CN105335814A - Online big data intelligent cloud auditing method and system - Google Patents
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Abstract
The invention discloses an online big data intelligent cloud auditing method and system. The method comprises data acquisition, data processing, data analysis and data storage. During the data acquisition, basic data of cloud auditing are acquired according to auditing items, and the basic data are updated in real time, wherein the basic data include dynamic data associated with enterprises and market big data. During the data processing, the basic data are processed to obtain first data used for data analysis. During the data analysis, the first data are analyzed according to a corresponding auditing model according to auditing demands to obtain second data used for production operation decision management of service enterprises. During the data storage, the second data are stored in a distributed cloud storage mode for later calling. According to the online big data intelligent cloud auditing method and system, online auditing is realized; the dynamic data and the market big data are acquired through online search, so that online dynamic auditing is realized; and the data are processed by adopting the corresponding auditing model according to the auditing demands to obtain the data used for the operation management decision of the service enterprises, so that real-time and efficient services can be provided for full life cycle of the enterprises from enterprise benefits.
Description
Technical Field
The invention relates to the technical field of information auditing, in particular to an online big data intelligent cloud auditing method and system.
Background
Cloud computing is a brand-new leading information technology, super computing and storage capacity are achieved by combining an IT technology and the Internet, and the driving force for promoting the rise of cloud computing is the development of high-speed Internet and virtualization technology, and the development of chips, hard disks and data centers which are cheaper and have powerful functions. Cloud computing can be regarded as a product of development and fusion of traditional computer and network technologies, such as distributed computing, parallel computing, utility computing, network storage, virtualization, load balancing and the like. There are many key technologies involved in cloud computing, including: communication, large-scale distributed storage technology, mass data processing technology, resource management, virtualization technology and the like.
Big data (bigdata), or huge data, refers to information that is too large in size to be captured, managed, processed, and organized in a reasonable time by current mainstream software tools to help enterprise business decisions to be more positive. Big data has 4V characteristics: volume (bulk), Velocity (high speed), Variety (multiple), Value (Value). Large data requires special techniques to efficiently process large amounts of data that are tolerant of elapsed time.
The coming of the mobile internet plus means the coming of the era of cross-border fusion, innovation drive, structure remodeling, humanity honoring and open ecology. The mobile internet plus is the internet plus all traditional industries, and the internet and the traditional industries are deeply fused by utilizing a computer technology, an information communication technology, a cloud computing technology and an internet platform, so that a new development state is created. The era of big data and cloud computing has penetrated into the fields of various industries and business functions and becomes an important component. According to the definition of an authoritative NIST, the existing cloud computing is mainly divided into three service modes, namely an infrastructure as a service (iaas) (infrastructure), which mainly provides infrastructure services for users, and comprises a computer, a server, a firewall, a storage device, a network device and the like; the platform is a platform (platform for platform application service), and mainly provides an application development, test and deployment platform for a user, that is, a complete system platform including application design, application development, application test, application deployment and application hosting is provided to the user as a service; software is a service saas (software application service), and is mainly used for providing software such as application programs for users. It can be said that the three service modes of cloud computing are from the perspective of hardware devices.
Amazon, Google, IBM, microsoft and other large companies abroad are all predecessors of cloud computing, and for example, Amazon provides computing and storage services for enterprises by using elastic computing cloud and simple storage service. Google docs is the earliest cloud computing application that was introduced, which, like microsoft's office online software, can process and search documents, forms, slides, and can also share with others over a network and set sharing rights. IBM introduced a "blue cloud" computing platform of "change game rules" in 2007, 11 months, bringing a ready-to-buy cloud computing platform for customers. Microsoft follows the cloud computing pace, and in 10 months in 2008, a Windows Azure operating system is introduced, so that after Windows replaces DOS, Microsoft subverts again to build a new cloud computing platform through the Internet, and Windows is really extended to the 'blue sky' from a PC. However, the research of cloud computing in foreign major companies only relates to the fields of public service, data processing, online storage or sharing and the like, belongs to the field of universal research and development, and does not relate to the related technology of combining the fields of cloud computing, big data and auditing.
With the increasing maturity of cloud computing technology and application, more users in China begin to apply cloud-based services, and more enterprises begin to fall into the ground for cloud computing. However, whether the search engine is a general hundred degree/google search engine or a dedicated service type dribble trip/58 city, etc., the personalized service is provided for the user by using the big data information, so the oriented object is mainly concentrated on the user or the consumer.
Traditional enterprise finance stays at the initial stage of accounting and accounting, and traditional auditing stays at the stages of checking and accounting and post accounting. Even in computer-aided auditing, such as accounting computerization systems of all units, the auditing system only realizes the datamation of vouchers, information and the like in the auditing process, only relates to historical data of a single enterprise to cause information isolated islands, and the consulting service is disposable, so that the conditions of all enterprises need to be known again every time, and data, industry dynamic data and the like among related enterprises cannot be comprehensively utilized. In order to meet the increasingly severe market challenges, it is urgently needed to mine and analyze and apply the mass financial data of the enterprise through technical means, and enhance the data utilization efficiency and market competitiveness of the enterprise.
