to actual conditions.

[0036] The implementation details of the technical solution of this application are described in detail below:

[0037] Figure 1 FIG. 1 is a flow chart showing an intelligent monitoring method for anesthesia status according to an embodiment of the present application. Figure 1 As shown, the intelligent monitoring method for anesthesia status includes at least steps S110 to S150, which are described in detail as follows:

[0038] Step S110, collecting physiological data of the anesthetized patient through a sensor device.

[0039] In one embodiment of the present application, the sensing device may include an electrocardiogram sensor, a blood pressure monitor, a respiratory rate sensor, a blood oxygen saturation monitor, etc., which can monitor the key physiological indicators of the patient under anesthesia in real time.

[0040] Specifically, an electrocardiogram sensor can record the electrical activity of the heart, helping doctors understand the patient's heart rate and rhythm. A blood pressure monitor can measure the patient's arterial blood pressure and assess the effect of anesthesia on blood pressure. A respiratory rate sensor can monitor the patient's breathing rate and breathing depth to reflect the effect of anesthesia on the respiratory system. A blood oxygen saturation monitor measures the oxygen content in the blood to ensure that the patient will not be in danger due to lack of oxygen during anesthesia.

[0041] In one embodiment of the present application, the sensor device can monitor the physiological data of the anesthetized patient in real time, such as heart rate, blood pressure, respiration, etc., to ensure the real-time and accuracy of the data. The real-time collection of these physiological data provides doctors with valuable information to help them evaluate the depth and effect of anesthesia, as well as the patient's response to anesthetic drugs. Doctors can adjust the anesthesia plan in a timely manner based on these data to ensure the safety and comfort of the patient during the operation. Collecting data through high-precision sensor devices reduces human errors and interference, and improves the accuracy and reliability of the data.

[0042] Step S120, performing hash and linear transformation processing on the physiological data and its corresponding tag information to generate an encryption key, encrypting the physiological data using the encryption key to generate encrypted data, and transmitting the encrypted data to a central monitoring platform via a routing transmission medium.

[0043] like Figure 2As shown, in one embodiment of the present application, the physiological data and its corresponding tag information are hashed and linearly transformed to generate an encryption key, the physiological data is encrypted by the encryption key to generate encrypted data, and the encrypted data is transmitted to a central monitoring platform via a routing transmission medium, including:

[0044] S210, traversing all sensor devices to form a transmission network, and selecting a transmission node from the transmission network;

[0045] S220, the transmission node combines the data collected by each sensor device, performs hash and linear transformation processing on the physiological data obtained after the combination and the corresponding tag information to generate an encryption key, and encrypts the physiological data using the encryption key to generate encrypted data;

[0046] S230, the transmission node transmits the encrypted data to the central monitoring platform via the routing transmission medium.

[0047] In one embodiment of the present application, all sensor devices are traversed to form a transmission network. First, all sensor devices in the network are detected, for example, by manually entering the device address, using broadcast messages, or through other network devices (such as gateways). After that, each sensor device is initialized, including configuring its network parameters, such as: IP address, MAC address, etc. Each sensor device needs to know other devices that are directly adjacent to it, which is achieved by exchanging messages in a specific format. Based on the results of the neighbor nodes traversing the network, a network topology diagram is constructed to describe the connection relationship between the sensor devices.

[0048] In addition, you can also choose a routing protocol suitable for the transmission network to ensure that data can be effectively transmitted in the network.

[0049] In one embodiment of the present application, the information of each sensor device in the transmission network is traversed to calculate the sensor device Corresponding transmission parameters for:

[0050] ;

[0051] in, P represents a preset transmission probability, which is used as the probability of each sensor device becoming a transmission node; r Indicates the number of rounds of transmission and selection of transmission nodes; mod indicates the remainder operation; represents the power of the sensor device, represents the data processing efficiency of the sensor device, The transmission parameters are obtained through the above calculations, and the node corresponding to the highest transmission parameter is selected from the sensor device as the transmission node, so as to ensure the data transmission quality and transmission efficiency in this way.

