CN112382386A

CN112382386A - Intelligent medical information pushing method and system based on big data

Info

Publication number: CN112382386A
Application number: CN202011263598.5A
Authority: CN
Inventors: 周玉娟
Original assignee: Individual
Current assignee: Individual
Priority date: 2020-04-19
Filing date: 2020-04-19
Publication date: 2021-02-19
Also published as: CN111540466A; CN111540466B; CN112382387A

Abstract

The embodiment of the disclosure provides an intelligent medical information pushing method and system based on big data, wherein a user behavior big data sample with behavior frequency as an arrangement mode is formed according to user behavior big data of a related intention search target, then the user behavior big data sample is processed into a plurality of user behavior big data sub-samples of a query intention decision tree which is divided by the related intention search target according to different query intentions in advance, query word frequency characteristics of a plurality of query words contained in each user behavior big data sub-sample are calculated to determine the query intention characteristics of each user behavior demand, and a medical information recommendation list of an intelligent medical service terminal is generated after an information recommendation thermodynamic diagram of the intelligent medical service terminal is generated. Therefore, the large data of the user behaviors of the target can be effectively mined and refined based on the related intentions of the patient user, and medical information recommendation can be provided for the patient user more pertinently.

Description

Intelligent medical information pushing method and system based on big data

Technical Field

The disclosure relates to the technical field of big data and intelligent medical treatment, in particular to a big data-based intelligent medical information pushing method and system.

Background

With the rapid development of intelligent medical treatment and internet technology, information recommendation based on intelligent medical treatment can help a patient user to more specifically search information related to the intention of the patient user. The inventor of the application finds that for each patient user, the medical case history situation of the patient user may arouse the requirement of the patient user on medical information search, and particularly due to the advantages of rapid development of the internet, privacy, convenience and the like, the internet intelligent medical treatment is gradually becoming an efficient channel for the patient user to obtain medical recommendation information. Based on the above, how to effectively mine and refine the user behavior big data of the target based on the related intentions of the patient user, so as to provide medical information recommendation for the patient user more pertinently is a technical problem to be solved in the field.

Disclosure of Invention

In order to overcome at least the above disadvantages in the prior art, an object of the present disclosure is to provide an intelligent medical information pushing method and system based on big data, which can effectively mine and refine the big data of user behavior of a target searched based on the related intentions of a patient user, so as to provide medical information recommendation for the patient user more specifically.

In a first aspect, the present disclosure provides a big data based smart medical information pushing method, which is applied to a big data medical cloud platform in communication connection with a plurality of smart medical service terminals, and the method includes:

acquiring user behavior big data of a related intention search target browsed by the intelligent medical service terminal after accessing a medical record, and forming a user behavior big data sample in a mode of arrangement according to behavior frequency according to the user behavior big data of the related intention search target;

processing the user behavior big data sample into a plurality of user behavior big data sub-samples of a query intention decision tree which is divided by the related intention search target according to different query intentions in advance, calculating query word frequency characteristics of a plurality of query words contained in each user behavior big data sub-sample, and taking the query word frequency characteristics as the query intention characteristics of the corresponding user behavior big data sub-sample;

taking the query intention characteristics as query intention characteristics of user behavior requirements mapped by corresponding user behavior big data subsamples, generating the query intention characteristics of each user behavior requirement, and generating an information recommendation thermodynamic diagram of the intelligent medical service terminal according to the query intention characteristics of each user behavior requirement;

and generating a medical information recommendation list of the intelligent medical service terminal according to the information recommendation thermodynamic diagram.

In a possible implementation manner of the first aspect, the step of processing the user behavior big data sample into a plurality of user behavior big data subsamples of query intention decision trees divided by the relevant intention search target according to different query intents in advance includes:

presetting a query intention decision tree divided according to different query intentions;

respectively processing the user behavior big data samples according to each query intention decision tree divided according to different query intentions to correspondingly obtain a plurality of intention decision object sequences;

selecting one of the plurality of intended decision object sequences as a first decision object sequence, and identifying a main decision object of the first decision object sequence and using the main decision object as a reference main decision object;

for other second intention decision object sequences except the first decision object sequence, respectively setting a main decision object of each second intention decision object sequence, and calculating item interaction information of a decision intention related item corresponding to any one main decision object in the main decision objects of each second intention decision object sequence and a decision intention related item corresponding to each reference main decision object of the first decision object sequence;

configuring any one main decision object as a reference main decision object which enables the project interaction information to be closest to the main decision object of the first decision object sequence, configuring the main decision object of each second intention decision object sequence in the rest intention decision object sequences as a corresponding reference main decision object, and respectively reconfiguring and distributing the rest intention decision object sequences with reference to the first decision object sequence according to the configuration result to obtain a plurality of configured intention decision object sequences together with the first decision object sequence;

calculating behavior interaction information and behavior frequency of each decision object sequence in the plurality of configured intention decision object sequences;

and dividing the user behavior big data sample according to the behavior interaction information and the behavior frequency of each decision object sequence in the configured plurality of intention decision object sequences to obtain a plurality of user behavior big data sub-samples.

In a possible implementation manner of the first aspect, the step of calculating query word frequency features of a plurality of query words included in each user behavior big data subsample includes:

calculating the repetition degree of a plurality of query words contained in each user behavior big data sub-sample to the respective corresponding user behavior big data sub-sample to obtain corresponding word frequency reversal frequency information;

and extracting a characteristic vector from the word frequency reversal frequency to obtain the query word frequency characteristics of a plurality of query words contained in each user behavior big data subsample.

In a possible implementation manner of the first aspect, the step of generating an information recommendation thermodynamic diagram of the intelligent medical service terminal according to the query intention characteristics of each user behavior demand includes:

obtaining query intention hotspot distribution of each user behavior demand output in each query intention decision tree according to the query intention characteristics of each user behavior demand, and dividing the query intention hotspot distribution to obtain a plurality of query intention hotspot units;

performing intention mining on the plurality of query intention hotspot units according to an intention mining script corresponding to each query intention decision tree respectively to obtain intention mining results corresponding to the plurality of query intention hotspot units, wherein the intention mining results comprise intention testing confidence degrees of the query intention hotspot units under the corresponding query intention decision trees;

and determining a graph connectivity relationship between the query intention hotspot units in each query intention decision tree according to the intention mining result corresponding to each query intention hotspot unit, and generating an information recommendation thermodynamic diagram of the intelligent medical service terminal according to the determined graph connectivity relationship between the query intention hotspot units in each query intention decision tree.

