A medical data distribution method and system based on big data value

Abstract

The present invention discloses a medical data distribution method and system based on big data value, which collects first medical data through big data to obtain basic information; obtains a first initial value evaluation result based on the basic information; obtains first medical data chain level information of the first medical data; obtains a first correlation coefficient based on the first medical data chain level information and the basic information; performs data analysis based on the first medical data chain level information and the first medical data to obtain a first consistency coefficient; obtains a flow distribution energy efficiency evaluation result through the first correlation coefficient, the first consistency coefficient and the first initial value evaluation result, and performs data distribution of the first medical data based on the flow distribution energy efficiency evaluation result. The method solves the technical problem that in the process of data screening in the prior art, the data cannot be accurately screened according to the data demand information, resulting in inaccurate correspondence between data and demand.

Description

Technical Field

[0001] The present invention relates to fields related to intelligent data distribution, and in particular to a medical data distribution method and system based on big data value. Background Art

[0002] Medical data refers to data related to medicine, such as pathological information, technical data, diagnosis and treatment data, etc. Medical data can be used to mine similar cases, recommend treatment plans, and make intelligent diagnoses, etc., to assist doctors in evaluating and treating patients. Medical data has become an important application resource at this stage. As a productive input resource, it can greatly improve production efficiency and provide more medical value. In the process of improving production efficiency and creating production value, medical data also has problems such as affecting the accuracy of judgment results and too much redundancy of medical data causing slow system operation. Therefore, how to intelligently screen and distribute medical data is an urgent problem to be solved.

[0003] However, in the process of implementing the technical solution of the invention in this application, it is found that the above technology has at least the following technical problems:

[0004] In the process of screening medical data in the prior art, there is a technical problem that the medical data cannot be accurately screened according to the data demand information, resulting in inaccurate correspondence between the medical data and the demand. Summary of the invention

[0005] The present application provides a medical data distribution method and system based on the value of big data, thereby solving the technical problem in the prior art that, during the process of medical data screening, medical data cannot be accurately screened according to data demand information, resulting in inaccurate correspondence between medical data and demand. The application achieves the technical effect of improving the correspondence between medical data and demand by accurately evaluating medical data based on demand information and basic information of medical data.

[0006] In view of the above problems, the present application proposes a medical data distribution method and system based on big data value.

[0007] In the first aspect, the present application provides a medical data distribution method based on big data value, the method comprising: collecting first medical data through big data to obtain basic information of the first medical data; performing an initial value assessment on the first medical data based on the basic information to obtain a first initial value assessment result; obtaining first medical data chain level information of the first medical data; obtaining a first correlation coefficient based on the first medical data chain level information and the basic information; performing data analysis based on the first medical data chain level information and the first medical data to obtain a first consistency coefficient; obtaining a flow allocation energy efficiency assessment result through the first correlation coefficient, the first consistency coefficient and the first initial value assessment result, and performing data allocation of the first medical data based on the flow allocation energy efficiency assessment result.

[0008] On the other hand, the present application also provides a medical data distribution system based on big data value, the system comprising: a first acquisition unit, the first acquisition unit is used to collect first medical data through big data to obtain basic information of the first medical data; a second acquisition unit, the second acquisition unit is used to perform an initial value assessment of the first medical data based on the basic information to obtain a first initial value assessment result; a third acquisition unit, the third acquisition unit is used to obtain first medical data chain level information of the first medical data; a fourth acquisition unit, the fourth acquisition unit is used to obtain a first correlation coefficient based on the first medical data chain level information and the basic information; a fifth acquisition unit, the fifth acquisition unit is used to perform data analysis based on the first medical data chain level information and the first medical data to obtain a first consistency coefficient; a first allocation unit, the first allocation unit is used to obtain a flow allocation energy efficiency assessment result through the first correlation coefficient, the first consistency coefficient and the first initial value assessment result, and perform data allocation of the first medical data based on the flow allocation energy efficiency assessment result.

[0009] In a third aspect, the present invention provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method described in the first aspect when executing the program.

[0010] In a fourth aspect, the present invention provides a computer program product, comprising a computer program and / or instructions, which, when executed by a processor, implement the steps of the method described in the first aspect.

[0011] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0012] By obtaining a first initial value evaluation result of the first medical data based on the basic information of the first medical data, performing correlation coefficient evaluation and consistency coefficient evaluation of the first medical data based on the first medical data chain level information, evaluating and analyzing data and demand through the three dimensions of value, relevance and consistency, and performing data distribution management of the first medical data based on the flow allocation energy efficiency evaluation result of the evaluation and analysis, the technical effect of accurately evaluating data based on demand information and basic data information and improving the correspondence between data and demand is achieved.

[0013] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 A flowchart of a medical data distribution method based on big data value for this application;

[0015] Figure 2 A schematic diagram of a process for obtaining a first initial value evaluation result of a medical data allocation method based on big data value in this application;

[0016] Figure 3 A schematic diagram of a process for obtaining a first consistency coefficient of a medical data allocation method based on big data value in this application;

[0017] Figure 4 A schematic diagram of a flow chart of obtaining a first correlation coefficient in a medical data allocation method based on big data value according to the present application;

[0018] Figure 5 A schematic diagram of the structure of a medical data distribution system based on big data value for this application;

[0019] Figure 6 This is a structural schematic diagram of an electronic device of the present application.