[0052] In one embodiment of the present application, the transmission node combines the data collected by each sensor device, performs hash and linear transformation processing on the physiological data obtained after the combination and its corresponding tag information to generate an encryption key, and encrypts the physiological data by the encryption key to generate encrypted data, including:

[0053] Each sensor device transmits the collected data to the transmission node;

[0054] The transmission node performs labeling processing on the collected data, and then combines the data to generate physiological data;

[0055] Acquire patient information of an anesthetized patient and label information of the physiological data, perform hash processing on the patient information and the label information, and generate an encryption key;

[0056] The physiological data and its corresponding tag information are hashed and linearly transformed to generate an encryption key, and the physiological data is encrypted using the encryption key to generate encrypted data.

[0057] In one embodiment of the present application, after the transmission node is determined, each sensor device transmits the collected data to the transmission node, and then the collected data is labeled by the transmission node. For example, data labels are generated based on information such as the source and type of each collected data, and then combined to generate physiological data.

[0058] In one embodiment of the present application, obtaining patient information of an anesthetized patient and label information of the physiological data, performing hash processing on the patient information and the label information, and generating an encryption key includes:

[0059] Acquiring patient information of an anesthetized patient and label information of the physiological data, and combining the patient information and label information to obtain a standardized information set;

[0060] Performing hash processing on each element in the information set to obtain a hash value set consisting of multiple hash values;

[0061] Performing a polynomial operation on multiple hash values ​​in the hash value set to generate a key candidate value;

[0062] The key candidate value is subjected to nonlinear transformation and iterative processing to generate an encryption key, and the physiological data is encrypted by using the encryption key to generate encrypted data.

[0063] In one embodiment of the present application, the patient information of the anesthetized patient and the label information of the physiological data are obtained, and the patient information and the label information are combined to obtain a standardized information set as follows: ,in Indicates i context information, and n represents the amount of data in the collection.

[0064] Afterwards, the information collection Hash each element in to get a hash value set consisting of multiple hash values: ;

[0065] The combined hash value set Perform an XOR operation ( ), get the preliminary key candidate value vector : ;

[0066] Afterwards, a randomly generated matrix A is used to map the key candidate value vector Perform matrix multiplication to obtain the updated first key candidate value sequence :

[0067] ;

[0068] in, Represents a large prime number, used in modular arithmetic to ensure that the result is within a reasonable range.

[0069] Afterwards, the first key candidate value sequence Perform polynomial operations to obtain a polynomial :

[0070] ;

[0071] in, is a sequence of candidate key values The elements in q is another large prime number, x represents the independent variable of the polynomial.

[0072] Afterwards, in the polynomial Add a random element, such as a randomly generated constant d and a randomly selected Value , calculate the updated second key candidate value sequence for:

[0073] ;

[0074] right Perform multiple nonlinear transformations, such as S-box replacement, round function, and permutation, etc. to increase the complexity and unpredictability of the key. Use the candidate key value after nonlinear transformation as input and perform multiple iterative processes. Each iteration can use different nonlinear transformation parameters, as well as combine mathematical elements such as matrix operations, polynomial operations, and modular operations. After multiple nonlinear transformations and iterative processes, the final encryption key is obtained, and the physiological data and its corresponding tag information are hashed and linearly transformed to generate an encryption key, and the physiological data is encrypted by the encryption key to generate encrypted data.

[0075] Through the key generation algorithm, a key with higher complexity and unpredictability can be generated. This method makes the generated key more secure and reliable in the fields of data encryption and network security, ensuring the privacy and security of patient information.

[0076] Step S130, decrypting the encrypted data through the central monitoring platform to obtain physiological data, and extracting feature vectors corresponding to each physiological dimension from the physiological data.

