CN117827462A - Financial report analysis integration method and system based on cloud computing - Google Patents
Financial report analysis integration method and system based on cloud computing Download PDFInfo
- Publication number
- CN117827462A CN117827462A CN202410061898.7A CN202410061898A CN117827462A CN 117827462 A CN117827462 A CN 117827462A CN 202410061898 A CN202410061898 A CN 202410061898A CN 117827462 A CN117827462 A CN 117827462A
- Authority
- CN
- China
- Prior art keywords
- cloud
- deployment
- data
- computing
- resource
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000004458 analytical method Methods 0.000 title claims abstract description 118
- 238000000034 method Methods 0.000 title claims abstract description 59
- 230000010354 integration Effects 0.000 title claims abstract description 17
- 238000012545 processing Methods 0.000 claims abstract description 79
- 238000003860 storage Methods 0.000 claims abstract description 39
- 238000013468 resource allocation Methods 0.000 claims abstract description 25
- 238000004891 communication Methods 0.000 claims abstract description 15
- 230000004927 fusion Effects 0.000 claims abstract description 12
- 238000007781 pre-processing Methods 0.000 claims description 14
- 230000035945 sensitivity Effects 0.000 claims description 14
- 238000010219 correlation analysis Methods 0.000 claims description 8
- 238000012216 screening Methods 0.000 claims description 6
- 230000001360 synchronised effect Effects 0.000 claims description 6
- 230000003044 adaptive effect Effects 0.000 claims description 5
- 238000011156 evaluation Methods 0.000 claims description 5
- 230000001915 proofreading effect Effects 0.000 claims description 4
- 238000004364 calculation method Methods 0.000 claims description 3
- 238000013507 mapping Methods 0.000 claims description 3
- 238000012549 training Methods 0.000 claims description 3
- 230000005540 biological transmission Effects 0.000 abstract description 7
- 238000013500 data storage Methods 0.000 abstract description 7
- 230000008569 process Effects 0.000 description 8
- 238000004422 calculation algorithm Methods 0.000 description 5
- 230000008859 change Effects 0.000 description 5
- 238000005516 engineering process Methods 0.000 description 5
- 230000008901 benefit Effects 0.000 description 4
- 238000006243 chemical reaction Methods 0.000 description 3
- 238000004140 cleaning Methods 0.000 description 2
- 238000007418 data mining Methods 0.000 description 2
- 238000000586 desensitisation Methods 0.000 description 2
- 238000010921 in-depth analysis Methods 0.000 description 2
- 238000007726 management method Methods 0.000 description 2
- 238000007637 random forest analysis Methods 0.000 description 2
- 238000006467 substitution reaction Methods 0.000 description 2
- 238000012706 support-vector machine Methods 0.000 description 2
- 238000003646 Spearman's rank correlation coefficient Methods 0.000 description 1
- 230000004931 aggregating effect Effects 0.000 description 1
- 230000002776 aggregation Effects 0.000 description 1
- 238000004220 aggregation Methods 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 238000012098 association analyses Methods 0.000 description 1
- 230000001364 causal effect Effects 0.000 description 1
- 230000008602 contraction Effects 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 238000013144 data compression Methods 0.000 description 1
- 238000013506 data mapping Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
- 230000014509 gene expression Effects 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 238000013105 post hoc analysis Methods 0.000 description 1
- 238000012913 prioritisation Methods 0.000 description 1
- 238000009877 rendering Methods 0.000 description 1
- 238000000638 solvent extraction Methods 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 238000005303 weighing Methods 0.000 description 1
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The application provides a financial report analysis integration method and a system based on cloud computing, and relates to the technical field of data processing, wherein the method comprises the following steps: determining resource allocation requirements, connecting a cloud computing platform, determining standby computing deployment, combining a self-adaptive cloud deployment model, coordinating the resource allocation requirements and the standby computing deployment to generate a cloud deployment scheme, executing report analysis by the cloud computing platform to generate a result multi-element group, performing fusion association of the multi-element group to generate a report analysis result, and finally defining a cloud storage space and establishing communication connection between the cloud storage space and an edge device end. The method mainly solves the problems that the prior art cannot automatically adapt and optimize resource allocation, is difficult to cope with the processing requirement of large-scale data, causes low resource utilization efficiency and slow task execution speed, and cannot guarantee the safety of data storage and the reliability of transmission. The data processing and analyzing capacity of the enterprise is improved, and the decision making efficiency and the competitiveness of the enterprise are improved.
Description
Technical Field
The application relates to the technical field of data processing, in particular to a financial report analysis integration method and system based on cloud computing.
Background
With the continuous expansion of business and the increasing of market competition, the amount of data that an enterprise needs to process and analyze is increasing. The traditional data processing and analyzing method cannot meet the demands of enterprises, and cannot realize rapid processing and analysis of mass data. With the change of market environment, enterprises need to acquire market information in time and respond quickly. Traditional financial report analysis methods are often post-hoc analysis, cannot meet the requirements of enterprises on real-time performance, cannot provide real-time financial report analysis results, and are very important in information exchange and collaboration among departments in modern enterprises. The existing method can not realize data sharing and cooperative work of quality inspection of all departments, and is inconvenient to exchange and share data with other enterprises or external institutions.
However, in the process of implementing the technical scheme of the invention in the embodiment of the application, the above technology is found to have at least the following technical problems:
the prior art cannot automatically adapt and optimize the resource allocation, and is difficult to cope with the processing requirement of large-scale data, so that the resource utilization efficiency is low, the task execution speed is slow, and the safety of data storage and the reliability of transmission cannot be ensured.
Disclosure of Invention
The method mainly solves the problems that the prior art cannot automatically adapt and optimize resource allocation, is difficult to cope with the processing requirement of large-scale data, causes low resource utilization efficiency and slow task execution speed, and cannot guarantee the safety of data storage and the reliability of transmission.
