CN117349030A - Medical digital system, method and equipment based on cloud computing cluster - Google Patents
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Abstract
The invention relates to the technical field of medical data, and discloses a medical digital system, a method and equipment based on a cloud computing cluster, wherein the medical digital system based on the cloud computing cluster comprises the following components: the medical data analysis module is used for carrying out data hierarchy classification processing on the medical data to obtain hierarchical medical data; the digital identification construction module is used for carrying out feature extraction on the hierarchical medical data to obtain data features, analyzing medical attributes corresponding to the hierarchical medical data, constructing digital association tags corresponding to the hierarchical medical data and creating a data exchange link of the hierarchical medical data; the computing resource cluster module is used for scheduling cloud computing resources corresponding to the medical data, and carrying out resource cluster processing on the resources in the cloud computing resources to obtain cluster resources; and the medical data processing module is used for executing data processing of the hierarchical medical data to obtain a processing result. The invention aims to improve the processing efficiency of medical digits based on cloud computing clusters.
Description
Technical Field
The invention relates to the technical field of medical data, in particular to a medical digital system, a method and equipment based on a cloud computing cluster.
Background
Medical digital processing refers to the process of processing, analyzing and managing medical data by using a computer and related technologies in the medical field, and relates to the conversion of medical data into an electronic form for better storage, transmission, sharing and analysis, and in order to improve the data processing efficiency of medical digital, the related cloud computing resources need to be clustered so as to better meet the computing requirements of the data.
The medical digital processing of the existing cloud computing cluster mainly adopts the construction of a corresponding cloud server, medical data to be processed is distributed to each cloud server in sequence for processing by dividing a physical server into a plurality of virtual machines, different application programs and operating systems can be independently operated through the cloud servers, but when related data are required to be scheduled in the use process of the system, the data are required to be scheduled from different cloud servers, and the corresponding resource use condition of the cloud servers is not analyzed, so that the medical digital processing efficiency is low.
Disclosure of Invention
The invention provides a medical digital system, a method and equipment based on a cloud computing cluster, and mainly aims to improve the processing efficiency of medical digital based on the cloud computing cluster.
In order to achieve the above object, the present invention provides a medical digital system based on a cloud computing cluster, which is characterized in that the medical digital system based on the cloud computing cluster includes:
the medical data analysis module is used for acquiring medical data to be processed, extracting a data tag of each piece of sub-data in the medical data, and carrying out data hierarchy classification processing on the medical data by combining the data tags to obtain hierarchical medical data;
the digital identification construction module is used for carrying out feature extraction on the hierarchical medical data to obtain data features, analyzing medical attributes corresponding to the hierarchical medical data, constructing a digital association tag corresponding to the hierarchical medical data by combining the medical attributes and the data features, and creating a data exchange link of the hierarchical medical data according to the digital association tag;
the computing resource cluster module is used for scheduling cloud computing resources corresponding to the medical data, collecting resource information corresponding to the cloud computing resources, wherein the resource information comprises: resource utilization information and resource parameter information, calculating a resource idle rate corresponding to each resource in the cloud computing resources according to the resource utilization information, and carrying out resource cluster processing on the resources in the cloud computing resources according to the resource parameter information to obtain cluster resources;
And the medical data processing module is used for setting a resource allocation scheme of the hierarchical medical data according to the resource idle rate, the cluster resources and the data exchange link, and executing data processing of the hierarchical medical data according to the resource allocation scheme to obtain a processing result.
Optionally, in combination with the data tag, performing data hierarchy classification processing on the medical data to obtain hierarchical medical data, including:
performing data cleaning treatment on the medical data to obtain target medical data;
calculating a label weight coefficient corresponding to each sub-label in the data label, and extracting a characteristic label in the data label according to the label weight coefficient;
calculating a support coefficient between each sub-label in the characteristic labels, and calculating a data entropy value corresponding to the target medical data;
and carrying out data hierarchy classification processing on the target medical data according to the data entropy value and the support coefficient to obtain hierarchical medical data.
Optionally, the calculating a support coefficient between each sub-tag in the feature tag includes:
calculating a support coefficient between each sub-label in the feature label by the following formula: Wherein A represents the support coefficient between each sub-tag in the feature tag, < >>The number of sub-tags representing the feature tag, a represents the tag serial number of the feature tag,/or->Representing the a-th and the a-th of the feature labelsTag length between a+1 tags, < >>Probability value representing the occurrence of the a-th tag of the feature tags,>probability value representing occurrence of (a+1) th tag among feature tags
Optionally, the calculating the data entropy value corresponding to the target medical data includes:
calculating a data entropy value corresponding to the target medical data through the following formula:wherein H represents the data entropy corresponding to the target medical data, b represents the serial number of the target medical data,/-the target medical data>Data quantity representing target medical data, +.>A set of random variables representing the a-th data of the target medical data,/a>Representing probabilities corresponding to a set of random variables for the a-th data in the target medical data.
Optionally, the feature extracting the hierarchical medical data to obtain a data feature includes:
extracting features of each piece of sub data in the hierarchical medical data to obtain sub data features, and calculating feature unreliability corresponding to the sub data features through the following formula: Wherein G represents the feature non-purity corresponding to the sub-data feature, d represents the feature sequence number of the sub-data feature, (-) -A>Representing sub-data featuresQuantity of features->Representing misclassification probability corresponding to the d-th feature in the sub-data features;
according to the characteristic non-purity, carrying out characteristic screening treatment on the sub-data characteristics to obtain target sub-data characteristics;
performing feature dimension reduction processing on the target sub-data features to obtain dimension reduction sub-data features, and analyzing feature linear relations among the dimension reduction sub-data features;
according to the characteristic linear relation, distributing the linear weight of the dimension reduction sub-data characteristic;
and carrying out linear weighted summation on the dimension reduction sub-data features according to the linear weight to obtain data features corresponding to the hierarchical medical data.
