Intelligent recommendation method, device and equipment for cardiovascular related monitoring information and medium

Abstract

The invention discloses a cardiovascular associated monitoring information intelligent recommendation method and device, equipment and a medium. The method comprises the steps of firstly obtaining current target user information and a current target time interval; if it is determined that the current target user information comprises the cardiovascular disease patient label, obtaining a current target user and current monitoring recommendation information; and if it is determined that the current user input information is a text information type, obtaining corresponding historical text information, forming current comprehensive input information by using the current user input information, the current monitoring recommendation information and the historical text information, and inputting the current comprehensive input information into a pre-training language model to obtain current intelligent recommendation information. According to the method, the patient label of the current target user, the current monitoring recommendation information, the current user input information and the historical text information can be combined to form the current comprehensive input information containing more information, and then the current comprehensive input information is input to the pre-training language model for the medical field; and more accurate intelligent recommendation information referring to more context information is obtained.

Description

Technical Field

[0001] The present invention relates to the field of natural language processing technology in artificial intelligence, and in particular to a method, device, equipment and medium for intelligently recommending cardiovascular-related monitoring information. Background Art

[0002] At present, natural language processing technology has been widely used in intelligent customer service systems. When a user has a conversation with an intelligent customer service system, the intelligent customer service system can give corresponding reply information based on the current text information sent by the user to achieve intelligent recommendation and reply of information. When the intelligent customer service system used by the user is a multi-domain intelligent system, although it can give corresponding replies to the user's questions and answers in various technical fields, when the user asks professional questions in a specific field such as the medical field, the replies given are often relatively general and inaccurate, and there is a situation where the relevance to the context is poor. Summary of the invention

[0003] The embodiments of the present invention provide a method, device, equipment and medium for intelligent recommendation of cardiovascular-related monitoring information, aiming to solve the problem in the prior art that when users use the intelligent customer service system to answer professional questions in a specific field such as the medical field, the answers given are often relatively general and inaccurate, and there is a poor correlation with the context.

[0004] In a first aspect, an embodiment of the present invention provides a method for intelligently recommending cardiovascular-related monitoring information, which is applied to a server and includes: In response to the information intelligent recommendation instruction, obtaining current target user information corresponding to the information intelligent recommendation instruction, and obtaining current target time interval corresponding to the intelligent recommendation instruction; If it is determined that the current target user information includes a cardiovascular disease patient tag, obtaining the current target user corresponding to the current target user information, and obtaining current monitoring recommendation information corresponding to the current target user and stored in a local storage space; If the current user input information is detected, the current information type of the current user input information is obtained; If it is determined that the current information type is a text information type, then historical text information corresponding to the current target user and whose information generation time is within the current target time interval is obtained, and the current user input information, the current monitoring recommendation information and the historical text information are used to form the current comprehensive input information and input it into the pre-trained language model to obtain the current intelligent recommendation information; wherein the pre-trained language model is a pre-trained language model for the medical field; Sending the current intelligent recommendation information to a current target user terminal corresponding to the current target user; If a current feedback tag is received which is sent by the current target user terminal and corresponds to the current intelligent recommendation information, the current feedback tag is added to the current intelligent recommendation information and stored in a local storage space.

[0005] In a second aspect, an embodiment of the present invention provides a device for intelligently recommending cardiovascular-related monitoring information, which is configured on a server and includes: a target user information acquisition unit, configured to, in response to an information intelligent recommendation instruction, acquire current target user information corresponding to the information intelligent recommendation instruction, and acquire current target time interval corresponding to the intelligent recommendation instruction; A monitoring recommendation information acquisition unit, configured to, if it is determined that the current target user information includes a cardiovascular disease patient tag, acquire the current target user corresponding to the current target user information, and acquire the current monitoring recommendation information corresponding to the current target user and stored in a local storage space; An information type acquisition unit, configured to acquire the current information type of the information entered by the current user if the current user enters information; A current intelligent recommendation information acquisition unit, for obtaining historical text information corresponding to the current target user and whose information generation time is within the current target time interval if the current information type is determined to be a text information type, and forming current comprehensive input information with the current user input information, the current monitoring recommendation information and the historical text information and inputting the information into a pre-trained language model to obtain current intelligent recommendation information; wherein the pre-trained language model is a pre-trained language model for the medical field; A current intelligent recommendation information sending unit, configured to send the current intelligent recommendation information to a current target user terminal corresponding to the current target user; The current feedback tag storage unit is used to add the current feedback tag to the current intelligent recommendation information and store it in a local storage space if the current feedback tag sent by the current target user terminal and corresponding to the current intelligent recommendation information is received.

[0006] In a third aspect, an embodiment of the present invention further provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the computer device executes the computer program, the method for intelligently recommending cardiovascular-related monitoring information as described in the first aspect above is implemented.

[0007] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for intelligently recommending cardiovascular-related monitoring information as described in the first aspect above is implemented.

[0008] The embodiment of the present invention provides a method, device, equipment and medium for intelligent recommendation of cardiovascular-related monitoring information. The method includes: in response to an information intelligent recommendation instruction, obtaining current target user information corresponding to the information intelligent recommendation instruction, and obtaining a current target time interval corresponding to the intelligent recommendation instruction; if it is determined that the current target user information includes a cardiovascular disease patient label, obtaining the current target user corresponding to the current target user information, and obtaining current monitoring recommendation information corresponding to the current target user and stored in a local storage space; if the current user input information is detected, then obtaining the current information type of the current user input information; if it is determined that the current information type is a text information type, then obtaining historical text information corresponding to the current target user and the information generation time within the current target time interval, using the current user input information, the current monitoring recommendation information and the historical text information to form the current comprehensive input information and input it into a pre-trained language model to obtain the current intelligent recommendation information; sending the current intelligent recommendation information to the current target user terminal corresponding to the current target user; if the current feedback label sent by the received current target user terminal and corresponding to the current intelligent recommendation information, then adding the current feedback label to the current intelligent recommendation information and storing it in the local storage space. Through the above method, the patient label of the current target user, the current monitoring recommendation information, the current user input information and the historical text information can be combined to form the current comprehensive input information containing more information, and then input into the pre-trained language model for the medical field to obtain more contextual information and more accurate intelligent recommendation information. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying any creative work.

