Business data management method and device and storage medium

Abstract

The embodiment of the invention provides a business data management method and device and a storage medium. The method comprises the following steps: acquiring business data of each business system in an enterprise; classifying the business data according to a preset field in an enterprise; based on the data quality model of each field, performing quality analysis on the business data of each field to obtain a quality score, and rejecting the business data of which the quality score does not reach a threshold value; the fields comprise one or more of market trend prediction, enterprise competition situation simulation, intelligent question and answer, intelligent writing, intelligent question selection, report management and data security; and storing the remaining business data to form data assets. The technical scheme provided by the invention is used for solving the problem that a large amount of data cannot be fully utilized in the prior art, so that the data use efficiency is improved.

Description

Technical Field

[0001] This document relates to the field of big data technology, and in particular to a business data governance method, device and storage medium. Background Art

[0002] With the rapid development of the Internet, data has become a new key production factor. If business data is limited to internal use and not deeply mined, it will gradually be forgotten, and its value will be difficult to realize, let alone play a role.

[0003] Traditional data utilization methods, including big data, artificial intelligence, and visualization engineering, have three defects:

[0004] 1. Big data: When the amount of data is small, big data is not very meaningful;

[0005] 2. Artificial Intelligence: When the amount of data is small or the data governance is imperfect, artificial intelligence algorithms cannot play a powerful role;

[0006] 3. Visualization: It only solves the role of data analysis from the perspective of display, but does not enhance the usability of business data from the essence of the business.

[0007] The above defects result in a large amount of data being unable to be fully utilized, thereby reducing the efficiency of data utilization. Summary of the invention

[0008] In order to solve at least one of the above technical problems, the present application aims to propose a high-performance, advanced integrated radio frequency switching amplifier circuit and terminal device.

[0009] In a first aspect, one or more embodiments of this specification provide a business data governance method, including:

[0010] Collect business data from various business systems within the enterprise;

[0011] Classify the business data according to preset fields within the enterprise;

[0012] Based on the data quality models of various fields, quality analysis is performed on the business data in various fields to obtain quality scores, and business data whose quality scores do not reach the threshold are eliminated; the various fields include: market trend forecasting, enterprise competition situation simulation, intelligent question and answer, intelligent writing, intelligent topic selection, report management and data security. One or more; and

[0013] Store the remaining business data to form data assets.

[0014] Furthermore, the method further comprises:

[0015] Business modeling is performed on the stored business data to obtain structured data, which is used to support specific business scenarios.

[0016] Furthermore, the method further comprises:

[0017] Receive keywords and text types from external input;

[0018] Selecting target data from the stored business data according to the keyword; and

[0019] Taking the target data and text type as input, corresponding text data is obtained based on a preset neural network model.

[0020] Furthermore, the method further comprises:

[0021] Creating a plurality of clusters with each of the fields as cluster centers; and

[0022] Classify business data, including:

[0023] Classifying the business data based on a preset classification model and the cluster center;

[0024] When there is business data that does not belong to any existing cluster, create a new cluster and set a new cluster center; and

[0025] Returns new clusters and new cluster centers.

[0026] Furthermore, the method further comprises:

[0027] monitoring the number of elements in the new cluster; and

[0028] When the number of elements in the new cluster reaches a threshold, a prompt message is issued.

[0029] In a second aspect, one or more embodiments of this specification provide a business data management device, including: a collection module, a classification module, an analysis module and a storage module

[0030] The acquisition module is used to collect business data of various business systems within the enterprise;

[0031] The classification module is used to classify the business data according to preset fields within the enterprise;

[0032] The analysis module is used to perform quality analysis on the business data in each field based on the data quality model of each field, obtain a quality score, and remove business data whose quality score does not reach a threshold; the fields include: market trend prediction, enterprise competition situation simulation, intelligent question and answer, intelligent writing, intelligent topic selection, report management and data security. One or more; and

[0033] The storage module is used to store the remaining service data.

