A supply chain financial risk control system based on artificial intelligence

Abstract

The present application discloses a supply chain financial risk control system based on artificial intelligence, which belongs to the field of supply chain financial technology, including an enterprise information management module to authenticate, register, and log in enterprise accounts, and record the enterprise's capital operation data; a supply chain relationship generation module to generate a supply chain; an artificial intelligence analysis module to perform in-depth analysis on target enterprises; an enterprise risk detection module to perform risk detection calculations on target enterprises and obtain enterprise risk detection results of target enterprises; a supply chain risk detection module to perform supply chain risk detection calculations on target supply chains and obtain supply chain risk detection results of the supply chain where the target enterprise is located; an artificial intelligence evaluation module to perform intelligent evaluation on target enterprises; a risk warning module to display and warn of enterprise risk detection results and supply chain risk detection results, and can judge whether there is a financial risk based on the enterprise risk detection results and supply chain risk detection results to avoid economic losses to the enterprise.

Description

Technical Field

[0001] The present application belongs to the field of supply chain finance technology, and specifically, relates to a supply chain finance risk control system based on artificial intelligence. Background Art

[0002] With the increasingly fierce market competition, many companies are facing the pressure of capital turnover, especially small and medium-sized enterprises, which may be unable to continue normal operations due to the inability to pay for goods immediately or the long payment cycle. Once the suppliers in the upper and middle reaches of the supply chain are short of funds, it will directly lead to stagnation of subsequent links or even a break in the chain.

[0003] Usually, in order to ease financial pressure, small and medium-sized enterprises will borrow money from upstream suppliers or provide advance payment services to downstream distributors. However, the development of small and medium-sized enterprises is unstable, and many of them go bankrupt. In the absence of collateral, credit records, and transaction vouchers, the loan risk of small and medium-sized enterprises is high. If upstream suppliers and downstream distributors do not understand the true operating conditions of the company well enough, and after providing large sums of money and leasing a large amount of goods, they find that they cannot control the risks afterwards, they will not only cause their own asset losses, but also the interdependence of all parties within the same supply chain. It is easy to cause the collapse of the entire supply chain like a domino effect, resulting in unpredictable losses. Summary of the invention

[0004] In order to solve the above problems and technical defects, the embodiment of the present application adopts the following technical solution, a supply chain financial risk control system based on artificial intelligence, including: The enterprise information management module is used to authenticate, register, and log in enterprise accounts, record the enterprise's capital operation data, and obtain the supply relationship between enterprises; The supply chain relationship generation module is used to establish a supply chain template, fill enterprises into the supply chain template according to the supply relationship between enterprises, and generate a supply chain; Artificial intelligence analysis module, which is used to deploy intelligent risk assessment models, use the intelligent risk assessment models to conduct in-depth analysis of target enterprises, and obtain in-depth analysis results; The enterprise risk detection module is used to perform risk detection calculations on the target enterprise based on the enterprise's capital operation data and obtain the enterprise risk detection results of the target enterprise; The supply chain risk detection module is used to perform supply chain risk detection calculations on the target supply chain based on the enterprise risk detection results and the supply relationship in the supply chain, and obtain the supply chain risk detection results of the supply chain where the target enterprise is located; The artificial intelligence evaluation module is used to conduct intelligent evaluation of target enterprises based on the in-depth analysis results and enterprise risk detection results to obtain risk evaluation results; The risk warning module is used to display the in-depth analysis results, risk assessment results, enterprise risk detection results of the target enterprise and the supply chain risk detection results of the supply chain in which it is located, to provide risk warnings to administrators and enable them to conduct risk control.

[0005] Preferably, the supply chain relationship generation module includes: an information filling module, a head relationship linking module and a tail relationship linking module; An information filling module is used to obtain the enterprise number of the target enterprise, the enterprise number of the upstream enterprise of the target enterprise, and the enterprise number of the downstream enterprise of the target enterprise, and fill in the enterprise number of the target enterprise; The primary relationship link module is used to fill in the enterprise number of the upstream enterprise of the target enterprise and generate a link to the enterprise number; The tail relationship link module is used to fill in the enterprise number of the downstream enterprise of the target enterprise and generate a link pointing to the enterprise number.

[0006] Furthermore, the capital operation data of the enterprise includes historical capital operation data and current target capital operation data. The process of performing risk detection calculation on the target enterprise is as follows: Obtain the historical capital operation data of the target enterprise, perform calculations and processing, and obtain the highest capital operation safety curve and the lowest capital operation safety curve; Obtain the target capital operation data to be calculated for the target enterprise, calculate and process the target capital operation data, and obtain the target capital operation curve; Calculate and compare the target capital operation curve with the highest safety curve of capital operation and the lowest safety curve of capital operation to determine whether the target capital operation curve is higher than the highest safety curve of capital operation or lower than the lowest safety curve of capital operation; If not, the target capital operation data of the target enterprise is judged to be safe operating capital; Otherwise, the target enterprise is judged to be a risky enterprise and the capital risk value of the target enterprise is calculated.

[0007] Furthermore, the fund risk value is calculated by first obtaining the extreme point in the curve portion where the target fund operation curve is higher than the highest safety curve for fund operation, and the extreme point in the curve portion where the target fund operation curve is lower than the lowest safety curve for fund operation, and calculating the fund risk value according to the extreme point and the highest safety curve for fund operation and the lowest safety curve for fund operation. The fund risk value calculation formula is as follows: α=∑ i=1 (Y i -U i ) 2 *T Among them, α is the capital risk value of the target capital operation data, Y i is the value of the ith extreme point, U iis the value corresponding to the i-th extreme point in the safety curve, and T is the risk coefficient.

[0008] Furthermore, the supply chain risk detection calculation is to first calculate the supply chain risk value of the supply chain in which the target enterprise is located according to the capital risk value. The calculation formula of the supply chain risk value is as follows: β=∑ j=1 (α j *√(M j *Q+N j *P) Among them, β is the supply chain risk value, ∑α j is the capital risk value of the j-th risk enterprise in the supply chain, M j is the number of links from the j-th risk enterprise to the upstream enterprise number, Q is the safety factor of the upstream enterprise, N j is the number of links from the j-th risk enterprise to the downstream enterprise number, and P is the safety factor of the upstream enterprise; Then determine whether the target supply chain is risky based on the supply chain risk value.

[0009] Furthermore, the determination of whether the target supply chain has risks is to preset a supply chain stability threshold, and when the supply chain risk value is greater than or equal to the supply chain stability threshold, the target supply chain is determined to have risks.

[0010] Preferably, the intelligent risk assessment model in the artificial intelligence analysis module includes an online intelligent assessment model and a local intelligent assessment model; The online intelligent evaluation model conducts online queries on the target enterprise, obtains the enterprise's past news and industry development prospects, deconstructs and analyzes the queried content, and obtains online analysis results; The local intelligent evaluation model obtains the target enterprise's financial operation data and the upstream and downstream supply relationships of the target enterprise, and conducts in-depth analysis based on the online analysis results to determine whether the target enterprise has potential risks and obtain in-depth analysis results.

[0011] Furthermore, the local intelligent evaluation model is locally deployed through the DeepSeek intelligent model, and the process of establishing, deploying and using it is as follows: Obtain locally stored enterprise data and supply chain data, preset feature parameters, use the DeepSeek intelligent model to extract features from enterprise data and supply chain data as sample data, and obtain enterprise feature data and supply chain feature data; A risk vector machine model is established by combining enterprise data and supply chain data using a dynamic particle swarm algorithm. The risk vector machine model is used to extract risk factors from enterprise feature data and supply chain feature data. Use DeepSeek intelligent model to conduct in-depth risk analysis on risk factors to obtain the safe fluctuation range, risk cause, risk development process, risk impact results and risk treatment control measures for each risk factor; Input the target enterprise data into the local intelligent evaluation model, analyze the risk factors in the target enterprise data, and determine whether the target enterprise will cause financial risks to the supply chain. If it will cause financial risks to the supply chain, output the cause of the target enterprise's risk, risk development process, risk impact results and risk handling control measures.

[0012] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the content of the supply chain financial risk control system based on artificial intelligence as described above is implemented.

[0013] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the content of the supply chain financial risk control system based on artificial intelligence as described above is implemented.

[0014] Compared with the prior art, the beneficial effects of the embodiments of the present application are: (1) This application establishes a supply chain template, fills enterprises into the supply chain template according to the supply relationship between enterprises, generates a supply chain, performs risk detection calculations, obtains the enterprise risk detection results of the target enterprise, and then performs supply chain risk detection calculations on the target supply chain to obtain the supply chain risk detection results. Based on the enterprise risk detection results and the supply chain risk detection results, it is determined whether there is a financial risk to avoid economic losses to the enterprise; (2) This application calculates historical fund operation data to obtain the highest fund operation safety curve and the lowest fund operation safety curve, and then calculates and processes the target fund operation data to obtain the target fund operation curve. The target fund operation curve is calculated and compared with the highest fund operation safety curve and the lowest fund operation safety curve, so as to accurately determine whether the target enterprise is a safe enterprise, allowing the administrator to deal with the risky enterprise accordingly to avoid more serious financial losses caused by the risky enterprise; (3) This application calculates the supply chain risk value of the target enterprise's supply chain based on the capital risk value, and then determines whether the target supply chain is risky based on the supply chain risk value. Depending on whether the supply chain is risky, corresponding measures are taken for the risky supply chain to protect the assets of other enterprises in the entire supply chain. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In the attached picture: Figure 1 A schematic diagram of the system structure of an embodiment of the present application; Figure 2 This is a schematic diagram of the device structure of an embodiment of the present application. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Generally, the components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations.

[0017] Example 1: Figure 1 As shown, a supply chain financial risk control system based on artificial intelligence includes: The enterprise information management module is used to authenticate, register, and log in enterprise accounts, record the enterprise's capital operation data, and obtain the supply relationship between enterprises; The capital operation data of an enterprise includes historical capital operation data and current target capital operation data.

[0018] The supply chain relationship generation module is used to establish a supply chain template, fill enterprises into the supply chain template according to the supply relationship between enterprises, and generate a supply chain; The supply chain relationship generation module includes: information filling module, head relationship link module and tail relationship link module; An information filling module is used to obtain the enterprise number of the target enterprise, the enterprise number of the upstream enterprise of the target enterprise, and the enterprise number of the downstream enterprise of the target enterprise, and fill in the enterprise number of the target enterprise; The primary relationship link module is used to fill in the enterprise number of the upstream enterprise of the target enterprise and generate a link to the enterprise number; The tail relationship link module is used to fill in the enterprise number of the downstream enterprise of the target enterprise and generate a link pointing to the enterprise number.

[0019] Artificial intelligence analysis module, which is used to deploy intelligent risk assessment models, use the intelligent risk assessment models to conduct in-depth analysis of target enterprises, and obtain in-depth analysis results; Intelligent risk assessment models include online intelligent assessment models and local intelligent assessment models; The online intelligent evaluation model conducts online queries on the target enterprise, obtains the enterprise's past news and industry development prospects, deconstructs and analyzes the queried content, and obtains online analysis results; The online intelligent evaluation model has multiple API interfaces, which are connected to the Deepseek model and the GPT Chat model. Through online search, the output content of multiple models is sorted to obtain online analysis results.

[0020] The local intelligent evaluation model obtains the target enterprise's financial operation data and the upstream and downstream supply relationships of the target enterprise, and conducts in-depth analysis based on the online analysis results to determine whether the target enterprise has potential risks and obtain in-depth analysis results.

[0021] The local intelligent evaluation model uses the open source Deepseek model for local deployment, uses locally stored enterprise data and supply chain data for training and use, and uses virtual network technology for data protection; The local intelligent evaluation model is deployed locally through the DeepSeek intelligent model. The process of establishing, deploying and using it is as follows: Obtain locally stored enterprise data and supply chain data, preset feature parameters, use the DeepSeek intelligent model to extract features from enterprise data and supply chain data as sample data, and obtain enterprise feature data and supply chain feature data; A dynamic particle swarm algorithm is used to combine enterprise data and supply chain data to establish a risk vector machine model, and the risk vector machine model is used to extract risk factors from enterprise feature data and supply chain feature data; Use DeepSeek intelligent model to conduct in-depth risk analysis on risk factors to obtain the safe fluctuation range, risk cause, risk development process, risk impact results and risk treatment control measures for each risk factor; Input the target enterprise data into the local intelligent evaluation model, analyze the risk factors in the target enterprise data, and determine whether the target enterprise will cause financial risks to the supply chain. If it will cause financial risks to the supply chain, output the cause of the target enterprise's risk, risk development process, risk impact results and risk handling control measures.

[0022] The enterprise risk detection module is used to perform risk detection calculations on the target enterprise based on the enterprise's capital operation data and obtain the enterprise risk detection results of the target enterprise; The process of risk detection and calculation for the target enterprise is as follows: Obtain the historical capital operation data of the target enterprise, calculate and process the historical capital operation data, and obtain the highest capital operation safety curve and the lowest capital operation safety curve; Obtain the target capital operation data to be calculated for the target enterprise, calculate and process the target capital operation data, and obtain the target capital operation curve; Calculate and compare the target capital operation curve with the highest safety curve of capital operation and the lowest safety curve of capital operation to determine whether the target capital operation curve is higher than the highest safety curve of capital operation or lower than the lowest safety curve of capital operation; If not, the target capital operation data of the target enterprise is judged to be safe operating capital; Otherwise, the target enterprise is judged to be a risky enterprise and the capital risk value of the target enterprise is calculated.

[0023] The capital risk value is calculated by first obtaining the extreme points in the curve part where the target capital operation curve is higher than the highest safety curve for capital operation, and the extreme points in the curve part where the target capital operation curve is lower than the lowest safety curve for capital operation, and then calculating the capital risk value based on the extreme points and the highest safety curve for capital operation and the lowest safety curve for capital operation.

[0024] The formula for calculating capital risk value is as follows: α=∑ i=1 (Y i -U i ) 2 *T Among them, α is the capital risk value of the target capital operation data, Y i is the value of the ith extreme point, U i is the value corresponding to the i-th extreme point in the safety curve, and T is the risk coefficient.

[0025] The supply chain risk detection module is used to perform supply chain risk detection calculations on the target supply chain based on the enterprise risk detection results and the supply relationship in the supply chain, and obtain the supply chain risk detection results of the supply chain where the target enterprise is located; The supply chain risk detection calculation is to first calculate the supply chain risk value of the target enterprise's supply chain based on the capital risk value. The calculation formula of the supply chain risk value is as follows: β=∑ j=1 (α j *√(M j *Q+N j *P) Among them, β is the supply chain risk value, ∑α j is the capital risk value of the j-th risk enterprise in the supply chain, M j is the number of links from the j-th risk enterprise to the upstream enterprise number, Q is the safety factor of the upstream enterprise, N j is the number of links from the j-th risk enterprise to the downstream enterprise number, and P is the safety factor of the upstream enterprise; Then determine whether the target supply chain is risky based on the supply chain risk value.

[0026] Judging whether the target supply chain is risky is to preset a supply chain stability threshold. When the supply chain risk value is greater than or equal to the supply chain stability threshold, the target supply chain is judged to be risky.

[0027] The artificial intelligence evaluation module is used to conduct intelligent evaluation of target enterprises based on the in-depth analysis results and enterprise risk detection results to obtain risk evaluation results; Risks are judged based on the results of in-depth analysis and enterprise risk detection, and the risk type, risk impact value, risk occurrence time and risk existence time are obtained. They are then evaluated to obtain risk avoidance and control measures.

[0028] The artificial intelligence evaluation module uses the open source Deepseek model for local deployment and is trained through locally stored enterprise data, supply chain data, risk detection result data, and manually added risk treatment control measures.

[0029] The risk warning module is used to display the in-depth analysis results, risk assessment results, enterprise risk detection results of the target enterprise and the supply chain risk detection results of the supply chain in which it is located, to provide risk warnings to administrators and enable them to conduct risk control.

[0030] It also includes the Enterprise Risk Impact Module and the Supply Chain Risk Impact Module; The enterprise risk impact module is used to calculate the impact value of risky enterprises based on the enterprise risk detection results. The administrator adopts corresponding control measures for risky enterprises based on the impact value. The formula for calculating the impact value of a venture enterprise is as follows: γ=α*(M*Q+N*P) / e Among them, γ is the impact value of the risk enterprise, M is the number of links from the risk enterprise to the upstream enterprise number, Q is the upstream enterprise safety factor, N is the number of links from the risk enterprise to the downstream enterprise number, P is the upstream enterprise safety factor, and e is the number of alternative enterprises. When the risk enterprise has no alternative enterprise, e is 1, and the impact value γ of the risk enterprise is the largest, indicating that the risk of the risk enterprise has an extremely high impact. Otherwise, the risk impact is extremely low.

[0031] The supply chain risk impact module is used to calculate the impact value of the risk supply chain based on the impact value of the risk enterprise. The administrator adopts corresponding control measures for the risk supply chain based on the impact value. The formula for calculating the impact value of the risk supply chain is as follows: ω=∑ j=1 (γ j *∂) Among them, ω is the impact value of the risk supply chain, γ j is the impact value of the j-th risk enterprise in the supply chain, and ∂ is the risk impact coefficient.

[0032] Example 2: Figure 2As shown, from a hardware level, the present application provides an embodiment of an electronic device that implements all or part of the contents of an artificial intelligence-based supply chain financial risk control system, wherein the electronic device includes a service processor and a distributed memory, wherein the service processor is connected to the memory, wherein the distributed memory stores a service self-management program configured to store machine-readable instructions, and wherein the service processor executes the service self-management program, and when the instructions are executed by the processor, the supply chain financial risk control system based on artificial intelligence as described above is implemented.

[0033] From the hardware level, in order to effectively improve the flexibility, versatility and efficiency of data collection, the present application provides an embodiment of an electronic device that contains all or part of the contents of a supply chain financial risk control system based on artificial intelligence, and the electronic device specifically includes the following contents: Processor, memory, communication interface and bus; wherein the processor, memory and communication interface communicate with each other through the bus; the communication interface is used to realize information transmission between the data acquisition device based on the distributed model and the core business system, user terminal and related database and other related equipment; the logic controller can be a desktop computer, a tablet computer and a mobile terminal, etc., but the present embodiment is not limited thereto. In the present embodiment, the logic controller can be implemented with reference to the embodiment of the supply chain financial risk control system based on artificial intelligence and the embodiment of the data acquisition device based on the distributed model in the embodiment, and the contents thereof are incorporated herein, and the repeated parts are not repeated.

[0034] It is understandable that the user terminal may include a smart phone, a tablet electronic device, a network set-top box, a portable computer, a desktop computer, a personal digital assistant (PDA), a vehicle-mounted device, a smart wearable device, etc. Among them, the smart wearable device may include smart glasses, a smart watch, a smart bracelet, etc.

[0035] In practical applications, the academic opinion annotation and analysis method can be performed on the electronic device side as described above, or all operations can be completed in the client device. The specific selection can be based on the processing capability of the client device and the limitations of the user's usage scenario, etc. This application does not limit this. If all operations are completed in the client device, the client device may also include a processor.

[0036] The above-mentioned client device may have a communication module (i.e., a communication unit), which can communicate with a remote server to realize data transmission with the server. The server may include a server on the task scheduling center side, and other implementation scenarios may also include a server on an intermediate platform, such as a server on a third-party server platform that has a communication link with the task scheduling center server. The server may include a single computer device, or a server cluster consisting of multiple servers, or a server structure of a distributed device.

[0037] Embodiment 3: The embodiments of the present application also provide a computer-readable storage medium capable of implementing all the contents of the artificial intelligence-based supply chain financial risk control system in the above embodiments whose execution subject is a server or a client. The computer-readable storage medium stores a computer program, which, when executed by a processor, implements all the contents of the artificial intelligence-based supply chain financial risk control system in the above embodiments whose execution subject is a server or a client.

[0038] The embodiments of the present application may be provided as methods, devices, or computer program products, and therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0039] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (apparatus), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0040] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0041] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0042] The above-mentioned embodiments only express the preferred implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for ordinary technicians in this field, several modifications, improvements and substitutions can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application.

Claims

1. A supply chain financial risk control system based on artificial intelligence, characterized in that: include: The enterprise information management module is used to authenticate, register, and log in enterprise accounts, record the enterprise's capital operation data, and obtain the supply relationship between enterprises; The supply chain relationship generation module is used to establish a supply chain template, fill enterprises into the supply chain template according to the supply relationship between enterprises, and generate a supply chain; Artificial intelligence analysis module, which is used to deploy intelligent risk assessment models, use the intelligent risk assessment models to conduct in-depth analysis of target enterprises, and obtain in-depth analysis results; The enterprise risk detection module is used to perform risk detection calculations on the target enterprise based on the enterprise's capital operation data and obtain the enterprise risk detection results of the target enterprise; The supply chain risk detection module is used to perform supply chain risk detection calculations on the target supply chain based on the enterprise risk detection results and the supply relationship in the supply chain, and obtain the supply chain risk detection results of the supply chain where the target enterprise is located; The artificial intelligence evaluation module is used to conduct intelligent evaluation of target enterprises based on the in-depth analysis results and enterprise risk detection results to obtain risk evaluation results; The risk warning module is used to display the in-depth analysis results, risk assessment results, enterprise risk detection results of the target enterprise and the supply chain risk detection results of the supply chain in which it is located, to provide risk warnings to administrators and enable them to conduct risk control.

2. According to claim 1, a supply chain financial risk control system based on artificial intelligence is characterized in that: The supply chain relationship generation module includes: an information filling module, a head relationship linking module and a tail relationship linking module; An information filling module is used to obtain the enterprise number of the target enterprise, the enterprise number of the upstream enterprise of the target enterprise, and the enterprise number of the downstream enterprise of the target enterprise, and fill in the enterprise number of the target enterprise; The primary relationship link module is used to fill in the enterprise number of the upstream enterprise of the target enterprise and generate a link to the enterprise number; The tail relationship link module is used to fill in the enterprise number of the downstream enterprise of the target enterprise and generate a link pointing to the enterprise number.

3. The supply chain financial risk control system based on artificial intelligence according to claim 2 is characterized in that: The capital operation data of the enterprise includes historical capital operation data and current target capital operation data. The process of risk detection and calculation for the target enterprise is as follows: Obtain the historical capital operation data of the target enterprise, perform calculations and processing, and obtain the highest capital operation safety curve and the lowest capital operation safety curve; Obtain the target capital operation data to be calculated for the target enterprise, calculate and process the target capital operation data, and obtain the target capital operation curve; Calculate and compare the target capital operation curve with the highest safety curve of capital operation and the lowest safety curve of capital operation to determine whether the target capital operation curve is higher than the highest safety curve of capital operation or lower than the lowest safety curve of capital operation; If not, the target capital operation data of the target enterprise is judged to be safe operating capital; Otherwise, the target enterprise is judged to be a risky enterprise and the capital risk value of the target enterprise is calculated.

4. The supply chain financial risk control system based on artificial intelligence according to claim 3 is characterized in that: The fund risk value is calculated by first obtaining the extreme point in the curve portion of the target fund operation curve that is higher than the highest safety curve of fund operation, and the extreme point in the curve portion of the target fund operation curve that is lower than the lowest safety curve of fund operation, and calculating the fund risk value according to the extreme point and the highest safety curve of fund operation and the lowest safety curve of fund operation. The fund risk value calculation formula is as follows: α=∑ i=1 (Y) i -U i ) 2 *T Among them, α is the capital risk value of the target capital operation data, Y i is the value of the ith extreme point, U i is the value corresponding to the i-th extreme point in the safety curve, and T is the risk coefficient.

5. The supply chain financial risk control system based on artificial intelligence according to claim 4 is characterized in that: The supply chain risk detection calculation is to first calculate the supply chain risk value of the target enterprise's supply chain based on the capital risk value. The calculation formula of the supply chain risk value is as follows: β=∑ j=1 (a j *√(M j *Q+N j *P)) Among them, β is the supply chain risk value, ∑α j is the capital risk value of the j-th risk enterprise in the supply chain, M j is the number of links from the j-th risk enterprise to the upstream enterprise number, Q is the safety factor of the upstream enterprise, N j is the number of links from the j-th risk enterprise to the downstream enterprise number, and P is the safety factor of the upstream enterprise; Then determine whether the target supply chain is risky based on the supply chain risk value.

6. The supply chain financial risk control system based on artificial intelligence according to claim 5 is characterized in that: The determination of whether the target supply chain has risks is to preset a supply chain stability threshold. When the supply chain risk value is greater than or equal to the supply chain stability threshold, the target supply chain is determined to have risks.

7. The supply chain financial risk control system based on artificial intelligence according to claim 1 is characterized in that: The intelligent risk assessment model includes an online intelligent assessment model and a local intelligent assessment model; The online intelligent evaluation model conducts online queries on the target enterprise, obtains the enterprise's past news and industry development prospects, deconstructs and analyzes the queried content, and obtains online analysis results; The local intelligent evaluation model obtains the target enterprise's financial operation data and the upstream and downstream supply relationships of the target enterprise, and conducts in-depth analysis based on the online analysis results to determine whether the target enterprise has potential risks and obtain in-depth analysis results.

8. The supply chain financial risk control system based on artificial intelligence according to claim 7 is characterized in that: The local intelligent evaluation model is locally deployed through the DeepSeek intelligent model. The process of establishing, deploying and using it is as follows: Obtain locally stored enterprise data and supply chain data, preset feature parameters, use the DeepSeek intelligent model to extract features from enterprise data and supply chain data as sample data, and obtain enterprise feature data and supply chain feature data; A risk vector machine model is established by combining enterprise data and supply chain data using a dynamic particle swarm algorithm. The risk vector machine model is used to extract risk factors from enterprise feature data and supply chain feature data. Use DeepSeek intelligent model to conduct in-depth risk analysis on risk factors to obtain the safe fluctuation range, risk cause, risk development process, risk impact results and risk treatment control measures for each risk factor; Input the target enterprise data into the local intelligent evaluation model, analyze the risk factors in the target enterprise data, and determine whether the target enterprise will cause financial risks to the supply chain. If it will cause financial risks to the supply chain, output the cause of the target enterprise's risk, risk development process, risk impact results and risk handling control measures.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the content of the supply chain financial risk control system based on artificial intelligence described in claim 1 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the content of the supply chain financial risk control system based on artificial intelligence described in claim 1 is implemented.

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