CN114185874A

CN114185874A - Big data based modeling method and device, development framework and equipment

Info

Publication number: CN114185874A
Application number: CN202210139412.8A
Authority: CN
Inventors: 康健; 贾贺; 薛铮; 付凯; 李常宝
Original assignee: CETC 15 Research Institute
Current assignee: CETC 15 Research Institute
Priority date: 2022-02-15
Filing date: 2022-02-15
Publication date: 2022-03-15

Abstract

The embodiment of the specification discloses a modeling method, a device, a development framework and equipment based on big data, wherein the method comprises the following steps: acquiring data to be processed; performing graphical arrangement and connection on the data to be processed to obtain a process construction result; and executing the flow construction result when a flow execution request is received. By adopting the modeling method provided by the embodiment of the specification, the process construction can be realized through graph dragging and connection, the process construction based on big data can be conveniently and quickly realized, and the method has higher applicability and expansibility; the process construction application can be quickly put into the production environment for use, the existing application development mode is improved, intermediate links are saved, and the time cost is saved; the use mode is simple and convenient, and the big data learning threshold is reduced; a great deal of technical details are shielded for non-professional business personnel, so that the data value is more concentrated, and the data business is more intuitively and conveniently processed.

Description

Big data based modeling method and device, development framework and equipment

Technical Field

The specification relates to the technical field of big data analysis, in particular to a modeling method, a modeling device and modeling equipment based on big data.

Background

With the improvement of computer storage capacity and the development of complex algorithms, the data volume increases exponentially in recent years, various algorithms and related technologies thereof are applied to various industries and fields, and accordingly, higher requirements are put forward on platforms and frames for supporting big data analysis application. In the traditional big data and machine learning field, a professional usually performs exploratory analysis and pretreatment on data, determines problem types according to different scenes, selects a proper algorithm, writes algorithm codes to operate, continuously debugs according to evaluation indexes, finally summarizes and summarizes a model and distributes and deploys the model, and finally, the model is applied to actual engineering. However, the traditional model construction method has some disadvantages: the method has high requirements on the quality of professional staff and the programming capability; the demand for the enlargement of data size and the reduction of time cost is increasingly conflicting; moreover, the model constructed by the traditional method is difficult to be rapidly applied, and the traditional modeling method is not flexible enough.

Therefore, a new method is needed, which can meet the modeling requirement of big data growing rapidly, reduce the requirement for the programming capability of modeling personnel, and can implement application rapidly and use flexibly.

Disclosure of Invention

The embodiment of the specification provides a modeling method, a modeling device, a development framework and a device based on big data, and is used for solving the following technical problems: the traditional model construction method has some disadvantages: the method has high requirements on the quality of professional staff and the programming capability; the demand for the enlargement of data size and the reduction of time cost is increasingly conflicting; moreover, the model constructed by the traditional method is difficult to be rapidly applied, and the traditional modeling method is not flexible enough.

In order to solve the above technical problem, the embodiments of the present specification are implemented as follows:

the modeling method based on big data provided by the embodiment of the specification comprises the following steps:

acquiring data to be processed;

performing graphical arrangement and connection on the data to be processed to obtain a process construction result;

and executing the flow construction result when a flow execution request is received.

Further, the graphically arranging and connecting the data to be processed to obtain a process construction result specifically includes:

based on the data to be processed, dragging an operator corresponding to the data to be processed from an operator library;

carrying out operator configuration on an operator corresponding to the data to be processed to obtain a configured operator;

and connecting the configured operators in a line connection mode to obtain a process construction result.

Further, the operator library comprises at least one operator, and the operator library comprises: operator basic information, an operator configuration page and a function realization code.

Further, the operator is a basic unit of the flow, and the operator includes: operator identification, operator configuration page, operator input data, operator execution method, operator output data and insight result.

Further, the operator identification is ID generated by the operator in the process and/or ID of the operator in the database.

Further, the performing operator configuration on the operator corresponding to the data to be processed to obtain a configured operator specifically includes:

selecting a data input source, a data output source, data preprocessing, algorithm parameter configuration, statistical mode setting, graph setting and script compiling of an operator corresponding to the data to be processed, and performing operator configuration on the operator corresponding to the data to be processed to obtain a configured operator.