[0010] Figure 1 A schematic diagram of an application scenario of the method for intelligently recommending cardiovascular-related monitoring information provided by an embodiment of the present invention; Figure 2 A schematic diagram of a process flow of a method for intelligently recommending cardiovascular-related monitoring information provided by an embodiment of the present invention; Figure 3 A schematic diagram of a sub-process of a method for intelligently recommending cardiovascular-related monitoring information provided by an embodiment of the present invention; Figure 4 A schematic diagram of a sub-process of a method for intelligently recommending cardiovascular-related monitoring information provided by an embodiment of the present invention; Figure 5A schematic diagram of a sub-process of a method for intelligently recommending cardiovascular-related monitoring information provided by an embodiment of the present invention; Figure 6 A schematic block diagram of an intelligent recommendation device for cardiovascular-related monitoring information provided by an embodiment of the present invention; Figure 7 A schematic block diagram of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0011] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0012] It should be understood that when used in this specification and the appended claims, the terms "include" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0013] It should also be understood that the terms used in this specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0014] It should be further understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0015] See also Figure 1 and Figure 2 , Figure 1 The embodiment of the present invention provides Figure 1 A schematic diagram of an application scenario of the method for intelligently recommending cardiovascular-related monitoring information provided by an embodiment of the present invention; Figure 2 Schematic diagram of the process of the intelligent recommendation method for cardiovascular-related monitoring information provided by an embodiment of the present invention; the intelligent recommendation method for cardiovascular-related monitoring information is applied to a server 10, and the server 10 is connected to a user terminal 20 for communication. Figure 2 As shown, the method includes steps S110 to S160.

[0016] S110. In response to an information intelligent recommendation instruction, obtain current target user information corresponding to the information intelligent recommendation instruction, and obtain current target time interval corresponding to the intelligent recommendation instruction.

[0017] In this embodiment, the technical solution is described with the server as the execution subject. An information intelligent recommendation system is deployed in the server (similar to an intelligent customer service platform, which can respond to users' questions or sent texts). When the user logs in to the information intelligent recommendation system in the server using a user terminal, he can interact with the information intelligent recommendation system in the server to obtain the required intelligent recommendation information.

[0018] Among them, when the user uses the user terminal to log in to the information intelligent recommendation system in the server, under the premise that the user authorizes to obtain his user information, the server can respond to the information intelligent recommendation instruction, obtain the current target user information corresponding to the information intelligent recommendation instruction, and the current target user information includes at least user ID, user name, user gender, user age, user patient label and other information. Moreover, the current target time interval corresponding to the current target user information can also be obtained based on the instruction generation time of the intelligent recommendation instruction. The information intelligent recommendation system in this application is a system that focuses more on making more accurate intelligent recommendations of monitoring information for users with various patient labels, and can provide more intelligent and accurate replies to information feedback sent by users in the information intelligent recommendation system.

[0019] In one embodiment, if Figure 3 As shown, step S110 includes: S111, obtaining current user classification label information in the current target user information, and determining a current acquisition period based on the current user classification label information and a preset label-period mapping relationship; S112, obtaining the instruction generation time of the information intelligent recommendation instruction, determining the start time of the current target time interval by the difference between the instruction generation time and the current acquisition cycle, and using the instruction generation time as the end time of the current target time interval; S113: Determine the current target time interval according to the start time of the current target time interval and the end time of the current target time interval.

[0020] In this embodiment, first, the current user classification label information can be obtained from the current target user information, such as a high-risk user classification label, a medium-risk user classification label or a low-risk user classification label, and a mapping relationship between each user classification label information and the acquisition cycle duration is also set in the server, such as setting a high-risk user classification label to correspond to acquisition cycle T1, a medium-risk user classification label to correspond to acquisition cycle T2, and a low-risk user classification label to correspond to acquisition cycle T3 (wherein T1, T2 and T3 are all positive numbers), and determining that the current user classification label information corresponds to the medium-risk user classification label, at this time, the current acquisition cycle can be determined as acquisition cycle T2 in the label-cycle mapping relationship.

[0021] Afterwards, combined with the instruction generation time of the information intelligent recommendation instruction and the current acquisition cycle (such as the acquisition cycle T2 in the above example), the difference between the instruction generation time and the current acquisition cycle can be used to determine the start time of the current target time interval, and the instruction generation time is used as the end time of the current target time interval. Finally, after completing the determination of the start time point and the end time point of the current target time interval, the determination of the current target time interval can be completed. Moreover, the current target time interval obtained is used to determine the communication history data corresponding to the current target user information (which can also be understood as historical context data).

[0022] S120: If it is determined that the current target user information includes a cardiovascular disease patient tag, obtain the current target user corresponding to the current target user information, and obtain current monitoring recommendation information corresponding to the current target user and stored in a local storage space.

[0023] In this embodiment, since the current target user information includes information on the user patient label, after parsing and obtaining it and determining that it is a cardiovascular disease patient label, the current target user corresponding to the current target user information can be further obtained, and the current monitoring recommendation information corresponding to the current target user and stored in the local storage space can be obtained.

[0024] Among them, the current monitoring recommendation information is the current monitoring recommendation information corresponding to the current target user, which is uploaded to the server by other business servers and edited and uploaded by the doctor user. For example, the current monitoring recommendation information includes the medical advice information for the current target user within the specified time period (this specified time period can be understood as the information effective time period corresponding to the current monitoring recommendation information), and more specifically can include the recommended diet information, medication information and daily rehabilitation exercise information, etc., and can also include the monitoring threshold of the key signs index for the current target user in the current specified time period (for example, once the key signs index of the current target user exceeds the monitoring threshold of the key signs index for multiple times in the current specified time period, it indicates that a timely review is required). Moreover, after the user uses the user terminal to log in to the information intelligent recommendation system of the server, the current monitoring recommendation information corresponding to the current target user obtained can be used as hidden information and not displayed on the display interface of the information intelligent recommendation system (such as a chat box display interface), or it can be non-hidden information directly displayed on the display interface of the information intelligent recommendation system.