In view of the foregoing, the present application provides a cloud computing-based financial report analysis integration method and system, and in a first aspect, the present application provides a cloud computing-based financial report analysis integration method, where the method includes: reading financial report data and a pre-analysis task, and determining resource allocation requirements; connecting a cloud computing platform, reading idle computing power configuration and cloud processing capacity of a resource pool, and determining standby computing deployment, wherein the standby computing deployment is provided with a cloud deployment mode identifier; combining an adaptive cloud deployment model, coordinating the resource configuration requirements with the standby computing deployment, and generating a cloud deployment scheme, wherein the cloud deployment scheme has at least one cloud deployment mode; transmitting the financial report data and the pre-analysis task to the cloud computing platform, and executing report analysis based on the cloud deployment scheme to generate a result multi-group; based on the correlation, carrying out fusion association of the result multi-groups to generate a report analysis result; and defining a cloud storage space, storing the report analysis result, and establishing communication connection between the cloud storage space and an edge device end.
In a second aspect, the present application provides a cloud computing-based financial reporting analysis integration system, the system comprising: the resource allocation demand determining module is used for reading financial report data and pre-analysis tasks and determining resource allocation demands; the platform connection module is used for connecting a cloud computing platform, reading idle computing power configuration and cloud processing capacity of a resource pool, and determining standby computing deployment, wherein the standby computing deployment is provided with a cloud deployment mode identifier; the cloud deployment scheme generation module is used for coordinating the resource configuration requirements and the standby computing deployment by combining with a self-adaptive cloud deployment model to generate a cloud deployment scheme, wherein the cloud deployment scheme at least has one cloud deployment mode; the report analysis execution module is used for transmitting the financial report data and the pre-analysis task to the cloud computing platform, executing report analysis based on the cloud deployment scheme and generating a result multi-group; the report analysis result generation module is used for carrying out fusion association of the result multiple groups based on the correlation to generate a report analysis result; and the communication connection establishment module is used for defining a cloud storage space, storing the report analysis result and establishing communication connection between the cloud storage space and the edge equipment end.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
the application provides a financial report analysis integration method and a system based on cloud computing, and relates to the technical field of data processing, wherein the method comprises the following steps: determining resource allocation requirements, connecting a cloud computing platform, determining standby computing deployment, combining a self-adaptive cloud deployment model, coordinating the resource allocation requirements and the standby computing deployment to generate a cloud deployment scheme, executing report analysis by the cloud computing platform to generate a result multi-element group, performing fusion association of the multi-element group to generate a report analysis result, and finally defining a cloud storage space and establishing communication connection between the cloud storage space and an edge device end.
The method mainly solves the problems that the prior art cannot automatically adapt and optimize resource allocation, is difficult to cope with the processing requirement of large-scale data, causes low resource utilization efficiency and slow task execution speed, and cannot guarantee the safety of data storage and the reliability of transmission. The data processing and analyzing capacity of the enterprise is improved, and the decision making efficiency and the competitiveness of the enterprise are improved.
The foregoing description is merely an overview of the technical solutions of the present application, and may be implemented according to the content of the specification in order to make the technical means of the present application more clearly understood, and in order to make the above-mentioned and other objects, features and advantages of the present application more clearly understood, the following detailed description of the present application will be given.
Drawings
For a clearer description of the technical solutions of the present application or of the prior art, the drawings used in the description of the embodiments or of the prior art will be briefly described below, it being obvious that the drawings in the description below are only exemplary and that other drawings can be obtained, without inventive effort, by a person skilled in the art from the drawings provided.
FIG. 1 is a schematic flow chart of a method for integrating analysis of financial reports based on cloud computing according to an embodiment of the present application;
FIG. 2 is a schematic flow chart of a method for generating a cloud deployment scenario in a cloud computing-based financial report analysis integration method according to an embodiment of the present application;
FIG. 3 is a schematic flow chart of a method for replacing sensitive data and preprocessing sensitive data in a cloud computing-based financial report analysis integration method according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a financial report analysis integration system based on cloud computing according to an embodiment of the present application.
Reference numerals illustrate: the system comprises a resource configuration requirement determining module 10, a platform connecting module 20, a cloud deployment scheme generating module 30, a report analysis executing module 40, a report analysis result generating module 50 and a communication connection establishing module 60.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
The method mainly solves the problems that the prior art cannot automatically adapt and optimize resource allocation, is difficult to cope with the processing requirement of large-scale data, causes low resource utilization efficiency and slow task execution speed, and cannot guarantee the safety of data storage and the reliability of transmission. The data processing and analyzing capacity of the enterprise is improved, and the decision making efficiency and the competitiveness of the enterprise are improved.
For a better understanding of the foregoing technical solutions, the following detailed description will be given with reference to the accompanying drawings and specific embodiments of the present invention:
example 1
A method for integrating financial report analysis based on cloud computing as shown in fig. 1, the method comprising:
reading financial report data and a pre-analysis task, and determining resource allocation requirements;
specifically, reading financial reporting data and pre-analyzing tasks determine resource allocation requirements including data storage, computing resources, and human resources. In terms of data storage, the amount of financial reporting data is typically large, requiring sufficient storage space to store the data. Depending on the size of the data volume and the growth rate, enterprises need to configure appropriately sized storage devices, such as cloud storage or high-performance local storage devices. In terms of computing resources, processing and analysis of financial reporting data requires significant computing power. Enterprises can select proper computing resources according to the complexity and the data size of analysis tasks. For example, a virtual machine instance or container service in a cloud computing service may be selected, and sufficient computing cores, memory, and storage resources may be configured according to actual requirements. In terms of human resources, financial reporting analysis requires specialized financial and data analysts. Enterprises need to configure personnel with related skills and experience, including data analysts, accountants, financial analysts, and the like.