Further, when the flow execution request is received, executing the flow construction result, specifically including:

and when a flow execution request is received, receiving a data analysis flow and data in the flow construction result, executing the data analysis flow, and obtaining an execution result of the to-be-processed data.

Further, when receiving the flow execution request, receiving a data analysis flow and data in the flow construction result, executing the data analysis flow, and obtaining an execution result of the to-be-processed data, specifically including:

when a flow execution request is received, receiving a data analysis flow and data constructed in a flow construction result, reading the structure of the data analysis flow, and determining an initial input operator, a flow ending operator, and a pre-operator and/or a post-operator of each operator included in the data analysis flow;

reading data to be operated in the data analysis process based on the initial input operator to obtain output data of a prepositive operator;

and calculating a post operator based on the output data of the pre operator to obtain the output data of the post operator until obtaining the execution result of the data to be processed.

Further, the determining of the initial input operator, the process ending operator, and the pre-operator and/or the post-operator of each operator included in the data analysis process specifically includes:

reading the structure of the data analysis process, calculating the out degree of each operator and the in degree of each operator included in the data analysis process, taking the operator with the in degree of 0 as an initial input operator, and taking the operator with the out degree of 0 as a process end operator;

and acquiring a prepositive operator and/or a postpositive operator of each operator included in the data analysis process based on the operator ID identification in the process construction result.

Further, the reading data to be operated in the data analysis process based on the initial input operator to obtain output data of a pre-operator specifically includes:

based on the initial input operator, establishing an initial operator example through operator identification and operator ID identification in the flow;

and inputting the operator configuration parameters as operators into an operator execution code, reading data to be operated in the data analysis flow, and obtaining output data of the prepositive operator.

Further, the calculating a post operator based on the output data of the pre operator to obtain the output data of the post operator specifically includes:

and inputting the output data of the pre-operator into a post-operator associated with the pre-operator, and calculating the post-operator to obtain the output data of the post-operator.

An embodiment of the present specification further provides a modeling apparatus based on big data, where the apparatus includes:

the acquisition module acquires data to be processed;

the construction module is used for carrying out graphical arrangement and connection on the data to be processed to obtain a process construction result;

and the execution module executes the flow construction result when receiving the flow execution request.

An embodiment of the present specification further provides a big data based development framework, where the framework includes: the system comprises a modeling module, a resource management and scheduling module and a data management module;

wherein the content of the first and second substances,

the data management module is used for receiving data to be processed;

the modeling module comprises a visual editing component and an analysis and calculation component;

the visual editing assembly is used for performing graphical arrangement and connection on the data to be processed to obtain a process construction result;

the analysis calculation component is used for executing the flow construction result when receiving a flow execution request.

An embodiment of the present specification further provides an electronic device, including:

at least one processor; and the number of the first and second groups,

a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,

the memory stores instructions executable by the at least one processor to enable the at least one processor to:

acquiring data to be processed;

The embodiment of the specification adopts at least one technical scheme which can achieve the following beneficial effects: the flow construction can be realized through drawing and connecting the graphs, the flow construction based on big data can be conveniently and quickly realized, and the method has higher applicability and expansibility; the process construction application can be quickly put into the production environment for use, the existing application development mode is improved, intermediate links are saved, and the time cost is saved; the use mode is simple and convenient, and the big data learning threshold is reduced; a great deal of technical details are shielded for non-professional business personnel, so that the data value is more concentrated, and the data business is more intuitively and conveniently processed.

Drawings

In order to more clearly illustrate the embodiments of the present specification or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only some embodiments described in the present specification, and for those skilled in the art, other drawings can be obtained according to the drawings without any creative effort.

FIG. 1 is a schematic diagram of a big data based modeling method provided by an embodiment of the present specification;

FIG. 2 is a business logic diagram of a development framework of a big data based modeling method according to an embodiment of the present disclosure;

FIG. 3 is a schematic structural diagram of an interactive analytical modeling tool platform provided in an embodiment of the present specification;

FIG. 4 is a schematic diagram illustrating a method for using a big data development framework according to an embodiment of the present disclosure;

fig. 5 is a schematic diagram of a modeling apparatus based on big data according to an embodiment of the present disclosure.

