A financial big data analysis and processing method and system based on cloud computing

Abstract

The present invention relates to the field of financial data protection, and discloses a financial big data analysis and processing method and system based on cloud computing, which includes the following steps: collecting and integrating financial data to be analyzed, obtaining complete target flow financial data, and analyzing all potential risks of the complete target flow financial data and the corresponding financial data source, and finally performing data protection and data recovery on the complete target flow financial data and the corresponding financial data source according to the potential risks, so as to achieve the purpose of protecting the financial data. The present invention can generate an encryption protection method for the financial data source and the corresponding financial data by judging whether the financial data in the financial data source is maliciously read, thereby maintaining the stability of the financial data source and ensuring the security and reliability of the financial data.

Description

Technical Field

[0001] The present invention relates to the field of financial data protection, and in particular to a financial big data analysis and processing method and system based on cloud computing. Background Art

[0002] Financial data covers all kinds of data generated in financial markets and financial institutions. These data are of great significance to investors, financial institutions and government departments. Financial data is the core of the operation of financial institutions, including sensitive information such as customer information, transaction records, and market conditions. The integrity and accuracy of these data are directly related to the stable operation of the financial system. Once the data is tampered with or destroyed, it may lead to transaction errors, system crashes or market chaos, which will have a serious impact on financial institutions and the entire financial market. At the same time, financial fraud is one of the important risks faced by the financial industry. By ensuring the integrity and accuracy of financial data, fraud can be effectively prevented. Since financial data is vulnerable to attacks, a financial big data analysis and processing method and system based on cloud computing is proposed. The purpose is to propose solutions through the loopholes and problems of cloud computing financial data, so as to maintain the stability of the financial system, protect user privacy, prevent financial fraud, and ensure the security and reliability of financial big data. Summary of the invention

[0003] The present invention overcomes the deficiencies of the prior art and provides a financial big data analysis and processing method and system based on cloud computing.

[0004] In order to achieve the above object, the technical solution adopted by the present invention is:

[0005] The first aspect of the present invention provides a financial big data analysis and processing method based on cloud computing, comprising the following steps:

[0006] Collect and integrate the financial data that needs to be analyzed, and pre-process the integrated financial data to obtain complete target flow financial data;

[0007] Conducting a potential risk analysis on the complete target flow financial data in the financial data source, and determining the potential risk of the financial data source and the potential risk of the complete target flow financial data based on the potential risk analysis result;

[0008] Perform data recovery and data protection on abnormal flow financial data, and perform data encryption control on abnormal financial data sources based on the potential risks of abnormal financial data sources.

[0009] Furthermore, in a preferred embodiment of the present invention, the financial data to be analyzed is collected and integrated, and the integrated financial data is preprocessed to obtain complete target flow financial data, specifically:

[0010] Acquire a financial data source, wherein the financial data source is a database that can collect financial data, and the financial data source includes a financial institution database and a financial market database;

[0011] Preset the financial data collection time range and financial data collection object, and collect all the flowing financial data of the financial data collection object within the financial data collection time range in real time through the financial data source, and mark them as target flowing financial sub-data;

[0012] Acquire the data formats of all target liquid finance sub-data, mark the data format of one of the target liquid finance sub-data as the base data format, and convert the data formats of all target liquid finance sub-data into the base data format to obtain all target liquid finance sub-data in a unified data format;

[0013] Integrate all target liquid financial sub-data in a unified data format to obtain target liquid financial data, identify missing values ​​in the target liquid financial data, and locate the positions of missing values ​​in the target liquid financial data;

[0014] The mean of the target liquid financial data is calculated, and an interpolation method is introduced. Based on the interpolation method, the mean of the target liquid financial data is reduced to fill the position of the missing value, and a box plot method is introduced to identify the outliers of the target liquid financial data. Finally, the outliers of the target liquid financial data are deleted to obtain the complete target liquid financial data.

[0015] Furthermore, in a preferred embodiment of the present invention, the potential risk analysis is performed on the complete target flow financial data in the financial data source, and the potential risk of the financial data source and the potential risk of the complete target flow financial data are determined based on the potential risk analysis result, specifically:

[0016] In the financial data source, a range that needs to be analyzed for potential risks is determined and marked as a standard range for potential risk analysis, wherein different potential risk analysis ranges are distributed in the financial data source, and the standard range for potential risk analysis is an analysis range determined in all potential risk analysis ranges;

[0017] Running the financial data source, during the operation of the financial data source, determining the actual number of times the complete target liquid financial data is read within the scope of the potential risk analysis standard, marking it as the actual number of times the complete target liquid financial data is read, and simultaneously obtaining the official number of times the complete target liquid financial data is recorded, marking it as the official number of times the complete target liquid financial data is read;

[0018] Determine whether the actual number of reads of the complete target mobile financial data is equal to the official number of reads, and if so, mark the financial data source as a healthy financial data source, and mark the complete target mobile financial data as healthy mobile financial data;

[0019] If not, a secondary analysis is performed on the number of reads of the complete target liquid financial data. If the actual number of reads of the complete target liquid financial data is less than the official number of reads, the complete target liquid financial data is continuously read from the financial data source until the actual number of reads of the complete target liquid financial data is equal to the official number of reads;

[0020] If the actual number of reads of the complete target liquid financial data is greater than the official number of reads, the financial data source is marked as an abnormal financial data source, and the complete target liquid financial data is marked as liquid financial data to be analyzed;

[0021] Perform potential risk identification analysis on abnormal financial data sources and liquid financial data to be analyzed, and determine all potential risks of abnormal financial data sources and liquid financial data to be analyzed based on the potential risk identification analysis results.