[0020] Explanation of the reference numerals: first obtaining unit 11 , second obtaining unit 12 , third obtaining unit 13 , fourth obtaining unit 14 , fifth obtaining unit 15 , first allocating unit 16 , electronic device 50 , processor 51 , memory 52 , input device 53 , output device 54 . DETAILED DESCRIPTION

[0021] This application solves the technical problem that in the process of data screening in the prior art, the data cannot be accurately screened according to the data demand information, resulting in inaccurate correspondence between data and demand, by providing a medical data distribution method and system based on the value of big data. The technical effect of improving the correspondence between data and demand is achieved by accurately evaluating the data based on the demand information and the basic information of the data. The embodiments of the present application are described below in conjunction with the accompanying drawings. It is known to those skilled in the art that with the development of technology and the emergence of new scenarios, the technical solutions provided by this application are also applicable to similar technical problems.

[0022] The terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the terms used in this way can be interchanged under appropriate circumstances. This is merely a way of distinguishing objects with the same properties when describing the embodiments of the present application. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, so that a process, method, system, product, or device that includes a series of units is not necessarily limited to those units, but may include other units that are not explicitly listed or inherent to these processes, methods, products, or devices.

[0023] Application Overview

[0024] Data analysis can help summarize experience, discover rules, and predict trends. Through the above process, it can assist decision-making. In order to ensure that decisions are more scientific, reasonable, and accurate, data needs to be accurately screened and identified. However, in the process of data screening in the prior art, there is a technical problem that the data cannot be accurately screened according to the data demand information, resulting in inaccurate correspondence between data and demand.

[0025] In response to the above technical problems, the overall idea of ​​the technical solution provided by this application is as follows:

[0026] The present application provides a medical data distribution method based on big data value, the method comprising: collecting first medical data through big data to obtain basic information of the first medical data; performing an initial value assessment on the first medical data based on the basic information to obtain a first initial value assessment result; obtaining first medical data chain level information of the first medical data; obtaining a first correlation coefficient based on the first medical data chain level information and the basic information; performing data analysis based on the first medical data chain level information and the first medical data to obtain a first consistency coefficient; obtaining a flow allocation energy efficiency assessment result through the first correlation coefficient, the first consistency coefficient and the first initial value assessment result, and performing data allocation of the first medical data based on the flow allocation energy efficiency assessment result.

[0027] After introducing the basic principles of the present application, various non-limiting implementation methods of the present application will be specifically described below in conjunction with the drawings in the specification.

[0028] Embodiment 1

[0029] like Figure 1 As shown, the present application provides a medical data distribution method based on big data value, the method comprising:

[0030] Step S100: collecting first medical data through big data to obtain basic information of the first medical data;

[0031] Step S200: performing an initial value assessment of the first medical data based on the basic information to obtain a first initial value assessment result;

[0032] Specifically, medical data refers to data related to medicine, such as pathological information, technical data, diagnosis and treatment data, etc. Medical data can be used to mine similar cases, recommend treatment plans, and make intelligent diagnoses, etc., to assist doctors in evaluating and treating patients. In the process of using data, it is necessary to analyze and process the data according to multiple dimensions such as the value of the data, the time of collection, and the degree of matching, so as to ensure the correspondence between the data and the needs. Therefore, it is necessary to evaluate and process the data. On the premise of obtaining the source permission of the data, the data is collected and stored to obtain the collected first medical data, wherein the collected information includes the basic information of the first medical data, and the basic information includes the source information of the data, the size and format of the data, the authentication information of the source, the source time of the data, the source location information of the data, etc. The acquisition of the basic information provides data layer support for the subsequent accurate analysis of the data. The initial value of the first medical data is evaluated based on the basic information, wherein the initial value reflects the value of the data itself before it does not match the needs. For example, when the data is obtained by an individual through simple guesswork, the initial value of the data itself is considered to be low, and when the data is obtained by a large number of experiments conducted by a medical institution, the value of the data itself is considered to be high. The data's initial value evaluation is performed based on the data acquisition subject, acquisition method, acquisition time and other dimensions to obtain the first initial value evaluation result. The data's initial value evaluation result provides support for subsequent accurate matching of data with demand.

[0033] Step S300: obtaining first medical data chain level information of the first medical data;

[0034] Step S400: obtaining a first correlation coefficient according to the first medical data link level information and the basic information;

[0035] Specifically, the first medical data link level refers to a data set after data collection and sorting under a predetermined demand target, which includes multiple demand-related data. According to the first medical data link level information, the demand target of the first medical data link level is obtained. For example, the first medical data link level can be the initial level of the heart data link, then the demand target is heart-related data, according to the first medical data link level information, the underlying rule data constructed by the first medical data link level is obtained, and the correlation is evaluated based on the underlying rule data and the basic information, and the first correlation coefficient is obtained according to the correlation evaluation result.

