A business risk detection method, system, electronic device and storage medium

Abstract

The embodiment of the application provides a kind of service risk detection method, system, electronic equipment and storage medium, method includes: deploying machine learning algorithm on edge computing center;The edge computing center obtains the service data of at least one service server, and utilizes the service data of at least one service server to detect risk, when detecting that the service server has risk, send risk prompt to at least one service server;When at least one service server receives the risk prompt of edge computing center, the service data is verified.Implementation of the above embodiment, the security of data is proved, and cost can be reduced.

Description

Technical Field

[0001] This application relates to the field of financial technology, and more specifically, to a business risk detection method, system, electronic device, and computer-readable storage medium. Background Technology

[0002] Traditional credit card fraud detection methods typically involve data processing and model training on centralized servers or in the cloud, followed by transmitting the detection results back to the end device. This approach has the following drawbacks: High data transmission latency: Because data needs to be transmitted from the end device to the cloud for processing, there is a high transmission latency, which may result in slower detection times. Lack of data privacy: Since all data needs to be transmitted to centralized servers or the cloud for processing, there is a risk of data leakage, especially if sufficient security measures are not taken during data processing and transmission. High bandwidth requirements: The need to transmit large amounts of data to centralized servers or the cloud for processing requires high bandwidth, which may lead to higher costs. Summary of the Invention

[0003] The purpose of this application is to provide a business risk detection method, system, electronic device, and storage medium that ensures data security, does not require high bandwidth, and can reduce costs.

[0004] Firstly, embodiments of this application provide a business risk detection method, including:

[0005] Deploy machine learning algorithms at edge computing centers;

[0006] The edge computing center acquires business data from at least one business server and uses the business data from the at least one business server to perform risk detection. When a risk is detected in the business server, a risk warning is sent to the at least one business server.

[0007] When the at least one business server receives a risk warning from the edge computing center, it verifies the business data.

[0008] In the above implementation, the edge computing center is located close to the business servers. The business servers do not require excessive bandwidth to send business data to the edge computing center, and the data transmission links are relatively short, thus reducing the risk of data leakage. Machine learning algorithms are deployed on the edge computing center. The edge computing center obtains business data from at least one business server and uses this data for risk detection. When a risk is detected in a business server, a risk alert is sent to at least one business server. When at least one business server receives the risk alert from the edge computing center, it verifies the business data. This ensures data security, saves resources, and achieves accurate risk detection for business operations.

[0009] Furthermore, the edge computing center has multiple locations, and the method further includes: deploying storage centers, which have multiple locations;

[0010] The at least one business server sends the business data to multiple storage centers;

[0011] When the distance from the edge computing center to the business server is less than a first distance threshold, the edge computing center obtains the business data of the at least one business server from at least one of the multiple storage centers based on the distance from the edge computing center to the multiple storage centers.

[0012] In the above implementation process, separating the storage classification function and the computing function can maximize the utilization of computer resources. When the distance from the edge computing center to the storage center is less than the first distance threshold, the edge computing can obtain the business data of the at least one business server from at least one of the multiple storage centers according to the distance from the edge computing center to the multiple storage centers. This enables rapid acquisition of business data and leverages the geographical and computing advantages of the edge computing center.

[0013] Furthermore, the edge computing center obtains information about the at least one business server from at least one of the plurality of storage centers based on the distance from the edge computing center to the plurality of storage centers, including:

[0014] When the distance from the edge computing center to the business server is greater than a first distance threshold, the edge computing center obtains the business data of the at least one business server from the at least one storage center based on the first distance from the edge computing center to the plurality of storage centers and the second distance from the edge computing center to the at least one business server.

[0015] In the above implementation process, separating the storage classification function and the computing function can maximize the utilization of computer resources. The edge computing center obtains the business data of the at least one business server from the at least one storage center based on the first distance from the edge computing center to the multiple storage centers and the second distance from the edge computing center to the at least one business server, thereby realizing the rapid acquisition of business data and leveraging the geographical and computing advantages of the edge computing center.