[0022] Furthermore, in a preferred embodiment of the present invention, the potential risk identification analysis is performed on the abnormal financial data source and the liquid financial data to be analyzed, and the potential risk of the abnormal financial data source and the potential risk of the liquid financial data to be analyzed are determined based on the potential risk identification analysis results, specifically:

[0023] In the abnormal financial data source, when the liquid financial data to be analyzed is read, data parameters of the abnormal financial data source are obtained, wherein the data parameters of the abnormal financial data source are data parameters of a financial institution database and a financial market database;

[0024] Based on data parameters of the abnormal financial data source, all source codes in the abnormal financial data source are parsed to obtain the source code of the abnormal financial data source, the source code of the abnormal financial data source is run and the location where the running error occurs when the source code is run is analyzed, and based on the location where the running error occurs when the source code is run, the abnormal source code of the abnormal financial data source is obtained;

[0025] Acquire a big data network, and based on the big data network, retrieve all potential risks that may be brought about by abnormal financial data sources when different abnormal source codes exist, and mark them as potential risks of abnormal financial data sources;

[0026] Calculate the Euclidean distance between the liquid financial data to be analyzed and the complete target liquid financial data, and preset the standard Euclidean distance. If the Euclidean distance between the liquid financial data to be analyzed and the complete target liquid financial data is not greater than the standard Euclidean distance, the liquid financial data to be analyzed is marked as a type of liquid financial data, wherein the type of liquid financial data is the liquid financial data to be analyzed that has not been read or tampered with.

[0027] If the Euclidean distance between the liquid financial data to be analyzed and the complete target liquid financial data is greater than the standard Euclidean distance, the liquid financial data to be analyzed will be marked as abnormal liquid financial data, the data missing position of the abnormal liquid financial data will be analyzed, the data missing position of the abnormal liquid financial data will be located, marked as the target data missing position, and all potential risks that exist when the data missing position of the abnormal liquid financial data is equal to the target data missing position will be retrieved in the big data network and marked as potential risks of abnormal liquid financial data.

[0028] Furthermore, in a preferred embodiment of the present invention, the data recovery and data protection of abnormal flow financial data, and the data encryption control of abnormal financial data sources based on the potential risks of abnormal financial data sources, are specifically:

[0029] Conduct risk assessment on potential risks of abnormal liquid financial data, determine the scope of impact brought by potential risks of abnormal liquid financial data, and preset the maximum scope of impact;

[0030] If the impact scope brought by the potential risk of abnormal liquid financial data is smaller than the maximum impact scope, a data recovery solution is retrieved based on the big data network at the data missing position of the abnormal liquid financial data and output, so that the abnormal liquid financial data does not have data missing position and is marked as healthy liquid financial data;

[0031] If the impact scope brought by the potential risk of abnormal liquid financial data is greater than the maximum impact scope, after the abnormal liquid financial data becomes healthy liquid financial data, the restored liquid financial data is backed up, and after the data is backed up, the restored financial data is deleted from the abnormal financial data source;

[0032] At the location where the running error occurs when the source code is running, a recovery solution for the abnormal source code of the abnormal financial data source is retrieved based on the big data network and output to obtain the restored financial data source, and the root directory location where the abnormal source code appeared is marked as the root directory location to be protected on the restored financial data source;

[0033] Based on the potential risks of abnormal financial data sources, data encryption processing is performed at the root directory location that needs to be protected on the restored financial data source, so that the restored financial data source is a healthy financial data source.

