An integrated data aggregation optimization method based on cloud computing

Abstract

An integrated data aggregation optimization method based on cloud computing relates to the technical field of data aggregation. The invention relates to the following steps: constructing a distribution map of medical institutions and acquiring a number of data storage points, acquiring economic operation data of medical institutions and generating preprocessed data, uploading the preprocessed data to the data storage point for storage, generating an aggregated data set and constructing a corresponding aggregated data map, visualizing the aggregated data map to obtain an aggregated data visualization diagram, acquiring derivative coefficients of data aggregation points and correlation coefficients between different data nodes, acquiring real-time economic operation data of medical institutions, and inputting the data into a preset correlation judgment model to obtain corresponding real-time related data, setting different correlation standards and generating different real-time feedback data. The invention relates to a method for optimizing the integration of data aggregation and the technology of data aggregation ...

Description

Technical Field

[0001] The present invention relates to the technical field of data aggregation, and in particular to an integrated data aggregation optimization method based on cloud computing. Background Art

[0002] The integrated data aggregation optimization method aims to utilize the computing resources and storage capabilities provided by the cloud computing platform, combined with advanced data aggregation algorithms and technologies, to achieve efficient integration, analysis and utilization of data. It improves the efficiency and scalability of data aggregation and reduces the cost of data processing through virtualization technology, distributed computing technology and service technology.

[0003] In the prior art, data aggregation is increasingly being used in the medical field, and can centrally process the economic operations of different medical institutions, thereby analyzing the economic operation data faster and better. However, the prior art has failed to make the abstract economic operation data intuitive and visual, nor has it been able to use the aggregated economic operation data to form a prediction mechanism for the potential economic operation of subsequent medical institutions. In view of the shortcomings of the prior art, the present invention provides an integrated data aggregation optimization method based on cloud computing. Summary of the invention

[0004] The purpose of the present invention is to provide an integrated data aggregation optimization method based on cloud computing.

[0005] The purpose of the present invention can be achieved by the following technical solution: an integrated data aggregation optimization method based on cloud computing, comprising the following steps:

[0006] Step S1: Collect the location information of each medical institution, construct a corresponding distribution map, and construct several data storage points according to the distribution map;

[0007] Step S2: Obtain economic operation data of each medical institution, pre-process the data to obtain pre-processed data, and upload the pre-processed data to a data storage point for storage;

[0008] Step S3: generating an aggregate data set in the data storage point, constructing a corresponding aggregate data map, and visualizing the aggregate data map to obtain an aggregate data visualization graph;

[0009] Step S4: Obtain the derivative coefficient of the data aggregation point according to the aggregated data visual graph, obtain the correlation coefficient between different data nodes, obtain the real-time economic operation data of the medical institution, and input it into the preset correlation judgment model to obtain the corresponding real-time related data;

[0010] Step S5: setting different association standards, and generating different real-time feedback data according to the association standards.

[0011] Furthermore, the location information of each medical institution is collected and a corresponding distribution map is constructed. The process of constructing several data storage points according to the distribution map includes:

[0012] The location information refers to the geographical location of each medical institution in the region to which it belongs. A distribution map of each medical institution in the region to which it belongs is constructed using GIS technology based on the collected location information.

[0013] Obtain an initial data storage point in the distribution map, and obtain the probability value of the initial data storage point being selected as the next data storage point based on the shortest distance between each other geographical location and the current existing data storage point;

[0014] The next data storage point is constructed at the geographical location corresponding to the maximum probability value, and this step is repeated until m data storage points are constructed.

[0015] Furthermore, the economic operation data of each medical institution is obtained, and pre-processed to obtain pre-processed data, and the process of uploading the pre-processed data to a data storage point for storage includes:

[0016] The economic operation data refers to the relevant data on economic operation generated by each medical institution after completing medical work;

[0017] Preprocessing the economic operation data, wherein the preprocessing includes outlier processing, missing value processing, and normalization processing, and marking the preprocessed economic operation data as preprocessed data;

[0018] A communication connection is established between each medical institution and its nearest data storage point, and the pre-processed data of each medical institution is uploaded to the data storage point with which the communication connection exists for storage.