[0016] Further, the edge computing center obtains the business data of the at least one business server from the at least one storage center based on a first distance from the edge computing center to the plurality of storage centers and a second distance from the edge computing center to the at least one business server, including:

[0017] The edge computing center obtains the weight of the first distance and the weight of the second distance, and obtains the business data of the at least one business server in the at least one storage center according to the weight of the first distance, the weight of the first distance, the second distance, and the weight of the second distance.

[0018] In the above implementation process, since the data transmitted between the edge computing center and the storage center, the data transmitted between the storage center and the business server, and the data transmitted between the edge computing center and the business server are different, a weight for the first distance and a weight for the second distance are set. Based on the weight of the first distance, the weight of the first distance, the second distance, and the weight of the second distance, the business data of the at least one business server is obtained in at least one storage center, which can realize the rapid acquisition of business data and give full play to the geographical and computing advantages of the edge computing center.

[0019] Furthermore, a computing unit is composed of multiple edge computing centers and at least one storage center; each edge computing center is used to process different business data;

[0020] Each edge computing center obtains its corresponding business data from the storage center.

[0021] In the above implementation process, different business data are suitable for different machine learning models. Therefore, by constructing different computing units, each computing unit includes multiple edge computing centers, and each edge computing center is used to process different business data, it is possible to accelerate the risk detection of business data, stop losses in time, and protect bank assets.

[0022] Furthermore, the method also includes:

[0023] In response to a model update instruction, the first edge computing center in the plurality of edge computing centers sends a work instruction to a second edge computing center that processes the same business data as the first edge computing center, so that the second edge computing center and the at least one storage center form a new computing unit;

[0024] The second edge computing center obtains business data from the storage center and performs risk detection on the business data of the at least one business server based on the business data;

[0025] The first edge computing center also stops obtaining the business data from the at least one storage center and updates the model of the first edge computing center based on historical model data.

[0026] In the above implementation process, model updates require a large number of operations. Therefore, in order to speed up the model update and maintain continuous monitoring of business data, based on the aforementioned implementation method of this application, a first edge computing center among multiple edge computing centers responds to the model update instruction and sends a work instruction to a second edge computing center that processes the same business data as the first edge computing center, so that the second edge computing center and at least one storage center form a new computing unit; the second edge computing center obtains business data from the storage center and performs risk detection on the business data of at least one business server based on the business data.

[0027] The first edge computing center also stops obtaining the business data from the at least one storage center and updates the model of the first edge computing center based on historical model data.

[0028] Furthermore, the storage center pushes business data to a message queue, and the edge computing center retrieves the business data from the message queue.

[0029] In the above implementation process, pushing business data through a message queue can avoid message congestion.

[0030] Secondly, embodiments of this application provide a business risk detection system, including:

[0031] An edge computing center is used to acquire business data from at least one business server and to perform risk detection using the business data from the at least one business server. When a risk is detected in a business server, a risk warning is sent to the at least one business server. When the at least one business server receives the risk warning from the edge computing center, it verifies the business data.

[0032] In the above implementation, the edge computing center is located close to the business servers. The business servers do not require excessive bandwidth to send business data to the edge computing center, and the data transmission links are relatively short, thus reducing the risk of data leakage. Machine learning algorithms are deployed on the edge computing center. The edge computing center obtains business data from at least one business server and uses this data for risk detection. When a risk is detected in a business server, a risk alert is sent to at least one business server. When at least one business server receives the risk alert from the edge computing center, it verifies the business data. This ensures data security, saves resources, and achieves accurate risk detection for business operations.

[0033] Thirdly, an electronic device provided in this application includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method as described in any of the first aspects.

[0034] Fourthly, embodiments of this application provide a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the method described in any of the first aspects.