Detailed Description

In order to make those skilled in the art better understand the technical solutions in the present specification, the technical solutions in the embodiments of the present specification will be clearly and completely described below with reference to the drawings in the embodiments of the present specification, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any inventive step based on the embodiments of the present disclosure, shall fall within the scope of protection of the present application.

Fig. 1 is a schematic diagram of a big data-based modeling method provided in an embodiment of the present specification, and as shown in fig. 1, the method includes:

step S101: and acquiring data to be processed.

In the embodiment of the present specification, the data to be processed may be production data or test data, and the format of the data to be processed is determined by a data source.

Step S103: and carrying out graphical arrangement and connection on the data to be processed to obtain a process construction result.

In an embodiment of this specification, the graphically arranging and connecting the data to be processed to obtain a process construction result specifically includes:

In an embodiment of the present specification, the operator library includes at least one operator, and the operator library includes: operator basic information, an operator configuration page and a function realization code.

In this specification, the operator is a basic unit of a flow, and the operator includes: operator identification, operator configuration page, operator input data, operator execution method, operator output data and insight result.

In the embodiment of the specification, the operator identification is an ID generated by the operator in the flow and/or an ID of the operator in the database.

In an embodiment of the present specification, the operators of the operator library include: general operators and special operators. Specifically, the general operator includes: the system comprises an input and output operator, a data preprocessing operator, a machine learning operator, a statistical analysis operator, a graph display operator, a natural language processing operator and the like, wherein a general operator can be updated along with the change of a business mode; the special subject operators are dedicated to business processing, such as special business operators in the fields of judicial, flight path, and the like. It should be noted that, when an operator changes, only the operator in the operator library needs to be updated.

In this embodiment of the present specification, the performing operator configuration on the operator corresponding to the to-be-processed data to obtain a configured operator specifically includes:

Step S105: and executing the flow construction result when a flow execution request is received.

In an embodiment of this specification, when the flow execution request is received, executing the flow construction result specifically includes:

In an embodiment of this specification, when receiving a flow execution request, receiving a data analysis flow and data in the flow construction result, executing the data analysis flow, and obtaining an execution result of the to-be-processed data, specifically including:

In an embodiment of this specification, the determining a starting input operator, a process ending operator, and a pre-operator and/or a post-operator of each operator included in the data analysis process specifically includes:

In an embodiment of this specification, the reading, based on the initial input operator, data to be operated in the data analysis process to obtain output data of a pre-operator specifically includes:

In this embodiment of the present specification, the calculating a post-operator based on the output data of the pre-operator to obtain the output data of the post-operator specifically includes:

By adopting the modeling method provided by the embodiment of the specification, the model can be further solidified into a model, so that a model list is formed, and different business requirements, namely the model output process, are met. Further, the production and testing can be performed based on the solidified model creation analysis processing task, and the solidified model can also be exported to be an application or a service. The derived model can be generally divided into two dimensions of a data model and a business model, wherein the data model is used for classification from a scientific angle and can be used for classification of statistical analysis, data retrieval, machine learning, deep learning, text analysis and the like; the business model focuses more on business performance, is used for solving practical problems, and has more pertinence to certain business scenes, for example, the business model is used for judicial data analysis, network data analysis, track data analysis and the like.

Furthermore, the data analysis processing flow and/or the model generated by the flow construction and execution process can be packaged into a service or an application, namely a service and application packaging process, and the exported service and/or application can be independently operated from the framework, so that the migration and the use are convenient. In the embodiment of the present specification, a service may provide a set of application program interfaces for big data analysis and processing, and a user may perform big data analysis and processing operations using an API only by knowing the function corresponding to the service API and calling API parameter specifications, and may obtain an expected processing result without considering the flow direction and the specific implementation of codes therein; in the embodiment of the present disclosure, an application may provide a big data analysis processing software entity, which generally includes a front-end designer and a back-end service portion, and a user may configure different data or parameters through an interface to call a service API to perform a big data analysis processing operation, thereby finally achieving result display.