[0019] Furthermore, the process of generating an aggregated data set in a data storage point and constructing a corresponding aggregated data graph includes:

[0020] Anonymize the basic information of the medical institutions in the pre-processed data, and aggregate the pre-processed data after the anonymization process in the data storage point, wherein the aggregation process refers to incorporating the pre-processed data containing the same relevant data into the same set to obtain an aggregated data set of the relevant data, wherein the relevant data refers to the sub-data segmented in the economic operation data;

[0021] Use ontology to define various entities and their relationships in the aggregated data set to obtain the semantic structure of the aggregated data map, use natural language processing technology to extract key information from text data, reason on text data to fill in the blanks in the aggregated data map, and establish the data structure of the aggregated data map in combination with the semantic structure, and fill the key information into the aggregated data map;

[0022] In the aggregated data graph, the relevant data therein is divided into "data nodes" and "data edges" for processing. The data nodes are the basic units in the aggregated data graph, representing the entities in the aggregated data set. The data edges are the association relationships between data nodes, representing the connection or association between data nodes. The data edges describe the semantic structure between data nodes.

[0023] Furthermore, the process of visualizing the aggregate data graph to obtain a visual graph of the aggregate data includes:

[0024] The relevant data corresponding to each aggregated data set is used as the data aggregation point of its aggregated data map, and each aggregated data map is visualized based on the data aggregation point. The visualization process refers to converting the data nodes and data edges in the aggregated data map into a tree diagram for display;

[0025] Taking the data aggregation point as the starting point, obtaining the first-level data nodes connected to the data aggregation point at the first level, wherein the first-level connection means that there is only one data edge with the data aggregation point, and connecting lines between the data aggregation point and each of its first-level data nodes;

[0026] Taking the primary data node as the starting point, obtain the secondary data node that is secondary-connected to the primary data node, wherein the secondary connection means that there is only one data edge with the primary data node, and a line is connected between the primary data node and each of its secondary data nodes;

[0027] In this way, data nodes at all levels are connected in sequence until all data nodes in the aggregate data graph are connected, and the aggregate data graph at this time is marked as an aggregate data visible graph.

[0028] Furthermore, the process of obtaining the derivative coefficient of the data aggregation point according to the aggregate data visualization diagram and obtaining the correlation coefficient between different data nodes includes:

[0029] Mark other data nodes in the aggregate data visualization graph corresponding to the data aggregation point as derived data nodes of the data aggregation point, and mark the number of derived data nodes as the derivative coefficient y of the data aggregation point;

[0030] Get all the data edges between the derived data node and its data aggregation point and the number of text words corresponding to each data edge, mark the number of text words of the first-level connected data edge as the first-level distance S1, and mark the number of text words of the second-level connected data edge as the second-level distance S2, and obtain the distances S at each level in turn. n ;

[0031] And set the corresponding weight values ​​for them, namely the first-level weight Q1, the second-level weight Q2, and the weights of each level Q n , obtain the correlation coefficient G between the derived data node and its data aggregation point;

[0032]

[0033] Among them, n represents the level number corresponding to the highest level data edge, and the correlation coefficient between each derived data node and its data aggregation point in the aggregated data visualization graph is obtained. Different data nodes are used as data aggregation points to obtain their derived data nodes and their correlation coefficients.

[0034] Furthermore, the process of obtaining the real-time economic operation data of the medical institution and inputting it into the preset association judgment model to obtain the corresponding real-time related data includes:

[0035] The real-time economic operation data refers to the relevant data on economic operation generated by each medical institution when performing medical work, which includes some data nodes;

[0036] The preset process of the association judgment model is as follows: generating an association judgment set according to different data aggregation points and their corresponding derived data nodes and association coefficients, and dividing the obtained association judgment set into a training set and a test set;

[0037] Construct a convolutional neural network, use different data aggregation points in the training set as input data of the convolutional neural network, use the corresponding derived data nodes and their correlation coefficients in the training set as output data of the convolutional neural network, and train the convolutional neural network to obtain an initial convolutional neural network;

[0038] The initial convolutional neural network is model verified using the test set, and the initial convolutional neural network with a test error threshold value less than or equal to the preset value is output as the corresponding association judgment model;

[0039] Some data nodes in real-time economic operation are respectively input into the association judgment model as data aggregation points to obtain real-time relevant data, where the real-time relevant data refers to the derived data nodes and their association coefficients corresponding to each data node in the real-time economic operation data.