In a possible implementation manner of the first aspect, the obtaining, according to the query intention feature of each user behavior demand, query intention hotspot distribution of each user behavior demand in each query intention decision tree, and dividing the query intention hotspot distribution to obtain a plurality of query intention hotspot units includes:

aiming at the query intention characteristics of each user behavior demand, acquiring an intention hotspot vector expression map of the user behavior demand according to the query intention hotspot distribution of the user behavior demand in each query intention decision tree, and taking the intention hotspot vector expression map as a hotspot vector distribution area, so that each user behavior demand is expressed as a hotspot vector distribution area consisting of the intention hotspot vector expression maps of the user behavior demand;

acquiring all similar hotspot vector distribution areas from the hotspot vector distribution area of each user behavior demand according to the vector distribution value of the hotspot vector distribution area corresponding to the user behavior demand to form a first hotspot vector distribution area distribution space;

aggregating hotspot distribution vectors in a hotspot vector distribution area corresponding to the user behavior demand in the first hotspot vector distribution area distribution space to obtain an aggregate vector object and an aggregate vector hierarchy;

calculating the negative query intention characteristic that a hotspot vector distribution area based on the user behavior demand does not contain a target demand vector according to the aggregation vector object and the aggregation vector hierarchy;

when each user behavior demand is calculated to obtain the negative query intention characteristics which do not contain the target demand vector in the hotspot vector distribution area centered on the user behavior demand, the user behavior demand which does not contain the target demand vector is obtained according to the negative query intention characteristics which do not contain the target demand vector and correspond to each user behavior demand;

obtaining a second hot spot vector distribution area distribution space according to the user behavior demand without the target demand vector, and processing the second hot spot vector distribution area distribution space to obtain an expression node set corresponding to the second hot spot vector distribution area distribution space;

and dividing the distribution of the query intention hot spots according to the expression node set to obtain a plurality of query intention hot spot units.

In a possible implementation manner of the first aspect, the dividing the distribution of the query intention hot spots according to the expression node set to obtain a plurality of query intention hot spot units includes:

calculating an expression node relation value and an expression dictionary component for the expression node set, taking the expression dictionary component as an initial value, and respectively processing a hotspot vector distribution area corresponding to the user behavior demand in the second hotspot vector distribution area distribution space according to the expression node relation value to obtain a corresponding expression node topological flow structure;

determining an expression characteristic meaning sub-label through the expression node topological flow structure, and determining a comparison target label according to the connection of label targets with the maximum vector value on each vector target associated with the comparison label by taking an expression characteristic meaning parent label of the expression node topological flow structure as the comparison label;

calculating semantic similarity between each label target in the topological flow structure of the expression node as a comparison object and the expression characteristic meaning sub-label and the comparison target label to obtain a semantic similarity result of the expression characteristic meaning sub-label and a semantic similarity result of the comparison target label;

obtaining a first fuzzy prediction result of the semantic flow information in the expression node topological flow structure by calculating the semantic flow information in the expression node topological flow structure;

performing threshold processing on the semantic similarity result of the comparison target label by using a continuously changed semantic similarity threshold to obtain a first similarity threshold list;

determining a first semantic similarity higher than a threshold in the first similarity threshold list, and taking a comparison target label with the maximum boundary semantic similarity in the first semantic similarity result as a target comparison target label, and taking a fuzzy label target in a second semantic similarity result with the semantic similarity larger than a set threshold in the semantic similarity result of the expression feature meaning sub-labels as a target fuzzy label target, by combining the first fuzzy prediction result;

calculating the semantic similarity between each label target and the expression characteristic meaning sub-label and the semantic similarity between the target fuzzy label targets in the semantic flow information in the expression node topological flow structure again to obtain a semantic similarity result of a second expression characteristic meaning sub-label and a semantic similarity result of the target fuzzy label targets;

calculating semantic flow direction information in the expression node topological flow direction structure obtained after the semantic similarity of the target comparison target label and other comparison target labels related to the target comparison target label is set to zero, and obtaining a second fuzzy prediction result of the internal semantic flow direction information in the expression node topological flow direction structure;

performing threshold processing on the semantic similarity result of the second expression characteristic meaning sub-label by using the continuously changed semantic similarity threshold to obtain a second similarity threshold list;

determining a third semantic similarity result which is higher than a threshold value in the second similarity threshold value list, and taking an expression feature meaning sub-tag with the maximum variation amplitude in the third semantic similarity result as a target expression feature meaning sub-tag by combining the second fuzzy prediction result, so as to obtain a plurality of query intention hot spot units of the user behavior demand distributed in a query intention hot spot of each query intention decision tree by using a hot spot part corresponding to each target expression feature meaning sub-tag.

In a possible implementation manner of the first aspect, the performing, according to the intention mining script corresponding to each query intention decision tree, intention mining on the plurality of query intention hotspot units to obtain the intention mining result corresponding to each of the plurality of query intention hotspot units includes:

performing intention mining processing on the plurality of query intention hotspot units according to an intention mining script corresponding to each query intention decision tree to obtain an intention mining probability graph corresponding to each query intention hotspot unit;

dividing the graph of the intention mining probability graph into a diversion probability graph and a non-diversion probability graph, wherein the diversion probability graph is a graph with characteristics similar to those of a diversion graph corresponding to the intention mining script, and the non-diversion probability graph is a graph with characteristics dissimilar to those of the diversion graph corresponding to the intention mining script;

respectively determining a first intention mining map node sequence of the diversion probability map and a second intention mining map node sequence of the non-diversion probability map;

determining an intention mining vector of the diversion probability map according to a first intention mining map node sequence of the diversion probability map, and simultaneously determining an intention mining vector of the non-diversion probability map by adopting a second intention mining map node sequence of the non-diversion probability map;

adopting the intention mining vector of the diversion probability map to represent the probability target interval of the diversion probability map, and simultaneously adopting the intention mining vector of the non-diversion probability map to represent the probability target interval of the non-diversion probability map;

detecting each first map unit of the diversion probability map in a probability target interval of the diversion probability map, and simultaneously detecting each second map unit of the non-diversion probability map in a probability target interval of the non-diversion probability map to obtain a first map unit set of the diversion probability map in the probability target interval and a second map unit set of the non-diversion probability map in the probability target interval;

and obtaining the intention mining result corresponding to each of the plurality of query intention hotspot units according to the diversion probability map and the non-diversion probability map.