[0034] Furthermore, the device further comprises: a modeling module;

[0035] The modeling module is used to perform business modeling on the stored business data to obtain structured data, and the structured data is used to support specific business scenarios.

[0036] Furthermore, the device also includes: a text generation module;

[0037] The text generation module is used to receive externally input keywords and text types; select target data from the stored business data according to the keywords; and obtain corresponding text data based on a preset neural network model with the target data and text type as input.

[0038] Furthermore, the classification module is also used to create multiple clusters with each of the fields as cluster centers; classify business data based on a preset classification model and the cluster centers; create a new cluster and set the cluster center when there is business data that does not belong to any existing cluster; and return the new cluster and the cluster center.

[0039] In a third aspect, one or more embodiments of this specification provide a storage medium, including:

[0040] Used to store computer executable instructions, which implement the method described in any one of the first aspects when executed.

[0041] Compared with the prior art, this application can at least achieve the following technical effects:

[0042] The present invention uniformly collects the business data of each business system by constructing various business systems within the enterprise. Afterwards, the available business data of each business field is screened out according to each business field. Furthermore, the quality analysis of the business data in each field is performed, and the business data whose quality score does not reach the threshold is eliminated to ensure the accuracy of the business data. Finally, the business data of each business field is uniformly stored in the business database corresponding to the business field. In this way, the internal data of the enterprise is unified, and the data is open and shared, which can solve the problem of data islands within the enterprise. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate one or more embodiments of this specification or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0044] Figure 1 A flowchart of a business data governance method provided for one or more embodiments of this specification;

[0045] Figure 2 A schematic diagram of a business data management device provided for one or more embodiments of this specification. DETAILED DESCRIPTION

[0046] In order to enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the following will be combined with the drawings in one or more embodiments of this specification to clearly and completely describe the technical solutions in one or more embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this document.

[0047] With the development of science and technology, every enterprise needs to sort out data to improve its efficiency. There are two common ways of sorting: the first is calibration and verification. For example, the finance department obtains the financial data of each department, verifies and calibrates the data, and finally obtains the company's revenue. The second is application. For example, the sales department shows the company's products to customers. Before the formal presentation, the sales department needs the purchasing department to provide raw material information and the product department to provide R&D information. After that, the sales department will merge the two types of information to get a product report.

[0048] The first case is the correspondence between data and data, which can be achieved by simply establishing a mapping relationship between the data. This means that in the first case, the staff does not need to know the meaning of the data, as long as the relationship is established. The second case is more complicated. First of all, the raw material information and R&D information cannot be directly displayed to customers. It needs to be processed to reflect the advantages of the product, otherwise for customers, the raw material information and R&D information may just be a number or a piece of code. Sufficient professional knowledge is required to process this data. For sales staff, they usually do not have enough professional knowledge to understand the raw material information and R&D information. Similarly, procurement staff do not understand sales and technology, and technical personnel do not understand sales and procurement. As a result, the data of the three departments can only be used within the department, and other departments cannot use it, which eventually formed three data islands.

[0049] In view of the above problems, the present application embodiment proposes a business data governance method, including the following steps:

[0050] Step 1: Collect business data from various business systems within the enterprise.

[0051] In the embodiment of the present application, each business system is a department commonly set up in an enterprise, such as a human resources department, a finance department, a product department, a sales department, and a purchasing department.

[0052] Step 2: Classify the business data according to the preset fields within the enterprise.

[0053] In the embodiments of the present application, each field includes one or more of: market trend forecasting, enterprise competition situation simulation, intelligent question and answer, intelligent writing, intelligent topic selection, report management, and data security. When classifying, determine what data is needed for each field, and classify the corresponding data into the corresponding category. For example, market trend forecasting requires knowing the contract amount, and the contract amount is classified into the market trend forecasting category. When answering questions raised by customers, intelligent question and answer needs to know which customers have signed contracts, and the customer names are classified into the intelligent question and answer category.