Disclosure of Invention
The invention provides an online big data intelligent cloud auditing method and system, and aims to solve the technical problem that online dynamic continuous auditing cannot be realized in the traditional information auditing field.
The technical scheme adopted by the invention is as follows:
according to one aspect of the invention, an online big data intelligent cloud auditing method is provided, which is based on the combination of cloud computing and big data, and comprises the following steps:
acquiring data, namely acquiring basic data of cloud audit according to an audit project, wherein the basic data comprises dynamic data associated with an enterprise, and updating the basic data in real time;
processing the data, namely processing the basic data to obtain first data for data analysis;
analyzing the data, namely analyzing the first data according to the audit requirement and a corresponding audit model to obtain second data for the production and operation decision management of the service enterprise;
and data storage, namely storing the second data in a distributed cloud storage mode for subsequent calling.
Further, the underlying data includes, but is not limited to: the system comprises enterprise self data, market information big data of an enterprise as a market main body, public policy information data, industry standard data, expert experience data and online operation log data.
Further, the data processing is to convert the basic data into the first data of the normalized data meeting the accounting criteria through one or more operations of data cleaning, data conversion, data integration and data loading.
Further, the data analysis is based on an intelligent auditing model of an auditor knowledge graph and/or a big data correlation auditing method model to obtain second data.
Further, the method of the invention also comprises the following steps:
and the data output is used for outputting the second data in a visual form meeting the personalized requirements of the user.
Further, audit projects are cost accounting, and basic data include but are not limited to: internal cost, external cost, industry comparison cost, industry standard cost, pre-cost, social average cost; wherein,
the internal cost comprises all tangible and intangible resource consumption cost items required for the whole process of internal decision, management, production and operation of the enterprise;
the external cost is a relevant cost index generated by economic environment, law and regulation environment, market supply and demand environment, regional environment, social public environment and resource environment of the enterprise except the enterprise;
the industry comparison cost refers to a leading cost index and a leading dynamic cost index of an enterprise which is used as a public reference, objectively exists and has cost comparison advantages in the industry where the enterprise is located;
the industry standard cost refers to the average cost for the same type of enterprises in the industry to produce the same type of goods or provide the same type of service;
the pre-cost comprises a potential cost item which can be reasonably predicted by an enterprise, can ensure the continuous operation of the enterprise and has a relatively superior cost;
the social average cost is a dynamic average cost index combining a related price index, a consumption index, a purchasing power index and economic tolerance in the time and space where an enterprise is located.
According to another aspect of the invention, an online big data intelligent cloud auditing system is provided, which is based on the combination of cloud computing and big data, and comprises:
the data acquisition unit is used for acquiring basic data of cloud audit according to the audit project, wherein the basic data comprises dynamic data related to enterprises, and updating the basic data in real time;
the data processing unit is used for processing the basic data to obtain first data for data analysis;
the data analysis unit is used for analyzing the first data according to the audit requirement and the corresponding audit model to obtain second data for the production and operation decision management of the service enterprise;
and the data storage unit is used for storing the second data in a distributed cloud storage mode for subsequent calling.
Further, the data acquisition unit adopts a NoSQL database, including:
the static data import module is used for receiving static data;
the dynamic data capturing module is used for capturing dynamic data;
and the dynamic data mining and analyzing module is used for mining and analyzing the data in the big data according to the requirements to obtain the required target data.
Further, the data storage unit includes:
the data encryption module is used for setting data access authority;
the analysis track storage module is used for identifying and storing the analysis track of the data;
and the suspicious point database storage module is used for updating the suspicious point database, calling the suspicious point database by the data analysis unit and automatically giving an early warning when similar information appears.
Further, the system of the present invention further comprises:
and the data output unit is used for outputting the second data in a visual form meeting the personalized requirements of the user.
The invention has the following beneficial effects:
according to the online big data intelligent cloud auditing method and system, the cloud computing and big data technology is applied to the field of traditional auditing, online auditing is achieved, dynamic data related to an enterprise are searched and collected online, online dynamic auditing is achieved, furthermore, data used for serving enterprise production and operation management decisions are obtained by processing the data through a corresponding auditing model according to auditing requirements, and real-time and efficient services can be provided for the whole life cycle of the enterprise from enterprise benefits. The big data is used for associating the previously dispersed data information islands, and integrating, mining and analyzing the data through the cloud computing technology, so that the guiding effect of the big data is exerted, meanwhile, the auditing method and the auditing system can get rid of the limitation of time and region, subvert the traditional auditing method, inject a brand-new auditing method and a cost value concept into intelligent enterprises and intelligent economy, liberate the economy of producers and service entities, and really realize the bidirectional liberation and economic ecological balance of producers and consumers.