[0034] Furthermore, in a preferred embodiment of the present invention, based on the potential risk of the abnormal financial data source, data encryption processing is performed at the root directory location to be protected on the restored financial data source, so that the restored financial data source is a healthy financial data source, specifically:

[0035] In the big data network, based on the potential risk retrieval of financial data sources, the anti-read source code can be applied to the root directory location that needs to be protected and marked as the target anti-read source code;

[0036] Obtain account information of all accounts that can read the restored financial data source, and update the target anti-reading source code using the account information of all accounts that can read the restored financial data source, and finally integrate the updated target anti-reading source code into the root directory location to be protected;

[0037] Performing a running test on the recovered financial data source with the target anti-reading source code updated, and during the running test, continuously determining whether the actual reading times of the recovered financial data source with the target anti-reading source code updated is equal to the official reading times, and if so, marking the recovered financial data source with the target anti-reading source code updated as a healthy financial data source;

[0038] When the restored financial data source in which the target anti-reading source code is updated is a healthy financial data source, data recovery is performed on the restored financial data in the healthy financial data source;

[0039] If not, then introduce the firewall source code at the location of the root directory to be protected, wherein the firewall source code sets a firewall at the port for reading the financial data source to prevent all instructions for reading the financial data source, and after preventing all instructions for reading the financial data source, determines whether the instruction for reading the financial data source is an official instruction, and if so, opens the reading, and if not, continues to prevent the reading, to obtain a healthy financial data source.

[0040] The second aspect of the present invention further provides a financial big data analysis and processing system based on cloud computing, the financial big data analysis and processing system comprising a memory and a processor, the memory storing the financial big data analysis and processing system, and the financial big data analysis and processing system when executed by the processor implements the following steps:

[0041] Collect and integrate the financial data that needs to be analyzed, and pre-process the integrated financial data to obtain complete target flow financial data;

[0042] Conducting a potential risk analysis on the complete target flow financial data in the financial data source, and determining the potential risk of the financial data source and the potential risk of the complete target flow financial data based on the potential risk analysis result;

[0043] Perform data recovery and data protection on abnormal flow financial data, and perform data encryption control on abnormal financial data sources based on the potential risks of abnormal financial data sources.

[0044] The present invention solves the technical defects existing in the background technology, and has the following beneficial effects: collecting and integrating the financial data to be analyzed, obtaining the complete target flow financial data, and analyzing all potential risks of the complete target flow financial data and the corresponding financial data source, and finally protecting and recovering the complete target flow financial data and the corresponding financial data source according to the potential risks, so as to achieve the purpose of protecting the financial data. The present invention can generate an encryption protection method for the financial data source and the corresponding financial data by judging whether the financial data in the financial data source has been maliciously read, thereby maintaining the stability of the financial data source and ensuring the security and reliability of the financial data. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, drawings of other embodiments can be obtained based on these drawings without paying creative work.

[0046] Figure 1 A flowchart of a financial big data analysis and processing method based on cloud computing is shown;

[0047] Figure 2 A flow chart of a method for performing data encryption control on abnormal financial data sources based on potential risks of abnormal financial data sources is shown;

[0048] Figure 3 A program view of a financial big data analysis and processing system based on cloud computing is shown. DETAILED DESCRIPTION

[0049] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0050] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0051] Figure 1A flowchart of a financial big data analysis and processing method based on cloud computing is shown, comprising the following steps:

[0052] S102: Collect and integrate the financial data to be analyzed, and perform data preprocessing on the integrated financial data to obtain complete target flow financial data;

[0053] S104: Performing a potential risk analysis on the complete target liquid financial data in the financial data source, and determining the potential risk of the financial data source and the potential risk of the complete target liquid financial data based on the potential risk analysis result;

[0054] S106: Perform data recovery and data protection on the abnormal flow financial data, and perform data encryption control on the abnormal financial data source based on the potential risks of the abnormal financial data source.

[0055] Furthermore, in a preferred embodiment of the present invention, the financial data to be analyzed is collected and integrated, and the integrated financial data is preprocessed to obtain complete target flow financial data, specifically:

[0056] Acquire a financial data source, wherein the financial data source is a database that can collect financial data, and the financial data source includes a financial institution database and a financial market database;

[0057] Preset the financial data collection time range and financial data collection object, and collect all the flowing financial data of the financial data collection object within the financial data collection time range in real time through the financial data source, and mark them as target flowing financial sub-data;

[0058] Acquire the data formats of all target liquid finance sub-data, mark the data format of one of the target liquid finance sub-data as the base data format, and convert the data formats of all target liquid finance sub-data into the base data format to obtain all target liquid finance sub-data in a unified data format;

[0059] Integrate all target liquid financial sub-data in a unified data format to obtain target liquid financial data, identify missing values ​​in the target liquid financial data, and locate the positions of missing values ​​in the target liquid financial data;

[0060] The mean of the target liquid financial data is calculated, and an interpolation method is introduced. Based on the interpolation method, the mean of the target liquid financial data is reduced to fill the position of the missing value, and a box plot method is introduced to identify the outliers of the target liquid financial data. Finally, the outliers of the target liquid financial data are deleted to obtain the complete target liquid financial data.

[0061] It should be noted that the financial data source is a database that can collect financial data. The financial data of mobile phones includes various financial data such as data in the trading system, market data, customer information, etc. The purpose of presetting the financial data collection time range and the financial data collection object is that if there is no definite time and object for the collection, the amount of data collected will be very large, which is not conducive to analysis and processing. The formats of the flowing financial data collected by different collection objects, that is, the target flowing financial sub-data, may be different, so it is necessary to unify the format of all target flowing financial sub-data and perform heterogeneous data integration to obtain the target flowing financial data. After data integration, since errors and omissions are prone to occur in the process of financial data collection, it is necessary to delete outliers and supplement missing values ​​for the target flowing financial data to make the financial data complete and error-free, and obtain complete target flowing financial data.