In a possible implementation manner of the first aspect, the step of obtaining an intention mining result corresponding to each of the plurality of query intention hotspot units according to the diversion probability map and the non-diversion probability map includes:

respectively determining a diversion comparison map unit of a first map unit set of the diversion probability map and a diversion comparison map unit of a second map unit set of the non-diversion probability map, and calculating diversion feature particles of the diversion probability map and diversion feature particles of the non-diversion probability map according to the diversion comparison map unit of the first map unit set and the diversion comparison map unit of the second map unit set;

according to the diversion feature particles of the diversion probability map and the diversion feature particles of the non-diversion probability map, comparing the diversion probability map with the non-diversion probability map to obtain a matching map unit pair of the diversion probability map and the non-diversion probability map;

dividing the intention mining probability map corresponding to the query intention hotspot unit into a plurality of corresponding intention mining probability blocks according to the matching map unit pair of the diversion probability map and the non-diversion probability map;

analyzing the positions of the plurality of intention mining probability blocks, and analyzing the position of each unit block in each intention mining probability block to obtain a position analysis result, wherein the position analysis result comprises a plurality of map node sequences determined as strong vectors;

dividing the position analysis result into a plurality of target map node sequences corresponding to the query intention hotspot unit, calculating to obtain a position confidence coefficient in each target map node sequence, calculating to obtain a ratio of each position confidence coefficient in each target map node sequence to a corresponding mean value, and obtaining a ratio sequence corresponding to each target map node sequence;

calculating ratio mean values of all ratio sequences, obtaining a maximum value of all ratio mean values as a global maximum ratio mean value, and generating a plurality of intention mining models of different mining labels according to the global maximum ratio mean value, wherein the mining labels of the plurality of intention mining models are sequentially increased from the priority, and the total number of the intention mining models is obtained by dividing the global maximum ratio mean value by a preset ratio and then rounding;

calculating to obtain a ratio mean value of a ratio sequence of each target map node sequence, dividing the ratio mean value by the preset ratio and then rounding to obtain a corresponding mining label of each target map node sequence, and respectively processing each ratio in the ratio sequence of the corresponding target map node sequence by using the intention mining model with the same mining label as the corresponding mining label of each target map node sequence to obtain a ratio degree mapping relation of each target map node sequence;

and processing the mean value, the mining label and the proportion degree mapping relation of each target map node sequence, the total number of the intention mining models and the corresponding reconstruction values to obtain the intention mining results corresponding to the plurality of inquiry intention hotspot units.

In a possible implementation manner of the first aspect, the step of determining a graph connectivity relationship between the query intent hotspot units in each query intent decision tree according to the respective intent mining results corresponding to the query intent hotspot units, and generating an information recommendation thermodynamic diagram of the intelligent medical service terminal according to the determined graph connectivity relationship between the query intent hotspot units in each query intent decision tree includes:

converting the query intention hotspot units into intention hotspot vector distribution graphs according to the intention mining results corresponding to the query intention hotspot units respectively;

selecting a plurality of vector distribution unit maps from the intention hotspot vector distribution map to form recommended thermal blocks, and determining hotspot intercommunication blocks among each recommended thermal block so as to determine a graph connectivity relation among the inquiry intention hotspot units in each inquiry intention decision tree according to the hotspot intercommunication blocks among each recommended thermal block;

extracting tag mapping codes of recommended item tags corresponding to all recommended thermal nodes in each recommended thermal image block according to the graph communication relation to obtain tag mapping code sequences, and extracting the types of the recommended item tags corresponding to each recommended thermal node in the hotspot intercommunication image block to obtain list sequences corresponding to the tag mapping code sequences;

after denoising processing is respectively carried out on the tag mapping code sequence and the list sequence, a group of recommended configuration parameters are randomly distributed to recommended item tag relation identifications corresponding to any recommended thermal node in the tag mapping code sequence to serve as recommended configuration parameters in an information recommendation process;

constructing an information recommendation model with the information recommendation quantity interval and the information recommendation sequence interval as variables according to the processed label mapping code sequence and the list sequence, and calculating a configuration parameter sequence of recommendation configuration parameters of the information recommendation process so as to obtain a target recommendation module of the information recommendation process;

discretizing the recommended thermal image blocks by using a target recommending module in the information recommending process, and calculating an information recommending quantity interval and an information recommending sequence interval of the discretization according to a discretization result;

if the discretized information recommendation quantity interval and the information recommendation sequence interval meet preset conditions, randomly selecting various types of label mapping codes by using a target recommendation module in the information recommendation process;

searching the label mapping codes of multiple types by using the recommended thermal image block and the hot spot intercommunication image block thereof, and calculating information recommendation quantization intervals of each type of label mapping code in different vector distribution unit maps;

and generating an information recommendation thermodynamic diagram of the intelligent medical service terminal according to the information recommendation quantization intervals of the mapping codes of each category label in different vector distribution unit diagrams.

In one possible implementation manner of the first aspect, the step of generating the medical information recommendation list of the intelligent medical service terminal according to the information recommendation thermodynamic diagram includes:

performing functional division on the information recommendation thermodynamic diagram to obtain at least one information recommendation functional area, wherein the information recommendation functions of all information recommendation nodes in the same information recommendation functional area are the same;

determining an information recommendation function and an information recommendation object of each information recommendation function area, and establishing a medical information recommendation list model according to the information recommendation function corresponding to each information recommendation function area and the information recommendation object of each information recommendation function area;

and generating a medical information recommendation list of the intelligent medical service terminal according to the medical information recommendation list model.

In a possible implementation manner of the first aspect, the step of generating the medical information recommendation list of the intelligent medical service terminal according to the medical information recommendation list model includes:

and acquiring medical hot spot information which is related to each medical information recommendation list node and is in a preset time period before the current time point according to each medical information recommendation list node in the medical information recommendation list model so as to generate a medical information recommendation list of the intelligent medical service terminal.

In a second aspect, the disclosed embodiment further provides a big data-based smart medical information pushing device, which is applied to a big data medical cloud platform in communication connection with a plurality of smart medical service terminals, where the device includes:

the acquisition module is used for acquiring user behavior big data of a related intention search target browsed by the intelligent medical service terminal after accessing medical records, and forming a user behavior big data sample in a mode of arrangement according to behavior frequency according to the user behavior big data of the related intention search target;

the computing module is used for processing the user behavior big data sample into a plurality of user behavior big data sub-samples of a query intention decision tree which is divided by the related intention search target according to different query intentions in advance, computing query word frequency characteristics of a plurality of query words contained in each user behavior big data sub-sample, and taking the query word frequency characteristics as the query intention characteristics of the corresponding user behavior big data sub-sample;

the first generation module is used for taking the query intention characteristics as the query intention characteristics of the user behavior requirements mapped by the corresponding user behavior big data subsample, generating the query intention characteristics of each user behavior requirement, and generating the information recommendation thermodynamic diagram of the intelligent medical service terminal according to the query intention characteristics of each user behavior requirement;

and the second generation module is used for generating a medical information recommendation list of the intelligent medical service terminal according to the information recommendation thermodynamic diagram.

