Internet hospital mobile terminal secure communication method and device based on message ferry

Abstract

The invention relates to the technical field of medical information, in particular to an internet hospital mobile terminal safety communication method and device based on message ferry, and the method comprises the steps: obtaining a medical data interaction request initiated by a mobile terminal, and carrying out the cluster classification of the data interaction request according to the semantic association degree of the medical data interaction request; acquiring patient medical file data corresponding to the data interaction request cluster, performing illness state evolution path analysis, and determining a data pre-caching strategy according to an illness state evolution path and evolution confidence; performing feature analysis on the data interaction request cluster and the ferry node, and determining a target ferry node through feature matching; and executing the data pre-caching strategy through the target ferry node and carrying out message ferry. By optimizing the data pre-caching strategy of the ferry node, safe, efficient and low-delay data interaction oriented to the medical scene is realized, and the data transmission efficiency of the Internet hospital mobile terminal is effectively improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of medical information technology, and in particular to an Internet hospital mobile terminal secure communication method and device based on message ferrying. BACKGROUND

[0002] With the rapid development of Internet medical services, Internet hospitals based on mobile terminals have become an important form of medical resource extension. The frequency of users performing interactive operations such as remote consultation, prescription acquisition, and diagnosis and treatment suggestion query through mobile terminals has significantly increased.

[0003] Traditional medical data interaction methods usually use centralized server architecture, which is prone to increased latency due to network congestion or single point failure. At the same time, medical data has high privacy and complexity, so introducing a message ferrying mechanism can effectively alleviate the communication bottlenecks and privacy risks caused by mobile terminals directly accessing medical system center nodes.

[0004] However, the data pre-caching strategy of the ferrying node in the existing message ferrying mechanism is usually statically configured or preloaded based on historical average access frequency, which lacks specific optimization for medical scenarios and fails to effectively consider the semantic features of medical data interaction requests, the disease evolution trend of individual patients, and the time sensitivity of medical data itself, resulting in decreased medical data cache hit rate and increased response latency. SUMMARY

[0005] In order to overcome the defects and deficiencies of the prior art, the present application provides an Internet hospital mobile terminal secure communication method and device based on message ferrying, which optimizes the data pre-caching strategy of the ferrying node to achieve secure, efficient, and low-latency data interaction for medical scenarios, effectively improving the data transmission efficiency of the Internet hospital mobile terminal.

[0006] In order to achieve the above purpose, the present application adopts the following technical solutions: In a first aspect, the present application provides an Internet hospital mobile terminal secure communication method based on message ferrying, comprising: S100, obtaining a medical data interaction request initiated by a mobile terminal, and classifying data interaction request clusters according to the semantic correlation degree of the medical data interaction request; S200, obtaining patient medical record data corresponding to the data interaction request cluster and performing disease evolution path analysis, determining a data pre-caching strategy according to the disease evolution path and evolution confidence; S300, performing feature analysis on the data interaction request cluster and the ferrying node respectively and determining a target ferrying node through feature matching; S400, executing the data pre-caching strategy through the target ferrying node and performing message ferrying.

[0007] Further, the specific steps of classifying data interaction request clusters in step S100 include: S110. Semantic parsing of each medical data interaction request is performed using a natural language processing model, and semantic feature vectors of each medical data interaction request are extracted. S120. Determine the semantic correlation degree between each medical data interaction request based on the semantic feature vector. The semantic correlation degree is the cosine similarity between the semantic feature vectors of the medical data interaction requests. S130. Medical data interaction requests with a semantic relevance greater than a preset semantic relevance threshold are divided into data interaction request clusters.

[0008] Furthermore, the specific steps for determining the data pre-caching strategy in step S200 include: S210. Obtain the patient's medical record data corresponding to the data interaction request cluster; S220. Construct a tree-like path diagram of the patient's disease evolution based on the patient's medical record data. The nodes in the disease evolution tree-like path diagram represent the disease evolution state, and the edges represent the disease evolution direction. S230. Determine the evolution confidence of each disease evolution path in the disease evolution tree diagram and take the disease evolution path with the highest evolution confidence as the target disease evolution path. S240. Use the disease evolution data corresponding to the target disease evolution path as pre-cached data and determine the cache refresh time window of the pre-cached data.