[0062] Furthermore, in a preferred embodiment of the present invention, the potential risk analysis is performed on the complete target flow financial data in the financial data source, and the potential risk of the financial data source and the potential risk of the complete target flow financial data are determined based on the potential risk analysis result, specifically:

[0063] In the financial data source, a range that needs to be analyzed for potential risks is determined and marked as a standard range for potential risk analysis, wherein different potential risk analysis ranges are distributed in the financial data source, and the standard range for potential risk analysis is an analysis range determined in all potential risk analysis ranges;

[0064] Running the financial data source, during the operation of the financial data source, determining the actual number of times the complete target liquid financial data is read within the scope of the potential risk analysis standard, marking it as the actual number of times the complete target liquid financial data is read, and simultaneously obtaining the official number of times the complete target liquid financial data is recorded, marking it as the official number of times the complete target liquid financial data is read;

[0065] Determine whether the actual number of reads of the complete target mobile financial data is equal to the official number of reads, and if so, mark the financial data source as a healthy financial data source, and mark the complete target mobile financial data as healthy mobile financial data;

[0066] If not, a secondary analysis is performed on the number of reads of the complete target liquid financial data. If the actual number of reads of the complete target liquid financial data is less than the official number of reads, the complete target liquid financial data is continuously read from the financial data source until the actual number of reads of the complete target liquid financial data is equal to the official number of reads;

[0067] If the actual number of reads of the complete target liquid financial data is greater than the official number of reads, the financial data source is marked as an abnormal financial data source, and the complete target liquid financial data is marked as liquid financial data to be analyzed.

[0068] It should be noted that before conducting a potential risk analysis on financial data, the scope of the analysis needs to be clearly determined, such as whether the financial data is a risk in the entire financial market, or a risk in a specific industry, company or financial product. After determining the standard scope of potential risk analysis, potential risks are determined within this scope. If there is a potential risk in financial data, first determine whether the financial data will be maliciously tampered with and read by others. The official reads recorded are not counted as malicious tampered reads. If all the read times recorded are the same as the official read times recorded, the financial data source is healthy and has not been tampered with and read. It is a healthy financial data source, and the financial data in the healthy financial data source is healthy mobile financial data. If all the read times recorded are different from the official read times recorded, it is necessary to determine whether the recorded read times are greater than the official times. If not, it may be that the financial data source is not fully recorded, and it is necessary to continue reading the complete target mobile financial data until the actual read times of the complete target mobile financial data are equal to the official read times. If it is greater than, it proves that the financial data source may have been maliciously tampered with, and the financial data source needs to be analyzed, so it is marked as an abnormal financial data source, and the complete target mobile financial data is marked as mobile financial data to be analyzed.

[0069] Furthermore, in a preferred embodiment of the present invention, the potential risk identification analysis is performed on the abnormal financial data source and the liquid financial data to be analyzed, and the potential risk of the abnormal financial data source and the potential risk of the liquid financial data to be analyzed are determined based on the potential risk identification analysis results, specifically:

[0070] In the abnormal financial data source, when the liquid financial data to be analyzed is read, data parameters of the abnormal financial data source are obtained, wherein the data parameters of the abnormal financial data source are data parameters of a financial institution database and a financial market database;

[0071] Based on data parameters of the abnormal financial data source, all source codes in the abnormal financial data source are parsed to obtain the source code of the abnormal financial data source, the source code of the abnormal financial data source is run and the location where the running error occurs when the source code is run is analyzed, and based on the location where the running error occurs when the source code is run, the abnormal source code of the abnormal financial data source is obtained;

[0072] Acquire a big data network, and based on the big data network, retrieve all potential risks that may be brought about by abnormal financial data sources when different abnormal source codes exist, and mark them as potential risks of abnormal financial data sources;

[0073] Calculate the Euclidean distance between the liquid financial data to be analyzed and the complete target liquid financial data, and preset the standard Euclidean distance. If the Euclidean distance between the liquid financial data to be analyzed and the complete target liquid financial data is not greater than the standard Euclidean distance, the liquid financial data to be analyzed is marked as a type of liquid financial data, wherein the type of liquid financial data is the liquid financial data to be analyzed that has not been read or tampered with.

[0074] If the Euclidean distance between the liquid financial data to be analyzed and the complete target liquid financial data is greater than the standard Euclidean distance, the liquid financial data to be analyzed will be marked as abnormal liquid financial data, the data missing position of the abnormal liquid financial data will be analyzed, the data missing position of the abnormal liquid financial data will be located, marked as the target data missing position, and all potential risks that exist when the data missing position of the abnormal liquid financial data is equal to the target data missing position will be retrieved in the big data network and marked as potential risks of abnormal liquid financial data.