Optionally, the constructing, by combining the medical attribute and the data feature, a digital association tag corresponding to the hierarchical medical data includes:
calculating an attribute hash value corresponding to the medical attribute, and generating an attribute abstract corresponding to the medical attribute according to the attribute hash value;
calculating the abstract association degree between the attribute abstracts, and extracting the associated abstracts corresponding to the hierarchical medical data from the attribute abstracts according to the abstract association degree;
Calculating feature confidence coefficient between the data features, and setting digital labels corresponding to the hierarchical medical data according to the feature confidence coefficient;
and combining the association abstract and the digital label to construct the digital association label corresponding to the hierarchical medical data.
Optionally, the creating a data exchange link of the hierarchical medical data according to the digital association tag includes:
analyzing the data structure of each piece of sub-data in the hierarchical medical data, and setting the link bandwidth of each piece of sub-data in the hierarchical medical data according to the data structure;
calculating data transmission delay corresponding to each piece of sub-data in the hierarchical medical data, and determining link delay of each piece of sub-data in the hierarchical medical data according to the data transmission delay;
acquiring a data transmission protocol corresponding to each piece of sub-data in the hierarchical medical data, and setting a link protocol of each piece of sub-data in the hierarchical medical data according to the data transmission protocol;
a data exchange link of the hierarchical medical data is created in conjunction with the digital association tag, the link bandwidth, the link latency, and the link protocol.
Optionally, the calculating, according to the resource usage information, a resource idle rate corresponding to each resource in the cloud computing resources includes:
determining occupied resources in the cloud computing resources according to the resource use information;
inquiring the occupation configuration corresponding to the occupied resource, and calculating the configuration weight corresponding to the occupation configuration;
scheduling a resource log corresponding to the cloud computing resource, and calculating the request resource quantity corresponding to the cloud computing resource according to the resource log;
calculating available configuration corresponding to the cloud computing resource according to the request resource quantity and the occupied configuration;
according to the available configuration and the configuration weight, calculating the residual computing power corresponding to each resource in the cloud computing resources through the following formula:wherein M represents the residual computing power corresponding to each resource in the cloud computing resources, +.>Configuration weights representing software resources of the ith configuration in the available configurations, +.>Representing the remaining computational capacity of the software resource in the ith configuration,/>Representing the configuration weights of the hardware resources in the ith configuration of the available configurations,representing the remaining computational capacity of the hardware resource in the ith configuration, i representing the serial number of the available configuration;
And determining the resource idle rate corresponding to each resource in the cloud computing resources according to the residual computing power.
In order to solve the above problems, the present invention further provides a medical digital method based on a cloud computing cluster, the method comprising:
acquiring medical data to be processed, extracting a data tag of each sub-data in the medical data, and carrying out data hierarchy classification processing on the medical data by combining the data tags to obtain hierarchical medical data;
extracting features of the hierarchical medical data to obtain data features, analyzing medical attributes corresponding to the hierarchical medical data, constructing a digital association tag corresponding to the hierarchical medical data by combining the medical attributes and the data features, and creating a data exchange link of the hierarchical medical data according to the digital association tag;
scheduling cloud computing resources corresponding to the medical data, and collecting resource information corresponding to the cloud computing resources, wherein the resource information comprises: resource utilization information and resource parameter information, calculating a resource idle rate corresponding to each resource in the cloud computing resources according to the resource utilization information, and carrying out resource cluster processing on the resources in the cloud computing resources according to the resource parameter information to obtain cluster resources;
Setting a resource allocation scheme of the hierarchical medical data according to the resource idle rate, the cluster resources and the data exchange link, and executing data processing of the hierarchical medical data according to the resource allocation scheme to obtain a processing result.
In order to solve the above problems, the present invention also provides an electronic device including:
at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor of the electronic device to perform the cloud computing cluster-based medical digital method described above.
In the embodiment of the invention, the data label of each sub-data in the medical data can be extracted, so that the data information corresponding to the medical data is known, the subsequent data hierarchy classification processing is facilitated. Therefore, the medical digital system, the method and the equipment based on the cloud computing cluster can improve the processing efficiency of medical digital based on the cloud computing cluster.
Drawings
FIG. 1 is a functional block diagram of a medical digital system based on a cloud computing cluster according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of a medical digital method based on a cloud computing cluster according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device for implementing the medical digital system based on the cloud computing cluster according to an embodiment of the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The terminology used in the embodiments of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this application and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise, the "plurality" generally includes at least two.
The words "if", as used herein, may be interpreted as "at … …" or "at … …" or "in response to a determination" or "in response to a detection", depending on the context. Similarly, the phrase "if determined" or "if detected (stated condition or event)" may be interpreted as "when determined" or "in response to determination" or "when detected (stated condition or event)" or "in response to detection (stated condition or event), depending on the context.
In addition, the sequence of steps in the method embodiments described below is only an example and is not strictly limited.