In addition to the objects, features and advantages described above, other objects, features and advantages of the present invention are also provided. The present invention will be described in further detail below with reference to the drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:
FIG. 1 is a schematic flow chart diagram of an online big data intelligent cloud auditing method according to a preferred embodiment of the invention;
FIG. 2 is a schematic flow chart diagram of an online big data intelligent cloud auditing method according to another preferred embodiment of the invention;
FIG. 3 is a schematic structural diagram of an online big data intelligent cloud auditing system according to a preferred embodiment of the present invention;
FIG. 4 is a schematic diagram of a data acquisition unit according to a preferred embodiment of the present invention;
FIG. 5 is a schematic diagram of a data storage unit according to a preferred embodiment of the present invention.
Detailed Description
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings.
The invention provides an online big data intelligent cloud auditing method, which is based on the combination of cloud computing and big data to innovate the traditional auditing method and mode. Referring to fig. 1, the method of the present embodiment includes:
step S100, data acquisition, namely acquiring basic data of cloud audit according to audit projects, wherein the basic data comprises dynamic data associated with enterprises, and updating the basic data in real time; the key improvement of the embodiment is that not only can historical static data of an enterprise related to the audit project be collected, but also various dynamic data related to the audit project can be collected through a big data system, so that online dynamic audit is realized, and more scientific guidance data is provided for production and operation decision management of the enterprise.
As a preferred implementation manner, in the cloud audit process of this embodiment, the NoSQL database is used for collecting basic data. NoSQL generally refers to a non-relational database, and the generation of the NoSQL database is to solve the challenges brought by large-scale data set multiple data types, especially the problem of big data application, and can establish a fast and extensible storage library for big data. In a traditional relational database, logical database setting needs to be carried out first, character length and type setting are carried out on each storage variable, and the data mode of the traditional relational database is static. In a big data environment, the data mode is dynamically changed, and the traditional database technology cannot solve the problem. Meanwhile, for the amplification of data types, data types such as documents, reports, pictures, audio and video cannot be stored in a relational database, and the data types become data information required by online cloud audit, so that a NoSQL database is required for data acquisition.
As a preferred implementation manner, in the data collection step of this embodiment, all basic data that can provide reference opinions for enterprise operation management development are collected online, which includes static interface import, dynamic data capture, dynamic data mining and analysis, and all data will be constructed into a large data center for processing by subsequent different auditing models or various data analysis methods. The basic data of the embodiment includes but is not limited to the following six aspects of data information:
1. enterprise self data: including financial data, asset data, warehousing data, sales data, internal control flow data, etc.
2. Market information big data of an enterprise as a market subject, such as related data of an industrial chain: the sensitivity of different industries to the data requirements of the industrial chain is different, the required data information is different, and the association degree and the integration requirement of the data are different. Taking a production type enterprise as an example, firstly, the components of raw materials need to be traced according to the produced products, the raw materials are traced to upstream suppliers, the supplier data can be displayed in a large scale and in regions, then, the material quotation information of the suppliers is obtained through industry associations, commodity price bureaus or cooperation with the suppliers and other channels, the reference is provided for the production enterprises, the suppliers are comprehensively compared and screened, and therefore the suppliers most suitable for the manufacturers are selected, and a long-term cooperation relationship is achieved. In addition, the data can be hierarchically referenced and information provided by the circulation of the produced products going back to the customers, wherein the enterprises are just the suppliers of the customers. For high and new technology enterprises and service type enterprises, the labor cost and the research and development cost are high, and the requirements on talent information can be emphasized more, so that the requirements of the enterprises can be aligned accurately through the arrangement of the talent information by the established large data center, the series of costs of enterprise recruitment, communication, running-in and the like are avoided, and the method becomes an important ring for enterprise cost control and creates value.
3. Public policy information data: the system comprises various policies and regulations data related to enterprises, such as economy, finance, tax, trade, law and the like at home and abroad.
4. Industry standard data: the system comprises related dynamic information of the industry where the enterprise is located and industry index data information.
5. Expert experience data: the series of online operations of experts are recorded and can be analyzed through computer technology, the machine learns according to different risk preferences, experience guidance in different fields and different decision styles of the experts, an artificial language is converted into an intelligent computer language, and online intelligent analysis and communication are completed.
6. Online operation log data: the system comprises the steps of identifying the identity of a user, such as financial staff or high-level management staff, intelligently analyzing the system according to the difference of the attention degree of the problem, intelligently matching the operation management problems which are most interested by the users at different posts or enterprises of different scales and different industries, and automatically providing reasonable suggestions for the enterprises in time and effectively.
Step S200, data processing is carried out, and basic data are processed to obtain first data for data analysis;
the data processing of the embodiment is to convert the basic data into the first data of the normalized data meeting the accounting criteria through one or more operations of data cleaning, data conversion, data integration and data loading. The data cleaning, data conversion, data integration and data loading are all existing data processing means, and detailed description thereof is omitted here.
As a better mode, in the data processing of this embodiment, a distributed processor system in a cloud computing platform is used, basic data acquired by a front end is imported into the distributed processor system, such as a distributed database or a distributed storage cluster, and cleaning or preprocessing is performed on the basis of the import, so that the processing requirement of mass data can be met, and the import amount per second can often reach hundreds of megabits, even megabits. Preferably, the distributed processor system integrates a dynamic load balancing and group management and allocation mechanism, and the platform can monitor the running state of each node of the whole system in real time and dynamically adjust and balance the load of different resources in the whole system range, thereby well solving the problems of reasonable use and effective management of a large-scale system.
