CN115662595A - User information management method and system based on online diagnosis and treatment system - Google Patents
User information management method and system based on online diagnosis and treatment system Download PDFInfo
- Publication number
- CN115662595A CN115662595A CN202211400383.2A CN202211400383A CN115662595A CN 115662595 A CN115662595 A CN 115662595A CN 202211400383 A CN202211400383 A CN 202211400383A CN 115662595 A CN115662595 A CN 115662595A
- Authority
- CN
- China
- Prior art keywords
- information
- target
- user
- change
- diagnosis
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Landscapes
- Medical Treatment And Welfare Office Work (AREA)
Abstract
The invention relates to the field of artificial intelligence, and discloses a user information management method and a user information management system based on an online diagnosis and treatment system, which are used for improving the user information management accuracy of the online diagnosis and treatment system. The method comprises the following steps: carrying out information change monitoring on the personal information of the user; when a diagnosis and treatment information change event is monitored, determining target change information in personal information of a user according to the diagnosis and treatment information change event; performing information attribute analysis on the target change information to obtain a target attribute type, and matching a target abnormality detection model corresponding to the target change information according to the target attribute type; inputting the target change information into a target abnormality detection model to perform information abnormality change detection to obtain an information abnormality detection result corresponding to each target user; and generating an abnormal alarm priority corresponding to the target change information according to the target attribute type, and carrying out online diagnosis and treatment monitoring on a plurality of target users according to the abnormal alarm priority and the information abnormal detection result.
Description
Technical Field
The invention relates to the field of artificial intelligence, in particular to a user information management method and system based on an online diagnosis and treatment system.
Background
At present, with the rapid development of artificial intelligence technology, the online diagnosis and treatment system greatly improves the living quality and the medical and scientific research level of users. The on-line diagnosis and treatment system can quickly diagnose the illness state through the description of images and characters, and improve the diagnosis and treatment efficiency.
However, the online diagnosis and treatment system does not timely perform real-time tracking and early warning on the information change condition of the user, so that the user information is analyzed with hysteresis and accuracy, namely the accuracy of the existing scheme is low.
Disclosure of Invention
The invention provides a user information management method and a user information management system based on an online diagnosis and treatment system, which are used for improving the user information management accuracy of the online diagnosis and treatment system.
The invention provides a user information management method based on an online diagnosis and treatment system, which comprises the following steps: acquiring user personal information of a plurality of target users from a preset online diagnosis and treatment system database, and respectively carrying out information change monitoring on the user personal information of each target user according to a preset diagnosis and treatment information management strategy; when a diagnosis and treatment information change event is monitored, determining target change information in the personal information of the user according to the diagnosis and treatment information change event; performing information attribute analysis on the target change information to obtain a target attribute type, and matching a target anomaly detection model corresponding to the target change information according to the target attribute type; inputting the target change information into the target abnormality detection model to perform information abnormality change detection to obtain an information abnormality detection result corresponding to each target user; and generating abnormal alarm priority corresponding to the target change information according to the target attribute type, and carrying out online diagnosis and treatment monitoring on the plurality of target users according to the abnormal alarm priority and the information abnormal detection result.
Optionally, in a first implementation manner of the first aspect of the present invention, the acquiring user personal information of a plurality of target users from a preset online medical system database, and performing information change monitoring on the user personal information of each target user according to a preset medical information management policy respectively includes: creating database connection channels between a plurality of target users and the online diagnosis and treatment system database; based on the database connection channel, respectively querying user personal information corresponding to each target user from the online diagnosis and treatment system database, wherein the user personal information comprises: user identity information and user historical diagnosis and treatment information; respectively setting diagnosis and treatment information management strategies corresponding to each target user according to the historical diagnosis and treatment information of the users; and generating a monitoring period according to the diagnosis and treatment information management strategy, and carrying out information change monitoring on the user personal information of each target user according to the monitoring period.
Optionally, in a second implementation manner of the first aspect of the present invention, when a medical information change event is monitored, determining target change information in the user personal information according to the medical information change event includes: performing listing conversion on the user personal information to generate a user information list corresponding to the user personal information; when a diagnosis and treatment information change event is monitored, extracting information change identification from the user information list to obtain target information change identification; and carrying out information detection and information identification on the target information change identification according to preset information change effective time to obtain target change information.
Optionally, in a third implementation manner of the first aspect of the present invention, the analyzing information attributes of the target change information to obtain a target attribute type, and matching a target anomaly detection model corresponding to the target change information according to the target attribute type includes: performing information attribute relation mapping analysis on the target change information according to a preset information attribute mapping relation to obtain a target attribute type; constructing a plurality of candidate anomaly detection models based on the online diagnosis and treatment system database; matching a target anomaly detection model corresponding to the target attribute type from the plurality of candidate anomaly detection models.
Optionally, in a fourth implementation manner of the first aspect of the present invention, the inputting the target change information into the target anomaly detection model to perform information anomaly change detection, so as to obtain an information anomaly detection result corresponding to each target user, includes: inputting the target variation information into the target anomaly detection model, wherein the target anomaly detection model comprises: a feature extraction network, an embedding layer, a convolution network and a normalization layer; extracting feature elements of the target change information through the feature extraction network to obtain a plurality of feature elements; inputting the plurality of characteristic elements into the embedding layer for vector coding to obtain characteristic input vectors; inputting the characteristic input vector into the convolution network for abnormal characteristic processing to obtain a target characteristic vector; inputting the target feature vector into the normalization layer for abnormal probability analysis, and outputting target probability data; and generating an information abnormity detection result corresponding to each target user according to the target probability data.