[0025] S130: If it is detected that the current user has entered information, obtain the current information type of the information entered by the current user.

[0026] In this embodiment, after the user logs in to the information intelligent recommendation system using a smart terminal, one of the common usage methods is to input the current user input information, such as text input information (such as Chinese text mainly composed of Chinese characters, English text mainly composed of English characters, digital text mainly composed of digital characters, etc.), voice input information, image input information, etc. However, no matter how the user enters the relevant information, the current information type of the current user input information can be obtained first.

[0027] In one embodiment, if Figure 4 As shown, step S130 includes: S131, obtaining the file format of the information entered by the current user; S132: If it is determined that the file format corresponds to a text file format, taking the text information type as the current information type; S133: If it is determined that the file format corresponds to a picture file format, taking the picture information type as the current information type; S134: If it is determined that the file format corresponds to a voice file format, the voice information type is used as the current information type.

[0028] In this embodiment, when determining the current information type of the information entered by the current user, the fastest implementation method is to obtain its file format, more specifically, to obtain the file suffix of the information entered by the user. After quickly determining the current information type based on the file suffix, you can further make a corresponding reply based on the current information type and the specific information content.

[0029] Specifically, if it is determined that the file format corresponds to a text file format, such as txt, the text information type is used as the current information type; if it is determined that the file format corresponds to a picture file format, such as jpg, the picture information type is used as the current information type; if it is determined that the file format corresponds to a voice file format, such as wma, the voice information type is used as the current information type. It can be seen that the current information type can be quickly determined by the above method.

[0030] S140. If it is determined that the current information type is a text information type, historical text information corresponding to the current target user and the information generation time within the current target time interval is obtained, and the current comprehensive input information is composed of the current user input information, the current monitoring recommendation information and the historical text information and input into the pre-trained language model to obtain the current intelligent recommendation information.

[0031] The pre-trained language model is a pre-trained language model for the medical field.

[0032] In this embodiment, if it is determined that the current information type is a text information type, it can be further determined to be more specifically a Chinese text mainly composed of Chinese characters, an English text mainly composed of English characters, or a digital text mainly composed of digital characters. For example, when it is determined that the current user input information is mainly input with Chinese characters (that is, the character proportion ratio of Chinese characters in the current user input information exceeds the first character proportion threshold), it means that the current target user is mainly asking questions, and the historical text information corresponding to the current target user and the information generation time within the current target time interval and the current guardianship recommendation information can be combined, and the above three types of information can be used to form a current comprehensive input information and input it into a pre-trained language model (such as a BioBERT model) to obtain the current intelligent recommendation information output by the pre-trained language model.

[0033] In one embodiment, if Figure 5 As shown, in step S140, the current user input information, the current monitoring recommendation information and the historical text information are used to form the current comprehensive input information and input into the pre-trained language model to obtain the current intelligent recommendation information, including: S141, according to the order of the current monitoring recommendation information, the historical text information and the current user input information, the current monitoring recommendation information, the historical text information and the current user input information are combined into the current comprehensive input information; S142. Input the current input vector corresponding to the current comprehensive input information into the pre-trained language model to obtain the current intelligent recommendation information.

[0034] In this embodiment, in order to combine more context information to input more accurate current intelligent recommendation information, the current guardianship recommendation information that is directly displayed or hidden can be used as the context data ranked first in the current comprehensive input information, and the historical text information can be used as the context data ranked in the middle of the current comprehensive input information. In combination with the current user input information, a current comprehensive input information that fully considers the current target user's current guardianship recommendation information, historical question information, and system historical reply information and other context data can be formed. The information is subjected to text tokenization (i.e., Tokenization, to divide the text into words) and text encoding (to map the words to a low-dimensional vector space) to obtain the current input vector and then input into the pre-trained language model to obtain the current intelligent recommendation information.

[0035] For example, the current monitoring recommendation information is "Measure blood pressure or heart rate every 2 hours every day, and avoid strenuous exercise and emotional excitement." The historical text information includes multiple user questions and corresponding system intelligent recommendation information, such as "I feel a little dizzy now, what should I do?" and "It is recommended to sit still for 10 minutes and measure blood pressure again." The current user input information is "The current measured blood pressure value is XX1." The above text is synthesized to form a current comprehensive input information containing rich context information and input into the pre-trained language model to obtain the current intelligent recommendation information.

[0036] In one embodiment, after step S142, the method further includes: The privacy protection degree of the current intelligent recommendation information is obtained based on a preset verification strategy; wherein the corresponding formula for obtaining the privacy protection degree of the intelligent recommendation information in the verification strategy is the privacy protection degree of the information , M is the total number of privacy entities included in the intelligent recommendation information, Sen i Indicates the privacy entity sensitive value of the i-th privacy entity in the intelligent recommendation information, Id i Represents the identifiability value of the i-th privacy entity in the intelligent recommendation information; If it is determined that the privacy protection level of the current intelligent recommendation information is greater than or equal to a preset privacy protection level threshold, a trustworthy protection label is correspondingly added to the current intelligent recommendation information to update the current intelligent recommendation information.