[0077] In one embodiment of the present application, the encrypted data is decrypted by a central monitoring platform to obtain physiological data, and feature vectors corresponding to each physiological dimension are extracted from the physiological data, including:

[0078] The central monitoring platform obtains the encrypted data, and decrypts the encrypted data to obtain the original physiological data;

[0079] The physiological data is preprocessed, and then the feature vectors corresponding to each physiological dimension are extracted.

[0080] In one embodiment of the present application, after obtaining the encrypted data, the central monitoring platform converts the encrypted data back to its original format, that is, the original physiological data. The specific decryption method can be the reverse process of the above process, which will not be described here.

[0081] The above process ensures the security of data during transmission and storage by encrypting physiological data, preventing data leakage and abuse. The decryption process is only performed on the central monitoring platform, further enhancing the security of data.

[0082] After decryption, the physiological data is preprocessed through the platform. Preprocessing may include steps such as data cleaning (such as removing noise, handling missing values, etc.), data conversion (such as converting data to a specific format or unit), and data standardization (such as scaling the data to a common scale). These steps are designed to improve the quality and consistency of the data for subsequent analysis.

[0083] After preprocessing, feature vectors are extracted from physiological data through the platform. Feature vectors are key statistics or patterns that represent each physiological dimension in the data set (such as heart rate, blood pressure, etc.). These feature vectors can be used for subsequent analysis, modeling, or prediction tasks. Methods for extracting feature vectors may include calculating statistics such as mean, standard deviation, maximum, minimum, kurtosis, skewness, etc.

[0084] The above process decrypts the encrypted data through the central monitoring platform to obtain the original physiological data. The feature vectors corresponding to each physiological dimension are extracted from the physiological data, such as the heart rate feature vector, blood pressure feature vector, etc., to provide a basis for subsequent analysis and judgment.

[0085] Step S140, obtaining characteristic indicators corresponding to each physiological dimension, comparing the characteristic vector with the characteristic indicator, and determining the current anesthetic state of the anesthetized patient.

[0086] In one embodiment of the present application, obtaining characteristic indicators corresponding to each physiological dimension, comparing the characteristic vector with the characteristic indicator, and determining the current anesthesia state of the anesthetized patient includes:

[0087] Obtain characteristic indicators corresponding to each physiological dimension;

[0088] Calculating a difference parameter between the feature vector and the feature index;

[0089] The current anesthetic state of the anesthetized patient is determined according to the difference parameter.

[0090] In one embodiment of the present application, the characteristic index is determined based on a large amount of historical data or medical knowledge, and is a key statistic or threshold that can reflect the physiological state. For example, for heart rate, the characteristic index may be a normal heart rate range; for blood pressure, the characteristic index may be a normal value or acceptable range of systolic and diastolic blood pressure.

[0091] The feature vector (representing the statistics or patterns of the current physiological data) extracted after preprocessing is compared with the characteristic index to calculate the difference parameters between them. These difference parameters can be numerical differences (such as the deviation between the actual heart rate and the normal heart rate range), proportional differences (such as the ratio of the actual blood pressure value to the normal blood pressure value), or other forms of measurement. The size and direction of the difference parameter can reflect the degree of deviation between the physiological state and the characteristic index.

[0092] If the difference parameters of multiple physiological dimensions are beyond the acceptable range, the patient is judged to be in a state of deep anesthesia or too shallow anesthesia; if the difference parameters are within the normal range, the patient is judged to be in a stable state of anesthesia. By extracting feature vectors and calculating difference parameters, the physiological state of anesthetized patients can be more comprehensively reflected. Combining the difference parameters of multiple physiological dimensions can more accurately evaluate the state of anesthesia and reduce the risk of misjudgment and missed diagnosis.

[0093] The above process can obtain the physiological data of anesthetized patients in real time through the central monitoring platform, and decrypt and pre-process it, making it possible to timely evaluate the anesthesia status. The acquisition of real-time data enables doctors to respond quickly to the physiological changes of patients and ensure the safety of patients.

[0094] Step S150, displaying the anesthesia state on a display.