[0054] It should be noted that there is no corresponding relationship between the domain and the aforementioned departments. It is just a specific business scenario. For example, market trend forecasting is not necessarily done by the sales department. The product department and the human resources department can also do it. However, these fields have a characteristic that they need to integrate data from multiple departments. For example, market trend forecasting requires sales data, product data, and even some financial data. Intelligent question and answer refers to a window opened by the enterprise to answer questions raised by users. This scenario covers the data of almost all departments within the enterprise. Intelligent topic selection refers to setting corresponding promotion examination questions for each department, which also covers the data of almost all departments within the enterprise. In addition, one business data can correspond to multiple categories. For example, when the business data is a contract, it can be used for both market trend forecasting and intelligent question and answering. At this time, the contract can be classified into market trend forecasting and intelligent question and answer respectively.

[0055] Step 3: Based on the data quality models of each field, conduct quality analysis on the business data in each field to obtain quality scores, and eliminate business data whose quality scores do not reach the threshold.

[0056] In the embodiments of the present application, each field has different requirements for data quality. Therefore, each field requires a corresponding quality model. For example, market trend prediction requires real-time and accuracy of data, while intelligent question answering focuses more on confidentiality and non-conflict between data. That is, each field has its own set of scoring quality standards, so each field has its own data quality model. The data quality model can be a threshold, a condition or a neural network.

[0057] Step 4: Store the remaining business data to form data assets.

[0058] In the embodiments of the present application, high-quality data is obtained by eliminating data that does not meet the quality requirements, thereby forming data assets. These data assets can be reused. For example, market trend forecasting requires the use of data from the last 5 or even 10 years, but market trend forecasting may need to be done once a year, which means that there must be a lot of data that will be used multiple times. For intelligent question-and-answer scenarios, as the number of inquiries increases, data assets will be reused in large quantities.

[0059] To sum up, based on the idea of ​​data governance, this application reclassifies conventional business data to form data assets that can be reused in various scenarios, thereby achieving efficient use of data from different business systems and breaking the data silos of each business system.

[0060] In actual scenarios, there are many fixed ways of display. For example, market trend forecasts and enterprise competition situation simulations need to form reports, and intelligent question and answer and intelligent writing need to form corresponding texts. In order to cope with this specific scenario, in an embodiment of the present application, business modeling is performed on the stored business data to obtain structured data, which is used to support specific business scenarios. Usually, the above method can further process data assets to increase the value of data assets.

[0061] In actual scenarios, business scenarios are complex and changeable, and new business forms will continue to emerge. Therefore, no matter how detailed the field classification is, it is difficult to cover all business fields. Improving the compatibility and scalability of data assets is an effective way to solve the above problems. In order to make data assets have good compatibility and scalability, this application provides two sets of solutions to deal with two situations respectively, specifically:

[0062] In the first case, the specific business is related to the set field but not the same. For example, technology development trend forecast. In terms of content, technology development trend forecast is definitely related to market trend forecast. But the specific parameters will be different. In terms of form, the formats of the two reports are generally the same, but there will be differences in the specific presentation of data. Therefore, if you only rely on the template and data of technology development trend forecast to write a technology development trend report, it is obviously impossible to meet the needs. For similar situations, the specific methods are as follows:

[0063] Receive external input keywords and text types; select target data from stored business data based on the keywords; use the target data and text type as input, and obtain corresponding text data based on a preset neural network model.

[0064] The embodiment of the present application constructs a neural network model based on the fact that neural networks are good at handling complex problems and existing data assets, so as to expand the scope of application of data assets and thus expand the business scope of the enterprise.