[0037] In this embodiment, in order to ensure that the current intelligent recommendation information sent to the user terminal by the information intelligent recommendation system has good privacy protection, the privacy protection degree of the current intelligent recommendation information can also be obtained by the verification strategy pre-deployed by the information intelligent recommendation system, wherein the corresponding formula for obtaining the privacy protection degree of the intelligent recommendation information in the verification strategy is the privacy protection degree of the information , M is the total number of privacy entities included in the intelligent recommendation information, Sen i Indicates the privacy entity sensitive value of the i-th privacy entity in the intelligent recommendation information, Id i Represents the identifiability value of the i-th privacy entity in the intelligent recommendation information. More specifically, when determining the total number of privacy entities in the current intelligent recommendation information, the current intelligent recommendation information can be compared and matched with multiple privacy entities included in a preset privacy entity set (for example, the preset privacy entity set includes privacy entities such as name, ID number, etc.), to determine the total number of privacy entities included in the current intelligent recommendation information and existing in the preset privacy entity set. Moreover, the multiple privacy entities included in the preset privacy entity set are preset with privacy entity sensitivity initial values ​​one by one. After completing the comparison and matching of the current intelligent recommendation information with the multiple privacy entities included in the preset privacy entity set, the privacy entity sensitivity initial value corresponding to each privacy entity in the current intelligent recommendation information can also be initially determined. Afterwards, the privacy entity sensitivity value can also be adjusted based on the number of occurrences of each privacy entity in the current intelligent recommendation information in the text. For example, each privacy entity subtracts its current privacy entity sensitivity value for each additional occurrence in the text of the current intelligent recommendation information, that is, the final privacy entity sensitivity value corresponding to the i-th privacy entity in the current intelligent recommendation information = the i-th privacy entity sensitivity initial value - k i ×Preset privacy entity sensitivity adjustment value, where k i is the total number of times the i-th privacy entity appears in the current intelligent recommendation information. When determining the identifiability value of the i-th privacy entity in the current intelligent recommendation information, the i-th privacy entity can be compared and matched with a preset identifiable entity set (the preset identifiable entity set includes multiple identifiable entities, and each identifiable entity corresponds to an identifiability value), so as to determine the identifiable entity with the same entity name as the i-th privacy entity and the corresponding identifiability value in the preset identifiable entity set, and use the identifiability value corresponding to the identifiable entity as the identifiability value of the i-th privacy entity.

[0038] It can be seen that through the above process, the verification strategy of the information intelligent recommendation system can quickly determine the privacy protection level of the current intelligent recommendation information, and when it is determined that the privacy protection level of the current intelligent recommendation information is greater than or equal to the preset privacy protection level threshold, it means that the current intelligent recommendation information has good privacy protection. At this time, a trusted protection label can be added to the current intelligent recommendation information to update the current intelligent recommendation information. For the current intelligent recommendation information with a trusted protection label, the information intelligent recommendation system can release it to the current target user terminal corresponding to the current target user without any interception or processing measures.

[0039] In one embodiment, after step S130, the method further includes: If it is determined that the current information type is an image information type, then acquiring image recognition text information corresponding to the current user input information based on the image text recognition model; If it is determined that the image recognition text information is physical sign detection data information, the previous physical sign detection data information corresponding to the current target user is obtained, and the previous physical sign detection data information and the image recognition text information are displayed side by side.

[0040] In this embodiment, if it is determined that the current information type is an image information type, it means that the current target user needs to intelligently identify the text content in the image information, such as the vital sign detection data information, before performing corresponding processing. At this time, the image recognition text information corresponding to the current user input information can be obtained based on an image text recognition model such as a convolutional recurrent neural network, and when it is determined that it is a vital sign detection data information and includes a number of vital sign detection data values, the previous vital sign detection data information corresponding to the current target user is obtained (for example, the time interval between the information generation time of the previous vital sign detection data information corresponding to the current target user and the entry time of the current user input information is less than the time interval between the information generation time of other vital sign detection data information and the entry time of the current user input information), and finally the previous vital sign detection data information and the image recognition text information are displayed in parallel, and the information is displayed in a parallel comparison manner so that the user can view the data change trend more intuitively.

[0041] S150: Send the current intelligent recommendation information to a current target user terminal corresponding to the current target user.

[0042] In this embodiment, after the current intelligent recommendation information corresponding to the current user input information sent by the current target user is obtained in the information intelligent recommendation system of the server, the server sends the current intelligent recommendation information to the current target user terminal corresponding to the current target user, so that the current target user can view it in time.

[0043] S160: If a current feedback tag is received and sent by the current target user terminal and corresponds to the current intelligent recommendation information, the current feedback tag is added to the current intelligent recommendation information and stored in a local storage space.

[0044] In this embodiment, in order to realize on-the-go training (i.e., continuous training) of the pre-trained language model, the user's current feedback label for each piece of intelligent recommendation information (such as a label for an accurate reply, or an inaccurate answer, or a label that cannot be judged whether it is accurate, etc.) can be continuously obtained, such as obtaining the current feedback label corresponding to the current intelligent recommendation information, and adding the current feedback label to the current intelligent recommendation information and storing it in a local storage space as a data set for subsequent continuous on-the-go training of the pre-trained language model.

[0045] In one embodiment, after step S160, the method further includes: If it is detected that the current system time meets the preset model online training conditions, a training set consisting of multiple stored intelligent recommendation information including feedback labels is obtained, and model training is performed on the pre-trained language model until the preset model training stop conditions are met to update the pre-trained language model.

[0046] In this embodiment, for example, the preset model online training condition is set to a time interval between the current system time and the last model online training that is equal to the preset model online training period. When it is detected that the current system time satisfies the preset model online training condition, it means that a training set consisting of multiple stored intelligent recommendation information including feedback tags is currently available (wherein the first three sentences of context information of each intelligent recommendation information in the training set are also known), and model training is performed on the pre-trained language model until the preset model training stop condition is met (such as convergence of the loss function, etc.) to update the pre-trained language model, thereby achieving continuous updating of the pre-trained language model so that it has better model performance.

[0047] It can be seen that the embodiment of the method can combine the patient label of the current target user, the current monitoring recommendation information, the current user input information and the historical text information to form the current comprehensive input information containing more information, and then input it into the pre-trained language model for the medical field to obtain more contextual information and more accurate intelligent recommendation information.

[0048] The embodiment of the present invention also provides a cardiovascular-related monitoring information intelligent recommendation device, which can be configured in a server and is used to execute any embodiment of the aforementioned cardiovascular-related monitoring information intelligent recommendation method. Figure 6 , Figure 6 This is a schematic block diagram of an intelligent recommendation device for cardiovascular-related monitoring information provided by an embodiment of the present invention. Figure 6 As shown, the cardiovascular-related monitoring information intelligent recommendation device 100 includes a target user information acquisition unit 110, a monitoring recommendation information acquisition unit 120, an information type acquisition unit 130, a current intelligent recommendation information acquisition unit 140, a current intelligent recommendation information sending unit 150 and a current feedback label storage unit 160.

[0049] The target user information acquisition unit 110 is used to respond to the information intelligent recommendation instruction, acquire the current target user information corresponding to the information intelligent recommendation instruction, and acquire the current target time interval corresponding to the intelligent recommendation instruction.