[0009] Furthermore, the specific steps for determining the evolutionary confidence level in step S230 include: S231. Extract the disease evolution paths from the disease evolution tree diagram and calculate the joint transition probability of all adjacent nodes on each disease evolution path: ; in, For the first The joint transition probability of all adjacent nodes on the disease evolution path. For the node Transfer to node The probability, Indicates the first The number of nodes in each disease progression path; S232. The joint transition probability of all adjacent nodes on the disease evolution path is used as the evolution confidence of each disease evolution path.

[0010] Furthermore, the specific steps for determining the cache refresh time window for the pre-cached data in step S240 include: S241. Obtain historical medical record data and filter out path-fitting cases, construct a path-fitting case dataset. Path-fitting cases are those in historical medical record data whose path similarity between the disease evolution path and the target disease evolution path is greater than a preset path similarity threshold. S242. Extract the disease evolution cycle of path-adapted cases from the path-adapted case dataset and take the weighted average of the disease evolution cycles of all path-adapted cases as the path evolution cycle. The weight is the ratio of the path similarity corresponding to the path-adapted case to the sum of the path similarities. S243. Extract the change characteristics of each data type in the disease evolution through the path-adapted case dataset, and use the Pearson correlation coefficient between the numerical change of each data type and the change of disease status as the correlation degree of disease evolution. S244. Obtain pre-cached data, determine the validity period of each data type in the pre-cached data, and determine the data value period by combining the correlation with the disease evolution: ,in, For data types The data value cycle For data types The corresponding data validity period, Represents data type The correlation between the disease progression; S245. The minimum value between the path evolution cycle of the disease evolution path and the data value cycle of the pre-cached data is used as the cache refresh time window for the pre-cached data.

[0011] Further, the determination of the target ferry node in step S300 includes: S310. Extract the request features of the data interaction request cluster and the node features of the ferry node and perform vector modeling to obtain the request feature vector and the node feature vector. S320. The weighted cosine similarity between the request feature vector and the node feature vector is used as the feature matching degree between the data interaction request cluster and the ferry node. S330. Select the ferry node with the highest feature matching degree as the target ferry node for the data interaction request cluster.

[0012] Secondly, the present invention provides a secure communication device for a mobile terminal of an internet hospital based on message transfer, comprising: The request cluster classification module is used to obtain medical data interaction requests initiated by mobile terminals and classify the data interaction request clusters according to the semantic relevance of the medical data interaction requests. A pre-caching strategy determination module connected to the request cluster classification module is configured to obtain patient medical record data corresponding to the data interaction request cluster and perform illness evolution path analysis, and determine a data pre-caching strategy according to the illness evolution path and evolution confidence; A target node determination module connected to the pre-caching strategy determination module is configured to perform feature analysis on the data interaction request cluster and the relay node respectively and determine a target relay node through feature matching. A pre-caching strategy execution module connected to the target node determination module is configured to execute the data pre-caching strategy through the target relay node and perform message relay.

[0013] In a third aspect, the present application provides an electronic device, comprising a processor and a memory, wherein the memory stores a computer program that can be called by the processor, and the processor executes the message relay-based secure communication method for the mobile terminal of the Internet hospital by calling the computer program stored in the memory.

[0014] In a fourth aspect, the present application provides a computer-readable storage medium storing instructions, which, when executed on a computer, cause the computer to execute the message relay-based secure communication method for the mobile terminal of the Internet hospital.