Step S300, analyzing data, namely analyzing the first data according to an audit model corresponding to the audit requirement to obtain second data for service enterprise operation management decision;
the audit requirement of this embodiment refers to a consultation service requirement of a user, that is, information or data for business management decision-making that the user wants to obtain, and the data analysis of this embodiment obtains a relevant conclusion by analyzing the relationship between financial data and non-financial data, and includes establishing a calculation intermediate table, and performing audit data analysis by using a financial audit analysis model, multidimensional data analysis, data mining, and the like.
As a preferred implementation manner, the data analysis in this embodiment adopts a distributed processor system in the cloud computing platform, such as a distributed database or a distributed storage cluster, so as to perform analysis and/or sorting, summarizing, and other processing on the mass data stored therein. Preferably, the distributed processor system integrates a dynamic load balancing and group management and allocation mechanism, and the platform can monitor the running state of each node of the whole system in real time and dynamically adjust and balance the load of different resources in the whole system range, thereby well solving the problems of reasonable use and effective management of a large-scale system.
As a preferred implementation, the data analysis of the present embodiment is based on intelligent analysis of machine learning in a big data environment, where machine learning refers to finding out unknown things from uncertain details. Common areas of machine learning are: speech recognition, character recognition (OCR), text classification, etc. In the online cloud audit, machine learning is utilized to analyze audit data in a big data environment, the clustering problem and the classification problem are solved, and frequent item sets are mined. For newly emerging text audit data types, machine learning can group them by feature through clustering application; correcting audit data information which is wrongly attributed by classification problems; frequent item set mining may then be used to audit frequently co-occurring features in the data, indicating that there is some correlation between them. Preferably, the data analysis of the present embodiment is more online to collect and discover audit information using correlation analysis. Because the big data technology provides unprecedented cross-domain and quantifiable dimensions, a large amount of relevant information of the audit problem can be recorded, calculated and analyzed. The causal relationship among things is not changed by the big data and cloud computing technology, but the development and utilization of the correlation relationship in the big data and cloud computing technology reduce the dependence of data analysis on the causal logic relationship, and even more, the data analysis based on the correlation relationship is prone to be applied, and the verification based on the correlation relationship analysis is an important characteristic of the big data and cloud computing technology. The online audit of the embodiment is converted from the traditional method of depending on causal relationship to collect and discover the audit evidence to the online method of collecting and discovering the audit evidence by using correlation relationship.
As a preferred implementation, the data analysis of this embodiment puts "sample as a whole" into the mode of online audit. Compared with a closed curing mode which depends on local and limited small data, digital accuracy and the like, the big data more emphasizes the universality, representativeness, relevance and timeliness of the data, so that the truth of the things is further approached, and the on-line big data intelligent cloud audit pursues the 'overall' and 'efficient' butt joint problem and the problem solution of the things. Around big data, when implementing online audit, utilize big data, cloud computing technology, use novel technological means and instrument such as distributed topology structure, cloud database, networking audit, data mining, solve most work on the line.
In a preferred implementation mode, the data analysis of the embodiment is based on an intelligent auditing model of an auditor knowledge graph and/or a big data correlation auditing method model to obtain the second data. The intelligent audit model and/or the big data correlation audit method model can be preset and stored on the cloud computing platform for data analysis and calling.
Taking data processing and data analysis in the medical industry as an example, for example, collecting price information of upstream products or materials in the medical industry, and then classifying: the method comprises the steps of comparing product information and time-sharing price information of traditional Chinese medicines, western medicines, Chinese and western medicines and the like with a certain product in a certain category in a medicine inventory management system of a user unit and the price at the same time, screening, automatically identifying the difference with industry information price information and dividing the difference interval, and then carrying out further analysis on the basis of basic information of the user unit and submitted other information, wherein the analysis can enable each product to be viewed independently, and each product of the user unit is cleaned instead of the traditional sampling mode or the traditional mode of directly selecting a product with a larger amount of money as the basis of analysis. The price information of upstream products or materials in the pharmaceutical industry is utilized to analyze, namely, related relation evidence is found to be used as further analysis, the traditional causal relation is not adopted to prove whether the price of the product is real or not, the relation between data is analyzed instead of traditional numbers, then the reason of the difference is further searched, and finally the ideal analysis effect is achieved. And the analysis track of the process can be identified and reserved as the data of the method library. The data analyzed by the big data not only is macroscopic data, but also comprises dynamic information of an enterprise, a doubt database, a method database and continuously updated data, and the occurrence of similar information can be automatically warned.
And S400, storing data, namely storing second data in a distributed cloud storage mode for subsequent calling.