[0050] In this embodiment, the technical solution is described with the server as the execution subject. An information intelligent recommendation system is deployed in the server (similar to an intelligent customer service platform, which can respond to users' questions or sent texts). When the user logs in to the information intelligent recommendation system in the server using a user terminal, he can interact with the information intelligent recommendation system in the server to obtain the required intelligent recommendation information.

[0051] Among them, when the user uses the user terminal to log in to the information intelligent recommendation system in the server, under the premise that the user authorizes to obtain his user information, the server can respond to the information intelligent recommendation instruction, obtain the current target user information corresponding to the information intelligent recommendation instruction, and the current target user information includes at least user ID, user name, user gender, user age, user patient label and other information. Moreover, the current target time interval corresponding to the current target user information can also be obtained based on the instruction generation time of the intelligent recommendation instruction. The information intelligent recommendation system in this application is a system that focuses more on making more accurate intelligent recommendations of monitoring information for users with various patient labels, and can provide more intelligent and accurate replies to information feedback sent by users in the information intelligent recommendation system.

[0052] In one embodiment, the target user information acquiring unit 110 is specifically used for: Acquire current user classification label information in the current target user information, and determine a current acquisition period based on the current user classification label information and a preset label-period mapping relationship; Obtaining the instruction generation time of the information intelligent recommendation instruction, determining the start time of the current target time interval by the difference between the instruction generation time and the current acquisition cycle, and using the instruction generation time as the end time of the current target time interval; The current target time interval is determined according to the current target time interval start time and the current target time interval end time.

[0053] In this embodiment, first, the current user classification label information can be obtained from the current target user information, such as a high-risk user classification label, a medium-risk user classification label or a low-risk user classification label, and a mapping relationship between each user classification label information and the acquisition cycle duration is also set in the server, such as setting a high-risk user classification label to correspond to acquisition cycle T1, a medium-risk user classification label to correspond to acquisition cycle T2, and a low-risk user classification label to correspond to acquisition cycle T3 (wherein T1, T2 and T3 are all positive numbers), and determining that the current user classification label information corresponds to the medium-risk user classification label, at this time, the current acquisition cycle can be determined as acquisition cycle T2 in the label-cycle mapping relationship.

[0054] Afterwards, combined with the instruction generation time of the information intelligent recommendation instruction and the current acquisition cycle (such as the acquisition cycle T2 in the above example), the difference between the instruction generation time and the current acquisition cycle can be used to determine the start time of the current target time interval, and the instruction generation time is used as the end time of the current target time interval. Finally, after completing the determination of the start time point and the end time point of the current target time interval, the determination of the current target time interval can be completed. Moreover, the current target time interval obtained is used to determine the communication history data corresponding to the current target user information (which can also be understood as historical context data).

[0055] The monitoring recommendation information acquisition unit 120 is used to obtain the current target user corresponding to the current target user information if it is determined that the current target user information includes a cardiovascular disease patient tag, and obtain the current monitoring recommendation information corresponding to the current target user and stored in the local storage space.

[0056] In this embodiment, since the current target user information includes information on the user patient label, after parsing and obtaining it and determining that it is a cardiovascular disease patient label, the current target user corresponding to the current target user information can be further obtained, and the current monitoring recommendation information corresponding to the current target user and stored in the local storage space can be obtained.

[0057] Among them, the current monitoring recommendation information is the current monitoring recommendation information corresponding to the current target user, which is uploaded to the server by other business servers and edited and uploaded by the doctor user. For example, the current monitoring recommendation information includes the medical advice information for the current target user within the specified time period (this specified time period can be understood as the information effective time period corresponding to the current monitoring recommendation information), and more specifically can include the recommended diet information, medication information and daily rehabilitation exercise information, etc., and can also include the monitoring threshold of the key signs index for the current target user in the current specified time period (for example, once the key signs index of the current target user exceeds the monitoring threshold of the key signs index for multiple times in the current specified time period, it indicates that a timely review is required). Moreover, after the user uses the user terminal to log in to the information intelligent recommendation system of the server, the current monitoring recommendation information corresponding to the current target user obtained can be used as hidden information and not displayed on the display interface of the information intelligent recommendation system (such as a chat box display interface), or it can be non-hidden information directly displayed on the display interface of the information intelligent recommendation system.

[0058] The information type acquisition unit 130 is used to acquire the current information type of the information entered by the current user if it is detected that the current user enters information.

[0059] In this embodiment, after the user logs in to the information intelligent recommendation system using a smart terminal, one of the common usage methods is to input the current user input information, such as text input information (such as Chinese text mainly composed of Chinese characters, English text mainly composed of English characters, digital text mainly composed of digital characters, etc.), voice input information, image input information, etc. However, no matter how the user enters the relevant information, the current information type of the current user input information can be obtained first.

[0060] In one embodiment, the information type obtaining unit 130 is specifically used to: Obtain the file format of the current user input information; If it is determined that the file format corresponds to a text file format, taking the text information type as the current information type; If it is determined that the file format corresponds to a picture file format, taking the picture information type as the current information type; If it is determined that the file format corresponds to a voice file format, the voice information type is used as the current information type.

[0061] In this embodiment, when determining the current information type of the information entered by the current user, the fastest implementation method is to obtain its file format, more specifically, to obtain the file suffix of the information entered by the user. After quickly determining the current information type based on the file suffix, you can further make a corresponding reply based on the current information type and the specific information content.

[0062] Specifically, if it is determined that the file format corresponds to a text file format, such as txt, the text information type is used as the current information type; if it is determined that the file format corresponds to a picture file format, such as jpg, the picture information type is used as the current information type; if it is determined that the file format corresponds to a voice file format, such as wma, the voice information type is used as the current information type. It can be seen that the current information type can be quickly determined by the above method.

[0063] The current intelligent recommendation information acquisition unit 140 is used to obtain historical text information corresponding to the current target user and the information generation time within the current target time interval if it is determined that the current information type is a text information type, and to form the current comprehensive input information with the current user input information, the current monitoring recommendation information and the historical text information and input it into the pre-trained language model to obtain the current intelligent recommendation information.

[0064] The pre-trained language model is a pre-trained language model for the medical field.

