Financial information management system based on cloud computing

Abstract

The invention discloses a financial information management system based on cloud computing, and relates to the technical field of information security. The system comprises a data acquisition end used for acquiring market data; the storage encryption unit is used for preprocessing and encrypting the market data and backing up ciphertext data; the model training unit is used for carrying out decryption by utilizing a secret key held by the model training unit, constructing a training data set according to decrypted data, and carrying out iterative training on the pre-training network by utilizing the training data set to obtain a risk early warning model; the management application unit is used for evaluating the target investment portfolio by using the risk early warning model, generating a visual report according to the target investment portfolio and the corresponding related data, evaluation time and evaluation result, and uploading the visual report to the cloud platform; wherein when the evaluation result exceeds a limit value, a warning signal is issued, and the warning signal and the corresponding visual report are simultaneously sent to the account of the person in charge. According to the invention, the automation degree and accuracy of management can be improved.

Description

Technical Field

[0001] The present invention relates to the field of information security technology, and particularly to a financial information management system based on cloud computing. Background Art

[0002] In the prior art, the construction and operation of financial information management systems face a series of challenges and deficiencies. Traditional financial information management systems often rely on local servers for data storage and processing, which not only limits the data processing capacity and efficiency but also increases the risks of data security and privacy protection. Although cloud computing technology has been gradually applied to the financial field in recent years, there are still many problems to be solved in the actual deployment and integration process. Therefore, there is an urgent need for a more efficient, secure, and intelligent solution to cope with the increasingly complex financial market environment. Summary of the Invention

[0003] The object of the present invention is to provide a financial information management system based on cloud computing, which can improve the automation degree and accuracy of management.

[0004] To achieve the above object, the present invention provides the following solutions:

[0005] A financial information management system based on cloud computing, comprising:

[0006] A data acquisition end for acquiring market data; the market data includes various portfolio data;

[0007] A storage and encryption unit for preprocessing and encrypting the market data, and backing up the encrypted ciphertext data; the preprocessing includes integration and cleaning;

[0008] A model training unit for decrypting the ciphertext data with its own held key, constructing a training data set according to the decrypted data, and iteratively training a pre-trained network with the training data set to obtain a risk warning model; the training data set includes portfolio data and corresponding training labels; the pre-trained network is constructed based on a value-at-risk model and an attention mechanism;

[0009] A management application unit for evaluating a target portfolio with the risk warning model, generating a visualization report based on the target portfolio, corresponding relevant data, evaluation time, and evaluation result, and uploading it to the cloud platform; wherein, when the evaluation result exceeds the limit value, a warning signal is issued, and the warning signal and the corresponding visualization report are simultaneously sent to the responsible person's account.

[0010] Optionally, the data acquisition end specifically includes:

[0011] External network data collection interface, which is used to collect investment portfolios and corresponding investment results on the Internet by using web crawlers to obtain external network investment portfolio data;

[0012] Internal network data collection interface, which is used to extract investment portfolios and corresponding investment results in the company's internal network to obtain internal network investment portfolio data.

[0013] Optionally, the storage encryption unit specifically includes:

[0014] Preprocessing module, which is used to integrate market data with similar attributes by using similarity calculation, and clean and delete duplicate market data to obtain preprocessed data;

[0015] Encryption module, which is used to encrypt and back up the preprocessed data by using the hash encryption algorithm to obtain encrypted ciphertext data.

[0016] Optionally, the preprocessing module specifically includes:

[0017] Similarity calculation sub-module, which is used to perform clustering analysis on market data and calculate the similarity between the data attributes of each cluster by using Jensen-Shannon divergence;

[0018] Integration and cleaning sub-module, which is used to integrate the data that meets the preset similarity threshold and clean and delete duplicate market data to obtain preprocessed data.

[0019] Optionally, the encryption module specifically includes:

[0020] Ciphertext generation sub-module, which is used to encrypt and back up the preprocessed data by using the hash encryption algorithm to obtain encrypted ciphertext data;

[0021] Public key generation sub-module, which is used to generate a public key; the public key is used for identity verification with the key held by the data decryptor itself.

[0022] Optionally, the model training unit specifically includes:

[0023] Decryption module, which is used to decrypt the ciphertext data by using the key held by itself;

[0024] Training dataset construction module, which is used to construct a training dataset according to the decrypted data;

[0025] Training module, which is used to iteratively train the pre-trained network by using the training dataset to obtain a risk warning model.