Optionally, in a fifth implementation manner of the first aspect of the present invention, the generating an abnormal alarm priority corresponding to the target change information according to the target attribute type, and performing online diagnosis and treatment monitoring on the plurality of target users according to the abnormal alarm priority and the information abnormality detection result includes: acquiring a weight value corresponding to the target change information based on the target attribute type; generating an abnormal alarm priority corresponding to the target change information according to the weight value; sorting the information anomaly detection results according to the anomaly alarm priority to obtain a target sequence; and carrying out online diagnosis and treatment monitoring on the plurality of target users according to the target sequence.
Optionally, in a sixth implementation manner of the first aspect of the present invention, the user information management method based on an online diagnosis and treatment system further includes: acquiring historical diagnosis and treatment information change events of a target user, and judging whether the target user has at least two diagnosis and treatment information change events according to the historical diagnosis and treatment information change events; if yes, calculating the interval time corresponding to the two diagnosis and treatment information change events; judging whether the interval time is greater than a preset time threshold value or not; and if so, adjusting the abnormal alarm priority corresponding to the target user.
The second aspect of the present invention provides a user information management system based on an online diagnosis and treatment system, including: the acquisition module is used for acquiring the user personal information of a plurality of target users from a preset online diagnosis and treatment system database and respectively monitoring the user personal information of each target user according to a preset diagnosis and treatment information management strategy; the monitoring module is used for determining target change information in the personal information of the user according to the diagnosis and treatment information change event when the diagnosis and treatment information change event is monitored; the analysis module is used for carrying out information attribute analysis on the target change information to obtain a target attribute type and matching a target abnormity detection model corresponding to the target change information according to the target attribute type; the detection module is used for inputting the target change information into the target abnormality detection model to carry out information abnormality change detection so as to obtain an information abnormality detection result corresponding to each target user; and the processing module is used for generating an abnormal alarm priority corresponding to the target change information according to the target attribute type and carrying out online diagnosis and treatment monitoring on the plurality of target users according to the abnormal alarm priority and the information abnormal detection result.
Optionally, in a first implementation manner of the second aspect of the present invention, the obtaining module is specifically configured to: creating database connection channels between a plurality of target users and the online diagnosis and treatment system database; based on the database connecting channel, respectively querying user personal information corresponding to each target user from the online diagnosis and treatment system database, wherein the user personal information comprises: user identity information and user historical diagnosis and treatment information; respectively setting diagnosis and treatment information management strategies corresponding to each target user according to the historical diagnosis and treatment information of the users; and generating a monitoring period according to the diagnosis and treatment information management strategy, and carrying out information change monitoring on the user personal information of each target user according to the monitoring period.
Optionally, in a second implementation manner of the second aspect of the present invention, the monitoring module is specifically configured to: listing conversion is carried out on the user personal information, and a user information list corresponding to the user personal information is generated; when a diagnosis and treatment information change event is monitored, extracting information change identification from the user information list to obtain target information change identification; and carrying out information detection and information identification on the target information change identification according to preset information change effective time to obtain target change information.
Optionally, in a third implementation manner of the second aspect of the present invention, the analysis module is specifically configured to: performing information attribute relation mapping analysis on the target change information according to a preset information attribute mapping relation to obtain a target attribute type; constructing a plurality of candidate anomaly detection models based on the online diagnosis and treatment system database; matching a target anomaly detection model corresponding to the target attribute type from the plurality of candidate anomaly detection models.
Optionally, in a fourth implementation manner of the second aspect of the present invention, the detection module is specifically configured to: inputting the target variation information into the target anomaly detection model, wherein the target anomaly detection model comprises: a feature extraction network, an embedding layer, a convolution network and a normalization layer; extracting feature elements of the target change information through the feature extraction network to obtain a plurality of feature elements; inputting the plurality of characteristic elements into the embedding layer for vector coding to obtain characteristic input vectors; inputting the characteristic input vector into the convolution network for abnormal characteristic processing to obtain a target characteristic vector; inputting the target feature vector into the normalization layer to perform abnormal probability analysis, and outputting target probability data; and generating an information abnormity detection result corresponding to each target user according to the target probability data.
Optionally, in a fifth implementation manner of the second aspect of the present invention, the processing module is specifically configured to: acquiring a weight value corresponding to the target change information based on the target attribute type; generating an abnormal alarm priority corresponding to the target change information according to the weight value; sorting the information anomaly detection results according to the anomaly alarm priority to obtain a target sequence; and carrying out online diagnosis and treatment monitoring on the plurality of target users according to the target sequence.
Optionally, in a sixth implementation manner of the second aspect of the present invention, the user information management system based on an online diagnosis and treatment system further includes: the adjustment module is used for acquiring historical diagnosis and treatment information change events of a target user and judging whether the target user has at least two diagnosis and treatment information change events according to the historical diagnosis and treatment information change events; if yes, calculating the interval time corresponding to the two diagnosis and treatment information change events; judging whether the interval time is greater than a preset time threshold value or not; and if so, adjusting the abnormal alarm priority corresponding to the target user.
The third aspect of the present invention provides a user information management device based on an online diagnosis and treatment system, including: a memory and at least one processor, the memory having instructions stored therein; the at least one processor calls the instructions in the memory to enable the online diagnosis and treatment system-based user information management device to execute the online diagnosis and treatment system-based user information management method.
A fourth aspect of the present invention provides a computer-readable storage medium having stored therein instructions, which, when executed on a computer, cause the computer to execute the above-mentioned user information management method based on an online medical system.