[0035] Fifthly, embodiments of this application provide a computer program product that, when run on a computer, causes the computer to perform the method described in any of the first aspects.

[0036] Other features and advantages disclosed in this application will be set forth in the following description, or some features and advantages may be inferred from the description or determined without doubt, or may be learned by practicing the above-described technology disclosed in this application.

[0037] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0038] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0039] Figure 1 A flowchart illustrating the business risk detection method provided in this application embodiment;

[0040] Figure 2 This is a schematic diagram of the business risk detection device provided in the embodiments of this application;

[0041] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0042] The technical solutions in the embodiments of this application will now be described with reference to the accompanying drawings.

[0043] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0044] Traditional credit card fraud detection methods typically involve data processing and model training on centralized servers or in the cloud, followed by transmitting the detection results back to the end device. This approach has the following drawbacks: High data transmission latency: Because data needs to be transmitted from the end device to the cloud for processing, there is a high transmission latency, which may result in slower detection times. Lack of data privacy: Since all data needs to be transmitted to a centralized server or cloud for processing, there is a risk of data leakage, especially if sufficient security measures are not taken during data processing and transmission. High bandwidth requirements: The need to transmit large amounts of data to a centralized server or cloud for processing requires high bandwidth, which may lead to higher costs.

[0045] See Figure 1 This application provides a business risk detection method that can be applied to a server for personalized masking of business data. The server can be a standalone server or a server cluster composed of multiple servers. It can also be a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence sampling point devices. See also... Figure 1 The methods include:

[0046] S1: Deploy machine learning algorithms on edge computing centers;

[0047] S2: The edge computing center obtains business data from at least one business server and uses the business data from the at least one business server to perform risk detection. When a risk is detected in the business server, a risk warning is sent to the at least one business server.

[0048] S3: When the at least one business server receives a risk warning from the edge computing center, it verifies the business data.

[0049] Business data includes data on loan business, deposit business, etc.

[0050] In the above embodiments, after the edge computing center receives business data, it uses the model deployed on the edge computing center to make predictions and determine whether the business data is risky. If there is a risk, it sends relevant information to the business server so that the server can issue an alert and allow staff to verify the business.

[0051] In the above implementation, the edge computing center is located close to the business servers. The business servers do not require excessive bandwidth to send business data to the edge computing center, and the data transmission links are relatively short, thus reducing the risk of data leakage. Machine learning algorithms are deployed on the edge computing center. The edge computing center obtains business data from at least one business server and uses this data for risk detection. When a risk is detected in a business server, a risk alert is sent to at least one business server. When at least one business server receives the risk alert from the edge computing center, it verifies the business data. This ensures data security, saves resources, and achieves accurate risk detection for business operations.

[0052] Furthermore, the edge computing center has multiple locations, and the method further includes: deploying storage centers, which have multiple locations;

[0053] The at least one business server sends the business data to multiple storage centers;

[0054] When the distance from the edge computing center to the business server is less than a first distance threshold, the edge computing center obtains the business data of the at least one business server from at least one of the multiple storage centers based on the distance from the edge computing center to the multiple storage centers.

[0055] When the distance from the edge computing center to the plurality of storage centers is less than the first distance threshold, the transmission time between the edge computing center and the business server is negligible. Therefore, it is not necessary to comprehensively consider the second distance from different business servers.

[0056] In the above implementation process, separating the storage classification function and the computing function can maximize the utilization of computer resources. When the distance from the edge computing center to the storage center is less than the first distance threshold, the edge computing can obtain the business data of the at least one business server from at least one of the multiple storage centers according to the distance from the edge computing center to the multiple storage centers. This enables rapid acquisition of business data and leverages the geographical and computing advantages of the edge computing center.