[0040] Furthermore, different association criteria are set, and the process of generating different real-time feedback data according to the association criteria includes:

[0041] Different association standards are set according to user needs, the association coefficients of the derived data nodes in the real-time related data are compared with the association standards respectively, the derived data nodes with association coefficients greater than or equal to the association standards are included in the real-time feedback data, and the real-time feedback data is sent to relevant personnel.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] 1. The present invention can integrate some medical institutions in the region by setting up several data storage points, without over-integration resulting in excessive data processing. By generating an aggregate data set around a single related data, constructing a corresponding aggregate data map, and then visualizing it to obtain a corresponding aggregate data visualization map, the abstract economic operation data can be made intuitive and visible.

[0044] 2. The present invention can reflect the data range extended by different data names by obtaining the derivative coefficients of each data aggregation point in the aggregate data visualization diagram. By obtaining the correlation coefficients between different data nodes and constructing a corresponding correlation judgment model, the corresponding real-time related data can be obtained according to the real-time economic operation data being generated. Different real-time feedback data can be generated by setting different correlation standards, which is conducive to predicting and providing feedback on the potential economic operation conditions of medical institutions. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0046] like Figure 1 As shown, an integrated data aggregation optimization method based on cloud computing includes the following steps:

[0047] Step S1: Collect the location information of each medical institution, construct a corresponding distribution map, and construct several data storage points according to the distribution map;

[0048] Step S2: Obtain economic operation data of each medical institution, pre-process the data to obtain pre-processed data, and upload the pre-processed data to a data storage point for storage;

[0049] Step S3: generating an aggregate data set in the data storage point, constructing a corresponding aggregate data map, and visualizing the aggregate data map to obtain an aggregate data visualization graph;

[0050] Step S4: Obtain the derivative coefficient of the data aggregation point according to the aggregated data visual graph, obtain the correlation coefficient between different data nodes, obtain the real-time economic operation data of the medical institution, and input it into the preset correlation judgment model to obtain the corresponding real-time related data;

[0051] Step S5: setting different association standards, and generating different real-time feedback data according to the association standards.

[0052] It should be further explained that, in the specific implementation process, the location information of each medical institution is collected and a corresponding distribution map is constructed. The process of constructing several data storage points according to the distribution map includes:

[0053] In the embodiment of the present invention, since it is necessary to aggregate the economic operation data of multiple different medical institutions, it is necessary to collect the location information of each medical institution, and the location information refers to the geographical location of each medical institution in the region to which it belongs, which is expressed in longitude and latitude;

[0054] Using GIS technology to construct a distribution map of each medical institution in its region based on the collected location information. The distribution map can not only reflect the geographical location of each medical institution, but also the distance relationship between each medical institution;

[0055] In the distribution map, an initial data storage point is constructed at the geographical location of any medical institution;

[0056] Get the shortest distance between each other geographical location and the current data storage point, and record the obtained shortest distance as d i , where i=1, 2, ..., k, k is the total number of geographical locations other than the data storage point;

[0057] Obtain the probability values ​​of each other geographical location being selected as the next data storage point, and record the obtained probability value as P i ;

[0058]

[0059] The next data storage point is constructed at the geographical location corresponding to the maximum probability value, and this step is repeated until m data storage points are constructed, and each constructed data storage point is uploaded to the distribution map for synchronization.

[0060] It should be further explained that, in the specific implementation process, the clinical medical data of each medical institution is obtained, and pre-processed to obtain pre-processed data, and the process of uploading the pre-processed data to the data storage point for storage includes:

[0061] After building the data storage point, it is necessary to obtain the economic operation data of each medical institution. The clinical medical data refers to the relevant data on economic operation generated by each medical institution after completing the medical work, including the following aspects:

[0062] Income data, including registration income, diagnosis and treatment income, examination income, treatment income, surgery income, nursing income, bed income, drug income, etc.;

[0063] Expenditure data, including personnel expenses, drug procurement costs, equipment purchase and maintenance costs, building maintenance costs, water and electricity and daily operating expenses;

[0064] Economic benefit indicators, including business revenue-expenditure ratio, average hospitalization cost, average outpatient cost, drug ratio, asset return rate, etc.;

[0065] Operational efficiency indicators, including bed occupancy rate, average length of stay, number of surgeries, number of outpatient visits, etc.;

[0066] Financial health indicators, including medical surplus ratio, debt-to-asset ratio, business cash flow ratio, etc.;

[0067] Preprocessing the collected economic operation data includes outlier processing, missing value processing, and normalization processing, and marking the economic operation data after the above three preprocessing as preprocessed data;

[0068] The outlier processing adopts the absolute median difference outlier processing method to clean up the abnormal data in the economic operation data, the missing value processing adopts the statistical filling method to fill the missing data in the economic operation data, and the normalization processing adopts the Z-Score standardization method to unify the clinical medical data format;

[0069] A communication connection is established between each medical institution and its nearest data storage point. One data storage point will have communication connections with multiple medical institutions at the same time. The pre-processed data of each medical institution is uploaded to the data storage point with which it has communication connection for storage.

[0070] It should be further explained that, in the specific implementation process, the process of generating an aggregated data set in a data storage point and constructing a corresponding aggregated data graph includes:

[0071] Anonymizing the basic information of the medical institution in the pre-processed data, wherein the anonymization refers to deleting, replacing or encrypting the basic information of the medical institution;

[0072] Aggregating the pre-processed data after anonymization in the data storage point, wherein the aggregation refers to including the pre-processed data containing the same relevant data in the same set to obtain an aggregated data set of the relevant data;

[0073] The relevant data refers to the more detailed sub-data in the economic operation data, such as registration income, personnel expenses, business revenue-expenditure ratio, bed occupancy rate, medical surplus rate, etc.;

[0074] Use ontology to define various entities and their relationships in the aggregated data set to obtain the semantic structure of the aggregated data map, use natural language processing technology to extract key information from text data, reason on text data to fill in the blanks in the aggregated data map, and establish the data structure of the aggregated data map in combination with the semantic structure, and fill the key information into the aggregated data map;

[0075] In the aggregated data graph, the relevant data therein is divided into "data nodes" and "data edges" for processing. The data nodes are the basic units in the aggregated data graph, representing the entities in the aggregated data set. The data edges are the association relationships between data nodes, representing the connection or association between data nodes. The data edges describe the semantic structure between data nodes.

[0076] It should be further explained that, in the specific implementation process, the process of visualizing the aggregate data map to obtain a visual graph of the aggregate data includes:

[0077] Since the aggregated data graph is converted from aggregated data sets, and the aggregated data sets are aggregated around a single related data, the related data corresponding to each aggregated data set is used as the data aggregation point of its aggregated data graph;

[0078] Based on the data aggregation point, the aggregated data map is visualized, wherein the visualization refers to converting the data nodes and data edges in the aggregated data map into a tree diagram for display;

[0079] Taking the data aggregation point as the starting point, obtain the data nodes connected to the data aggregation point at the first level, where the first-level connection means that there is only one data edge with the data aggregation point, and mark the corresponding data nodes as first-level data nodes, and connect the data aggregation point and each of its first-level data nodes;

[0080] Taking the primary data node as the starting point, obtain the data node that is secondary connected to the primary data node, where secondary connection means that there is only one data edge with the primary data node, and mark the corresponding data node as a secondary data node, and connect the primary data node with each of its secondary data nodes;

[0081] In this way, the third-level data nodes, fourth-level data nodes, fifth-level data nodes, and so on are connected in turn until all data nodes in the aggregate data graph are connected. The aggregate data graph at this time is marked as an aggregate data visual graph, and the same method is used to obtain aggregate data visual graphs of different aggregate data sets.

[0082] It should be further explained that, in the specific implementation process, the process of obtaining the derivative coefficient of the data aggregation point according to the aggregate data visual graph and obtaining the correlation coefficient between different data nodes includes:

[0083] Taking any data aggregation point as an example, other data nodes in the aggregated data visualization graph corresponding to the data aggregation point are marked as derived data nodes of the data aggregation point, and the number of derived data nodes is marked as the derivative coefficient y of the data aggregation point;

[0084] Based on the derivative coefficient, obtain the correlation coefficient G between each derivative data node and its data aggregation point. Taking any derivative data node as an example, obtain all data edges between the derivative data node and its data aggregation point, and obtain the number of text words corresponding to each data edge;

[0085] The number of text words corresponding to the first-level connected data edges is marked as the first-level distance S1, and the number of text words corresponding to the second-level connected data edges is marked as the second-level distance S2, and the distances S of each level are obtained in sequence. n , and set corresponding weight values ​​for them, namely, first-level weight Q1, second-level weight Q2, ..., weights at all levels Q n , obtain the correlation coefficient between the derived data node and its data aggregation point;

[0086]

[0087] Among them, n represents the level number corresponding to the highest level data edge, and the correlation coefficient between each derived data node and its data aggregation point in the aggregated data visibility graph is obtained, and then different data nodes are used as data aggregation points, and the correlation coefficient between their derived data nodes and them is obtained.

[0088] It should be further explained that, in the specific implementation process, the process of obtaining the real-time economic operation data of the medical institution and inputting it into the preset association judgment model to obtain the corresponding real-time related data includes:

[0089] The real-time economic operation data refers to the relevant data on economic operation generated by each medical institution when performing medical work, which includes some data nodes;

[0090] The preset process of the association judgment model is as follows: generating an association judgment set according to different data aggregation points and their corresponding derived data nodes and association coefficients, and dividing the obtained association judgment set into a training set and a test set;

[0091] Construct a convolutional neural network, use different data aggregation points in the training set as input data of the convolutional neural network, use the corresponding derived data nodes and their correlation coefficients in the training set as output data of the convolutional neural network, and train the convolutional neural network to obtain an initial convolutional neural network;

[0092] The initial convolutional neural network is model verified using the test set, and the initial convolutional neural network with a test error threshold value less than or equal to the preset value is output as the corresponding association judgment model;

[0093] Some data nodes in the collected real-time economic operation data are input into the association judgment model as data aggregation points respectively, and the derived data nodes and their association coefficients corresponding to each data node in the real-time economic operation data are obtained through the association judgment model, that is, real-time related data.

[0094] It should be further explained that, in the specific implementation process, different correlation standards are set, and the process of generating different real-time feedback data according to the correlation standards includes:

[0095] Different association standards are set according to user needs, and the association coefficients of the derived data nodes in the real-time related data are compared with the association standards respectively, and the derived data nodes whose association coefficients are greater than or equal to the association standards are included in the corresponding real-time feedback data;

[0096] The real-time feedback data will be sent to relevant personnel to prompt the derivative data nodes that will appear in the current economic operation, including registration income, personnel expenses, business revenue-expenditure ratio, bed occupancy rate, medical surplus rate, etc.

[0097] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. An integrated data aggregation optimization method based on cloud computing, characterized in that: The following steps are involved: Step S1: Collect the location information of each medical institution, construct a corresponding distribution map, and construct several data storage points according to the distribution map; Step S2: Obtain economic operation data of each medical institution, pre-process the data to obtain pre-processed data, and upload the pre-processed data to a data storage point for storage; Step S3: generating an aggregate data set in the data storage point, constructing a corresponding aggregate data map, and visualizing the aggregate data map to obtain an aggregate data visualization graph; Step S4: Obtain the derivative coefficient of the data aggregation point according to the aggregated data visual graph, obtain the correlation coefficient between different data nodes, obtain the real-time economic operation data of the medical institution, and input it into the preset correlation judgment model to obtain the corresponding real-time related data; Step S5: setting different association criteria, and generating different real-time feedback data according to the association criteria; The process of obtaining the derivative coefficients of the data aggregation points and the correlation coefficients of different data nodes includes: Mark other data nodes in the aggregate data visualization graph corresponding to the data aggregation point as derived data nodes of the data aggregation point, and mark the number of derived data nodes as the derivative coefficient y of the data aggregation point; Get all the data edges between the derived data node and its data aggregation point and the number of text words corresponding to each data edge, mark the number of text words of the first-level connected data edge as the first-level distance S1, and mark the number of text words of the second-level connected data edge as the second-level distance S2, and obtain the distances S at each level in turn. n ; And set the corresponding weight values ​​for them, namely the first-level weight Q1, the second-level weight Q2, and the weights of each level Q n , obtain the correlation coefficient G between the derived data node and its data aggregation point; Where n represents the level number corresponding to the highest level data edge, obtain the correlation coefficient between each derived data node and its data aggregation point in the aggregated data visualization graph, take different data nodes as data aggregation points, and obtain their derived data nodes and their correlation coefficients; The process of obtaining real-time economic operation data and its real-time related data includes: The real-time economic operation data refers to the relevant data on economic operation generated by each medical institution when performing medical work, which includes some data nodes; The preset process of the association judgment model is as follows: generating an association judgment set according to different data aggregation points and their corresponding derived data nodes and association coefficients, and dividing the obtained association judgment set into a training set and a test set; Construct a convolutional neural network, use different data aggregation points in the training set as input data of the convolutional neural network, use the corresponding derived data nodes and their correlation coefficients in the training set as output data of the convolutional neural network, and train the convolutional neural network to obtain an initial convolutional neural network; The initial convolutional neural network is model verified using the test set, and the initial convolutional neural network with a test error threshold value less than or equal to the preset value is output as the corresponding association judgment model; Some data nodes in the real-time economic operation data are respectively input as data aggregation points into the association judgment model to obtain real-time relevant data, where the real-time relevant data refers to the derived data nodes and their association coefficients corresponding to each data node in the real-time economic operation data.

2. The integrated data aggregation optimization method based on cloud computing according to claim 1 is characterized in that: The process of building a distribution map of medical institutions and several data storage points includes: The location information refers to the geographical location of each medical institution in the region to which it belongs. A distribution map of each medical institution in the region to which it belongs is constructed using GIS technology based on the collected location information. Obtain an initial data storage point in the distribution map, and obtain the probability value of the initial data storage point being selected as the next data storage point based on the shortest distance between each other geographical location and the current existing data storage point; The next data storage point is constructed at the geographical location corresponding to the maximum probability value, and this step is repeated until m data storage points are constructed.

3. The integrated data aggregation optimization method based on cloud computing according to claim 2 is characterized in that: The process of obtaining economic operation data and preprocessing data includes: The economic operation data refers to the relevant data on economic operation generated by each medical institution after completing medical work; Preprocessing the economic operation data, wherein the preprocessing includes outlier processing, missing value processing, and normalization processing, and marking the preprocessed economic operation data as preprocessed data; A communication connection is established between each medical institution and its nearest data storage point, and the pre-processed data of each medical institution is uploaded to the data storage point with which the communication connection exists for storage.

4. The integrated data aggregation optimization method based on cloud computing according to claim 3 is characterized in that: The process of generating an aggregated data set and building the corresponding aggregated data graph includes: Anonymize the basic information of the patient in the pre-processed data, and aggregate the pre-processed data after the anonymization process in the data storage point, wherein the aggregation process refers to incorporating the pre-processed data containing the same relevant data into the same set to obtain an aggregated data set of the relevant data, wherein the relevant data refers to the sub-data segmented in the economic operation data; Use ontology to define various entities and their relationships in the aggregated data set to obtain the semantic structure of the aggregated data map, use natural language processing technology to extract key information from text data, reason on text data to fill in the blanks in the aggregated data map, and establish the data structure of the aggregated data map in combination with the semantic structure, and fill the key information into the aggregated data map; In the aggregated data graph, the relevant data therein is divided into "data nodes" and "data edges" for processing. The data nodes are the basic units in the aggregated data graph, representing the entities in the aggregated data set. The data edges are the association relationships between the data nodes, representing the connection or association between the data nodes. The data edges describe the semantic structure between the data nodes.

5. The integrated data aggregation optimization method based on cloud computing according to claim 4 is characterized in that: The process of obtaining a visual representation of aggregated data involves: The relevant data corresponding to each aggregated data set is used as the data aggregation point of its aggregated data map, and each aggregated data map is visualized based on the data aggregation point. The visualization process refers to converting the data nodes and data edges in the aggregated data map into a tree diagram for display; Taking the data aggregation point as the starting point, obtaining the first-level data nodes connected to the data aggregation point at the first level, wherein the first-level connection means that there is only one data edge with the data aggregation point, and connecting lines between the data aggregation point and each of its first-level data nodes; Taking the primary data node as the starting point, obtain the secondary data node that is secondary-connected to the primary data node, wherein the secondary connection means that there is only one data edge with the primary data node, and a line is connected between the primary data node and each of its secondary data nodes; In this way, data nodes at all levels are connected in sequence until all data nodes in the aggregate data graph are connected, and the aggregate data graph at this time is marked as an aggregate data visible graph.

6. The integrated data aggregation optimization method based on cloud computing according to claim 5 is characterized in that: The process of generating different real-time feedback data according to the associated criteria includes: Different association standards are set according to user needs, the association coefficients of the derived data nodes in the real-time related data are compared with the association standards respectively, the derived data nodes with association coefficients greater than or equal to the association standards are included in the real-time feedback data, and the real-time feedback data is sent to relevant personnel.

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