[0095] In one embodiment of the present application, a display method corresponding to the anesthesia state is determined according to a preset display method, and displayed on a display.

[0096] In one embodiment of the present application, the display method may include text description, graphic display, color coding, sound prompt, etc. The selection of these methods should be based on medical standards, the usage habits of medical staff, and the technical feasibility of the system. The preset display method may be stored in the system configuration file or set by the medical staff through the user interface.

[0097] According to the evaluation results of the anesthesia status (such as deep anesthesia, stable anesthesia, too shallow anesthesia, etc.), the system needs to match the evaluation results with the preset display method. The matching process may involve programming techniques such as logical judgment and conditional branching to ensure that the appropriate display method is correctly selected.

[0098] In this embodiment, the display content may include a text description of the anesthesia state, a chart (such as a curve chart of physiological indicators such as heart rate and blood pressure), a color code (such as using different colors to represent different anesthesia states), etc. Through the preset display method, the central monitoring platform can display the anesthesia state information on the display in a suitable form. This step not only helps medical staff to quickly understand the patient's anesthesia state, but also improves the efficiency and quality of medical services.

[0099] In the technical solution of the present application, the physiological data of an anesthetized patient is collected by a sensor device; the physiological data and its corresponding tag information are hashed and linearly transformed to generate an encryption key, the physiological data is encrypted by the encryption key to generate encrypted data, and the encrypted data is transmitted to a central monitoring platform through a routing transmission medium; the encrypted data is decrypted by the central monitoring platform to obtain physiological data, and characteristic vectors corresponding to each physiological dimension are extracted from the physiological data; characteristic indicators corresponding to each physiological dimension are obtained, and the characteristic vectors are compared with the characteristic indicators to determine the current anesthesia state of the anesthetized patient; and the anesthesia state is displayed on a display. The above process realizes the real-time collection, encrypted transmission, accurate decryption and feature analysis of the physiological data of anesthetized patients by integrating sensor devices, data encryption technology, central monitoring platform and feature extraction algorithm, ensuring the efficiency, security and privacy protection of anesthesia data transmission.

[0100] The following describes an embodiment of the device of the present application, which can be used to execute the intelligent monitoring method of the anesthetic state in the above-mentioned embodiment of the present application. It can be understood that the device can be a computer program (including program code) running in a computer device, for example, the device is an application software; the device can be used to execute the corresponding steps in the method provided in the embodiment of the present application. For details not disclosed in the embodiment of the device of the present application, please refer to the embodiment of the intelligent monitoring method of the anesthetic state in the above-mentioned embodiment of the present application.

[0101] Figure 3 A block diagram of an intelligent monitoring system for anesthesia status according to an embodiment of the present application is shown.

[0102] Reference Figure 3 As shown, according to an embodiment of the present application, an intelligent monitoring system for anesthesia status includes:

[0103] The collecting unit 310 is used to collect physiological data of the anesthetized patient through a sensor device;

[0104] The encryption unit 320 is used to perform hash and linear transformation processing on the physiological data and its corresponding tag information to generate an encryption key, encrypt the physiological data using the encryption key to generate encrypted data, and transmit the encrypted data to the central monitoring platform via a routing transmission medium;

[0105] The feature unit 330 is used to decrypt the encrypted data through the central monitoring platform to obtain physiological data, and extract feature vectors corresponding to each physiological dimension from the physiological data;

[0106] The state unit 340 is used to obtain the characteristic index corresponding to each physiological dimension, compare the characteristic vector with the characteristic index, and determine the current anesthesia state of the anesthetized patient;

[0107] The display unit 350 is used to display the anesthesia state on a display.

[0108] In the present application, based on the aforementioned scheme, the physiological data and its corresponding label information are hashed and linearly transformed to generate an encryption key, the physiological data is encrypted by the encryption key to generate encrypted data, and the encrypted data is transmitted to the central monitoring platform via a routing transmission medium, including: traversing all sensor devices to form a transmission network, and selecting a transmission node from the transmission network; the transmission node combines the data collected by each sensor device, hashes and linearly transforms the physiological data and its corresponding label information obtained after the combination to generate an encryption key, and encrypts the physiological data by the encryption key to generate encrypted data; the transmission node transmits the encrypted data to the central monitoring platform via the routing transmission medium.