In a third aspect, the disclosed embodiment further provides a smart medical information pushing system based on big data, where the smart medical information pushing system based on big data includes a big data medical cloud platform and a plurality of smart medical service terminals in communication connection with the big data medical cloud platform;

the big data medical cloud platform is used for acquiring user behavior big data of a related intention search target browsed by the intelligent medical service terminal after accessing medical records, and forming a user behavior big data sample in a mode of arrangement according to behavior frequency according to the user behavior big data of the related intention search target;

the big data medical cloud platform is used for processing the user behavior big data samples into a plurality of user behavior big data sub-samples of a query intention decision tree which are divided by the related intention search target according to different query intentions in advance, calculating query word frequency characteristics of a plurality of query words contained in each user behavior big data sub-sample, and taking the query word frequency characteristics as the query intention characteristics of the corresponding user behavior big data sub-sample;

the big data medical cloud platform is used for taking the query intention characteristics as query intention characteristics of user behavior requirements mapped by corresponding user behavior big data sub-samples, generating the query intention characteristics of each user behavior requirement, and generating an information recommendation thermodynamic diagram of the intelligent medical service terminal according to the query intention characteristics of each user behavior requirement;

the big data medical cloud platform is used for generating a medical information recommendation list of the intelligent medical service terminal according to the information recommendation thermodynamic diagram.

In a fourth aspect, the disclosed embodiment further provides a big data medical cloud platform, where the big data medical cloud platform includes a processor, a machine-readable storage medium, and a network interface, where the machine-readable storage medium, the network interface, and the processor are connected through a bus system, the network interface is used for being in communication connection with at least one smart medical service terminal, the machine-readable storage medium is used for storing a program, an instruction, or a code, and the processor is used for executing the program, the instruction, or the code in the machine-readable storage medium to execute the big data-based smart medical information pushing method in the first aspect or any one of possible designs in the first aspect.

In a fifth aspect, an embodiment of the present disclosure provides a computer-readable storage medium, where instructions are stored, and when executed, cause a computer to perform the big data-based intelligent medical information pushing method in the first aspect or any one of the possible designs of the first aspect.

Based on any one of the above aspects, the present disclosure forms a user behavior big data sample in an arrangement mode according to the user behavior big data of the related intention search target, then processes the user behavior big data sample into a plurality of user behavior big data sub-samples of the query intention decision tree divided in advance according to different query intentions with the related intention search target, calculates query word frequency characteristics of a plurality of query words contained in each user behavior big data sub-sample to determine query intention characteristics of each user behavior demand, and generates a medical information recommendation thermodynamic diagram of the intelligent medical service terminal according to the query word frequency characteristics, and then generates a medical information recommendation list of the intelligent medical service terminal. Therefore, the large data of the user behaviors of the target can be effectively mined and refined based on the related intentions of the patient user, and medical information recommendation can be provided for the patient user more pertinently.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings needed to be used in the embodiments will be briefly described below.

Fig. 1 is a schematic view of an application scenario of a big data-based intelligent medical information pushing system according to an embodiment of the present disclosure;

fig. 2 is a schematic flowchart illustrating a method for pushing intelligent medical information based on big data according to an embodiment of the disclosure;

fig. 3 is a schematic diagram illustrating functional modules of a big data-based intelligent medical information pushing device according to an embodiment of the disclosure;

fig. 4 is a block diagram illustrating a structure of a big data medical cloud platform for implementing the big data-based smart medical information pushing method according to the embodiment of the present disclosure.

Detailed Description

The present disclosure is described in detail below with reference to the drawings, and the specific operation methods in the method embodiments can also be applied to the device embodiments or the system embodiments.

Fig. 1 is an interaction diagram of an intelligent medical information pushing system 10 based on big data according to an embodiment of the present disclosure. The big data based smart medical information pushing system 10 may include a big data medical cloud platform 100 and a smart medical service terminal 200 communicatively connected to the big data medical cloud platform 100. The big data based intelligent medical information pushing system 10 shown in fig. 1 is only one possible example, and in other possible embodiments, the big data based intelligent medical information pushing system 10 may also include only one of the components shown in fig. 1 or may also include other components.

In this embodiment, the smart medical services terminal 200 may include a mobile device, a tablet computer, a laptop computer, or any combination thereof. In some embodiments, the mobile device may include a smart home device, a wearable device, a smart mobile device, a virtual reality device, an augmented reality device, or the like, or any combination thereof. In some embodiments, the smart home devices may include control devices of smart electrical devices, smart monitoring devices, smart televisions, smart cameras, and the like, or any combination thereof. In some embodiments, the wearable device may include a smart bracelet, a smart lace, smart glass, a smart helmet, a smart watch, a smart garment, a smart backpack, a smart accessory, or the like, or any combination thereof. In some embodiments, the smart mobile device may include a smartphone, a personal digital assistant, a gaming device, and the like, or any combination thereof. In some embodiments, the virtual reality device and/or the augmented reality device may include a virtual reality helmet, virtual reality glass, a virtual reality patch, an augmented reality helmet, augmented reality glass, an augmented reality patch, or the like, or any combination thereof. For example, the virtual reality device and/or augmented reality device may include various virtual reality products and the like.

In this embodiment, the internet of things cloud big data medical cloud platform 100 and the smart medical service terminal 200 in the big data based smart medical information pushing system 10 may execute the big data based smart medical information pushing method described in the following method embodiment in a matching manner, and the detailed description of the method embodiment below may be referred to in the execution steps of the big data medical cloud platform 100 and the smart medical service terminal 200.

In order to solve the technical problem in the foregoing background, fig. 2 is a schematic flowchart of a big-data-based smart medical information pushing method according to an embodiment of the present disclosure, which can be executed by the big-data medical cloud platform 100 shown in fig. 1, and the big-data-based smart medical information pushing method is described in detail below.

Step S110, obtaining the user behavior big data of the related intention search target browsed by the intelligent medical service terminal 200 after accessing the medical record, and forming a user behavior big data sample in which the behavior frequency is the arrangement mode according to the user behavior big data of the related intention search target.

Step S120, processing the user behavior big data sample into a plurality of user behavior big data sub-samples of a query intention decision tree which is divided by related intention search targets according to different query intents in advance, calculating query word frequency characteristics of a plurality of query words contained in each user behavior big data sub-sample, and taking the query word frequency characteristics as the query intention characteristics of the corresponding user behavior big data sub-sample.

