Family enterprise governance cooperation management and control system

Abstract

The invention relates to the technical field of family trust, and discloses a family enterprise governance cooperation management and control system, which comprises a service product management module used for managing service products of family trust; the service execution management module is used for managing execution of service products; the asset list input module is used for inputting asset data of the customer; the risk diagnosis module is used for diagnosing customer risks; the file management module is used for encrypting and managing files and data which are uploaded by the user and related to the specific customer in a digital file form; the system can help an enterprise to realize customer full life cycle management including customer data collection, customer follow-up, sales opportunity tracking, auxiliary order talking, project management, expert resource combination and the like, has a powerful customer property risk diagnosis tool and a block chain encryption technology, can help the enterprise to improve the sales efficiency, and has a good market prospect. And a privacy data security solution is provided for the client.

Description

Technical Field

[0001] The present invention relates to the field of family trust technology, and more specifically, to a family business governance collaborative management and control system. Background Art

[0002] A family trust is a trust structure established by family members to manage and protect family wealth, achieve wealth inheritance and family planning. A family trust transfers family property, assets or inheritance to a trust fund, which is managed and distributed by the trustee (usually a professional trust institution or a part of the family members). The purpose of a family trust is to achieve the following goals:

[0003] Wealth protection: Family trusts can help family members protect family wealth from external risks, such as lawsuits, debt collection, etc. By placing family property in a trust, it can be clearly separated from personal property, thereby protecting the safety of the property.

[0004] Wealth inheritance: Family trusts provide family members with an effective way to plan and manage the inheritance of wealth. By setting up a trust plan, family members can determine the method, time and conditions for wealth distribution to ensure the inheritance and continuation of wealth within the family.

[0005] Tax advantages: In some countries or regions, family trusts can provide certain tax advantages. For example, trust property may enjoy tax policies such as exemption or deferral of property tax and inheritance tax.

[0006] Management professionalization: By entrusting a professional trust institution or trustee to manage family property, the professional level and efficiency of property management can be effectively improved. The trustee is responsible for implementing the trust plan and managing and investing the trust assets according to established rules and guidelines.

[0007] Family governance: Family trusts can be used as a tool for family governance to help regulate the rights and obligations among family members, reduce conflicts and disputes within the family, and maintain family harmony and stability.

[0008] In the prior art, individuals or organizations of family trusts use general office systems to conduct digital office and document management, which is unable to ensure the confidentiality and circulation protection of specific documents in family trust services. Summary of the invention

[0009] The present invention provides a family business governance collaborative management and control system to solve the technical problem in the related art that it is impossible to protect the confidentiality and circulation of specific documents in family trust services.

[0010] The present invention provides a family business governance collaboration management and control system, including:

[0011] User management module, which is used to manage users;

[0012] Service product management module, which is used to manage the service products of family trusts;

[0013] A service execution management module, which is used to manage the execution of service products;

[0014] Customer management module, which is used to manage customers;

[0015] Information visualization module, which is used to display various data indicators related to family offices;

[0016] Asset list entry module, which is used to enter the customer's asset data;

[0017] Risk diagnosis module, which is used to diagnose customer risks;

[0018] The file management module is used to encrypt and manage the files and materials uploaded by users and related to specific customers in the form of digital files.

[0019] Furthermore, the service execution management module manages the following aspects during the service product execution process:

[0020] The selection of the responsible expert is done by the responsible expert himself / herself by logging into the expert terminal to decide whether to serve the client, or the project director may designate;

[0021] After the product service solves the problem for the customer, the project director completes the delivery, or the expert logs in to the system to complete the delivery. After clicking Complete Delivery, the project product status will change to Customer Pending Confirmation;

[0022] Cost management, which is used to manage the costs incurred when performing service products;

[0023] To-do management, used to record to-do items in the execution process of management service products;

[0024] Customer follow-up, which is used to record and manage interactions and communications with customers;

[0025] File management is used for users to upload and store files and information related to specific customers.

[0026] Furthermore, the customer's risk corresponds to multiple risk items, and each risk item is evaluated separately;

[0027] Risk items correspond to multiple risk levels. Risk levels are divided into three levels: high, medium, and low. Each risk item is set with evaluation conditions corresponding to each level. The evaluation conditions are matched according to the current customer's asset data to determine the risk item as a risk level that meets the evaluation conditions.

[0028] Furthermore, the method for the file management module to encrypt and decrypt digital files includes:

[0029] Step 101, first generate a key pair using the OpenGPG key generator;

[0030] Step 102, encrypt the digital file using the public key and upload it to the decentralized network;

[0031] Step 103, downloading the digital archive file from the IPFS decentralized network and decrypting it using the private key;

[0032] Encrypting digital files with public keys and uploading them to the IPFS decentralized network includes the following steps:

[0033] A. Select digital archive file: Select the digital archive file to be encrypted;

[0034] B. Public key encryption: Use the public key to encrypt digital archive files using an asymmetric encryption algorithm;

[0035] C. Upload IPFS: upload the encrypted digital archive file to the IPFS decentralized network;

[0036] D. Generate download address: After the upload is successful, the IPFS network node will generate a unique identifier for the digital archive file and return the content address.

[0037] The present invention provides a family business governance collaborative control method, which includes executing the following steps through the aforementioned family business governance collaborative control system:

[0038] Obtain the family business operation data set D and the decision model parameter set P, where D includes financial data, market data, and personnel data, and normalize the data set D to obtain D′;

[0039] The normalized data D′ is input into a self-organizing map network for processing. The SOM network includes an input layer and a competition layer. The weights of the winning neuron and its neighboring neurons are updated by calculating the distance between the input data and the weight vector of the competition layer neurons until the network converges.

[0040] The data processed by the SOM network is input into the generative adversarial network, the generator G generates the visualization data V, and the discriminator D discriminates the generated visualization data;

[0041] The visualization data V and decision model parameters P are input into the decision model to generate decision recommendations R including operation strategy, market strategy, human resource strategy, financial strategy and risk warning.

[0042] Furthermore, the weight update formula of the self-organizing map network is:

[0043] w j (t+1)=w j (t)+α(t)h cj (t)(xw j (t))

[0044] Among them, α(t) is the learning rate, h cj (t) is the Gaussian neighborhood function, x is the input data, w j is the weight vector.

[0045] Furthermore, the decision model includes:

[0046] Feature extraction layer: Use convolutional neural networks to process visualization data;

[0047] Parameter encoding layer: Use a fully connected layer to process enterprise parameters;

[0048] Fusion layer: concatenates the outputs of the feature extraction layer and the parameter encoding layer;

[0049] Decision generation layer: contains 5 parallel sub-networks, which generate decision suggestions of different dimensions.

[0050] The present invention provides an application of a family business governance collaboration control system in family business governance collaboration, and the aforementioned family business governance collaboration control system is used to complete the following collaboration contents:

[0051] 1. Collaboratively link the four roles of family office, experts, channels and customers. The four roles can access the family business governance collaborative management and control system through four different service terminals;

[0052] 2. The service items for a customer include more than one service product;

[0053] Clients and family offices associated with a service project have the right to view and manage all service products associated with the service project;

[0054] Experts and channels only have the authority to view and manage the service products associated with them;

[0055] 3. Generate a risk assessment report for the customer based on the customer's information. The risk assessment report includes the risk level of the risk items associated with the customer;

[0056] The risk items associated with a customer can be specified by the customer or obtained based on the content input by the customer. For example, the customer can enter text content such as legal documents and link to the risk items by identifying keywords in the text.

[0057] 4. Family offices can acquire content through embedded large language models;

[0058] 5. After the service product is accepted, the fee details for the customer will be automatically generated based on the service product information set by the family office; and the fees for experts and channels will be automatically settled based on the commission ratio of the service product to experts and channels set by the family office.

[0059] Furthermore, the method of applying the aforementioned family business governance collaborative management and control system to complete the file distribution specified by the customer includes:

[0060] Generate a key pair using the OpenGPG key generator;

[0061] Use the public key to encrypt the digital file specified by the customer and upload it to the IPFS decentralized network;

[0062] The intended allocation time specified by the customer. When the specified intended allocation time arrives, the files and private keys specified by the customer will be automatically sent to the relevant beneficiaries.

[0063] The present invention provides a computer storage medium for storing computer-readable instructions, which can be executed on a computer system. When the computer-readable instructions are executed, the aforementioned family business governance collaborative management and control system can be run.

[0064] The beneficial effects of the present invention are:

[0065] Intuitive and easy to use: The front-end interface is simple and intuitive, allowing users to easily understand and operate the various functions of the system. The friendly user experience reduces the learning cost and improves the user's work efficiency.

[0066] Multi-platform support: The front-end application supports multiple platforms, including Web and mobile devices. Users can use the system flexibly on different devices and complete tasks anytime and anywhere.

[0067] Customizability: Provides a certain degree of front-end customization, allowing family offices to personalize settings and adjust the interface according to their own needs. This customizability enables the system to better adapt to the workflow and business characteristics of family offices.

[0068] Data visualization: Present important information through data visualization methods such as charts and reports, so that users can intuitively understand business conditions and data trends. This helps users make accurate decisions and better manage and analyze data.

[0069] Responsive design: Adopting responsive design, it can automatically adapt to different screen sizes and resolutions. This allows the system to achieve good display effects on different devices and provide a consistent user experience.

[0070] Efficiency: Through automation and process optimization, the work efficiency of family offices is improved. It provides functions such as assisting in negotiation, project management, incubation and running, etc., to help family offices improve sales efficiency and income.

[0071] Security: Multiple security measures are adopted to protect the data of the family office. Asymmetric encryption, blockchain technology and permission control are used to ensure the security, reliability and confidentiality of customer data and prevent data leakage and loss.

[0072] Scalability: The architecture is designed with scalability in mind and can meet the needs of family offices of different sizes. It supports distributed deployment and cloud services, can be expanded according to business growth, and provides good performance and stability.

[0073] Expert resources: We cooperate with the family office industry association and build an expert resource library to provide family offices with professional guidance and support in the industry, helping them to obtain more resources and opportunities in their development.

[0074] Ecological Alliance: Promotes cooperation and sharing among family offices. Through functions such as customer source delivery and resource sharing of big names, the family office alliance has been built to help family offices develop together and achieve win-win results in the industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 It is a module schematic diagram of a family business governance collaboration management and control system of the present invention;

[0076] Figure 2 is a flow chart of a method for encrypting and decrypting a digital file of the present invention;

[0077] Figure 3 is a module schematic diagram of a computer storage medium of the present invention;

[0078] Figure 4 It is a flow chart of a family business governance collaboration method of the present invention.

[0079] In the figure: user management module 101, service product management module 102, service execution management module 103, customer management module 104, information visualization module 105, asset list entry module 106, risk diagnosis module 107, and file management module 108. DETAILED DESCRIPTION

[0080] The subject matter described herein will now be discussed with reference to example embodiments. It should be understood that the discussion of these embodiments is only to enable those skilled in the art to better understand and implement the subject matter described herein, and the functions and arrangements of the elements discussed may be changed without departing from the scope of protection of the contents of this specification. Each example may omit, replace or add various processes or components as needed. In addition, the features described relative to some examples may also be combined in other examples.

[0081] In at least one embodiment of the present invention, a family business governance collaboration management system is provided. Figure 1 As shown, including:

[0082] A user management module 101, which is used to manage users;

[0083] User management includes adding, deleting and changing users, and managed user information includes user login accounts and passwords;

[0084] A service product management module 102, which is used to manage the service products of the family trust;

[0085] Users can customize the name, charging method, price, commission rate, etc. of the service product, and manage the description of the service product.

[0086] A service execution management module 103, which is used to manage the execution of service products;

[0087] Manage the following aspects during the service product execution process:

[0088] The responsible expert can select the responsible expert by logging into the expert terminal. Whether to agree to serve the customer can also be determined by the project director;

[0089] After the product service solves the problem for the customer, the project director can complete the delivery, or the expert can log in to the system to complete the delivery. After clicking Complete Delivery, the project product status will change to Customer Pending Confirmation; the customer can log in through the Family Office Client: Digital Butler to confirm the product service and conduct acceptance.

[0090] Cost management is used to manage the costs incurred when executing service products, such as expert expenses, channel expenses, etc.

[0091] To-do management, used to record to-do items in the execution process of management service products;

[0092] Help users quickly record and manage to-do items so as to better plan and manage their time and tasks. Users can record and manage their to-do items, including activity title, participants, time, location and other information.

[0093] Customer follow-up is used to record and manage the interaction and communication records with customers in order to better understand customer needs, improve service quality and increase customer satisfaction.

[0094] Document management allows users to upload and store documents and materials related to specific customers, such as contracts, invoices, photos, etc. These documents can be conveniently stored in the system's files and can be easily found through the search function.

[0095] A customer management module 104, which is used to manage customers;

[0096] Customer management includes adding, deleting and changing customers, and can manage customer related information;

[0097] The customer's relevant information includes family relationships, income and expenditure, assets and liabilities, customer projects, signed contract orders, collections, invoices, etc. The above-mentioned family relationships, income and expenditure, assets and liabilities and other information can be added, deleted, modified, etc.

[0098] Collection management is mainly used to record, calculate, monitor and analyze payments received and receivable.

[0099] Invoice management can help customers better manage and process invoice data, improve financial management efficiency, and avoid financial losses caused by incorrect information.

[0100] An information visualization module 105, which is used to display various data indicators related to the family office;

[0101] In one embodiment of the present invention, the data indicators include: total number of customers, total number of projects, total number of contracts, and total contract amount;

[0102] When you click on different dimensional data indicators in the display interface, the middle chart will show the growth trend curve for 30 days;

[0103] The information visualization module can support the display of data in a variety of statistical charts, such as pie charts, curve charts, line charts, etc.

[0104] An asset list entry module 106, which is used to enter the client's asset data;

[0105] In one embodiment of the present invention, asset data is entered through a family asset inventory form, and then the data in table form is imported, and the asset data is extracted by parsing the fields of the table. The asset data includes customer profile, family relationships, movable and immovable property, liquid assets, equity, debts and credits, annual income, annual expenditure, etc.

[0106] A risk diagnosis module 107, which is used to diagnose customer risks;

[0107] The customer's risk corresponds to multiple risk items, and each risk item is evaluated separately;

[0108] Risk items correspond to multiple risk levels;

[0109] In one embodiment of the present invention, risk levels are divided into three levels: high, medium, and low. Each risk item is set with evaluation conditions corresponding to each level. The evaluation conditions are matched according to the asset data of the current customer to determine the risk item as a risk level that meets the evaluation conditions.

[0110] For example, if a risk item is inheritance risk, the evaluation conditions corresponding to the three risk levels are: the number of heirs who cannot inherit according to their wishes is greater than 3, the number of heirs who cannot inherit according to their wishes is equal to 2, and the number of heirs who cannot inherit according to their wishes is less than 2.

[0111] The risk diagnosis module is a management tool specifically designed to identify, assess and monitor customer risks. This function is based on advanced risk management theories and methods, and is designed to help customers better understand their own risk status, warn of risk events in advance, and formulate response measures in a timely manner, thereby reducing business risks and losses. It analyzes wealth preservation, targeted inheritance, health care protection, asset allocation, etc. from multiple dimensions.

[0112] The file management module 108 is used to encrypt and manage the files and materials uploaded by the user and related to the specific customer in the form of digital files;

[0113] In one embodiment of the present invention, Figure 2 As shown, the method for the file management module to encrypt and decrypt digital files includes:

[0114] Step 101, first generate a key pair (i.e., a public key and a private key) using the OpenGPG key generator;

[0115] Methods for generating key pairs include:

[0116] OpenGPG key generator: OpenGPG official provides or third-party tools that support GPG standards;

[0117] Enter the private key password: used to strengthen the protection of the private key, that is, when using the private key for decryption, signing or other sensitive operations, you need to provide the password associated with the private key. This password is set by the user and is used to encrypt and protect the private key file.

[0118] Generate public and private keys: The public key is used to encrypt information. Anyone can encrypt information using the public key. The private key is used to decrypt information encrypted by the public key. Only individuals with the corresponding private key can decrypt the information.

[0119] Step 102, encrypt the digital file using the public key and upload it to the IPFS decentralized network, or other decentralized networks such as private blockchain, public blockchain, alliance blockchain, etc.;

[0120] The following steps are involved:

[0121] A. Select digital archive file: Select the digital archive file to be encrypted;

[0122] B. Public key encryption: Use the public key to encrypt digital archive files using an asymmetric encryption algorithm (such as RSA);

[0123] C. Upload IPFS: Upload the encrypted digital archive files to the IPFS decentralized network. The IPFS network is connected through a peer-to-peer network, which enables file distribution and load balancing, making it an ideal decentralized storage platform.

[0124] D. Generate download address: After the upload is successful, the IPFS network node will generate a unique identifier for the digital archive file and return the content address;

[0125] Step 103, downloading the digital archive file from the IPFS decentralized network and decrypting it using the private key;

[0126] E. Access the download address: After successfully uploading the file to the IPFS network in step 102, a download address is generated. Using a browser to access the address will automatically download the encrypted file.

[0127] F. Private key decryption: Enter the private key or import the private key;

[0128] G. Enter the private key password: Enter the private key password;

[0129] H. Decryption successful: Select the encrypted digital archive file downloaded from the IPFS network, click Start decryption, and after the three elements F, G, and H are verified correctly, the file is decrypted successfully. At this time, you can view the original content of the digital archive normally.

[0130] By separating the compression and encryption processes and adopting a decentralized storage system, the file management module improves the stability and anti-attack capabilities of the system while ensuring the security of digital archive content. This optimization solution better meets the needs of secure storage and sharing of digital archives.

[0131] Compared with traditional digital archive management systems, it has the following advantages:

[0132] 1. Hybrid encryption scheme:

[0133] Key point: A hybrid encryption scheme is adopted, which combines symmetric key encryption and asymmetric key encryption, giving full play to the advantages of both.

[0134] Protection point: Efficient file encryption is achieved through symmetric key encryption, and symmetric keys are securely transmitted through asymmetric keys, improving the security and performance of the system.

[0135] 2. Separate compression and encryption processes:

[0136] Key point: Separate the compression and encryption steps to prevent the password from being leaked when the compression tool processes them simultaneously.

[0137] Protection point: Improves the security of passwords. Even if the password is leaked, the attacker still needs to decrypt the compressed file, which enhances the confidentiality of the digital archive content.

[0138] 3. Decentralized storage system:

[0139] Key point: Using a decentralized storage system, such as IPFS, prevents the risks of single point failure and centralized storage services.

[0140] Protection point: Through content addressing and distributed networks, the availability and robustness of files are improved, and file loss due to attacks or suspension of centralized services is prevented.

[0141] 4. IPFS Integration:

[0142] Key point: Integrate IPFS as a storage platform and take advantage of its distributed, content-addressed features.

[0143] Protection point: IPFS provides a safer and more reliable way to store files. It identifies files by hashing, prevents file duplication, and distributes files across the network, improving file distribution efficiency and security.

[0144] 5. Application of asymmetric encryption algorithm:

[0145] Key point: Use asymmetric encryption algorithms, such as RSA, for public key encryption, digital signatures, and key exchange.

[0146] Protection point: Through asymmetric encryption, secure key transfer and digital signature are achieved, ensuring the integrity and authenticity of file transmission.

[0147] In addition to IPFS, there are other decentralized storage and distributed file system alternatives, each with its own unique features and uses. Here are some possible alternatives:

[0148] Filecoin: Filecoin is a decentralized storage network built on the IPFS protocol. It introduces blockchain technology to enable storage providers to be paid in the form of Filecoin tokens. Filecoin provides more economic incentives to encourage more nodes to provide storage services.

[0149] Sia: Sia is a decentralized storage platform that uses blockchain technology to build a network that allows users to rent hard drive space from other users. Files are segmented and encrypted, and then distributed across multiple nodes on the network to ensure security and reliability.

[0150] Storj: Storj is a distributed cloud storage platform that uses blockchain technology to create a decentralized storage network. It allows users to rent storage space from other users and ensures the security and integrity of data through smart contracts.

[0151] Swarm: Swarm is a decentralized storage protocol in the Ethereum ecosystem that aims to provide a distributed storage solution for DApps. It allows users to distribute data across multiple nodes of the network to improve availability and censorship resistance.

[0152] Maidsafe: Maidsafe is a decentralized, self-encrypted storage network. It uses distributed networks and encryption technology to ensure the security and privacy of data. Maidsafe's goal is to provide a secure, private, and highly scalable decentralized storage solution.

[0153] These alternatives are all dedicated to solving different problems in the field of distributed storage, and have their own advantages and characteristics in terms of performance, reliability, security, etc.

[0154] It should be noted that the aforementioned users refer to individuals, companies or organizations representing family offices, and clients refer to individuals or families served by family offices.

[0155] The technical architecture of the present invention provides efficient, secure and reliable solutions for family offices by integrating advanced information technology and blockchain technology.

[0156] The technical architecture of the present invention is based on advanced information technology and blockchain technology. It adopts a multi-level architecture, including front-end applications, back-end services and underlying infrastructure.

[0157] In terms of front-end applications, the present invention provides an intuitive and easy-to-use user interface that can be accessed through a web page or a mobile application. Users can interact with the system through the interface and use various functional modules.

[0158] Backend services are the core part of this invention, which include various functional modules and business logic. These services support data processing, storage and management, such as big data risk diagnosis tools, customer service, etc. Backend services are also combined with blockchain technology to achieve data encryption, permission control and security assurance.

[0159] The underlying infrastructure is the basic environment that supports the operation of the entire system. It includes servers, network equipment, databases, etc., to ensure the stability and reliability of the system. The underlying infrastructure usually adopts a distributed architecture to handle high concurrency and large-scale data processing requirements. In addition, the present invention may also use cloud services to provide elasticity and scalability.

[0160] In general, the technical architecture of the present invention provides efficient, secure and reliable solutions for family offices by integrating advanced information technology and blockchain technology.

[0161] In one embodiment of the present invention, the family business governance collaborative management and control system also embeds a large prediction model for content acquisition, and the specific large language model may be ChatGPT (ChatGenerativePre-trainedTransformer).

[0162] In one embodiment of the present invention, the family business governance collaborative management and control system is further provided with an automated allocation module, which is used to automatically generate an asset allocation plan for the client according to the allocation conditions set by the client.

[0163] Here are some of the main technical components:

[0164] SpringBoot: The present invention uses SpringBoot as the backend framework, which provides the ability of rapid development and deployment, and has high scalability and flexibility.

[0165] IPFS (InterPlanetary File System): IPFS is a distributed file system. The present invention uses IPFS to store and share data files to ensure the security and reliability of data.

[0166] Asymmetric encryption: To protect the security and confidentiality of customer data, the present invention uses an asymmetric encryption algorithm, such as RSA or ECC, to encrypt and decrypt sensitive information.

[0167] Current limiting: In order to prevent the system from being maliciously attacked or overloaded, the present invention implements a current limiting mechanism to control the concurrent access volume by monitoring the request frequency and resource consumption.

[0168] Distributed transactions: Since the present invention may involve multiple services and database operations, in order to ensure data consistency, the system adopts a distributed transaction management mechanism, such as asynchronous transactions based on message queues or a two-phase commit protocol.

[0169] In addition to the above technical components, the present invention may also include other technologies and tools, such as database management systems (such as MySQL or MongoDB), message queues (such as Kafka or RabbitMQ), containerization technologies (such as Docker or Kubernetes), etc., to support the scalability, reliability and high performance of the system.

[0170] As the first computer system in China dedicated to family business governance collaboration, the present invention can complete the whole process of assisting the family office in the process of family business governance collaboration. Specifically, at least one embodiment of the present invention provides a family business governance collaboration method, such as Figure 4 As shown, the aforementioned family business governance collaboration and control system is used to complete the following collaboration contents:

[0171] 1. Collaboratively link the four roles of family office (user), expert, channel and customer. The four roles can access the family business governance collaborative management and control system through four different service terminals;

[0172] 2. The service items for a customer include more than one service product;

[0173] Clients and family offices associated with a service project have the right to view and manage all service products associated with the service project;

[0174] Experts and channels only have the authority to view and manage the service products associated with them;

[0175] 3. Generate a risk assessment report for the customer based on the customer's information. The risk assessment report includes the risk level of the risk items associated with the customer;

[0176] The risk items associated with a customer can be specified by the customer or obtained based on the content input by the customer. For example, the customer can enter text content such as legal documents and link to the risk items by identifying keywords in the text.

[0177] 4. Family offices can acquire content through embedded large language models;

[0178] 5. After the service product is accepted, the fee details for the customer will be automatically generated based on the service product information set by the family office; and the fees for experts and channels will be automatically settled based on the commission ratio of the service product to experts and channels set by the family office.

[0179] At least one embodiment of the present invention provides a method for applying the aforementioned family business governance collaboration management system to complete the file allocation specified by the customer, including:

[0180] Generate a key pair using the OpenGPG key generator;

[0181] Use the public key to encrypt the digital file specified by the customer and upload it to the IPFS decentralized network;

[0182] The intended allocation time specified by the customer. When the intended allocation time arrives, the files and private keys specified by the customer will be automatically sent to the relevant beneficiaries;

[0183] The files designated by the client are associated with the supervisor, beneficiary and executor. The supervisor and executor have the authority to send the files designated by the client to the relevant beneficiaries. Automatic sending at the designated intended allocation time can avoid the situation where the supervisor and executor are unable to perform their duties and still ensure that the files designated by the client can reach the beneficiary to make the rights and interests associated with the files effective.

[0184] The present invention provides a computer storage medium, such as Figure 3 As shown, it is used to store computer-readable instructions, which can be executed on a computer system. When the computer-readable instructions are executed, the aforementioned family business governance collaborative management and control system can be run.

[0185] As an important form of corporate organization, family businesses play an important role in global economic development. However, family businesses face the following technical problems in the governance process:

[0186] 1. Inefficient data processing

[0187] Traditional family business data processing methods mainly rely on manual statistics and simple data analysis tools, which are difficult to effectively process and integrate multi-dimensional data such as finance, market, and personnel.

[0188] Insufficient analysis of the correlation between data leads to insufficient basis for decision making

[0189] 2. Visualization is not intuitive

[0190] In the existing technology, the visualization method of enterprise data is too simple, such as simple line charts, bar charts, etc.

[0191] Lack of comprehensive display capabilities for multi-dimensional data

[0192] There is a lack of effective association mechanism between visualization results and decision models

[0193] 3. Single mechanism for generating decision suggestions

[0194] Existing decision support systems are mostly based on fixed rule engines

[0195] Difficulty adapting to the rapidly changing business environment of family businesses

[0196] Decision recommendations lack dynamic adjustment capabilities and explainability

[0197] 4. Difficulty in model update and maintenance

[0198] Traditional decision-making models are difficult to update in a timely manner based on new data

[0199] The model updating process may lead to the loss of historical experience

[0200] Lack of effective model performance evaluation mechanism.

[0201] The present invention provides a family business governance collaborative management method, which solves the above technical problems based on the aforementioned family business governance collaborative management system, and includes the following steps:

[0202] Step 1: Data acquisition and preprocessing

[0203] Obtain various operational data of family businesses, including financial data, market data, personnel data, etc., recorded as data set D = {d 1 ,d 2 ,…,d n},in:

[0204] d i Represents the i-th data sample, i=1,2,…,n

[0205] n represents the total number of data samples

[0206] At the same time, obtain the relevant parameters of the existing enterprise decision model P = {p 1 ,p 2 ,…,p m},in:

[0207] p j represents the jth decision parameter, j = 1, 2, ..., m

[0208] m represents the total number of decision parameters

[0209] Normalize the data set D using the Min-Max normalization method: in:

[0210] d min Represents the minimum value in the data set

[0211] d max Represents the maximum value in the data set

[0212] N(d) represents the normalized data value, and its value range is [0,1]

[0213] The normalized data is recorded as D′={N(d 1 ),N(d 2 ),…,N(d n )}.

[0214] Step 2, SOM network processing

[0215] Construct a SOM network with an input layer and a competitive layer. The number of neurons in the input layer is equal to the data dimension k, and the neurons in the competitive layer are arranged in a two-dimensional grid of a×b. Among them:

[0216] k represents the feature dimension of the data

[0217] a and b represent the number of rows and columns of the competition layer grid, respectively.

[0218] For input data x∈D′: 1) Calculate its weight vector w for each neuron in the competition layer j The Euclidean distance of: in:

[0219] x i Represents the i-th component of the input data x

[0220] w ji represents the i-th component of the j-th neuron weight vector

[0221] Find the winning neuron c with the smallest distance:

[0222] c = argmin j d(x,w j )

[0223] Update the weights of the winning neuron and its neighboring neurons: w j (t+1)=w j (t)+α(t)h cj (t)(xw j (t)) where:

[0224] is the learning rate, α 0 is the initial learning rate, T is the maximum number of iterations

[0225] is the Gaussian neighborhood function

[0226] is the neighborhood radius, σ 0 is the initial neighborhood radius

[0227] r c and r jare the position vectors of the winning neuron and the jth neuron respectively

[0228] Repeat the above steps until the convergence condition is met: the weight change for two consecutive iterations is less than the preset threshold ∈.

[0229] Step 3: GAN network construction and training

[0230] Construct a GAN network, including the generator G and the discriminator D:

[0231] Generator G structure:

[0232] Input layer: receives the feature vector processed by SOM, with a dimension of k

[0233] Hidden layer 1: fully connected layer, using ReLU activation function

[0234] Hidden layer 2: fully connected layer, using ReLU activation function

[0235] Output layer: fully connected layer, using Tanh activation function, the output dimension is the visualization data dimension l

[0236] Discriminator D structure:

[0237] Input layer: receives visualization data of dimension l

[0238] Hidden layer 1: fully connected layer, using LeakyReLU activation function

[0239] Hidden layer 2: fully connected layer, using LeakyReLU activation function

[0240] Output layer: fully connected layer, using Sigmoid activation function, outputting a single probability value

[0241] Training process:

[0242] Generator loss function:

[0243]

[0244] L G Represents the loss value of the generator, which is used to measure the difference between the data generated by the generator and the real data distribution. The smaller the value, the closer the data generated by the generator is to the real data distribution.

[0245] It represents mathematical expectation, which is the expectation of the following expression under the corresponding distribution.

[0246] z is the input noise variable of the generator, z~p z 9z) means z follows distribution p z (z).

[0247] G(z) represents the data generated by the generator G with noise z as input.

[0248] D(G(z)) represents the judgment result of the discriminator D on the data G(z) generated by the generator. It is a probability value, which indicates the probability that the discriminator believes that G(z) is real data.

[0249] Discriminator loss function:

[0250]

[0251] in:

[0252] L D Represents the loss value of the discriminator, which is used to measure the discriminator's ability to distinguish between real data and generated data. The smaller the value, the stronger the discriminator's ability to distinguish.

[0253] x is the real data variable, x~p data (x) means x follows the distribution of real data p data (x).

[0254] D(x) represents the judgment result of the discriminator D on the real data x. It is a probability value, which indicates the probability that the discriminator believes that x is the real data.

[0255] This item measures the discriminator's ability to distinguish real data. It is hoped that the discriminator can judge the real data as real as accurately as possible, that is, D(x) is close to 1, and the value of this item is close to 0.

[0256] This item measures the discriminator's ability to distinguish generated data. It is hoped that the discriminator can judge the generated data as false as accurately as possible, that is, D(G(z)) is close to 0, and the value of this item is close to 0.

[0257] The Adam optimizer is used for optimization, with a learning rate of 0.0002 and β 1 =0.5,β 2 =0.999

[0258] Step 4: Decision model construction

[0259] Construct a decision model with a multi-layer neural network structure:

[0260] Input Layer:

[0261] Receive visualization data V = {v 1 ,v 2 ,...,v l}in:

[0262] v iRepresents the i-th visualization feature, i = 1, 2, ..., l

[0263] l represents the total number of visualized features

[0264] Receiving enterprise parameters P = {p 1 ,p 2 ,...,p m}

[0265] Feature extraction layer: Use convolutional neural network to process visualization data:

[0266] Convolution operation: C(V) = Conv(V,K), where:

[0267] K is a 3×3 convolution kernel used to extract local features.

[0268] Conv represents the standard convolution operation

[0269] Pooling operation: F v =MaxPool(C(V)) where MaxPool represents the maximum pooling operation, which is used to reduce dimension and extract main features

[0270] Parameter encoding layer: Use a fully connected layer to process enterprise parameters: F p =ReLU(W p P+b p )

[0271] Fusion layer: concatenates the outputs of the feature extraction layer and the parameter encoding layer: F combined =Concat(F v ,F p )

[0272] Decision generation layer: contains 5 parallel sub-networks, which generate decision suggestions of different dimensions:

[0273] Operational decision: R op =Softmax(W op F combined +b op )

[0274] Market Decision: R market =Softmax(W market F combined +b market )

[0275] Human resource decision making: hr =Softmax(W hr F combined +b hr )

[0276] Financial Decisions: Rfinance =Softmax(W finance F combined +b finance 0

[0277] Risk warning: R risk =Sigmoid(W risk F combined +b risk )

[0278] Final decision suggestion R = {R op ,R market ,R hr ,R finance ,R risk}

[0279] Example 2

[0280] This embodiment provides a method for solving the problem of feature loss when SOM and GAN are fused, which is improved on the basis of embodiment 1:

[0281] Step 1: Feature importance evaluation

[0282] Define the feature importance evaluation function: in:

[0283] w i is the weight of the i-th feature, obtained by principal component analysis (PCA)

[0284] var(d i ) is the variance of the i-th feature

[0285] Step 2: Feature Compensation Mechanism

[0286] Calculate the feature importance of the data after SOM processing: for d SOM ∈D SOM , calculate I(d SOM )

[0287] Set the important feature threshold θ: θ = μ·max d∈D I(d) where μ is the threshold coefficient, ranging from [0,1]

[0288] Characteristic compensation: For I(d SOM )<θ:

[0289] Calculate the similarity with the original data:

[0290] Select the original data point with the highest similarity for feature compensation: d comp =λd SOM +(1-λ)d oriWhere λ is the compensation coefficient, which is dynamically adjusted according to the similarity: λ = sim(d SOM ,d ori )

[0291] Example 3

[0292] This embodiment provides a method for solving the compatibility problem between the visualization data generated by GAN and the enterprise decision-making model:

[0293] Step 1: Adaptation layer design

[0294] Construct a three-layer neural network as the adaptation layer T(V): 1) Input layer: The dimension is the same as the visualization data V generated by GAN 2) Hidden layer: Use ReLU activation function 3) Output layer: The dimension is the same as the input dimension required by the decision model

[0295] Step 2: Adaptation layer training

[0296] Loss function design: L adapt =αL mse +βL kl +γL struct in:

[0297] is the mean square error loss

[0298] L kl =D KL (F(T(V),P)‖F(V,P)) is the KL divergence loss

[0299] is the structural similarity loss

[0300] α, β, γ are weight coefficients

[0301] D KL Represents the Kullback-Leibler divergence, which is used to measure the difference between two probability distributions

[0302] S(·) represents the structural feature extraction function

[0303] Optimization process: Use Adam optimizer to minimize L adapt : in:

[0304] θ t Represents the model parameters at the tth iteration

[0305] η is the learning rate

[0306] represents the gradient operator with respect to the parameter θ

[0307] Step 3: Adaptation data generation

[0308] For the visualization data V generated by GAN: 1) Transformed by the adaptation layer: V adapt =T(V)2) V adapt Input the decision model to generate decision recommendations.

[0309] Example 4

[0310] This embodiment provides a decision model explainability method based on the attention mechanism:

[0311] Step 1: Attention Mechanism Design

[0312] Define two attention vectors:

[0313] Visualizing Data Attention: α V =Softmax(W V tanh(U V V))

[0314] Parameter attention: α P =Softmax(W P tanh(U P P))W V , U V , W P , U P is a trainable weight matrix

[0315] Attention Weighting:

[0316] Visualize data weighting:

[0317] Parameter weighting:

[0318] Step 2: Interpretability Analysis

[0319] Feature importance analysis: For each feature i, calculate its importance score:

[0320] Parameter dependency analysis: For each parameter j, calculate its dependency:

[0321] Decision path tracking: record the features and parameter sequences with the highest attention weights to form a decision chain diagram

[0322] Example 5

[0323] This embodiment provides a model dynamic update method:

[0324] Step 1: Incremental learning mechanism

[0325] Define the data update window size w and update frequency f

[0326] For new data D new :

[0327] If |D new |< w, temporarily store it in the buffer

[0328] If |D new | ≥ w or the cumulative time reaches f, trigger an update

[0329] Step 2, model update strategy

[0330] SOM network update:

[0331] Fix the winning neurons in the fixed part

[0332] Only update the neuron weights w related to the new data j (t + 1) = w j (t) + α′(t)h cj (t)(x new - w j (t)) where α′(t) is the reduced learning rate

[0333] GAN network update:

[0334] Fix the discriminator D parameters

[0335] Update the generator G parameters: where is the generator loss based on the new data

[0336] Decision model update:

[0337] Calculate the difference between the old and new decisions: ΔR = ∥R new - R old ∥ 2

[0338] If ΔR > τ (preset threshold), then update the decision model parameters: where

[0339] Step 3, verify the update effect

[0340] Define the verification metrics:

[0341] Visualization quality metric: Q v = SSIM(V new , V target )

[0342] Decision accuracy metric: Q d = Accuracy(R new , R target )

[0343] Model stability indicators:

[0344] Overall score: Score = w 1 Q v +w 2 Q d +w 3 Q s where w 1 、w 2 、w 3 is the weight coefficient

[0345] Decide whether to roll back the update based on the score: If Score < δ (preset threshold), roll back to the model state before the update

[0346] Example 6: Practical application example

[0347] This embodiment takes a family business group as an example to illustrate the specific application process of Embodiment 1:

[0348] Step 1: Data acquisition and preprocessing

[0349] Collect the business group's operating data from January to December 2022:

[0350] Table 1 Monthly statistics of financial data

[0351]

[0352]

[0353] Table 2 Monthly statistics of market data

[0354]

[0355] Table 3 Monthly statistics of personnel data

[0356]

[0357]

[0358] The enterprise decision model parameters include:

[0359] Cost control threshold: p 1 =0.7

[0360] Inventory turnover rate standard: p 2 =4

[0361] Market share target: p 3 =15%

[0362] Customer satisfaction benchmark: p 4 =85

[0363] 2. Data processing results

[0364] Normalized data example (taking December 2022 as an example): N(d 1 )=0.85(operating income)N(d 5 )=0.78(market share)N(d 8 ) = 0.92 (total number of employees)

[0365] SOM network processing results:

[0366] Grid size: 6×6

[0367] Winning neuron distribution: financial indicators are mainly distributed in the upper left area of ​​the grid, market indicators are mainly distributed in the upper right area, and personnel indicators are mainly distributed in the lower half of the grid.

[0368] Weight convergence threshold ∈=0.001

[0369] 3. Visualization Results

[0370] The visualization data generated by the GAN network includes:

[0371] Business trend chart:

[0372] Horizontal axis: January to December 2022

[0373] Vertical axis: Normalized values ​​of various indicators Features: Clearly shows the coordinated upward trend of revenue, profit and market share Risk warning chart:

[0374] Display various risk indicators in the form of radar charts, focus on the fluctuation range of asset-liability ratio and staff turnover rate4, and generate decision suggestions

[0375] Based on the visualized data, the decision model generates recommendations including:

[0376] Operational strategy recommendations:

[0377] It is recommended to set the revenue target for Q1 2023 at 330 million yuan. Cost control target: reduce raw material costs by 3%.

[0378] Inventory optimization: Reduce inventory turnover days from 45 days to 40 days

[0379] Marketing strategy suggestions:

[0380] Focus on developing the East China market, expected to increase market share by 1.2%

[0381] Increase investment in product innovation, aiming to improve customer satisfaction to 90 points Optimize product structure and increase the proportion of high-end products to 40%

[0382] HR advice:

[0383] It is recommended to recruit 30 technical personnel in Q1 2023, optimize the salary system, and increase the performance bonus ratio to 40% of the annual salary.

[0384] Formulate a talent development plan, focusing on training 15 middle-level managers Financial strategy suggestions:

[0385] Optimize financing structure and increase the proportion of long-term loans to 60%

[0386] Accounts receivable management: shorten the collection period to 45 days

[0387] It is recommended to increase R&D investment to 5% of revenue

[0388] Risk warning suggestions:

[0389] Risk of raw material price fluctuations: It is recommended to sign a long-term agreement with suppliers

[0390] Risk of brain drain: It is recommended to improve the employee stock ownership plan

[0391] Market competition risk: It is recommended to strengthen the layout of patent technology

[0392] 5. Implementation Effect

[0393] After the company adopted the above decision-making suggestions, the following results were achieved in Q1 2023:

[0394] Operating income increased by 15% year-on-year

[0395] Market share increased to 14.2%

[0396] 12% increase in employee satisfaction

[0397] Reduce operating costs by 5%

[0398] Through the application of this embodiment, the feasibility and effectiveness of this method in actual enterprise management are verified.

[0399] The family business governance collaborative control method provided in this embodiment achieves the following beneficial effects by deeply integrating SOM with GAN and deeply coupling it with the decision-making model:

[0400] Improve data processing efficiency

[0401] Efficient dimensionality reduction and clustering of multidimensional data are achieved through SOM network

[0402] The automation level of data preprocessing has been significantly improved

[0403] Realizes automatic extraction of data features and correlation analysis

[0404] Enhanced visualization

[0405] The visualization results generated by GAN are more intuitive and easy to understand

[0406] Can display data relationships in multiple dimensions at the same time

[0407] Visualization results and decision models are deeply coupled

[0408] Improve decision quality

[0409] Decision-making recommendations are more comprehensive, covering multiple dimensions such as operations, marketing, human resources, finance and risks

[0410] Improving the interpretability of the decision process through the attention mechanism

[0411] Decision-making suggestions are highly targeted and operational

[0412] Implementing dynamic optimization

[0413] Models can be incrementally updated based on new data

[0414] Avoid loss of important information through feature compensation mechanism

[0415] Possessing a complete update effect verification mechanism

[0416] Reduce management costs

[0417] Reduced manual data processing and analysis workload

[0418] Improved decision-making efficiency and shortened decision-making cycle

[0419] Reduced risk of wrong decision making

[0420] Improve adaptability

[0421] The system can be adapted to different sizes and types of family businesses

[0422] Good scalability and maintainability

[0423] Ability to respond quickly to changes in the business environment

[0424] Through practical application verification, enterprises adopting the method of the present invention have achieved significant improvements in the following aspects: - Operating income increased by 15% year-on-year - Market share increased by 1.2 percentage points - Employee satisfaction increased by 12% - Operating costs decreased by 5% - Decision-making cycle shortened by 40%

[0425] These effects fully demonstrate the practical value of the present invention in improving the governance efficiency of family businesses.

[0426] The above describes an embodiment of the present embodiment, but the present embodiment is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present embodiment, ordinary technicians in this field can also make many forms, all of which are within the protection of the present embodiment.

Claims

1. A family business governance collaboration and control system, characterized in that: include: User management module, which is used to manage users; Service product management module, which is used to manage the service products of family trusts; A service execution management module, which is used to manage the execution of service products; Customer management module, which is used to manage customers; Information visualization module, which is used to display various data indicators related to family offices; Asset list entry module, which is used to enter the customer's asset data; Risk diagnosis module, which is used to diagnose customer risks; The file management module is used to encrypt and manage the files and materials uploaded by users and related to specific customers in the form of digital files.

2. A family business governance collaboration and management system according to claim 1, characterized in that: The service execution management module manages the following aspects during the service product execution process: The selection of the responsible expert is done by the responsible expert himself / herself by logging into the expert terminal to decide whether to serve the client, or the project director may designate; After the product service solves the problem for the customer, the project director completes the delivery, or the expert logs in to the system to complete the delivery. After clicking Complete Delivery, the project product status will change to Customer Pending Confirmation; Cost management, which is used to manage the costs incurred when performing service products; To-do management, used to record to-do items in the execution process of management service products; Customer follow-up, which is used to record and manage interactions and communications with customers; File management is used for users to upload and store files and information related to specific customers.

3. A family business governance collaboration management and control system according to claim 1, characterized in that: The customer's risk corresponds to multiple risk items, and each risk item is evaluated separately; Risk items correspond to multiple risk levels, which are divided into three levels: high, medium and low. Each risk item has evaluation conditions corresponding to each level. The evaluation conditions are matched according to the current customer's asset data to determine the risk item as a risk level that meets the evaluation conditions.

4. A family business governance collaboration management and control system according to claim 1, characterized in that: The method for the file management module to encrypt and decrypt digital files includes: Step 101, first generate a key pair using the OpenGPG key generator; Step 102, encrypt the digital file using the public key and upload it to the decentralized network; Step 103, downloading the digital archive file from the IPFS decentralized network and decrypting it using the private key; Encrypting digital files with public keys and uploading them to the IPFS decentralized network includes the following steps: A. Select digital archive file: Select the digital archive file to be encrypted; B. Public key encryption: Use the public key to encrypt digital archive files using an asymmetric encryption algorithm; C. Upload IPFS: upload the encrypted digital archive file to the IPFS decentralized network; D. Generate download address: After the upload is successful, the IPFS network node will generate a unique identifier for the digital archive file and return the content address.

5. A family business governance collaborative management method, characterized in that: The following steps are performed by the family business governance collaborative management and control system as described in any one of claims 1 to 4: Obtain the family business operation data set D and the decision model parameter set P, where D includes financial data, market data, and personnel data, and normalize the data set D to obtain D′; The normalized data D′ is input into a self-organizing map network for processing. The SOM network includes an input layer and a competition layer. The weights of the winning neuron and its neighboring neurons are updated by calculating the distance between the input data and the weight vector of the competition layer neurons until the network converges. The data processed by the SOM network is input into the generative adversarial network, the generator G generates the visualization data V, and the discriminator D discriminates the generated visualization data; The visualization data V and decision model parameters P are input into the decision model to generate decision recommendations R including operation strategy, market strategy, human resource strategy, financial strategy and risk warning.

6. A family business governance collaborative management method according to claim 5, characterized in that: The weight update formula of the self-organizing map network is: w j (t+1)=w j (t)+α(t)h cj (t)(x-w j (t)) Among them, α(t) is the learning rate, h cj (t) is the Gaussian neighborhood function, x is the input data, w j is the weight vector.

7. A family business governance collaborative management method according to claim 6, characterized in that: The decision model includes: Feature extraction layer: Use convolutional neural networks to process visualization data; Parameter encoding layer: Use a fully connected layer to process enterprise parameters; Fusion layer: concatenates the outputs of the feature extraction layer and the parameter encoding layer; Decision generation layer: contains 5 parallel sub-networks, which generate decision suggestions of different dimensions.

8. An application of a family business governance collaboration control system in family business governance collaboration, characterized in that: Apply the aforementioned family business governance collaboration and control system to complete the following collaboration content:

1. Collaboratively link the four roles of family office, experts, channels and customers. The four roles can access the family business governance collaborative management and control system through four different service terminals; 2. The service items for a customer include more than one service product; Clients and family offices associated with a service project have the right to view and manage all service products associated with the service project; Experts and channels only have the authority to view and manage the service products associated with them; 3. Generate a risk assessment report for the customer based on the customer's information. The risk assessment report includes the risk level of the risk items associated with the customer; The risk items associated with a customer can be specified by the customer or obtained based on the content input by the customer. For example, the customer can enter text content such as legal documents and link to the risk items by identifying keywords in the text.

4. Family offices can acquire content through embedded large language models; 5. After the service product is accepted, the fee details for the customer will be automatically generated based on the service product information set by the family office; and the fees for experts and channels will be automatically settled based on the commission ratio of the service product to experts and channels set by the family office.

9. The application of the family business governance collaboration control system according to claim 8 in family business governance collaboration is characterized in that: The method of applying the aforementioned family business governance collaborative control system to complete the document distribution specified by the customer includes: Generate a key pair using the OpenGPG key generator; Use the public key to encrypt the digital file specified by the customer and upload it to the IPFS decentralized network; The intended allocation time specified by the customer. When the specified intended allocation time arrives, the files and private keys specified by the customer will be automatically sent to the relevant beneficiaries.

10. A computer storage medium, characterized in that: It is used to store computer-readable instructions, which can be executed on a computer system. When the computer-readable instructions are executed, the family business governance collaborative management and control system as described in any one of claims 1-7 can be run.

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