An intelligent medical information data encryption method, system, device and medium

Abstract

This invention discloses a smart medical information data encryption method, system, device, and medium, relating to the field of data encryption technology. The method includes: preprocessing medical information data to obtain device wear curves and device correlation curves; obtaining device usage codes; obtaining patient diagnostic characteristics based on patient medical information; and setting encryption codes for patient medical data. This invention addresses the problem that existing technologies for encrypting medical information typically involve multiple encryptions of user passwords or rearranging characters, without combining encryption with the medical data itself. This leads to the potential leakage of patient data when attackers from other industries attempt to steal the data, using decryption techniques targeting patient information.

Description

Technical Field

[0001] This invention relates to the field of data encryption technology, specifically to a method, system, device, and medium for encrypting smart medical information data. Background Technology

[0002] Medical information refers to various data and information related to patients, diseases, treatments, research, and medical institutions in the medical field, which are collected, analyzed, organized, and disseminated. The accuracy and reliability of medical information play a crucial role in the decision-making of medical personnel and the health management of patients. With the continuous advancement of technology and the development of the medical system, the importance of medical information is becoming increasingly prominent. Data encryption is a commonly used security technology in medical information systems. It prevents unauthorized personnel from accessing and reading sensitive data by converting data into an incomprehensible form.

[0003] Existing improvements to medical information data encryption typically use existing password libraries or custom password characters to encrypt medical data. The encryption methods are usually multiple encryptions of user passwords or rearrangement of characters, without combining encryption with the medical data itself. This means that when hackers from other industries steal medical information data, they can exploit the patient's password or personal information to leak the patient's medical data. For example, Chinese patent application CN106357608A discloses a method for encrypting and decrypting privacy data for personal medical and health data. This scheme encrypts privacy data by converting the password of the personal medical and health data account into ciphertext using password characters. However, this encryption method only uses the account password and password characters. If the generated ciphertext cannot be combined with the data in the medical information, technicians from other industries implementing privacy data encryption algorithms are at risk of cracking the account password and password characters. Furthermore, existing AI algorithms could be used to decipher the password conversion process, leading to the leakage of user privacy data. Therefore, it is necessary to improve existing medical information data encryption methods. Summary of the Invention

[0004] This invention aims to at least partially solve one of the technical problems in the prior art by proposing a smart medical information data encryption method, system, device, and medium. This addresses the issue that the encryption of medical information in the prior art typically involves multiple encryptions of user passwords or rearrangement of characters, without combining encryption with medical data. This leads to the problem that when individuals from other industries attempt to steal medical information data, they may use decryption methods starting with the patient's user password or personal information, potentially resulting in the leakage of the patient's medical data.

[0005] To achieve the above objectives, in a first aspect, the present invention provides a method for encrypting smart medical information data, comprising:

[0006] The medical information data is preprocessed, and the device wear curve and device correlation curve are obtained from the medical information data based on the preprocessing results; the device usage code is obtained based on the device wear curve and device correlation curve.

[0007] When a patient seeks medical attention, the patient's diagnostic characteristics are obtained based on the patient's medical information.

[0008] Encryption codes for patient medical data are set based on device usage codes and patient diagnostic characteristics.

[0009] Furthermore, the medical information data is preprocessed, and based on the preprocessing results, the device wear curve and device correlation curve in the medical information data are obtained, including:

[0010] Acquire medical information data, including medical device usage data, medical personnel work data, and patient medical treatment data; preprocess the medical information data using medical feature extraction methods, including:

[0011] A Cartesian coordinate system is established, denoted as the instrument wear coordinate system. The horizontal axis of the instrument wear coordinate system is in units of time, and the vertical axis is in units of wear percentage. Based on the usage data of all medical devices, a wear analysis sub-method is used to record the relationship between the usage time and wear level of each medical device in the instrument wear coordinate system. The wear analysis sub-method includes: for any medical device, the quantity of the medical device is denoted as J, and all medical devices are denoted as α1 to αJ. For any αn among α1 to αJ, the wear level of αn is obtained and expressed as a percentage, denoted as wear percentage n. The usage time of αn from the start of use to the present is recorded as the usage time of αn, where n is a positive integer less than or equal to J and greater than or equal to 1.

[0012] Based on the percentage of wear and tear and the duration of use of all medical devices, a scatter plot is drawn in the device wear coordinate system, and the curve obtained by fitting the scatter plot is recorded as the wear curve.

[0013] Furthermore, medical feature extraction methods also include:

[0014] Based on the processing method of medical devices, the loss analysis sub-method is used to process all medical devices, and all the loss curves obtained are fitted in the device loss coordinate system. The fitted curves are denoted as multimode loss curves.