In the technical scheme provided by the invention, information change monitoring is carried out on the personal information of a user; when a diagnosis and treatment information change event is monitored, determining target change information in the personal information of the user according to the diagnosis and treatment information change event; performing information attribute analysis on the target change information to obtain a target attribute type, and matching a target abnormality detection model corresponding to the target change information according to the target attribute type; inputting the target change information into a target abnormality detection model to perform information abnormality change detection to obtain an information abnormality detection result corresponding to each target user; according to the method, information change monitoring is carried out on each target user, monitored change information is subjected to abnormity analysis through a pre-constructed target abnormity detection model, and then online diagnosis and treatment monitoring is carried out on the plurality of target users according to abnormity analysis results, so that the user information management accuracy of the online diagnosis and treatment system is improved.
Drawings
Fig. 1 is a schematic diagram of an embodiment of a user information management method based on an online diagnosis and treatment system according to an embodiment of the present invention;
fig. 2 is a schematic diagram of another embodiment of a user information management method based on an online diagnosis and treatment system according to an embodiment of the present invention;
fig. 3 is a schematic diagram of an embodiment of a user information management system based on an online diagnosis and treatment system according to an embodiment of the present invention;
fig. 4 is a schematic diagram of another embodiment of a user information management system based on an online diagnosis and treatment system according to an embodiment of the present invention.
Detailed Description
The embodiment of the invention provides a user information management method and system based on an online diagnosis and treatment system, which are used for improving the user information management accuracy of the online diagnosis and treatment system. The terms "first," "second," "third," "fourth," and the like in the description and in the claims, as well as in the drawings, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It will be appreciated that the data so used may be interchanged under appropriate circumstances such that the embodiments described herein may be practiced otherwise than as specifically illustrated or described herein. Moreover, the terms "comprises," "comprising," or "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
For convenience of understanding, a specific flow of the embodiment of the present invention is described below, and referring to fig. 1, an embodiment of a user information management method based on an online diagnosis and treatment system according to the embodiment of the present invention includes:
101. acquiring user personal information of a plurality of target users from a preset online diagnosis and treatment system database, and respectively carrying out information change monitoring on the user personal information of each target user according to a preset diagnosis and treatment information management strategy;
it can be understood that the executing subject of the present invention may be a user information management system based on an online diagnosis and treatment system, and may also be a terminal or a server, which is not limited herein. The embodiment of the present invention is described by taking a server as an execution subject.
It should be noted that the preset online diagnosis and treatment system database stores historical monitoring data of all users using the online diagnosis and treatment system, wherein the users can acquire personal information of the users through data acquisition instruments in the online diagnosis and treatment system, and the personal information of the users includes, but is not limited to, blood pressure, heart rate, body temperature, weight and height. In addition, the online diagnosis and treatment system also comprises an expert diagnosis and treatment database, wherein the expert diagnosis and treatment database stores user data corresponding to different diseases, and after receiving the user data, the background decision-making system judges the diseases according to the expert diagnosis and treatment database and then issues corresponding diagnosis and treatment analysis results. The online diagnosis and treatment system can be arranged at an intelligent hospital terminal, a user personal terminal and the like, the user personal information is uploaded through a cloud computing platform after the online diagnosis and treatment system collects the user personal information, the user personal information uploaded by batch online diagnosis and treatment systems can be processed in a centralized mode, the online diagnosis and treatment system sharing one cloud computing platform can be networked to realize centralized management, after the data of the online diagnosis and treatment system is uploaded, preliminary analysis processing is carried out through the cloud computing platform, if multiple items of data in the user personal information are within a standard range, the background decision system is not uploaded temporarily, diagnosis and treatment instructions are sent to the online diagnosis and treatment system to optimize the user personal information, if the multiple items of data in the user personal information are not within the standard range, the user personal information is uploaded to the background decision system, the diagnosis and treatment instructions are made by the background decision system and sent back to the cloud computing platform, and the cloud computing platform sends the diagnosis and treatment system to the online diagnosis and treatment system. In addition, in the embodiment, the user personal information of each target user is encrypted and packaged, and further, the user personal information of each target user is independently stored and monitored in information change. Specifically, the server collects user personal information for providing medical services, extracts personal ID information from a tag entering a predetermined data reading area, receives personal security information through a data interface, checks whether the input personal security information is the same as stored personal ID information, receives a request item through the interface, searches the requested data in data corresponding to the extracted personal ID information by the server, acquires user personal information of a plurality of target users, and further, the server performs information change monitoring on each user personal information through a preset diagnosis and treatment information management policy.
It should be noted that, when information change monitoring is performed, the server performs real-time detection through a preset field detection function, which is used for performing information change monitoring, and can improve timeliness and accuracy when the personal information of the user is detected.
102. When a diagnosis and treatment information change event is monitored, determining target change information in the personal information of the user according to the diagnosis and treatment information change event;
specifically, after detecting that a field change event occurs to the personal information of the user, determining a change field identifier of the personal information of the user, determining a target change field corresponding to the change field identifier from a preset API information database according to the change field identifier, returning the target change field to the personal information of the user through the API access interface,
it should be noted that, when the API interface document is identified to be changed, the server may automatically identify the changed field portion, extract the changed field data and update the changed field data into the API database, and then map the changed field data with the field actually used by the program, so that when the API is changed, the server may find out the required field value according to the field mapping table in the database, and directly extract the corresponding changed field from the API database, thereby ensuring the running continuity.