Connecting a cloud computing platform, reading idle computing power configuration and cloud processing capacity of a resource pool, and determining standby computing deployment, wherein the standby computing deployment is provided with a cloud deployment mode identifier;
specifically, the system establishes a connection with the cloud computing platform through a specific network protocol or API interface. This connection may be real-time or near real-time, depending on the needs of the system and the configuration of the cloud computing platform. Once the connection is established, the system begins reading the idle computing power configuration and cloud processing capabilities from the resource pool of the cloud computing platform. Such information may include, but is not limited to, availability of resources such as CPU, memory, storage, and network bandwidth. Based on the read idle computing power and processing power, the system automatically determines available backup computing deployments. These deployments may be one or more virtual machines, containers, or other computing units. Each backup computing deployment is tagged with one or more cloud deployment pattern identifications to distinguish its characteristics from application scenarios. For example, some deployments may be labeled as "high performance computing" modes, suitable for large-scale mathematical computation or 3D rendering; while other deployments may be labeled "high availability" mode, primarily for online business processes requiring high stability and redundancy. By the method, the system can automatically determine the resource allocation requirement, effectively utilize the idle computing power and processing capacity of the cloud computing platform, quickly generate report analysis results, and ensure safe storage and reliable transmission of data.
Combining an adaptive cloud deployment model, coordinating the resource configuration requirements with the standby computing deployment, and generating a cloud deployment scheme, wherein the cloud deployment scheme has at least one cloud deployment mode;
specifically, first, the system obtains financial reporting data and information about the pre-analysis tasks, including data volume, processing complexity, time limit requirements, and the like. Such information is used to evaluate the required resource configuration, including computing power, storage space, network bandwidth, and the like. Next, the system reads the idle computing power configuration and cloud processing capabilities from the resource pool of the cloud computing platform, and determines available backup computing deployments. These deployments typically exist in the form of virtual machines, containers, or other computing units, with corresponding cloud deployment pattern identifications. Then, the system is combined with the self-adaptive cloud deployment model, and matching is carried out according to the resource configuration requirements and the information of standby computing deployment. This model is automatically adapted and optimized based on the task and resource characteristics to find the best deployment scenario. Based on the matching result of the adaptive cloud deployment model, the system generates one or more cloud deployment schemes. These schemes detail how resources on a cloud computing platform are used to meet the needs of the system, including the number of computing nodes, storage configuration, network topology, etc.
Transmitting the financial report data and the pre-analysis task to the cloud computing platform, and executing report analysis based on the cloud deployment scheme to generate a result multi-group;
specifically, first, the system performs necessary preprocessing on the financial reporting data and the pre-analysis tasks, including data format conversion, cleaning, and sorting, etc. To ensure secure transmission of data, encryption techniques are used to encrypt the financial reporting data. And transmitting the encrypted data to the cloud computing platform through a secure network connection. And carrying out corresponding configuration and deployment. This includes launching the corresponding computing node, configuring storage and network resources, setting access controls, and the like. Based on the cloud deployment scenario, the system begins performing the analysis tasks of the financial reporting data. Including various operations such as querying, screening, aggregating, visualizing, etc. of the data. In the analysis process, the system can utilize the parallel processing capacity of the cloud computing platform to improve the analysis efficiency and accuracy. After the analysis is complete, the system integrates the results generated into one or more result tuples. These tuples contain deep analysis and key insight of financial reports and can be used directly for decision support or further data mining work.
Based on the correlation, carrying out fusion association of the result multi-groups to generate a report analysis result;
specifically, first, the system performs in-depth analysis on each result in the result multi-set, and extracts key features and indexes. For each result in the result tuple, the system calculates its correlation coefficient with the other results. The correlation coefficients may be calculated using different algorithms and models, such as pearson correlation coefficients, spearman rank correlation coefficients, and the like. Based on the calculated correlation coefficient, the system performs fusion association on the result multi-element group. The results with the higher correlation coefficients are combined to form one or more association groups. These associations reflect the inherent links and interactions between different outcomes. Based on the result tuples after fusion association, the system generates a final report analysis result. The results integrate a plurality of related analysis results, and improve the comprehensiveness and credibility of analysis.
And defining a cloud storage space, storing the report analysis result, and establishing communication connection between the cloud storage space and an edge device end.
Specifically, first, the system demarcates a specific cloud storage space on the cloud computing platform for storing the results of the financial report analysis. This may be a virtual storage volume or container with sufficient capacity to hold the analysis results. To ensure data security, the system performs the necessary backup and redundancy configuration when storing the results of the financial report analysis. Data may be stored at multiple locations or nodes to prevent loss or corruption of the data. The system adopts encryption technology to encrypt the analysis result of the financial report stored in the cloud storage space, so as to ensure the security of the data. At the same time, a strict access control policy is set, and only authorized users or devices can access the data. In order to enable the edge device side to access the data in the cloud storage space, the system establishes a stable communication connection. This may be accomplished through a dedicated network connection, VPN, or other secure communication protocol. In order to ensure the real-time property and consistency of the data, the system can realize the real-time data synchronization with the edge equipment. When the data in the cloud storage space changes, the changes can be timely synchronized to the edge equipment end, and the latest state of the data is ensured. Assume that a large enterprise uses a cloud computing platform for financial report analysis. After the analysis is completed, the system stores the results in the delineated cloud storage space. In order to ensure the safety of data, the system performs data backup and redundancy configuration, and simultaneously adopts an encryption technology to perform data protection. Only authorized departments or project teams can access this data. In addition, the enterprise also establishes stable communication connection, so that the edge equipment end can access and synchronize the data in the cloud storage space in real time.