[0026] Optionally, the training module specifically includes:

[0027] A model construction sub-module for constructing a pre-trained network according to a value-at-risk model and an attention mechanism;

[0028] A model training sub-module for inputting the training data set into the pre-trained network, training according to a gradient descent strategy with the goal of minimizing the loss between the network output and the training labels, and determining the trained network as a risk warning model.

[0029] Optionally, the management application unit specifically includes:

[0030] An evaluation module for evaluating a target portfolio using the risk warning model to obtain an evaluation result; the evaluation result includes a risk type and a corresponding risk quantification value;

[0031] A report generation module for generating a visual report based on the target portfolio, corresponding relevant data, evaluation time, and evaluation result, and uploading it to the cloud platform;

[0032] An early warning module for issuing a warning signal when the evaluation result exceeds the limit, and sending the warning signal and the corresponding visual report to the responsible person's account at the same time.

[0033] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0034] The present invention discloses a financial information management system based on cloud computing. The method includes a data collection end for collecting market data; a storage and encryption unit for preprocessing and encrypting market data, and backing up the ciphertext data; a model training unit for decrypting using its own held key, constructing a training data set based on the decrypted data, and iteratively training a pre-trained network using the training data set to obtain a risk warning model; a management application unit for evaluating a target portfolio using the risk warning model, and generating a visual report based on the target portfolio, corresponding relevant data, evaluation time, and evaluation result, and uploading it to the cloud platform; wherein, when the evaluation result exceeds the limit, a warning signal is issued, and the warning signal and the corresponding visual report are sent to the responsible person's account at the same time. The present invention can improve the automation degree and accuracy of management. Description of the Drawings

[0035] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required to be used in the embodiments. Obviously, the following described drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0036] Figure 1Schematic diagram of the financial information management system based on cloud computing of the present invention. Detailed implementation manners

[0037] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0038] The purpose of the present invention is to provide a financial information management system based on cloud computing, which can improve the automation degree and accuracy of management.

[0039] To make the above objects, features, and advantages of the present invention more obvious and understandable, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific implementation manners.

[0040] As Figure 1 shown, the present invention provides a financial information management system based on cloud computing, including:

[0041] A data acquisition end, used to acquire market data; the market data includes a variety of portfolio data;

[0042] A storage encryption unit, used to preprocess and encrypt the market data, and back up the encrypted ciphertext data; the preprocessing includes integration and cleaning;

[0043] A model training unit, used to decrypt the ciphertext data with its own held key, construct a training data set according to the decrypted data, and iteratively train a pre-trained network with the training data set to obtain a risk warning model; the training data set includes portfolio data and corresponding training labels; the pre-trained network is constructed based on a value-at-risk model and an attention mechanism;

[0044] A management application unit, used to evaluate a target portfolio with the risk warning model, and generate a visualization report based on the target portfolio, corresponding relevant data, evaluation time, and evaluation result, and upload it to the cloud platform; wherein, when the evaluation result exceeds the limit value, a warning signal is issued, and the warning signal and the corresponding visualization report are sent to the responsible person's account at the same time.

[0045] As a specific implementation manner, the data acquisition end specifically includes:

[0046] An external network data acquisition interface, used to acquire portfolio and corresponding investment results on the Internet with a web crawler to obtain external network portfolio data;

[0047] The intranet data collection interface is used to extract the investment portfolio and the corresponding investment results in the company's intranet to obtain intranet investment portfolio data.

[0048] As a specific implementation manner, the storage encryption unit specifically includes:

[0049] The preprocessing module is used to integrate market data with similar attributes by using similarity calculation, and clean and delete duplicate market data to obtain preprocessed data;

[0050] The encryption module is used to encrypt and back up the preprocessed data by using the hash encryption algorithm to obtain encrypted ciphertext data.

[0051] As a specific implementation manner, the preprocessing module specifically includes:

[0052] The similarity calculation sub-module is used to perform clustering analysis on market data and calculate the similarity between the data attributes of each cluster by using Jensen-Shannon divergence;

[0053] The integration and cleaning sub-module is used to integrate the data that meets the preset similarity threshold and clean and delete duplicate market data to obtain preprocessed data.

[0054] As a specific implementation manner, the encryption module specifically includes:

[0055] The ciphertext generation sub-module is used to encrypt and back up the preprocessed data by using the hash encryption algorithm to obtain encrypted ciphertext data;

[0056] The public key generation sub-module is used to generate a public key; the public key is used for identity verification with the key held by the data decryptor itself.

[0057] As a specific implementation manner, the model training unit specifically includes:

[0058] The decryption module is used to decrypt the ciphertext data by using the key held by itself;

[0059] The training data set construction module is used to construct a training data set according to the decrypted data;

[0060] The training module is used to iteratively train the pre-trained network by using the training data set to obtain a risk warning model.