[0015] Let the maximum absolute value of the slope in the multimode loss curve be denoted as β0; for any loss curve β in the instrument loss coordinate system, let the maximum absolute value of the slope in the loss curve β be denoted as β1. Let β be the slope ratio; obtain the slope ratio of all loss curves, and denote the maximum and minimum values ​​among all slope ratios as deviation slope A and deviation slope B; when the value of deviation slope A minus deviation slope B is less than or equal to β0, the multimode loss curve is denoteed as the instrument loss curve; when the value of deviation slope A minus deviation slope B is greater than β0, the loss curves corresponding to deviation slope A and deviation slope B are removed from the instrument loss coordinate system, and then all loss curves in the instrument loss coordinate system are fitted, and the fitted curve is denoteed as the instrument loss curve.

[0016] Furthermore, medical feature extraction methods also include:

[0017] For any medical device, based on medical information data, the number of times medical personnel use the medical device is denoted as Pα; the number of times patients use the medical device during their medical visits is denoted as Dα. A device-emphasis algorithm is used to obtain the device's emphasis parameters. The device-emphasis algorithm includes: Where F is the emphasis parameter, z1 is the number of records of medical personnel using medical devices in the medical information data, z2 is the number of records of patients using medical devices during diagnosis in the medical information data, and J is the number of medical devices.

[0018] The key parameters of all medical devices are obtained based on the analytical methods used for medical devices.

[0019] Establish a Cartesian coordinate system, denoted as the device-related coordinate system. The unit of the X-axis of the device-related coordinate system is the number of times, and the unit of the Y-axis is the emphasis parameter. Based on the quantity and emphasis parameter of each medical device, mark points in the device-related coordinate system and denot them as related points. Connect adjacent related points on the horizontal axis with a curve and denot the resulting curve as the device-related curve.

[0020] Furthermore, obtaining the device usage code based on the device wear curve and device correlation curve includes:

[0021] In the instrument association coordinate system, when the instrument association curve intersects with the X-axis, the multiple independent closed regions formed by the instrument association curve and the X-axis are denoted as association region 1 to association region N. The sum of the areas of all association regions above the X-axis in association region 1 to association region N is denoted as the positive association value, and the sum of the areas of all association regions below the X-axis in association region 1 to association region N is denoted as the negative association value.

[0022] In the instrument wear coordinate system, the intersection points of the instrument wear curve with all wear curves are denoted as wear intersection point 1 to wear intersection point M, respectively. The absolute values ​​of the slopes of the instrument wear curves at all wear intersection points are denoted as γ1 to γM, respectively. The absolute values ​​of γ1 to γM are denoted as the average wear slope. The maximum absolute value of the slope in the instrument wear curve is denoted as slope MAX. The slope MAX is not necessarily equal to β0.

[0023] Set the instrument usage code as an array szz[4], where szz[4] = {positive correlation value, negative correlation value, mean loss slope, maximum slope}.

[0024] Furthermore, when a patient seeks medical attention, the diagnostic characteristics of the patient obtained based on the patient's medical information include:

[0025] When a patient visits a clinic, the name of the department the patient visited is obtained and recorded as the visiting department name. The number of characters in the Chinese name corresponding to the visiting department name is recorded as G. The remainder obtained by dividing G by the array coefficient is recorded as the conversion value. The array coefficient is the number of elements in the array szz.

[0026] The symptoms registered in the patient's medical information are recorded as real-time symptoms, and the medical devices used to treat the real-time symptoms are recorded as real-time devices. The slope ratio of the wear curve corresponding to the real-time device is obtained and recorded as the real-time ratio. When the real-time ratio is greater than or equal to 1, the value of the real-time ratio rounded up is recorded as the device value. When the real-time ratio is less than 1, the value of 1 divided by the real-time ratio and rounded up is recorded as the device value. The conversion value and the device value are recorded as the patient's diagnostic characteristics.

[0027] Furthermore, the encryption codes for patient medical data, based on device usage codes and patient diagnostic characteristics, include:

[0028] When a patient uses medical devices during a visit, the patient's conversion value is recorded as i, the patient's device value is recorded as j, the i-th element in the array szz[4] is converted to base j, and the resulting array szz[4] is recorded as the encryption code. The encryption code is used to encrypt the patient's medical data.

[0029] When a patient does not use medical devices during a visit, the remainder obtained by dividing the device value by the array coefficient is recorded as the device conversion value. The patient's device conversion value is recorded as i, and the patient's device value is recorded as j. The i-th element in the array szz[4] is converted to base j, and the resulting array szz[4] is recorded as the encryption code. The encryption code is used to encrypt the patient's medical data.