[0065] In this embodiment, if it is determined that the current information type is a text information type, it can be further determined to be more specifically a Chinese text mainly composed of Chinese characters, an English text mainly composed of English characters, or a digital text mainly composed of digital characters. For example, when it is determined that the current user input information is mainly input with Chinese characters (that is, the character proportion ratio of Chinese characters in the current user input information exceeds the first character proportion threshold), it means that the current target user is mainly asking questions, and the historical text information corresponding to the current target user and the information generation time within the current target time interval and the current guardianship recommendation information can be combined, and the above three types of information can be used to form a current comprehensive input information and input it into a pre-trained language model (such as a BioBERT model) to obtain the current intelligent recommendation information output by the pre-trained language model.

[0066] In one embodiment, the current intelligent recommendation information acquisition unit 140 is specifically used to: According to the order of the current monitoring recommendation information, the historical text information and the current user input information, the current monitoring recommendation information, the historical text information and the current user input information are combined into the current comprehensive input information; The current input vector corresponding to the current comprehensive input information is input into the pre-trained language model to obtain the current intelligent recommendation information.

[0067] In this embodiment, in order to combine more context information to input more accurate current intelligent recommendation information, the current guardianship recommendation information that is directly displayed or hidden can be used as the context data ranked first in the current comprehensive input information, and the historical text information can be used as the context data ranked in the middle of the current comprehensive input information. In combination with the current user input information, a current comprehensive input information that fully considers the current target user's current guardianship recommendation information, historical question information, and system historical reply information and other context data can be formed. The information is subjected to text tokenization (i.e., Tokenization, to divide the text into words) and text encoding (to map the words to a low-dimensional vector space) to obtain the current input vector and then input into the pre-trained language model to obtain the current intelligent recommendation information.

[0068] For example, the current monitoring recommendation information is "Measure blood pressure or heart rate every 2 hours every day, and avoid strenuous exercise and emotional excitement." The historical text information includes multiple user questions and corresponding system intelligent recommendation information, such as "I feel a little dizzy now, what should I do?" and "It is recommended to sit still for 10 minutes and measure blood pressure again." The current user input information is "The current measured blood pressure value is XX1." The above text is synthesized to form a current comprehensive input information containing rich context information and input into the pre-trained language model to obtain the current intelligent recommendation information.

[0069] In one embodiment, the current intelligent recommendation information obtaining unit 140 is further used to: The privacy protection degree of the current intelligent recommendation information is obtained based on a preset verification strategy; wherein the corresponding formula for obtaining the privacy protection degree of the intelligent recommendation information in the verification strategy is the privacy protection degree of the information , M is the total number of privacy entities included in the intelligent recommendation information, Sen i Indicates the privacy entity sensitive value of the i-th privacy entity in the intelligent recommendation information, Id i Represents the identifiability value of the i-th privacy entity in the intelligent recommendation information; If it is determined that the privacy protection level of the current intelligent recommendation information is greater than or equal to a preset privacy protection level threshold, a trustworthy protection label is correspondingly added to the current intelligent recommendation information to update the current intelligent recommendation information.

[0070] In this embodiment, in order to ensure that the current intelligent recommendation information sent to the user terminal by the information intelligent recommendation system has good privacy protection, the privacy protection degree of the current intelligent recommendation information can also be obtained by the verification strategy pre-deployed by the information intelligent recommendation system, wherein the corresponding formula for obtaining the privacy protection degree of the intelligent recommendation information in the verification strategy is the privacy protection degree of the information , M is the total number of privacy entities included in the intelligent recommendation information, Sen i Indicates the privacy entity sensitive value of the i-th privacy entity in the intelligent recommendation information, Id i Represents the identifiability value of the i-th privacy entity in the intelligent recommendation information. More specifically, when determining the total number of privacy entities in the current intelligent recommendation information, the current intelligent recommendation information can be compared and matched with multiple privacy entities included in a preset privacy entity set (for example, the preset privacy entity set includes privacy entities such as name, ID number, etc.), to determine the total number of privacy entities included in the current intelligent recommendation information and existing in the preset privacy entity set. Moreover, the multiple privacy entities included in the preset privacy entity set are preset with privacy entity sensitivity initial values ​​one by one. After completing the comparison and matching of the current intelligent recommendation information with the multiple privacy entities included in the preset privacy entity set, the privacy entity sensitivity initial value corresponding to each privacy entity in the current intelligent recommendation information can also be initially determined. Afterwards, the privacy entity sensitivity value can also be adjusted based on the number of occurrences of each privacy entity in the current intelligent recommendation information in the text. For example, each privacy entity subtracts its current privacy entity sensitivity value for each additional occurrence in the text of the current intelligent recommendation information, that is, the final privacy entity sensitivity value corresponding to the i-th privacy entity in the current intelligent recommendation information = the i-th privacy entity sensitivity initial value - k i ×Preset privacy entity sensitivity adjustment value, where k i is the total number of times the i-th privacy entity appears in the current intelligent recommendation information. When determining the identifiability value of the i-th privacy entity in the current intelligent recommendation information, the i-th privacy entity can be compared and matched with a preset identifiable entity set (the preset identifiable entity set includes multiple identifiable entities, and each identifiable entity corresponds to an identifiability value), so as to determine the identifiable entity with the same entity name as the i-th privacy entity and the corresponding identifiability value in the preset identifiable entity set, and use the identifiability value corresponding to the identifiable entity as the identifiability value of the i-th privacy entity.

[0071] It can be seen that through the above process, the verification strategy of the information intelligent recommendation system can quickly determine the privacy protection level of the current intelligent recommendation information, and when it is determined that the privacy protection level of the current intelligent recommendation information is greater than or equal to the preset privacy protection level threshold, it means that the current intelligent recommendation information has good privacy protection. At this time, a trusted protection label can be added to the current intelligent recommendation information to update the current intelligent recommendation information. For the current intelligent recommendation information with a trusted protection label, the information intelligent recommendation system can release it to the current target user terminal corresponding to the current target user without any interception or processing measures.