Further, in the method, the deployment modes include public cloud, private cloud, hybrid cloud and community cloud deployment modes, and the reading of the resource pool idle computing power configuration and cloud processing capability includes:
reading real-time operation conditions of the cloud computing platform, and determining available resource configuration mapped to a cloud deployment mode, wherein the available resource configuration comprises the resource pool idle computing power configuration and the cloud processing capacity;
and identifying the availability resource configuration, performing resource screening based on task characteristics, identifying a cloud deployment mode, and generating the standby computing deployment.
Specifically, the real-time operation condition of the cloud computing platform is read, the available resource configuration is determined according to the cloud deployment mode, the current resource condition can be better known, and resource screening and identification are performed according to the task characteristics, so that standby computing deployment is generated. And acquiring the running condition data of the cloud computing platform in real time by using a monitoring tool or an API interface. Such live data includes, but is not limited to, utilization of computing resources, storage capacity, network bandwidth, and the like. And then analyzing available resources of the current cloud computing platform according to the real-time condition data. And determining available resource configurations in different modes in combination with the cloud deployment mode. And identifying idle computing power configuration in the resource pool, namely underutilized computing resources. And analyzing the overall processing capacity of the cloud computing platform, including computing, storage, network and other aspects. The appropriate resources are screened based on the characteristics of the task, such as compute intensive, I/O intensive, memory requirements, etc. And determining the optimal resource configuration suitable for task execution according to the screening result. And identifying a proper cloud deployment mode for the current task or application according to the available resource configuration and the task characteristics. The selected cloud deployment mode is ensured to meet the requirements of tasks and fully utilize the existing resources. A backup computing deployment scenario for a current task or application is generated. The solution should include selection of resource pools, resource configuration and adjustment suggestions, suggestions for cloud deployment patterns, and the like.
Further, the method of the present application, after generating the backup computing deployment, includes:
reading cloud processing records of a historical time interval, and supervising and training a resource prediction model;
combining the resource prediction model, carrying out dynamic expansion prediction of resources in a preset time zone by taking the standby calculation deployment as a base line, and determining a time zone prediction result;
and adjusting the standby computing deployment based on the time zone prediction result.
Specifically, first, a time interval in which a query is required, for example, a certain time period in the past is determined. This time interval should be chosen according to the actual requirements to ensure that useful data can be obtained. A connection is established with a database storing historical data or log files through an appropriate database connection tool or API. This may be a relational database (e.g., mySQL, postgreSQL, etc.) or a non-relational database (e.g., mongoDB, cassandra, etc.). Executing the written query statement and acquiring corresponding data from the database. Such data should include information on resource usage, task execution time, system load, etc. The required features and labels are extracted from the history database or log file. Features may include historical load, resource usage, time stamps, etc., with tags being the corresponding resource demands over a period of time in the future. An appropriate supervised learning model is selected based on the requirements and the characteristics of the data. Such as Support Vector Machines (SVMs), random Forest (Random Forest), or neural networks (neurolnterworks), etc. The selected model is trained using historical cloud processing records. And combining the resource prediction model to perform dynamic resource expansion prediction in a preset time zone. Predicting the resource demand change in a period of time in the future, and carrying out dynamic expansion and contraction of the resources according to the demand change. And (5) inputting the characteristic data such as the current time stamp, the historical load, the resource utilization rate and the like into the previously trained resource prediction model. The model will predict the resource demand over a period of time in the future based on these features. Based on the output of the model, predicting the change trend of the resource demand in a future period of time. This may include an increase or decrease in resource demand, as well as a specific point in time of the change. The impact of resource demand changes on system performance and task execution is evaluated. And according to the time zone prediction result, the existing standby computing deployment is adjusted. The adjustment content comprises resource configuration, selection and optimization of cloud deployment modes and the like, so as to ensure that future resource requirements can be met. The storage capacity is adjusted to increase or decrease the storage capacity according to the predicted storage demand. Including adding storage devices, optimizing storage configurations, or implementing data compression.
Further, as shown in fig. 2, the method of the present application, the coordinating the resource configuration requirement with the backup computing deployment includes:
identifying a cloud deployment mode identifier based on the resource configuration requirement, and determining a mode allocation requirement;
mapping the mode allocation requirements and the standby computing deployment to perform supply and demand proofreading;
if the data is supplied and required, balancing the data security and the processing energy efficiency, deploying the pre-analysis task, and determining the cloud deployment scheme;
if the supply is smaller than the demand, determining a target cloud deployment mode, reading the operation configuration information of the resource pool, determining collaborative computing deployment based on time sequence priority, and generating the cloud deployment scheme.
Specifically, first, according to the characteristics of resource allocation requirements, an appropriate cloud deployment mode is identified. Including public clouds, private clouds, and hybrid clouds. Public cloud provides shared computing resources, pay-per-demand, private cloud adopts all infrastructure technologies of public cloud and stores the infrastructure technologies locally, and hybrid cloud is combination of public cloud and private cloud. For each cloud deployment mode, a respective mode allocation requirement is analyzed. Public clouds require large-scale investment to obtain returns, and are suitable for applications with large demand, private clouds require large-scale investment to obtain returns, and are suitable for applications with high security requirements, and hybrid clouds require comprehensive consideration of the advantages of public clouds and private clouds to achieve optimal performance and cost benefits. And determining a cloud deployment mode which is most suitable for the resource configuration requirement, and defining the allocation requirement of the mode. This includes investment requirements for infrastructure, platforms and software, as well as maintenance and upgrade requirements. The determined apportioned demand is mapped with the previously generated backup computing deployment. And analyzing whether the standby computing deployment can meet the current allocation requirement, and performing supply and demand proofreading. And if the resource provided by the standby computing deployment is larger than the current demand, carrying out the condition processing of the supply and demand. If the spare computing deployment is insufficient to meet the demand, then a less-than-demand case is handled. In the case of supply and demand, there is a need to balance the tradeoff between data security and processing energy efficiency. And properly deploying the pre-analysis task according to the weighing result, and determining a final cloud deployment scheme. In the case where supply is smaller than demand, it is necessary to determine a target cloud deployment mode. And reading the operation configuration information of the resource pool, and determining collaborative computing deployment according to the time sequence priority. And generating a final cloud deployment scheme to meet the resource requirements.