[0030] Secondly, the present invention also proposes a smart medical information data encryption system, including a code acquisition module, a diagnostic code acquisition module, and a data encryption module;

[0031] The code acquisition module is used to preprocess medical information data and obtain the device wear curve and device correlation curve from the medical information data based on the preprocessing results; the device usage code is then obtained based on the device wear curve and device correlation curve.

[0032] The diagnostic code acquisition module is used to obtain the patient's diagnostic characteristics based on the patient's medical information when the patient visits the hospital.

[0033] The data encryption module is used to set encryption codes for patient medical data based on device usage codes and patient diagnostic characteristics.

[0034] Thirdly, the present invention provides a storage medium having a computer program stored thereon, which, when executed by a processor, performs the steps of the method as described in any of the preceding claims.

[0035] Fourthly, the present invention provides an electronic device including a processor and a memory, the memory storing computer-readable instructions that, when executed by the processor, perform the steps of the method described in any of the preceding claims.

[0036] The beneficial effects of this invention are as follows: First, the invention preprocesses medical information data and obtains device wear curves and device correlation curves based on the preprocessing results. Then, it obtains device usage codes based on these curves. Next, when a patient visits the hospital, the invention obtains the patient's diagnostic characteristics based on the patient's medical information. Finally, it sets an encryption code for the patient's medical data based on the device usage code and the patient's diagnostic characteristics. The advantage of this approach is that by obtaining the device wear curves and device correlation curves and further obtaining the device usage code, the device usage code can be combined with the medical information data as an encryption code. When an attacker attempts to crack the device usage code, the encryption is compromised. Firstly, it is necessary to acquire real-time wear and tear data of the equipment. This acquisition process involves a large amount of data access and decryption, increasing the risk of being discovered as a potential breacher. This enhances the confidentiality of the equipment usage code for non-medical personnel. However, by combining the patient's diagnostic characteristics at the time of their visit with the equipment usage code to obtain an encrypted code, a code tailored to the patient's diagnostic information can be generated in real time based on the equipment usage code to encrypt patient data. This further enhances the confidentiality of the encrypted code for medical personnel. Moreover, if the encrypted code is leaked, the scope of investigation for potential breachers can be minimized based on knowledge of the patient's diagnostic characteristics. Attached Figure Description

[0037] Figure 1 This is a schematic diagram of the system of the present invention;

[0038] Figure 2 This is a flowchart illustrating the steps of the method of the present invention;

[0039] Figure 3 This is a schematic diagram illustrating the acquisition of the device wear curve according to the present invention;

[0040] Figure 4 This is a schematic diagram of the associated region of the present invention;

[0041] Figure 5 This is a schematic diagram of the electronic device of the present invention. Detailed Implementation

[0042] The technical solutions of 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.

[0043] Example 1, please refer to Figure 1 As shown, this application provides a smart medical information data encryption system, including a code acquisition module, a diagnostic code acquisition module, and a data encryption module;

[0044] The code acquisition module is used to preprocess medical information data and obtain the device wear curve and device correlation curve from the medical information data based on the preprocessing results; the device usage code is obtained based on the device wear curve and device correlation curve.

[0045] The code acquisition module is configured with a device curve acquisition strategy and a code generation strategy. The device curve acquisition strategy includes:

[0046] Acquire medical information data, including medical device usage data, medical personnel work data, and patient medical treatment data; preprocess the medical information data using medical feature extraction methods, including:

[0047] A Cartesian coordinate system is established, denoted as the instrument wear coordinate system. The horizontal axis of the instrument wear coordinate system is in units of time, and the vertical axis is in units of wear percentage. Based on the usage data of all medical devices, the wear analysis sub-method is used to record the relationship between the usage time and wear degree of each medical device in the instrument wear coordinate system.

[0048] The loss analysis sub-method includes: for any medical device, the number of medical devices is denoted as J, and all medical devices are denoted as α1 to αJ respectively. For any αn among α1 to αJ, the degree of loss of αn is obtained and expressed as a percentage, denoted as the loss percentage n. The duration of αn from the start of use to the present is denoted as the usage duration of αn, where n is a positive integer less than or equal to J and greater than or equal to 1.

[0049] In the specific implementation process, in this embodiment, when acquiring all medical devices, only medical devices that are in normal use are counted. Medical devices α that cannot be used due to damage or accidents are not included in the acquisition scope.

[0050] Based on the percentage of wear and tear and the duration of use of all medical devices, a scatter plot is drawn in the device wear coordinate system, and the curve obtained by fitting the scatter plot is recorded as the wear curve.