[0072] In one embodiment, the cardiovascular-related monitoring information intelligent recommendation device 100 further includes: An image text recognition unit, configured to obtain image recognition text information corresponding to the current user input information based on an image text recognition model if it is determined that the current information type is an image information type; The data parallel display unit is used to obtain the previous physical sign detection data information corresponding to the current target user if it is determined that the image recognition text information is physical sign detection data information, and to display the previous physical sign detection data information and the image recognition text information in parallel.

[0073] In this embodiment, if it is determined that the current information type is an image information type, it means that the current target user needs to intelligently identify the text content in the image information, such as the vital sign detection data information, before performing corresponding processing. At this time, the image recognition text information corresponding to the current user input information can be obtained based on an image text recognition model such as a convolutional recurrent neural network, and when it is determined that it is a vital sign detection data information and includes a number of vital sign detection data values, the previous vital sign detection data information corresponding to the current target user is obtained (for example, the time interval between the information generation time of the previous vital sign detection data information corresponding to the current target user and the entry time of the current user input information is less than the time interval between the information generation time of other vital sign detection data information and the entry time of the current user input information), and finally the previous vital sign detection data information and the image recognition text information are displayed in parallel, and the information is displayed in a parallel comparison manner so that the user can view the data change trend more intuitively.

[0074] The current intelligent recommendation information sending unit 150 is used to send the current intelligent recommendation information to the current target user terminal corresponding to the current target user.

[0075] In this embodiment, after the current intelligent recommendation information corresponding to the current user input information sent by the current target user is obtained in the information intelligent recommendation system of the server, the server sends the current intelligent recommendation information to the current target user terminal corresponding to the current target user, so that the current target user can view it in time.

[0076] The current feedback tag storage unit 160 is used to add the current feedback tag to the current intelligent recommendation information and store it in a local storage space if the current feedback tag sent by the current target user terminal and corresponding to the current intelligent recommendation information is received.

[0077] In this embodiment, in order to realize on-the-go training (i.e., continuous training) of the pre-trained language model, the user's current feedback label for each piece of intelligent recommendation information (such as a label for an accurate reply, or an inaccurate answer, or a label that cannot be judged whether it is accurate, etc.) can be continuously obtained, such as obtaining the current feedback label corresponding to the current intelligent recommendation information, and adding the current feedback label to the current intelligent recommendation information and storing it in a local storage space as a data set for subsequent continuous on-the-go training of the pre-trained language model.

[0078] In one embodiment, the cardiovascular-related monitoring information intelligent recommendation device 100 further includes: The model online training unit is used to obtain a training set consisting of multiple stored intelligent recommendation information including feedback tags if it is detected that the current system time meets the preset model online training conditions, and perform model training on the pre-trained language model until the preset model training stop conditions are met, so as to update the pre-trained language model.

[0079] In this embodiment, for example, the preset model online training condition is set to a time interval between the current system time and the last model online training that is equal to the preset model online training period. When it is detected that the current system time satisfies the preset model online training condition, it means that a training set consisting of multiple stored intelligent recommendation information including feedback tags is currently available (wherein the first three sentences of context information of each intelligent recommendation information in the training set are also known), and model training is performed on the pre-trained language model until the preset model training stop condition is met (such as convergence of the loss function, etc.) to update the pre-trained language model, thereby achieving continuous updating of the pre-trained language model so that it has better model performance.

[0080] It can be seen that the implementation example of the device can combine the patient label of the current target user, the current monitoring recommendation information, the current user input information and the historical text information to form the current comprehensive input information containing more information, and then input it into the pre-trained language model for the medical field to obtain more contextual information and more accurate intelligent recommendation information.

[0081] The above-mentioned intelligent recommendation device for cardiovascular-related monitoring information can be implemented in the form of a computer program. The computer program can be used in Figure 7 Runs on the computer device shown.

[0082] See also Figure 7 , Figure 7 The schematic block diagram of a computer device provided by an embodiment of the present invention is shown in FIG. The computer device integrates any one of the cardiovascular-related monitoring information intelligent recommendation devices provided by an embodiment of the present invention.

[0083] See also Figure 7 The computer device 400 includes a processor 402 , a memory and a network interface 405 connected via a system bus 401 , wherein the memory may include a storage medium 403 and an internal memory 404 .

[0084] The storage medium 403 can store an operating system 4031 and a computer program 4032. The computer program 4032 includes program instructions, and when the program instructions are executed, the processor 402 can execute the above-mentioned cardiovascular-related monitoring information intelligent recommendation method.

[0085] The processor 402 is used to provide computing and control capabilities to support the operation of the entire computer device.

[0086] The internal memory 404 provides an environment for the operation of the computer program 4032 in the storage medium 403. When the computer program 4032 is executed by the processor 402, the processor 402 can execute the above-mentioned intelligent recommendation method for cardiovascular-related monitoring information.

[0087] The network interface 405 is used to communicate with other devices over the network. Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0088] The processor 402 is used to run a computer program 4032 stored in the memory to implement the above-mentioned intelligent recommendation method for cardiovascular-related monitoring information.

[0089] It should be understood that in the embodiment of the present invention, the processor 402 may be a central processing unit (CPU), and the processor 402 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0090] It can be understood by those skilled in the art that all or part of the processes in the method for implementing the above embodiment can be completed by instructing the relevant hardware through a computer program. The computer program includes program instructions, and the computer program can be stored in a storage medium, which is a computer-readable storage medium. The program instructions are executed by at least one processor in the computer device to implement the process steps of the embodiment of the above method.

[0091] Therefore, the present invention also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, wherein the computer program includes program instructions. When the program instructions are executed by a processor, the processor executes the above-mentioned intelligent recommendation method for cardiovascular-related monitoring information.

[0092] The computer-readable storage medium may be a USB flash drive, a mobile hard disk, a read-only memory (ROM), a magnetic disk, or an optical disk, etc., which are computer-readable storage media that can store program codes.

[0093] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0094] In the several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of each unit is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed.

[0095] The steps in the method of the embodiment of the present invention can be adjusted in order, combined and deleted according to actual needs. The units in the device of the embodiment of the present invention can be combined, divided and deleted according to actual needs. In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0096] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, terminal, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention.