[0057] Furthermore, the edge computing center obtains information about the at least one business server from at least one of the plurality of storage centers based on the distance from the edge computing center to the plurality of storage centers, including:

[0058] When the distance from the edge computing center to the business server is greater than a first distance threshold, the edge computing center obtains the business data of the at least one business server from the at least one storage center based on the first distance from the edge computing center to the plurality of storage centers and the second distance from the edge computing center to the at least one business server.

[0059] When the distance from the edge computing center to the plurality of storage centers is greater than the first distance threshold, the transmission time between the edge computing center and the business server cannot be ignored. Therefore, it is necessary to comprehensively consider the second distance from different business servers.

[0060] In the above implementation process, separating the storage classification function and the computing function can maximize the utilization of computer resources. The edge computing center obtains the business data of the at least one business server from the at least one storage center based on the first distance from the edge computing center to the multiple storage centers and the second distance from the edge computing center to the at least one business server, thereby realizing the rapid acquisition of business data and leveraging the geographical and computing advantages of the edge computing center.

[0061] Further, the edge computing center obtains the business data of the at least one business server from the at least one storage center based on a first distance from the edge computing center to the plurality of storage centers and a second distance from the edge computing center to the at least one business server, including:

[0062] The edge computing center obtains the weight of the first distance and the weight of the second distance, and obtains the business data of the at least one business server in the at least one storage center according to the weight of the first distance, the weight of the first distance, the second distance, and the weight of the second distance.

[0063] In the above implementation process, since the data transmitted between the edge computing center and the storage center, the data transmitted between the storage center and the business server, and the data transmitted between the edge computing center and the business server are different, a weight for the first distance and a weight for the second distance are set. Based on the weight of the first distance, the weight of the first distance, the second distance, and the weight of the second distance, the business data of the at least one business server is obtained in at least one storage center, which can realize the rapid acquisition of business data and give full play to the geographical and computing advantages of the edge computing center.

[0064] Furthermore, a computing unit is composed of multiple edge computing centers and at least one storage center; each edge computing center is used to process different business data;

[0065] Each edge computing center obtains its corresponding business data from the storage center.

[0066] In the above implementation process, different business data are suitable for different machine learning models. Therefore, by constructing different computing units, each computing unit includes multiple edge computing centers, and each edge computing center is used to process different business data, it is possible to accelerate the risk detection of business data, stop losses in time, and protect bank assets.

[0067] Furthermore, the method also includes:

[0068] In response to a model update instruction, the first edge computing center in the plurality of edge computing centers sends a work instruction to a second edge computing center that processes the same business data as the first edge computing center, so that the second edge computing center and the at least one storage center form a new computing unit;

[0069] The second edge computing center obtains business data from the storage center and performs risk detection on the business data of the at least one business server based on the business data;

[0070] The first edge computing center also stops obtaining the business data from the at least one storage center and updates the model of the first edge computing center based on historical model data.

[0071] In the above implementation process, model updates require a large number of operations. Therefore, in order to speed up the model update and maintain continuous monitoring of business data, based on the aforementioned implementation method of this application, a first edge computing center among multiple edge computing centers responds to the model update instruction and sends a work instruction to a second edge computing center that processes the same business data as the first edge computing center, so that the second edge computing center and at least one storage center form a new computing unit; the second edge computing center obtains business data from the storage center and performs risk detection on the business data of at least one business server based on the business data.

[0072] The first edge computing center also stops obtaining the business data from the at least one storage center and updates the model of the first edge computing center based on historical model data.

[0073] Furthermore, the storage center pushes business data to a message queue, and the edge computing center retrieves the business data from the message queue.

[0074] In the above implementation process, pushing business data through a message queue can avoid message congestion.

[0075] This application also provides an external business risk detection system, including:

[0076] An edge computing center is used to acquire business data from at least one business server and to perform risk detection using the business data from the at least one business server. When a risk is detected in a business server, a risk warning is sent to the at least one business server. When the at least one business server receives the risk warning from the edge computing center, it verifies the business data.