[0036] Further, the relevance evaluation process includes classification relevance and semantic analysis relevance. The classification relevance refers to the classification relevance between the demand target and the first medical data. When the classification of the demand target is a large classification and the classification of the first medical data is a sub-classification of the large classification, it is considered that the classification relevance between the first medical data and the demand target is high; when the classification of the first medical data is a large classification and the classification of the demand target is a sub-classification of the large classification, it is necessary to further evaluate the relevance according to the specific demand content. The relevance after semantic analysis is the evaluation result of the content relevance based on the semantic recognition analysis result after the semantic analysis of the demand target and the first medical data. The final first relevance coefficient is obtained based on the classification relevance and the relevance after semantic analysis. The acquisition of the first relevance coefficient lays a solid foundation for the subsequent accurate matching evaluation of the first medical data and the demand data.

[0037] Step S500: performing data analysis according to the first medical data chain level information and the first medical data to obtain a first consistency coefficient;

[0038] Step S600: Obtain a flow allocation energy efficiency evaluation result through the first correlation coefficient, the first consistency coefficient and the first initial value evaluation result, and perform data allocation of the first medical data based on the flow allocation energy efficiency evaluation result.

[0039] Specifically, the first consistency coefficient is an evaluation parameter that reflects the consistency of the viewpoints of the first medical data and the first medical data reflecting the same problem, wherein the other data are the data that have been collected and included in the first medical data chain level information, and the first medical data chain level information is analyzed, and the content description information of each data is obtained according to the semantic parsing result of the data, and the data describing the same problem as the first medical data is screened based on the content description information, and the consistency of the viewpoints is identified according to the screening result of the data. For example, when the collected data is the diagnosis result for the same gastric ulcer picture, the first medical data is the result data of diagnosing benign gastric ulcer, and the other data is the result data of diagnosing malignant gastric ulcer, then at this time, there is inconsistency between the diagnosis results of the first medical data and the other data, and the first consistency coefficient is low at this time, and the final evaluation result needs to be comprehensively evaluated based on the first consistency coefficient and the authority of the first medical data. Through the first correlation coefficient, the first consistency coefficient and the first initial value evaluation result, the correlation and matching degree of the first medical data and the first medical data chain level information, and the value of the first medical data relative to the first medical data chain level are evaluated, and the flow allocation energy efficiency evaluation result is obtained according to the evaluation result, and the data allocation of the first medical data is performed based on the flow allocation energy efficiency evaluation result. The first initial value evaluation result of the first medical data is obtained according to the basic information of the first medical data, and the correlation coefficient and consistency coefficient of the first medical data are evaluated based on the first medical data chain level information. The data and demand are evaluated and analyzed through the three dimensions of value, correlation and consistency. The data allocation management of the first medical data is performed according to the flow allocation energy efficiency evaluation result of the evaluation analysis, so as to achieve the technical effect of accurately evaluating the data according to the demand information and the basic information of the data and improving the correspondence between the data and the demand.

[0040] Furthermore, if Figure 2 As shown, the initial value evaluation of the first medical data is performed based on the basic information to obtain a first initial value evaluation result. Step S200 of the present application also includes:

[0041] Step S210: Obtaining first source information of the first medical data according to the basic information;

[0042] Step S220: obtaining first publishing location information and verification material integrity information of the first medical data based on the basic information;

[0043] Step S230: performing source authentication evaluation of the first medical data based on the first publishing location information and the integrity of the verification data to obtain a first authentication evaluation result;

[0044] Step S240: Perform an authority evaluation on the first medical data using the first authentication evaluation result and the first source information, and obtain the first initial value evaluation result based on the authority evaluation result and the first authentication evaluation result.

[0045] Specifically, the first source information of the first medical data refers to the information of the publisher of the first medical data. Further, based on the publisher information of the first medical data, and subject to the permission of the publisher, the generation process of the first medical data is obtained from the first publisher, such as scientific research experiment generation, feature evaluation generation, experimental verification generation, expert review generation, etc. The acquisition of the first source information provides support for subsequent accurate initial value assessment. The first publishing location is the upload IP information of the first medical data. In order to accurately detect the data, accurately check for false data and abnormal data, and verify the reliability of the data based on the upload location and upload time of the data.

[0046] Furthermore, the process of conducting the initial price system evaluation also includes an evaluation of the completeness of the verification information of the publisher of the first medical data. In order to further ensure the security and reliability of the data, in the process of conducting the first medical data evaluation, the information of the publisher of the first medical data is obtained, and the completeness of the information provided by the publisher is evaluated based on the information. The higher the completeness, the higher the authentication degree of the first authentication evaluation result. Based on the evaluation result of the first authentication degree and the information of the publisher of the first medical data, the authority of the first medical data is evaluated, wherein the authority is a parameter that reflects the reliability of the data, and the initial value evaluation result of the first medical data is obtained based on the authoritative evaluation result.