By adopting the modeling method provided by the embodiment of the specification, the process construction can be realized through graph dragging and connection, the process construction based on big data can be conveniently and quickly realized, and the method has higher applicability and expansibility; the process construction application can be quickly put into the production environment for use, the existing application development mode is improved, intermediate links are saved, and the time cost is saved; the use mode is simple and convenient, and the big data learning threshold is reduced; a great deal of technical details are shielded for non-professional business personnel, so that the data value is more concentrated, and the data business is more intuitively and conveniently processed.

In order to further understand the modeling method provided by the embodiments of the present specification, the following description will be made in conjunction with a business logic diagram. Fig. 2 is a business logic diagram of a development framework of a modeling method based on big data according to an embodiment of the present specification.

As shown in fig. 2, the development framework provided in the present specification mainly includes a process building and executing process, a model producing process, and a service and application packaging process.

In the embodiment of the present specification, the process building and executing process is mainly used for displaying a big data analysis processing process, an operator component list, a model list, a service and application list.

In an embodiment of the present specification, the operator component list comprises at least one type of operator component. In embodiments of the present description, the list of computing group components includes: general operators and special operators. Specifically, the general operator includes: the system comprises an input and output operator, a data preprocessing operator, a machine learning operator, a statistical analysis operator, a graph display operator, a natural language processing operator and the like, wherein a general operator can be updated along with the change of a business mode; the special subject operators are dedicated to business processing, such as special business operators in the fields of judicial, flight path, and the like. It should be noted that, when an operator changes, only the operator in the operator component list needs to be updated.

In the embodiment of the present specification, the model is a frame after a process is constructed using an operator, a set of relatively complete data or business processing processes is recorded, and the model is derived through experiments. In embodiments of the present specification, a plurality of models are provided that process different services or data, and the models constitute a list of models.

In the embodiment of the present specification, the service and application are results obtained by encapsulating a complete processing flow after a model or an experiment has been sufficiently tested, and are independent individuals (generally, project execution packages) that can be deployed independently from the framework, and the exported application can be used directly by a user and only needs to provide data to be processed; the exported service can provide a series of data processing processes, and the data processing processes of the users can be packaged on the basis of the exported service for secondary development and used as a part of the data processing processes of the users. In the development framework provided by the present specification, services and applications are presented in the form of a list.

In the process of flow construction and execution, interactive modeling is provided in a mode of dragging and connecting operator components, and external instructions such as operator configuration, flow execution and the like are received. It should be noted that, in the interactive modeling process, when the operators are connected, the connection order between the operators is determined according to the execution order of the to-be-processed service corresponding to the to-be-processed data or the execution logic of the to-be-processed data at the algorithm level, with the type of the to-be-processed data as a reference.

In the embodiment of the specification, the flow is formed and executed by using an interactive design module, a task execution engine and an operator library in a cooperative manner. The interactive design module provides a human-computer interaction interface for constructing an analysis processing flow, operators are arranged in the interaction interface in a graphical arrangement mode, and a plurality of operators in the interface are connected in a line connection mode, so that data transmission and data flow direction control among the operators are realized; the task execution engine is used for receiving a flow execution request sent by a user in the interactive design module, receiving a data analysis flow and parameters constructed in the interactive design, analyzing and executing the data analysis flow by using the calculation engine, outputting an insight result, and displaying execution process information and the insight result to the user through an interactive design interface, wherein in a specific embodiment, the calculation engine can be a Spark calculation engine; the operator library provides operator entities participating in data analysis process construction in the interactive design interface, and in a specific embodiment, the operator library can comprise operator basic information, an operator configuration page and function implementation codes. It should be noted that, the operator configuration in the interactive interface means to configure parameters required for the operator to execute itself. Specifically, the input and output operators configure connection modes of different data sources and select the data sources; verifying the evaluation operator to configure the evaluation index; and the algorithm class operator configures the parameters of the corresponding algorithm.

In order to further understand the modeling method provided by the embodiment of the present specification, the embodiment of the present specification further provides an interactive analytical modeling tool platform, as shown in fig. 3. Fig. 3 is a schematic structural diagram of an interactive analytical modeling tool platform provided in an embodiment of the present specification, and as shown in fig. 3, a platform overall structure separates a front-end interface and a back-end processing and computing service into a visual editing component and an analytical computing component, where the visual editing component corresponds to an interactive design module in a process of building and executing a process, provides a human-computer interaction interface, drags modeling components such as operators to a modeling work area for visual modeling, and receives and stores operator and process configuration parameters, input/output data, data transmission between operators, and result display; the task execution engine and the operator library part in the process of establishing and executing the corresponding flow of the analysis and calculation assembly provide operator data support for the visual editing assembly, input data and calculation parameters transmitted in the analysis flow are received, the big data calculation process is executed, result data and each operator insight data are generated, and the result data and each operator insight data are returned to the visual editing assembly for data display.