Further, the method comprises the following steps:
reading a parallel processing task, and if the parallel processing task is not empty and is supplied and required, generating a resource coordination instruction;
based on the resource coordination instruction, judging whether the standby computing deployment meets the resource allocation requirement of synchronous processing of the parallel processing task;
if yes, performing resource coordination configuration based on the parallel processing task on the standby computing deployment;
and if the task priority is not met, the parallel processing tasks are subjected to priority sorting, and a task processing sequence is generated, wherein the priority sorting standard is at least set based on task grades and task time limits.
Specifically, the parallel processing tasks to be processed are read from a task queue or workflow management. These tasks may be a collection of tasks that are processed simultaneously by multiple computing nodes or processes. It is checked whether the parallel processing task is non-empty and it is determined whether the current resource is being supplied over demand. Supply and demand means that the current resources can meet the demands of more tasks. If the parallel processing task is not empty and the resource supply is over-demand, a corresponding resource coordination instruction is generated. These instructions are used to direct the rational allocation and scheduling of resources to meet the resource requirements of multiple tasks. Based on the resource coordination instruction, whether the current standby computing deployment meets the resource configuration requirement of synchronous processing of the parallel processing task is judged. Synchronous processing requires resources to be able to timely and accurately provide support for multiple tasks. And if the standby computing deployment meets the resource configuration requirement, performing resource coordination configuration based on the parallel processing task. The configuration process may include partitioning of tasks, dynamic allocation of resources, priority setting, etc., to ensure that tasks can be efficiently and orderly executed. If the standby computing deployment does not meet the resource configuration requirements, the parallel processing tasks need to be prioritized. Prioritization is based on criteria such as task class and time limit settings to ensure that important or time-critical tasks can obtain resources preferentially. And generating a task processing sequence according to the priority ordering result. The processing sequence arranges tasks according to the priority order and guides reasonable scheduling and allocation of resources.
Further, as shown in fig. 3, before transmitting the financial report data and the pre-analysis task to the cloud computing platform, the method of the present application includes:
performing data confidentiality grade assessment on the financial report data, performing data division and extracting sensitive data, wherein the sensitive data is marked with sensitivity;
traversing a secret database to match a data secret mode based on the sensitivity, wherein the data secret mode comprises key encryption and privacy processing;
preprocessing the sensitive data based on the data confidentiality mode, and determining preprocessed sensitive data;
and replacing the sensitive data with the preprocessing sensitive data according to the financial report data.
In particular, security level assessment is performed on the financial reporting data to identify the sensitivity level of the different data items. The security level of the data can be assessed according to its nature, purpose and exposure risk. The financial reporting data is divided into different data sets based on the security level assessment results. Data can be generally divided into three levels of low sensitivity, medium sensitivity, and high sensitivity, each corresponding to a different security requirement and access control. Data with high sensitivity is extracted from the partitioned different data sets. Such data may include critical financial indicators, customer information, vendor information, etc., which may be of particular concern for privacy and privacy protection. And carrying out sensitivity identification on the extracted sensitive data, and determining the security level and corresponding management requirement of each piece of data. For highly sensitive data, encryption of the key may be used to keep the secret. First, an encryption algorithm and a key are determined, and then sensitive data is encrypted using the encryption algorithm. The encrypted data cannot be read directly, and only the person with the correct key can decrypt and access the data. For some data that is more sensitive but does not require complete encryption, privacy may be provided by way of privacy processing. Including data desensitization, anonymization, etc. By processing sensitive data by these methods, the privacy of the data can be protected while maintaining partial availability of the data. The security database is traversed to determine the appropriate data security means. The associated data security means and algorithms are retrieved from the database based on the sensitivity and security requirements of the data. For each sensitive data item, a suitable encryption algorithm or privacy handling method is selected and a corresponding privacy handling is performed. First, the preprocessing target of the sensitive data is specified. The method comprises the operations of data cleaning, format conversion, data aggregation and the like, and a proper preprocessing method is selected according to a confidentiality mode and data sensitivity. For encrypted data, it may be necessary to select a preprocessing method capable of supporting the processing of the encrypted data; for sensitive data of the privacy process, a corresponding desensitization or anonymization technique is selected. Sensitive data is processed according to the selected preprocessing method. For example, operations such as data deduplication, missing value padding, and outlier processing are performed on data to be cleaned, and operations such as format conversion and transcoding are performed on data to be converted. During the preprocessing, it is ensured that any sensitive information is not revealed and the integrity of the data is maintained. All sensitive data are ensured to be correctly identified and positioned through data mapping and regular expressions. And generating corresponding replacement data according to the type and format of the preprocessed sensitive data. For numerical sensitive data, random numbers or fixed values may be used for substitution, and for textual sensitive data, words or phrases of similar semantics may be used for substitution. Ensuring that the replaced data has similar characteristics and uses as the original sensitive data. After the sensitive data location is identified, the original sensitive data is replaced using the generated replacement data. Other parts of the data are not changed in the replacement process, and the integrity and the accuracy of the data are maintained. After the data replacement is completed, whether the replacement result is correct or not is verified. Checking whether the replaced data is consistent with the original sensitive data or not, and simultaneously ensuring that the semantics and the purposes of the data are not changed. If a replacement error or inconsistency is found, corresponding adjustments and corrections are required.
Further, the method of the present application, based on the correlation, performs fusion association of the result multi-sets, including:
performing inter-group correlation analysis on the result multi-group to determine an inter-group correlation sequence;
performing intra-group correlation analysis on the result multi-group to determine an intra-group correlation sequence;
carrying out hierarchical association on the inter-group association sequence and the intra-group association sequence to determine a report analysis system;
carrying out overall evaluation on the report analysis system to determine a comprehensive analysis result;
and determining the report analysis result based on the report analysis system and the comprehensive analysis result.
Specifically, a plurality of result tuples are selected and their correlations are analyzed. Statistical methods or data mining techniques are used to determine the relevance between these groups. These associations may represent relationships or trends between variables. Based on the inter-group correlation analysis, an inter-group correlation sequence is determined. The association sequence may represent a dependency or a causal relationship between different result tuples. Each result multi-tuple is subjected to an in-depth analysis to determine correlations between elements within the group. This approach may reveal patterns and structures within the results. Based on the intra-set correlation analysis, a correlation sequence within each resulting multi-set is determined. These association sequences describe the relationships between the elements within the group. And integrating and comparing the determined inter-group association sequence with the intra-group association sequence. Attempts are made to build hierarchical relationships to better understand the structure of the data and results. Based on the hierarchical association analysis, a complete report analysis system is constructed. The system should be able to fully and systematically expose the underlying links and structures of the data. And comprehensively evaluating the report analysis system by using an evaluation standard or index. Evaluating its integrity, accuracy, practicality, etc. And obtaining a comprehensive analysis result based on the overall evaluation. And combining the report analysis system with the comprehensive analysis result to obtain a final report analysis result.
Example two
Based on the same inventive concept as the cloud computing-based financial report analysis integration method of the foregoing embodiment, as shown in fig. 4, the present application provides a cloud computing-based financial report analysis integration system, which includes:
the resource allocation requirement determining module 10, wherein the resource allocation requirement determining module 10 is used for reading financial report data and pre-analysis tasks and determining resource allocation requirements;
the platform connection module 20 is used for connecting a cloud computing platform, reading idle computing power configuration and cloud processing capacity of a resource pool, and determining standby computing deployment, wherein the standby computing deployment is provided with a cloud deployment mode identifier;
the cloud deployment scheme generation module 30 is configured to coordinate the resource configuration requirement and the standby computing deployment in combination with an adaptive cloud deployment model to generate a cloud deployment scheme, where the cloud deployment scheme has at least one cloud deployment mode;
a report analysis execution module 40, wherein the report analysis execution module 40 is configured to transmit the financial report data and the pre-analysis task to the cloud computing platform, execute report analysis based on the cloud deployment scheme, and generate a result multi-tuple;
the report analysis result generation module 50 is used for generating a report analysis result by carrying out fusion association of the result multiple groups based on the correlation by the report analysis result analysis module 50;
the communication connection establishment module 60 is configured to define a cloud storage space, store the report analysis result, and establish a communication connection between the cloud storage space and an edge device.
Further, the system further comprises:
the standby computing deployment generation module is used for reading the real-time operation condition of the cloud computing platform and determining the available resource configuration mapped to a cloud deployment mode, wherein the available resource configuration comprises the resource pool idle computing power configuration and the cloud processing capacity; and identifying the availability resource configuration, performing resource screening based on task characteristics, identifying a cloud deployment mode, and generating the standby computing deployment.
Further, the system further comprises:
the deployment adjustment module is used for reading cloud processing records of the historical time interval and supervising and training a resource prediction model; combining the resource prediction model, carrying out dynamic expansion prediction of resources in a preset time zone by taking the standby calculation deployment as a base line, and determining a time zone prediction result; and adjusting the standby computing deployment based on the time zone prediction result.
Further, the system further comprises:
the cloud deployment scheme generation module is used for identifying a cloud deployment mode identifier based on the resource configuration requirement and determining a mode allocation requirement; mapping the mode allocation requirements and the standby computing deployment to perform supply and demand proofreading; if the data is supplied and required, balancing the data security and the processing energy efficiency, deploying the pre-analysis task, and determining the cloud deployment scheme; if the supply is smaller than the demand, determining a target cloud deployment mode, reading the operation configuration information of the resource pool, determining collaborative computing deployment based on time sequence priority, and generating the cloud deployment scheme.
Further, the system further comprises:
the task processing sequence generating module is used for reading the parallel processing task, and generating a resource coordination instruction if the parallel processing task is not empty and is supplied to the request; based on the resource coordination instruction, judging whether the standby computing deployment meets the resource allocation requirement of synchronous processing of the parallel processing task; if yes, performing resource coordination configuration based on the parallel processing task on the standby computing deployment; and if the task priority is not met, the parallel processing tasks are subjected to priority sorting, and a task processing sequence is generated, wherein the priority sorting standard is at least set based on task grades and task time limits.
Further, the system further comprises:
the preprocessing sensitive data determining module is used for carrying out data security level assessment on the financial report data, carrying out data division and extracting sensitive data, and identifying sensitivity of the sensitive data; traversing a secret database to match a data secret mode based on the sensitivity, wherein the data secret mode comprises key encryption and privacy processing; preprocessing the sensitive data based on the data confidentiality mode, and determining preprocessed sensitive data; and replacing the sensitive data with the preprocessing sensitive data according to the financial report data.
Further, the system further comprises:
the comprehensive analysis result determining module is used for carrying out inter-group correlation analysis on the result multi-element group and determining an inter-group correlation sequence; performing intra-group correlation analysis on the result multi-group to determine an intra-group correlation sequence; carrying out hierarchical association on the inter-group association sequence and the intra-group association sequence to determine a report analysis system; carrying out overall evaluation on the report analysis system to determine a comprehensive analysis result; and determining the report analysis result based on the report analysis system and the comprehensive analysis result.
The foregoing detailed description of the cloud computing-based financial report analysis integration method will be clear to those skilled in the art, and the cloud computing-based financial report analysis integration system in this embodiment is described more simply for the system disclosed in the embodiments, since it corresponds to the embodiment disclosure method, and the relevant points refer to the description of the method section.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (8)
1. A cloud computing-based financial report analysis integration method, the method comprising:
reading financial report data and a pre-analysis task, and determining resource allocation requirements;
connecting a cloud computing platform, reading idle computing power configuration and cloud processing capacity of a resource pool, and determining standby computing deployment, wherein the standby computing deployment is provided with a cloud deployment mode identifier;
combining an adaptive cloud deployment model, coordinating the resource configuration requirements with the standby computing deployment, and generating a cloud deployment scheme, wherein the cloud deployment scheme has at least one cloud deployment mode;
transmitting the financial report data and the pre-analysis task to the cloud computing platform, and executing report analysis based on the cloud deployment scheme to generate a result multi-group;
based on the correlation, carrying out fusion association of the result multi-groups to generate a report analysis result;
and defining a cloud storage space, storing the report analysis result, and establishing communication connection between the cloud storage space and an edge device end.
2. The method of claim 1, wherein the deployment modes include public cloud, private cloud, hybrid cloud, and community cloud deployment modes, the reading resource pool idle computing power configuration and cloud processing capabilities comprising:
reading real-time operation conditions of the cloud computing platform, and determining available resource configuration mapped to a cloud deployment mode, wherein the available resource configuration comprises the resource pool idle computing power configuration and the cloud processing capacity;
and identifying the availability resource configuration, performing resource screening based on task characteristics, identifying a cloud deployment mode, and generating the standby computing deployment.
3. The method of claim 2, wherein after generating the backup computing deployment, comprising:
reading cloud processing records of a historical time interval, and supervising and training a resource prediction model;
combining the resource prediction model, carrying out dynamic expansion prediction of resources in a preset time zone by taking the standby calculation deployment as a base line, and determining a time zone prediction result;
and adjusting the standby computing deployment based on the time zone prediction result.
4. The method of claim 1, wherein the coordinating the resource configuration requirements with the backup computing deployment comprises:
identifying a cloud deployment mode identifier based on the resource configuration requirement, and determining a mode allocation requirement;
mapping the mode allocation requirements and the standby computing deployment to perform supply and demand proofreading;
if the data is supplied and required, balancing the data security and the processing energy efficiency, deploying the pre-analysis task, and determining the cloud deployment scheme;
if the supply is smaller than the demand, determining a target cloud deployment mode, reading the operation configuration information of the resource pool, determining collaborative computing deployment based on time sequence priority, and generating the cloud deployment scheme.
5. The method as recited in claim 4, comprising:
reading a parallel processing task, and if the parallel processing task is not empty and is supplied and required, generating a resource coordination instruction;
based on the resource coordination instruction, judging whether the standby computing deployment meets the resource allocation requirement of synchronous processing of the parallel processing task;
if yes, performing resource coordination configuration based on the parallel processing task on the standby computing deployment;
and if the task priority is not met, the parallel processing tasks are subjected to priority sorting, and a task processing sequence is generated, wherein the priority sorting standard is at least set based on task grades and task time limits.
6. The method of claim 1, wherein prior to transmitting the financial reporting data and the pre-analysis task to the cloud computing platform, comprising:
performing data confidentiality grade assessment on the financial report data, performing data division and extracting sensitive data, wherein the sensitive data is marked with sensitivity;
performing data confidentiality grade assessment on the financial report data, performing data division and extracting sensitive data, wherein the sensitive data is marked with sensitivity;
preprocessing the sensitive data based on the data confidentiality mode, and determining preprocessed sensitive data;
and replacing the sensitive data with the preprocessing sensitive data according to the financial report data.
7. The method of claim 1, wherein the performing the fusion association of the result tuples based on correlation comprises:
performing inter-group correlation analysis on the result multi-group to determine an inter-group correlation sequence;
performing intra-group correlation analysis on the result multi-group to determine an intra-group correlation sequence;
carrying out hierarchical association on the inter-group association sequence and the intra-group association sequence to determine a report analysis system;
carrying out overall evaluation on the report analysis system to determine a comprehensive analysis result;
and determining the report analysis result based on the report analysis system and the comprehensive analysis result.