103. Performing information attribute analysis on the target change information to obtain a target attribute type, and matching a target abnormality detection model corresponding to the target change information according to the target attribute type;
firstly, the server obtains all attribute sorting schemes by using a full-permutation algorithm for information in target change information, respectively adds connection attribute information to the attribute sorting schemes according to connection attributes between every two attribute indexes in the target change information, performs dimensionality reduction and clustering on all the attribute sorting schemes through an MDS (minimum-mean-square) algorithm, performs visual analysis, obtains a target attribute type through interactive analysis and information attribute analysis between every two views, and matches a target anomaly detection model corresponding to the target change information according to the target attribute type.
It should be noted that, when performing model matching, the server determines a keyword according to a target attribute, further, the server performs a corresponding target attribute type according to the determined keyword, and meanwhile, the server performs field type analysis according to the target change information to determine a corresponding field type, and finally, the service area performs model matching according to the target attribute type and the field type to determine a corresponding target anomaly detection model.
104. Inputting the target change information into a target abnormality detection model to perform information abnormality change detection to obtain an information abnormality detection result corresponding to each target user;
specifically, the server calculates an evaluation threshold for evaluating normality and abnormality based on detected target fluctuation information and environmental data, compares the calculated evaluation threshold with an abnormality evaluation value to determine abnormality determination of abnormality, calculates an abnormality arrival time corresponding to each evaluation determination value based on a fluctuation characteristic and an abnormality evaluation value calculated based on the stored evaluation threshold and an addition operation time when the evaluation threshold exceeds a failure state determination value indicating a failure state of abnormality, displays the abnormality, and obtains an information abnormality detection result corresponding to each target user.
105. And generating an abnormal alarm priority corresponding to the target change information according to the target attribute type, and carrying out online diagnosis and treatment monitoring on a plurality of target users according to the abnormal alarm priority and the information abnormal detection result.
Specifically, the server generates an abnormal alarm priority corresponding to the target change information according to the target attribute type, wherein the server receives the fault detection message, respectively obtains the serial number from the received fault detection message, and generates the abnormal alarm priority corresponding to the target change information according to the serial number. Further, the server performs online diagnosis and treatment monitoring on a plurality of target users according to the abnormal alarm priority and the information abnormality detection result, specifically, the server mainly determines a corresponding response level according to the abnormal alarm priority, performs corresponding strategy generation according to the information abnormality detection result, determines a corresponding coping strategy, and finally performs online diagnosis and treatment monitoring according to the response level and the coping strategy.
In the embodiment of the invention, information change monitoring is carried out on the personal information of a user; when a diagnosis and treatment information change event is monitored, determining target change information in the personal information of the user according to the diagnosis and treatment information change event; performing information attribute analysis on the target change information to obtain a target attribute type, and matching a target abnormality detection model corresponding to the target change information according to the target attribute type; inputting the target change information into a target abnormality detection model to perform information abnormality change detection to obtain an information abnormality detection result corresponding to each target user; according to the method, the information change monitoring is carried out on each target user, the monitored change information is subjected to abnormality analysis through a pre-constructed target abnormality detection model, and then the online diagnosis and treatment monitoring is carried out on the plurality of target users according to the abnormality analysis result, so that the user information management accuracy of the online diagnosis and treatment system is improved.
Referring to fig. 2, another embodiment of the user information management method based on an online diagnosis and treatment system according to the embodiment of the present invention includes:
201. acquiring user personal information of a plurality of target users from a preset online diagnosis and treatment system database, and respectively carrying out information change monitoring on the user personal information of each target user according to a preset diagnosis and treatment information management strategy;
specifically, database connecting channels between a plurality of target users and an online diagnosis and treatment system database are established; based on a database connecting channel, respectively inquiring user personal information corresponding to each target user from an online diagnosis and treatment system database, wherein the user personal information comprises: user identity information and user historical diagnosis and treatment information; respectively setting diagnosis and treatment information management strategies corresponding to each target user according to historical diagnosis and treatment information of the users; and generating a monitoring period according to the diagnosis and treatment information management strategy, and carrying out information change monitoring on the user personal information of each target user according to the monitoring period.
It should be noted that the user personal information of a plurality of target users is stored by the partition data, wherein the online diagnosis and treatment system database is used for simultaneously containing the digital field and other data in the same channel in the frame. Therefore, the server can combine fields and data in a common information field and simultaneously transmit in the same channel frame by frame through a digital switching network, the switching network including a common port having a digital interface connected to the port, the digital interface being capable of separating and combining the fields and the data fields, the digital interface being connected to the server for creating a database connection channel between a plurality of target users and an online medical system database, further, the server indicates information of configuration of resources constituting a storage system in the database through user personal information, detects a change in setting a preset storage region to extract resources constituting an invalid preset storage region of the changed preset storage region as monitoring target resources, acquires performance information of information representing a monitored state to determine the target resources from the storage system, judges whether the performance information of the monitoring target resources matches a preset policy, and sets a medical information management policy corresponding to each target user according to user historical medical information; the method and the system for monitoring the diagnosis and treatment information generate a monitoring period according to the diagnosis and treatment information management strategy, and information change monitoring is carried out on the user personal information of each target user according to the monitoring period.
202. When a diagnosis and treatment information change event is monitored, determining target change information in the personal information of the user according to the diagnosis and treatment information change event;
specifically, listing conversion is carried out on the user personal information to generate a user information list corresponding to the user personal information; when a diagnosis and treatment information change event is monitored, extracting information change identification from a user information list to obtain target information change identification; and carrying out information detection and information identification on the target information change identification according to the preset information change effective time to obtain target change information.
The method comprises the steps of acquiring a data form type of user personal information, generating corresponding type data, acquiring the data form information of the user personal information, performing listing conversion on the user personal information, generating a corresponding data list, namely a user information list corresponding to the user personal information, further setting and processing missing data columns of the user information list by a server according to a preset standard data list template matched with the type data to generate a corresponding modified user information list, and extracting information change identification from the user information list when a diagnosis and treatment information change event is monitored to obtain a target information change identification; and carrying out information detection and information identification on the target information change identification according to the preset information change effective time to obtain target change information.
When monitoring is carried out, the service can determine a change field identifier of user personal information after detecting that a field change event occurs to the user personal information, determine a target change field corresponding to the change field identifier from a preset API information database according to the change field identifier, return the target change field to the user personal information through an API access interface, carry out information detection and information identification on the target information change identifier according to the preset information change effective time to obtain target change information, and map the target change information with a field actually used by a program to realize that when the API changes, a server can find out a required field value according to a field mapping table in the database, directly extract a corresponding change field from the API database and ensure the running continuity.
203. Performing information attribute analysis on the target change information to obtain a target attribute type, and matching a target abnormality detection model corresponding to the target change information according to the target attribute type;
specifically, according to a preset information attribute mapping relationship, performing information attribute relationship mapping analysis on the target change information to obtain a target attribute type; constructing a plurality of candidate anomaly detection models based on an online diagnosis and treatment system database; a target anomaly detection model corresponding to the target attribute type is matched from the plurality of candidate anomaly detection models.
The server acquires the historical user attribute information, stores or updates the historical user attribute information to a local information storage table so as to establish a mapping relation between an attribute information storage address and an interface, inserts the historical user attribute information into the target change information, and sends the target change information to the server for attribute relation mapping to obtain a target attribute type.
Before model screening, a server acquires target data, wherein the target data comprises target historical data and target real-time data, the target data is preprocessed and subjected to feature extraction, the target historical data is used as a training set, a plurality of local feature-based parallel and integrated anomaly detection models are trained, the trained parallel and integrated anomaly detection models are used for deducing the target real-time data, and abnormal data of a target are screened, wherein the server matches the target anomaly detection models corresponding to target attribute types from a plurality of candidate anomaly detection models.
204. Inputting the target change information into a target abnormality detection model to perform information abnormality change detection to obtain an information abnormality detection result corresponding to each target user;
specifically, the target variation information is input into a target anomaly detection model, wherein the target anomaly detection model includes: a feature extraction network, an embedding layer, a convolution network and a normalization layer; extracting feature elements of the target change information through a feature extraction network to obtain a plurality of feature elements; inputting a plurality of characteristic elements into the embedding layer for vector coding to obtain characteristic input vectors; inputting the feature input vector into a convolution network for abnormal feature processing to obtain a target feature vector; inputting the target feature vector into a normalization layer for abnormal probability analysis, and outputting target probability data; and generating an information anomaly detection result corresponding to each target user according to the target probability data.
The method includes the steps of determining n samples and label matrixes corresponding to the n samples in a training data set, determining whether an ith sample contains an object indicated by a jth label or not by using elements in the label matrixes, extracting feature matrixes of the n samples by using a feature extraction network, predicting confidence of the object indicated by the jth label contained in the ith sample by using elements in a predicted label matrix of the feature matrixes by using a feature mapping network, obtaining a plurality of feature elements according to the label matrixes and the predicted label matrixes, performing anomaly probability analysis by calculating maximum probability of feature input vectors in a transfer process by using a Viterbi algorithm during recognition, outputting target probability data, and generating an information anomaly detection result corresponding to each target user according to the target probability data.
205. Acquiring a weight value corresponding to the target change information based on the target attribute type;
206. generating an abnormal alarm priority corresponding to the target change information according to the weight value;
207. sorting the information anomaly detection results according to the anomaly alarm priority to obtain a target sequence;
specifically, evaluation attribute index information of an evaluation target is obtained, each evaluation attribute index information comprises a plurality of pieces of evaluation index information, an evaluation index score of each evaluation index is calculated based on the evaluation index information, an evaluation index weight vector and an evaluation attribute index weight vector are determined by adopting an analytic hierarchy process, and a weight value corresponding to target variation information is obtained based on the evaluation index weight vector. And the server receives an abnormal alarm priority corresponding to the target change information generated according to the weight value, and sorts the information abnormality detection results according to the abnormal alarm priority to obtain a target sequence.
208. And carrying out online diagnosis and treatment monitoring on a plurality of target users according to the target sequence.
Optionally, historical diagnosis and treatment information change events of the target user are obtained, and whether the target user has at least two diagnosis and treatment information change events is judged according to the historical diagnosis and treatment information change events; if yes, calculating the interval time corresponding to the two diagnosis and treatment information change events; judging whether the interval time is greater than a preset time threshold value or not; if so, adjusting the abnormal alarm priority corresponding to the target user.
The method comprises the steps of calculating a change click event of a data storage area where a target user is located, wherein the change time ratio of an effective field in each change period is set to be 0.45 by default, calculating the number of the effective field of the target user in one change period, setting the effective field number as 3 by default, calculating the time interval between two continuous field changes, judging whether the interval time is greater than a preset time threshold, and if so, adjusting the abnormal alarm priority corresponding to the target user.
In the embodiment of the invention, information change monitoring is carried out on the personal information of a user; when a diagnosis and treatment information change event is monitored, determining target change information in personal information of a user according to the diagnosis and treatment information change event; performing information attribute analysis on the target change information to obtain a target attribute type, and matching a target abnormality detection model corresponding to the target change information according to the target attribute type; inputting the target change information into a target abnormality detection model to perform information abnormality change detection to obtain an information abnormality detection result corresponding to each target user; according to the method, the information change monitoring is carried out on each target user, the monitored change information is subjected to abnormality analysis through a pre-constructed target abnormality detection model, and then the online diagnosis and treatment monitoring is carried out on the plurality of target users according to the abnormality analysis result, so that the user information management accuracy of the online diagnosis and treatment system is improved.
In the above description of the user information management method based on the online diagnosis and treatment system in the embodiment of the present invention, the following description of the user information management system based on the online diagnosis and treatment system in the embodiment of the present invention refers to fig. 3, and an embodiment of the user information management system based on the online diagnosis and treatment system in the embodiment of the present invention includes:
an obtaining module 301, configured to obtain user personal information of multiple target users from a preset online diagnosis and treatment system database, and perform information change monitoring on the user personal information of each target user according to a preset diagnosis and treatment information management policy;
a monitoring module 302, configured to determine target change information in the user personal information according to a diagnosis and treatment information change event when the diagnosis and treatment information change event is monitored;
an analysis module 303, configured to perform information attribute analysis on the target change information to obtain a target attribute type, and match a target anomaly detection model corresponding to the target change information according to the target attribute type;
the detection module 304 is configured to input the target change information into the target anomaly detection model to perform information anomaly detection, so as to obtain an information anomaly detection result corresponding to each target user;
the processing module 305 is configured to generate an abnormal alarm priority corresponding to the target change information according to the target attribute type, and perform online diagnosis and treatment monitoring on the plurality of target users according to the abnormal alarm priority and the information abnormal detection result.
In the embodiment of the invention, information change monitoring is carried out on the personal information of a user; when a diagnosis and treatment information change event is monitored, determining target change information in personal information of a user according to the diagnosis and treatment information change event; performing information attribute analysis on the target change information to obtain a target attribute type, and matching a target abnormality detection model corresponding to the target change information according to the target attribute type; inputting the target change information into a target abnormality detection model to perform information abnormality change detection to obtain an information abnormality detection result corresponding to each target user; according to the method, information change monitoring is carried out on each target user, monitored change information is subjected to abnormity analysis through a pre-constructed target abnormity detection model, and then online diagnosis and treatment monitoring is carried out on the plurality of target users according to abnormity analysis results, so that the user information management accuracy of the online diagnosis and treatment system is improved.
Referring to fig. 4, another embodiment of the user information management system based on the online diagnosis and treatment system according to the embodiment of the present invention includes:
an obtaining module 301, configured to obtain user personal information of multiple target users from a preset online diagnosis and treatment system database, and perform information change monitoring on the user personal information of each target user according to a preset diagnosis and treatment information management policy;
a monitoring module 302, configured to determine target change information in the user personal information according to a diagnosis and treatment information change event when the diagnosis and treatment information change event is monitored;
an analysis module 303, configured to perform information attribute analysis on the target change information to obtain a target attribute type, and match a target anomaly detection model corresponding to the target change information according to the target attribute type;
a detection module 304, configured to input the target change information into the target anomaly detection model to perform information anomaly detection, so as to obtain an information anomaly detection result corresponding to each target user;
the processing module 305 is configured to generate an abnormal alarm priority corresponding to the target change information according to the target attribute type, and perform online diagnosis and treatment monitoring on the multiple target users according to the abnormal alarm priority and the information abnormality detection result.
Optionally, the obtaining module 301 is specifically configured to: creating database connection channels between a plurality of target users and the online diagnosis and treatment system database; based on the database connection channel, respectively querying user personal information corresponding to each target user from the online diagnosis and treatment system database, wherein the user personal information comprises: user identity information and user historical diagnosis and treatment information; respectively setting diagnosis and treatment information management strategies corresponding to each target user according to the historical diagnosis and treatment information of the users; and generating a monitoring period according to the diagnosis and treatment information management strategy, and carrying out information change monitoring on the user personal information of each target user according to the monitoring period.
Optionally, the monitoring module 302 is specifically configured to: performing listing conversion on the user personal information to generate a user information list corresponding to the user personal information; when a diagnosis and treatment information change event is monitored, extracting information change identification from the user information list to obtain target information change identification; and carrying out information detection and information identification on the target information change identification according to preset information change effective time to obtain target change information.
Optionally, the analysis module 303 is specifically configured to: performing information attribute relation mapping analysis on the target change information according to a preset information attribute mapping relation to obtain a target attribute type; constructing a plurality of candidate anomaly detection models based on the online diagnosis and treatment system database; matching a target anomaly detection model corresponding to the target attribute type from the plurality of candidate anomaly detection models.
Optionally, the detecting module 304 is specifically configured to: inputting the target variation information into the target anomaly detection model, wherein the target anomaly detection model comprises: a feature extraction network, an embedding layer, a convolution network and a normalization layer; extracting feature elements of the target change information through the feature extraction network to obtain a plurality of feature elements; inputting the plurality of characteristic elements into the embedding layer for vector coding to obtain characteristic input vectors; inputting the characteristic input vector into the convolution network for abnormal characteristic processing to obtain a target characteristic vector; inputting the target feature vector into the normalization layer for abnormal probability analysis, and outputting target probability data; and generating an information abnormity detection result corresponding to each target user according to the target probability data.
Optionally, the processing module 305 is specifically configured to: acquiring a weight value corresponding to the target change information based on the target attribute type; generating an abnormal alarm priority corresponding to the target change information according to the weight value; sorting the information abnormity detection results according to the abnormity alarm priority to obtain a target sequence; and carrying out online diagnosis and treatment monitoring on the plurality of target users according to the target sequence.