[0061] As a specific implementation manner, the training module specifically includes:

[0062] The model construction sub-module is used to construct a pre-trained network according to the value-at-risk model and the attention mechanism;

[0063] A model training sub-module, configured to input the training data set into the pre-trained network, and perform training according to the gradient descent strategy with the goal of minimizing the loss between the network output and the training labels, and determine the trained network as a risk warning model.

[0064] As a specific implementation, the management application unit specifically includes:

[0065] An evaluation module, configured to evaluate a target investment portfolio by using the risk warning model to obtain an evaluation result; the evaluation result includes a risk type and a corresponding risk quantification value;

[0066] A report generation module, configured to generate a visual report based on the target investment portfolio, corresponding relevant data, evaluation time, and evaluation result, and upload it to the cloud platform;

[0067] An early warning module, configured to issue a warning signal when the evaluation result exceeds the limit, and send the warning signal and the corresponding visual report to the responsible person's account at the same time.

[0068] Based on the above technical solutions, the following embodiments are provided.

[0069] System architecture

[0070] Data acquisition end: responsible for acquiring market data, including investment portfolio data of the external network and the internal network.

[0071] Storage and encryption unit: preprocess and encrypt the acquired data, and perform backup.

[0072] Model training unit: use the encrypted data to perform model training and construct a risk warning model.

[0073] Management application unit: use the risk warning model to perform evaluation, generate a visual report, and issue a warning when the risk exceeds the limit.

[0074] Implementation steps

[0075] 1. Data acquisition

[0076] External network data acquisition: Use web crawler technology to collect investment portfolio and corresponding investment result data from public channels such as Internet financial platforms and stock exchanges through the external network data acquisition interface.

[0077] Internal network data acquisition: Extract investment portfolio and corresponding investment result data from the company's internal database through the internal network data acquisition interface.

[0078] 2. Data preprocessing and encryption

[0079] Similarity calculation: Conduct clustering analysis on the collected market data, and use Jensen-Shannon divergence to calculate the similarity between the data attributes of each cluster.

[0080] Data integration and cleaning: Integrate the data that meets the preset similarity threshold, delete duplicate data, and obtain the preprocessed data.

[0081] Data encryption: Use the hash encryption algorithm to encrypt the preprocessed data, generate ciphertext data, and make a backup.

[0082] Public key generation: Generate a public key for identity verification with the key held by the data decryptor itself.

[0083] 3. Model training

[0084] Data decryption: Use the key held by itself to decrypt the ciphertext data to obtain the original data.

[0085] Training dataset construction: Construct a training dataset based on the decrypted data, including portfolio data and corresponding training labels.

[0086] Model construction: Build a pre-training network based on the Value at Risk (VaR) model and the attention mechanism.

[0087] Model training: Input the training dataset into the pre-training network, aiming to minimize the loss between the network output and the training labels, and train according to the gradient descent strategy to obtain a risk warning model.

[0088] 4. Risk assessment and warning

[0089] Risk assessment: Use the trained risk warning model to evaluate the target investment portfolio, and obtain evaluation results, including risk types and corresponding risk quantification values.

[0090] Report generation: Generate a visualization report based on the target investment portfolio, corresponding relevant data, evaluation time, and evaluation results, and upload it to the cloud platform.

[0091] Warning release: When the evaluation result exceeds the preset limit, release a warning signal, and send the warning signal and the corresponding visualization report to the responsible person's account at the same time.

[0092] Technical details

[0093] Data acquisition end

[0094] External network data acquisition interface: Use the requests and BeautifulSoup libraries in Python to write a web crawler to collect investment portfolio data on the Internet.

[0095] Internal network data collection interface: Extract portfolio data in the internal network through the company's internal API interface or database connection.

[0096] Storage encryption unit

[0097] Similarity calculation sub-module: Use the scikit-learn library in Python for clustering analysis and calculate the Jensen-Shannon divergence.

[0098] Integration and cleaning sub-module: Use the Pandas library for data integration and cleaning.

[0099] Encryption module: Use the hashlib library for hash encryption to generate ciphertext data.

[0100] Model training unit

[0101] Decryption module: Use a symmetric encryption algorithm (such as AES) for data decryption.

[0102] Training dataset construction module: Organize the decrypted data into a format suitable for model training.

[0103] Model construction sub-module: Use the TensorFlow or PyTorch framework to construct a pre-trained network based on the value-at-risk model and attention mechanism.

[0104] Model training sub-module: Use the gradient descent algorithm for model training to optimize network parameters.