[0015] Compared with the prior art, the present application has the following advantages and beneficial effects: The present application introduces a data interaction request cluster classification based on semantic correlation degree, a data pre-caching strategy driven by illness evolution path analysis, and an intelligent relay node selection mechanism based on node feature matching, thereby realizing an efficient, secure and low-latency data communication method for a medical scenario, dynamically adjusting the cache content and refresh timing according to the individual illness changes of patients, and effectively improving the data transmission efficiency in time-sensitive medical scenarios. BRIEF DESCRIPTION OF DRAWINGS

[0016] Other features, objects and advantages of the present application will become more apparent from the following detailed description of non-limiting embodiments, made with reference to the accompanying drawings: Figure 1 is a flowchart of the message relay-based secure communication method for the mobile terminal of the Internet hospital provided by the embodiments of the present application; Figure 2 is a flowchart of step S100 provided by the embodiments of the present application; Figure 3 is a flowchart of step S200 provided by the embodiments of the present application; Figure 4 is a flowchart of step S240 provided by the embodiments of the present application; Figure 5 is a flowchart of step S300 provided by the embodiments of the present application; Figure 6 is a structural schematic diagram of an Internet hospital mobile terminal secure communication device based on message ferry provided by an embodiment of the present application; Figure 7 is a structural schematic diagram of an electronic device. DETAILED DESCRIPTION

[0017] The technical solutions of the present application will be described in detail below with reference to the drawings and specific embodiments. It should be understood that the specific features in the embodiments and the specific embodiments of the present application are detailed descriptions of the technical solutions of the present application, and are not limitations of the technical solutions of the present application. In the case of no conflict, the technical features in the embodiments and the specific embodiments can be combined with each other.

[0018] Please refer to Figure 1 , Figure 1 is a whole flow schematic diagram of an Internet hospital mobile terminal secure communication method based on message ferry provided by an embodiment of the present application, and specifically includes the following steps: S100, obtaining a medical data interaction request initiated by a mobile terminal, and classifying the data interaction request cluster according to the semantic correlation degree of the medical data interaction request; By classifying the medical data interaction request at the semantic level, a request cluster with high semantic correlation and similar data requirements is constructed, so that the subsequent disease evolution path analysis, data pre-caching strategy formulation and ferry node selection can be concentrated and optimized around the request cluster, thereby improving the processing efficiency, data hit rate and response accuracy of the system. Please refer to Figure 2 , Figure 2 is a flow schematic diagram of step S100 provided by an embodiment of the present application, including: S110, performing semantic analysis on each medical data interaction request by a natural language processing model and extracting a semantic feature vector of each medical data interaction request, and performing structured expression of the natural language medical request submitted by the user at the semantic level, converting each request into a calculable feature vector, wherein the natural language processing model is any one of a Med-BERT model, a BioBERT model and a ClinicalBERT model; S120, determining the semantic correlation degree between each medical data interaction request according to the semantic feature vector, the semantic correlation degree being the cosine similarity between the semantic feature vectors of the medical data interaction requests, which can identify requests with similar meanings but different expressions, avoid semantic fragmentation caused by expression differences, and ensure that requests with similar semantics can be correctly classified, thereby providing support for unified caching and communication strategies for high correlation requests; S130, divide the medical data interaction request with a semantic correlation degree greater than a preset semantic correlation degree threshold into a data interaction request cluster, aggregate the requests facing the same disease, treatment stage or medical behavior together, so that the subsequent disease evolution path reasoning and data cache scheduling are more targeted and have batch processing advantages.