Step S130, using the query intention characteristics as the query intention characteristics of the user behavior requirements mapped by the corresponding user behavior big data subsample, generating the query intention characteristics of each user behavior requirement, and generating the information recommendation thermodynamic diagram of the intelligent medical service terminal 200 according to the query intention characteristics of each user behavior requirement.

In step S140, a medical information recommendation list of the intelligent medical service terminal 200 is generated according to the information recommendation thermodynamic diagram.

In this embodiment, the medical record may be a record of the medical activities of the patient user, such as examination, diagnosis, treatment, etc., of the occurrence, development, and outcome of the disease of the patient user by the relevant medical staff during the medical treatment. For example, the medical records may be written in a prescribed format and requirement by summarizing, sorting, and comprehensively analyzing the collected data, and the medical records may be recorded in the big data medical cloud platform 100 by medical staff, and a patient user may access the big data medical cloud platform 100 through a medical service terminal to view medical records at any time.

The patient user may be in various information searching intentions in the process of viewing medical cases, information searching is carried out on the basis of the related intention searching target, at the moment, the user behavior big data related to the patient user can be obtained, and a user behavior big data sample in which the behavior frequency is arranged is formed according to the user behavior big data of the related intention searching target.

The action frequency may refer to the number of times of repetition of the search action generated by the patient user, or the duration, and may be used to indicate the attention degree of the patient user for a certain search target content.

The query term may refer to a series of keywords, such as input or clicked keywords, generated by the patient user based on a certain search target content.

Based on the above steps, the present embodiment forms a user behavior big data sample according to the user behavior big data of the related intention search target, the user behavior big data sample is processed into a plurality of user behavior big data sub-samples of the query intention decision tree divided by the related intention search target according to different query intentions in advance, the query word frequency feature of a plurality of query words contained in each user behavior big data sub-sample is calculated to determine the query intention feature of each user behavior requirement, and thus the medical information recommendation thermodynamic diagram of the smart medical service terminal 200 is generated and then the medical information recommendation list of the smart medical service terminal 200 is generated. Therefore, the large data of the user behaviors of the target can be effectively mined and refined based on the related intentions of the patient user, and medical information recommendation can be provided for the patient user more pertinently.

In one possible implementation manner, regarding step S120, considering that there may be many query intentions mixed in the big data sample of the user behavior, step S120 may be specifically implemented by the following exemplary sub-steps for facilitating the accurate recommendation, which will be described in detail below.

And a substep S121, presetting a query intention decision tree divided according to different query intentions.

And a substep S122, respectively processing the user behavior big data samples according to each query intention decision tree divided according to different query intentions, and correspondingly obtaining a plurality of intention decision object sequences.

And a substep S123 of selecting one of the plurality of intended decision object sequences as a first decision object sequence, and identifying a main decision object of the first decision object sequence and using the main decision object as a reference main decision object.

In the substep S124, for other second intended decision object sequences except the first decision object sequence, the main decision object of each second intended decision object sequence is set, and the item interaction information between the decision intention related item corresponding to any one of the main decision objects of each second intended decision object sequence and the decision intention related item corresponding to each reference main decision object of the first decision object sequence is calculated.

The item interaction information may refer to information generated by information interaction between items related to decision intentions, such as information generated in the process of transferring from one item to another item.

And a substep S125, configuring any one of the main decision objects as a reference main decision object that makes the item interaction information closest to the main decision object of the first decision object sequence, configuring the main decision object of each second intention decision object sequence in the remaining intention decision object sequences as a corresponding reference main decision object, and referring to the configuration result, performing reconfiguration distribution on the remaining intention decision object sequences with reference to the first decision object sequence, respectively, and obtaining a plurality of configured intention decision object sequences together with the first decision object sequence.

And a substep S126, calculating behavior interaction information and behavior frequency of each decision object sequence in the configured plurality of intention decision object sequences.

And a substep S127 of dividing the user behavior big data sample according to the behavior interaction information and the behavior frequency of each decision object sequence in the configured plurality of intention decision object sequences to obtain a plurality of user behavior big data subsamples.

In a possible implementation manner, still referring to step S120, in the process of calculating the query word frequency characteristics of the plurality of query words included in each user behavior big data sub-sample, the following exemplary sub-steps may be specifically implemented, which are described in detail below.

And a substep S128, calculating the repetition degree of the plurality of query words contained in each user behavior big data subsample to the corresponding user behavior big data subsample, and obtaining corresponding word frequency reversal frequency information.

In this embodiment, term frequency-reversal frequency information (TF-IDF) may be used to indicate the repetition degree of the query term for each corresponding user behavior big data subsample. In detail, the importance of the query term is increased in proportion to the number of times the query term appears in the corresponding big data subsample of the user behavior, so that the importance of the query term can be used as a measure or rating of the degree of correlation between the big data subsample of the user behavior and the query of the user.

And a substep S129, extracting a characteristic vector from the word frequency reversal frequency to obtain the query word frequency characteristics of a plurality of query words contained in each user behavior big data subsample.

In one possible implementation, step S130 may be implemented by the following exemplary sub-steps, which are described in detail below.

And the substep S131, obtaining the query intention hotspot distribution of each user behavior demand output in each query intention decision tree according to the query intention characteristics of each user behavior demand, and dividing the query intention hotspot distribution to obtain a plurality of query intention hotspot units.

And a substep S132 of performing intention mining processing on the plurality of query intention hotspot units according to the intention mining script corresponding to each query intention decision tree to obtain respective intention mining results of the plurality of query intention hotspot units.

In this embodiment, the intent mining result may include an intent test confidence of the query intent hotspot unit under the corresponding query intent decision tree.

And a substep S133, determining a graph connectivity relationship between the query intent hotspot units in each query intent decision tree according to the respective intent mining results corresponding to the query intent hotspot units, and generating an information recommendation thermodynamic diagram of the intelligent medical service terminal 200 according to the determined graph connectivity relationship between the query intent hotspot units in each query intent decision tree.

For example, in sub-step S131, this may be embodied by the following exemplary implementation:

(1) aiming at the query intention characteristics of each user behavior demand, acquiring an intention hotspot vector expression map of the user behavior demand according to the query intention hotspot distribution of the user behavior demand in each query intention decision tree, and taking the intention hotspot vector expression map as a hotspot vector distribution area, so that each user behavior demand is expressed as a hotspot vector distribution area consisting of the intention hotspot vector expression maps of the user behavior demands.