[0105] Management application unit

[0106] Evaluation module: Use the trained risk warning model to evaluate the target portfolio.

[0107] Report generation module: Use the Matplotlib or Seaborn library to generate visual reports.

[0108] Warning module: Send warning signals and reports to the responsible person's account through the API interface of the cloud platform.

[0109] Implementation effect

[0110] Through the above system, financial institutions can collect and process market data in real time, use advanced risk warning models for accurate evaluation, and issue warnings in a timely manner when the risk exceeds the limit. This not only improves the efficiency of risk management, but also enhances the scientificity and timeliness of decision-making, providing strong support for the stable operation of financial institutions.

[0111] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The same or similar parts among the various embodiments can be referred to each other.

[0112] In this article, specific examples are used to elaborate on the principles and implementation modes of the present invention. The description of the above embodiments is only used to help understand the core idea of the present invention. At the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation modes and application scopes. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. A financial information management system based on cloud computing, characterized in that: include: A data collection terminal is used to collect market data; the market data includes a variety of investment portfolio data; A storage encryption unit, used for preprocessing and encrypting the market data, and backing up the encrypted ciphertext data; The pretreatment includes integration and cleaning; A model training unit, used to decrypt the ciphertext data using a key held by itself, and to construct a training data set based on the decrypted data, and to iteratively train the pre-trained network using the training data set to obtain a risk warning model; The training data set includes investment portfolio data and corresponding training labels; The pre-trained network is constructed based on a risk value model and an attention mechanism; The management application unit is used to evaluate the target investment portfolio using the risk warning model, and generate a visual report based on the target investment portfolio and corresponding relevant data, evaluation time and evaluation results, and upload it to the cloud platform; when the evaluation result exceeds the limit, an alarm signal is issued, and the alarm signal and the corresponding visual report are sent to the account of the person in charge at the same time.

2. The cloud computing-based financial information management system according to claim 1, characterized in that: The data collection end specifically includes: The external network data collection interface is used to collect investment portfolios and corresponding investment results on the Internet using web crawlers to obtain external network investment portfolio data; The intranet data collection interface is used to extract the investment portfolio and corresponding investment results in the company's intranet to obtain the intranet investment portfolio data.

3. The cloud computing-based financial information management system according to claim 1, characterized in that: The storage encryption unit specifically includes: The preprocessing module is used to integrate market data with similar attributes by using similarity calculation, and to clean and delete duplicate market data to obtain preprocessed data; The encryption module is used to encrypt and back up the pre-processed data using a hash encryption algorithm to obtain encrypted ciphertext data.

4. The financial information management system based on cloud computing according to claim 3 is characterized in that: The preprocessing module specifically includes: The similarity calculation submodule is used to perform cluster analysis on market data and calculate the similarity between the attributes of each cluster data using Jensen-Shannon divergence; The integration and cleaning submodule is used to integrate the data that meets the preset similarity threshold and clean and delete the duplicate market data to obtain the pre-processed data.

5. The cloud computing-based financial information management system according to claim 3, characterized in that: The encryption module specifically includes: The ciphertext generation submodule is used to encrypt and back up the pre-processed data using a hash encryption algorithm to obtain encrypted ciphertext data; The public key generation submodule is used to generate a public key; the public key is used to authenticate the identity with the key held by the data decryption party itself.

6. The financial information management system based on cloud computing according to claim 1, characterized in that: The model training unit specifically includes: A decryption module, used to decrypt the ciphertext data using a key held by itself; A training data set construction module, used to construct a training data set according to the decrypted data; The training module is used to iteratively train the pre-trained network using the training data set to obtain a risk warning model.

7. The cloud computing-based financial information management system according to claim 6, characterized in that: The training module specifically includes: The model building submodule is used to build a pre-trained network based on the risk value model and attention mechanism; The model training submodule is used to input the training data set into the pre-trained network, train according to the gradient descent strategy with the goal of minimizing the loss between the network output and the training label, and determine the trained network as a risk warning model.

8. The financial information management system based on cloud computing according to claim 1, characterized in that: The management application unit specifically includes: An evaluation module, used to evaluate the target investment portfolio using the risk warning model to obtain an evaluation result; the evaluation result includes a risk type and a corresponding risk quantification value; A report generation module, used to generate a visual report based on the target investment portfolio and corresponding relevant data, evaluation time and evaluation results, and upload it to the cloud platform; The early warning module is used to issue an alarm signal when the evaluation result exceeds the limit, and send the alarm signal and the corresponding visual report to the account of the person in charge at the same time.

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