[0077] In the above implementation, the edge computing center is located close to the business servers. The business servers do not require excessive bandwidth to send business data to the edge computing center, and the data transmission links are relatively short, thus reducing the risk of data leakage. Machine learning algorithms are deployed on the edge computing center. The edge computing center obtains business data from at least one business server and uses this data for risk detection. When a risk is detected in a business server, a risk alert is sent to at least one business server. When at least one business server receives the risk alert from the edge computing center, it verifies the business data. This ensures data security, saves resources, and achieves accurate risk detection for business operations.

[0078] In some embodiments, the edge computing center has multiple locations, and the at least one business server is further configured to send the business data to the multiple storage centers; the edge computing center is further configured to, when the distance from the edge computing center to the business server is less than a first distance threshold, obtain the business data of the at least one business server from at least one of the multiple storage centers according to the distance from the edge computing center to the multiple storage centers.

[0079] In some embodiments, the edge computing center is further configured to, when the distance from the edge computing center to the plurality of storage centers is greater than a first distance threshold, obtain the business data of the at least one business server from the at least one storage center based on the first distance from the edge computing center to the plurality of storage centers and the second distance from the edge computing center to the at least one business server.

[0080] In some embodiments, the edge computing center is further configured to obtain the weight of the first distance and the weight of the second distance, and to obtain the business data of the at least one business server in the at least one storage center based on the first distance, the weight of the first distance, the second distance, and the weight of the second distance.

[0081] In some embodiments, a computing unit is composed of multiple edge computing centers and at least one storage center; each edge computing center is used to process different business data.

[0082] Each of the edge computing centers is also used to obtain the business data corresponding to the edge computing center from the storage center.

[0083] In some embodiments, a first edge computing center among the plurality of edge computing centers is used to send a work instruction to a second edge computing center that processes the same business data as the first edge computing center in response to a model update instruction, so that the second edge computing center and the at least one storage center form a new computing unit;

[0084] The second edge computing center is used to obtain business data from the storage center and perform risk detection on the business data of the at least one business server based on the business data;

[0085] The first edge computing center is also configured to stop obtaining the business data from the at least one storage center and update the model of the first edge computing center based on historical model data.

[0086] Also used for

[0087] In some embodiments, the storage center is further configured to push business data to a message queue, and the edge computing center retrieves the business data from the message queue.

[0088] This application also provides an electronic device, please refer to [link to application]. Figure 3 , Figure 3 This is a structural block diagram of an electronic device provided in an embodiment of this application. The electronic device may include a processor 31, a communication interface 32, a memory 33, and at least one communication bus 34. The communication bus 34 is used to enable direct communication between these components. In this embodiment, the communication interface 32 of the electronic device is used for signaling or data communication with other node devices. The processor 31 may be an integrated circuit chip with signal processing capabilities.

[0089] The processor 31 described above can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor, or the processor 31 can be any conventional processor.

[0090] The memory 33 may be, but is not limited to, random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), etc. The memory 33 stores computer-readable instructions. When these computer-readable instructions are executed by the processor 31, the electronic device can perform the aforementioned operations. Figures 1 to 2 The various steps involved in the method implementation examples.

[0091] Alternatively, the electronic device may also include a storage controller and an input / output unit.

[0092] The memory 33, storage controller, processor 31, peripheral interface, and input / output unit are electrically connected directly or indirectly to achieve data transmission or interaction. For example, these components can be electrically connected to each other through one or more communication buses 34. The processor 31 is used to execute executable modules stored in the memory 33, such as software function modules or computer programs included in electronic devices.

[0093] The input / output unit is used to provide users with the ability to create tasks and to set optional start periods or preset execution times for those tasks, thereby enabling user-server interaction. The input / output unit may be, but is not limited to, a mouse and keyboard.

[0094] Understandable. Figure 3 The structure shown is for illustrative purposes only; the electronic device may also include components that are more advanced than those shown. Figure 3 The more or fewer components shown, or having the same Figure 3 The different configurations shown. Figure 3 The components shown can be implemented using hardware, software, or a combination thereof.

[0095] This application also provides a storage medium storing instructions. When the instructions are run on a computer, the computer program is executed by a processor to implement the method described in the method embodiment. To avoid repetition, the method will not be described again here.

[0096] This application also provides a computer program product that, when run on a computer, causes the computer to perform the method described in the method embodiment.

[0097] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0098] In addition, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0099] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0100] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application. It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0101] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0102] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

Claims

1. A business risk detection method, characterized in that, include: Deploy machine learning algorithms at edge computing centers; The edge computing center acquires business data from at least one business server and uses the business data from the at least one business server to perform risk detection. When a risk is detected in the business server, a risk warning is sent to the at least one business server. When the at least one business server receives a risk alert from the edge computing center, it verifies the business data; The edge computing center is located close to the business server, and the business server sends business data to the edge computing center; The edge computing center has multiple locations, and the method further includes: deploying storage centers, which have multiple locations; the at least one business server sends the business data to the multiple storage centers; when the distance from the edge computing center to the business server is less than a first distance threshold, the edge computing center obtains the business data of the at least one business server from at least one of the multiple storage centers according to the distance from the edge computing center to the multiple storage centers.

2. The business risk detection method according to claim 1, characterized in that, The edge computing center obtains information about at least one business server from at least one of the multiple storage centers based on the distance from the edge computing center to the multiple storage centers, including: When the distance from the edge computing center to the business server is greater than a first distance threshold, the edge computing center obtains the business data of the at least one business server from the at least one storage center based on the first distance from the edge computing center to the plurality of storage centers and the second distance from the edge computing center to the at least one business server.

3. The business risk detection method according to claim 2, characterized in that, The edge computing center obtains the business data of the at least one business server from the at least one storage center based on a first distance from the edge computing center to the plurality of storage centers and a second distance from the edge computing center to the at least one business server, including: The edge computing center obtains the weight of the first distance and the weight of the second distance, and obtains the business data of the at least one business server in the at least one storage center according to the first distance, the weight of the first distance, the second distance, and the weight of the second distance.

4. The business risk detection method according to claim 3, characterized in that, A computing unit consists of multiple edge computing centers and at least one storage center; each edge computing center is used to process different business data. Each edge computing center obtains its corresponding business data from the storage center.

5. The business risk detection method according to claim 4, characterized in that, The method further includes: In response to a model update instruction, the first edge computing center in the plurality of edge computing centers sends a work instruction to the second edge computing center, which processes the same business data as the first edge computing center, so that the second edge computing center and the at least one storage center form a new computing unit; The second edge computing center obtains business data from the storage center and performs risk detection on the business data of the at least one business server based on the business data; The first edge computing center also stops obtaining the business data from the at least one storage center and updates the model of the first edge computing center based on historical model data.

6. The business risk detection method according to claim 1, characterized in that, The storage center pushes business data to a message queue, and the edge computing center retrieves the business data from the message queue.

7. A business risk detection system, characterized in that, include: An edge computing center is used to acquire business data from at least one business server and use the business data from the at least one business server to perform risk detection. When a risk is detected in the business server, a risk warning is sent to the at least one business server. When the at least one business server receives a risk alert from the edge computing center, it verifies the business data; The business risk detection system also includes: The edge computing center is located close to the business server, and the business server sends business data to the edge computing center; there are multiple edge computing centers, and the method further includes: deploying storage centers, which are multiple; the at least one business server sends the business data to multiple storage centers; when the distance from the edge computing center to the business server is less than a first distance threshold, the edge computing center obtains the business data of the at least one business server from at least one of the multiple storage centers according to the distance from the edge computing center to the multiple storage centers.

8. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of the method as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed on a computer, cause the computer to perform the method as described in any one of claims 1-6.

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