[0075] It should be noted that when a financial data source is maliciously read, it is marked as an abnormal financial data source. It is judged that the source code of the financial data source has been maliciously tampered with, resulting in the phenomenon of data being read. Therefore, all source codes in the abnormal financial data source are parsed to find the abnormal source code of the abnormal financial data source, and all potential risks that may be brought by the abnormal source code of the abnormal financial data source are retrieved. Use a big data network for retrieval, where the big data network is equivalent to a database that stores historical data of various schemes. The liquid financial data to be analyzed is the financial data collected after the financial data source is maliciously read, and may not be similar to the complete target liquid financial data. If the Euclidean distance between the data is not greater than the standard value, it proves that the similarity between the data is high, which means that even if the financial data source is maliciously read, the financial data to be obtained has not been tampered with. On the contrary, if the Euclidean distance between the liquid financial data to be analyzed and the complete target liquid financial data is greater than the standard Euclidean distance, it proves that the liquid financial data to be analyzed has been tampered with and marked as abnormal liquid financial data. It is necessary to determine the data missing position of the abnormal liquid financial data, that is, the target data missing position, and give the target data missing position to retrieve its potential risk in the big data network.

[0076] Figure 2 A flow chart of a method for performing data encryption control on abnormal financial data sources based on potential risks of abnormal financial data sources is shown, including the following steps:

[0077] S202: Perform data recovery and data protection on abnormally flowing financial data, and determine the location of the root directory that needs to be protected on the financial data source;

[0078] S204: Based on the potential risk of the abnormal financial data source, data encryption processing is performed at the root directory location that needs to be protected on the restored financial data source, so that the restored financial data source is a healthy financial data source.

[0079] Furthermore, in a preferred embodiment of the present invention, the data recovery and data protection of abnormal flow financial data, and the data encryption control of abnormal financial data sources based on the potential risks of abnormal financial data sources, are specifically:

[0080] Conduct risk assessment on potential risks of abnormal liquid financial data, determine the scope of impact brought by potential risks of abnormal liquid financial data, and preset the maximum scope of impact;

[0081] If the impact scope brought by the potential risk of abnormal liquid financial data is smaller than the maximum impact scope, a data recovery solution is retrieved based on the big data network at the data missing position of the abnormal liquid financial data and output, so that the abnormal liquid financial data does not have data missing position and is marked as healthy liquid financial data;

[0082] If the impact scope brought by the potential risk of abnormal liquid financial data is greater than the maximum impact scope, after the abnormal liquid financial data becomes healthy liquid financial data, the restored liquid financial data is backed up, and after the data is backed up, the restored financial data is deleted from the abnormal financial data source;

[0083] At the location where a runtime error occurs during source code execution, a recovery solution for the abnormal source code of the abnormal financial data source is retrieved based on the big data network and output to obtain a restored financial data source. The root directory location where the abnormal source code appeared is marked as the root directory location that needs to be protected on the restored financial data source.

[0084] It should be noted that it is necessary to determine whether the scope of influence of the potential risk of abnormal flow financial data exceeds the controllable scope, that is, the maximum scope of influence. Among them, the scope of influence includes but is not limited to the potential risk of abnormal flow financial data that may cause a decline in corporate profits, the collapse of the financial system, etc. If the scope of influence is less than the maximum scope of influence, it proves that even if the abnormal flow financial data is stolen, it will not have a deep impact, and only the data needs to be restored with missing data; on the contrary, if the scope of influence is large, the abnormal flow financial data needs to be backed up and deleted to prevent the financial data from being leaked again and causing irreversible consequences. The purpose of the backup is to facilitate the recovery of the data after the financial data source is repaired, so that the data is no longer stolen maliciously. At the location where the running error occurs when the source code is running, the recovery plan of the abnormal source code of the abnormal financial data source is retrieved based on the big data network and output to obtain the restored financial data source, where the restored financial data source is a financial data source with complete source code, but the restored financial data source is easy to continue to be attacked, resulting in the theft of financial data, so it is necessary to determine the root directory location of the abnormal source code on the restored financial data source to achieve the optimization of the financial data source.

[0085] Furthermore, in a preferred embodiment of the present invention, based on the potential risk of the abnormal financial data source, data encryption processing is performed at the root directory location to be protected on the restored financial data source, so that the restored financial data source is a healthy financial data source, specifically:

[0086] In the big data network, based on the potential risk retrieval of financial data sources, the anti-read source code can be applied to the root directory location that needs to be protected and marked as the target anti-read source code;

[0087] Obtain account information of all accounts that can read the restored financial data source, and update the target anti-reading source code using the account information of all accounts that can read the restored financial data source, and finally integrate the updated target anti-reading source code into the root directory location to be protected;

[0088] Performing a running test on the recovered financial data source with the target anti-reading source code updated, and during the running test, continuously determining whether the actual reading times of the recovered financial data source with the target anti-reading source code updated is equal to the official reading times, and if so, marking the recovered financial data source with the target anti-reading source code updated as a healthy financial data source;

[0089] When the restored financial data source in which the target anti-reading source code is updated is a healthy financial data source, data recovery is performed on the restored financial data in the healthy financial data source;

[0090] If not, then introduce the firewall source code at the location of the root directory to be protected, wherein the firewall source code sets a firewall at the port for reading the financial data source to prevent all instructions for reading the financial data source, and after preventing all instructions for reading the financial data source, determines whether the instruction for reading the financial data source is an official instruction, and if so, opens the reading, and if not, continues to prevent the reading, to obtain a healthy financial data source.