[0051] Based on the processing method of medical devices, the loss analysis sub-method is used to process all medical devices, and all the loss curves obtained are fitted in the device loss coordinate system. The fitted curves are denoted as multimode loss curves.

[0052] Let the maximum absolute value of the slope in the multimode loss curve be denoted as β0; for any loss curve β in the instrument loss coordinate system, let the maximum absolute value of the slope in the loss curve β be denoted as β1. Let β be the slope ratio; obtain the slope ratio of all loss curves, and denote the maximum and minimum values ​​among all slope ratios as deviation slope A and deviation slope B; when the value of deviation slope A minus deviation slope B is less than or equal to β0, the multimode loss curve is denoteed as the instrument loss curve; when the value of deviation slope A minus deviation slope B is greater than β0, the loss curves corresponding to deviation slope A and deviation slope B are removed from the instrument loss coordinate system, and then all loss curves in the instrument loss coordinate system are fitted, and the fitted curve is denoteed as the instrument loss curve.

[0053] For specific implementation details, please refer to [link / reference]. Figure 3 As shown, the dashed lines corresponding to CC1 to CC6 are the loss curves, and CC0 is the multimode loss curve obtained by fitting CC1 to CC6. Through data analysis, the maximum absolute value of the slope in the multimode loss curve is 1.5, and the slope ratios of all loss curves are 0.5, 0.7, 1, 1.4, 2, and 2.5, respectively. Through calculation, it can be found that the value of 2.5-0.5 is greater than 1.5. Therefore, the loss curves with slope ratios of 2.5 and 0.5 are removed from the instrument loss coordinate system, and then all loss curves in the instrument loss coordinate system are fitted again. The fitted curve is recorded as the instrument loss curve.

[0054] In the specific implementation process, by eliminating loss curves with excessively large slope ratios, it is possible to prevent the obtained instrument loss curve from being affected by certain medical devices that are not easily damaged or are relatively easily damaged, thus preventing the instrument loss curve from reflecting the overall loss situation of medical devices.

[0055] For any medical device, based on medical information data, the number of times medical personnel use the medical device is denoted as Pα; the number of times patients use the medical device during their medical visits is denoted as Dα. A device-emphasis algorithm is used to obtain the device's emphasis parameters. The device-emphasis algorithm includes: Where F is the emphasis parameter, z1 is the number of records of medical personnel using medical devices in the medical information data, z2 is the number of records of patients using medical devices during diagnosis in the medical information data, and J is the number of medical devices.

[0056] In a specific implementation process, for example, in a data processing process, the number of medical devices is 100, the number of records of medical personnel using medical devices in the medical information data is 10, the number of records of patients using medical devices during diagnosis in the medical information data is 5, the number of times medical personnel use medical devices is 20, and the number of times patients use medical devices during medical treatment is 5. Then, the emphasis parameter of the medical device can be calculated to be 1.75. In this embodiment, the emphasis parameter can be an integer or a negative number.

[0057] The key parameters of all medical devices are obtained based on the analytical methods used for medical devices.

[0058] Establish a Cartesian coordinate system, denoted as the device-related coordinate system. The unit of the X-axis of the device-related coordinate system is the number of times, and the unit of the Y-axis is the emphasis parameter. Based on the quantity and emphasis parameter of each medical device, mark points in the device-related coordinate system and denot them as related points. Connect adjacent related points on the horizontal axis with a curve and denot the resulting curve as the device-related curve.

[0059] For specific implementation details, please refer to [link / reference]. Figure 4 As shown, the point corresponding to ○ is the associated point, the curve corresponding to WW0 is the instrument associated curve, WW2 and WW4 are the associated regions above the X-axis, and WW1 and WW3 are the associated regions below the X-axis.

[0060] The code generation strategy includes: In the device association coordinate system, when the device association curve intersects with the X-axis, the multiple independent closed regions formed by the device association curve and the X-axis are denoted as association region 1 to association region N. The sum of the areas of all association regions above the X-axis in association region 1 to association region N is denoted as the positive association value, and the sum of the areas of all association regions below the X-axis in association region 1 to association region N is denoted as the negative association value.

[0061] In the instrument wear coordinate system, the intersection points of the instrument wear curve with all wear curves are denoted as wear intersection point 1 to wear intersection point M, respectively. The absolute values ​​of the slopes of the instrument wear curves at all wear intersection points are denoted as γ1 to γM, respectively. The absolute values ​​of γ1 to γM are denoted as the average wear slope. The maximum absolute value of the slope in the instrument wear curve is denoted as slope MAX. The slope MAX is not necessarily equal to β0.