Optionally, the user information management system based on the online diagnosis and treatment system further includes:
the adjusting module 306 is configured to obtain a historical diagnosis and treatment information change event of a target user, and determine whether the target user has at least two diagnosis and treatment information change events according to the historical diagnosis and treatment information change event; if yes, calculating the interval time corresponding to the two diagnosis and treatment information change events; judging whether the interval time is greater than a preset time threshold value or not; and if so, adjusting the abnormal alarm priority corresponding to the target user.
In the embodiment of the invention, information change monitoring is carried out on the personal information of a user; when a diagnosis and treatment information change event is monitored, determining target change information in the personal information of the user according to the diagnosis and treatment information change event; performing information attribute analysis on the target change information to obtain a target attribute type, and matching a target abnormality detection model corresponding to the target change information according to the target attribute type; inputting the target change information into a target abnormality detection model to perform information abnormality change detection to obtain an information abnormality detection result corresponding to each target user; according to the method, the information change monitoring is carried out on each target user, the monitored change information is subjected to abnormality analysis through a pre-constructed target abnormality detection model, and then the online diagnosis and treatment monitoring is carried out on the plurality of target users according to the abnormality analysis result, so that the user information management accuracy of the online diagnosis and treatment system is improved.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A user information management method based on an online diagnosis and treatment system is characterized by comprising the following steps:
acquiring user personal information of a plurality of target users from a preset online diagnosis and treatment system database, and respectively carrying out information change monitoring on the user personal information of each target user according to a preset diagnosis and treatment information management strategy;
when a diagnosis and treatment information change event is monitored, determining target change information in the personal information of the user according to the diagnosis and treatment information change event;
performing information attribute analysis on the target change information to obtain a target attribute type, and matching a target abnormality detection model corresponding to the target change information according to the target attribute type;
inputting the target change information into the target abnormality detection model to perform information abnormality change detection to obtain an information abnormality detection result corresponding to each target user;
and generating abnormal alarm priority corresponding to the target change information according to the target attribute type, and carrying out online diagnosis and treatment monitoring on the plurality of target users according to the abnormal alarm priority and the information abnormal detection result.
2. The method for managing user information based on an online medical system according to claim 1, wherein the step of obtaining user personal information of a plurality of target users from a preset online medical system database and performing information change monitoring on the user personal information of each target user according to a preset medical information management policy comprises:
creating database connection channels between a plurality of target users and the online diagnosis and treatment system database;
based on the database connecting channel, respectively querying user personal information corresponding to each target user from the online diagnosis and treatment system database, wherein the user personal information comprises: user identity information and user historical diagnosis and treatment information;
respectively setting diagnosis and treatment information management strategies corresponding to each target user according to the historical diagnosis and treatment information of the users;
and generating a monitoring period according to the diagnosis and treatment information management strategy, and carrying out information change monitoring on the user personal information of each target user according to the monitoring period.
3. The method for managing user information based on an online medical system according to claim 1, wherein the determining target change information in the user personal information according to the medical information change event when the medical information change event is monitored comprises:
performing listing conversion on the user personal information to generate a user information list corresponding to the user personal information;
when a diagnosis and treatment information change event is monitored, extracting information change identification from the user information list to obtain target information change identification;
and carrying out information detection and information identification on the target information change identification according to preset information change effective time to obtain target change information.
4. The user information management method based on the online diagnosis and treatment system according to claim 1, wherein the performing information attribute analysis on the target variation information to obtain a target attribute type, and matching a target anomaly detection model corresponding to the target variation information according to the target attribute type includes:
performing information attribute relation mapping analysis on the target change information according to a preset information attribute mapping relation to obtain a target attribute type;
constructing a plurality of candidate anomaly detection models based on the online diagnosis and treatment system database;
matching a target anomaly detection model corresponding to the target attribute type from the plurality of candidate anomaly detection models.
5. The user information management method based on the online diagnosis and treatment system according to claim 1, wherein the inputting the target change information into the target abnormality detection model for information abnormality change detection to obtain an information abnormality detection result corresponding to each target user comprises:
inputting the target variation information into the target anomaly detection model, wherein the target anomaly detection model comprises: a feature extraction network, an embedding layer, a convolution network and a normalization layer;
extracting feature elements of the target change information through the feature extraction network to obtain a plurality of feature elements;
inputting the plurality of characteristic elements into the embedding layer for vector coding to obtain characteristic input vectors;
inputting the characteristic input vector into the convolution network for abnormal characteristic processing to obtain a target characteristic vector;
inputting the target feature vector into the normalization layer for abnormal probability analysis, and outputting target probability data;
and generating an information anomaly detection result corresponding to each target user according to the target probability data.
6. The user information management method based on the online diagnosis and treatment system according to claim 1, wherein the generating of the abnormal alarm priority corresponding to the target change information according to the target attribute type and the online diagnosis and treatment monitoring of the plurality of target users according to the abnormal alarm priority and the information abnormality detection result comprise:
acquiring a weight value corresponding to the target change information based on the target attribute type;
generating an abnormal alarm priority corresponding to the target change information according to the weight value;
sorting the information abnormity detection results according to the abnormity alarm priority to obtain a target sequence;
and carrying out online diagnosis and treatment monitoring on the target users according to the target sequence.
7. The method for managing user information based on an online diagnosis and treatment system according to claim 1, further comprising:
acquiring historical diagnosis and treatment information change events of a target user, and judging whether the target user has at least two diagnosis and treatment information change events according to the historical diagnosis and treatment information change events;
if yes, calculating the interval time corresponding to the two diagnosis and treatment information change events;
judging whether the interval time is greater than a preset time threshold value or not;
and if so, adjusting the abnormal alarm priority corresponding to the target user.