[0019] S200, obtain patient medical record data corresponding to the data interaction request cluster and perform disease evolution path analysis, and determine a data pre-cache strategy according to the disease evolution path and the evolution confidence; In the mobile Internet medical scene, the timeliness requirement for medical response is high, and the traditional “on-demand pull” data access mode is difficult to meet the timeliness requirement. Through disease evolution path analysis, the upcoming disease state change can be predicted, and the related medical data can be cached in advance, so as to realize the change from “passive response” to “active supply”, improve the timeliness and accuracy of data acquisition, and reduce system communication delay and load. Please refer to Figure 3 , Figure 3 is a flowchart of step S200 provided by the embodiment of the present application, comprising: S210, obtaining patient medical record data corresponding to the data interaction request cluster; S220, constructing a disease evolution tree path graph of the patient according to the patient medical record data, wherein the nodes in the disease evolution tree path graph represent the disease evolution state, and the edges represent the disease evolution direction. By constructing the disease evolution tree path graph, the stage and branching of disease development can be effectively revealed, and data basis is provided for evolution confidence evaluation and pre-cache decision. The way of constructing the disease evolution tree path graph of the patient includes path modeling based on clinical rules, state transition analysis based on medical record sequence mining (such as Markov model), disease evolution modeling based on deep learning model (such as LSTM model or Transformer model), and common path extraction method based on graph mining and path clustering; S230, determining the evolution confidence of each disease evolution path in the disease evolution tree path graph and taking the disease evolution path with the highest evolution confidence as the target disease evolution path. In multi-path evolution, only part of the disease evolution paths have high frequency or logical reasonableness in the clinic, so the most representative disease evolution path needs to be selected as the decision basis of the pre-cache strategy. The joint transition probability is used to quantify the confidence degree and occurrence probability of each path in actual clinical evolution. Taking the joint transition probability as the evolution confidence of each disease evolution path reflects the statistical support strength of the disease evolution along a certain path, and then the target disease evolution path with the most representativeness and prediction value is selected from multiple paths. The specific steps of determining the evolution confidence in step S230 include: S231, extract the disease evolution path in the disease evolution tree path diagram and calculate the joint transition probability of all adjacent nodes on each disease evolution path; ; wherein, is the joint transition probability of all adjacent nodes on the i-th disease evolution path, is the joint transition probability of all adjacent nodes on the i-th disease evolution path, is the probability of transitioning from node to node , wherein node and node are two adjacent nodes on the disease evolution path, which can be obtained by analyzing and counting a large number of historical cases based on historical medical record data, represents the number of nodes of the i-th disease evolution path, represents the joint transition probability from the initial node to the tail node , i.e., the joint transition probability is the product of all transition probabilities in the disease evolution path; S232, taking the joint transition probability of all adjacent nodes on the disease evolution path as the evolution confidence of each disease evolution path; S240, taking the disease evolution data corresponding to the target disease evolution path as pre-cached data and determining the cache refresh time window of the pre-cached data, wherein the disease evolution data includes diagnosis data, examination and test data, treatment scheme data and medical behavior data; In the data pre-caching strategy, the cache refresh time window is used to control the effective duration and update frequency of the pre-cached data, and its core role is to balance the freshness of the data and the utilization efficiency of the cache resources. By reasonably setting the refresh window, it can ensure that the data in the cache still has timeliness and clinical reference value when the user initiates a request, avoiding cache invalidation or redundant occupation of storage space. In particular, in the medical scene, the data sensitivity under different disease states is different, and formulating an accurate refresh cycle is of great significance to ensure the accuracy of diagnosis and treatment data, improve response speed and reduce system burden. Please refer to Figure 4 , Figure 4 is a flowchart of step S240 provided by the embodiment of the present application, comprising: S241, obtaining historical medical record data and screening out path-adapted cases to construct a path-adapted case data set. The path-adapted case is a case whose path similarity between the disease evolution path and the target disease evolution path is greater than a preset path similarity threshold in the historical medical record data. The path-adapted case can provide experience of data changes in a similar disease evolution process as an important reference for predicting the evolution rhythm of the target path, ensuring that the subsequent analysis is based on representative real disease course data, and improving the accuracy and reliability of the cache strategy;​ S242. Extract the disease evolution cycle of path-adapted cases from the path-adapted case dataset and use the weighted average of the disease evolution cycles of all path-adapted cases as the path evolution cycle. The weight is the ratio of the path similarity corresponding to the path-adapted case to the sum of the path similarities. The disease evolution cycle reflects the speed of the patient's state change under the path and is one of the core bases for judging the cache refresh time window. By transforming historical medical record data into dynamic update basis, the pre-caching strategy can be closer to the actual development rhythm of the disease. S243. Extract the change characteristics of each data type in the disease evolution through the path-adapted case dataset, and use the Pearson correlation coefficient between the numerical change of each data type and the change of disease status as the disease evolution correlation degree. The disease evolution correlation degree is used to quantify the sensitivity and correlation of various medical data in the disease evolution. S244. Obtain pre-cached data, determine the validity period of each data type in the pre-cached data, and determine the data value period by combining the correlation with the disease evolution: ,in, For data types The data value cycle For data types The corresponding data validity period can be obtained through medical clinical guidelines or through statistical analysis of historical medical record data. The steps for obtaining validity period based on statistical analysis of historical medical record data include: performing a time span analysis on the same data type in historical cases, and using the average time from the generation of that data type to its first update as the data validity period. Represents data type The correlation between the disease progression and the disease progression. For data types The absolute value of the correlation between disease progression and disease evolution is used. The sign of the correlation only indicates the directional relationship between data changes and disease progression. Therefore, the absolute value is used to represent the strength of the correlation to disease progression to avoid directional interference with the logic. The '1' indicates that the data validity period is used as a benchmark to ensure that when... At that time, the data value cycle is still the data validity period; S245. The minimum value between the path evolution cycle of the disease evolution path and the data value cycle of the pre-cached data is used as the cache refresh time window of the pre-cached data. By comprehensively considering the disease development rhythm and data validity, a refresh boundary that will not cause cache failure due to data aging or disease changes is found, ensuring a balance between the stability and timeliness of the caching strategy.

[0020] S300: Perform feature analysis on the data interaction request cluster and the ferry node respectively, and determine the target ferry node through feature matching; Due to the differences in network bandwidth, computing power, cache state and geographical location of different ferry nodes, intelligent matching of the most suitable data transfer node according to the request characteristics can significantly improve the load balancing capability and reduce the communication delay of the system, please refer to Figure 5 , Figure 5 is a flowchart of step S300 provided by the embodiment of the application, comprising: S310, extracting the request characteristics of the data interaction request cluster and the node characteristics of the ferry node and performing vector modeling to obtain the request characteristic vector and the node characteristic vector, providing a quantitative basis for accurate matching between complex characteristics; S320, taking the weighted cosine similarity of the request characteristic vector and the node characteristic vector as the feature matching degree of the data interaction request cluster and the ferry node, since the importance of different request characteristics in matching is different (such as being more sensitive to urgency), the key factors are highlighted through weighted processing, so that the matching is closer to the actual demand; S330, taking the ferry node with the highest feature matching degree as the target ferry node of the data interaction request cluster, the target ferry node is used to undertake the pre-cached data and subsequent data transmission tasks, effectively reducing the waiting time and failure rate in the data scheduling process, and improving the stability of the communication link and the service perception quality.

[0021] S400, performing data pre-caching strategy and message ferrying through the target ferry node.

[0022] In the embodiment of the application, the determination method of the set parameters such as the weighted weight, the preset semantic correlation threshold and the preset path similarity threshold can be: constructing a data set by obtaining medical data interaction requests, patient medical record data and historical medical record data, substituting into calculating the feature matching degree, the semantic correlation degree and the path similarity, and obtaining the judgment results of the experts on the feature matching degree of the data interaction request cluster and the ferry node, the semantic correlation degree between the medical data interaction requests and the path similarity of the disease evolution path in the historical medical record data and the target disease evolution path, importing the calculated feature matching degree, semantic correlation degree, path similarity and judgment results into fitting software, and outputting the weighted weight, preset semantic correlation threshold and preset path similarity threshold that meet the maximum judgment accuracy.

[0023] Please refer to Figure 6 , Figure 6 is a structural schematic diagram of the internet hospital mobile terminal secure communication device based on message ferrying provided by the embodiment of the application, comprising: The request cluster classification module 210 is used for obtaining the medical data interaction request initiated by the mobile terminal, and classifying the data interaction request cluster according to the semantic correlation degree of the medical data interaction request; The pre-caching strategy determination module 220 is connected with the request cluster classification module 210, and is used to obtain patient medical record data corresponding to the data interaction request cluster and perform illness evolution path analysis, and determine a data pre-caching strategy according to the illness evolution path and the evolution confidence; The target node determination module 230 is connected with the pre-caching strategy determination module 220, and is used to perform feature analysis on the data interaction request cluster and the relay node respectively, and determine a target relay node through feature matching; The pre-caching strategy execution module 240 is connected with the target node determination module 230, and is used to execute the data pre-caching strategy through the target relay node and perform message relaying.

[0024] In the embodiment of the present application, the request cluster classification module 210 is used to obtain a medical data interaction request initiated by a mobile terminal, and perform data interaction request cluster classification according to the semantic correlation degree of the medical data interaction request, including: Performing semantic analysis on each medical data interaction request through a natural language processing model and extracting a semantic feature vector of each medical data interaction request; Determining the semantic correlation degree between each medical data interaction request according to the semantic feature vector, and the semantic correlation degree is the cosine similarity between the semantic feature vectors of the medical data interaction requests; Dividing the medical data interaction requests with a semantic correlation degree greater than a preset semantic correlation degree threshold into a data interaction request cluster.

[0025] In the embodiment of the present application, the pre-caching strategy determination module 220 is used to obtain patient medical record data corresponding to the data interaction request cluster and perform illness evolution path analysis, and determine a data pre-caching strategy according to the illness evolution path and the evolution confidence, including: Obtaining patient medical record data corresponding to the data interaction request cluster; Constructing an illness evolution tree path graph of the patient according to the patient medical record data, wherein a node in the illness evolution tree path graph represents an illness evolution state, and an edge represents an illness evolution direction; Determining the evolution confidence of each illness evolution path in the illness evolution tree path graph and taking the illness evolution path with the highest evolution confidence as a target illness evolution path, wherein the specific steps of determining the evolution confidence include: Extracting the illness evolution paths in the illness evolution tree path graph and calculating the joint transition probability of all adjacent nodes on each illness evolution path; Taking the joint transition probability of all adjacent nodes on the illness evolution path as the evolution confidence of each illness evolution path; Taking the illness evolution data corresponding to the target illness evolution path as pre-caching data and determining a cache refresh time window of the pre-caching data, including: The historical medical record data is acquired, and path adaptation cases are screened out, a path adaptation case data set is constructed, and the path adaptation cases are cases in which the path similarity between the path of disease evolution and the target path of disease evolution in the historical medical record data is greater than a preset path similarity threshold; The path adaptation case data set is used to extract the disease evolution cycle of the path adaptation cases, and a weighted average of the disease evolution cycles of all the path adaptation cases is taken as the path evolution cycle, and the weight is the ratio of the path similarity corresponding to the path adaptation case to the sum of the path similarities; The path adaptation case data set is used to extract the change characteristics of each data type in the disease evolution, and the Pearson correlation coefficient between the numerical change amount and the disease state change amount corresponding to each data type is taken as the disease evolution correlation degree; The pre-cached data is acquired, the data validity period corresponding to each data type in the pre-cached data is determined, and the data value cycle is determined in combination with the disease evolution correlation degree; The minimum value in the path evolution cycle of the path of disease evolution and the data value cycle of the pre-cached data is taken as the cache refresh time window of the pre-cached data.

[0026] In the embodiment of the application, the target node determination module 230 is configured to perform feature analysis on the data interaction request cluster and the ferry node respectively, and determine the target ferry node through feature matching, including: The request features of the data interaction request cluster and the node features of the ferry node are extracted and vector modeling is performed, to obtain the request feature vector and the node feature vector; The weighted cosine similarity of the request feature vector and the node feature vector is taken as the feature matching degree of the data interaction request cluster and the ferry node; The ferry node with the highest feature matching degree is taken as the target ferry node of the data interaction request cluster.

[0027] The above-mentioned parameters and steps of realizing the corresponding functions of each unit module in the internet hospital mobile terminal secure communication device based on message ferry of the application can refer to the parameters and steps in the embodiments of the internet hospital mobile terminal secure communication method based on message ferry, and will not be repeated here.

[0028] Please refer to Figure 7 The embodiment of the application further provides an electronic device 300, including a memory 310, a processor 320 and a communication bus 330; the memory 310 and the processor 320 are connected through the communication bus 330. The memory 310 stores the internet hospital mobile terminal secure communication method based on message ferry provided by the above-mentioned embodiments, which can be loaded and executed by the processor 320.

[0029] The memory 310 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 310 can include a program storage area and a data storage area, wherein the program storage area can store instructions for implementing an operating system, instructions for at least one function, and instructions for implementing the message ferrying-based secure communication method of the Internet hospital mobile terminal provided by the above-mentioned embodiments, etc.; and the data storage area can store data involved in the message ferrying-based secure communication method of the Internet hospital mobile terminal provided by the above-mentioned embodiments, etc.

[0030] The processor 320 can include one or more processing cores. The processor 320 invokes data stored in the memory 310, executes various functions and processes data of the present application by running or executing instructions, programs, code sets or instruction sets stored in the memory 310. The processor 320 can be at least one of an Application Specific Integrated Circuit (ASIC), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a Central Processing Unit (CPU), a controller, a microcontroller, and a microprocessor. It can be understood that, for different devices, the electronic device used to implement the functions of the processor 320 described above can also be other devices, and the embodiments of the present application are not limited specifically.

[0031] The communication bus 330 can include a path for transmitting information between the above-mentioned components. The communication bus 330 can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The communication bus 330 can be divided into an address bus, a data bus, a control bus, etc. For the convenience of representation, Figure 7 Only one double-headed arrow is used in the figure, but it does not mean that there is only one bus or only one type of bus.

[0032] The embodiments of the present application provide a computer readable storage medium, which stores a computer program capable of being loaded and executed by a processor to implement the message ferrying-based secure communication method of the Internet hospital mobile terminal provided by the above-mentioned embodiments.

[0033] In the embodiments of the present application, the computer readable storage medium can be a tangible device that holds or stores a sequence of instructions for use by an instruction execution device. The computer readable storage medium can be, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination thereof. Specifically, the computer readable storage medium can be a portable computer diskette, a hard disk, a U disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, an optical disk, a magnetic disk, a mechanical encoding device, and any combination thereof.

[0034] The terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0035] The above description is merely preferred embodiments of the present application and a description of the principles of the technology used. Those skilled in the art should understand that the scope of the application involved in the present application is not limited to the technical solutions formed by the specific combinations of the above technical features, and should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above application concept. For example, the technical solutions formed by mutually replacing the above features with the technical features applied in the present application (but not limited to) having similar functions.

Claims

1. A message ferrying based secure communication method for Internet hospital mobile terminal, characterized in that, Comprise: S100, acquire the medical data interaction request initiated by the mobile terminal, and classify the data interaction request cluster according to the semantic correlation degree of the medical data interaction request; S200, acquire the patient medical record data corresponding to the data interaction request cluster and perform path analysis of the disease evolution, determine the data pre-caching strategy according to the path of the disease evolution and the evolution confidence; S300, respectively analyze the features of the data interaction request cluster and the transfer node, and determine the target transfer node through feature matching; S400, execute the data pre-caching strategy through the target transfer node and perform message transfer. 2.The method of claim 1, wherein, The specific steps of classifying the data interaction request cluster in step S100 include: S110, perform semantic analysis on each medical data interaction request through a natural language processing model and extract the semantic feature vector of each medical data interaction request; S120, determine the semantic correlation degree between each medical data interaction request according to the semantic feature vector, and the semantic correlation degree is the cosine similarity between the semantic feature vectors of the medical data interaction requests; S130, divide the medical data interaction requests with a semantic correlation degree greater than a preset semantic correlation degree threshold into a data interaction request cluster. 3.The method of claim 1, wherein, The specific steps of determining the data pre-caching strategy in step S200 include: S210, acquire the patient medical record data corresponding to the data interaction request cluster; S220, construct a patient disease evolution tree path graph according to the patient medical record data, wherein the nodes in the disease evolution tree path graph represent the disease evolution state, and the edges represent the disease evolution direction; S230, determine the evolution confidence of each disease evolution path in the disease evolution tree path graph and take the disease evolution path with the highest evolution confidence as the target disease evolution path; S240, take the disease evolution data corresponding to the target disease evolution path as pre-caching data and determine the cache refresh time window of the pre-caching data.

4. The method of claim 3, wherein the message ferrying-based secure communication method for the Internet hospital mobile terminal is characterized by, The specific steps of determining the evolution confidence in step S230 include: S231, extract the disease evolution paths in the disease evolution tree path graph and calculate the joint transition probability of all adjacent nodes on each disease evolution path: ； in, For the first The joint transition probability of all adjacent nodes on the disease evolution path. For the node Transfer to node The probability, Indicates the first The number of nodes in each disease progression path; S232, take the joint transition probability of all adjacent nodes on the disease evolution path as the evolution confidence of each disease evolution path.

5. The method of claim 3, wherein the message ferrying-based secure communication method for the Internet hospital mobile terminal is characterized by, The specific steps of determining the cache refresh time window of the pre-caching data in step S240 include: S241, acquire historical medical record data and filter out path adaptation cases, construct a path adaptation case data set, and the path adaptation case is a case in the historical medical record data whose path similarity between the disease evolution path and the target disease evolution path is greater than a preset path similarity threshold; S242, extract the disease evolution period of the path adaptation case through the path adaptation case data set, and take the weighted mean of the disease evolution periods of all path adaptation cases as the path evolution period, and the weight is the ratio of the path similarity corresponding to the path adaptation case to the sum of the path similarities; S243, extract the change characteristics of each data type in the disease evolution through the path adaptation case data set, and take the Pearson correlation coefficient between the numerical change amount and the disease state change amount corresponding to each data type as the disease evolution correlation degree; S244, acquiring pre-cached data determines the data validity period corresponding to each data type in the pre-cached data and determines the data value period in combination with the illness evolution correlation degree: wherein, is the data value period of the data type , is the data validity period corresponding to the data type , represents the illness evolution correlation degree of the data type . S245, taking the minimum value in the path evolution period of the disease evolution path and the data value period of the pre-cached data as the cache refresh time window of the pre-cached data. 6.The method of claim 1, wherein, The determining the target ferry node in step S300 comprises: S310, extracting the request features of the data interaction request cluster and the node features of the ferry node and performing vector modeling to obtain the request feature vector and the node feature vector; S320, taking the weighted cosine similarity of the request feature vector and the node feature vector as the feature matching degree of the data interaction request cluster and the ferry node; S330, taking the ferry node with the highest feature matching degree as the target ferry node of the data interaction request cluster.

7. The Internet hospital mobile terminal secure communication device based on message relay, used for implementing the Internet hospital mobile terminal secure communication method based on message relay in any one of claims 1-6, characterized in that, The device comprises: The request cluster classification module is configured to acquire the medical data interaction request initiated by the mobile terminal, and perform data interaction request cluster classification according to the semantic correlation degree of the medical data interaction request. The pre-caching strategy determination module is connected to the request cluster classification module, and is configured to acquire the patient medical record data corresponding to the data interaction request cluster and perform disease evolution path analysis, and determine the data pre-caching strategy according to the disease evolution path and the evolution confidence. The target node determination module is connected to the pre-caching strategy determination module, and is configured to perform feature analysis on the data interaction request cluster and the ferry node respectively, and determine the target ferry node through feature matching. The pre-caching strategy execution module is connected to the target node determination module, and is configured to execute the data pre-caching strategy through the target ferry node and perform message ferry.

8. An electronic device comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; characterized in that the processor executes the message ferry-based mobile terminal secure communication method of the internet hospital according to any one of claims 1-6 by calling the computer program stored in the memory.

9. A computer-readable storage medium, characterized in that, The computer program is stored in the memory, and when the computer program runs on the computer, the computer executes the message ferry-based mobile terminal secure communication method of the internet hospital according to any one of claims 1-6.

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