[0047] Furthermore, if Figure 3 As shown, the data is parsed according to the first medical data link level information and the first medical data to obtain a first consistency coefficient. Step S500 of the present application also includes:

[0048] Step S510: performing data semantic analysis on the first medical data to obtain a first medical data analysis result;

[0049] Step S520: performing semantic matching of data in the first medical data chain hierarchical information based on the first medical data parsing result to obtain a first matching data set;

[0050] Step S530: performing consistency evaluation on the first medical data parsing result based on the first matching data set to obtain a first consistency evaluation result;

[0051] Step S540: Correct the first consistency evaluation result according to the authoritative evaluation result to obtain the first consistency coefficient.

[0052] Specifically, the semantic analysis is a data analysis result that can be understood by a computer after performing text recognition and performing an in-depth analysis of grammar and sentence meaning in combination with the recognized context, title and other data, that is, the first medical data analysis result. Similarly, after semantic analysis of all data in the first medical data chain hierarchical information, a semantic recognition set is obtained. According to the first medical data analysis result, the recognition set is subjected to data semantic matching, wherein the matching process is to find a data set in the recognition set that is analyzed as the same problem as the first medical data analysis result, and the first matching data set is obtained according to the matching result. According to the first matching data set, based on the semantic analysis result, a consistency assessment is performed on the first matching data set with the first medical data analysis result.

[0053] Furthermore, the evaluation process includes the number of consistent opinions, the degree and number of inconsistent opinions, etc. The proportion of the number and degree is used as the first condition for evaluating the first consistency coefficient, and the authoritative evaluation results of each opinion are used as the correction condition for the first consistency evaluation coefficient. That is, according to the authoritative evaluation results of the first medical data, the first consistency evaluation results are corrected, and the first consistency coefficient is obtained according to the correction results. By performing semantic recognition of data, analyzing data information of different opinions on the same problem, and performing consistency evaluation of the first medical data, the consistency coefficient is corrected based on the authoritative evaluation results, so as to achieve the technical effect of making the final flow distribution energy efficiency evaluation results of the first medical data more in line with the data itself and the needs.

[0054] Further, such as Figure 4 As shown, the first correlation coefficient is obtained according to the first medical data link level information and the basic information, and step S400 of the present application also includes:

[0055] Step S410: obtaining data requirement information of the first medical data link according to the first medical data link level information;

[0056] Step S420: obtaining data classification information of the first medical data according to the basic information;

[0057] Step S430: obtaining a first correlation coefficient according to the data classification information and the data requirement information;

[0058] Step S440: obtaining a second correlation coefficient according to the semantic parsing result of the basic information and the data demand information;

[0059] Step S450: Obtaining the first correlation coefficient based on the first correlation coefficient and the second correlation coefficient.

[0060] Specifically, the first medical data link demand information is a predetermined demand target of the construction rules of the first medical data link level, and the data demand information of the first medical data link is obtained according to the first medical data link level information. The classification standard of the first predetermined data is obtained, and the data classification information of the first medical data is obtained according to the basic information. Further, one data may be included in multiple data classifications. Therefore, it is necessary to evaluate the data classification of the first medical data according to the degree of correlation between the data and each classification, and obtain the data classification information according to the evaluation result. The data classification information includes the multiple classifications to which the first medical data belongs and the correlation of each classification. The first correlation coefficient is the correlation coefficient obtained according to the classification correlation evaluation of the data classification information and the data demand information, wherein the higher the coefficient, the higher the correlation of the classification.

[0061] The second correlation coefficient refers to an evaluation coefficient of the relevance of the content, and is evaluated based on the semantic parsing result of the basic information of the first medical data and the matching degree of the content of the demand information, and the second correlation coefficient is obtained based on the evaluation result. The first correlation coefficient between the first medical data and the demand information is obtained based on the first correlation coefficient and the second correlation coefficient. By obtaining the first correlation coefficient and the second correlation coefficient, the evaluation result of the first correlation coefficient is more scientific and accurate, thereby laying a solid foundation for subsequent accurate data division and allocation.

[0062] Furthermore, step S400 of the present application also includes:

[0063] Step S460: obtaining a set correlation coefficient evaluation threshold;

[0064] Step S461: determining whether the first correlation coefficient meets the set correlation coefficient evaluation threshold;

[0065] Step S462: When the first correlation coefficient does not meet the set correlation coefficient evaluation threshold, the first medical data is removed from the first medical data chain hierarchy information.

[0066] Specifically, the set correlation coefficient evaluation threshold is a preset threshold for data screening based on correlation. Preferably, the set correlation coefficient evaluation threshold is set to 15%. Based on the set correlation coefficient evaluation threshold, the correlation check of the first medical data is performed. When the first medical data does not meet the set correlation coefficient evaluation threshold, it indicates that the first medical data is low-correlation data relative to the first medical data chain level. At this time, the first medical data is low-value data relative to the first medical data chain level. At this time, in order to ensure the fluency of the system, the first medical data is removed from the first medical data chain level. When the first medical data meets the set correlation coefficient evaluation threshold, it indicates that the first medical data is high-correlation data relative to the first medical data chain level. At this time, the first medical data can be distributed according to the subsequent evaluation. By eliminating low-correlation data, the technical effect of ensuring data accuracy and improving the system's computing speed is achieved.

[0067] Furthermore, step S600 of the present application also includes:

[0068] Step S610: performing information coding operation according to the correlation coefficient, the consistency coefficient and the initial value evaluation result to obtain a first information entropy set, wherein the first information entropy set contains information entropy that uniquely corresponds to the correlation coefficient, the consistency coefficient and the initial value evaluation result;

[0069] Step S620: sorting the first information entropy set by numerical value to construct a multi-level feature analysis model;

[0070] Step S630: input the first correlation coefficient, the first consistency coefficient and the first initial value evaluation result into the multi-level feature analysis model to obtain the flow distribution energy efficiency evaluation result.

[0071] Specifically, the correlation coefficient is used as the first feature, the consistency coefficient is used as the second feature, and the initial value evaluation result is used as the third feature. The first feature, the second feature, and the third feature are subjected to information coding theory operations to obtain the first information entropy corresponding to the first feature, the second information entropy corresponding to the second feature, and the third information entropy corresponding to the third feature. The larger the information entropy, the less important the feature is in this demand. According to the comparison result of the size of the information entropy, the sorting result of the features is obtained, and the features are prioritized according to the sorting result of the features. According to the order of entropy values ​​from small to large, the features are classified by a recursive algorithm in turn, and finally the multi-level feature analysis model is constructed. The first correlation coefficient, the first consistency coefficient, and the first initial value evaluation result of the first medical data are input into the multi-level feature analysis model to obtain the flow distribution energy efficiency evaluation result. Through the obtained classification features, the information entropy of various features is calculated, and the features with the smallest information entropy are selected and prioritized. At the same time, the information entropy of other classification features is recursively classified according to the same method, so that the multi-level feature analysis model finally constructed is more accurately classified. By inputting the data of each feature of the first medical data into the constructed multi-level feature analysis model, the flow allocation energy efficiency evaluation results applicable to the first medical data and the target demand data can be quickly and accurately matched, and the data allocation management of the first medical data is performed based on the flow allocation energy efficiency evaluation results, so as to achieve the technical effect of accurately evaluating the data according to the demand information and the basic information of the data, and improving the correspondence between the data and the demand.

[0072] Furthermore, step S600 of the present application also includes:

[0073] Step S610: determining whether the authority evaluation result meets a first predetermined evaluation threshold;

[0074] Step S620: when the authority evaluation result meets a first predetermined evaluation threshold, obtaining a first correction parameter;

[0075] Step S630: performing parameter correction on the flow distribution energy efficiency evaluation result according to the first correction parameter to obtain a first correction result;

[0076] Step S640: performing data allocation of the first medical data using the first correction result.

[0077] Specifically, the first predetermined evaluation threshold is a preset threshold for authoritative judgment. According to the set first predetermined evaluation threshold, the authoritative evaluation result of the first medical data is verified. When the authoritative evaluation result of the first medical data does not meet the first predetermined evaluation threshold, the first medical data is subjected to routine processing; when the authoritative evaluation result of the first medical data meets the first predetermined evaluation threshold, a first correction parameter is obtained at this time, and the first correction parameter is adjusted according to the degree of satisfaction between the authoritative evaluation result and the first predetermined evaluation threshold, that is, the higher the authority, the larger the first correction parameter. The correction parameter is used as a correction parameter for the first initial value evaluation result and the first consistency coefficient, and data allocation of the first medical data is performed according to the corrected result.

[0078] In summary, the medical data distribution method and system based on big data value provided by this application have the following technical effects:

[0079] 1. By obtaining the first initial value evaluation result of the first medical data according to the basic information of the first medical data, performing correlation coefficient evaluation and consistency coefficient evaluation of the first medical data based on the first medical data chain level information, performing evaluation and analysis of data and demand through the three dimensions of value, relevance and consistency, and performing data distribution management of the first medical data according to the flow allocation energy efficiency evaluation result of the evaluation and analysis, the technical effect of accurately evaluating the data according to the demand information and the basic information of the data and improving the correspondence between the data and the demand is achieved.

[0080] 2. By adopting the method of semantic recognition of data, analyzing data information of different viewpoints on the same problem, and conducting consistency evaluation of the first medical data, and correcting the consistency coefficient through authoritative evaluation results, the technical effect of making the final flow distribution energy efficiency evaluation result of the first medical data more in line with the data itself and the needs is achieved.

[0081] 3. Due to the method of obtaining the first correlation coefficient and the second correlation coefficient, the evaluation result of the first correlation coefficient is more scientific and accurate, thereby laying a solid foundation for subsequent accurate data division and allocation.

[0082] 4. By adopting the method of constructing a multi-level feature analysis model by inputting the data of each feature of the first medical data, it is possible to quickly and accurately match the flow allocation energy efficiency evaluation results applicable to the first medical data and the target demand data, and to manage the data allocation of the first medical data through the flow allocation energy efficiency evaluation results, thereby achieving the technical effect of accurately evaluating the data based on the demand information and the basic information of the data, and improving the correspondence between the data and the demand.

[0083] Embodiment 2

[0084] Based on the same inventive concept as the medical data distribution method based on big data value in the aforementioned embodiment, the present invention also provides a medical data distribution system based on big data value, such as Figure 5 As shown, the system comprises:

[0085] A first obtaining unit 11, the first obtaining unit 11 is used to collect first medical data through big data to obtain basic information of the first medical data;

[0086] A second obtaining unit 12, the second obtaining unit 12 is used to perform an initial value evaluation of the first medical data based on the basic information to obtain a first initial value evaluation result;

[0087] A third obtaining unit 13, the third obtaining unit 13 is used to obtain first medical data chain level information of the first medical data;

[0088] A fourth obtaining unit 14, the fourth obtaining unit 14 is used to obtain a first correlation coefficient according to the first medical data link level information and the basic information;

[0089] a fifth obtaining unit 15, the fifth obtaining unit 15 being configured to perform data analysis according to the first medical data chain level information and the first medical data to obtain a first consistency coefficient;

[0090] The first allocation unit 16 is used to obtain a flow allocation energy efficiency evaluation result through the first correlation coefficient, the first consistency coefficient and the first initial value evaluation result, and perform data allocation of the first medical data based on the flow allocation energy efficiency evaluation result.

[0091] Furthermore, the system also includes:

[0092] a sixth obtaining unit, the sixth obtaining unit being configured to obtain first source information of the first medical data according to the basic information;

[0093] a seventh obtaining unit, the seventh obtaining unit being used to obtain first publishing location information and verification material integrity information of the first medical data based on the basic information;

[0094] an eighth obtaining unit, configured to perform image noise evaluation on the first image set to obtain a first image noise evaluation result;

[0095] a ninth obtaining unit, the ninth obtaining unit being used to perform source authentication evaluation of the first medical data through the first publishing location information and the integrity of the verification data, and obtain a first authentication evaluation result;

[0096] A tenth obtaining unit is used to perform an authority evaluation on the first medical data using the first authentication evaluation result and the first source information, and obtain the first initial value evaluation result based on the authority evaluation result and the first authentication evaluation result.

[0097] Furthermore, the system also includes:

[0098] an eleventh obtaining unit, the eleventh obtaining unit being used to perform data semantic analysis on the first medical data to obtain a first medical data analysis result;

[0099] a twelfth obtaining unit, the twelfth obtaining unit being used to perform semantic matching of data in the first medical data chain hierarchical information based on the first medical data parsing result to obtain a first matching data set;

[0100] a thirteenth obtaining unit, the thirteenth obtaining unit being used to perform a consistency evaluation of the first medical data parsing result based on the first matching data set to obtain a first consistency evaluation result;

[0101] A fourteenth obtaining unit is used to correct the first consistency evaluation result according to the authoritative evaluation result to obtain the first consistency coefficient.

[0102] Furthermore, the system also includes:

[0103] a fifteenth obtaining unit, the fifteenth obtaining unit being used to obtain data requirement information of the first medical data link according to the first medical data link level information;

[0104] a sixteenth obtaining unit, the sixteenth obtaining unit being configured to obtain data classification information of the first medical data according to the basic information;

[0105] a seventeenth obtaining unit, the seventeenth obtaining unit being configured to obtain a first correlation coefficient according to the data classification information and the data requirement information;

[0106] An eighteenth obtaining unit, the eighteenth obtaining unit being configured to obtain a second correlation coefficient according to a semantic parsing result of the basic information and the data demand information;

[0107] A nineteenth obtaining unit is configured to obtain the first correlation coefficient based on the first correlation coefficient and the second correlation coefficient.

[0108] Furthermore, the system also includes:

[0109] a twentieth obtaining unit, the twentieth obtaining unit being used to obtain a set correlation coefficient evaluation threshold;

[0110] A first judgment unit, the first judgment unit is used to judge whether the first correlation coefficient meets the set correlation coefficient evaluation threshold;

[0111] The first elimination unit is used to eliminate the first medical data from the first medical data chain level information when the first correlation coefficient does not meet the set correlation coefficient evaluation threshold.

[0112] Furthermore, the system also includes:

[0113] A twenty-first obtaining unit, the twenty-first obtaining unit is used to perform information coding theory operation according to the correlation coefficient, the consistency coefficient and the initial value evaluation result to obtain a first information entropy set, wherein the first information entropy set contains information entropy that uniquely corresponds to the correlation coefficient, the consistency coefficient and the initial value evaluation result;

[0114] A first construction unit, the first construction unit is used to sort the first information entropy set by numerical value to construct a multi-level feature analysis model;

[0115] The twenty-second obtaining unit is used to input the first correlation coefficient, the first consistency coefficient and the first initial value evaluation result into the multi-level feature analysis model to obtain the flow distribution energy efficiency evaluation result.

[0116] Furthermore, the system also includes:

[0117] A second judgment unit, the second judgment unit is used to judge whether the authority evaluation result meets a first predetermined evaluation threshold;

[0118] A twenty-third obtaining unit, the twenty-third obtaining unit is used to obtain a first correction parameter when the authoritative evaluation result meets a first predetermined evaluation threshold;

[0119] A twenty-fourth obtaining unit, the twenty-fourth obtaining unit is used to perform parameter correction of the flow direction allocation energy efficiency evaluation result according to the first correction parameter to obtain a first correction result;

[0120] A second allocating unit is used to distribute the first medical data according to the first correction result.

[0121] The foregoing Figure 1 The various variations and specific examples of a medical data distribution method based on big data value in Example 1 are also applicable to a medical data distribution system based on big data value in this embodiment. Through the above detailed description of a medical data distribution method based on big data value, technical personnel in this field can clearly know the implementation method of a medical data distribution system based on big data value in this embodiment, so for the sake of conciseness of the specification, it will not be described in detail here.

[0122] Exemplary Electronic Devices

[0123] Reference below Figure 6 To describe the electronic device of the present application.

[0124] Figure 6 The figure shows a schematic structural diagram of an electronic device according to the present application.

[0125] Based on the inventive concept of a medical data distribution method based on big data value in the aforementioned embodiment, the present invention also provides an electronic device, as shown below. Figure 6 The electronic device according to the present application is described as follows. The electronic device may be the mobile device itself, or a stand-alone device independent of the mobile device, on which a computer program is stored, and when the program is executed by a processor, the steps of any of the methods described above are implemented.

[0126] like Figure 6 As shown, the electronic device 50 includes one or more processors 51 and a memory 52 .

[0127] The processor 51 may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device 50 to perform desired functions.

[0128] The memory 52 may include one or more computer program products, and the computer program product may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, a random access memory (RAM) and / or a cache memory (cache), etc. The non-volatile memory may include, for example, a read-only memory (ROM), a hard disk, a flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 51 may execute the program instructions to implement the methods of the various embodiments of the present application described above and / or other desired functions.

[0129] In one example, the electronic device 50 may further include: an input device 53 and an output device 54 , and these components are interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0130] The embodiment of the present invention provides a method for distributing medical data based on the value of big data, the method comprising: collecting first medical data through big data to obtain basic information of the first medical data; performing an initial value evaluation of the first medical data based on the basic information to obtain a first initial value evaluation result; obtaining first medical data chain level information of the first medical data; obtaining a first correlation coefficient according to the first medical data chain level information and the basic information; performing data analysis according to the first medical data chain level information and the first medical data to obtain a first consistency coefficient; obtaining a flow distribution energy efficiency evaluation result through the first correlation coefficient, the first consistency coefficient and the first initial value evaluation result, and performing data distribution of the first medical data based on the flow distribution energy efficiency evaluation result. The method solves the technical problem that in the process of screening medical data in the prior art, the medical data cannot be accurately screened according to the data demand information, resulting in inaccurate correspondence between medical data and demand, and the technical effect of improving the correspondence between medical data and demand by accurately evaluating medical data according to the demand information and the basic information of medical data.

[0131] Through the description of the above implementation methods, the technicians in the relevant field can clearly understand that the present application can be implemented by means of software plus necessary general hardware, and of course, it can also be implemented by special hardware including special integrated circuits, special CPUs, special memories, special components, etc. In general, all functions completed by computer programs can be easily implemented with corresponding hardware, and the specific hardware structures used to implement the same function can also be various, such as analog circuits, digital circuits or special circuits. However, for the present application, software program implementation is a better implementation method in more cases. Based on such an understanding, the technical solution of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a readable storage medium, such as a computer floppy disk, U disk, mobile hard disk, ROM, RAM, disk or optical disk, etc., including a number of instructions to enable a computer device to execute the methods described in each embodiment of the present application.

[0132] In the above embodiments, all or part of the embodiments may be implemented by software, hardware, firmware or any combination thereof. When implemented by software, all or part of the embodiments may be implemented in the form of a computer program product.

[0133] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from a computer-readable storage medium to another computer-readable storage medium, and the available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)), etc.

[0134] It should be understood that "one embodiment" or "an embodiment" mentioned throughout the specification means that specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. It should be understood that in various embodiments of the present application, the size of the sequence number of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the present application.

[0135] In addition, the terms "system" and "network" are often used interchangeably in this article. The term "and / or" in this article is only a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0136] It should be understood that in this application, "B corresponding to A" means that B is associated with A, and B can be determined based on A. However, it should also be understood that determining B based on A does not mean determining B only based on A, but B can also be determined based on A and / or other information.

[0137] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0138] In short, the above is only a preferred embodiment of the technical solution of this application, and is not intended to limit the protection scope of this application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of this application should be included in the protection scope of this application.

Claims

1. A medical data distribution method based on big data value, characterized in that: The method comprises: Collecting first medical data through big data to obtain basic information of the first medical data; Performing an initial value assessment of the first medical data based on the basic information to obtain a first initial value assessment result; Obtaining first medical data chain level information of the first medical data; Obtaining a first correlation coefficient according to the first medical data link level information and the basic information; Performing data analysis based on the first medical data chain level information and the first medical data to obtain a first consistency coefficient; Obtaining a flow distribution energy efficiency evaluation result through the first correlation coefficient, the first consistency coefficient and the first initial value evaluation result, and performing data distribution of the first medical data based on the flow distribution energy efficiency evaluation result; The performing an initial value assessment of the first medical data based on the basic information to obtain a first initial value assessment result further includes: Obtaining first source information of the first medical data according to the basic information; Based on the basic information, obtain first publishing location information and verification data integrity information of the first medical data; Performing source authentication evaluation of the first medical data based on the first publishing location information and the integrity of the verification data to obtain a first authentication evaluation result; Performing an authority evaluation of the first medical data using the first authentication evaluation result and the first source information, and obtaining the first initial value evaluation result based on the authority evaluation result and the first authentication evaluation result; Perform information coding operation according to the correlation coefficient, the consistency coefficient and the initial value evaluation result to obtain a first information entropy set, wherein the first information entropy set contains information entropy that uniquely corresponds to the correlation coefficient, the consistency coefficient and the initial value evaluation result; Sorting the results of numerical order of the first information entropy set to construct a multi-level feature analysis model; The first correlation coefficient, the first consistency coefficient and the first initial value evaluation result are input into the multi-level feature analysis model to obtain the flow distribution energy efficiency evaluation result.

2. The method according to claim 1, characterized in that The performing data analysis according to the first medical data link level information and the first medical data to obtain a first consistency coefficient also includes: Performing data semantic analysis on the first medical data to obtain a first medical data analysis result; Performing semantic matching of data in the first medical data chain hierarchical information based on the first medical data parsing result to obtain a first matching data set; Performing a consistency evaluation on the first medical data parsing result based on the first matching data set to obtain a first consistency evaluation result; The first consistency evaluation result is corrected according to the authoritative evaluation result to obtain the first consistency coefficient.

3. The method according to claim 1, characterized in that The obtaining of a first correlation coefficient according to the first medical data link level information and the basic information also includes: Obtaining data requirement information of the first medical data link according to the first medical data link level information; Obtaining data classification information of the first medical data according to the basic information; Obtaining a first correlation coefficient according to the data classification information and the data requirement information; Obtaining a second correlation coefficient according to the semantic parsing result of the basic information and the data demand information; The first correlation coefficient is obtained based on the first correlation coefficient and the second correlation coefficient.

4. The method according to claim 3, characterized in that The method further comprises: Get the set correlation coefficient evaluation threshold; Determining whether the first correlation coefficient meets the set correlation coefficient evaluation threshold; When the first correlation coefficient does not meet the set correlation coefficient evaluation threshold, the first medical data is removed from the first medical data chain level information.

5. The method according to claim 1, characterized in that The method further comprises: Determining whether the authority evaluation result meets a first predetermined evaluation threshold; When the authoritative evaluation result meets a first predetermined evaluation threshold, obtaining a first correction parameter; Perform parameter correction on the flow distribution energy efficiency evaluation result according to the first correction parameter to obtain a first correction result; Data distribution of the first medical data is performed using the first correction result.

6. A medical data distribution system based on big data value, characterized in that: The system comprises: A first obtaining unit, the first obtaining unit is used to collect first medical data through big data to obtain basic information of the first medical data; a second obtaining unit, the second obtaining unit being used to perform an initial value evaluation of the first medical data based on the basic information to obtain a first initial value evaluation result; a third obtaining unit, the third obtaining unit being used to obtain first medical data chain level information of the first medical data; a fourth obtaining unit, the fourth obtaining unit being configured to obtain a first correlation coefficient according to the first medical data link level information and the basic information; a fifth obtaining unit, configured to perform data analysis according to the first medical data chain level information and the first medical data to obtain a first consistency coefficient; a first allocation unit, the first allocation unit being configured to obtain a flow allocation energy efficiency evaluation result through the first correlation coefficient, the first consistency coefficient and the first initial value evaluation result, and to perform data allocation of the first medical data based on the flow allocation energy efficiency evaluation result; a sixth obtaining unit, the sixth obtaining unit being configured to obtain first source information of the first medical data according to the basic information; a seventh obtaining unit, the seventh obtaining unit being used to obtain first publishing location information and verification material integrity information of the first medical data based on the basic information; a ninth obtaining unit, the ninth obtaining unit being used to perform source authentication evaluation of the first medical data through the first publishing location information and the integrity of the verification data, and obtain a first authentication evaluation result; a tenth obtaining unit, the tenth obtaining unit being used to perform an authority evaluation of the first medical data through the first authentication evaluation result and the first source information, and to obtain the first initial value evaluation result according to the authority evaluation result and the first authentication evaluation result; A twenty-first obtaining unit, the twenty-first obtaining unit is used to perform information coding theory operation according to the correlation coefficient, the consistency coefficient and the initial value evaluation result to obtain a first information entropy set, wherein the first information entropy set contains information entropy that uniquely corresponds to the correlation coefficient, the consistency coefficient and the initial value evaluation result; A first construction unit, the first construction unit is used to sort the first information entropy set by numerical value to construct a multi-level feature analysis model; The twenty-second obtaining unit is used to input the first correlation coefficient, the first consistency coefficient and the first initial value evaluation result into the multi-level feature analysis model to obtain the flow distribution energy efficiency evaluation result.

7. An electronic device, characterized in that: The method comprises a processor and a memory; the memory is used for storing; and the processor is used for executing the method according to any one of claims 1 to 5 by calling.

8. A computer program product comprising a computer program and / or instructions, characterized in that: When the computer program and / or the instructions are executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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