In practice, a server-side device deployed by a cloud computing cluster-based medical digital system may be composed of one or more devices. The medical digital system based on the cloud computing cluster can be realized as follows: service instance, virtual machine, hardware device. For example, the cloud computing cluster-based medical digital system may be implemented as a business instance deployed on one or more devices in a cloud node. Briefly, the live service system may be understood as a software deployed on a cloud node, for providing a service of medical digits based on a cloud computing cluster to each user side. Alternatively, the cloud computing cluster-based medical digital system may also be implemented as a virtual machine deployed on one or more devices in a cloud node. The virtual machine is provided with application software for managing each user side. Or, the medical digital system based on the cloud computing cluster can be further realized as a service end formed by a plurality of hardware devices of the same or different types, and one or more hardware devices are arranged for providing medical digital services based on the cloud computing cluster for each user end.
In an implementation form, the medical digital system based on the cloud computing cluster and the user side are mutually adapted. Namely, the medical digital system based on the cloud computing cluster is used as an application installed on a cloud service platform, and the user side is used as a client side for establishing communication connection with the application; or realizing the medical digital system based on the cloud computing cluster as a website, and realizing the user side as a webpage; and then, or the medical digital system based on the cloud computing cluster is realized as a cloud service platform, and the user side is realized as an applet in the instant messaging application.
Referring to fig. 1, a functional block diagram of a medical digital system based on a cloud computing cluster according to an embodiment of the present invention is shown.
The medical digital system 100 based on the cloud computing cluster can be arranged in a cloud server, and in an implementation form, the medical digital system can be used as one or more service devices, can be used as an application to be installed on a cloud (such as a server of a live service operator, a server cluster and the like), or can be developed into a website. According to the implemented functions, the cloud computing cluster-based medical digital system 100 includes a medical data analysis module 101, a digital identification construction module 102, a computing resource cluster module 103, and a medical data processing module 104. The module referred to in the present invention refers to a series of computer program segments capable of being executed by a processor of an electronic device and of performing a fixed function, which are stored in a memory of the electronic device.
In the embodiment of the invention, in the medical digital based on the cloud computing cluster, each module can be independently realized and called with other modules. The invoking can be understood that a certain module can be connected with a plurality of modules of another type and provide corresponding services for the plurality of modules connected with the certain module, and in the medical digital system based on the cloud computing cluster provided by the embodiment of the invention, the application range of the medical digital architecture based on the cloud computing cluster can be adjusted by adding the modules and directly invoking the modules without modifying program codes, so that the cluster type horizontal expansion is realized, and the purpose of rapidly and flexibly expanding the medical digital system based on the cloud computing cluster is achieved. In practical applications, the modules may be disposed in the same device or different devices, or may be service instances disposed in virtual devices, for example, in a cloud server.
The following description is directed to each component of the medical digital system based on the cloud computing cluster and a specific workflow, respectively, with reference to specific embodiments:
the medical data analysis module 101 is configured to obtain medical data to be processed, extract a data tag of each sub-data in the medical data, and perform data hierarchy classification processing on the medical data in combination with the data tag to obtain hierarchical medical data.
In the embodiment of the invention, the data label of each piece of sub-data in the medical data can be extracted, so that the data information corresponding to the medical data can be known, and the subsequent data hierarchical classification processing is convenient, wherein the medical data is the related data of various patients recorded in medical treatment, such as the data of illness state, treatment scheme, recovery degree and the like, the data label is the description information corresponding to each piece of data in the medical data, and optionally, the data label of each piece of sub-data in the medical data can be extracted through a label extracting tool, and the label extracting tool is compiled by a script language.
According to the invention, the medical data is subjected to data hierarchical classification processing, so that the medical data is more standardized and systematic, and the processing efficiency of subsequent data is improved, wherein the hierarchical medical data is obtained by performing corresponding rule classification hierarchical processing on the medical data.
As an embodiment of the present invention, in combination with the data tag, the data hierarchy classification processing is performed on the medical data to obtain hierarchical medical data, including: performing data cleaning processing on the medical data to obtain target medical data, calculating a tag weight coefficient corresponding to each sub-tag in the data tag, extracting a characteristic tag in the data tag according to the tag weight coefficient, calculating a support coefficient between each sub-tag in the characteristic tag, calculating a data entropy value corresponding to the target medical data, and performing data hierarchical classification processing on the target medical data according to the data entropy value and the support coefficient to obtain hierarchical medical data.
The target medical data are data obtained by removing invalid data in the medical data, the tag weight coefficient represents the importance degree corresponding to each sub-tag in the data tag, the characteristic tag is a representative tag in the data tag, the support coefficient represents the support degree corresponding to each sub-tag in the characteristic tag, and the data entropy value represents the data information amount corresponding to the target medical data.
Optionally, the data cleaning process for the medical data may be implemented by a data cleaning tool, for example, a keyle tool, calculating a tag weight coefficient corresponding to each sub-tag in the data tag may be implemented by a weight calculator, extracting a feature tag in the data tag according to a value of the tag weight coefficient, calculating a data entropy value corresponding to the target medical data, and performing data hierarchical classification process for the target medical data according to the data entropy value and the support coefficient, so as to obtain hierarchical medical data.
Optionally, as an optional embodiment of the present invention, the calculating a support coefficient between each sub-tag in the feature tag includes:
Calculating the distance between each sub-label in the characteristic label by the following formulaSupport coefficient:wherein A represents the support coefficient between each sub-tag in the feature tag, < >>The number of sub-tags representing the feature tag, a represents the tag serial number of the feature tag,/or->Representing the tag length between the a-th tag and the a+1-th tag in the feature tag,/->Probability value representing the occurrence of the a-th tag of the feature tags,>a probability value representing the occurrence of the (a+1) th tag among the feature tags.
Optionally, as an optional embodiment of the present invention, the calculating a data entropy value corresponding to the target medical data includes:
calculating a data entropy value corresponding to the target medical data through the following formula:wherein H represents the data entropy corresponding to the target medical data, b represents the serial number of the target medical data,/-the target medical data>Data quantity representing target medical data, +.>A set of random variables representing the a-th data of the target medical data,/a>Representing probabilities corresponding to a set of random variables for the a-th data in the target medical data.
The digital identification construction module 102 is configured to perform feature extraction on the hierarchical medical data to obtain data features, analyze medical attributes corresponding to the hierarchical medical data, combine the medical attributes and the data features, construct a digital association tag corresponding to the hierarchical medical data, and create a data exchange link of the hierarchical medical data according to the digital association tag.
In the embodiment of the invention, the useful characteristics of the hierarchical medical data can be obtained by extracting the characteristics of the hierarchical medical data so as to facilitate the subsequent data analysis and processing, thereby reducing the complexity and noise interference of the hierarchical medical data, wherein the data characteristics are data characterization in the hierarchical medical data.
In the embodiment of the present invention, the feature extraction of the hierarchical medical data to obtain data features includes: extracting features from each piece of sub data in the hierarchical medical data to obtain sub data features, calculating feature non-purity corresponding to the sub data features, performing feature screening processing on the sub data features according to the feature non-purity to obtain target sub data features, performing feature dimension reduction processing on the target sub data features to obtain dimension reduction sub data features, analyzing feature linear relations among the dimension reduction sub data features, distributing linear weights of the dimension reduction sub data features according to the feature linear relations, and performing linear weighted summation on the dimension reduction sub data features according to the linear weights to obtain data features corresponding to the hierarchical medical data.
The characteristic feature is a data representation of each piece of sub-data in the hierarchical medical data, the feature non-purity represents a feature confusion degree corresponding to the sub-data feature, the dimension reduction sub-data feature is a feature obtained after the target sub-data feature is reduced from a high latitude to a low latitude, the feature linear relationship is a relationship existing between the dimension reduction sub-data features, and the linear weight is an importance degree of the corresponding relationship between the dimension reduction sub-data features.
Further, in the embodiment of the present invention, feature extraction on each sub-data in the hierarchical medical data may be implemented by a principal component analysis method, feature non-purity corresponding to the sub-data feature is calculated, feature screening processing is performed on the sub-data feature according to the feature non-purity to obtain a target sub-data feature, feature dimension reduction processing is performed on the target sub-data feature to obtain a dimension reduction sub-data feature, a feature linear relationship between the dimension reduction sub-data features is analyzed, a linear weight of the dimension reduction sub-data feature is allocated according to the feature linear relationship, and linear weighted summation is performed on the dimension reduction sub-data feature according to the linear weight to obtain a data feature corresponding to the hierarchical medical data.
Further, as an optional embodiment of the present invention, the calculating the feature non-purity corresponding to the sub-data feature includes:
calculating the feature non-purity corresponding to the sub-data feature by the following formula:wherein G represents the feature non-purity corresponding to the sub-data feature, d represents the feature sequence number of the sub-data feature, (-) -A>Feature quantity representing sub-data features, +.>And representing the misclassification probability corresponding to the d-th feature in the sub-data features.
In the embodiment of the invention, by combining the medical attributes and the data characteristics, the digital association tag corresponding to the hierarchical medical data is constructed, so that the association relationship between the hierarchical medical data can be established, the related data can be conveniently and quickly processed in the subsequent data processing, wherein the medical attributes are medical properties corresponding to the hierarchical medical data, such as diagnostic data type or personal information type, the digital association tag is a digital identifier between the hierarchical medical data, the rapid exchange is convenient in the subsequent data interaction, and optionally, the analysis of the medical attributes corresponding to the hierarchical medical data can be realized through an alpha-confidence coefficient method.
In the embodiment of the present invention, the constructing, by combining the medical attribute and the data feature, a digital association tag corresponding to the hierarchical medical data includes: calculating attribute hash values corresponding to the medical attributes, generating attribute digests corresponding to the medical attributes according to the attribute hash values, calculating the digest association degree between the attribute digests, extracting the association digests corresponding to the hierarchical medical data from the attribute digests according to the digest association degree, calculating the feature confidence degree between the data features, setting digital labels corresponding to the hierarchical medical data according to the feature confidence degree, and constructing the digital association labels corresponding to the hierarchical medical data by combining the association digests and the digital labels.
The attribute hash value is a value of a fixed length generated after the medical attribute is subjected to hash calculation, the attribute digest is summary information generated by the attribute hash value, the summary association degree represents the association degree between the attribute digests, the association digest is summary information with the largest association degree in the attribute digests, the feature confidence degree represents the credibility between the data features, and the digital label is a digital identification of the credibility corresponding to the hierarchical medical data.
Optionally, calculating the attribute hash value corresponding to the medical attribute may be implemented by a hash algorithm, for example, SHA-256 algorithm, generating an attribute digest corresponding to the medical attribute may be implemented by a hash function, for example, MD5 function, calculating a digest association degree between the attribute digests may be implemented by pearson coefficient method, extracting an association digest corresponding to the hierarchical medical data from the attribute digests may be implemented by left function, calculating a feature confidence degree between the data features may be implemented by a confidence degree calculator, the confidence degree calculator is compiled by Java language, a digital tag corresponding to the hierarchical medical data may be set according to a value of the feature confidence degree, for example, the confidence degree is 80%, the digital tag may be set to 0.8, and the association digest and the digital tag may be combined together to obtain a digital association tag corresponding to the hierarchical medical data, for example, the guardian medical data-5, representing guardian related data of the patient, and 5 representing a data association level in guardian.
According to the invention, the data exchange link of the hierarchical medical data is created according to the digital association tag, so that data interaction can be rapidly performed during subsequent data processing, and the data processing efficiency is improved, wherein the data exchange link is a corresponding transmission channel during data interaction of the hierarchical medical data.
As one embodiment of the present invention, the creating a data exchange link of the hierarchical medical data according to the digital association tag includes: analyzing a data structure of each sub-data in the hierarchical medical data, setting a link bandwidth of each sub-data in the hierarchical medical data according to the data structure, calculating a data transmission delay corresponding to each sub-data in the hierarchical medical data, determining a link delay of each sub-data in the hierarchical medical data according to the data transmission delay, acquiring a data transmission protocol corresponding to each sub-data in the hierarchical medical data, setting a link protocol of each sub-data in the hierarchical medical data according to the data transmission protocol, and creating a data exchange link of the hierarchical medical data by combining the digital association tag, the link bandwidth, the link delay and the link protocol.
The data structure is a data composition architecture of each sub-data in the hierarchical medical data, the link bandwidth is the data quantity which can be accommodated by a link when the hierarchical medical data performs data interaction, the data transmission delay is the data transmission time of each sub-data in the hierarchical medical data, the link delay is the link transmission delay time when the hierarchical medical data performs data interaction, the data transmission protocol is the transmission rule when the hierarchical medical data performs data interaction, and the link protocol is the comprehensive transmission rule when the hierarchical medical data performs data interaction.
Optionally, the analyzing the data structure of each sub-data in the hierarchical medical data may be implemented by a syntax analysis method, each sub-data in the hierarchical medical data may be determined by a capacity required by the data structure, a data transmission delay corresponding to each sub-data in the hierarchical medical data may be calculated by a ping command method, a link delay of each sub-data in the hierarchical medical data may be set by sequentially staggering according to a duration and a value of the data transmission delay, a data transmission protocol corresponding to each sub-data in the hierarchical medical data may be obtained by a network analysis tool, for example, a Wireshark tool, may be implemented by using the data transmission protocol as a link protocol of each sub-data in the hierarchical medical data, and combining the link bandwidth, the link delay and the link protocol according to the digital association tag, respectively, to create a switching link of corresponding association data in the hierarchical medical data.
The computing resource cluster module 103 is configured to schedule cloud computing resources corresponding to the medical data, collect resource information corresponding to the cloud computing resources, where the resource information includes: and carrying out resource cluster processing on the resources in the cloud computing resources according to the resource parameter information to obtain cluster resources.
According to the embodiment of the invention, the use condition and the availability of each resource of the cloud computing resource can be known by collecting the resource information corresponding to the cloud computing resource, so that the resource is effectively planned and allocated, the resource utilization rate is improved, the resource overload or idle is avoided, wherein the cloud computing resource is software and hardware required by the medical data processing, the resource information is basic information such as state information corresponding to the cloud computing resource, optionally, the cloud computing resource corresponding to the medical data can be scheduled through a resource scheduling algorithm such as a shortest job priority algorithm, the resource information corresponding to the cloud computing resource can be collected through an information collecting tool, and the information collecting tool is compiled by a programming language.
According to the method and the device for setting the follow-up resource allocation scheme, the resource idle rate corresponding to each resource in the cloud computing resources is calculated according to the resource use information, so that the idle degree of each resource in the cloud computing resources can be known, and the setting accuracy of the follow-up resource allocation scheme is further improved, wherein the resource idle rate represents the resource remaining amount corresponding to each resource in the cloud computing resources.
As an embodiment of the present invention, the calculating, according to the resource usage information, a resource idle rate corresponding to each resource in the cloud computing resources includes: determining occupied resources in the cloud computing resources according to the resource usage information, inquiring occupied configuration corresponding to the occupied resources, calculating configuration weights corresponding to the occupied configuration, scheduling resource logs corresponding to the cloud computing resources, calculating request resource amounts corresponding to the cloud computing resources according to the resource logs, calculating available configurations corresponding to the cloud computing resources according to the request resource amounts and the occupied configuration, calculating residual computing power corresponding to each resource in the cloud computing resources according to the available configurations and the configuration weights, and determining resource idle rates corresponding to each resource in the cloud computing resources according to the residual computing power.
The occupied resource is a resource which is already used in the cloud computing resource, the occupied configuration is a use configuration corresponding to the occupied resource, such as a memory configuration, the configuration weight represents an importance degree corresponding to the occupied configuration, the resource log is log information of resource use condition and activity corresponding to the cloud computing resource, the request resource amount is a resource consumption corresponding to a task to be processed in the cloud computing resource, the available configuration is a residual available idle resource in the cloud computing resource, and the residual computing power is a residual computing performance corresponding to each resource in the cloud computing resource.
Optionally, the occupied resources in the cloud computing resources may be determined by recording the usage resources in the resource usage information, the configuration weight corresponding to the occupied configuration may be obtained by calculating by a BP neural network, a resource log corresponding to the cloud computing resources may be scheduled by a scheduling tool, the scheduling tool is compiled by a scripting language, a waiting processing task is determined according to a waiting computation request in the resource log, a required resource corresponding to the waiting processing task is calculated, a request resource amount corresponding to the cloud computing resources is obtained according to the required resource, a request configuration corresponding to the request resource amount is determined, and the available configuration corresponding to the cloud computing resources is calculated by combining the request configuration and the occupied configuration, so that a resource idle rate corresponding to each resource in the cloud computing resources may be determined by calculating a ratio of the remaining computation power to a total computation power corresponding to each resource in the cloud computing resources.
Optionally, as an optional embodiment of the present invention, the calculating, according to the available configuration and the configuration weight, a remaining computing power corresponding to each resource in the cloud computing resources includes:
calculating the residual computing power corresponding to each resource in the cloud computing resources through the following formula:wherein M represents the residual computing power corresponding to each resource in the cloud computing resources, +.>Configuration weights representing software resources of the ith configuration in the available configurations, +.>Representing the remaining computational capacity of the software resource in the ith configuration,/>Representing the configuration weight of the hardware resource in the ith configuration of the available configurations,/or->Representing the remaining computational capacity of the hardware resources in the i-th configuration, i representing the serial number of the available configuration.
According to the method and the device, the resources in the cloud computing resources are subjected to resource clustering according to the resource parameter information, so that a plurality of resources in the cloud computing resources can be integrated and shared, the utilization rate of the resources is improved, the resources can be dynamically allocated and regulated according to actual requirements, and the requirements of users can be better met by the resources, wherein the clustered resources are obtained after the resources in the cloud computing resources are integrated together, and optionally, the resources in the cloud computing resources can be subjected to resource clustering according to the information similarity by calculating the information similarity in the resource parameter information.
The medical data processing module 104 is configured to set a resource allocation scheme of the hierarchical medical data according to the resource idle rate, the cluster resources and the data exchange link, and perform data processing of the hierarchical medical data according to the resource allocation scheme, so as to obtain a processing result.
According to the invention, the resource allocation scheme of the hierarchical medical data is set according to the resource idle rate, the cluster resources and the data exchange link, so that the hierarchical medical data can be rapidly processed, and the data processing efficiency is improved, wherein the resource allocation scheme is an optimal resource allocation strategy corresponding to the hierarchical medical data when being processed, alternatively, the resource allocation scheme for setting the hierarchical medical data can be realized through a scheme generator, and the scheme generator is compiled by a programming language.
In the embodiment of the invention, the data label of each sub-data in the medical data can be extracted, so that the data information corresponding to the medical data is known, the subsequent data hierarchy classification processing is facilitated. Therefore, the medical digital system based on the cloud computing cluster can improve the processing efficiency of medical digital based on the cloud computing cluster.
Referring to fig. 2, a flow chart of a medical digital method based on a cloud computing cluster according to an embodiment of the invention is shown. In this embodiment, the medical digital method based on the cloud computing cluster includes:
acquiring medical data to be processed, extracting a data tag of each sub-data in the medical data, and carrying out data hierarchy classification processing on the medical data by combining the data tags to obtain hierarchical medical data;
extracting features of the hierarchical medical data to obtain data features, analyzing medical attributes corresponding to the hierarchical medical data, constructing a digital association tag corresponding to the hierarchical medical data by combining the medical attributes and the data features, and creating a data exchange link of the hierarchical medical data according to the digital association tag;
scheduling cloud computing resources corresponding to the medical data, and collecting resource information corresponding to the cloud computing resources, wherein the resource information comprises: resource utilization information and resource parameter information, calculating a resource idle rate corresponding to each resource in the cloud computing resources according to the resource utilization information, and carrying out resource cluster processing on the resources in the cloud computing resources according to the resource parameter information to obtain cluster resources;
Setting a resource allocation scheme of the hierarchical medical data according to the resource idle rate, the cluster resources and the data exchange link, and executing data processing of the hierarchical medical data according to the resource allocation scheme to obtain a processing result.
Fig. 3 is a schematic structural diagram of an electronic device for implementing a medical digital system based on a cloud computing cluster according to an embodiment of the present invention.
The electronic device 1 may comprise a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may further comprise a computer program stored in the memory 11 and executable on the processor 10, such as a medical digital program based on a cloud computing cluster.
The processor 10 may be formed by an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or may be formed by a plurality of integrated circuits packaged with the same function or different functions, including one or more central processing units (Central Processing Unit, CPU), a microprocessor, a digital processing chip, a graphics processor, a combination of various control chips, and so on. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects various components of the entire electronic device using various interfaces and lines, executes programs or modules stored in the memory 11 (for example, executes a medical digital program based on a cloud computing cluster, etc.), and invokes data stored in the memory 11 to perform various functions of the electronic device and process the data.
The memory 11 includes at least one type of readable storage medium including flash memory, a removable hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device, such as a mobile hard disk of the electronic device. The memory 11 may in other embodiments also be an external storage device of the electronic device, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the electronic device. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device. The memory 11 may be used not only for storing application software installed in an electronic device and various types of data, such as codes of medical digital programs based on cloud computing clusters, but also for temporarily storing data that has been output or is to be output.
The communication bus 12 may be a peripheral component interconnect standard (Peripheral Component Interconnect, PCI) bus, or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. The bus is arranged to enable a connection communication between the memory 11 and at least one processor 10 etc.
The communication interface 13 is used for communication between the electronic device and other devices, including a network interface and a user interface. Optionally, the network interface may include a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), typically used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a Display (Display), an input unit such as a Keyboard (Keyboard), or alternatively a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the electronic device and for displaying a visual user interface.
Fig. 3 shows only an electronic device with components, it being understood by a person skilled in the art that the structure shown in fig. 3 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than shown, or may combine certain components, or may be arranged in different components.
For example, although not shown, the electronic device may further include a power source (such as a battery) for supplying power to the respective components, and preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management, and the like are implemented through the power management device. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure detection circuit, power converter or inverter, power status indicator, etc. The electronic device may further include various sensors, bluetooth modules, wi-Fi modules, etc., which are not described herein.
It should be understood that the embodiments described are for illustrative purposes only and are not limited to this configuration in the scope of the patent application.
The cloud computing cluster based medical digital program stored in the memory 11 in the electronic device 1 is a combination of instructions that, when executed in the processor 10, may implement:
acquiring medical data to be processed, extracting a data tag of each sub-data in the medical data, and carrying out data hierarchy classification processing on the medical data by combining the data tags to obtain hierarchical medical data;
Extracting features of the hierarchical medical data to obtain data features, analyzing medical attributes corresponding to the hierarchical medical data, constructing a digital association tag corresponding to the hierarchical medical data by combining the medical attributes and the data features, and creating a data exchange link of the hierarchical medical data according to the digital association tag;
scheduling cloud computing resources corresponding to the medical data, and collecting resource information corresponding to the cloud computing resources, wherein the resource information comprises: resource utilization information and resource parameter information, calculating a resource idle rate corresponding to each resource in the cloud computing resources according to the resource utilization information, and carrying out resource cluster processing on the resources in the cloud computing resources according to the resource parameter information to obtain cluster resources;
setting a resource allocation scheme of the hierarchical medical data according to the resource idle rate, the cluster resources and the data exchange link, and executing data processing of the hierarchical medical data according to the resource allocation scheme to obtain a processing result.
In particular, the specific implementation method of the above instructions by the processor 10 may refer to the description of the relevant steps in the corresponding embodiment of the drawings, which is not repeated herein.
Further, the modules/units integrated in the electronic device 1 may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as separate products. The computer readable storage medium may be volatile or nonvolatile. For example, the computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM).
In the several embodiments provided by the present invention, it should be understood that the disclosed systems, methods, and apparatus may be implemented in other ways. For example, the system embodiments described above are merely illustrative, e.g., the division of the modules is merely a logical function division, and other manners of division may be implemented in practice.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof.
The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. Multiple units or systems as set forth in the system claims may also be implemented by means of one unit or system in software or hardware. The terms first, second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (10)
1. A cloud computing cluster-based medical digital system, the cloud computing cluster-based medical digital system comprising:
the medical data analysis module is used for acquiring medical data to be processed, extracting a data tag of each piece of sub-data in the medical data, and carrying out data hierarchy classification processing on the medical data by combining the data tags to obtain hierarchical medical data;
the digital identification construction module is used for carrying out feature extraction on the hierarchical medical data to obtain data features, analyzing medical attributes corresponding to the hierarchical medical data, constructing a digital association tag corresponding to the hierarchical medical data by combining the medical attributes and the data features, and creating a data exchange link of the hierarchical medical data according to the digital association tag;
The computing resource cluster module is used for scheduling cloud computing resources corresponding to the medical data, collecting resource information corresponding to the cloud computing resources, wherein the resource information comprises: resource utilization information and resource parameter information, calculating a resource idle rate corresponding to each resource in the cloud computing resources according to the resource utilization information, and carrying out resource cluster processing on the resources in the cloud computing resources according to the resource parameter information to obtain cluster resources;
and the medical data processing module is used for setting a resource allocation scheme of the hierarchical medical data according to the resource idle rate, the cluster resources and the data exchange link, and executing data processing of the hierarchical medical data according to the resource allocation scheme to obtain a processing result.
2. The cloud computing cluster-based medical digital system of claim 1, wherein the performing data hierarchy classification processing on the medical data in combination with the data tag to obtain hierarchical medical data comprises:
performing data cleaning treatment on the medical data to obtain target medical data;
calculating a label weight coefficient corresponding to each sub-label in the data label, and extracting a characteristic label in the data label according to the label weight coefficient;
Calculating a support coefficient between each sub-label in the characteristic labels, and calculating a data entropy value corresponding to the target medical data;
and carrying out data hierarchy classification processing on the target medical data according to the data entropy value and the support coefficient to obtain hierarchical medical data.
3. The cloud computing cluster-based medical digital system of claim 2, wherein the computing support coefficients between each sub-label in the feature label comprises:
calculating a support coefficient between each sub-label in the feature label by the following formula:wherein A represents the support coefficient between each sub-tag in the feature tag, < >>The number of sub-tags representing the feature tag, a represents the tag serial number of the feature tag,/or->Representing the tag length between the a-th tag and the a+1-th tag in the feature tag,/->Probability value representing the occurrence of the a-th tag of the feature tags,>a probability value representing the occurrence of the (a+1) th tag among the feature tags.
4. The cloud computing cluster-based medical digital system of claim 2, wherein the computing the data entropy value corresponding to the target medical data comprises:
calculating a data entropy value corresponding to the target medical data through the following formula: Wherein H represents the data entropy corresponding to the target medical data, b represents the serial number of the target medical data,/-the target medical data>Data quantity representing target medical data, +.>A set of random variables representing the a-th data of the target medical data,/a>Representing probabilities corresponding to a set of random variables for the a-th data in the target medical data.
5. The cloud computing cluster-based medical digital system of claim 1, wherein the feature extraction of the hierarchical medical data to obtain data features comprises:
extracting features of each piece of sub data in the hierarchical medical data to obtain sub data features, and calculating feature unreliability corresponding to the sub data features through the following formula:wherein G represents the feature non-purity corresponding to the sub-data feature, d represents the feature sequence number of the sub-data feature, (-) -A>Feature quantity representing sub-data features, +.>Representing misclassification probability corresponding to the d-th feature in the sub-data features;
according to the characteristic non-purity, carrying out characteristic screening treatment on the sub-data characteristics to obtain target sub-data characteristics;
performing feature dimension reduction processing on the target sub-data features to obtain dimension reduction sub-data features, and analyzing feature linear relations among the dimension reduction sub-data features;
According to the characteristic linear relation, distributing the linear weight of the dimension reduction sub-data characteristic;
and carrying out linear weighted summation on the dimension reduction sub-data features according to the linear weight to obtain data features corresponding to the hierarchical medical data.
6. The cloud computing cluster-based medical digital system of claim 1, wherein the constructing a digital association tag corresponding to the hierarchical medical data in combination with the medical attribute and the data feature comprises:
calculating an attribute hash value corresponding to the medical attribute, and generating an attribute abstract corresponding to the medical attribute according to the attribute hash value;
calculating the abstract association degree between the attribute abstracts, and extracting the associated abstracts corresponding to the hierarchical medical data from the attribute abstracts according to the abstract association degree;
calculating feature confidence coefficient between the data features, and setting digital labels corresponding to the hierarchical medical data according to the feature confidence coefficient;
and combining the association abstract and the digital label to construct the digital association label corresponding to the hierarchical medical data.
7. The cloud computing cluster-based medical digital system of claim 1, wherein said creating a data exchange link for the hierarchical medical data from the digital association labels comprises:
Analyzing the data structure of each piece of sub-data in the hierarchical medical data, and setting the link bandwidth of each piece of sub-data in the hierarchical medical data according to the data structure;
calculating data transmission delay corresponding to each piece of sub-data in the hierarchical medical data, and determining link delay of each piece of sub-data in the hierarchical medical data according to the data transmission delay;
acquiring a data transmission protocol corresponding to each piece of sub-data in the hierarchical medical data, and setting a link protocol of each piece of sub-data in the hierarchical medical data according to the data transmission protocol;
a data exchange link of the hierarchical medical data is created in conjunction with the digital association tag, the link bandwidth, the link latency, and the link protocol.
8. The cloud computing cluster-based medical digital system of claim 1, wherein the computing a resource idleness corresponding to each of the cloud computing resources according to the resource usage information comprises:
determining occupied resources in the cloud computing resources according to the resource use information;
inquiring the occupation configuration corresponding to the occupied resource, and calculating the configuration weight corresponding to the occupation configuration;
Scheduling a resource log corresponding to the cloud computing resource, and calculating the request resource quantity corresponding to the cloud computing resource according to the resource log;
calculating available configuration corresponding to the cloud computing resource according to the request resource quantity and the occupied configuration;
according to the available configuration and the configuration weight, calculating the residual computing power corresponding to each resource in the cloud computing resources through the following formula:wherein M represents the residual computing power corresponding to each resource in the cloud computing resources, +.>Configuration weights representing software resources of the ith configuration in the available configurations, +.>Representing the remaining computational capacity of the software resource in the ith configuration,/>Representing the configuration weights of the hardware resources in the ith configuration of the available configurations,representing the remaining computational capacity of the hardware resource in the ith configuration, i representing the serial number of the available configuration;
and determining the resource idle rate corresponding to each resource in the cloud computing resources according to the residual computing power.
9. A cloud computing cluster-based medical digital method, the method comprising:
acquiring medical data to be processed, extracting a data tag of each sub-data in the medical data, and carrying out data hierarchy classification processing on the medical data by combining the data tags to obtain hierarchical medical data;
Extracting features of the hierarchical medical data to obtain data features, analyzing medical attributes corresponding to the hierarchical medical data, constructing a digital association tag corresponding to the hierarchical medical data by combining the medical attributes and the data features, and creating a data exchange link of the hierarchical medical data according to the digital association tag;
scheduling cloud computing resources corresponding to the medical data, and collecting resource information corresponding to the cloud computing resources, wherein the resource information comprises: resource utilization information and resource parameter information, calculating a resource idle rate corresponding to each resource in the cloud computing resources according to the resource utilization information, and carrying out resource cluster processing on the resources in the cloud computing resources according to the resource parameter information to obtain cluster resources;
setting a resource allocation scheme of the hierarchical medical data according to the resource idle rate, the cluster resources and the data exchange link, and executing data processing of the hierarchical medical data according to the resource allocation scheme to obtain a processing result.
10. An electronic device, the electronic device comprising:
at least one processor; the method comprises the steps of,
A memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the cloud computing cluster-based medical digital method as recited in claim 9.
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