[0065] In the second case, with the iteration and update of technology, new businesses will continue to be generated. These new businesses are likely to be unable to be solved using existing data assets, and the business data needs to be reclassified. For this scenario, in an embodiment of the present application, multiple clusters are created with each of the fields as cluster centers; business data is classified based on a preset classification model and the cluster centers; when there is business data that does not belong to any existing cluster, a new cluster is created and the cluster center is set; the new cluster and the cluster center are returned. Through the above method, it can help enterprises monitor whether there are new businesses in real time, and can classify business data based on new businesses in real time, thereby improving the scalability of data assets. The algorithm for implementing the above method can be a K-means clustering algorithm, a single-channel clustering algorithm, and other clustering algorithms available to those skilled in the art.

[0066] Preferably, in order to further help the enterprise confirm whether a new business is generated, in the embodiment of the present application, the number of elements in the new cluster is monitored; when the number of elements in the new cluster reaches a threshold, a prompt message is issued. By calculating the number of suspected new businesses, it is detected whether the new business volume has reached a certain scale. If so, a prompt is issued to provide a reference for the enterprise's decision-making.

[0067] The present application embodiment proposes a business data management device, such as Figure 2 As shown, it includes: a collection module 201, a classification module 202, an analysis module 203 and a storage module 204;

[0068] The collection module 201 is used to collect business data from various business systems within the enterprise.

[0069] The classification module 202 is used to classify business data according to preset fields within the enterprise.

[0070] The analysis module 203 is used to perform quality analysis on the business data in each field based on the data quality model of each field, obtain quality scores, and eliminate business data whose quality scores do not reach the threshold; each field includes: market trend forecasting, enterprise competition situation simulation, intelligent question and answer, intelligent writing, intelligent topic selection, report management and data security. One or more.

[0071] The storage module 204 is used to store the remaining service data.

[0072] In the embodiment of the present application, the device further includes: a modeling module;

[0073] The modeling module is used to perform business modeling on the stored business data to obtain structured data, which is used to support specific business scenarios.

[0074] In the embodiment of the present application, the apparatus further comprises: a text generation module;

[0075] The text generation module is used to receive external input keywords and text types; select target data from the stored business data based on the keywords; and obtain corresponding text data based on the preset neural network model with the target data and text type as input.

[0076] In an embodiment of the present application, the classification module 202 is also used to create multiple clusters with each field as the cluster center; classify business data based on a preset classification model and cluster center; when there is business data that does not belong to any existing cluster, create a new cluster and set the cluster center; and return the new cluster and cluster center.

[0077] The present application provides a storage medium, including:

[0078] Used to store computer executable instructions, which implement the method described in any of the above embodiments when executed.

[0079] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0080] In the 1930s, improvements to a technology could be clearly distinguished as hardware improvements (for example, improvements to the circuit structure of diodes, transistors, switches, etc.) or software improvements (improvements to the method flow). However, with the development of technology, many improvements to the method flow today can be regarded as direct improvements to the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved method flow into the hardware circuit. Therefore, it cannot be said that an improvement in a method flow cannot be implemented using a hardware entity module. For example, a programmable logic device (PLD) (such as a field programmable gate array (FPGA)) is such an integrated circuit whose logical function is determined by the user's programming of the device. Designers can "integrate" a digital method on a PLD by programming it themselves, without having to ask a chip manufacturer to design and produce a dedicated integrated circuit chip. Moreover, nowadays, instead of manually making integrated circuit chips, this kind of programming is mostly implemented by "logic compiler" software, which is similar to the software compiler used when developing and writing programs, and the original code before compilation must also be written in a specific programming language, which is called hardware description language (HDL). There is not only one HDL, but many kinds, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc. The most commonly used ones are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also know that it is only necessary to program the method flow slightly in the above-mentioned hardware description languages ​​and program it into the integrated circuit, and then it is easy to obtain the hardware circuit that implements the logic method flow.