8. A user information management system based on an online diagnosis and treatment system is characterized by comprising:
the acquisition module is used for acquiring the user personal information of a plurality of target users from a preset online diagnosis and treatment system database and respectively monitoring the user personal information of each target user according to a preset diagnosis and treatment information management strategy;
the monitoring module is used for determining target change information in the personal information of the user according to the diagnosis and treatment information change event when the diagnosis and treatment information change event is monitored;
the analysis module is used for carrying out information attribute analysis on the target change information to obtain a target attribute type and matching a target abnormity detection model corresponding to the target change information according to the target attribute type;
the detection module is used for inputting the target change information into the target abnormality detection model to carry out information abnormality change detection so as to obtain an information abnormality detection result corresponding to each target user;
and the processing module is used for generating an abnormal alarm priority corresponding to the target change information according to the target attribute type and carrying out online diagnosis and treatment monitoring on the plurality of target users according to the abnormal alarm priority and the information abnormal detection result.
9. The system for managing user information based on an online diagnosis and treatment system according to claim 8, wherein the obtaining module is specifically configured to:
creating database connection channels between a plurality of target users and the online diagnosis and treatment system database;
based on the database connecting channel, respectively querying user personal information corresponding to each target user from the online diagnosis and treatment system database, wherein the user personal information comprises: user identity information and user historical diagnosis and treatment information;
respectively setting diagnosis and treatment information management strategies corresponding to each target user according to the historical diagnosis and treatment information of the users;
and generating a monitoring period according to the diagnosis and treatment information management strategy, and carrying out information change monitoring on the user personal information of each target user according to the monitoring period.
10. The system for managing user information based on an online diagnosis and treatment system according to claim 8, wherein the monitoring module is specifically configured to:
performing listing conversion on the user personal information to generate a user information list corresponding to the user personal information;
when a diagnosis and treatment information change event is monitored, extracting information change identification from the user information list to obtain target information change identification;
and carrying out information detection and information identification on the target information change identification according to preset information change effective time to obtain target change information.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211400383.2A CN115662595A (en) | 2022-11-09 | 2022-11-09 | User information management method and system based on online diagnosis and treatment system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211400383.2A CN115662595A (en) | 2022-11-09 | 2022-11-09 | User information management method and system based on online diagnosis and treatment system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115662595A true CN115662595A (en) | 2023-01-31 |
Family
ID=85016950
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211400383.2A Pending CN115662595A (en) | 2022-11-09 | 2022-11-09 | User information management method and system based on online diagnosis and treatment system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115662595A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115778570A (en) * | 2023-02-09 | 2023-03-14 | 岱川医疗(深圳)有限责任公司 | Endoscope detection method, control device and detection system |
-
2022
- 2022-11-09 CN CN202211400383.2A patent/CN115662595A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115778570A (en) * | 2023-02-09 | 2023-03-14 | 岱川医疗(深圳)有限责任公司 | Endoscope detection method, control device and detection system |
| CN115778570B (en) * | 2023-02-09 | 2023-06-27 | 岱川医疗(深圳)有限责任公司 | Endoscope detection method, control device and detection system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112017789B (en) | Triage data processing method, triage data processing device, triage data processing equipment and triage data processing medium | |
| CN111177714A (en) | Abnormal behavior detection method and device, computer equipment and storage medium | |
| CN113270197A (en) | Health prediction method, system and storage medium based on artificial intelligence | |
| CN115688760B (en) | Intelligent diagnosis guiding method, device, equipment and storage medium | |
| CN114359787A (en) | Target attribute identification method and device, computer equipment and storage medium | |
| CN113822366A (en) | Service index abnormality detection method and device, electronic equipment and storage medium | |
| CN115858794B (en) | Abnormal log data identification method for network operation safety monitoring | |
| CN117809124B (en) | Medical image association calling method and system based on multi-feature fusion | |
| CN111415760B (en) | Doctor recommendation method, doctor recommendation system, computer equipment and storage medium | |
| CN115662595A (en) | User information management method and system based on online diagnosis and treatment system | |
| CN116959725A (en) | Disease risk prediction method based on multi-mode data fusion | |
| CN118312816A (en) | Cluster weighted clustering integrated medical data processing method and system based on member selection | |
| CN114647636A (en) | Big data anomaly detection method and system | |
| CN112732690B (en) | Stabilizing system and method for chronic disease detection and risk assessment | |
| CN113223701A (en) | Sudden heart disease prediction method based on Transformer-MHP model | |
| CN116030984B (en) | User physical examination system and physical examination method based on intelligent health station | |
| CN113537407B (en) | Image data evaluation processing method and device based on machine learning | |
| CN113349792B (en) | Method, apparatus, device and medium for classifying multi-lead electrocardiosignal | |
| CN112380224B (en) | Mass big data system for massive heterogeneous multidimensional data acquisition | |
| CN115392351A (en) | Risk user identification method and device, electronic equipment and storage medium | |
| CN113627997A (en) | Data processing method and device, electronic equipment and storage medium | |
| CN115831339B (en) | Medical system risk management and control pre-prediction method and system based on deep learning | |
| CN115953724B (en) | User data analysis and management method, device, equipment and storage medium | |
| CN117555501B (en) | Cloud printer operation and data processing method based on edge calculation and related device | |
| CN116564423B (en) | Method and system for constructing microbial metagenome database |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |