CN112990767B - Vertical consumption medical SaaS production data calculation method, system, terminal and medium - Google Patents
Vertical consumption medical SaaS production data calculation method, system, terminal and medium Download PDFInfo
- Publication number
- CN112990767B CN112990767B CN202110421503.6A CN202110421503A CN112990767B CN 112990767 B CN112990767 B CN 112990767B CN 202110421503 A CN202110421503 A CN 202110421503A CN 112990767 B CN112990767 B CN 112990767B
- Authority
- CN
- China
- Prior art keywords
- production
- saas
- data
- platform
- classification
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000004519 manufacturing process Methods 0.000 title claims abstract description 351
- 238000004364 calculation method Methods 0.000 title claims abstract description 26
- 238000012795 verification Methods 0.000 claims abstract description 75
- 238000013139 quantization Methods 0.000 claims abstract description 37
- 230000012010 growth Effects 0.000 claims description 24
- 239000002131 composite material Substances 0.000 claims description 23
- 238000011002 quantification Methods 0.000 claims description 22
- 238000000034 method Methods 0.000 claims description 19
- 230000015654 memory Effects 0.000 claims description 15
- 238000004590 computer program Methods 0.000 claims description 9
- 238000013524 data verification Methods 0.000 claims description 9
- 210000001503 joint Anatomy 0.000 claims 2
- 238000011161 development Methods 0.000 description 15
- 230000018109 developmental process Effects 0.000 description 15
- 230000008859 change Effects 0.000 description 8
- 238000011160 research Methods 0.000 description 7
- 238000011156 evaluation Methods 0.000 description 6
- 238000010586 diagram Methods 0.000 description 5
- 230000006870 function Effects 0.000 description 4
- 238000007726 management method Methods 0.000 description 3
- 238000013499 data model Methods 0.000 description 2
- 238000011835 investigation Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 238000010606 normalization Methods 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 230000003698 anagen phase Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 238000005192 partition Methods 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000012358 sourcing Methods 0.000 description 1
- 230000001360 synchronised effect Effects 0.000 description 1
- 238000012384 transportation and delivery Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/067—Enterprise or organisation modelling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/23—Updating
- G06F16/2365—Ensuring data consistency and integrity
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/25—Integrating or interfacing systems involving database management systems
- G06F16/254—Extract, transform and load [ETL] procedures, e.g. ETL data flows in data warehouses
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0637—Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- Theoretical Computer Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Economics (AREA)
- Educational Administration (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Development Economics (AREA)
- Databases & Information Systems (AREA)
- Game Theory and Decision Science (AREA)
- Marketing (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Computer Security & Cryptography (AREA)
- Medical Treatment And Welfare Office Work (AREA)
Abstract
The invention relates to a vertical consumption medical SaaS production data calculation method, a system, a terminal and a medium, which are characterized in that the data type of production data is determined based on a linear time line, the production data is standardized into one or more SaaS production classification items and one or more self-sufficient platform production classification items, and a standardized quantization result corresponding to each production classification item is obtained; verifying quality elements of the standardized quantization result; if the verification is successful, a SaaS production business classification combination and a platform business classification combination which are respectively formed by classification items required by each SaaS business closed loop and classification items required by each platform business closed loop are obtained, and finally, business indexes of the configured SaaS production business classification combination and platform business classification combination are calculated.
Description
Technical Field
The invention relates to the field of vertical consumption medical SaaS (software as a service) service, in particular to a method, a system, a terminal and a medium for calculating vertical consumption medical SaaS production data.
Background
Today, the consumer medical industry is facing a fast growing phase, and in the face of a constantly changing environment, the upgrading of consumption, capital attention and internet surge are all pushing it to an unprecedented level. The consumption medical software is produced at the same time, the software is a service product, and the production quantitative standard of a software body is data production capacity and data quality.
The traditional consumption medical software has the following problems in data production: 1, service logic and data architecture change according to different clients, service and data architecture standardization cannot be achieved, and data quality is uneven; 2. data cannot be summarized in real time or high-cost implementation is invested; 3. medical data cannot be effectively monitored and protected and cannot be used for industry evaluation; 4. socialized and mobilization data is lacking. 5. And can not be used for industry evaluation and policy research.
Therefore, the data standardization cannot be completed through the traditional software, and the universal quantitative calculation cannot be performed through the data analysis mode of the traditional medical software. The problem of traditional software production also leads to inaccurate, asymmetric and untimely information acquired by related institutions and industry/field professional institutions, and inconsistent statistical calibers, so that the development conditions of industries and individual enterprises cannot be accurately evaluated according to data calculation.
Disclosure of Invention
In view of the above disadvantages of the prior art, an object of the present invention is to provide a method, a system, a terminal, and a medium for calculating vertical consumption medical SaaS production data, which are used to solve the problems that the conventional software in the prior art cannot achieve standardization of medical data and real-time summarization or invest high cost, lacks references of socialized and mobile data, and has inaccurate, asymmetric, and untimely acquired information, inconsistent statistical calibers, and cannot accurately evaluate the development conditions of industries and individual enterprises according to data calculation.
In order to achieve the above objects and other related objects, the present invention provides a method for calculating vertical consumer medical SaaS production data, the method comprising: respectively determining the data types of the SaaS production data and the self-sufficient platform production data based on the linear timeline; respectively standardizing the SaaS production data and the self-sufficient platform production data into one or more SaaS production classification items and one or more self-sufficient platform production classification items based on the determined data types, and obtaining standardized quantification results respectively corresponding to the SaaS production classification items and the respective self-sufficient platform production classification items; verifying quality elements corresponding to each SaaS production classification item and the standardized quantization result of each platform production classification item respectively to obtain a verification result corresponding to verification success or verification failure; if the verification is successful, respectively configuring each SaaS production classification item and each given platform production classification item into a classification item required by the SaaS service closed loop and a classification item required by the platform service closed loop based on the vertical consumption medical standard, and obtaining a SaaS production service classification combination and a platform service classification combination which are respectively formed by each SaaS service closed loop required classification item and each platform service closed loop required classification item; and respectively calculating the service indexes of the configured SaaS production service classification combination and platform service classification combination.
In an embodiment of the present invention, the data types of the SaaS production data include: one or more of SaaS functional interface calling data, SaaS system log data, SaaS service data and SaaS flow data; and/or the data types of the self-sufficient platform production data comprise: the self-sufficient platform functional interface calls one or more of data, self-sufficient platform log data, self-sufficient platform business data, and self-sufficient platform traffic data.
In an embodiment of the invention, the normalizing the SaaS production data and the self-sufficient platform production data into one or more SaaS production classification items and one or more self-sufficient platform production classification items respectively based on the determined data types and obtaining the normalized quantification results respectively corresponding to each SaaS production classification item and each self-sufficient platform production classification item includes: standardizing the SaaS production data and the self-sufficient platform data into one or more SaaS production classification items and one or more self-sufficient platform production classification items according to classification item labels of the SaaS production data and the self-sufficient platform data respectively based on the data types of the SaaS production data and the self-sufficient platform production data; and respectively obtaining standardized quantitative results corresponding to each SaaS production classification item and each platform production classification item according to the SaaS production classification items and each platform production classification item.
In an embodiment of the present invention, the verifying the quality elements corresponding to the standardized quantization results of each SaaS production classification item and each platform production classification item respectively includes: and respectively verifying quality elements corresponding to each SaaS production classification item and the standardized quantization result of each platform production classification item based on CAP theorem to obtain a verification result corresponding to verification success or verification failure. Wherein, the quality elements corresponding to the standardized result of each SaaS production classification item comprise: one or more elements of consistency, accuracy, time linear integrity and attribute correctness; and/or the quality elements corresponding to the standardized quantification results of the production classification items of the platform respectively comprise: one or more of logical consistency, topological consistency, associative consistency, attribute correctness, and time-linear integrity elements.
In an embodiment of the present invention, the classification items required by the SaaS service closed loop include: classification items required by basic service closed loop and classification items required by extended service closed loop; the classification items required by each basic service closed loop form a basic service combination and the classification items required by each extended service closed loop form an extended service combination.
In an embodiment of the present invention, the service index includes: one or more of a productivity index, a composite growth rate, and a productivity weight; wherein the productivity index, composite growth rate, and productivity weight are associated with the linear timeline.
In an embodiment of the present invention, the method further includes: if the verification fails, respectively re-determining the SaaS production data and the data types of the self-sufficient platform production data based on the linear timeline; respectively standardizing the SaaS production data and the self-sufficient platform production data into one or more SaaS production classification items and one or more self-sufficient platform production classification items based on the determined data types, and obtaining standardized quantification results respectively corresponding to the SaaS production classification items and the respective self-sufficient platform production classification items; verifying quality elements corresponding to each SaaS production classification item and the standardized quantization result of each platform production classification item respectively to obtain a verification result corresponding to verification success or verification failure; if the verification is successful, respectively configuring each SaaS production classification item and each given platform production classification item into a classification item required by the SaaS service closed loop and a classification item required by the platform service closed loop based on the vertical consumption medical standard, and obtaining a SaaS production service classification combination and a platform service classification combination which are respectively formed by each SaaS service closed loop required classification item and each platform service closed loop required classification item; and respectively calculating the service indexes of the configured SaaS production service classification combination and platform service classification combination.
To achieve the above and other related objects, the present invention provides a vertical consumer medical SaaS production data calculation system, comprising: the type determining module is used for respectively determining the data types of the SaaS production data and the self-sufficient platform production data based on the linear time line; the data standardization module is connected with the type determination module and is used for respectively standardizing the SaaS production data and the self-sufficient platform production data into one or more SaaS production classification items and one or more self-sufficient platform production classification items based on the determined data types and obtaining standardization quantification results respectively corresponding to each SaaS production classification item and each self-sufficient platform production classification item; the data verification module is connected with the data standardization module and is used for verifying quality elements corresponding to each SaaS production classification item and the standardized quantization result of each platform production classification item so as to obtain a verification result corresponding to verification success or verification failure; the service configuration module is connected with the data verification module and is used for respectively configuring each SaaS production classification item and each given platform production classification item into a classification item required by the closed loop of the SaaS service and a classification item required by the closed loop of the platform service on the basis of the vertical consumption medical standard under the condition of successful verification, and obtaining a SaaS production service classification combination and a platform service classification combination which are respectively formed by the classification item required by the closed loop of each SaaS service and the classification item required by the closed loop of each platform service; and the calculation module is connected with the service configuration module and is used for respectively calculating the service indexes of the configured SaaS production service classification combination and platform service classification combination.
To achieve the above and other related objects, the present application provides a vertical consumer medical SaaS production data computing terminal, including: one or more memories for storing computer programs; one or more processors for executing the vertical consumption medical SaaS production data calculation method.
In order to achieve the above and other related objects, the present application provides a computer storage medium storing a computer program, where the computer program is executed to implement the method for calculating vertical consumer medical SaaS production data.
As described above, the present invention is a vertical consumer medical SaaS production data calculation method, system, terminal, and medium, and has the following beneficial effects:
1. the industry standard definition is converged, and the data architecture and the data quality are standardized;
2. enterprise clients can gather related data in real time to carry out enterprise operation per se;
3. the medical data model is unified, and better monitoring protection is obtained;
4. integrating a socialization and mobilization platform, carrying out on-line linkage on doctors and patients, and producing socialization and mobilization data;
5. can be used for industry research evaluation and policy research.
Drawings
Fig. 1 is a schematic flow chart illustrating a vertical consumer medical SaaS production data calculation method according to an embodiment of the present invention.
Fig. 2 is a schematic flow chart illustrating a vertical consumer medical SaaS production data calculation method according to an embodiment of the present invention.
Fig. 3 is a schematic structural diagram of a vertical consumer medical SaaS production data computing system according to an embodiment of the present invention.
Fig. 4 is a schematic structural diagram of a vertical consumer medical SaaS production data computing terminal according to an embodiment of the present invention.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.
Throughout the specification, when a part is referred to as being "connected" to another part, this includes not only a case of being "directly connected" but also a case of being "indirectly connected" with another element interposed therebetween. In addition, when a certain part is referred to as "including" a certain component, unless otherwise stated, other components are not excluded, but it means that other components may be included.
The terms first, second, third, etc. are used herein to describe various elements, components, regions, layers and/or sections, but are not limited thereto. These terms are only used to distinguish one element, component, region, layer or section from another element, component, region, layer or section. Thus, a first element, component, region, layer or section discussed below could be termed a second element, component, region, layer or section without departing from the scope of the present invention.
Also, as used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context indicates otherwise. It will be further understood that the terms "comprises," "comprising," and/or "comprising," when used in this specification, specify the presence of stated features, operations, elements, components, items, species, and/or groups, but do not preclude the presence, or addition of one or more other features, operations, elements, components, items, species, and/or groups thereof. The terms "or" and/or "as used herein are to be construed as inclusive or meaning any one or any combination. Thus, "A, B or C" or "A, B and/or C" means "any of the following: a; b; c; a and B; a and C; b and C; A. b and C ". An exception to this definition will occur only when a combination of elements, functions or operations are inherently mutually exclusive in some way.
The embodiment of the invention provides a vertical consumption medical SaaS production data calculation method, which solves the problems that the traditional software in the prior art cannot realize medical data standardization and real-time summarization or input high cost, does not have reference to socialized and mobile data, and cannot accurately evaluate the development conditions of industries and individual enterprises and the like due to inaccurate, asymmetrical and untimely acquired information, inconsistent statistical calibers and inaccurate data calculation. The vertical consumption medical SaaS of the invention produces novel socialized and mobile data which cannot be produced by traditional software, and unifies medical data standards; the enterprise client can not only collect relevant data in real time to carry out enterprise operation per se, but also can carry out industry investigation and evaluation and policy research on the calculation of production data.
The SaaS is a software layout model, and is designed for network delivery by application, and is convenient for users to host, deploy and access through the internet. The SaaS provider builds all network infrastructures, software and hardware operation platforms required by informatization for enterprises, is responsible for a series of services such as implementation in the early stage and maintenance in the later stage, and can use the information system through the Internet without purchasing software and hardware, building a machine room and recruiting IT personnel.
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings so that those skilled in the art can easily implement the embodiments of the present invention. The present invention may be embodied in many different forms and is not limited to the embodiments described herein.
As shown in fig. 1, a flow diagram of a vertical consumer medical SaaS production data calculation method in an embodiment of the present invention is shown.
The method comprises the following steps:
step S11: based on the linear timeline, the SaaS production data and the data categories of the production data by the self-sufficient platform are respectively determined.
Optionally, the data types of the SaaS production data generated in the SaaS production process and the self-sufficient platform production data generated in the self-sufficient platform data production process are determined based on the linear timeline, respectively.
The SaaS production process is a vertical consumption medical SaaS data production process, and the platform data production process is a socialization and mobile platform production process docked with the vertical consumption medical SaaS.
It is to be noted that the production quantification standard of the SaaS production process is based on the data volume of a linear timeline; the production quantification criteria for the self-sufficient platform data production process are based on the amount of data in the linear timeline.
Optionally, the data types of the SaaS production data include: one or more of SaaS functional interface calling data, SaaS system log data, SaaS service data and SaaS flow data; and/or the data types of the self-sufficient platform production data comprise: the self-sufficient platform functional interface calls one or more of data, self-sufficient platform log data, self-sufficient platform service data and self-sufficient platform flow data;
optionally, each SaaS production data includes: one or more of quantity information, attribute information, geographic information, data validity information, format information, and production time information.
Optionally, each self-sufficient platform production data comprises: one or more of self-sufficient platform sourcing information, quantity information, attribute information, geographic information, data validity information, format information, and production time information.
Optionally, the data types of the SaaS production data and the data produced by the self-sufficient platform are determined respectively and concurrently executed in a stream flow mode, so that the collection and classification efficiency is improved.
Step S12: respectively standardizing the SaaS production data and the self-sufficient platform production data into one or more SaaS production classification items and one or more self-sufficient platform production classification items based on the determined data types, and obtaining standardized quantification results respectively corresponding to the SaaS production classification items and the respective self-sufficient platform production classification items.
Optionally, the normalizing the SaaS production data and the self-sufficient platform production data into one or more SaaS production classification items and one or more self-sufficient platform production classification items respectively based on the determined data types, and obtaining normalized quantification results respectively corresponding to each SaaS production classification item and each self-sufficient platform production classification item includes: standardizing the SaaS production data and the self-sufficient platform data into one or more SaaS production classification items and one or more self-sufficient platform production classification items according to classification item labels of the SaaS production data and the self-sufficient platform data respectively based on the data types of the SaaS production data and the self-sufficient platform production data; and respectively obtaining standardized quantitative results corresponding to each SaaS production classification item and each platform production classification item according to the SaaS production classification items and each platform production classification item.
Specifically, based on the data type of the SaaS production data, obtaining one or more SaaS production classification items corresponding to the classification item label from the data type of the SaaS production data according to the classification item label of the SaaS production data; obtaining one or more self-sufficient platform production classification items corresponding to classification item labels of self-sufficient platform production data in a data type based on the data type of the self-sufficient platform production data according to the classification item labels of the self-sufficient platform data; respectively obtaining standardized quantitative results which are respectively corresponding to the SaaS production classification items in advance according to the SaaS production classification items; and respectively obtaining standardized quantitative results which are respectively corresponding to the respective platform production classification items in advance according to the self-sufficient platform production classification items. Wherein each category corresponds to a category item.
For example, after the SaaS-class functional interface calls the data type ETL, generating functional interface call classification items, which are denoted as n classes, and the number of linear timelines corresponding to the n classes of classification items is denoted as Qn. It should be noted that the log classification item, the traffic classification item, and the like are all classified and counted by the linear timeline in the above manner.
Or standardizing the self-supporting platform function interface calling data type ETL to generate function interface calling classification items, and marking as n types, wherein the number of linear timelines corresponding to each classification item of the n types is marked as Qn. The log classification item, the service classification item, the flow classification item and the like are classified and counted by the linear time line according to the mode.
Optionally, according to a bottom database management system used by the SaaS system, batch-wise classification and quantification of the SaaS production data and the self-sufficient platform production data based on linear time are performed;
taking a MySQL database management system as an example, the MySQL binlog operation log is extracted in real time through a real-time online log synchronous analysis tool, and the batch-by-batch classification quantification based on linear time is executed through a computer program which is generated through a flink/spark and has industry standard business logic according to a specific industry standard and a data table. Since the binlog _ format parameter in MySQL must be set to row mode and the binlog _ row _ image parameter must be set to full mode at this time, otherwise the quantization result is not accurate in the classification items.
Optionally, the normalized quantization result is written in a stream mode, and a slowly-changing dimension mode is adopted for recording the historical result set.
Step S13: and respectively verifying the quality elements corresponding to the SaaS production classification items and the standardized quantization results of the platform production classification items to obtain verification results corresponding to verification success or verification failure.
Optionally, the verifying the quality elements corresponding to the SaaS production classification items and the standardized quantization results of the platform production classification items respectively includes: and respectively verifying quality elements corresponding to each SaaS production classification item and the standardized quantization result of each platform production classification item based on CAP theorem to obtain a verification result corresponding to verification success or verification failure. Wherein, the quality elements corresponding to the standardized result of each SaaS production classification item comprise: one or more elements of consistency, accuracy, time linear integrity and attribute correctness; and/or the quality elements corresponding to the standardized quantification results of the production classification items of the platform respectively comprise: one or more of logical consistency, topological consistency, associative consistency, attribute correctness, and time-linear integrity elements.
Specifically, according to the CAP (Consistency, Availability, Partition tolerance) theorem, the quality elements of the SaaS production data classification and quantization results of the platform data classification and quantization results are verified respectively by using a data final Consistency method. For the quality elements of each SaaS production classification item, the aspects such as consistency, accuracy, time linear integrity, attribute correctness and the like are related to the aspects such as logic consistency, topological consistency, correlation consistency, attribute correctness, time linear integrity and the like of the quality elements of each platform production classification item.
Optionally, based on the verification standard, the quality elements corresponding to each SaaS production classification item and the standardized quantization results of each platform production classification item are verified respectively, and when the verification standard is met, a verification result corresponding to successful verification is obtained; otherwise, obtaining the verification result corresponding to the verification failure.
Preferably, the verification standard may set different scoring weights for each quality element, and determine whether verification is successful according to the lowest scoring standard for each element or the scoring weighted scoring for each quality element.
Step S14: if the verification is successful, based on the vertical consumption medical standard, configuring each SaaS production classification item and each given platform production classification item into a classification item required by the SaaS service closed loop and a classification item required by the platform service closed loop respectively, and obtaining a SaaS production service classification combination and a platform service classification combination formed by each SaaS production classification item required by the SaaS service closed loop and each platform service closed loop.
Optionally, if the verification is successful, configuring one or more SaaS production classification items as classification items required by a basic service closed loop based on a vertical consumption medical industry standard, and forming a basic service combination; configuring one or more SaaS production classification items into classification items required by an expanded service closed loop based on the vertical consumption medical industry standard to form an expanded service combination; based on the utilization of self-sufficient platform production data, the respective platform production classification items are configured into classification items required by platform service closed loop, and a platform service combination is formed.
Step S15: and respectively calculating the service indexes of the configured SaaS production service classification combination and platform service classification combination.
Optionally, the service index includes: one or more of a productivity index, a composite growth rate, and a productivity weight; wherein the productivity index, composite growth rate, and productivity weight are associated with the linear timeline. The enterprise client can provide data decision capability through the productivity index, the composite growth rate and the productivity weight which are calculated by different classification item combinations and by combining with advanced mathematical models and other modes, and then make timely decisions by combining with actual business conditions, so that safer, more accurate and more scientific enterprise development is carried out. When the coverage rate of vertical consumer medical SaaS in the market is high, the vertical consumer medical SaaS can represent the consumer medical industry and becomes an industry head service provider, and the comprehensive development condition and the development speed of the industry can be reflected by the productivity index and the composite growth rate probably. Similarly, under the condition of the same statistical period, the productivity indexes, the composite growth rate and the productivity weight values of different enterprise customers through the basic service combination can be quantitatively compared. And quantitatively evaluating the development conditions of the industry of different enterprise customers and different regions. For the society, the development of the industry can be known more accurately, the standardized data model is unified, better monitoring protection is obtained, the precision of industry research evaluation and policy research is higher, and the industry can obtain more policy support and capital attention.
Optionally, for the calculation of the productivity index, a base period (a period serving as a basis for comparison) and a report period (a period indicating a change condition) in the linear timeline interval are specified according to the linear timeline interval of the corresponding classification item in each classification combination;
determining the sum of the number of the corresponding classification items of the business classification item combination needing to be reflected in the base period, and recording the sum as the base period
(ii) a The weight of each classification item in the combination corresponding to the base period
(ii) a And determining the corresponding classification of the service classification item combination needing to be reflected in the report periodThe sum of the number of terms is recorded as
The weight corresponding to each classification item in the base period in the combination
And calculating the productivity index K of the business combination data:
according to the above formula, a basic service combination productivity index K1 corresponding to a basic service combination, an extended service combination productivity index K2 corresponding to an extended service combination, and a platform service combination productivity index K3 corresponding to a platform service combination can be obtained.
For example, explicitly with 2017 month 07 as the base period, the oral basic service closed loop includes: the patient classification item quantization result amount Q0pn, weight W0pn, reservation classification item quantization result Q0an, weight W0an, charge quantization result amount Q0cn, weight W0 cn. Specifically, with 2017, 8 months as a report period, the oral basic service closed loop comprises: the patient classification item quantitative result Q1pn with weight W1pn, the reservation classification item quantitative result Q1an, the fee-based quantitative result Q1cn with weight W1pn, and the weight W1 pn. The K value that can be obtained to the report period of 8 months in 2017 is calculated using equation (1).
Optionally, for the composite growth rate, according to the continuous linear timeline change or the asynchronous long-value time change interval, the report period results k (t) of each service index at different time nodes t may be obtained, and the composite growth rate GR of k (t) is calculated:
according to the above formula, a basic service combination composite growth rate GR1 corresponding to a basic service combination, an extended service combination composite growth rate GR2 corresponding to an extended service combination, and a platform service combination composite growth rate GR3 corresponding to a platform service combination can be obtained, and the development condition of an enterprise client can be determined according to the composite growth rate. And quantitatively evaluating the development of the enterprise client in different periods.
For example, with 7 months in 2017 as a base period and 8 months in 2017 as a report period, the growth rate of 8 months can be obtained by formula (2).
Optionally, for the productivity weight, since the data is from data production of vertical consumption medical SaaS, the basic service, the extended service, and the platform service classification item data elements of the productivity weight include attributes of tenants, and each service combination productivity weight aix can be calculated by separately using specific tenants and different geographic attributes:
based on the productivity weight aix, various business customers may be quantitatively evaluated, wherein,
the corresponding weight in the combination in the report period is used for each classification item.
According to the above formula, a productivity weight aix1 of the basic service combination corresponding to the basic service combination, a productivity weight aix2 of the extended service combination corresponding to the extended service combination, and a productivity weight aix3 of the platform service combination corresponding to the platform service combination can be obtained.
For example, taking 2017 month as the base period, and 2017 month 8 as the report period, the productivity score of 8 months is calculated by the weight:
wherein, the oral basic service closed loop specifically takes 2017, month 07 as a base period, and comprises the following steps: the patient classification item quantization result amount Q0pn, weight W0pn, reservation classification item quantization result Q0an, weight W0an, charge quantization result amount Q0cn, weight W0 cn. Specifically, with 2017, 8 months as a report period, the oral basic service closed loop comprises: the patient classification item quantitative result Q1pn with weight W1pn, the reservation classification item quantitative result Q1an, the fee-based quantitative result Q1cn with weight W1pn, and the weight W1 pn. The in-production score of 8 months is formed by the scores of all enterprise customers, and the scores can be specifically decomposed to all enterprise customers according to the unique attribute of the tenants in the SaaS.
Optionally, the total index sum (K) is obtained according to the classification index K of the classification item quantization result calculated by weighted average, where sum (K) is the comprehensive development condition of all enterprise customers who depend on SaaS as a whole.
Optionally, as shown in fig. 2, the method includes: if the verification fails, respectively re-determining the SaaS production data and the data types of the self-sufficient platform production data based on the linear timeline; respectively standardizing the SaaS production data and the self-sufficient platform production data into one or more SaaS production classification items and one or more self-sufficient platform production classification items based on the determined data types, and obtaining standardized quantification results respectively corresponding to the SaaS production classification items and the respective self-sufficient platform production classification items;
verifying quality elements corresponding to each SaaS production classification item and the standardized quantization result of each platform production classification item respectively to obtain a verification result corresponding to verification success or verification failure; if the verification is successful, respectively configuring each SaaS production classification item and each given platform production classification item into a classification item required by the SaaS service closed loop and a classification item required by the platform service closed loop based on the vertical consumption medical standard, and obtaining a SaaS production service classification combination and a platform service classification combination which are respectively formed by each SaaS service closed loop required classification item and each platform service closed loop required classification item; and respectively calculating the service indexes of the configured SaaS production service classification combination and platform service classification combination.
Optionally, if the verification fails, that is, if the quantization source has a problem and the result set is inaccurate, the quantization source data of the batch is re-standardized from the error time point by itself.
Optionally, the method further includes: the binlog log is stored in a limited time so as to quickly recover to the error time point before carrying out the re-operation when the error occurs.
Similar to the principle of the embodiment, the invention provides a vertical consumption medical SaaS production data computing system.
Specific embodiments are provided below in conjunction with the attached figures:
fig. 3 shows a schematic structural diagram of a vertical consumer medical SaaS production data computing system according to an embodiment of the present invention.
The system comprises:
the category determination module 31 is configured to determine data categories of the SaaS production data and the self-sufficient platform production data based on the linear timeline;
a data standardization module 32, connected to the category determination module 31, for standardizing the SaaS production data and the self-sufficient platform production data into one or more SaaS production classification items and one or more self-sufficient platform production classification items, respectively, based on the determined data category, and obtaining standardized quantization results corresponding to each SaaS production classification item and each self-sufficient platform production classification item;
the data verification module 33 is connected with the data standardization module 32 and is used for verifying quality elements corresponding to each SaaS production classification item and the standardized quantization results of the platform production classification items respectively so as to obtain verification results corresponding to verification success or verification failure;
a service configuration module 34 connected to the data verification module 33, configured to configure, based on the vertical consumption medical standard, each SaaS production classification item and each platform production classification item into a classification item required by a SaaS service closed loop and a classification item required by a platform service closed loop, respectively, and obtain a SaaS production service classification combination and a platform service classification combination formed by each classification item required by the SaaS service closed loop and each classification item required by the platform service closed loop, respectively;
and the calculating module 35 is connected to the service configuration module 34, and is configured to calculate service indexes of the configured SaaS production service classification combination and platform service classification combination, respectively.
Optionally, the category determining module 31 is configured to determine, based on the linear timeline, data categories of SaaS production data generated in the SaaS production process and self-sufficient platform production data generated in the self-sufficient platform data production process, respectively. The SaaS production process is a vertical consumption medical SaaS data production process, and the platform data production process is a socialization and mobile platform production process docked with the vertical consumption medical SaaS.
It is to be noted that the production quantification standard of the SaaS production process is based on the data volume of a linear timeline; the production quantification criteria for the self-sufficient platform data production process are based on the amount of data in the linear timeline.
Optionally, the data types of the SaaS production data include: one or more of SaaS functional interface calling data, SaaS system log data, SaaS service data and SaaS flow data; and/or the data types of the self-sufficient platform production data comprise: the self-sufficient platform functional interface calls one or more of data, self-sufficient platform log data, self-sufficient platform service data and self-sufficient platform flow data;
optionally, the type determining module 31 determines that the data types of the SaaS production data and the self-sufficient platform production data are concurrently executed in a stream mode, so as to improve the collection and classification efficiency.
Optionally, the data standardization module 32 is configured to standardize the SaaS production data and the self-sufficient platform data into one or more SaaS production classification items and one or more self-sufficient platform production classification items according to classification item labels of the SaaS production data and the self-sufficient platform production data, respectively, based on the data types of the SaaS production data and the self-sufficient platform production data; and respectively obtaining standardized quantitative results corresponding to each SaaS production classification item and each platform production classification item according to the SaaS production classification items and each platform production classification item.
Optionally, the data normalization module 32 quantifies the SaaS production data and the self-sufficient platform production data in batch classifications based on linear time according to a bottom-level database management system used by the SaaS system.
Optionally, the data normalization module 32 writes the normalized quantization result in a stream mode, and records the historical result set in a slowly changing dimension mode.
Optionally, the data verification module 33 is configured to verify quality elements corresponding to each SaaS production classification item and a standardized quantization result of each platform production classification item based on CAP theorem, so as to obtain a verification result corresponding to a verification success or a verification failure. Wherein, the quality elements corresponding to the standardized result of each SaaS production classification item comprise: one or more elements of consistency, accuracy, time linear integrity and attribute correctness; and/or the quality elements corresponding to the standardized quantification results of the production classification items of the platform respectively comprise: one or more of logical consistency, topological consistency, associative consistency, attribute correctness, and time-linear integrity elements.
Optionally, the data verification module 33 is configured to verify quality elements corresponding to each SaaS production classification item and a standardized quantization result of each platform production classification item based on a verification standard, and obtain a verification result corresponding to successful verification when the verification standard is met; otherwise, obtaining the verification result corresponding to the verification failure.
Preferably, the verification standard may set different scoring weights for each quality element, and determine whether verification is successful according to the lowest scoring standard for each element or the scoring weighted scoring for each quality element.
Optionally, the service configuration module 34 is configured to configure one or more SaaS production classification items as classification items required by a basic service closed loop to form a basic service combination based on a vertical consumer medical industry standard in case of successful verification; configuring one or more SaaS production classification items into classification items required by an expanded service closed loop based on the vertical consumption medical industry standard to form an expanded service combination; based on the utilization of self-sufficient platform production data, the respective platform production classification items are configured into classification items required by platform service closed loop, and a platform service combination is formed.
Optionally, the service index includes: one or more of a productivity index, a composite growth rate, and a productivity weight; wherein the productivity index, composite growth rate, and productivity weight are associated with the linear timeline.
Optionally, the calculating module 35 is configured to calculate a productivity index, and specify a base period (a period serving as a basis for comparison) and a report period (a period indicating a change condition) in the linear timeline interval according to the linear timeline interval of the corresponding classification item in each classification combination; determining the sum of the number of the corresponding classification items of the business classification item combination needing to be reflected in the base period, and recording the sum as the base period
(ii) a The weight of each classification item in the combination corresponding to the base period
(ii) a And determining the sum of the number of the corresponding classification items of the service classification item combination needing to be reflected in the report period, and recording the sum as
The weight corresponding to each classification item in the base period in the combination
And calculating the productivity index K of the business combination data:
according to the above formula, a basic service combination productivity index K1 corresponding to a basic service combination, an extended service combination productivity index K2 corresponding to an extended service combination, and a platform service combination productivity index K3 corresponding to a platform service combination can be obtained.
Optionally, the calculating module 35 is configured to calculate a composite growth rate, obtain report period results k (t) of service indexes of each service combination at different time nodes t according to a continuous linear timeline change or an asynchronous long-value time change interval, and perform composite growth rate GR calculation on k (t):
according to the above formula, a basic service combination composite growth rate GR1 corresponding to a basic service combination, an extended service combination composite growth rate GR2 corresponding to an extended service combination, and a platform service combination composite growth rate GR3 corresponding to a platform service combination can be obtained, and the development condition of an enterprise client can be determined according to the composite growth rate. And quantitatively evaluating the development of the enterprise client in different periods.
Optionally, the calculating module 35 is configured to calculate the productivity weight, and since the data is from data production of vertical consumption medical SaaS, the basic service, the extended service, and the platform service classification item data elements of the data include attributes of tenants, the calculating module may separately calculate the combined productivity weight aix of each service with specific tenants and different geographic attributes:
based on the productivity weight aix, various business customers may be quantitatively evaluated, wherein,
the corresponding weight in the combination in the report period is used for each classification item.
According to the above formula, a productivity weight aix1 of the basic service combination corresponding to the basic service combination, a productivity weight aix2 of the extended service combination corresponding to the extended service combination, and a productivity weight aix3 of the platform service combination corresponding to the platform service combination can be obtained.
Optionally, the calculating module 35 is further configured to calculate a total index, and obtain a total index sum (K) according to the classification index K of the classification item quantization result calculated by weighted average, where sum (K) is a comprehensive development condition of all enterprise customers that integrally depend on SaaS.
Optionally, the system further includes: the data re-standardization module is connected with the data verification module 33 and used for re-determining the SaaS production data and the data types of the data produced by the self-sufficient platform respectively on the basis of the linear timeline under the condition of failure of verification; respectively standardizing the SaaS production data and the self-sufficient platform production data into one or more SaaS production classification items and one or more self-sufficient platform production classification items based on the determined data types, and obtaining standardized quantification results respectively corresponding to the SaaS production classification items and the respective self-sufficient platform production classification items; verifying quality elements corresponding to each SaaS production classification item and the standardized quantization result of each platform production classification item respectively to obtain a verification result corresponding to verification success or verification failure; if the verification is successful, respectively configuring each SaaS production classification item and each given platform production classification item into a classification item required by the SaaS service closed loop and a classification item required by the platform service closed loop based on the vertical consumption medical standard, and obtaining a SaaS production service classification combination and a platform service classification combination which are respectively formed by each SaaS service closed loop required classification item and each platform service closed loop required classification item; and respectively calculating the service indexes of the configured SaaS production service classification combination and platform service classification combination.
As shown in fig. 4, a schematic structural diagram of a vertical consumer medical SaaS production data computing terminal 40 in the embodiment of the present application is shown.
The vertical consumer medical SaaS production data calculation terminal 40 includes: a memory 41 and a processor 42, the memory 41 being for storing computer programs; the processor 42 runs a computer program to implement the vertical consumer medical SaaS production data calculation method shown in fig. 1.
Alternatively, the number of the memories 41 may be one or more, the number of the processors 42 may be one or more, and fig. 4 illustrates one example.
Optionally, the processor 42 in the vertical consumption medical SaaS production data computing terminal 40 may load one or more instructions corresponding to processes of an application program into the memory 41 according to the steps shown in fig. 1, and the processor 42 runs the application program stored in the first memory 41, so as to implement various functions in the vertical consumption medical SaaS production data computing method shown in fig. 1.
Optionally, the memory 41 may include, but is not limited to, a high speed random access memory, a non-volatile memory. Such as one or more magnetic disk storage devices, flash memory devices, or other non-volatile solid-state storage devices; the Processor 42 may include, but is not limited to, a Central Processing Unit (CPU), a Network Processor (NP), and the like; the Integrated Circuit may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, or a discrete hardware component.
Optionally, the Processor 42 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the Integrated Circuit may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, or a discrete hardware component.
The present invention also provides a computer-readable storage medium storing a computer program which, when executed, implements the method for calculating vertical consumer medical SaaS production data as shown in fig. 1. The computer-readable storage medium may include, but is not limited to, floppy diskettes, optical disks, CD-ROMs (compact disc-read only memories), magneto-optical disks, ROMs (read-only memories), RAMs (random access memories), EPROMs (erasable programmable read only memories), EEPROMs (electrically erasable programmable read only memories), magnetic or optical cards, flash memory, or other type of media/machine-readable medium suitable for storing machine-executable instructions. The computer readable storage medium may be a product that is not accessed by the computer device or may be a component that is used by an accessed computer device.
In summary, the method, the system, the terminal and the medium for calculating the vertical consumption medical SaaS production data are used for solving the problems that the traditional software in the prior art cannot realize medical data standardization and real-time summarization or input high cost, does not have reference to socialized and mobile data, has inaccurate, asymmetric and untimely acquired information and inconsistent statistical calibers, cannot accurately evaluate the development conditions of industries and individual enterprises according to data calculation, and the like. The vertical consumption medical SaaS of the invention produces novel socialized and mobile data which cannot be produced by traditional software, and unifies medical data standards; the enterprise client can not only collect relevant data in real time to carry out enterprise operation per se, but also can carry out industry investigation and evaluation and policy research on the calculation of production data. Therefore, the invention effectively overcomes various defects in the prior art and has high industrial utilization value.
The foregoing embodiments are merely illustrative of the principles of the present invention and its efficacy, and are not to be construed as limiting the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical spirit of the present invention be covered by the claims of the present invention.
Claims (9)
1. A vertical consumption medical SaaS production data calculation method is characterized by comprising the following steps:
respectively determining data types of SaaS production data generated in a SaaS production process and self-sufficient platform production data generated in a self-sufficient platform data production process based on a linear timeline; the data types of the SaaS production data comprise: one or more of SaaS functional interface calling data, SaaS system log data, SaaS service data and SaaS flow data; and/or the data types of the self-sufficient platform production data comprise: the self-sufficient platform functional interface calls one or more of data, self-sufficient platform log data, self-sufficient platform service data and self-sufficient platform flow data; the self-sufficient platform data production process is a socialized and mobile platform production process in butt joint with vertical consumption medical SaaS;
respectively standardizing the SaaS production data and the self-sufficient platform production data into one or more SaaS production classification items and one or more self-sufficient platform production classification items based on the determined data types, and obtaining standardized quantification results respectively corresponding to the SaaS production classification items and the respective self-sufficient platform production classification items;
verifying quality elements corresponding to each SaaS production classification item and the standardized quantization result of each platform production classification item respectively to obtain a verification result corresponding to verification success or verification failure;
if the verification is successful, respectively configuring each SaaS production classification item and each given platform production classification item into a classification item required by the SaaS service closed loop and a classification item required by the platform service closed loop based on the vertical consumption medical standard, and obtaining a SaaS production service classification combination and a platform service classification combination which are respectively formed by each SaaS service closed loop required classification item and each platform service closed loop required classification item;
and respectively calculating the service indexes of the configured SaaS production service classification combination and platform service classification combination.
2. The method of calculating vertical consumer medical SaaS production data according to claim 1, wherein the normalizing the SaaS production data and the self-sufficient platform production data into one or more SaaS production classification items and one or more self-sufficient platform production classification items, respectively, based on the determined data type, and obtaining the normalized quantification results corresponding to each SaaS production classification item and each self-sufficient platform production classification item comprises:
standardizing the SaaS production data and the self-sufficient platform data into one or more SaaS production classification items and one or more self-sufficient platform production classification items according to classification item labels of the SaaS production data and the self-sufficient platform data respectively based on the data types of the SaaS production data and the self-sufficient platform production data;
and respectively obtaining standardized quantitative results corresponding to each SaaS production classification item and each platform production classification item according to the SaaS production classification items and each platform production classification item.
3. The method for calculating the vertical consumer medical service (SaaS) production data according to claim 1, wherein the verifying the quality elements corresponding to the standardized quantization results of each of the SaaS production classification items and each of the platform production classification items comprises:
based on CAP theorem, respectively verifying quality elements corresponding to each SaaS production classification item and the standardized quantization result of each platform production classification item to obtain verification results corresponding to verification success or verification failure;
wherein, the quality elements corresponding to the standardized result of each SaaS production classification item comprise: one or more elements of consistency, accuracy, time linear integrity and attribute correctness; and/or the quality elements corresponding to the standardized quantification results of the production classification items of the platform respectively comprise: one or more of logical consistency, topological consistency, associative consistency, attribute correctness, and time-linear integrity elements.
4. The method for calculating the vertical consumer medical SaaS production data according to claim 1, wherein the classification items required by the SaaS service closed loop comprise: classification items required by basic service closed loop and classification items required by extended service closed loop; the classification items required by each basic service closed loop form a basic service combination and the classification items required by each extended service closed loop form an extended service combination.
5. The method for calculating vertical consumer medical-like SaaS production data according to claim 1, wherein the business index comprises: one or more of a productivity index, a composite growth rate, and a productivity weight; wherein the productivity index, composite growth rate, and productivity weight are associated with the linear timeline.
6. The method for calculating vertical consumer medical-like SaaS production data according to claim 1, further comprising:
if the verification fails, respectively re-determining the data types of the SaaS production data and the self-sufficient platform production data based on the linear timeline;
respectively standardizing the SaaS production data and the self-sufficient platform production data into one or more SaaS production classification items and one or more self-sufficient platform production classification items based on the determined data types, and obtaining standardized quantification results respectively corresponding to the SaaS production classification items and the respective self-sufficient platform production classification items;
verifying quality elements corresponding to each SaaS production classification item and the standardized quantization result of each platform production classification item respectively to obtain a verification result corresponding to verification success or verification failure;
if the verification is successful, respectively configuring each SaaS production classification item and each given platform production classification item into a classification item required by the SaaS service closed loop and a classification item required by the platform service closed loop based on the vertical consumption medical standard, and obtaining a SaaS production service classification combination and a platform service classification combination which are respectively formed by each SaaS service closed loop required classification item and each platform service closed loop required classification item;
and respectively calculating the service indexes of the configured SaaS production service classification combination and platform service classification combination.
7. A vertical consumer medical-like SaaS production data computing system, the system comprising:
a class determination module for determining data classes of SaaS production data generated in a SaaS production process and self-sufficient platform production data generated in a self-sufficient platform data production process, respectively, based on a linear timeline; the data types of the SaaS production data comprise: one or more of SaaS functional interface calling data, SaaS system log data, SaaS service data and SaaS flow data; and/or the data types of the self-sufficient platform production data comprise: the self-sufficient platform functional interface calls one or more of data, self-sufficient platform log data, self-sufficient platform service data and self-sufficient platform flow data; the self-sufficient platform data production process is a socialized and mobile platform production process in butt joint with vertical consumption medical SaaS;
the data standardization module is connected with the type determination module and is used for respectively standardizing the SaaS production data and the self-sufficient platform production data into one or more SaaS production classification items and one or more self-sufficient platform production classification items based on the determined data types and obtaining standardization quantification results respectively corresponding to each SaaS production classification item and each self-sufficient platform production classification item;
the data verification module is connected with the data standardization module and is used for verifying quality elements corresponding to each SaaS production classification item and the standardized quantization result of each platform production classification item so as to obtain a verification result corresponding to verification success or verification failure;
the service configuration module is connected with the data verification module and is used for respectively configuring each SaaS production classification item and each given platform production classification item into a classification item required by the closed loop of the SaaS service and a classification item required by the closed loop of the platform service on the basis of the vertical consumption medical standard under the condition of successful verification, and obtaining a SaaS production service classification combination and a platform service classification combination which are respectively formed by the classification item required by the closed loop of each SaaS service and the classification item required by the closed loop of each platform service;
and the calculation module is connected with the service configuration module and is used for respectively calculating the service indexes of the configured SaaS production service classification combination and platform service classification combination.
8. A vertical consumption medical SaaS production data computing terminal is characterized by comprising:
a memory for storing a computer program;
a processor for performing the method of calculating vertical consumer medical SaaS production data according to any of claims 1 to 6.
9. A computer storage medium characterized in that a computer program is stored, which when executed implements the vertical consumer medical-like SaaS production data calculation method according to any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110421503.6A CN112990767B (en) | 2021-04-20 | 2021-04-20 | Vertical consumption medical SaaS production data calculation method, system, terminal and medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110421503.6A CN112990767B (en) | 2021-04-20 | 2021-04-20 | Vertical consumption medical SaaS production data calculation method, system, terminal and medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112990767A CN112990767A (en) | 2021-06-18 |
| CN112990767B true CN112990767B (en) | 2021-08-20 |
Family
ID=76341219
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110421503.6A Active CN112990767B (en) | 2021-04-20 | 2021-04-20 | Vertical consumption medical SaaS production data calculation method, system, terminal and medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112990767B (en) |
Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105335814A (en) * | 2015-09-25 | 2016-02-17 | 湖南中德安普大数据网络科技有限公司 | Online big data intelligent cloud auditing method and system |
| CN105389619A (en) * | 2014-08-31 | 2016-03-09 | 通用电气公司 | Methods and systems for improving connections within healthcare ecosystem |
| CN105976109A (en) * | 2016-05-05 | 2016-09-28 | 云神科技投资股份有限公司 | Intelligent auditing method and system based on big data |
| CN106293872A (en) * | 2016-07-27 | 2017-01-04 | 云南电网有限责任公司信息中心 | A kind of SLA resources balance management-control method based on resource pool |
| CN106503257A (en) * | 2016-11-15 | 2017-03-15 | 北京京东金融科技控股有限公司 | Distributed transaction server method and system based on binlog compensation mechanism |
| CN107430613A (en) * | 2015-03-23 | 2017-12-01 | 甲骨文国际公司 | Knowledge-intensive data handling system |
| CN109036539A (en) * | 2018-08-22 | 2018-12-18 | 广东省中医院（广州中医药大学第二附属医院 广州中医药大学第二临床医学院 广东省中医药科学院） | A kind of irritable bowel syndrome clinical data platform designing method |
| CN110147931A (en) * | 2019-04-16 | 2019-08-20 | 国家电网有限公司 | Intelligent panoramic system and intelligent panoramic power grid |
| CN110415831A (en) * | 2019-07-18 | 2019-11-05 | 天宜(天津)信息科技有限公司 | A kind of medical treatment big data cloud service analysis platform |
| CN110766332A (en) * | 2019-10-29 | 2020-02-07 | 重庆亚德科技股份有限公司 | Comprehensive quality index monitoring system for regional sanitation |
| CN111105860A (en) * | 2019-12-18 | 2020-05-05 | 青岛科技大学 | Intelligent prediction, analysis and optimization system for accurate motion big data for chronic disease rehabilitation |
| CN111125061A (en) * | 2019-12-18 | 2020-05-08 | 甘肃省卫生健康统计信息中心(西北人口信息中心) | Method for standardizing and promoting health medical big data |
| CN111951935A (en) * | 2019-05-16 | 2020-11-17 | 上海宽带技术及应用工程研究中心 | Medical cloud system, method, system and medium for medical big data processing |
| CN112133445A (en) * | 2020-10-21 | 2020-12-25 | 万达信息股份有限公司 | Cardiovascular disease management service method and system |
| CN112349404A (en) * | 2020-11-03 | 2021-02-09 | 中国人民解放军总医院 | Multi-center medical equipment big data cloud platform based on cloud-edge-end architecture |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9489647B2 (en) * | 2008-06-19 | 2016-11-08 | Csc Agility Platform, Inc. | System and method for a cloud computing abstraction with self-service portal for publishing resources |
| US20190304582A1 (en) * | 2018-04-03 | 2019-10-03 | Patient Oncology Portal, Inc. | Methods and System for Real Time, Cognitive Integration with Clinical Decision Support Systems featuring Interoperable Data Exchange on Cloud-Based and Blockchain Networks |
| US10970660B1 (en) * | 2018-07-16 | 2021-04-06 | Coupa Software Incorporated | Community-based data analysis in a software-as-a-service system using a deep learning classifier |
| CN109409908A (en) * | 2018-10-10 | 2019-03-01 | 北京长城华冠汽车技术开发有限公司 | Customer value classification method and device, computer-readable medium |
| CN111507395A (en) * | 2020-04-15 | 2020-08-07 | 赛诺数据科技(南京)有限公司 | Marketing big data modeling method and platform |
| CN112559491A (en) * | 2020-12-17 | 2021-03-26 | 山东健康医疗大数据有限公司 | Method and system for realizing adaptive management based on data model in medical industry |
-
2021
- 2021-04-20 CN CN202110421503.6A patent/CN112990767B/en active Active
Patent Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105389619A (en) * | 2014-08-31 | 2016-03-09 | 通用电气公司 | Methods and systems for improving connections within healthcare ecosystem |
| CN107430613A (en) * | 2015-03-23 | 2017-12-01 | 甲骨文国际公司 | Knowledge-intensive data handling system |
| CN105335814A (en) * | 2015-09-25 | 2016-02-17 | 湖南中德安普大数据网络科技有限公司 | Online big data intelligent cloud auditing method and system |
| CN105976109A (en) * | 2016-05-05 | 2016-09-28 | 云神科技投资股份有限公司 | Intelligent auditing method and system based on big data |
| CN106293872A (en) * | 2016-07-27 | 2017-01-04 | 云南电网有限责任公司信息中心 | A kind of SLA resources balance management-control method based on resource pool |
| CN106503257A (en) * | 2016-11-15 | 2017-03-15 | 北京京东金融科技控股有限公司 | Distributed transaction server method and system based on binlog compensation mechanism |
| CN109036539A (en) * | 2018-08-22 | 2018-12-18 | 广东省中医院（广州中医药大学第二附属医院 广州中医药大学第二临床医学院 广东省中医药科学院） | A kind of irritable bowel syndrome clinical data platform designing method |
| CN110147931A (en) * | 2019-04-16 | 2019-08-20 | 国家电网有限公司 | Intelligent panoramic system and intelligent panoramic power grid |
| CN111951935A (en) * | 2019-05-16 | 2020-11-17 | 上海宽带技术及应用工程研究中心 | Medical cloud system, method, system and medium for medical big data processing |
| CN110415831A (en) * | 2019-07-18 | 2019-11-05 | 天宜(天津)信息科技有限公司 | A kind of medical treatment big data cloud service analysis platform |
| CN110766332A (en) * | 2019-10-29 | 2020-02-07 | 重庆亚德科技股份有限公司 | Comprehensive quality index monitoring system for regional sanitation |
| CN111105860A (en) * | 2019-12-18 | 2020-05-05 | 青岛科技大学 | Intelligent prediction, analysis and optimization system for accurate motion big data for chronic disease rehabilitation |
| CN111125061A (en) * | 2019-12-18 | 2020-05-08 | 甘肃省卫生健康统计信息中心(西北人口信息中心) | Method for standardizing and promoting health medical big data |
| CN112133445A (en) * | 2020-10-21 | 2020-12-25 | 万达信息股份有限公司 | Cardiovascular disease management service method and system |
| CN112349404A (en) * | 2020-11-03 | 2021-02-09 | 中国人民解放军总医院 | Multi-center medical equipment big data cloud platform based on cloud-edge-end architecture |
Non-Patent Citations (4)
| Title |
|---|
| The Framework of Medical Simulation Education and Evaluation System for Supporting SaaS;Jia Shang;《2010 IEEE 34th Annual Computer Software and Applications Conference Workshops》;20101101;参见第93-97页 * |
| 基于SaaS模式的医疗设备软件质量评价研究与应用;徐鑫;《中国医疗设备》;20180430;第33卷(第4期);参见第134-138页 * |
| 基于云计算的医院信息技术平台的构建与研究;许敏;《中国优秀硕士学位论文全文数据库 信息科技辑》;20140815(第8期);参见第5章 * |
| 基于工业互联网的智能矿山研究与设计;李首滨 等;《中国煤炭》;20210228;第47卷(第2期);参见第59-65页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112990767A (en) | 2021-06-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11625387B2 (en) | Structuring data | |
| US20200104186A1 (en) | Executing algorithms in parallel | |
| US11176154B1 (en) | Collaborative dataset management system for machine learning data | |
| CN107993143A (en) | A kind of Credit Risk Assessment method and system | |
| US10915850B2 (en) | Objective evidence-based worker skill profiling and training activation | |
| US20150220868A1 (en) | Evaluating Data Quality of Clinical Trials | |
| WO2014099127A1 (en) | Dynamic model data facility and automated operational model building and usage | |
| WO2003096237A2 (en) | Electronic data processing system and method of using an electronic data processing system for automatically determining a risk indicator value | |
| US9304991B2 (en) | Method and apparatus for using monitoring intent to match business processes or monitoring templates | |
| TW201142630A (en) | Method for training and using a classification model with association rule models | |
| KR20160104064A (en) | A multidimensional recursive learning process and system used to discover complex dyadic or multiple counterparty relationships | |
| CN110728422A (en) | Building information model, method, device and settlement system for construction project | |
| CN112100219B (en) | Report generation method, device, equipment and medium based on database query processing | |
| CN114880405A (en) | Data lake-based data processing method and system | |
| CN112365070A (en) | Power load prediction method, device, equipment and readable storage medium | |
| Jameel et al. | Analyses the performance of data warehouse architecture types | |
| CN112990767B (en) | Vertical consumption medical SaaS production data calculation method, system, terminal and medium | |
| Ben-Assuli et al. | Data mining techniques utilizing latent class models to evaluate emergency department revisits | |
| CN113554350A (en) | Activity evaluation method and apparatus, electronic device and computer readable storage medium | |
| US20170109637A1 (en) | Crowd-Based Model for Identifying Nonconsecutive Executions of a Business Process | |
| US11227288B1 (en) | Systems and methods for integration of disparate data feeds for unified data monitoring | |
| CN113610575B (en) | Product sales prediction method and prediction system | |
| Yalaoui et al. | A survey on data quality: principles, taxonomies and comparison of approaches | |
| WO2022227213A1 (en) | Industry recommendation method and apparatus, computer device and storage medium | |
| US20220171662A1 (en) | Transitioning of computer-related services based on performance criteria |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| PE01 | Entry into force of the registration of the contract for pledge of patent right | ||
| PE01 | Entry into force of the registration of the contract for pledge of patent right |
Denomination of invention: Calculation methods, systems, terminals, and media for vertical consumer medical SaaS production data Effective date of registration: 20231127 Granted publication date: 20210820 Pledgee: China Minsheng Banking Corp Shanghai branch Pledgor: SHANGHAI LINKEDCARE INFORMATION TECHNOLOGY Co.,Ltd. Registration number: Y2023310000785 |