[0109] In the present application, based on the aforementioned scheme, the transmission node combines the data collected by each sensor device, performs hash and linear transformation processing on the physiological data obtained after the combination and its corresponding label information to generate an encryption key, and encrypts the physiological data through the encryption key to generate encrypted data, including: each sensor device transmits the collected data to the transmission node; the transmission node labels the collected data, and then combines them to generate physiological data; obtains patient information of an anesthetized patient and label information of the physiological data, hashes the patient information and the label information to generate an encryption key; performs hash and linear transformation processing on the physiological data and its corresponding label information to generate an encryption key, and encrypts the physiological data through the encryption key to generate encrypted data.

[0110] In the present application, based on the aforementioned scheme, the patient information of an anesthetized patient and the label information of the physiological data are obtained, the patient information and the label information are hashed, and an encryption key is generated, including: obtaining the patient information of an anesthetized patient and the label information of the physiological data, combining the patient information and the label information to obtain a standardized information set; hashing each element in the information set to obtain a hash value set consisting of multiple hash values; performing polynomial operations on multiple hash values ​​in the hash value set to generate key candidate values; performing nonlinear transformation and iterative processing on the key candidate values ​​to generate an encryption key, and encrypting the physiological data with the encryption key to generate encrypted data.

[0111] In the present application, based on the aforementioned scheme, the encrypted data is decrypted by the central monitoring platform to obtain physiological data, and the characteristic vectors corresponding to each physiological dimension are extracted from the physiological data, including: the central monitoring platform obtains the encrypted data, and decrypts the encrypted data to obtain the original physiological data; the physiological data is preprocessed, and then the characteristic vectors corresponding to each physiological dimension are extracted.

[0112] In the present application, based on the aforementioned scheme, the characteristic indicators corresponding to each physiological dimension are obtained, the characteristic vectors are compared with the characteristic indicators, and the current anesthetic state of the anesthetized patient is determined, including: obtaining the characteristic indicators corresponding to each physiological dimension; calculating the difference parameters between the characteristic vectors and the characteristic indicators; and determining the current anesthetic state of the anesthetized patient according to the difference parameters.

[0113] In the present application, based on the aforementioned solution, displaying the corresponding level of the anesthesia state in the display includes: determining the display method corresponding to the anesthesia state according to a preset display method, and displaying it in the display.

[0114] In the technical solution of the present application, the physiological data of an anesthetized patient is collected by a sensor device; the physiological data and its corresponding tag information are hashed and linearly transformed to generate an encryption key, the physiological data is encrypted by the encryption key to generate encrypted data, and the encrypted data is transmitted to a central monitoring platform through a routing transmission medium; the encrypted data is decrypted by the central monitoring platform to obtain physiological data, and characteristic vectors corresponding to each physiological dimension are extracted from the physiological data; characteristic indicators corresponding to each physiological dimension are obtained, and the characteristic vectors are compared with the characteristic indicators to determine the current anesthesia state of the anesthetized patient; and the anesthesia state is displayed on a display. The above process realizes the real-time collection, encrypted transmission, accurate decryption and feature analysis of the physiological data of anesthetized patients by integrating sensor devices, data encryption technology, central monitoring platform and feature extraction algorithm, ensuring the efficiency, security and privacy protection of anesthesia data transmission.

[0115] Figure 4 A schematic diagram of the structure of a computer system suitable for implementing an electronic device of an embodiment of the present application is shown.

[0116] It should be noted that the computer system 400 of the electronic device shown in the figure is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0117] The computer system 400 includes a central processing unit (CPU) 401, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 402 or the program loaded from the storage part 408 to the random access memory (RAM) 403, such as executing the method described in the above embodiment. Various programs and data required for system operation are also stored in the RAM 403. The CPU 401, the ROM 402 and the RAM 403 are connected to each other through the bus 404. The input / output (I / O) interface 405 is also connected to the bus 404.