In the embodiment of the specification, the task execution engine provides a distributed big data analysis processing execution framework, distributed data storage and task engine management. Specifically, the execution framework includes a spark, tensorflow, and other mainstream big data execution frameworks, and the execution framework may also be other frameworks, and the specific type of the execution framework does not limit the present application. The distributed data storage comprises a distributed file storage system, a distributed relational database, a document database and other large data storage schemes aiming at different application scenes. The task engine management provides management functions of registering access, expansion, modification, deletion and the like of the execution framework environment.

In this specification, the operator library is a set of a framework-owned operator and a user-defined upload operator, where the operator is a basic unit for performing data analysis processing in a flow, and each operator includes the following elements: the method comprises the steps of configuring a page, operator identification, inputting data, executing a method, outputting data and an insight result. In addition, the framework supports a user to define code editing and generating operators, and the user can write modeling component codes by using multiple programming languages and upload the modeling component codes to the framework to be used as the operators.

In an embodiment of the present specification, the platform structure of the present specification further includes a resource management and scheduling module, configured to manage various framework resources and system resources in the framework. The frame resources comprise operators, models, applications, services and the like, and the system resources comprise a CPU (central processing unit), a GPU (graphic processing unit), a memory, flow and the like. A user can check the utilization rate of the CPU/GPU, the memory consumption, the I/O condition and the like through a resource display board provided by the framework, and perform resource scheduling in a mode of modifying resource quotas, so that reasonable resource distribution is realized.

In order to further understand the modeling method provided by the embodiments of the present specification, the following description will be made in conjunction with a specific use method. Fig. 4 is a schematic diagram of a method for using a big data development framework provided in an embodiment of this specification, which specifically includes:

step S401: and inputting the data to be processed into a big data development framework.

In this specification, the data to be processed may be production data or test data, the format of the data to be processed is determined by a data source, and the big data development framework provided in this specification may be compatible with different data formats.

In the embodiment of the description, the data to be processed is accessed to the big data development framework through the data management module in the big data development framework, so that data support is provided for process construction.

Step S403: and based on the data to be processed, carrying out operator dragging and/or connection to obtain an analysis flow.

In the embodiment of the present specification, the operator management module provided based on the development framework can use the existing operator in the operator library, and can also customize the operator, that is, the operator is generated by a custom-written algorithm component or a business logic component, and further use the customized operator in the modeling interactive interface to meet the personalized requirements.

Step S405: and carrying out operator configuration based on the analysis flow to obtain configured flow data.

After the analysis flow is obtained, the operator corresponding to the analysis flow is further configured according to the content and structure of the data to be processed. Specifically, the operator configuration includes: selecting one or more of a data input source, a data output source, data preprocessing, algorithm parameter configuration, statistical mode setting, graph setting and script compiling.

In the embodiments of the present specification, the configured flow data is stored in a database.

Step S407: and after receiving the flow execution request, analyzing and processing the configured flow data.

In the embodiment of the specification, a flow execution request is received through a task analysis engine, flow data of the configuration is read from a database, and a flow execution process and operator execution conditions are displayed in a console of an interactive interface through a communication execution protocol. In this specification embodiment, the communication execution protocol may be WebSocket.