[0062] Set the instrument usage code as an array szz[4], where szz[4] = {positive correlation value, negative correlation value, mean loss slope, maximum slope}.

[0063] In the specific implementation process, for example, in a data processing process, the positive correlation value is 20, the negative correlation value is 15, the maximum slope is 2, and the absolute values ​​of the slopes of the device wear curves at all wear intersections are 0.5, 0.7, 1, 1.4, 2 and 2.5 respectively. The average wear slope is calculated to be 1.35, so the device usage code is {20, 15, 1.35, 2}.

[0064] The diagnostic code acquisition module is used to obtain the patient's diagnostic characteristics based on the patient's medical information when the patient visits the hospital.

[0065] The diagnostic code acquisition module is configured with a patient feature extraction strategy, which includes:

[0066] When a patient visits a clinic, the name of the department the patient visited is obtained and recorded as the visiting department name. The number of characters in the Chinese name corresponding to the visiting department name is recorded as G. The remainder obtained by dividing G by the array coefficient is recorded as the conversion value. The array coefficient is the number of elements in the array szz.

[0067] In the specific implementation process, for example, in a data processing process, if the number of characters in the Chinese name of the department being treated is 7, then the conversion value is 3;

[0068] The symptoms registered in the patient's medical information are recorded as real-time symptoms, and the medical devices used to treat the real-time symptoms are recorded as real-time devices. The slope ratio of the wear curve corresponding to the real-time device is obtained and recorded as the real-time ratio. When the real-time ratio is greater than or equal to 1, the value of the real-time ratio rounded up is recorded as the device value. When the real-time ratio is less than 1, the value of 1 divided by the real-time ratio and rounded up is recorded as the device value. The conversion value and the device value are recorded as the patient's diagnostic characteristics.

[0069] In the specific implementation process, by comparing the implementation ratio with 1, the purpose is to ensure that the obtained device value is a positive integer greater than or equal to 1, which helps the subsequent encryption process.

[0070] The data encryption module is used to set encryption codes for patient medical data based on device usage codes and patient diagnostic characteristics.

[0071] The data encryption module is configured with an encryption code generation strategy, which includes:

[0072] When a patient uses medical devices during a visit, the patient's conversion value is recorded as i, the patient's device value is recorded as j, the i-th element in the array szz[4] is converted to base j, and the resulting array szz[4] is recorded as the encryption code. The encryption code is used to encrypt the patient's medical data.

[0073] In the specific implementation process, for example, if a medical device is used during the patient's medical treatment, and the patient's conversion value is 3 and the device's value is 8, then through analysis, it can be concluded that the third element of the array szz[4] should be converted to octal and the array szz[4] should be recorded as the encryption code and the patient's medical data should be encrypted.

[0074] When a patient does not use medical devices during a visit, the remainder obtained by dividing the device value by the array coefficient is recorded as the device conversion value. The patient's device conversion value is recorded as i, and the patient's device value is recorded as j. The i-th element in the array szz[4] is converted to base j, and the resulting array szz[4] is recorded as the encryption code. The encryption code is used to encrypt the patient's medical data.

[0075] Example 2, please refer to Figure 2 As shown, the present invention provides a method for encrypting smart medical information data, comprising:

[0076] Step S1 involves preprocessing the medical information data and obtaining the device wear curve and device correlation curve from the medical information data based on the preprocessing results; obtaining the device usage code based on the device wear curve and device correlation curve; Step S1 includes:

[0077] Step S101: Obtain medical information data, which includes medical device usage data, medical personnel work data, and patient medical treatment data; preprocess the medical information data using a medical feature extraction method, which includes:

[0078] Step S102: Establish a Cartesian coordinate system, denoted as the instrument wear coordinate system, where the unit of the horizontal axis of the instrument wear coordinate system is time, and the unit of the vertical axis is the percentage of wear. Based on the usage data of all medical devices in the medical device usage data, use the wear analysis sub-method to record the relationship between the usage time and the degree of wear of each medical device in the instrument wear coordinate system.

[0079] Step S1021, the loss analysis sub-method includes: for any medical device, the number of medical devices is denoted as J, and all medical devices are denoted as α1 to αJ respectively. For any αn among α1 to αJ, the degree of loss of αn is obtained and expressed as a percentage, denoted as the loss percentage n. The duration of αn from the start of use to the present is denoted as the usage duration of αn, where n is a positive integer less than or equal to J and greater than or equal to 1.

[0080] Step S1022: Based on the percentage of wear and tear of all medical devices and the duration of use, a scatter plot is drawn in the device wear coordinate system, and the curve obtained by fitting the scatter plot is recorded as the wear curve.

[0081] Step S1023: Based on the processing method of medical devices, use the loss analysis sub-method to process all medical devices, and fit all the obtained loss curves in the device loss coordinate system. The fitted curve is recorded as the multimode loss curve.

[0082] Step S1024: The maximum absolute value of the slope in the multimode loss curve is denoted as β0; for any loss curve β in the instrument loss coordinate system, the maximum absolute value of the slope in the loss curve β is denoted as β1. Let β be the slope ratio; obtain the slope ratio of all loss curves, and denote the maximum and minimum values ​​among all slope ratios as deviation slope A and deviation slope B; when the value of deviation slope A minus deviation slope B is less than or equal to β0, the multimode loss curve is denoteed as the instrument loss curve; when the value of deviation slope A minus deviation slope B is greater than β0, the loss curves corresponding to deviation slope A and deviation slope B are removed from the instrument loss coordinate system, and then all loss curves in the instrument loss coordinate system are fitted, and the fitted curve is denoteed as the instrument loss curve.

[0083] Step S1025: For any medical device, based on medical information data, record the number of times medical personnel use the medical device as Pα; record the number of times patients use the medical device during their medical visits as Dα; and use a device-emphasis algorithm to obtain the emphasis parameters of the medical device. The device-emphasis algorithm includes: Where F is the emphasis parameter, z1 is the number of records of medical personnel using medical devices in the medical information data, z2 is the number of records of patients using medical devices during diagnosis in the medical information data, and J is the number of medical devices.

[0084] Step S1026: Obtain the key parameters of all medical devices based on the analysis method for medical devices;

[0085] Step S1027: Establish a Cartesian coordinate system, denoted as the device-related coordinate system. The unit of the X-axis of the device-related coordinate system is the number of times, and the unit of the Y-axis is the emphasis parameter. Based on the quantity and emphasis parameter of each medical device, mark points in the device-related coordinate system and denot them as related points. Connect adjacent related points on the horizontal axis with a curve and denot the resulting curve as the device-related curve.

[0086] Step S1 further includes:

[0087] Step S103: In the instrument association coordinate system, when the instrument association curve intersects with the X-axis, the multiple independent closed regions formed by the instrument association curve and the X-axis are denoted as association region 1 to association region N. The sum of the areas of all association regions above the X-axis in association region 1 to association region N is denoted as the positive association value, and the sum of the areas of all association regions below the X-axis in association region 1 to association region N is denoted as the negative association value.

[0088] Step S104: In the instrument wear coordinate system, the intersection points of the instrument wear curve with all wear curves are denoted as wear intersection point 1 to wear intersection point M, respectively. The absolute values ​​of the slopes of the instrument wear curves at all wear intersection points are denoted as γ1 to γM, respectively. The absolute values ​​of γ1 to γM are denoted as the average wear slope. The maximum absolute value of the slope in the instrument wear curve is denoted as slope MAX. The slope MAX is not necessarily equal to β0.

[0089] Step S105: Set the instrument usage code to array szz[4], where szz[4] = {positive correlation value, negative correlation value, mean loss slope, maximum slope}.

[0090] Step S2: When a patient visits the clinic, the patient's diagnostic characteristics are obtained based on the patient's medical information. Step S2 includes the following sub-steps:

[0091] Step S201: When a patient visits a doctor, obtain the name of the department where the patient visits the doctor and record it as the name of the department. Record the number of characters in the Chinese name corresponding to the name of the department as G and record the remainder obtained by dividing G by the array coefficient as the conversion value. Here, the array coefficient is the number of elements in the array szz.

[0092] Step S202: Record the symptoms registered in the patient's medical information as real-time symptoms, record the medical devices used to treat the real-time symptoms as real-time devices, obtain the slope ratio of the wear curve corresponding to the real-time device, and record it as the real-time ratio; when the real-time ratio is greater than or equal to 1, record the value of the real-time ratio rounded up as the device value; when the real-time ratio is less than 1, record the value of 1 divided by the real-time ratio and rounded up as the device value; record the conversion value and the device value as the patient's diagnostic characteristics.

[0093] Step S3: Set encryption codes for patient medical data based on device usage codes and patient diagnostic characteristics. Step S3 includes:

[0094] Step S301: When a patient uses a medical device during a medical visit, the patient's conversion value is recorded as i, the patient's device value is recorded as j, the i-th element in the array szz[4] is converted to base j, and the resulting array szz[4] is recorded as the encryption code. The encryption code is used to encrypt the patient's medical data.