[0118] The following components are connected to the I / O interface 405: an input section 406 including a keyboard, a mouse, etc.; an output section 407 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the I / O interface 405 as needed. A removable medium 411, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 410 as needed so that a computer program read therefrom is installed into the storage section 408 as needed.

[0119] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication part 409, and / or installed from a removable medium 411. When the computer program is executed by the central processing unit (CPU) 401, various functions defined in the system of the present application are executed.

[0120] It should be noted that the computer-readable medium shown in the embodiment of the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, device or device. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable computer program. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, which may send, propagate, or transmit programs for use by or in conjunction with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0121] The flowchart and block diagram in the accompanying drawings illustrate the possible architecture, functions and operations of the system, method and computer program product according to various embodiments of the present application. Wherein, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0122] The units involved in the embodiments described in this application may be implemented by software or hardware, and the units described may also be set in a processor. The names of these units do not, in some cases, constitute limitations on the units themselves.

[0123] According to one aspect of the present application, a computer program product or computer program is provided, the computer program product or computer program comprising computer instructions, the computer instructions being stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and when the processor executes the computer instructions, the intelligent monitoring method for the anesthetic state described in the above embodiment is implemented.

[0124] As another aspect, the present application also provides a computer-readable medium, which may be included in the electronic device described in the above embodiment; or may exist independently without being assembled into the electronic device. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by an electronic device, the electronic device implements the method described in the above embodiment.

[0125] It should be noted that, although several modules or units of the equipment for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present application, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into being embodied by multiple modules or units.

[0126] Through the description of the above implementation methods, it is easy for those skilled in the art to understand that the example implementation methods described here can be implemented by software, or by combining software with necessary hardware. Therefore, the technical solution according to the implementation method of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the implementation method of the present application.

[0127] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the embodiments disclosed herein. The present application is intended to cover any variations, uses or adaptations of the present application, which follow the general principles of the present application and include common knowledge or customary technical means in the art that are not disclosed in the present application.

[0128] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. An intelligent monitoring method for anesthesia status, characterized in that: include: Traverse all sensor devices to form a transmission network, and select a transmission node from the transmission network; specifically, traverse the information of each sensor device in the transmission network, calculate the sensor device Corresponding transmission parameters for: ； in, P represents a preset transmission probability, which is used as the probability of each sensor device becoming a transmission node; r Indicates the number of rounds of transmission and selection of transmission nodes; mod indicates the remainder operation; represents the power of the sensor device, represents the data processing efficiency of the sensor device, represents the amount of data to be transmitted; the transmission parameter is obtained through the above calculation, and the node corresponding to the highest transmission parameter is selected from the sensor device as the transmission node; Each sensor device transmits the collected data to a transmission node, and the transmission node labels the collected data and then combines them to generate physiological data; The patient information of the anesthetized patient and the label information of the physiological data are obtained, and the patient information and the label information are combined to obtain a standardized information set as follows: ,in Indicates i context information, n represents the amount of data in the collection; The information collection Hash each element in to get a hash value set consisting of multiple hash values: ; For the hash value set Perform XOR operation on multiple hash values ​​in , get the preliminary key candidate value vector ; Afterwards, a randomly generated matrix A is used to map the key candidate value vector Perform matrix multiplication to obtain the updated first key candidate value sequence : ;in, represents a large prime number; Afterwards, the first key candidate value sequence Perform polynomial operations to obtain a polynomial : ； in, is a sequence of candidate values ​​for the key The elements in q is another large prime number, x represents the independent variable of the polynomial; Afterwards, in the polynomial Adding an element of randomness: a randomly generated constant d and a randomly selected Value , calculate the updated second key candidate value sequence for: ； For the second key candidate value sequence Performing nonlinear transformation and iterative processing to generate an encryption key, and encrypting the physiological data using the encryption key to generate encrypted data; The transmission node transmits the encrypted data to a central monitoring platform via a routing transmission medium; Decrypting the encrypted data through a central monitoring platform to obtain physiological data, and extracting feature vectors corresponding to each physiological dimension from the physiological data; Acquire characteristic indicators corresponding to each physiological dimension, compare the characteristic vector with the characteristic indicator, and determine the current anesthesia state of the anesthetized patient; The anesthetic state is displayed on a display.