In an embodiment of this specification, a flow execution process is executed in a task analysis engine, and the specific process includes: the task analysis engine reads the flow structure, determines initial input operators and flow end operators, and calculates the prepositive operator and the postpositive operator of each operator; creating an initial operator example through the operator identification and the ID identification generated in the process, inputting the configuration parameters as operators, executing operator codes, and reading data to be operated in the process; after receiving the output data of the pre-operator, the task analysis engine transmits the data serving as input data into a post-operator associated with the pre-operator, and continues to complete the data calculation process of the post-operator; and finally, the task execution engine finishes all the process finishing operators, summarizes the execution result and returns the result to the interactive interface for display. In one embodiment of the present specification, the calculation of the initial input operator and the process end operator is obtained by the out-degree and in-degree of the operator. An operator with an in-degree of 0 is used as an initial input operator, and a general flow is usually driven by data, so that the flow starts to be used as data input, an input operator node corresponds to the flow, an operator with an out-degree of 0 is used as a flow end operator, and the corresponding operator is generally a graphic display, statistics, verification or data output operator.

In this embodiment, the operator identifier may be an ID generated in the flowchart, or may be an ID of the operator in the database. The information of the operator is stored in a database, and the port and the execution class information corresponding to the operator are obtained through the ID of the database; and acquiring configuration information of the operator during the construction of the flow chart through the flow chart ID.

Step S409: and carrying out data insight on the operator execution result and judging whether the operator execution result meets the requirement.

In the embodiment of the present specification, the operator execution result is an operator execution result in the process of executing the flow or after the process of executing the flow. When data insights are carried out on operator execution results, different indexes are set aiming at different algorithm types, and the method mainly comprises the following steps: accuracy, recall, F1 values, AUC (Area Under ROC Curve), ROC (receiver operating characterization Curve) for the classification algorithm, mean absolute error, mean square error, root mean square error for the regression algorithm, and self-set indicators in the user configuration. It should be noted that the self-set index in the user configuration is a subjective judgment standard of the user on the operator execution result. The self-set indexes in the user configuration can include: whether the data is in a certain numerical value range, the number of the data meeting a certain condition, whether the track data meets requirements, the operator execution speed and the like. The self-set indexes in the user configuration are commonly used in thematic operators for judging the completion degree and the execution efficiency of the business and the like. The F1 value is based on the harmonic mean of recall and accuracy, i.e., the recall and accuracy are evaluated in combination.

If the operator execution result is an execution pass, the embodiments of the present specification may also derive a flow of the execution pass as a model. In this embodiment, the development framework further includes a model management module, which can manage the derived model, and the content of the model management module includes: model information editing, model process viewing, authorization to other users, publishing to public models and the like.

In the embodiments of the present specification, the flow or model through which the pass is performed may be further derived as a service. The development framework provided by the embodiment of the description can also comprise a service management and monitoring module, can be used for packaging the experiment process selected by the user, packaging the experiment process into a service after a series of steps such as packaging, configuring example resources, containerized deployment and the like, and managing the service, so that a big data analysis processing API can be conveniently provided for the outside. Meanwhile, the module comprises service monitoring and statistics of service running conditions, calling conditions and the like; the system also comprises a service export and migration function which can be independently deployed from the platform.

On the basis of the service, an application can also be generated. The one-stop development framework provided in the embodiments of the present specification may further include an application management and monitoring module, and when a user constructs an application, the user may select a suitable application front-end template to bind a data display control therein with a service API. The platform uniformly packages, deploys, registers and tests the front-end template and the service, and the application passing the test can be issued to other users through the platform for use, or an application entity is exported by using an export application function, so that the platform deployment is separated. In addition, the user can also perform secondary development according to the service, perform customized modification and functional extension on the analysis processing function provided by the service in other projects or applications, and finally complete the function desired by the user. The user may register these applications in the platform by uploading a package of items or using an access address. In addition, the platform also provides an application management and application monitoring function, and can manage and monitor the application.

The above details a modeling method based on big data, and accordingly, the present specification further provides a modeling apparatus based on big data, as shown in fig. 5. The big data-based modeling device comprises:

an obtaining module 501, which obtains data to be processed;

the construction module 503 is configured to perform graphical layout and connection on the data to be processed to obtain a process construction result;

the execution module 505 executes the flow construction result when receiving the flow execution request.

wherein the content of the first and second substances,

the data management module is used for receiving data to be processed;

at least one processor; and the number of the first and second groups,

acquiring data to be processed;

The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the embodiments of the apparatus, the electronic device, and the nonvolatile computer storage medium, since they are substantially similar to the embodiments of the method, the description is simple, and the relevant points can be referred to the partial description of the embodiments of the method.