[0091] It should be noted that since the restored financial data source can still be maliciously read, the source code that prevents malicious reading is retrieved based on the big data network, that is, the target anti-reading source code, and it is set that only official accounts can read the restored financial data source, and finally the target anti-reading source code is integrated into the root directory location that needs to be protected. After the target anti-reading source code is integrated into the root directory location that needs to be protected, if the number of recorded reads during the operation test of the restored financial data source is still not equal to the standard value, it proves that only integrating the target anti-reading source code is useless, and a firewall needs to be set up to reject all read instructions first, and analyze different instructions to determine whether they are official instructions. Official instructions are information reading instructions issued by official accounts. Official instructions can be read openly, and unofficial instructions are prevented from reading, so as to obtain a healthy financial data source.

[0092] like Figure 3 As shown, the second aspect of the present invention further provides a financial big data analysis and processing system based on cloud computing, the financial big data analysis and processing system comprises a memory 31 and a processor 32, the memory 31 stores the financial big data analysis and processing system, and when the financial big data analysis and processing system is executed by the processor 32, the following steps are implemented:

[0093] Collect and integrate the financial data that needs to be analyzed, and pre-process the integrated financial data to obtain complete target flow financial data;

[0094] Conducting a potential risk analysis on the complete target flow financial data in the financial data source, and determining the potential risk of the financial data source and the potential risk of the complete target flow financial data based on the potential risk analysis result;

[0095] Perform data recovery and data protection on abnormal flow financial data, and perform data encryption control on abnormal financial data sources based on the potential risks of abnormal financial data sources.

[0096] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A financial big data analysis and processing method based on cloud computing, characterized in that: The following steps are involved: Collect and integrate the financial data that needs to be analyzed, and pre-process the integrated financial data to obtain complete target flow financial data; Conducting a potential risk analysis on the complete target flow financial data in the financial data source, and determining the potential risk of the financial data source and the potential risk of the complete target flow financial data based on the potential risk analysis result; Perform data recovery and data protection on abnormal flow financial data, and perform data encryption control on abnormal financial data sources based on their potential risks; The potential risk analysis of the complete target flow financial data is performed in the financial data source, and the potential risk of the financial data source and the potential risk of the complete target flow financial data are determined based on the potential risk analysis result, specifically: In the financial data source, a range that needs to be analyzed for potential risks is determined and marked as a standard range for potential risk analysis, wherein different potential risk analysis ranges are distributed in the financial data source, and the standard range for potential risk analysis is an analysis range determined in all potential risk analysis ranges; Running the financial data source, during the operation of the financial data source, determining the actual number of times the complete target liquid financial data is read within the scope of the potential risk analysis standard, marking it as the actual number of times the complete target liquid financial data is read, and simultaneously obtaining the official number of times the complete target liquid financial data is recorded, marking it as the official number of times the complete target liquid financial data is read; Determine whether the actual number of reads of the complete target mobile financial data is equal to the official number of reads, and if so, mark the financial data source as a healthy financial data source, and mark the complete target mobile financial data as healthy mobile financial data; If not, a secondary analysis is performed on the number of reads of the complete target liquid financial data. If the actual number of reads of the complete target liquid financial data is less than the official number of reads, the complete target liquid financial data is continuously read from the financial data source until the actual number of reads of the complete target liquid financial data is equal to the official number of reads; If the actual number of reads of the complete target liquid financial data is greater than the official number of reads, the financial data source is marked as an abnormal financial data source, and the complete target liquid financial data is marked as liquid financial data to be analyzed; Conduct potential risk identification analysis on abnormal financial data sources and liquid financial data to be analyzed, and determine all potential risks of abnormal financial data sources and all potential risks of liquid financial data to be analyzed based on the potential risk identification analysis results; The potential risk identification and analysis of the abnormal financial data source and the liquid financial data to be analyzed, and the potential risk of the abnormal financial data source and the potential risk of the liquid financial data to be analyzed are determined based on the potential risk identification and analysis results, specifically: In the abnormal financial data source, when the liquid financial data to be analyzed is read, data parameters of the abnormal financial data source are obtained, wherein the data parameters of the abnormal financial data source are data parameters of a financial institution database and a financial market database; Based on data parameters of the abnormal financial data source, all source codes in the abnormal financial data source are parsed to obtain the source code of the abnormal financial data source, the source code of the abnormal financial data source is run and the location where the running error occurs when the source code is run is analyzed, and based on the location where the running error occurs when the source code is run, the abnormal source code of the abnormal financial data source is obtained; Acquire a big data network, and based on the big data network, retrieve all potential risks that may be brought about by abnormal financial data sources when different abnormal source codes exist, and mark them as potential risks of abnormal financial data sources; Calculate the Euclidean distance between the liquid financial data to be analyzed and the complete target liquid financial data, and preset the standard Euclidean distance. If the Euclidean distance between the liquid financial data to be analyzed and the complete target liquid financial data is not greater than the standard Euclidean distance, the liquid financial data to be analyzed is marked as a type of liquid financial data, wherein the type of liquid financial data is the liquid financial data to be analyzed that has not been read or tampered with. If the Euclidean distance between the liquid financial data to be analyzed and the complete target liquid financial data is greater than the standard Euclidean distance, the liquid financial data to be analyzed will be marked as abnormal liquid financial data, the data missing position of the abnormal liquid financial data will be analyzed, the data missing position of the abnormal liquid financial data will be located, marked as the target data missing position, and all potential risks that exist when the data missing position of the abnormal liquid financial data is equal to the target data missing position will be retrieved in the big data network and marked as potential risks of abnormal liquid financial data.