In this embodiment, all data, including copies and backups, is stored in the contract, service level agreement and geographical locations allowed by the regulations. Preferably, the data storage establishes data access control, and more preferably, data encryption and/or data level differentiation is performed on the data, and the data is stored separately. The cloud storage data is safe and confidential, and the space, the acquisition cost and the maintenance cost of hardware equipment of an enterprise are saved. In addition, through cloud data interaction and authority management, an enterprise information system is not an information isolated island any more, an enterprise can ask for data from partners, suppliers and agents at any time, and data transmission is directly carried out at the cloud, so that the efficiency is greatly improved.
In another embodiment, referring to fig. 2, the method of the present invention further comprises:
and step S500, outputting the data, namely outputting the second data in a visual form meeting the personalized requirements of the user.
In the step, data or data analysis results are converted into patterns, images, tables, files and other forms by using computer graphics, image processing technology or other office software such as excel and the like, and interactive processing can be carried out, so that information can be expressed more clearly and can be delivered to customers as service products.
The cloud computing and big data technology is applied to the field of traditional auditing, online auditing is achieved, dynamic data are collected through online searching, online dynamic auditing is achieved, furthermore, corresponding auditing models are adopted to process the data according to auditing requirements to obtain data for serving enterprise operation management decisions, and real-time and efficient service can be provided for the whole life cycle of an enterprise from enterprise interests. The big data is used for associating the previously dispersed data information islands, and integrating, mining and analyzing the data through the cloud computing technology, so that the guiding effect of the big data is exerted.
The online big data intelligent auditing method of the embodiment is explained by taking cost accounting as a special case of an online continuous dynamic auditing project:
the method comprises the steps of acquiring data, wherein enterprise cost refers to various production costs generated for producing a certain product and comprises all aspects of active labor and physical and chemical labor of the production cost of the product, namely the total cost of the enterprise is sigma PiQi, i belongs to 1-n, wherein P is the price of a single cost item, Q is the number of the single cost item, i is the ith cost item, and n is n cost items. The method mainly comprises six aspects of internal cost, external cost, industry comparative cost, industry standard cost, preposed cost and social average cost, wherein six costs are defined as follows:
1. the internal cost comprises all tangible and intangible resource consumption cost items needed for internal decision, management, production and operation of the whole process of the enterprise. The internal cost here is static, closed, cured historical cost data.
2. The external cost refers to a relevant cost index generated by economic environment, legal and legal environment, market supply and demand environment, regional environment, social public environment and resource environment of the enterprise, which are not the enterprise itself. In the embodiment, external costs related to enterprises are collected through the cloud computing platform and the big data system and serve as one of contents of basic data.
3. The industry comparison cost refers to a leading cost index and a leading dynamic cost index of an enterprise which is used as a public reference, objectively exists and has cost comparison advantages in the industry where the enterprise is located.
4. The industry standard cost refers to the average cost for the same type of enterprises in the industry to produce the same type of goods or provide the same type of service; industry standard costs are typically organized by a wide range of professional companies or governing bodies, with a wide range of adaptability.
5. The pre-cost comprises a potential cost item which can be reasonably predicted by an enterprise, can ensure the continuous operation of the enterprise and has a relatively superior cost; such as environmental pollution abatement costs, carbon emission prevention costs, and technology renewal costs.
6. The social average cost is a dynamic average cost index combining a related price index, a consumption index, a purchasing power index and economic tolerance in the time and space where an enterprise is located.
The cost parameters are not only recorded and presented by various static chart data and character data information of enterprises, such as various raw material information tables, public raw material consumption tables, wage accounting tables, purchase comparison tables, cost distribution tables, intermediate product cost accounting tables, sales detail tables and the like; meanwhile, various dynamic data information can be collected, such as public tax policy information, timely exchange rate ratio information, customer integrity archive information, tax invoice statistical information, industry standard value information and social average cost information of the same product; and the system further comprises related data information which combines market big data to analyze the potential environmental pollution and treatment cost of the same industry and the technology updating speed to form a threat index for the enterprise. According to the embodiment, the cloud computing platform and the big data system are used for collecting the six cost data, a more scientific data source is provided for subsequent cost accounting, scientific and reasonable cost prediction is obtained, the risk early warning capability of enterprises is improved, and the leading advantage is kept in market competition and stands in a non-abortive place.
Secondly, data processing, namely performing graphical modeling on the production process and the cost decision process of various products of a company by a system, and abstracting a mathematical model from the graphical modeling; then, the system cleans, screens, converts and loads the acquired data information according to the requirements of the model on the relevant cost parameters and the requirement application sent by the user, and transmits the data information to the data analysis module for specific data analysis work.
And thirdly, data analysis, wherein the establishment of a dynamic cost model and the setting of rules based on a big data environment are the basis for dynamic data analysis, and the processed cost data information is automatically analyzed mainly by establishing a series of dynamic models to obtain corresponding valuable output information.
Because different enterprises involve various costs, different types of enterprises produce different product flows, various intermediate products and final products, and the manufacturing process of each product may change due to the purchase of new equipment and the improvement of production steps, the dynamic cost algorithm model must have high flexibility and expandability to adapt to the change of the actual production process requirements at any time, thereby simplifying and abstracting the complex actual problems into a reasonable computer data structure process. And then, a computer algorithm is used for analyzing and solving the problems, the manual operation of a cost accountant is replaced, the intelligent cost accounting work is carried out, and the cost decision is helped to be carried out.
In the data analysis process, setting a specific dynamic cost model and an algorithm rule is a key, deep mining and analysis are carried out on various cost parameters by utilizing a big data technology, the cost parameters are combined with various models to obtain data information of different combinations, and multidimensional analysis and display are carried out from various angles of cost decision to obtain comprehensive guidance suggestions.
And fourthly, data storage, namely a cloud-based data storage and management mode.
And fifthly, data visualization output: and outputting visual data facing the personalized requirements of enterprise users. The system supports various output forms such as graphs, images, charts, characters and the like, and a user can select the expression mode of cost accounting and decision results according to the requirement of most clearly expressing the requirement of the user.
The embodiment aims at the fact that the big data intelligent cloud audit of cost accounting is established on the basis of big data, six costs are established, namely, internal cost, external cost, industry comparison cost, industry standard cost, front cost and social average cost, the whole operation process from product design inside an enterprise to production, sales and after-sales service is surrounded by internal management and control of all the costs of the enterprise, the enterprise is further ensured to be in long-distance and overall development strategic targets, constant cost advantages of the enterprise are sought, analysis data of the enterprise and competitors of the enterprise are provided by a special method, managers are helped to form and evaluate enterprise strategies, and accordingly competitive advantages are created, and the purpose that the enterprise effectively adapts to the environment with continuous external changes is achieved. For example, through cost accounting, the enterprise can be ensured to take a dominant position in the bidding process.
The big data intelligent audit cost management of the embodiment starts to reduce cost activities from a design stage and a development planning stage, establishes a cost benefit concept, improves cost prediction and decision level from all directions and the whole process, is linked with the overall benefit of an enterprise, treats the cost and the control problem thereof by using the dynamic cost benefit concept, treats the necessity and rationality of investment from the comparative analysis of investment and output, determines the rise and fall of the cost from the perspective of the benefit, and performs dynamic management of the cost by taking the benefit as the center; the innovative concept of science and technology is established, and the technology is insisted on being combined with the economy. Effectively adopts new technology, new equipment, new process and new material, and depends on modern scientific technology to reduce the product cost. Meanwhile, the technology content of the product is considered to be included in the cost accounting, so that the enterprise can make correct decisions conveniently.
According to another aspect of the present invention, an online big data intelligent cloud auditing system is further provided, based on the combination of cloud computing and big data, the online big data intelligent cloud auditing system of the present embodiment corresponds to the online big data intelligent cloud auditing method of the above embodiment, with reference to fig. 3, the system of the present embodiment includes:
the data acquisition unit 100 is used for acquiring basic data of cloud audit according to the audit project, wherein the basic data comprises dynamic data related to enterprises, and updating the basic data in real time; the key of the embodiment lies in that not only can historical static data of an enterprise related to the audit project be collected, but also various dynamic data related to the audit project can be collected through a big data system, so that online dynamic audit is realized, and more scientific guidance data is provided for the operation decision of the enterprise.
As a preferred implementation manner, in the cloud audit process of this embodiment, the NoSQL database is used for collecting basic data. NoSQL generally refers to a non-relational database, and the generation of the NoSQL database is to solve the challenges brought by large-scale data set multiple data types, especially the problem of big data application, and can establish a fast and extensible storage library for big data. In a traditional relational database, logical database setting needs to be carried out first, character length and type setting are carried out on each storage variable, and the data mode of the traditional relational database is static. In a big data environment, the data mode is dynamically changed, and the traditional database technology cannot solve the problem. Meanwhile, for the amplification of data types, data types such as documents, reports, pictures, audio and video cannot be stored in a relational database, and the data types become data information required by online cloud audit, so that a NoSQL database is required for data acquisition.
As a preferred embodiment, referring to fig. 4, the data acquisition unit 100 includes:
a static data import module 110, configured to receive static data;
a dynamic data capture module 120, configured to capture dynamic data;
and the dynamic data mining and analyzing module 130 is used for mining and analyzing the data in the big data according to the requirement to obtain the required target data.
The data acquisition unit 100 of this embodiment acquires all basic data capable of providing reference opinions for enterprise operation management development on line, including static data, further including dynamic data capture and dynamic data mining and analysis, and all data will constitute a big data center for subsequent processing by different auditing models or various data analysis methods. The basic data of the embodiment includes but is not limited to the following six aspects of data information: the data of the enterprises themselves and the market information data of the enterprises which are taken as market main bodies, such as related data of the industrial chain, public policy information data, industry standard data, expert experience data and online operation log data. The above data definitions refer to method embodiments, and are not described herein again.
The data processing unit 200 is configured to process the basic data to obtain first data for data analysis;
the data processing unit 200 converts the basic data into the first data of the normalized data meeting the accounting criteria through one or more operations of data cleaning, data conversion, data integration and data loading. The data cleaning, data conversion, data integration and data loading are all existing data processing means, and detailed description thereof is omitted here.