[0081] The controller can be implemented in any appropriate manner, for example, the controller can take the form of a microprocessor or processor and a computer-readable medium storing a computer-readable program code (such as software or firmware) that can be executed by the (micro)processor, a logic gate, a switch, an application-specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320. The memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art also know that in addition to implementing the controller in a purely computer-readable program code manner, the controller can be implemented in the form of a logic gate, a switch, an application-specific integrated circuit, a programmable logic controller, and an embedded microcontroller by logically programming the method steps. Therefore, this controller can be considered as a hardware component, and the devices included therein for implementing various functions can also be regarded as structures within the hardware component. Or even, the devices for implementing various functions can be regarded as both software modules for implementing the method and structures within the hardware component.

[0082] The methods, devices, modules or units described in the above embodiments may be implemented by a computer chip or entity, or by a product having a certain function. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, 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.

[0083] For the convenience of description, the above devices are described in terms of functions and are divided into various units. Of course, when implementing the embodiments of this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0084] It will be appreciated by those skilled in the art that one or more embodiments of this specification may be provided as methods, methods or computer program products. Therefore, one or more embodiments of this specification may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0085] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (methods), and computer program products according to the embodiments of this specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0086] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0087] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0088] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0089] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0090] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules 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 disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0091] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0092] One or more embodiments of the present specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. One or more embodiments of the present specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0093] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the method embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0094] The above description is only an embodiment of this document and is not intended to limit this document. For those skilled in the art, this document may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of this document should be included in the scope of the claims of this document.

Claims

1. A business data governance method, characterized in that: include: Collect business data from various business systems within the enterprise; Classify the business data according to preset fields within the enterprise; Based on the data quality models of various fields, quality analysis is performed on the business data in various fields to obtain quality scores, and business data whose quality scores do not reach the threshold are eliminated; the various fields include: market trend forecasting, enterprise competition situation simulation, intelligent question and answer, intelligent writing, intelligent topic selection, report management and data security. as well as Store the remaining business data to form data assets.

2. The method according to claim 1, characterized in that The method further comprises: Business modeling is performed on the stored business data to obtain structured data, which is used to support specific business scenarios.

3. The method according to claim 1, characterized in that The method further comprises: Receive keywords and text types from external input; Selecting target data from the stored business data according to the keyword; and Taking the target data and text type as input, corresponding text data is obtained based on a preset neural network model.

4. The method according to claim 1, characterized in that: The method further comprises: Creating a plurality of clusters with each of the fields as cluster centers; and Classify business data, including: Classifying the business data based on a preset classification model and the cluster center; When there is business data that does not belong to any existing cluster, a new cluster is created and a new cluster center is set; and Returns new clusters and new cluster centers.

5. The method according to claim 4, characterized in that The method further comprises: monitoring the number of elements in the new cluster; and When the number of elements in the new cluster reaches a threshold, a prompt message is issued.

6. A business data management device, characterized in that: include: Acquisition module, classification module, analysis module and storage module The acquisition module is used to collect business data of various business systems within the enterprise; The classification module is used to classify the business data according to preset fields within the enterprise; The analysis module is used to perform quality analysis on the business data in each field based on the data quality model of each field, obtain quality scores, and eliminate business data whose quality scores do not reach the threshold; the fields include: market trend prediction, enterprise competition situation simulation, intelligent question and answer, intelligent writing, intelligent topic selection, report management and data security. as well as The storage module is used to store the remaining service data.

7. The device according to claim 6, characterized in that The device also includes: a modeling module; The modeling module is used to perform business modeling on the stored business data to obtain structured data, and the structured data is used to support specific business scenarios.

8. The device according to claim 6, characterized in that The device also includes: a text generation module; The text generation module is used to receive externally input keywords and text types; select target data from the stored business data according to the keywords; and obtain corresponding text data based on a preset neural network model with the target data and text type as input.

9. The device according to claim 6, characterized in that The classification module is also used to create multiple clusters with each of the fields as cluster centers; classify business data based on a preset classification model and the cluster centers; when there is business data that does not belong to any existing cluster, create a new cluster and set the cluster center; and return the new cluster and the cluster center.

10. A storage medium, characterized in that: include: Used to store computer executable instructions, which implement the method according to any one of claims 1 to 5 when executed.

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