2. The intelligent monitoring method of anesthesia state according to claim 1, characterized in that: The encrypted data is decrypted by the central monitoring platform to obtain physiological data, and feature vectors corresponding to each physiological dimension are extracted from the physiological data, including: The central monitoring platform obtains the encrypted data, and decrypts the encrypted data to obtain the original physiological data; The physiological data is preprocessed, and then the feature vectors corresponding to each physiological dimension are extracted.

3. The intelligent monitoring method of anesthesia state according to claim 1, characterized in that: Acquiring characteristic indicators corresponding to each physiological dimension, comparing the characteristic vector with the characteristic indicator, and determining the current anesthesia state of the anesthetized patient, including: Obtain characteristic indicators corresponding to each physiological dimension; Calculating a difference parameter between the feature vector and the feature index; The current anesthetic state of the anesthetized patient is determined according to the difference parameter.

4. The intelligent monitoring method of anesthesia state according to claim 1, characterized in that: The corresponding level of the anesthesia state is displayed on the display, including: According to a preset display method, the display method corresponding to the anesthesia state is determined and displayed on the display.

5. An intelligent monitoring system for anesthesia status, characterized in that: include: The collection unit is used to traverse all sensor devices to form a transmission network and select a transmission node from the transmission network; specifically, traverse the information of each sensor device in the transmission network and calculate the sensor device Corresponding transmission parameters for: ； in, P represents a preset transmission probability, which is used as the probability of each sensor device becoming a transmission node; r Indicates the number of rounds of transmission and selection of transmission nodes; mod indicates the remainder operation; represents the power of the sensor device, represents the data processing efficiency of the sensor device, represents the amount of data to be transmitted; the transmission parameters are obtained through the above calculation, and the node corresponding to the highest transmission parameter is selected from the sensor device as the transmission node; each sensor device transmits the collected data to the transmission node, and the transmission node labels the collected data and then combines them to generate physiological data; The encryption unit is used to obtain the patient information of the anesthetized patient and the label information of the physiological data, and combine the patient information and the label information to obtain a standardized information set as follows: ,in Indicates i context information, n represents the amount of data in the collection; The information collection Hash each element in to get a hash value set consisting of multiple hash values: ; For the hash value set Perform XOR operation on multiple hash values ​​in , get the preliminary key candidate value vector ; Afterwards, a randomly generated matrix A is used to map the key candidate value vector Perform matrix multiplication to obtain the updated first key candidate value sequence : ； in, represents a large prime number; Afterwards, the first key candidate value sequence Perform polynomial operations to obtain a polynomial : ； in, is a sequence of candidate values ​​for the key The elements in q is another large prime number, x represents the independent variable of the polynomial; Afterwards, in the polynomial Adding an element of randomness: a randomly generated constant d and a randomly selected Value , calculate the updated second key candidate value sequence for: ； For the second key candidate value sequence Performing nonlinear transformation and iterative processing to generate an encryption key, and encrypting the physiological data using the encryption key to generate encrypted data; The transmission node transmits the encrypted data to a central monitoring platform via a routing transmission medium; Decrypting the encrypted data through a central monitoring platform to obtain physiological data, and extracting feature vectors corresponding to each physiological dimension from the physiological data; Acquire characteristic indicators corresponding to each physiological dimension, compare the characteristic vector with the characteristic indicator, and determine the current anesthesia state of the anesthetized patient; The anesthetic state is displayed on a display.

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