8. A cloud computing-based financial reporting analysis integration system, the system comprising:
the resource allocation demand determining module is used for reading financial report data and pre-analysis tasks and determining resource allocation demands;
the platform connection module is used for connecting a cloud computing platform, reading idle computing power configuration and cloud processing capacity of a resource pool, and determining standby computing deployment, wherein the standby computing deployment is provided with a cloud deployment mode identifier;
the cloud deployment scheme generation module is used for coordinating the resource configuration requirements and the standby computing deployment by combining with a self-adaptive cloud deployment model to generate a cloud deployment scheme, wherein the cloud deployment scheme at least has one cloud deployment mode;
the report analysis execution module is used for transmitting the financial report data and the pre-analysis task to the cloud computing platform, executing report analysis based on the cloud deployment scheme and generating a result multi-group;
the report analysis result generation module is used for carrying out fusion association of the result multiple groups based on the correlation to generate a report analysis result;
and the communication connection establishment module is used for defining a cloud storage space, storing the report analysis result and establishing communication connection between the cloud storage space and the edge equipment end.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410061898.7A CN117827462B (en) | 2024-01-16 | 2024-01-16 | Financial report analysis integration method and system based on cloud computing |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410061898.7A CN117827462B (en) | 2024-01-16 | 2024-01-16 | Financial report analysis integration method and system based on cloud computing |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117827462A true CN117827462A (en) | 2024-04-05 |
| CN117827462B CN117827462B (en) | 2024-06-21 |
Family
ID=90513457
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410061898.7A Active CN117827462B (en) | 2024-01-16 | 2024-01-16 | Financial report analysis integration method and system based on cloud computing |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117827462B (en) |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110265081A1 (en) * | 2010-04-26 | 2011-10-27 | Vmware, Inc. | Droplet execution engine for dynamic server application deployment |
| CN107241384A (en) * | 2017-05-03 | 2017-10-10 | 复旦大学 | A kind of content distribution service priority scheduling of resource method based on many cloud frameworks |
| CN109271257A (en) * | 2018-10-11 | 2019-01-25 | 郑州云海信息技术有限公司 | A kind of method and apparatus of virtual machine (vm) migration deployment |
| CN112685179A (en) * | 2020-12-28 | 2021-04-20 | 跬云(上海)信息科技有限公司 | Resource deployment system and method based on cost on cloud |
| CN114844791A (en) * | 2022-07-06 | 2022-08-02 | 北京悦游信息技术有限公司 | Cloud service automatic management and distribution method and system based on big data and storage medium |
| CN115983788A (en) * | 2022-12-23 | 2023-04-18 | 广东亿迅科技有限公司 | Private cloud deployment method and device based on operation and maintenance big data optimization |
| CN116542800A (en) * | 2023-06-29 | 2023-08-04 | 天津星耀九洲科技有限公司 | Intelligent financial statement analysis system based on cloud AI technology |
| CN116700920A (en) * | 2023-05-15 | 2023-09-05 | 中国工商银行股份有限公司 | Cloud primary hybrid deployment cluster resource scheduling method and device |
| CN117076077A (en) * | 2023-08-18 | 2023-11-17 | 上海墅字科技有限公司 | Planning and scheduling optimization method based on big data analysis |
-
2024
- 2024-01-16 CN CN202410061898.7A patent/CN117827462B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110265081A1 (en) * | 2010-04-26 | 2011-10-27 | Vmware, Inc. | Droplet execution engine for dynamic server application deployment |
| CN107241384A (en) * | 2017-05-03 | 2017-10-10 | 复旦大学 | A kind of content distribution service priority scheduling of resource method based on many cloud frameworks |
| CN109271257A (en) * | 2018-10-11 | 2019-01-25 | 郑州云海信息技术有限公司 | A kind of method and apparatus of virtual machine (vm) migration deployment |
| CN112685179A (en) * | 2020-12-28 | 2021-04-20 | 跬云(上海)信息科技有限公司 | Resource deployment system and method based on cost on cloud |
| CN114844791A (en) * | 2022-07-06 | 2022-08-02 | 北京悦游信息技术有限公司 | Cloud service automatic management and distribution method and system based on big data and storage medium |
| CN115983788A (en) * | 2022-12-23 | 2023-04-18 | 广东亿迅科技有限公司 | Private cloud deployment method and device based on operation and maintenance big data optimization |
| CN116700920A (en) * | 2023-05-15 | 2023-09-05 | 中国工商银行股份有限公司 | Cloud primary hybrid deployment cluster resource scheduling method and device |
| CN116542800A (en) * | 2023-06-29 | 2023-08-04 | 天津星耀九洲科技有限公司 | Intelligent financial statement analysis system based on cloud AI technology |
| CN117076077A (en) * | 2023-08-18 | 2023-11-17 | 上海墅字科技有限公司 | Planning and scheduling optimization method based on big data analysis |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117827462B (en) | 2024-06-21 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Lv et al. | Microservice deployment in edge computing based on deep Q learning | |
| US10303690B1 (en) | Automated identification and classification of critical data elements | |
| Bellavista et al. | Machine learning for predictive diagnostics at the edge: An IIoT practical example | |
| CN109542057A (en) | Novel maintenance model and its construction method based on virtual Machine Architecture | |
| CN112700192B (en) | Wharf logistics business object processing method based on spark Internet of things | |
| CN117371952B (en) | Multi-project collaborative management system | |
| CN115564071A (en) | Method and system for generating data labels of power Internet of things equipment | |
| CN114817739B (en) | Industrial big data processing system based on artificial intelligence algorithm | |
| CN117236855A (en) | Biological feed warehouse management system and method | |
| CN117827462B (en) | Financial report analysis integration method and system based on cloud computing | |
| CN117436768A (en) | Unified supervision index method based on data management | |
| CN116389486B (en) | Method and system for realizing operation analysis of multiple cloud resources | |
| CN114757448B (en) | Manufacturing inter-link optimal value chain construction method based on data space model | |
| Bychkov et al. | Support for managing the survivability of energy systems based on a combinatorial approach | |
| Kireev et al. | Monitoring system for the housing and utility services based on the digital technologies iiot, big data, data mining, edge and cloud computing | |
| Bai et al. | An OEE improvement method based on TOC | |
| CN110415136B (en) | Service capability evaluation system and method for power dispatching automation system | |
| CN115314400A (en) | Network system anomaly detection method and device, electronic equipment and storage medium | |
| CN114819367A (en) | Public service platform based on industrial internet | |
| Li | Supply Chain Efficiency and Effectiveness Management Using Decision Support Systems | |
| CN113379344A (en) | Intelligent logistics processing method and system based on cloud computing processing | |
| Zhou et al. | Optimization Control Strategy of Electricity Information Acquisition System Based on Edge-Cloud Computing Collaboration | |
| CN117151496B (en) | Enterprise architecture alignment method, device, equipment and storage medium | |
| Sarathchandra et al. | Resource aware scheduler for distributed stream processing in cloud native environments | |
| Hordiichuk-Bublivska et al. | A modified federated singular value decomposition method for big data and ML optimization in IIoT systems |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant |