A power grid enterprise fund security monitoring system and method

Abstract

The present application relates to a kind of funds security monitoring system and method of power grid enterprise, belong to big data technical field, solve the problems such as the insufficient speed of identification and reaction of existing monitoring method to guarantee fund risk, insufficient informationization support of fund supervision.The system includes: fund monitoring processing module and security prevention and control module, fund monitoring processing module includes: monitoring module is in real time to the fund inflow from multiple user nodes and the fund outflow to power generation enterprise monitoring;Fund flow safety determination module determines the fund flow safety before fund inflow fund pool;Prediction module predicts the safety value of fund pool after current fund flow in and out fund pool before fund flow in and out fund pool;Judgment module determines current fund flow as first unsafe fund flow or safe fund flow according to whether the safety value of fund pool exceeds the pre-set fund pool safety boundary threshold, to allow safe fund flow in and out fund pool.Full-process monitoring and real-time early warning are carried out to the transaction fund of power grid enterprise.

Description

Technical Field

[0001] This invention relates to the field of big data technology, and in particular to a fund security monitoring system and method for power grid enterprises. Background Technology

[0002] The existing power grid funds suffer from the following problems: 1. Supervision mainly relies on bank fund supervision and the accountability system of accounting personnel, resulting in a relatively singular supervisory body and a lack of top-level design; 2. Electricity revenue is dispersed and substantial, and the system for collecting and organizing cash flow is loosely structured, making it prone to asset loss and excessive cost expenditures; 3. The review of fund information is concentrated in the financial accounting department, resulting in low information transparency, difficulty in ensuring the authenticity of transactions, and a lack of a supervisory system that runs through the entire business process and facilitates business collaboration; 4. The identification and response speed to fund risks is not sensitive enough, and the information technology support for fund supervision is insufficient. Summary of the Invention

[0003] Based on the above analysis, the embodiments of the present invention aim to provide a fund security monitoring system and method for power grid enterprises, in order to solve the problems of existing single regulatory body, dispersed electricity revenue, low information transparency, difficulty in ensuring the identification and response speed of fund risks, and insufficient information support for fund supervision.

[0004] On one hand, embodiments of the present invention provide a fund security monitoring system for power grid enterprises, comprising: a fund monitoring and processing module and a security control module. The fund monitoring and processing module includes: a security control module comprising an early warning module, a freeze command generation module, and a unfreeze command generation module; a monitoring module for real-time monitoring of fund inflows from multiple user nodes and fund outflows to power generation enterprises; a fund flow security determination module for determining the security of fund flows to the fund pool before funds flow into the fund pool; a prediction module for predicting the security value of the fund pool after the current fund flow enters or leaves the fund pool before the current fund flow enters or leaves the fund pool; and a judgment module for determining whether the current fund flow is a first unsafe fund flow or a safe fund flow based on whether the security value of the fund pool exceeds a preset fund pool security boundary threshold, so as to allow the safe fund flow to enter or leave the fund pool.

[0005] The beneficial effects of the above technical solution are as follows: The fund flow security determination module can determine the security of the fund flow to the fund pool before the funds flow into the fund pool, thereby ensuring the security of the fund flow entering the fund pool. The prediction module can predict the security value of the fund pool after the current fund flow enters or leaves the fund pool before the fund flow enters or leaves the fund pool, thereby determining whether the impact of the fund flow entering or leaving the fund pool on the fund pool is within the allowable range of the fund pool.

[0006] Based on further improvements to the above system, the security control module includes: an early warning module for issuing an early warning for the first unsafe fund flow; a freeze instruction generation module for generating a freeze instruction based on the early warning of the first unsafe fund flow and sending it to the transaction fund supervision bank through a remote control mechanism; and an unfreezing instruction generation module for generating an unfreezing instruction when the first unsafe fund flow is predicted to enter or leave the fund pool again, and the security value of the fund pool does not exceed the security boundary threshold of the fund pool, and sending it to the transaction fund supervision bank through the remote control mechanism.

[0007] The beneficial effects of the above technical solution are as follows: when it is determined that the cash flow or cash pool is insecure, a freeze command is generated through a remote mechanism; and when the cash flow or cash pool meets the requirements again after a series of adjustments, a unfreeze command is generated through a remote mechanism, thereby monitoring the funds of the power grid company and ensuring the safety of funds.

[0008] Based on further improvements to the above system, the pre-set security boundary thresholds include a cash flow security boundary threshold and a cash pool security boundary threshold. The cash flow security determination module further includes an influencing factor determination submodule, a weight determination submodule, and a cash flow credibility determination submodule. The influencing factor determination submodule is configured to determine multiple influencing factors affecting the security of the power grid company's transaction funds based on a historical database. The weight determination submodule is configured to determine the weight of each influencing factor based on the influencing factors. The cash flow credibility determination submodule is configured to determine the credibility of the cash flow based on the weight of each influencing factor and its corresponding initial credibility value. Specifically, based on the cash flow credibility and the cash flow security boundary threshold, it is determined whether the cash flow to be flowed into the cash pool is safe. When the credibility of the cash flow is greater than or equal to the cash flow security boundary threshold, the cash flow to be flowed into the cash pool is determined to be safe. When the credibility of the cash flow is less than the cash flow security boundary threshold, the credibility of the cash flow is determined to exceed the cash flow security boundary threshold.

[0009] Based on further improvements to the above system, the early warning module is further configured to, when it is determined that the credibility of the fund flow exceeds the fund flow security boundary threshold, designate the determined fund flow as the second unsafe fund flow and issue an early warning for the second unsafe fund flow; the freeze instruction generation module is further configured to, based on the early warning of the second unsafe fund flow, generate a freeze instruction and send it to the transaction fund supervision bank through the remote control mechanism; and the adjustment module is configured to, perform standardized analysis on each influencing factor, and adjust the corresponding initial credibility value of each influencing factor based on the analysis results; the unfreeze instruction generation module is configured to, adjust the corresponding initial credibility value of each influencing factor until the credibility of the second unsafe fund flow changes to be greater than or equal to the fund flow security boundary threshold, generate an unfreeze instruction, and send it to the transaction fund supervision bank through the remote control mechanism.

[0010] Based on further improvements to the above system, the influencing factor determination submodule is configured to determine the following influencing factors on the security of the power grid transaction funds based on historical databases: fund liquidity, working capital turnover, working capital loss, credit rating of the trading counterparty, and inflation rate; the weight determination submodule is configured to construct an m×n indicator matrix A for the n influencing factors of m fund flows:

[0011]

[0012] The index matrix A is dimensionless to transform it into a standardized target matrix B:

[0013]

[0014] Based on the standardized target matrix B, the entropy value H of each indicator is calculated using the following formula. j :

[0015]

[0016] in, Assume that when f ij =0,f ij lnf ij =0, k is the Boltzmann constant, let k>0;

[0017] Based on the entropy values ​​H of the aforementioned indicators j The index entropy weight w is calculated using the following formula. j :

[0018]

[0019] Based on further improvements to the above system, the fund flow credibility determination submodule is configured to calculate the credibility of the fund flow using the following formula before the funds flow into the fund pool:

[0020] CF(E)=w1*CF(E1)+w2*CF(E2)+…+w n *CF(E n )

[0021] Wherein, CF(E1), CF(E2)...CF(E n ) represents the confidence level of each influencing factor, w1, w2...w n The entropy weights of each influencing factor on the security of cash flow are given.

[0022] Based on further improvements to the above system, the early warning module further includes a static strength determination submodule for fund flows and a comprehensive credibility determination submodule: the static strength determination submodule for fund flows is configured to determine the static strength of the fund flows after determining that the fund flows to flow into the fund pool are safe; the comprehensive credibility determination submodule for comprehensive credibility determination is configured to determine the comprehensive credibility of each fund flow based on the credibility of the fund flows and the static strength of the fund flows, wherein the fund pool includes multiple fund flows; the comprehensive credibility of the fund pool is determined based on the comprehensive credibility of each fund flow and the comprehensive credibility of the fund pool is used as the safety value of the fund pool.

[0023] Based on further improvements to the above system, the static strength determination submodule for cash flow is configured to calculate the static strength of cash flow using the following formula:

[0024] CF(H,E)=MB(H,E)-MD(H,E)

[0025]

[0026] Wherein, the posterior probability is: P(H) is the prior probability based on conclusion H; LS is called the sufficiency measure; MB is the growth of trust and MD is the growth of distrust.

[0027] Based on further improvements to the above system, the comprehensive credibility determination submodule is configured to calculate the comprehensive credibility CF(H) of each fund flow using the following formula:

[0028] CF(H) = CF(H, E) * CF(E)

[0029] The fund flows in the entire fund pool are denoted as CF1(H), CF2(H), CF3(H), ..., CF n (H),

[0030] The following formula is used to predict the safe value of the fund pool after the current inflow and outflow of funds:

[0031] The formula for capital inflow is as follows:

[0032] CF 1，2 (H)=CF1(H)+CF2(H)-CF1(H)*CF2(H)

[0033] CF 1,2,3 (H)=CF 1，2 (H)+CF3(H)-CF 1,2 (H)*CF3(H) ......

[0035] CF 1,2,3......n (H)=CF 1,,2,3...n-1 (H)+CF n (H)-CF 1,2,3...n-1 (H)*CF n (H)

[0036] The formula for cash outflow is as follows:

[0037]

[0038] On the other hand, embodiments of the present invention provide a method for monitoring the security of funds in a power grid enterprise, comprising: real-time monitoring of fund inflows from multiple user nodes and fund outflows to power generation enterprises; determining the safety of fund flows into the fund pool before they flow into the pool, and predicting the safety value of the fund pool after the current fund flows in and out of the pool before they enter or leave the pool; determining whether the current fund flow is a first unsafe fund flow or a safe fund flow based on whether the safety value of the fund pool exceeds a pre-set safety boundary threshold, allowing the safe fund flow to enter or leave the pool, and issuing an early warning for the first unsafe fund flow; generating a freezing command based on the early warning of the first unsafe fund flow, and sending it to the transaction fund supervision bank through a remote control mechanism; and generating an unfreezing command when the first unsafe fund flow is predicted to enter or leave the pool again, and the safety value of the pool does not exceed the safety boundary threshold, and sending it to the transaction fund supervision bank through the remote control mechanism.

[0039] The beneficial effects of the aforementioned further improvements are as follows: Before funds flow into the fund pool, the safety of the funds flowing into the pool is determined, thus ensuring the safety of the funds entering the pool. Before funds flow into or out of the pool, the safety value of the pool after the current funds flow in or out is predicted, thus determining whether the impact of the funds flowing in or out of the pool is within the pool's allowable range. If it is determined that the funds flow or the pool is unsafe, a freeze command is generated remotely; and after a series of adjustments, if the funds flow or the pool meets the requirements again, a unfreeze command is generated remotely, thereby monitoring the power grid company's funds and ensuring fund safety.

[0040] Based on a further improvement of the above method, the pre-set security boundary threshold includes a fund flow security boundary threshold and a fund pool security boundary threshold. Specifically, determining the security of the fund flow to be flowed into the fund pool before the funds flow into the pool further includes: determining multiple influencing factors on the security of the power grid company's transaction funds based on a historical database, and determining the weight of each influencing factor based on these factors; determining the credibility of the fund flow based on the weights of each influencing factor and their corresponding initial credibility values; and determining whether the fund flow to be flowed into the fund pool is safe based on the credibility of the fund flow and the fund flow security boundary threshold. Specifically, when the credibility of the fund flow is greater than or equal to the fund flow security boundary threshold, the fund flow to be flowed into the fund pool is determined to be safe; and when the credibility of the fund flow is less than the fund flow security boundary threshold, the credibility of the fund flow is determined to exceed the fund flow security boundary threshold.

[0041] Compared with the prior art, the present invention can achieve at least one of the following beneficial effects:

[0042] 1. Before funds flow into the fund pool, the safety of the incoming funds is determined, thus ensuring the safety of the funds entering the pool. Before funds flow into or out of the pool, the safety value of the pool after the current funds flow in or out is predicted, thus determining whether the impact of the funds flowing in or out of the pool is within the pool's allowable range. If the funds flow or pool is determined to be unsafe, a freeze command is generated remotely; and if the adjusted funds flow or pool meets the requirements again, an unfreeze command is generated remotely, thereby monitoring the power grid company's funds and ensuring fund safety.

[0043] 2. This invention is suitable for transitioning to a fully market-based electricity trading process, and uses the supervision of trading funds as an entry point to build an artificial intelligence-based system platform as a specific application; focusing on the security of trading funds, which is of utmost concern to the power grid, an uncertainty reasoning algorithm model based on artificial intelligence deep learning is constructed to monitor and provide real-time early warning of trading funds throughout the entire process.

[0044] 3. The power grid enterprise transaction fund supervision system of this invention is designed for all entities, regulatory systems, regulatory banks, and government verification departments in the power trading process. It ensures the security, transparency, and reliable verification of all information in fund supervision through on-chain storage and verification, and guarantees the automatic execution of business processes under specified conditions through smart contracts, reducing the costs and risks of manual operation. It also enables business interconnection and data sharing among various institutions and roles based on blockchain technology.

[0045] In this invention, the above-described technical solutions can be combined with each other to achieve more preferred combinations. Other features and advantages of this invention will be set forth in the following description, and some advantages may become apparent from the description or be learned by practicing the invention. The objects and other advantages of this invention can be realized and obtained from what is particularly pointed out in the description and drawings. Attached Figure Description

[0046] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts.

[0047] Figure 1 This is a block diagram of a fund security monitoring system for power grid enterprises according to an embodiment of the present invention.

[0048] Figure 2 This is a structural diagram of a fund security monitoring system for power grid enterprises according to an embodiment of the present invention.

[0049] Figure 3 This is a flowchart of a method for monitoring the financial security of power grid enterprises according to an embodiment of the present invention.

[0050] Figure 4 This is a flowchart of the power grid retail business process according to an embodiment of the present invention.

[0051] Figure 5 This is a schematic diagram of the flow of transaction funds according to an embodiment of the present invention. Detailed Implementation

[0052] Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not intended to limit the scope of the present invention.

[0053] One specific embodiment of the present invention discloses a fund security monitoring system for power grid enterprises. For example... Figure 1As shown, the fund security monitoring system of a power grid enterprise includes: a fund monitoring and processing module 100 and a security control module 200. The fund monitoring and processing module 100 includes a monitoring module 102, a fund flow security determination module 104, a prediction module 106, and a judgment module 108. The security control module 200 includes an early warning module 202, a freeze command generation module 204, and a unfreeze command generation module 206. The monitoring module 102 is used to monitor fund inflows from multiple user nodes and fund outflows to power generation enterprises in real time. The fund flow security determination module 104 is used to determine the security of fund flows into the fund pool before funds flow into the pool. The prediction module 106 is used to predict the security value of the fund pool after the current fund flow enters or leaves the pool. The judgment module 108 is used to determine whether the current fund flow is a first unsafe fund flow or a safe fund flow based on whether the security value of the fund pool exceeds a pre-set fund pool security boundary threshold, allowing safe fund flows to enter or leave the fund pool. The early warning module 202 is used to issue an early warning for the first unsafe fund flow. The freeze instruction generation module 204 generates a freeze instruction based on the early warning of the first unsafe fund flow and sends it to the transaction fund supervision bank via a remote control mechanism. The unfreeze instruction generation module 206 generates an unfreeze instruction when the first unsafe fund flow is predicted to enter or leave the fund pool again, and the safety value of the fund pool does not exceed the safety boundary threshold of the fund pool. This instruction is then sent to the transaction fund supervision bank via a remote control mechanism.

[0054] Compared to existing technologies, the fund flow security determination module provided in this embodiment can determine the security of fund flows into the fund pool before the funds flow in, thereby ensuring the security of the funds entering the fund pool. The prediction module can predict the security value of the fund pool after the current fund flow enters or leaves the fund pool, thereby determining whether the impact of the fund flow entering or leaving the fund pool is within the allowable range of the fund pool. If it is determined that the fund flow or fund pool is unsafe, a freeze command is generated through a remote mechanism, and if the fund flow or fund pool meets the requirements again after a series of adjustments, a unfreeze command is generated through a remote mechanism, thereby monitoring the funds of the power grid company and ensuring fund security.

[0055] The following text will refer to Figure 1 and Figure 2 This document provides a detailed description of a fund security monitoring system for power grid enterprises according to embodiments of the present invention. Figure 1 As shown, the fund security monitoring system of the power grid enterprise includes: fund monitoring and processing module 100, security control module 200, and data entry module.

[0056] The data entry module includes digital identity management data, security protocol management data, and evidence storage and blockchain management data. Digital identity management is used to create accounts. Digital identity management involves real-name authentication, transaction qualification authentication, and the participation of both buyers and sellers. To ensure the security of user information and transaction data, the system uses IBE (Identity Based Encryption), and only users with the correct IDs can decrypt private information. Accounts are created by the account holder. After account creation, an authorization key is distributed, and each signing of a security agreement or execution of an operation is authorized using this key. Security protocol management involves the power user and the power grid establishing a principal-agent relationship, the regulatory system creating a security agreement and selecting a regulatory bank, the online electronic signing of various security agreements by the parties involved in the fund transaction, and finally, the bank confirming the security agreement to begin the fund supervision process. This process monitors the entire lifecycle of transaction funds, synchronizes the progress of security agreements in real time, provides interfaces for adding, deleting, modifying, and querying protocols, and timestamps and stores the agreements on the blockchain for verification by all parties. Evidence storage and blockchain management uses an artificial intelligence system to automatically identify and store the necessary information for each transaction step for verification purposes.

[0057] The fund monitoring and processing module 100 includes a monitoring module 102, a fund flow security determination module 104, a prediction module 106, and a judgment module 108. The monitoring module 102 is used to monitor fund inflows from multiple user nodes and fund outflows to power generation enterprises in real time. The fund flow security determination module 104 is used to determine the security of fund flows into the fund pool before funds flow into the pool. The fund flow security determination module 104 further includes an influencing factor determination submodule, a weight determination submodule, and a fund flow credibility determination submodule. The influencing factor determination submodule is configured to determine multiple influencing factors affecting the security of the power grid enterprise's transaction funds based on a historical database. The weight determination submodule is configured to determine the weight of each influencing factor based on the influencing factors. The fund flow credibility determination submodule is configured to determine the credibility of the fund flow based on the weight of each influencing factor and the corresponding initial credibility value. Pre-set security boundary thresholds include a fund flow security boundary threshold and a fund pool security boundary threshold. Based on the credibility of the fund flow and the fund flow safety boundary threshold, the safety of the fund flow to be injected into the fund pool is determined. When the credibility of the fund flow is greater than or equal to the fund flow safety boundary threshold, the fund flow to be injected into the fund pool is determined to be safe. When the credibility of the fund flow is less than the fund flow safety boundary threshold, the credibility of the fund flow is determined to exceed the fund flow safety boundary threshold. The prediction module 106 is used to predict the safety value of the fund pool after the current fund flow enters or leaves the fund pool. The judgment module 108 is used to determine whether the current fund flow is the first unsafe fund flow or a safe fund flow based on whether the safety value of the fund pool exceeds the preset fund pool safety boundary threshold, so as to allow the safe fund flow to enter or leave the fund pool.

[0058] The security control module 200 includes an early warning module 202, a freeze instruction generation module 204, an unfreeze instruction generation module 206, and an adjustment module. The early warning module 202 is used to issue an early warning for the first unsafe fund flow. The early warning module 202 is further configured to, when the credibility of a determined fund flow exceeds the fund flow security boundary threshold, designate the determined fund flow as the second unsafe fund flow and issue an early warning for the second unsafe fund flow. The freeze instruction generation module 204 is used to generate a freeze instruction based on the early warning of the first unsafe fund flow and send it to the transaction fund supervision bank via a remote control mechanism. The freeze instruction generation module 204 is further configured to generate a freeze instruction based on the early warning of the second unsafe fund flow and send it to the transaction fund supervision bank via a remote control mechanism. The adjustment module is configured to perform standardized analysis on each influencing factor and adjust the initial credibility value of each influencing factor based on the analysis results. The unfreeze instruction generation module 206 is used to generate an unfreeze instruction when the first unsafe fund flow is predicted to enter or leave the fund pool again, and the safety value of the fund pool does not exceed the fund pool security boundary threshold, and sends it to the transaction fund supervision bank via a remote control mechanism. The unfreezing instruction generation module 206 is configured to adjust the initial credibility values ​​of each influencing factor until the credibility of the second unsafe fund flow changes to be greater than or equal to the fund flow security boundary threshold, generate an unfreezing instruction, and send it to the transaction fund supervision bank through a remote control mechanism.

[0059] The influencing factor determination submodule is configured to identify the following influencing factors affecting the security of power grid transaction funds based on historical databases: fund liquidity, working capital turnover, working capital loss, credit rating of the trading counterparty, and inflation rate. The weight determination submodule is configured to construct an m×n indicator matrix A for the n influencing factors of m fund flows.

[0060]

[0061] The index matrix A is dimensionless to transform it into a standardized target matrix B:

[0062]

[0063] Based on the standardized objective matrix B, the entropy value H of each indicator is calculated using the following formula. j :

[0064]

[0065] in, Assume that when f ij =0,f ij ln f ij =0, k is the Boltzmann constant, let k>0; based on the entropy values ​​H of each index j The index entropy weight w is calculated using the following formula.j :

[0066]

[0067] The cash flow credibility determination submodule is configured to calculate the credibility of the cash flow using the following formula before funds flow into the cash pool:

[0068] CF(E)=w1*CF(E1)+w2*CF(E2)+…+w n *CF(E n )

[0069] Wherein, CF(E1), CF(E2)...CF(E n ) represents the confidence level of each influencing factor, w1, w2...w n The entropy weights of each influencing factor on the security of cash flow are given.

[0070] The early warning module 202 further includes a sub-module for determining the static strength of fund flows and a sub-module for determining the overall credibility: the sub-module for determining the static strength of fund flows is configured to determine the static strength of fund flows after determining that the fund flows to be flowed into the fund pool are safe; the sub-module for determining the overall credibility of fund flows is configured to determine the overall credibility of each fund flow based on the credibility of the fund flows and the static strength of the fund flows, wherein the fund pool includes multiple fund flows; the overall credibility of the fund pool is determined based on the overall credibility of each fund flow and the overall credibility of the fund pool is used as the safety value of the fund pool.

[0071] The submodule for determining the static strength of cash flow is configured to calculate the static strength of cash flow using the following formula:

[0072] CF(H,E)=MB(H,E)-MD(H,E)

[0073]

[0074] Wherein, the posterior probability is: P(H) is the prior probability based on conclusion H; LS is called the sufficiency measure; MB is the growth of trust and MD is the growth of distrust.

[0075] The overall credibility determination submodule is configured to calculate the overall credibility CF(H) of each fund flow using the following formula:

[0076] CF(H) = CF(H, E) * CF(E),

[0077] The fund flows in the entire fund pool are denoted as CF1(H), CF2(H), CF3(H), ..., CF n (H) uses the following formula to predict the safe value of the fund pool after the current inflow and outflow of funds:

[0078] The formula for capital inflow is as follows:

[0079] CF 1，2 (H)=CF1(H)+CF2(H)-CF1(H)*CF2(H)

[0080] CF 1，2，3 (H)=CF 1，2 (H)+CF3(H)-CF 1，2 (H)*CF3(H) ......

[0082] CF 1，2，3......n (H)=CF 1，2，3...n-1 (H)+CF n (H)-CF 1，2，3......n-1 (H)*CF n (H)

[0083] The formula for cash outflow is as follows:

[0084]

[0085] The following text will refer to Figure 2 The document provides a detailed description of the fund security monitoring system of power grid companies using specific examples.

[0086] The data entry module includes digital identity management, security protocol management, and on-chain evidence storage management. Digital identity management is used to create accounts. Digital identity management involves real-name authentication, transaction qualification authentication, and the participation of both buyers and sellers. To ensure the security of user information and transaction data, the system uses IBE (Identity Based Encryption), and only users with the correct IDs can decrypt private information. Accounts are created by the account holder. After account creation, an authorization key is distributed, and each signing of a security agreement or execution of an operation is authorized using this key. Security protocol management involves the power user and the power grid establishing a principal-agent relationship, the regulatory system creating a security agreement and selecting a supervising bank, the online electronic signing of various security agreements by the parties involved in the fund transaction, and finally, the bank confirming the security agreement to begin the fund supervision process. This process monitors the entire lifecycle of transaction funds, synchronizes the progress of security agreements in real time, provides interfaces for adding, deleting, modifying, and querying protocols, and timestamps and stores the agreements on the blockchain for verification by all parties. On-chain evidence storage management uses an artificial intelligence system to automatically identify and store the necessary information for each transaction step for verification purposes.

[0087] The funds monitoring and processing module includes real-time monitoring, scheduling management, and data analysis. Real-time monitoring refers to the use of an artificial intelligence system to monitor the source, amount, dynamics, and scheduling of funds in the fund pool around the clock, enabling tracking and ensuring fund security. Scheduling management refers to the allocation or use of funds in the fund pool according to relevant permissions. The system allocates permissions based on different security levels. Routine scheduling requires operation based on submitted scheduling plans and approved applications. Emergency scheduling is only available at the highest security level, and requires subsequent application and approval. The scheduling process is monitored in real-time by artificial intelligence and recorded on the blockchain for traceability. Data analysis refers to the use of a series of artificial intelligence analytical methods to budget working capital, analyze and predict business needs, including relationship graphs, data graphs, data rules, and data collision analysis.

[0088] The security control module includes a fund pool early warning system, a remote control mechanism, and access control. The fund pool early warning system uses artificial intelligence (AI) to set security indicators for transaction funds through learning capabilities and employs AI algorithms to determine the safety and trustworthiness of the funds. If the flow of funds within the fund pool exceeds the system's set security threshold, the AI ​​will immediately issue an early warning and send a fund freezing order to the supervising bank. The remote control mechanism establishes an instruction interface between the fund supervision system and the transaction fund supervising bank. The system can issue instructions to freeze, allocate, and unfreeze funds within the fund pool, and the bank will respond to these instructions. Access control allows the system to assign different permissions to different objects based on the security levels set in the security protocol, with each object having a unique authorization key.

[0089] The following text will refer to Figure 3 The present invention provides a detailed description of a method for monitoring the financial security of power grid enterprises according to embodiments thereof.

[0090] refer to Figure 3 The fund security monitoring method for power grid enterprises includes: Step S302, real-time monitoring of fund inflows from multiple user nodes and fund outflows to power generation enterprises. Step S304, before funds flow into the fund pool, determining the safety of the fund flow to be flowing into the fund pool, and predicting the safety value of the fund pool after the current fund flow enters or leaves the fund pool. Step S306, based on whether the safety value of the fund pool exceeds the pre-set fund pool safety boundary threshold, determining whether the current fund flow is the first unsafe fund flow or a safe fund flow, allowing the safe fund flow to enter or leave the fund pool, and issuing an early warning for the first unsafe fund flow. Step S308, based on the early warning of the first unsafe fund flow, generating a freeze instruction and sending it to the transaction fund supervision bank through a remote control mechanism. Step S310, when the first unsafe fund flow is predicted to enter or leave the fund pool again, and the safety value of the fund pool does not exceed the fund pool safety boundary threshold, generating an unfreeze instruction and sending it to the transaction fund supervision bank through a remote control mechanism.

[0091] The pre-set security boundary thresholds include a fund flow security boundary threshold and a fund pool security boundary threshold. Step S304, before funds flow into the fund pool, further includes determining the security of the fund flow to be flowed into the fund pool by: determining multiple influencing factors of the security of the power grid company's transaction funds based on historical databases, and determining the weight of each influencing factor based on the influencing factors; determining the credibility of the fund flow based on the weight of each influencing factor and the corresponding initial credibility value; and determining whether the fund flow to be flowed into the fund pool is safe based on the credibility of the fund flow and the fund flow security boundary threshold, wherein when the credibility of the fund flow is greater than or equal to the fund flow security boundary threshold, the fund flow to be flowed into the fund pool is determined to be safe, and when the credibility of the fund flow is less than the fund flow security boundary threshold, the credibility of the fund flow is determined to exceed the fund flow security boundary threshold.

[0092] The following text will refer to Figure 4 and Figure 5 This paper provides a detailed description of the methods for monitoring the financial security of power grid companies, using specific examples.

[0093] refer to Figure 4 Step 1: Registration: General electricity users or large users involved in the retail sector complete real-name authentication, transaction qualification authentication, and security agreement signing on the fund supervision system. Finally, the system administrator reviews the materials, and the regulatory agency successfully creates and activates the system after the review is passed. In this step, all operation records, supporting documents, and identity information will be stored on the blockchain for evidence.

[0094] Step 2: Establishing an Agency Relationship: The electricity user authorizes the power grid company to purchase electricity on their behalf, and both parties sign an agency agreement to determine the agency fee standard. In this step, all operation records, supporting documents, and identity information will be stored on the blockchain for evidence.

[0095] Step 3: User Electricity Top-up: Users must ensure sufficient balance in their electricity account, i.e., prepay electricity fees. These funds are held in the bank as a transaction deposit. To ensure the security of the account funds, neither the user, the power grid, nor the bank can use them arbitrarily. When the deposit balance falls below the power grid company's security threshold for the user, the system will automatically remind the user to top up. In this step, all operation records, supporting documents, and identity information will be stored on the blockchain for evidence.

[0096] Step 4: Power Grid Companies Conduct Electricity Purchase Transactions: The AI ​​learning system analyzes the financial data in the database to predict business demand. Combining the power grid company's budget with historical data on user electricity consumption and prices, it estimates the amount of electricity to be purchased, the type of electricity purchased, and the purchase price. The purchased amount should meet the electricity needs of all users represented by the power grid company. The decision-making level integrates the system's estimated data with actual demand to make electricity market transaction decisions. In this step, all operation records, supporting documents, and identity information will be stored on the blockchain for evidence.

[0097] Step 5: Set a safety boundary for working capital: Relevant financial management personnel determine safety indicators, such as the current ratio, quick ratio, current asset turnover, and accounts receivable turnover. This safety boundary is updated regularly, and the specific update cycle is strongly correlated with the inventory turnover days. Transfers in the capital pool must comply with the plan and be subject to approval. The artificial intelligence system tracks the entire process of the transfer in real time, focusing on funds with large amounts and unclear reasons for changes, and records them on the blockchain for future traceability.

[0098] Step 6: Reference Figure 5 Outflow of funds from the power grid: Power grid companies engage in market-based transactions with power generation companies through the power trading platform, incurring operating expenses. During this process, all operation records, supporting documents, and identity information will be stored on the blockchain for verification.

[0099] Step 7: Reference Figure 5 Grid cash inflow: Utilizing IoT technology, the electricity meter data of agent users is connected to the internet, and cash inflow is correlated with electricity consumption in real time. At the end of the month, the power grid company processes the accounts, officially confirming and summarizing operating revenue. In this step, all operation records, supporting documents, and identity information are stored on the blockchain for evidence.

[0100] Step 8: Funds Pool Early Warning: The AI ​​system connects with the bank to monitor cash and cash flow in the funds pool in real time during transactions, ensuring the regulatory platform's control over fund security. Based on the AI's machine learning capabilities, the system automatically identifies regulated funds exceeding security boundaries and issues a funds pool early warning, issuing a fund freeze order to the bank via a remote control mechanism. Regulatory personnel, using the access control mechanism, dynamically trace and adjust the early warning funds according to their permissions to ensure compliance with security protocols. After system review and approval, the AI ​​then issues an unfreezing order to the bank via the remote control mechanism. In this step, all operation records, supporting documents, and identity information are stored on the blockchain for evidence.

[0101] The operation steps of the fund pool early warning function in artificial intelligence are as follows:

[0102] (1) Based on historical databases, determine the influencing factors on the security of transaction funds in the company's capital database: capital liquidity, working capital turnover, working capital depletion, credit rating of the trading counterparty, inflation rate, etc., and confirm the weight w of each influencing factor on the security of funds, with the total weight not exceeding 1. As the sample size increases and the progress of machine learning in artificial intelligence deepens, subsequent weighting factors w can be autonomously allocated by the artificial intelligence system.

[0103] There are various methods for determining weights. Considering the complexity of the attributes of various influencing factors, this invention adopts the entropy weight method. It uses the entropy value of historical data results for objective analysis, determines the weight based on the amount of information provided by the entropy value, determines the intrinsic relationship between information levels, and enhances the credibility of the weights.

[0104] Basic steps:

[0105] 1.1 Constructing the indicator matrix

[0106] Based on historical data from the company's cash database, and using the current working capital in the company's cash pool as the evaluation object, an m×n matrix is ​​constructed for n influencing factors of m cash flows.

[0107] A(), as shown below:

[0108]

[0109] 1.2 Indicator Standardization

[0110] The indicator matrix A is dimensionless using a standardization method. Different standardization methods are used for different indicator types; they are categorized into three types: cost-based indicators, benefit-based indicators, and fixed indicators. Specifically, cost-based indicators T1 represent indicators that are more important the smaller their value; benefit-based indicators T2 represent indicators that are more important the larger their value; and fixed indicators T3 represent indicators that are more important the closer they are to a fixed value. The formulas for cost-based indicators, benefit-based indicators, and fixed indicators are shown below:

[0111] Cost-related indicators:

[0112] Benefit-oriented indicators:

[0113] Fixed indicators:

[0114] Where y represents the dimensionless indicator. T1 represents cost-based indicators, T2 represents benefit-based indicators, and T3 represents fixed indicators. Based on the currently listed influencing factors, cost-based indicators include working capital losses, benefit-based indicators include capital liquidity, working capital turnover, and credit rating of trading partners, and fixed indicators include the inflation rate.

[0115] After standardization, the standardized target matrix B is constructed, as shown below:

[0116]

[0117] 1.3 Calculate the entropy value H of the index

[0118] Suppose we define the entropy value of the j-th index as H j The formulas for calculating the entropy values ​​of each indicator are as follows:

[0119]

[0120] in, Assume that when f ij =0,f ij ln f ij =0, k is the Boltzmann constant, let k>0.

[0121] 1.4 Determine the index entropy weight w

[0122] The entropy weight w of the j-th index is defined based on the entropy value calculated by the formula. j As shown below:

[0123]

[0124] (2) Extract the complete analysis sample S to form a sample set as evidence (E) for the analysis of the security of transaction funds in order to select the sample fund pool to be analyzed.

[0125] (3) Analyze the credibility (dynamic strength) CF(E) of each influencing factor on a certain capital flow. CF takes values ​​in (0,1]. The larger the value of CF, the higher the credibility of the conclusion (H) that the capital is in a safe state (different factors have different credibility factors).

[0126] CF(E)=w1*CF(E1)+w2*CF(E2)+…+w n *CF(E n )

[0127] Wherein, CF(E1), CF(E2), ..., CF(E n ) represents the confidence level of each influencing factor, w1, w2, ... w n The weights of each influencing factor on the security of the capital chain are denoted by CF(E), which represents the dynamic strength of the entire capital chain.

[0128] (4) Set a threshold limit λ1 for the credibility factor. The threshold is given by system experts to evaluate the security of the cash flow itself, and takes a value in (0.6,1], which refers to the minimum credibility of the knowledge that the system can accept. The threshold specifies the limit on the availability of the corresponding knowledge. Only when the credibility of E reaches or exceeds this limit, that is, CF(E) is greater than or equal to λ1, can the dynamic strength of the funds be applied. Conversely, if the credibility exceeds the security boundary, a fund security warning is issued, and the artificial intelligence system issues an instruction to freeze the funds related to the knowledge. The system operation traces back to the previous level and checks the credibility of multiple influencing factors CF(E) in the cash flow in the database. n Standardized analysis is conducted, and data below the standard is processed directly or indirectly. Direct processing, for example, if the company's working capital depletion rate is too high, it is necessary to reduce waste in the process of using funds and lower the depletion rate. Indirect processing, for example, if the credit rating of a trading partner is low and cannot be changed in the short term, it is necessary to adjust other influencing factors, such as increasing working capital to ensure that the liquidity of funds can compensate for the credit rating problem of the trading partner.

[0129] The setting of the λ1 parameter reflects, to some extent, the company's emphasis on the comprehensive influencing factors of cash flow.

[0130] <![CDATA[Range of values of λ1]]> The importance attached to the dynamic security of fund flows Trust in the company's existing cash pool [0.6,0.7) Do not pay attention trust [0.7,0.8) generally generally [0.8,0.9) Pay attention to distrust [0.9,1) Great importance Extreme distrust

[0131] (5) Determine the static strength CF(H,E) of the fund flow, that is, the systematic credibility before it enters (or flows out) the fund pool. It takes a value in (0,1], which represents the degree of support it provides for conclusion H when the evidence corresponding to the premise E is true.

[0132] The general form of knowledge is IF E THEN H CF(H,E). E is the prerequisite for the generation of knowledge. It can be a simple condition or multiple simple conditions can be connected into a compound condition using AND and OR.

[0133] For example: E = E1 AND E2 AND (E3 OR E4);

[0134] 5.1 Calculate the posterior probability P(H / E)

[0135]

[0136] Here, P(H) is the prior probability given by the system expert for conclusion H, which is based on historical data without considering any new evidence. P(H / E) is the posterior probability. As new evidence is obtained, the confidence in H changes. Based on the values ​​of P(E) and LS, the prior probability is transformed into the posterior probability. LS is called the sufficiency measure, used to represent the degree of support E for H, that is, the degree of necessity for E to be true for H. The LS value is initially given by the domain expert, and later given by artificial intelligence based on the deep learning function of artificial intelligence.

[0137] 5.2 Calculate the trust growth rate (MB) and distrust growth rate (MD).

[0138]

[0139] Where MB is the confidence growth, which represents the confidence growth that makes conclusion H true due to the appearance of evidence that matches premise E; MD is the distrust growth, which represents the confidence growth that makes conclusion H true due to the appearance of evidence that matches premise E. MB and MD are mutually exclusive.

[0140] 5.3 Determine the static strength CF(H,E) of the knowledge.

[0141] CF(H,E)=MB(H,E)-MD(H,E)

[0142]

[0143] (6) The comprehensive credibility index is used to evaluate the security of the cash flow r, CF(H), that is, the security evaluation index of the cash flow. The security evidence of the cash flow is a comprehensive summary of the evidence of various influencing factors on the cash flow chain.

[0144] CF(H) = CF(H, E) * CF(E)

[0145] The funding chains in the entire funding pool are denoted as CF1(H), CF2(H), CF3(H), ... CF n (H)

[0146] (7) Evaluation of the safety of funds in the fund pool h using a comprehensive credibility index. The recursive calculation method is adopted, starting from CF1(H), and proceeding step by step according to the order of each fund flow entering and leaving the fund pool. Whenever a fund flow is added, the credibility of H increases by one point, and whenever a fund flow is removed, the credibility of H decreases by one point, until the credibility of H is finally calculated.

[0147] The formula for capital inflow is as follows:

[0148] CF 1，2 (H)=CF1(H)+CF2(H)-CF1(H)*CF2(H)

[0149] CF 1,2,3 (H)=CF 1,2 (H)+CF3(H)-CF 1，2 (H)*CF3(H) ......

[0151] CF 1,2,3......n (H)=CF 1,2,3......n-1 (H)+CF n (H)-CF 1,2,3......n-1 (H)*CF n (H)

[0152] The formula for cash outflow is as follows:

[0153] ......

[0155]

[0156] (8) Conduct risk simulations of fund flows in and out of the fund pool, predict the safety of the fund pool after the flow, and set a threshold limit λ2 to consider the systematic safety of the fund flow and predict its impact on the safety of the fund pool. The threshold is given by system experts and takes a value in (0.6,1], which refers to the minimum credibility of the knowledge that the system can accept. The threshold sets limits on the inflow and outflow of funds. Only when the credibility of the fund pool reaches or exceeds this limit, i.e., CF... 1，2......n (H) When λ² is greater than or equal to λ, the dynamic strength of the cash flow can be utilized, and cash flows are allowed to enter or leave the cash pool. Conversely, if the credibility exceeds the safety boundary, it indicates that the existing funds in the cash pool are insufficient to support the cash movement, triggering a cash security warning. The AI ​​system then issues a freeze order, prohibiting the relevant cash flow. The system operation reverts to the previous level, waiting for the cash pool's comprehensive credibility evaluation to be updated after the enterprise conducts other cash transactions, and re-verifies the simulated CF. 1,2......n If (H) is greater than or equal to λ2, the simulated fund movement can proceed.

[0157] The setting of the λ1 parameter reflects, to some extent, the company's emphasis on the comprehensive influencing factors of cash flow.

[0158] <![CDATA[Range of values of λ2]]> The importance attached to the company's existing cash pool Level of trust in the security of fund flows [0.6,0.7) Do not pay attention trust [0.7,0.8) generally generally [0.8,0.9) Pay attention to distrust [0.9,1) Great importance Extreme distrust

[0159] Step 9: Funds Unfreezing: Relevant personnel modify fund-related information, upload relevant fund supervision and unfreezing certification materials, and connect with the regulatory authorities for verification through the aforementioned system. After verification, an unfreezing instruction is sent to the bank via an AI-powered automatic transfer smart contract. In this step, all fund transfer records, approval records, and fund details will be stored on the blockchain for evidence.

[0160] Compared with the prior art, the present invention can achieve at least one of the following beneficial effects:

[0161] 1. Before funds flow into the fund pool, the safety of the incoming funds is determined, thus ensuring the safety of the funds entering the pool. Before funds flow into or out of the pool, the safety value of the pool after the current funds flow in or out is predicted, thus determining whether the impact of the funds flowing in or out of the pool is within the pool's allowable range. If the funds flow or pool is determined to be unsafe, a freeze command is generated remotely; and if the adjusted funds flow or pool meets the requirements again, an unfreeze command is generated remotely, thereby monitoring the power grid company's funds and ensuring fund safety.

[0162] 2. This invention is suitable for transitioning to a fully market-based electricity trading process, and uses the supervision of trading funds as an entry point to build an artificial intelligence-based system platform as a specific application; focusing on the security of trading funds, which is of utmost concern to the power grid, an uncertainty reasoning algorithm model based on artificial intelligence deep learning is constructed to monitor and provide real-time early warning of trading funds throughout the entire process.

[0163] 3. The power grid enterprise transaction fund supervision system of this invention is designed for all entities, regulatory systems, regulatory banks, and government verification departments in the power trading process. It ensures the security, transparency, and reliable verification of all information in fund supervision through on-chain storage and verification, and guarantees the automatic execution of business processes under specified conditions through smart contracts, reducing the costs and risks of manual operation. It also enables business interconnection and data sharing among various institutions and roles based on blockchain technology.

[0164] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware, and the program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.

[0165] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A fund security monitoring system for power grid enterprises, characterized in that, include: The system includes a fund monitoring and processing module and a security control module. The fund monitoring and processing module comprises: a monitoring module for real-time monitoring of fund inflows from multiple user nodes and fund outflows to power generation enterprises; a fund flow security determination module for determining the security of fund flows into the fund pool before they flow in; a prediction module for predicting the security value of the fund pool after the current fund flow enters or leaves the fund pool before it enters or leaves, thereby determining whether the impact of the fund flow entering or leaving the fund pool is within the allowable range of the fund pool; and a judgment module for determining whether the current fund flow is a first unsafe fund flow or a safe fund flow based on whether the security value of the fund pool exceeds a pre-set fund pool security boundary threshold, so as to allow the safe fund flow to enter or leave the fund pool. The pre-set security boundary thresholds include a cash flow security boundary threshold and a cash pool security boundary threshold. The cash flow security determination module further includes an influencing factor determination submodule, a weight determination submodule, and a cash flow credibility determination submodule. The influencing factor determination submodule is configured to determine multiple influencing factors affecting the security of the power grid company's transaction funds based on historical databases. The weight determination submodule is configured to determine the weight of each influencing factor based on the influencing factors; The cash flow credibility determination submodule is configured to determine the credibility of the cash flow based on the weights of each influencing factor and the corresponding initial credibility values; Specifically, the safety of funds flowing into the fund pool is determined based on the credibility of the fund flow and the safety boundary threshold of the fund flow. When the credibility of the fund flow is greater than or equal to the fund flow security boundary threshold, the fund flow to be flowed into the fund pool is determined to be safe; when the credibility of the fund flow is less than the fund flow security boundary threshold, the credibility of the fund flow is determined to exceed the fund flow security boundary threshold. The security control module includes: an early warning module for issuing an early warning for the first unsafe fund flow; a freeze command generation module for generating a freeze command based on the early warning of the first unsafe fund flow and sending it to the transaction fund supervision bank via a remote control mechanism; and an unfreezing command generation module for generating an unfreezing command when the first unsafe fund flow is predicted to enter or leave the fund pool again, and the safety value of the fund pool does not exceed the safety boundary threshold of the fund pool, and sending it to the transaction fund supervision bank via the remote control mechanism. The early warning module further includes: a static strength determination submodule for fund flows, configured to determine the static strength of the fund flows after determining that the fund flows to flow into the fund pool are safe; and a comprehensive credibility determination submodule configured to determine the comprehensive credibility of each fund flow based on the credibility of the fund flows and the static strength of the fund flows, wherein the fund pool includes multiple fund flows; and to determine the comprehensive credibility of the fund pool based on the comprehensive credibility of each fund flow and use the comprehensive credibility of the fund pool as the safety value of the fund pool.

2. The fund security monitoring system for power grid enterprises according to claim 1, characterized in that, The early warning module is further configured to, when it is determined that the credibility of the fund flow exceeds the security boundary threshold of the fund flow, designate the determined fund flow as the second unsafe fund flow and issue an early warning for the second unsafe fund flow; The freeze instruction generation module is further configured to generate a freeze instruction based on the warning of the second unsafe fund flow, and send it to the transaction fund supervision bank through the remote control mechanism; as well as The module is configured to perform standardized analysis on each of the influencing factors and adjust the initial confidence values ​​of each influencing factor based on the analysis results. The unfreezing instruction generation module is configured to adjust the initial credibility values ​​of each influencing factor until the credibility of the second insecure fund flow changes to be greater than or equal to the fund flow security boundary threshold, generate an unfreezing instruction, and send it to the transaction fund supervision bank through the remote control mechanism.

3. The fund security monitoring system for power grid enterprises according to claim 2, characterized in that, The influencing factors determination submodule is configured to determine the following influencing factors on the security of the power grid transaction funds based on historical databases: fund liquidity, working capital turnover, working capital loss, credit rating of the trading counterparty, and inflation rate. The weight determination submodule is configured to construct an m×n indicator matrix A for n influencing factors of m capital flows: The index matrix A is dimensionless to transform it into a standardized target matrix B: Based on the standardized target matrix B, the entropy value H of each indicator is calculated using the following formula. j : in, Assume that when f ij =0,f ij lnf ij =0, k is the Boltzmann constant, let k>0; Based on the entropy values ​​H of the aforementioned indicators j The index entropy weight w is calculated using the following formula. j :

4. The fund security monitoring system for power grid enterprises according to claim 3, characterized in that, The cash flow credibility determination submodule is configured to calculate the credibility of the cash flow using the following formula before the funds flow into the cash pool: CF(E)=w1*CF(E1)+w2*CF(E2)+…+w n *CF(E n ) Wherein, CF(E1), CF(E2), ..., CF(E n ) represents the confidence level of each influencing factor, w1, w2, ..., w n The entropy weights of each influencing factor on the security of cash flow are given.

5. The fund security monitoring system for power grid enterprises according to claim 4, characterized in that, The cash flow static strength determination submodule is configured to calculate the static strength of the cash flow using the following formula: CF(H,E)=MB(H,E)-MD(H,E) Wherein, the posterior probability is: P(H) is the prior probability based on conclusion H; LS is called the sufficiency measure; MB is the growth of trust and MD is the growth of distrust.

6. The fund security monitoring system for power grid enterprises according to claim 5, characterized in that, The comprehensive credibility determination submodule is configured to calculate the comprehensive credibility CF(H) of each fund flow using the following formula: CF(H) = CF(H, E) * CF(E) The fund flows in the entire fund pool are denoted as CF1(H), CF2(H), CF3(H), ..., CF n (H), The following formula is used to predict the safe value of the fund pool after the current inflow and outflow of funds: The formula for capital inflow is as follows: CF 1，2 (H)=CF1(H)+CF2(H)-CF1(H)*CF2(H) CF 1，2，3 (H)=CF 1，2 (H)+CF3(H)-CF 1，2 (H)*CF3(H) ...... CF 1，2，3...n (H)=CF 1，2，3......n-1 (H)+CF n (H)-CF 1,2，3......n-1 (H)*CF n (H) The formula for cash outflow is as follows:

7. A method for monitoring the financial security of power grid enterprises, characterized in that, include: Real-time monitoring of fund inflows from multiple user nodes and fund outflows to power generation companies; Before funds flow into the fund pool, the safety of the funds flowing into the fund pool is determined, and before the funds flow into or out of the fund pool, the safety value of the fund pool after the current funds flow into or out of the fund pool is predicted, thereby determining whether the impact of the funds flow into or out of the fund pool on the fund pool is within the allowable range of the fund pool. Based on whether the safety value of the fund pool exceeds the preset safety boundary threshold of the fund pool, the current fund flow is determined as either a first unsafe fund flow or a safe fund flow. The safe fund flow is allowed to enter or leave the fund pool, and an early warning is issued for the first unsafe fund flow. Based on the warning of the first unsafe fund flow, a freeze order is generated and sent to the transaction fund supervision bank through a remote control mechanism; as well as When the first unsafe fund flow is predicted to enter or leave the fund pool again, and the safety value of the fund pool does not exceed the safety boundary threshold of the fund pool, an unfreezing instruction is generated and sent to the transaction fund supervision bank through the remote control mechanism. The pre-set security boundary thresholds include a fund flow security boundary threshold and a fund pool security boundary threshold. Determining the security of fund flows into the fund pool before they flow in further includes: Based on historical databases, multiple influencing factors on the security of the power grid company's transaction funds are identified, and the weight of each influencing factor is determined based on these factors. The credibility of the cash flow is determined based on the weights of each influencing factor and their corresponding initial credibility values. Based on the credibility of the fund flow and the security boundary threshold of the fund flow, it is determined whether the fund flow to be injected into the fund pool is safe. Specifically, when the credibility of the fund flow is greater than or equal to the fund flow security boundary threshold, the fund flow to be flowing into the fund pool is determined to be safe; and when the credibility of the fund flow is less than the fund flow security boundary threshold, the credibility of the fund flow is determined to exceed the fund flow security boundary threshold. The security control module includes: an early warning module for issuing an early warning for the first unsafe fund flow; a freeze command generation module for generating a freeze command based on the early warning of the first unsafe fund flow and sending it to the transaction fund supervision bank via a remote control mechanism; and an unfreezing command generation module for generating an unfreezing command when the first unsafe fund flow is predicted to enter or leave the fund pool again, and the safety value of the fund pool does not exceed the safety boundary threshold of the fund pool, and sending it to the transaction fund supervision bank via the remote control mechanism. The early warning module further includes: a static strength determination submodule for fund flows, configured to determine the static strength of the fund flows after determining that the fund flows to flow into the fund pool are safe; and a comprehensive credibility determination submodule configured to determine the comprehensive credibility of each fund flow based on the credibility of the fund flows and the static strength of the fund flows, wherein the fund pool includes multiple fund flows; and to determine the comprehensive credibility of the fund pool based on the comprehensive credibility of each fund flow and use the comprehensive credibility of the fund pool as the safety value of the fund pool.

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