As a better way, the data processing unit 200 in this embodiment adopts a distributed processor system in a cloud computing platform, imports basic data acquired at a front end into the distributed processor system, such as a distributed database or a distributed storage cluster, and performs cleaning or preprocessing on the basis of the importation, so as to meet the processing requirement of mass data, and the import amount per second can often reach hundreds of megabits, even megabits. Preferably, the distributed processor system integrates a dynamic load balancing and group management and allocation mechanism, and the platform can monitor the running state of each node of the whole system in real time and dynamically adjust and balance the load of different resources in the whole system range, thereby well solving the problems of reasonable use and effective management of a large-scale system.
The data analysis unit 300 is used for analyzing the first data according to the auditing requirement and a corresponding auditing model to obtain second data for service enterprise operation management decision;
as a preferred implementation manner, the data analysis unit 300 in this embodiment uses a distributed processor system in a cloud computing platform, such as a distributed database or a distributed storage cluster, so as to perform processing such as analysis and/or classification and summarization on mass data stored therein. Preferably, the distributed processor system integrates a dynamic load balancing and group management and allocation mechanism, and the platform can monitor the running state of each node of the whole system in real time and dynamically adjust and balance the load of different resources in the whole system range, thereby well solving the problems of reasonable use and effective management of a large-scale system.
As a preferred implementation, the data analysis unit 300 performs intelligent analysis based on machine learning in a big data environment, where machine learning refers to finding out unknown things from uncertain details. Common areas of machine learning are: speech recognition, character recognition (OCR), text classification, etc. In the online cloud audit, machine learning is utilized to analyze audit data in a big data environment, the clustering problem and the classification problem are solved, and frequent item sets are mined. For newly emerging text audit data types, machine learning can group them by feature through clustering application; correcting audit data information which is wrongly attributed by classification problems; frequent item set mining may then be used to audit frequently co-occurring features in the data, indicating that there is some correlation between them. Preferably, the data analysis of the present embodiment is more online to collect and discover audit information using correlation analysis. Because the big data technology provides unprecedented cross-domain and quantifiable dimensions, a large amount of relevant information of the audit problem can be recorded, calculated and analyzed. The causal relationship among things is not changed by the big data and cloud computing technology, but the development and utilization of the correlation relationship in the big data and cloud computing technology reduce the dependence of data analysis on the causal logic relationship, and even more, the data analysis based on the correlation relationship is prone to be applied, and the verification based on the correlation relationship analysis is an important characteristic of the big data and cloud computing technology. The online audit of the embodiment is converted from the traditional method of depending on causal relationship to collect and discover the audit evidence to the online method of collecting and discovering the audit evidence by using correlation relationship.
As a preferred embodiment, the data analysis unit 300 of the present embodiment implants "sample-total" into the online auditing mode. Compared with the method which depends on local limited small data, numerical accuracy and a closed curing mode, the large data more emphasizes the universality, representativeness, relevance and timeliness of the data, so that the truth of the object is further approached, and the on-line large data intelligent cloud audit pursues the 'overall' and 'high efficiency' of the object, accurately docks the object and solves the problem. Around big data, when implementing online audit, utilize big data, cloud computing technology, use novel technological means and instrument such as distributed topology structure, cloud database, networking audit, data mining, solve most work on the line.
As a preferred implementation manner, the data analysis unit 300 of this embodiment obtains the second data based on the intelligent audit model of the auditor knowledge graph and/or the big data correlation audit method model. The intelligent audit model and/or the big data correlation audit method model can be preset and stored on the cloud computing platform for data analysis and calling.
The data storage unit 400 is configured to store the second data in a distributed cloud storage manner for subsequent invocation.
As a preferred embodiment, referring to fig. 5, the data storage unit 400 includes:
a data encryption module 410 for setting data access authority;
an analysis track storage module 420, configured to identify and store an analysis track of the data;
and the suspicious node library storage module 430 is used for updating the suspicious node library, calling the suspicious node library by the data analysis unit, and automatically giving an early warning when similar information appears.
In this embodiment, all data, including copies and backups, is stored in the contract, service level agreement and geographical locations allowed by the regulations. Preferably, data access control is established for the data storage through the data encryption module 410, and more preferably, data encryption and/or data level differentiation are performed on the data and stored separately. The cloud storage data is safe and confidential, and the space, the acquisition cost and the maintenance cost of hardware equipment of an enterprise are saved. In addition, through cloud data interaction and authority management, an enterprise information system is not an information isolated island any more, an enterprise can ask for data from partners, suppliers and agents at any time, and data transmission is directly carried out at the cloud, so that the efficiency is greatly improved. Preferably, the analysis track storage module 420 stores the data processing process and provides the data processing process for the method library to call, so that the intelligent level of cloud audit is improved. Preferably, the suspicious node database is updated through the suspicious node database storage module 430, so that automatic early warning when similar information appears is realized, real-time dynamic audit of enterprises is realized, and audit requirements of the enterprise in the whole life cycle are met.
Optionally, the system of the present invention further comprises:
and a data output unit 500 for outputting the second data in a visual form satisfying the user personalized requirements.