(2) And acquiring all similar hotspot vector distribution areas from the hotspot vector distribution area of each user behavior demand according to the vector distribution value of the hotspot vector distribution area corresponding to the user behavior demand to form a first hotspot vector distribution area distribution space.

(3) And aggregating hotspot distribution vectors in a hotspot vector distribution area corresponding to the user behavior requirement in the first hotspot vector distribution area distribution space to obtain an aggregate vector object and an aggregate vector hierarchy.

(4) And calculating the negative query intention characteristic that the hotspot vector distribution area based on the user behavior demand does not contain the target demand vector according to the aggregation vector object and the aggregation vector level.

(5) And when each user behavior demand is calculated to obtain the negative query intention characteristics of the hotspot vector distribution region centered on the user behavior demand, obtaining the user behavior demand without the target demand vector according to the negative query intention characteristics which are corresponding to each user behavior demand and do not contain the target demand vector.

(6) And obtaining a second hot spot vector distribution area distribution space according to the user behavior demand without the target demand vector, and processing the second hot spot vector distribution area distribution space to obtain an expression node set corresponding to the second hot spot vector distribution area distribution space.

(7) And dividing the distribution of the query intention hot spots according to the expression node set to obtain a plurality of query intention hot spot units.

For example, in an alternative example, the embodiment may calculate an expression node relation value and an expression dictionary component for the expression node set, and respectively process the hotspot vector distribution areas corresponding to the user behavior requirement in the second hotspot vector distribution area distribution space according to the expression node relation value by using the expression dictionary component as an initial value, so as to obtain corresponding expression node topological flow structures.

On the basis, the expression characteristic meaning sub-label can be determined through the expression node topological flow structure, and the comparison target label is determined by taking the expression characteristic meaning parent label of the expression node topological flow structure as the comparison label and connecting the label targets with the maximum vector value on each vector target associated with the comparison label. And then, calculating semantic similarity between each label target as a comparison object in the topological flow structure of the expression node and the expression characteristic meaning sub-labels and the comparison target labels to obtain semantic similarity results of the expression characteristic meaning sub-labels and the comparison target labels.

Therefore, a first fuzzy prediction result of the semantic flow information in the expression node topological flow structure can be obtained by calculating the semantic flow information in the expression node topological flow structure, and a first similarity threshold list is obtained by performing threshold processing on the semantic similarity result of the comparison target label by using a continuously-changed semantic similarity threshold. And then, determining a first semantic similarity higher than a threshold in the first similarity threshold list, and taking a comparison target label with the maximum boundary semantic similarity in the first semantic similarity result as a target comparison target label and taking a fuzzy label target in a second semantic similarity result with the semantic similarity higher than a set threshold in the semantic similarity results of the expression feature meaning sub-labels as a target fuzzy label target by combining the first fuzzy prediction result.

On the basis, the semantic similarity between each label target and the expression characteristic meaning sub-label and the semantic similarity between the target fuzzy label targets in the semantic flow information in the topological flow structure of the expression node can be calculated again to obtain a semantic similarity result of the second expression characteristic meaning sub-label and a semantic similarity result of the target fuzzy label targets. Then, semantic flow information in the expression node topological flow structure obtained after the semantic similarity of the target comparison target label and other comparison target labels related to the target comparison target label is set to zero is calculated, and a second fuzzy prediction result of the inner semantic flow information in the expression node topological flow structure is obtained. And thirdly, performing threshold processing on the semantic similarity result of the second expression characteristic meaning sub-label by using a continuously changed semantic similarity threshold to obtain a second similarity threshold list.

In this way, a third semantic similarity result higher than the threshold in the second similarity threshold list can be determined, and the expression feature meaning sub-tag with the largest variation amplitude in the third semantic similarity result is taken as a target expression feature meaning sub-tag in combination with the second fuzzy prediction result, so that a plurality of query intention hot spot units distributed in the query intention hot spot of each query intention decision tree for the user behavior demand are obtained from the hot spot part corresponding to each target expression feature meaning sub-tag.

As another example, in sub-step S132, the following exemplary implementation may be implemented:

(1) and performing intention mining processing on the plurality of query intention hotspot units according to the intention mining script corresponding to each query intention decision tree to obtain an intention mining probability graph corresponding to each query intention hotspot unit.

(2) And carrying out graph division on the intention mining probability graph, and dividing the graph of the intention mining probability graph into a flow guide probability graph and a non-flow guide probability graph, wherein the flow guide probability graph is a graph similar to the characteristics of the flow guide graph corresponding to the intention mining script, and the non-flow guide probability graph is a graph dissimilar to the characteristics of the flow guide graph corresponding to the intention mining script.

(3) And respectively determining a first intention mining map node sequence of the diversion probability map and a second intention mining map node sequence of the non-diversion probability map.

(4) And determining an intention mining vector of the diversion probability map according to the first intention mining map node sequence of the diversion probability map, and simultaneously determining an intention mining vector of the non-diversion probability map by adopting a second intention mining map node sequence of the non-diversion probability map.

(5) And expressing the probability target interval of the diversion probability map by adopting the intention mining vector of the diversion probability map, and expressing the probability target interval of the non-diversion probability map by adopting the intention mining vector of the non-diversion probability map.

(6) And detecting each first map unit of the diversion probability map in a probability target interval of the diversion probability map, and simultaneously detecting each second map unit of the non-diversion probability map in a probability target interval of the non-diversion probability map to obtain a first map unit set of the diversion probability map in the probability target interval and a second map unit set of the non-diversion probability map in the probability target interval.

(7) And obtaining intention mining results corresponding to the plurality of inquiry intention hotspot units according to the diversion probability map and the non-diversion probability map.

For example, in one possible example, the present embodiment may determine the diversion comparison map units of the first map unit set of the diversion probability map and the diversion comparison map units of the second map unit set of the non-diversion probability map, respectively, and calculate the diversion feature particles of the diversion probability map and the diversion feature particles of the non-diversion probability map based on the diversion comparison map units of the first map unit set and the diversion comparison map units of the second map unit set.

On the basis, the diversion probability map and the non-diversion probability map can be compared according to the diversion feature particles of the diversion probability map and the diversion feature particles of the non-diversion probability map to obtain a matching map unit pair of the diversion probability map and the non-diversion probability map. And then, dividing the intention mining probability map corresponding to the query intention hotspot unit into a plurality of corresponding intention mining probability blocks according to the matching map unit pairs of the diversion probability map and the non-diversion probability map. Then, the positions of the plurality of intention mining probability blocks are analyzed, and the position of each unit block in each intention mining probability block is analyzed to obtain a position analysis result, wherein the position analysis result comprises a plurality of map node sequences determined as strong vectors. For example, a strong vector may be used to represent a vector having a vector extension length greater than a predetermined length.

Then, the position analysis result may be divided into a plurality of target map node sequences corresponding to the query intention hotspot unit, and a position confidence in each target map node sequence is obtained through calculation, and a ratio of each position confidence in each target map node sequence to the corresponding mean value is obtained through calculation, so as to obtain a ratio sequence corresponding to each target map node sequence. And then, calculating to obtain ratio mean values of all the ratio sequences, obtaining the maximum value of all the ratio mean values as a global maximum ratio mean value, and generating a plurality of intention mining models of different mining labels according to the global maximum ratio mean value, wherein the mining labels of the plurality of intention mining models are sequentially increased from the priority, and the total number of the intention mining models is obtained by dividing the global maximum ratio mean value by a preset ratio and then rounding.

Therefore, the ratio mean value of the ratio sequence of each target map node sequence can be calculated, the ratio mean value is divided by a preset ratio and then is rounded to obtain a corresponding mining label of each target map node sequence, and each proportion in the ratio sequence of the corresponding target map node sequence is respectively processed by using an intention mining model with the mining label being the same as the corresponding mining label of each target map node sequence to obtain the proportion degree mapping relation of each target map node sequence. Therefore, the mean value, the mining label and the proportion degree mapping relation of each target map node sequence, the total number of the intention mining models and the corresponding reconstruction values can be processed, and the intention mining results corresponding to the plurality of inquiry intention hotspot units are obtained.

As another example, in sub-step S133, the following exemplary implementation may be implemented:

(1) and converting the query intention hotspot units into intention hotspot vector distribution graphs according to the respective intention mining results corresponding to the query intention hotspot units.

(2) And selecting a plurality of vector distribution unit graphs from the intention hot spot vector distribution graph to form recommended thermal blocks respectively, determining hot spot intercommunication blocks among the recommended thermal blocks, and determining graph communication relations among the inquiry intention hot spot units in each inquiry intention decision tree according to the hot spot intercommunication blocks among the recommended thermal blocks.

(3) And extracting the label mapping codes of the recommended item labels corresponding to all the recommended thermal nodes in each recommended thermal image block according to the graph communication relation to obtain a label mapping code sequence, and extracting the category of the recommended item label corresponding to each recommended thermal node in the hotspot intercommunication image block to obtain a list sequence corresponding to the label mapping code sequence.

(4) And after denoising processing is respectively carried out on the tag mapping code sequence and the list sequence, a group of recommended configuration parameters are randomly distributed to a recommended item tag relation identifier corresponding to any recommended thermal node in the tag mapping code sequence to serve as recommended configuration parameters in the information recommendation process.

(5) And constructing an information recommendation model with the information recommendation quantity interval and the information recommendation sequence interval as variables according to the processed label mapping code sequence and the list sequence, and calculating a configuration parameter sequence of recommendation configuration parameters in the information recommendation process so as to obtain a target recommendation module in the information recommendation process.

(6) Discretizing the recommended thermal image blocks by using a target recommending module in the information recommending process, and calculating an information recommending quantity interval and an information recommending sequence interval of the discretization according to a discretization result.

(7) And if the discretized information recommendation quantity interval and the information recommendation sequence interval meet preset conditions, randomly selecting various types of label mapping codes by using a target recommendation module in the information recommendation process.

(8) And searching the label mapping codes of multiple types by using the recommended thermal image block and the hot spot intercommunication image block thereof, and calculating the information recommendation quantization interval of each type of label mapping code in different vector distribution unit maps.

(9) And generating an information recommendation thermodynamic diagram of the intelligent medical service terminal 200 according to the information recommendation quantization intervals of the mapping codes of each category label in the different vector distribution unit diagrams.

In one possible implementation, step S140 may be implemented by the following exemplary sub-steps, which are described in detail below.

And a substep S141, performing functional division on the information recommendation thermodynamic diagram to obtain at least one information recommendation functional region, wherein the information recommendation functions of all information recommendation nodes in the same information recommendation functional region are the same.

And a substep S142, determining the information recommendation function and the information recommendation object of each information recommendation function region, and establishing a medical information recommendation list model according to the information recommendation function corresponding to each information recommendation function region and the information recommendation object of each information recommendation function region.

In the substep S143, a medical information recommendation list of the intelligent medical service terminal 200 is generated according to the medical information recommendation list model.

For example, in sub-step S143, the present embodiment may acquire, according to each medical information recommendation list node in the medical information recommendation list model, medical hotspot information associated with the medical information recommendation list node in a preset time period before the current time point, so as to generate a medical information recommendation list of the intelligent medical service terminal 200.

Fig. 3 is a schematic diagram illustrating functional modules of a smart medical information pushing apparatus 300 based on big data according to an embodiment of the present disclosure, in this embodiment, functional modules of the smart medical information pushing apparatus 300 based on big data may be divided according to an embodiment of a method executed by the big data medical cloud platform 100, that is, the following functional modules corresponding to the smart medical information pushing apparatus 300 based on big data may be used to execute various embodiments of the method executed by the big data medical cloud platform 100. The big data-based smart medical information pushing apparatus 300 may include an obtaining module 310, a calculating module 320, a first generating module 330, and a second generating module 340, wherein the functions of the functional modules of the big data-based smart medical information pushing apparatus 300 are described in detail below.

The obtaining module 310 is configured to obtain the user behavior big data of the related intention search target browsed by the intelligent medical service terminal 200 after accessing the medical record, and form a user behavior big data sample in which the behavior frequency is an arrangement mode according to the user behavior big data of the related intention search target. The obtaining module 310 may be configured to perform the step S110, and the detailed implementation of the obtaining module 310 may refer to the detailed description of the step S110.

The calculating module 320 is configured to process the user behavior big data sample into a plurality of user behavior big data sub-samples of a query intention decision tree divided by a related intention search target according to different query intents in advance, calculate query word frequency features of a plurality of query words included in each user behavior big data sub-sample, and use the query word frequency features as query intention features of the corresponding user behavior big data sub-sample. The calculating module 320 may be configured to perform the step S120, and the detailed implementation of the calculating module 320 may refer to the detailed description of the step S120.

The first generating module 330 is configured to use the query intention features as the query intention features of the user behavior requirements mapped by the corresponding user behavior big data sub-sample, generate the query intention features of each user behavior requirement, and generate the information recommendation thermodynamic diagram of the intelligent medical service terminal 200 according to the query intention features of each user behavior requirement. The first generating module 330 may be configured to execute the step S130, and the detailed implementation of the first generating module 330 may refer to the detailed description of the step S130.

The second generating module 340 is configured to generate a medical information recommendation list of the intelligent medical service terminal 200 according to the information recommendation thermodynamic diagram. The second generating module 340 may be configured to execute the step S140, and for a detailed implementation of the second generating module 340, reference may be made to the detailed description of the step S140.

It should be noted that the division of the modules of the above apparatus is only a logical division, and the actual implementation may be wholly or partially integrated into one physical entity, or may be physically separated. And these modules can be realized in the form of software called by processing element; or may be implemented entirely in hardware; and part of the modules can be realized in the form of calling software by the processing element, and part of the modules can be realized in the form of hardware. For example, the obtaining module 310 may be a processing element separately set up, or may be implemented by being integrated into a chip of the apparatus, or may be stored in a memory of the apparatus in the form of program code, and the processing element of the apparatus calls and executes the functions of the obtaining module 310. Other modules are implemented similarly. In addition, all or part of the modules can be integrated together or can be independently realized. The processing element described herein may be an integrated circuit having signal processing capabilities. In implementation, each step of the above method or each module above may be implemented by an integrated logic circuit of hardware in a processor element or an instruction in the form of software.

For example, the above modules may be one or more integrated circuits configured to implement the above methods, such as: one or more Application Specific Integrated Circuits (ASICs), or one or more microprocessors (DSPs), or one or more Field Programmable Gate Arrays (FPGAs), among others. For another example, when some of the above modules are implemented in the form of a processing element scheduler code, the processing element may be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor that can call program code. As another example, these modules may be integrated together, implemented in the form of a system-on-a-chip (SOC).

Fig. 4 shows a hardware structure diagram of a big data medical cloud platform 100 for implementing the above control device according to an embodiment of the present disclosure, and as shown in fig. 4, the big data medical cloud platform 100 may include a processor 110, a machine-readable storage medium 120, a bus 130, and a transceiver 140.

In a specific implementation process, at least one processor 110 executes computer-executable instructions stored in the machine-readable storage medium 120 (for example, the obtaining module 310, the calculating module 320, the first generating module 330, and the second generating module 340 included in the smart medical-information pushing apparatus 300 based on big data shown in fig. 3), so that the processor 110 may execute the smart medical-information pushing method based on big data according to the above method embodiment, where the processor 110, the machine-readable storage medium 120, and the transceiver 140 are connected through the bus 130, and the processor 110 may be configured to control the transceiving action of the transceiver 140, so as to perform data transceiving with the smart medical service terminal 200.

For a specific implementation process of the processor 110, reference may be made to the above-mentioned various method embodiments executed by the big data medical cloud platform 100, which implement principles and technical effects similar to each other, and details of this embodiment are not described herein again.

In the embodiment shown in fig. 4, it should be understood that the Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the present invention may be embodied directly in a hardware processor, or in a combination of the hardware and software modules within the processor.

The machine-readable storage medium 120 may comprise high-speed RAM memory and may also include non-volatile storage NVM, such as at least one disk memory.

The bus 130 may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended ISA (EISA) bus, or the like. The bus 130 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, the buses in the figures of the present application are not limited to only one bus or one type of bus.

In addition, the embodiment of the disclosure also provides a readable storage medium, in which computer execution instructions are stored, and when a processor executes the computer execution instructions, the intelligent medical information pushing method based on big data is implemented.

Finally, it should be noted that: the above embodiments are only used for illustrating the technical solutions of the present disclosure, and not for limiting the same; while the present disclosure has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art will understand that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the 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 disclosure.

Claims

1. A big data based intelligent medical information pushing method is applied to a big data medical cloud platform in communication connection with a plurality of intelligent medical service terminals, and comprises the following steps:

generating a medical information recommendation list of the intelligent medical service terminal according to the information recommendation thermodynamic diagram;

the behavior frequency refers to the repetition number or duration of the search behavior generated by the patient user, and is used for representing the attention degree of the patient user for searching the target content;

the query term refers to a keyword generated by the patient user based on the search target content, and the keyword comprises an input keyword or a clicked keyword;

the medical record is the record of the medical activity process of the patient user in the process of the patient user's treatment, such as the occurrence, development and outcome of the patient user's disease, examination, diagnosis, treatment and the like, by the relevant medical staff.

2. The intelligent medical information pushing method based on big data according to claim 1, wherein the step of processing the big user behavior data sample into a plurality of big user behavior data subsamples of query intention decision trees divided by the related intention search target according to different query intents in advance comprises:

3. The intelligent medical information pushing method based on big data according to claim 1, wherein the step of calculating query word frequency characteristics of a plurality of query words contained in each big data subsample of user behavior comprises:

4. The intelligent medical information pushing method based on big data as claimed in claim 1, wherein the step of generating the information recommendation thermodynamic diagram of the intelligent medical service terminal according to the query intention characteristics of each user behavior demand includes:

5. The intelligent medical information pushing method based on big data as claimed in claim 4, wherein the step of obtaining the distribution of query intention hotspots of each user behavior demand in each query intention decision tree according to the query intention characteristics of each user behavior demand, and dividing the distribution of query intention hotspots to obtain a plurality of query intention hotspot units comprises:

6. The intelligent medical information pushing method based on big data as claimed in claim 4, wherein the step of performing intent mining on the plurality of query intent hotspot units according to the intent mining script corresponding to each query intent decision tree to obtain the intent mining results corresponding to the plurality of query intent hotspot units respectively comprises:

7. The method as claimed in claim 1, wherein the step of determining a graph connectivity relationship between the query intent hotspot units in each query intent decision tree according to the respective intent mining results corresponding to the query intent hotspot units, and generating an information recommendation thermodynamic diagram of the intelligent medical service terminal according to the determined graph connectivity relationship between the query intent hotspot units in each query intent decision tree includes:

8. The intelligent medical information pushing method based on big data according to any one of claims 1-7, wherein the step of generating a medical information recommendation list of the intelligent medical service terminal according to the information recommendation thermodynamic diagram includes:

9. The intelligent medical information pushing method based on big data according to claim 8, wherein the step of generating the medical information recommendation list of the intelligent medical service terminal according to the medical information recommendation list model comprises:

10. The smart medical information pushing system based on big data is characterized by comprising a big data medical cloud platform and a plurality of smart medical service terminals in communication connection with the big data medical cloud platform;

the big data medical cloud platform is used for generating a medical information recommendation list of the intelligent medical service terminal according to the information recommendation thermodynamic diagram;