2. According to the financial big data analysis and processing method based on cloud computing described in claim 1, it is characterized in that: The financial data to be analyzed is collected and integrated, and the integrated financial data is preprocessed to obtain complete target flow financial data, specifically: Acquire a financial data source, wherein the financial data source is a database that can collect financial data, and the financial data source includes a financial institution database and a financial market database; Preset the financial data collection time range and financial data collection object, and collect all the flowing financial data of the financial data collection object within the financial data collection time range in real time through the financial data source, and mark them as target flowing financial sub-data; Acquire the data formats of all target liquid finance sub-data, mark the data format of one of the target liquid finance sub-data as the base data format, and convert the data formats of all target liquid finance sub-data into the base data format to obtain all target liquid finance sub-data in a unified data format; Integrate all target liquid financial sub-data in a unified data format to obtain target liquid financial data, identify missing values ​​in the target liquid financial data, and locate the positions of missing values ​​in the target liquid financial data; The mean of the target liquid financial data is calculated, and an interpolation method is introduced. Based on the interpolation method, the mean of the target liquid financial data is filled into the position of the missing value, and a box plot method is introduced to identify the outliers of the target liquid financial data. Finally, the outliers of the target liquid financial data are deleted to obtain the complete target liquid financial data.

3. According to the financial big data analysis and processing method based on cloud computing described in claim 1, it is characterized in that: The data recovery and data protection of abnormal flow financial data, and the data encryption control of abnormal financial data sources based on the potential risks of abnormal financial data sources, are specifically: Conduct risk assessment on potential risks of abnormal liquid financial data, determine the scope of impact brought by potential risks of abnormal liquid financial data, and preset the maximum scope of impact; If the impact scope brought by the potential risk of abnormal liquid financial data is smaller than the maximum impact scope, a data recovery solution is retrieved based on the big data network at the data missing position of the abnormal liquid financial data and output, so that the abnormal liquid financial data does not have data missing position and is marked as healthy liquid financial data; If the impact scope brought by the potential risk of abnormal liquid financial data is greater than the maximum impact scope, after the abnormal liquid financial data becomes healthy liquid financial data, the restored liquid financial data is backed up, and after the data is backed up, the restored financial data is deleted from the abnormal financial data source; At the location where the running error occurs when the source code is running, a recovery solution for the abnormal source code of the abnormal financial data source is retrieved based on the big data network and output to obtain the restored financial data source, and the root directory location where the abnormal source code appeared is marked as the root directory location to be protected on the restored financial data source; Based on the potential risks of abnormal financial data sources, data encryption processing is performed at the root directory location that needs to be protected on the restored financial data source, so that the restored financial data source is a healthy financial data source.

4. According to the financial big data analysis and processing method based on cloud computing described in claim 3, it is characterized in that: Based on the potential risk of abnormal financial data sources, data encryption processing is performed at the root directory location that needs to be protected on the restored financial data source, so that the restored financial data source is a healthy financial data source, specifically: In the big data network, based on the potential risks of financial data sources, anti-read source code that can be used in the root directory location to be protected is retrieved and marked as the target anti-read source code; Obtain account information of all accounts that can read the restored financial data source, and update the target anti-reading source code with the account information of all accounts that can read the restored financial data source, and finally integrate the updated target anti-reading source code into the root directory location to be protected; Performing a running test on the recovered financial data source with the target anti-reading source code updated, and during the running test, continuously determining whether the actual reading times of the recovered financial data source with the target anti-reading source code updated is equal to the official reading times, and if so, marking the recovered financial data source with the target anti-reading source code updated as a healthy financial data source; When the restored financial data source in which the target anti-reading source code is updated is a healthy financial data source, data recovery is performed on the restored financial data in the healthy financial data source; If not, then introduce the firewall source code at the location of the root directory to be protected, wherein the firewall source code sets a firewall at the port for reading the financial data source to prevent all instructions for reading the financial data source, and after preventing all instructions for reading the financial data source, determines whether the instruction for reading the financial data source is an official instruction, and if so, opens the reading, and if not, continues to prevent the reading, to obtain a healthy financial data source.