[0097] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for intelligently recommending cardiovascular-related monitoring information, applied to a server, characterized in that: include: In response to the information intelligent recommendation instruction, obtaining current target user information corresponding to the information intelligent recommendation instruction, and obtaining current target time interval corresponding to the intelligent recommendation instruction; If it is determined that the current target user information includes a cardiovascular disease patient tag, obtaining the current target user corresponding to the current target user information, and obtaining the current monitoring recommendation information corresponding to the current target user and stored in the local storage space; If the current user input information is detected, the current information type of the current user input information is obtained; If it is determined that the current information type is a text information type, then historical text information corresponding to the current target user and whose information generation time is within the current target time interval is obtained, and the current user input information, the current monitoring recommendation information and the historical text information are used to form the current comprehensive input information and input it into the pre-trained language model to obtain the current intelligent recommendation information; wherein the pre-trained language model is a pre-trained language model for the medical field; Sending the current intelligent recommendation information to a current target user terminal corresponding to the current target user; If a current feedback tag is received which is sent by the current target user terminal and corresponds to the current intelligent recommendation information, the current feedback tag is added to the current intelligent recommendation information and stored in a local storage space.

2. The method for intelligently recommending cardiovascular-related monitoring information according to claim 1, characterized in that: The obtaining of the current information type of the information entered by the current user includes: Obtain the file format of the current user input information; If it is determined that the file format corresponds to a text file format, taking the text information type as the current information type; If it is determined that the file format corresponds to a picture file format, taking the picture information type as the current information type; If it is determined that the file format corresponds to a voice file format, the voice information type is used as the current information type.

3. The intelligent recommendation method for cardiovascular-related monitoring information according to claim 1, characterized in that: The current user input information, the current monitoring recommendation information and the historical text information are used to form the current comprehensive input information and input it into the pre-trained language model to obtain the current intelligent recommendation information, including: According to the order of the current monitoring recommendation information, the historical text information and the current user input information, the current monitoring recommendation information, the historical text information and the current user input information are combined into the current comprehensive input information; The current input vector corresponding to the current comprehensive input information is input into the pre-trained language model to obtain the current intelligent recommendation information.

4. The method for intelligently recommending cardiovascular-related monitoring information according to claim 3, characterized in that: After the step of inputting the current input vector corresponding to the current comprehensive input information into the pre-trained language model to obtain the current intelligent recommendation information, the method further includes: The privacy protection degree of the current intelligent recommendation information is obtained based on a preset verification strategy; wherein the corresponding formula for obtaining the privacy protection degree of the intelligent recommendation information in the verification strategy is the privacy protection degree of the information , M is the total number of privacy entities included in the intelligent recommendation information, Sen i Indicates the privacy entity sensitive value of the i-th privacy entity in the intelligent recommendation information, Id i Represents the identifiability value of the i-th privacy entity in the intelligent recommendation information; If it is determined that the privacy protection level of the current intelligent recommendation information is greater than or equal to a preset privacy protection level threshold, a trustworthy protection label is correspondingly added to the current intelligent recommendation information to update the current intelligent recommendation information.

5. The method for intelligently recommending cardiovascular-related monitoring information according to claim 1, characterized in that: After the step of obtaining the current information type of the current user input information if the current user input information is detected, the method further includes: If it is determined that the current information type is an image information type, then acquiring image recognition text information corresponding to the current user input information based on the image text recognition model; If it is determined that the image recognition text information is physical sign detection data information, the previous physical sign detection data information corresponding to the current target user is obtained, and the previous physical sign detection data information and the image recognition text information are displayed side by side.

6. The method for intelligently recommending cardiovascular-related monitoring information according to claim 1, characterized in that: After the step of adding the current feedback tag to the current intelligent recommendation information and storing it in a local storage space if the current feedback tag sent by the current target user terminal and corresponding to the current intelligent recommendation information is received, the method further includes: If it is detected that the current system time meets the preset model online training conditions, a training set consisting of multiple stored intelligent recommendation information including feedback labels is obtained, and model training is performed on the pre-trained language model until the preset model training stop conditions are met to update the pre-trained language model.

7. The method for intelligently recommending cardiovascular-related monitoring information according to claim 1, characterized in that: The obtaining of the current target user information corresponding to the information intelligent recommendation instruction and the obtaining of the current target time interval corresponding to the intelligent recommendation instruction include: Acquire current user classification label information in the current target user information, and determine a current acquisition period based on the current user classification label information and a preset label-period mapping relationship; Obtaining the instruction generation time of the information intelligent recommendation instruction, determining the start time of the current target time interval by the difference between the instruction generation time and the current acquisition cycle, and using the instruction generation time as the end time of the current target time interval; The current target time interval is determined according to the current target time interval start time and the current target time interval end time.

8. An intelligent recommendation device for cardiovascular-related monitoring information, configured on a server, characterized in that: include: a target user information acquisition unit, configured to, in response to an information intelligent recommendation instruction, acquire current target user information corresponding to the information intelligent recommendation instruction, and acquire current target time interval corresponding to the intelligent recommendation instruction; A monitoring recommendation information acquisition unit, configured to, if it is determined that the current target user information includes a cardiovascular disease patient tag, acquire the current target user corresponding to the current target user information, and acquire the current monitoring recommendation information corresponding to the current target user and stored in a local storage space; An information type acquisition unit, configured to acquire the current information type of the information entered by the current user if the current user enters information; A current intelligent recommendation information acquisition unit, for obtaining historical text information corresponding to the current target user and whose information generation time is within the current target time interval if the current information type is determined to be a text information type, and forming current comprehensive input information with the current user input information, the current monitoring recommendation information and the historical text information and inputting the information into a pre-trained language model to obtain current intelligent recommendation information; wherein the pre-trained language model is a pre-trained language model for the medical field; A current intelligent recommendation information sending unit, configured to send the current intelligent recommendation information to a current target user terminal corresponding to the current target user; The current feedback tag storage unit is used to add the current feedback tag to the current intelligent recommendation information and store it in a local storage space if the current feedback tag sent by the current target user terminal and corresponding to the current intelligent recommendation information is received.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the computer device executes the computer program, the method for intelligently recommending cardiovascular-related monitoring information according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for intelligently recommending cardiovascular-related monitoring information according to any one of claims 1 to 7 is implemented.

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