The apparatus, the electronic device, the nonvolatile computer storage medium and the method provided in the embodiments of the present description correspond to each other, and therefore, the apparatus, the electronic device, and the nonvolatile computer storage medium also have similar advantageous technical effects to the corresponding method.

In the 90 s of the 20 th century, improvements in a technology could clearly distinguish between improvements in hardware (e.g., improvements in circuit structures such as diodes, transistors, switches, etc.) and improvements in software (improvements in process flow). However, as technology advances, many of today's process flow improvements have been seen as direct improvements in hardware circuit architecture. Designers almost always obtain the corresponding hardware circuit structure by programming an improved method flow into the hardware circuit. Thus, it cannot be said that an improvement in the process flow cannot be realized by hardware physical modules. For example, a Programmable Logic Device (PLD), such as a Field Programmable Gate Array (FPGA), is an integrated circuit whose Logic functions are determined by programming the Device by a user. A digital system is "integrated" on a PLD by the designer's own programming without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Furthermore, nowadays, instead of manually making an Integrated Circuit chip, such Programming is often implemented by "logic compiler" software, which is similar to a software compiler used in program development and writing, but the original code before compiling is also written by a specific Programming Language, which is called Hardware Description Language (HDL), and HDL is not only one but many, such as abel (advanced Boolean Expression Language), ahdl (alternate Hardware Description Language), traffic, pl (core universal Programming Language), HDCal (jhdware Description Language), lang, Lola, HDL, laspam, hardward Description Language (vhr Description Language), vhal (Hardware Description Language), and vhigh-Language, which are currently used in most common. It will also be apparent to those skilled in the art that hardware circuitry that implements the logical method flows can be readily obtained by merely slightly programming the method flows into an integrated circuit using the hardware description languages described above.

The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, and an embedded microcontroller, examples of which include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic for the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may thus be considered a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.

The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functionality of the various elements may be implemented in the same one or more software and/or hardware implementations in implementing one or more embodiments of the present description.

As will be appreciated by one skilled in the art, the present specification embodiments may be provided as a method, system, or computer program product. Accordingly, embodiments of the present description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present description may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and so forth) having computer-usable program code embodied therein.

The description has been presented with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the description. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data optimization apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data optimization apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data optimization apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data optimization apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.

The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.

Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.

It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.

This description may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.

The above description is only an example of the present specification, and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A big data based modeling method, the method comprising:

acquiring data to be processed;

2. The method according to claim 1, wherein the graphically arranging and connecting the data to be processed to obtain a process construction result specifically comprises:

3. The method of claim 2, wherein the operator library comprises at least one operator, the operator library comprising: operator basic information, an operator configuration page and a function realization code.

4. The method of claim 3, wherein the operator is a basic unit of a flow, the operator comprising: operator identification, operator configuration page, operator input data, operator execution method, operator output data and insight result.

5. The method of claim 4, wherein the operator identification is an ID generated by the operator in the process flow and/or an ID of the operator in the database.

6. The method according to claim 2, wherein the performing operator configuration on the operator corresponding to the data to be processed to obtain the configured operator specifically comprises:

7. The method of claim 1, wherein the executing the flow construction result when receiving a flow execution request specifically comprises:

8. The method of claim 1, wherein the receiving a flow execution request, receiving a data analysis flow and data in the flow construction result, and executing the data analysis flow to obtain an execution result of the to-be-processed data specifically comprises:

9. The method according to claim 8, wherein the determining of the start input operator, the end of the process, and the pre-operator and/or the post-operator of each operator included in the data analysis process specifically comprises:

10. The method according to claim 9, wherein the reading data to be operated in the data analysis process based on the initial input operator to obtain output data of a pre-operator comprises:

11. The method according to claim 8, wherein the calculating a post-operator based on the output data of the pre-operator to obtain the output data of the post-operator comprises:

12. An apparatus for big data based modeling, the apparatus comprising:

the acquisition module acquires data to be processed;

13. A big-data based development framework, the framework comprising: the system comprises a modeling module, a resource management and scheduling module and a data management module;

wherein the content of the first and second substances,

the data management module is used for receiving data to be processed;

14. An electronic device, comprising:

at least one processor; and the number of the first and second groups,

acquiring data to be processed;