[0095] Step S302: When the patient does not use medical devices during the consultation, the remainder obtained by dividing the device value by the array coefficient is recorded as the device conversion value, the patient's device conversion value is recorded as i, the patient's device value is recorded as j, the i-th element in the array szz[4] is converted to base j, and the resulting array szz[4] is recorded as the encryption code. The encryption code is used to encrypt the patient's medical data.

[0096] Example 3, please refer to Figure 5 As shown, this application provides an electronic device including a processor 401 and a memory 402. The memory 402 stores computer-readable instructions. When the computer-readable instructions are executed by the processor 401, the steps in the above method are performed. Through the above technical solution, the processor 401 and the memory 402 are interconnected and communicate with each other via a communication bus and / or other forms of connection mechanism (not shown). The memory 402 stores a computer program executable by the processor 401. When the electronic device is running, the processor 401 executes the computer program to perform the method in any optional implementation of the above embodiments, to achieve the following functions: first, preprocessing medical information data, and obtaining the device wear curve and device association curve from the medical information data based on the preprocessing results; obtaining the device usage code based on the device wear curve and device association curve; then, when a patient visits a doctor, obtaining the patient's diagnostic characteristics based on the patient's medical information; and finally, setting an encryption code for the patient's medical data based on the device usage code and the patient's diagnostic characteristics.

[0097] Example 4: This application provides a storage medium storing a computer program. When the computer program is executed by a processor, it performs the steps in the above method. Through the above technical solution, when the computer program is executed by a processor, it performs the method in any optional implementation of the above embodiments to achieve the following functions: First, preprocessing the medical information data and obtaining the device wear curve and device association curve from the medical information data based on the preprocessing results; obtaining the device usage code based on the device wear curve and device association curve; then, when a patient visits the doctor, obtaining the patient's diagnostic characteristics based on the patient's medical information; finally, setting an encryption code for the patient's medical data based on the device usage code and the patient's diagnostic characteristics.

[0098] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0099] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0100] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the displayed or discussed mutual couplings or direct couplings or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

Claims

1. A method for encrypting smart medical information data, characterized in that, include: Medical information data is preprocessed, and the device wear curve and device correlation curve are obtained from the medical information data based on the preprocessing results. Obtain the device usage code based on the device wear curve and device correlation curve; When a patient seeks medical attention, the patient's diagnostic characteristics are obtained based on the patient's medical information. Encryption codes for patient medical data are set based on device usage codes and patient diagnostic characteristics; Preprocessing of medical information data, and obtaining device wear curves and device correlation curves from the medical information data based on the preprocessing results, including: Acquire medical information data, including medical device usage data, medical personnel work data, and patient medical treatment data; preprocess the medical information data using medical feature extraction methods, including: A Cartesian coordinate system is established, denoted as the instrument wear coordinate system. The horizontal axis of the instrument wear coordinate system is in units of time, and the vertical axis is in units of wear percentage. Based on the usage data of all medical devices, a wear analysis sub-method is used to record the relationship between the usage time and wear level of each medical device in the instrument wear coordinate system. The wear analysis sub-method includes: for any medical device, the quantity of the medical device is denoted as J, and all medical devices are denoted as α1 to αJ. For any αn among α1 to αJ, the wear level of αn is obtained and expressed as a percentage, denoted as wear percentage n. The usage time of αn from the start of use to the present is recorded as the usage time of αn, where n is a positive integer less than or equal to J and greater than or equal to 1. Based on the percentage of wear and tear of all medical devices and the duration of use, a scatter plot is drawn in the device wear coordinate system, and the curve obtained by fitting the scatter plot is recorded as the wear curve. Medical feature extraction methods also include: Based on the processing method of medical devices, the loss analysis sub-method is used to process all medical devices, and all the resulting loss curves are fitted in the device loss coordinate system. The fitted curves are denoted as multimode loss curves. Let the maximum absolute value of the slope in the multimode loss curve be denoted as β0; for any loss curve β in the instrument loss coordinate system, let the maximum absolute value of the slope in the loss curve β be denoted as β1. Let β be the slope ratio; obtain the slope ratio of all loss curves, and denote the maximum and minimum values ​​among all slope ratios as deviation slope A and deviation slope B; when the value of deviation slope A minus deviation slope B is less than or equal to β0, the multimode loss curve is denoteed as the instrument loss curve; when the value of deviation slope A minus deviation slope B is greater than β0, the loss curves corresponding to deviation slope A and deviation slope B are removed from the instrument loss coordinate system, and then all loss curves in the instrument loss coordinate system are fitted, and the fitted curve is denoteed as the instrument loss curve. Medical feature extraction methods also include: For any medical device, based on medical information data, the number of times medical personnel use the medical device is denoted as Pα; the number of times patients use the medical device during their medical visits is denoted as Dα. A device-emphasis algorithm is used to obtain the device's emphasis parameters. The device-emphasis algorithm includes: Where F is the emphasis parameter, z1 is the number of records of medical personnel using medical devices in the medical information data, z2 is the number of records of patients using medical devices during diagnosis in the medical information data, and J is the number of medical devices. The key parameters of all medical devices are obtained based on the analytical methods used for medical devices. Establish a Cartesian coordinate system, denoted as the device-related coordinate system. The unit of the X-axis of the device-related coordinate system is the number of times, and the unit of the Y-axis is the emphasis parameter. Based on the quantity and emphasis parameter of each medical device, mark points in the device-related coordinate system and denote them as related points. Connect adjacent related points on the horizontal axis with a curve and denote the resulting curve as the device-related curve. The device usage code is obtained based on the device wear curve and the device correlation curve, including: In the instrument association coordinate system, when the instrument association curve intersects with the X-axis, the multiple independent closed regions formed by the instrument association curve and the X-axis are denoted as association region 1 to association region N. The sum of the areas of all association regions above the X-axis in association region 1 to association region N is denoted as the positive association value, and the sum of the areas of all association regions below the X-axis in association region 1 to association region N is denoted as the negative association value. In the instrument wear coordinate system, the intersection points of the instrument wear curve with all wear curves are denoted as wear intersection point 1 to wear intersection point M, respectively. The absolute values ​​of the slopes of the instrument wear curves at all wear intersection points are denoted as γ1 to γM, respectively. The absolute values ​​of γ1 to γM are denoted as the average wear slope. The maximum absolute value of the slope in the instrument wear curve is denoted as slope MAX. The slope MAX is not necessarily equal to β0. Set the instrument usage code as an array szz[4], where szz[4] = {positive correlation value, negative correlation value, mean loss slope, maximum slope}.

2. The method for encrypting smart medical information data according to claim 1, characterized in that, When a patient seeks medical attention, the diagnostic characteristics obtained based on the patient's medical information include: When a patient visits a clinic, the name of the department the patient visited is obtained and recorded as the visiting department name. The number of characters in the Chinese name corresponding to the visiting department name is recorded as G. The remainder obtained by dividing G by the array coefficient is recorded as the conversion value. The array coefficient is the number of elements in the array szz. The symptoms registered in the patient's medical information are recorded as real-time symptoms, and the medical devices used to treat the real-time symptoms are recorded as real-time devices. The slope ratio of the wear curve corresponding to the real-time device is obtained and recorded as the real-time ratio. When the real-time ratio is greater than or equal to 1, the value of the real-time ratio rounded up is recorded as the device value. When the real-time ratio is less than 1, the value of 1 divided by the real-time ratio and rounded up is recorded as the device value. The conversion value and the device value are recorded as the patient's diagnostic characteristics.

3. The method for encrypting smart medical information data according to claim 2, characterized in that, The encryption code for patient medical data, based on device usage codes and patient diagnostic characteristics, includes: When a patient uses medical devices during a visit, the patient's conversion value is recorded as i, the patient's device value is recorded as j, the i-th element in the array szz[4] is converted to base j, and the resulting array szz[4] is recorded as the encryption code. The encryption code is used to encrypt the patient's medical data. When a patient does not use medical devices during a visit, the remainder obtained by dividing the device value by the array coefficient is recorded as the device conversion value. The patient's device conversion value is recorded as i, and the patient's device value is recorded as j. The i-th element in the array szz[4] is converted to base j, and the resulting array szz[4] is recorded as the encryption code. The encryption code is used to encrypt the patient's medical data.

4. A smart medical information data encryption system, applicable to the smart medical information data encryption method according to any one of claims 1-3, characterized in that, This includes a code acquisition module, a diagnostic code acquisition module, and a data encryption module; The code acquisition module is used to preprocess medical information data and obtain the device wear curve and device correlation curve from the medical information data based on the preprocessing results; the device usage code is then obtained based on the device wear curve and device correlation curve. The diagnostic code acquisition module is used to obtain the patient's diagnostic characteristics based on the patient's medical information when the patient visits the hospital. The data encryption module is used to set encryption codes for patient medical data based on device usage codes and patient diagnostic characteristics.

5. An electronic device, characterized in that, It includes a processor and a memory, the memory storing computer-readable instructions, which, when executed by the processor, perform the steps of the method as described in any one of claims 1-3.

6. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it performs the steps of the method as described in any one of claims 1-3.

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