The data output unit 500 converts data or data analysis results into a form of a graph, an image, a table, a file, etc. by using computer graphics, an image processing technology, or other office software, such as excel, and can perform interactive processing, so that information can be expressed more clearly and delivered to a customer as a service product.
From the above description, it can be known that, according to the online big data intelligent cloud auditing method and system, by applying the cloud computing and big data technology to the traditional auditing field, online auditing is realized, and dynamic data is acquired through online searching, so that online dynamic auditing is realized. The big data is used for associating the previously dispersed data information islands, and integrating, mining and analyzing the data through the cloud computing technology, so that the guiding effect of the big data is exerted, meanwhile, the auditing method and the auditing system can get rid of the limitation of time and region, subvert the traditional auditing method, inject a brand-new auditing method and a cost value concept into intelligent enterprises and intelligent economy, liberate the economy of producers and service entities, and really realize the bidirectional liberation and economic ecological balance of producers and consumers.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. An online big data intelligent cloud auditing method is characterized in that based on the combination of cloud computing and big data, the method comprises the following steps:
acquiring data, namely acquiring basic data of cloud audit according to an audit project, wherein the basic data comprises dynamic data associated with an enterprise, and updating the basic data in real time;
processing the basic data to obtain first data for data analysis;
analyzing the data, namely analyzing the first data according to an audit model corresponding to the audit requirement to obtain second data for the production and operation decision management of the service enterprise;
and data storage, namely storing the second data in a distributed cloud storage mode for subsequent calling.
2. The online big data intelligent cloud audit method of claim 1,
the underlying data includes, but is not limited to: the system comprises enterprise self data, market information big data of an enterprise as a market main body, public policy information data, industry standard data, expert experience data and online operation log data.
3. The online big data intelligent cloud audit method of claim 1,
the data processing is to convert the basic data into the first data of the normalized data meeting the accounting criteria through one or more operations of data cleaning, data conversion, data integration and data loading.
4. The online big data intelligent cloud audit method of claim 1,
and the data analysis is based on an intelligent auditing model of an auditor knowledge graph and/or a big data correlation auditing method model to obtain the second data.
5. The online big data intelligent cloud auditing method of claim 1, further comprising:
and the data output is used for outputting the second data in a visual form meeting the personalized requirements of the user.
6. The online big data intelligent cloud audit method of claim 1,
the audit project is cost accounting, and the basic data includes but is not limited to: internal cost, external cost, industry comparison cost, industry standard cost, pre-cost, social average cost; wherein,
the internal cost comprises all tangible and intangible resource consumption cost items required for the whole process of internal decision, management, production and operation of the enterprise;
the external cost is a relevant cost index generated by economic environment, law and regulation environment, market supply and demand environment, regional environment, social public environment and resource environment of the enterprise except the enterprise;
the industry comparison cost refers to a leading cost index and a leading dynamic cost index of an enterprise which is used as a public reference, objectively exists and has cost comparison advantages in the industry where the enterprise is located;
the industry standard cost refers to the average cost for the same type of enterprises in the industry to produce the same type of goods or provide the same type of service;
the pre-cost comprises a potential cost item which can be reasonably predicted by an enterprise, can ensure the continuous operation of the enterprise and has a relatively superior cost;
the social average cost is a dynamic average cost index combining a related price index, a consumption index, a purchasing power index and economic tolerance in the time and space of an enterprise.
7. The utility model provides an online big data intelligence cloud audit system which characterized in that, based on the combination of cloud computing and big data, includes:
the data acquisition unit is used for acquiring basic data of cloud audit according to audit projects, wherein the basic data comprises dynamic data related to enterprises, and updating the basic data in real time;
the data processing unit is used for processing the basic data to obtain first data for data analysis;
the data analysis unit is used for analyzing the first data according to the audit requirement and the corresponding audit model to obtain second data for the production and operation decision management of the service enterprise;
and the data storage unit is used for storing the second data in a distributed cloud storage mode for subsequent calling.
8. The online big data intelligent cloud audit system of claim 7,
the data acquisition unit adopts a NoSQL database and comprises:
the static data import module is used for receiving static data;
the dynamic data capturing module is used for capturing dynamic data;
and the dynamic data mining and analyzing module is used for mining and analyzing the data in the big data according to the requirements to obtain the required target data.
9. The online big data intelligent cloud audit system of claim 7,
the data storage unit includes:
the data encryption module is used for setting data access authority;
the analysis track storage module is used for identifying and storing the analysis track of the data;
and the suspicious point database storage module is used for updating the suspicious point database, calling the suspicious point database by the data analysis unit and automatically giving an early warning when similar information appears.
10. The online big data intelligent cloud audit system according to any one of claims 7 to 9, further comprising:
and the data output unit is used for outputting the second data in a visual form meeting the personalized requirements of the user.
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| CN111385226A (en) * | 2020-03-18 | 2020-07-07 | 薛爱君 | Network resource allocation method, system and network equipment applied to online office |
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| CN113516375A (en) * | 2021-06-21 | 2021-10-19 | 苏州长城开发科技有限公司 | Maturity evaluation system and method for black light factory |
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