5. A financial big data analysis and processing system based on cloud computing, characterized in that: The financial big data analysis and processing system comprises a memory and a processor, wherein the memory stores a financial big data analysis and processing method program, and when the financial big data analysis and processing method program is executed by the processor, the steps of the financial big data analysis and processing method according to any one of claims 1 to 4 are implemented: Collect and integrate the financial data that needs to be analyzed, and pre-process the integrated financial data to obtain complete target flow financial data; Conducting a potential risk analysis on the complete target flow financial data in the financial data source, and determining the potential risk of the financial data source and the potential risk of the complete target flow financial data based on the potential risk analysis result; Perform data recovery and data protection on abnormal flow financial data, and perform data encryption control on abnormal financial data sources based on their potential risks; The potential risk analysis of the complete target flow financial data is performed in the financial data source, and the potential risk of the financial data source and the potential risk of the complete target flow financial data are determined based on the potential risk analysis result, specifically: In the financial data source, a range that needs to be analyzed for potential risks is determined and marked as a standard range for potential risk analysis, wherein different potential risk analysis ranges are distributed in the financial data source, and the standard range for potential risk analysis is an analysis range determined in all potential risk analysis ranges; Running the financial data source, during the operation of the financial data source, determining the actual number of times the complete target liquid financial data is read within the scope of the potential risk analysis standard, marking it as the actual number of times the complete target liquid financial data is read, and simultaneously obtaining the official number of times the complete target liquid financial data is recorded, marking it as the official number of times the complete target liquid financial data is read; Determine whether the actual number of reads of the complete target mobile financial data is equal to the official number of reads, and if so, mark the financial data source as a healthy financial data source, and mark the complete target mobile financial data as healthy mobile financial data; If not, a secondary analysis is performed on the number of reads of the complete target liquid financial data. If the actual number of reads of the complete target liquid financial data is less than the official number of reads, the complete target liquid financial data is continuously read from the financial data source until the actual number of reads of the complete target liquid financial data is equal to the official number of reads; If the actual number of reads of the complete target liquid financial data is greater than the official number of reads, the financial data source is marked as an abnormal financial data source, and the complete target liquid financial data is marked as liquid financial data to be analyzed; Conduct potential risk identification analysis on abnormal financial data sources and liquid financial data to be analyzed, and determine all potential risks of abnormal financial data sources and all potential risks of liquid financial data to be analyzed based on the potential risk identification analysis results; The potential risk identification and analysis of the abnormal financial data source and the liquid financial data to be analyzed, and the potential risk of the abnormal financial data source and the potential risk of the liquid financial data to be analyzed are determined based on the potential risk identification and analysis results, specifically: In the abnormal financial data source, when the liquid financial data to be analyzed is read, data parameters of the abnormal financial data source are obtained, wherein the data parameters of the abnormal financial data source are data parameters of a financial institution database and a financial market database; Based on data parameters of the abnormal financial data source, all source codes in the abnormal financial data source are parsed to obtain the source code of the abnormal financial data source, the source code of the abnormal financial data source is run and the location where the running error occurs when the source code is run is analyzed, and based on the location where the running error occurs when the source code is run, the abnormal source code of the abnormal financial data source is obtained; Acquire a big data network, and based on the big data network, retrieve all potential risks that may be brought about by abnormal financial data sources when different abnormal source codes exist, and mark them as potential risks of abnormal financial data sources; Calculate the Euclidean distance between the liquid financial data to be analyzed and the complete target liquid financial data, and preset the standard Euclidean distance. If the Euclidean distance between the liquid financial data to be analyzed and the complete target liquid financial data is not greater than the standard Euclidean distance, the liquid financial data to be analyzed is marked as a type of liquid financial data, wherein the type of liquid financial data is the liquid financial data to be analyzed that has not been read or tampered with. If the Euclidean distance between the liquid financial data to be analyzed and the complete target liquid financial data is greater than the standard Euclidean distance, the liquid financial data to be analyzed will be marked as abnormal liquid financial data, the data missing position of the abnormal liquid financial data will be analyzed, the data missing position of the abnormal liquid financial data will be located, marked as the target data missing position, and all potential risks that exist when the data missing position of the abnormal liquid financial data is equal to the target data missing position will be retrieved in the big data network and marked as potential risks of abnormal liquid financial data.

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