Traffic data processing method, system and device, medium and computer program

Abstract

The invention provides a traffic data processing method, system and device, a medium and a computer program, the processing method is applied to a decision engine, and the processing method comprises the following steps: obtaining a rule packet based on a received release rule request through a post-management application of the decision engine; a flow data switching proportion corresponding to rule packet pushing is configured through a back management application of the decision engine, and the flow data switching proportion is pushed to an online application of the decision engine; and switching the traffic data based on the traffic data switching proportion through the online application of the decision engine. According to the invention, through interaction between the post-management application and the online application in the decision engine, the traffic data switching proportion corresponding to rule packet pushing is configured, and switching processing is performed on the traffic data based on the traffic data switching proportion, so that smooth transition of release and update of the rule packet can be ensured in a large-traffic data transaction scene, and the traffic processing efficiency is improved. The stable operation of the system is not influenced, and the flexibility and stability of flow data management are improved.

Description

Technical Field

[0001] The present disclosure relates to the field of information processing technology, and in particular to a method, system, device, medium and computer program for processing flow data. Background Art

[0002] With the rapid development of the Internet, the number of user visits and transaction volumes faced by business systems has increased dramatically. How to effectively manage and optimize traffic data distribution has become the key to improving system stability and user experience. Traditional traffic data switching methods often rely on manual configuration or simple polling mechanisms, which are difficult to cope with complex and changing business needs and challenges in high-concurrency scenarios. In particular, when launching new functions or adjusting rules, it is impossible to ensure a smooth transition between new and old rules, which has an impact on the business. Summary of the invention

[0003] The technical problem to be solved by the present disclosure is to overcome the defect that the flow data switching method in the prior art cannot ensure the smooth transition of the new and old rules, affecting the stable operation of the system, and to provide a flow data processing method, system, device, medium and computer program.

[0004] The present invention solves the above technical problems through the following technical solutions:

[0005] A first aspect of the present disclosure provides a method for processing traffic data, the method being applied to a decision engine, the method comprising:

[0006] Obtaining a rule package based on a received publishing rule request through a back-end management application of the decision engine;

[0007] The flow data switching ratio corresponding to the rule package push is configured through the back-end management application of the decision engine, and the flow data switching ratio is pushed to the online application of the decision engine;

[0008] The flow data is switched based on the flow data switching ratio through the online application of the decision engine.

[0009] Preferably, the processing method further comprises:

[0010] Pushing the rule package to a remote dictionary service through a backend application of the decision engine, so as to push the rule package to an online application of the decision engine through the remote dictionary service;

[0011] The rule package is loaded into the memory through the online application of the decision engine, and the rule package is initially run in the memory.

[0012] Preferably, the processing method further comprises:

[0013] Grayscale verification is performed on the rule package. If the verification fails, the rule package that fails the verification is rolled back; if the verification passes, the traffic data switching ratio corresponding to the old rule package is switched to the traffic data switching ratio corresponding to the new rule package.

[0014] Preferably, the processing method further comprises:

[0015] registering the rule package to a registration center through an online application of the decision engine;

[0016] The post-management application of the decision engine determines the publishing status of the rule package according to the existence of the rule package corresponding to each online application in the registration center.

[0017] Preferably, the step of determining the publishing status of the rule package according to the existence of the rule package corresponding to each online application in the registration center through the back-end management application of the decision engine includes:

[0018] The back-end application of the decision engine determines whether there is a rule package corresponding to each online application in the registration center. If so, it is determined that the rule package corresponding to the rule publishing request has been published; if not, it is determined that the rule package corresponding to the rule publishing request has not been published.

[0019] Preferably, there are multiple rule packages, and the processing method further includes:

[0020] Select the best rule package from multiple rule packages as the final rule package;

[0021] and / or,

[0022] The rule package includes at least one of a routing rule package, a weight allocation rule package, and a condition judgment rule package.

[0023] A second aspect of the present disclosure provides a traffic data processing system, the processing system being applied to a decision engine, the processing system comprising:

[0024] An acquisition module, used for acquiring a rule package based on a received publishing rule request through a back-end management application of a decision engine;

[0025] A configuration module, configured to configure the flow data switching ratio corresponding to the push of the rule package through the back-end application of the decision engine, and push the flow data switching ratio to the online application of the decision engine;

[0026] The first switching processing module is used to perform switching processing on the flow data based on the flow data switching ratio through the online application of the decision engine.

[0027] Preferably, the processing system further comprises:

[0028] A push module, configured to push the rule package to a remote dictionary service through a backend application of the decision engine, so as to push the rule package to an online application of the decision engine through the remote dictionary service;

[0029] A loading module is used to load the rule package into the memory through the online application of the decision engine, and to perform the initial operation on the rule package in the memory.

[0030] Preferably, the processing system further comprises:

[0031] A verification module, used to perform grayscale verification on the rule package, and if the verification fails, call the fallback processing module; if the verification passes, call the second switching processing module;

[0032] The rollback processing module is used to perform rollback processing on the rule packages that fail the verification;

[0033] The second switching processing module is used to switch the traffic data switching ratio corresponding to the old rule package to the traffic data switching ratio corresponding to the new rule package.

[0034] Preferably, the processing system further comprises:

[0035] A registration module, used for registering the rule package to a registration center through an online application of the decision engine;

[0036] The determination module is used to determine the publishing status of the rule package according to the existence of the rule package corresponding to each online application in the registration center through the back-end management application of the decision engine.

[0037] Preferably, the determining module includes:

[0038] A judgment unit, configured to judge whether a rule package corresponding to each online application exists in the registration center through a back-end management application of the decision engine, and if so, call the first determination unit; if not, call the second determination unit;

[0039] The first determining unit is used to determine that the rule package corresponding to the rule publishing request has been published;

[0040] The second determining unit is used to determine that the rule package corresponding to the rule publishing request has not been published.

[0041] Preferably, there are multiple rule packages, and the processing system further includes:

[0042] A selection module is used to select the best rule package from multiple rule packages as the final rule package;

[0043] and / or,

[0044] The rule package includes at least one of a routing rule package, a weight allocation rule package, and a condition judgment rule package.

[0045] A third aspect of the present disclosure provides an electronic device, including a memory, a processor, and a computer program stored in the memory and used to run on the processor, and when the processor executes the computer program, the method for processing traffic data described in the first aspect is implemented.

[0046] A fourth aspect of the present disclosure provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method for processing traffic data described in the first aspect is implemented.

[0047] A fifth aspect of the present disclosure provides a computer program product, including a computer program, which, when executed by a processor, implements the method for processing traffic data as described in the first aspect.

[0048] On the basis of being in accordance with the common sense in the art, the above-mentioned preferred conditions can be arbitrarily combined to obtain the preferred embodiments of the present disclosure.

[0049] The positive and progressive effects of this disclosure are:

[0050] The present invention configures the traffic data switching ratio corresponding to the push of the rule package through the interaction between the back-end management application and the online application in the decision engine, and switches the traffic data based on the traffic data switching ratio, thereby ensuring that in the scenario of large traffic data transactions, the release and update of the rule package can be smoothly transitioned without affecting the stable operation of the system, thereby improving the flexibility and stability of traffic data management. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 This is a flowchart of the method for processing traffic data provided in Example 1 of the present disclosure.

[0052] Figure 2 A module diagram of a flow data processing system provided in Example 2 of the present disclosure.

[0053] Figure 3 This is a schematic diagram of the structure of an electronic device for implementing the method for processing flow data according to Embodiment 3 of the present disclosure. DETAILED DESCRIPTION

[0054] The present disclosure is further described below by way of examples, but the present disclosure is not limited to the scope of the examples.

[0055] Prefixes such as "first" and "second" are used in the embodiments of the present disclosure only to distinguish different description objects, and have no limiting effect on the position, order, priority, quantity or content of the described objects. The use of prefixes such as ordinal numbers to distinguish description objects in the embodiments of the present disclosure does not constitute a limitation on the described objects. For the statement of the described objects, please refer to the description in the context of the claims or embodiments, and no unnecessary limitation should be constituted due to the use of such prefixes. In addition, in the description of the present embodiment, unless otherwise specified, the meaning of "plurality" is two or more.

[0056] In the embodiments of the present disclosure, the collection, storage, use, processing, transmission, provision and disclosure of user personal information involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0057] Example 1

[0058] Figure 1 This is a flow chart of a method for processing traffic data provided in Example 1 of the present disclosure. The processing method is applied to a decision engine. This embodiment builds a highly configurable intelligent decision engine to support multiple types of rule definitions and introduces big data algorithms, such as machine learning and statistical analysis, to perform real-time analysis on traffic data and provide decision support for the decision engine. It should be noted that the decision engine is responsible for the definition, parsing and execution of rules, and provides a flexible configuration interface and powerful computing capabilities. Figure 1 As shown, the processing method includes:

[0059] S1. Obtaining a rule package based on a received publishing rule request through a backend application of a decision engine;

[0060] In this embodiment, after clicking on the publish rule request on the back-end management application page of the decision engine, the rules are compiled into a rule package in the back-end management application.

[0061] In this embodiment, the rule package includes at least one of a routing rule package, a weight allocation rule package, and a condition judgment rule package.

[0062] S2. Configure the traffic data switching ratio corresponding to the rule package push through the back-end application of the decision engine, and push the traffic data switching ratio to the online application of the decision engine;

[0063] In this embodiment, according to the instructions of the decision engine and according to business needs, the traffic data allocation ratio between different rule packages is set, and the traffic data entering the system is allocated and scheduled to implement a flexible traffic data proportional switching mechanism. Specifically, the traffic data switching ratio of different versions of the same rule service can be adjusted and published in the back-end application page of the decision engine, which can support operations such as grayscale verification of small business traffic. Champion rules can also be configured to automatically implement the optimal rule package switching.

[0064] S3. Switching the traffic data based on the traffic data switching ratio is performed through the online application of the decision engine.

[0065] In this embodiment, in the online application of the decision engine, the traffic data switching ratio and related configuration of different versions of the rule package are obtained in real time to select the rule package to be executed.

[0066] This embodiment configures the traffic data switching ratio corresponding to the push of the rule package through the interaction between the back-end management application and the online application in the decision engine, and switches the traffic data based on the traffic data switching ratio, ensuring that in the scenario of large traffic data transactions, the release and update of the rule package can be smoothly transitioned without affecting the stable operation of the system, thereby improving the flexibility and stability of traffic data management.

[0067] In an optional embodiment, the processing method further includes:

[0068] Pushing the rule package to the remote dictionary service through the backend application of the decision engine, so as to push the rule package to the online application of the decision engine through the remote dictionary service;

[0069] In this embodiment, after clicking on the publish rule request on the back-end application page of the decision engine, the rules are compiled into rule packages in the back-end application, pushed to redis (remote dictionary service), and then the deployment status in zookeeper (registration center) is polled.

[0070] The rule package is loaded into the memory through the online application of the decision engine, and the rule package is initially run in the memory.

[0071] In this embodiment, after receiving the push in the online application of the decision engine, the rule package is loaded and hot loaded to speed up the time consumption for the first use;

[0072] In this embodiment, a traffic data preheating and gradual increase mechanism is introduced to gradually increase the traffic data switching ratio of the new rule package in the early stage of rule release until it is completely taken over. The system's operating status and performance indicators (such as response time, throughput, error rate, etc.) during the rule switching process are monitored in real time. Once an abnormality is found, the alarm mechanism is immediately triggered to notify relevant personnel to handle it, so as to ensure a smooth switching process.

[0073] In an optional embodiment, the processing method further includes:

[0074] Grayscale verification is performed on the rule package. If the verification fails, the rule package that fails the verification is rolled back; if the verification passes, the traffic data switching ratio corresponding to the old rule package is switched to the traffic data switching ratio corresponding to the new rule package.

[0075] In this embodiment, the grayscale verification function is supported, that is, in the early stage of the new rule launch, only a small part of the traffic is directed to the new rule to evaluate its performance and stability. A fast fallback mechanism is provided. Once a problem is found in the new rule package, the traffic data switching ratio corresponding to the new rule package can be immediately switched back to the traffic data switching ratio corresponding to the old rule package to ensure business continuity.

[0076] In addition, the architecture can increase or decrease the number of online applications at will, which is managed and dynamically loaded by Zookeeper, with good flexibility and scalability; the newly released rule packages can support proportional verification, automatic switching of rules in champion challenges, and automatic offline of rule packages according to specified monitoring rules, which is convenient for business personnel to verify new rules in a timely manner and deal with inappropriate rules.

[0077] In an optional embodiment, the processing method further includes:

[0078] S21. Registering the rule package to the registration center through the online application of the decision engine;

[0079] In this embodiment, after all online applications of the decision engine are loaded and preheated, that is, the current rule package can provide services, the current rule package is registered to Zookeeper, that is, the content queried by the above polling. Using Zookeeper to register rule packages can dynamically expand the capacity of online applications without doing additional operations, and the current online rule package can be viewed on the back-end application page.

[0080] S22. The back-end management application of the decision engine determines the publishing status of the rule package according to the existence of the rule package corresponding to each online application in the registration center.

[0081] In this embodiment, after the rule package is published in the back-end application page of the decision engine, the deployment status is polled until all online applications are deployed, and then it can be considered that the current rule package has been fully published.

[0082] In an optional embodiment, step S22 includes:

[0083] The back-end application of the decision engine determines whether there is a rule package corresponding to each online application in the registration center. If so, it is determined that the rule package corresponding to the rule request has been published; if not, it is determined that the rule package corresponding to the rule request has not been published.

[0084] This embodiment integrates big data algorithms and intelligent decision-making engines, communicates through efficient data exchange protocols and interfaces, and collaborates to complete the task of proportional traffic switching, thereby realizing efficient traffic control strategies, including grayscale verification, fast rollback, champion challenge and other functions, ensuring that in high-traffic trading scenarios, the release and update of rule packages can be smoothly transitioned without affecting the stable operation of the trading system.

[0085] In an optional embodiment, there are multiple rule packages, and the processing method further includes:

[0086] Select the best rule package from multiple rule packages as the final rule package;

[0087] In this embodiment, a champion challenge function is introduced to allow multiple candidate rule packages to run in parallel, and the best rule package is automatically selected as the final rule package based on actual performance.

[0088] This embodiment effectively solves the problem of rule publishing and updating in high-traffic transaction scenarios by integrating intelligent decision engines, big data algorithms, and distributed deployment technologies, which not only improves the flexibility and stability of traffic management, but also reduces operation and maintenance costs, providing a strong guarantee for the continuous and stable operation of the business system. Specifically, the decision engine is deployed using distributed architecture and load balancing technology to support traffic processing in high-concurrency scenarios. The scalability and availability of the system in high-concurrency scenarios are ensured, the load balancing of the decision engine is achieved, and resource utilization is optimized, thereby improving the processing efficiency of traffic data; by designing a set of efficient rule package publishing processes, it is ensured that during high-traffic transactions, the release and update of rule packages can minimize the impact on the transaction system, and by supporting multiple types of rule definitions and flexible traffic allocation strategies, the needs of different business scenarios are met. Furthermore, through grayscale verification, fast rollback and other mechanisms, business continuity is ensured during the launch of new rules, and intelligent decision-making for traffic management is achieved by integrating big data algorithms and machine learning technologies.

[0089] Example 2

[0090] Corresponding to the aforementioned embodiment of a method for processing flow data, the present disclosure also provides an embodiment of a system for processing flow data.

[0091] Figure 2This is a module diagram of a flow data processing system provided in Embodiment 2 of the present disclosure. The processing system is applied to a decision engine. This embodiment builds a highly configurable intelligent decision engine to support various types of rule definitions and introduces big data algorithms, such as machine learning and statistical analysis, to perform real-time analysis on flow data and provide decision support for the decision engine. It should be noted that the decision engine is responsible for the definition, parsing and execution of rules, and provides a flexible configuration interface and powerful computing capabilities. Figure 2 As shown, the processing system includes: an acquisition module 21, a configuration module 22, and a first switching processing module 23;

[0092] An acquisition module 21, configured to acquire a rule package based on a received publishing rule request through a backend application of a decision engine;

[0093] In this embodiment, after clicking on the publish rule request on the back-end management application page of the decision engine, the rules are compiled into a rule package in the back-end management application.

[0094] In this embodiment, the rule package includes at least one of a routing rule package, a weight allocation rule package, and a condition judgment rule package.

[0095] A configuration module 22, configured to configure a flow data switching ratio corresponding to the rule package push through a back-end management application of the decision engine, and push the flow data switching ratio to an online application of the decision engine;

[0096] In this embodiment, according to the instructions of the decision engine and according to business needs, the traffic data allocation ratio between different rule packages is set, and the traffic data entering the system is allocated and scheduled to implement a flexible traffic data proportional switching mechanism. Specifically, the traffic data switching ratio of different versions of the same rule service can be adjusted and published in the back-end application page of the decision engine, which can support operations such as grayscale verification of small business traffic. Champion rules can also be configured to automatically implement the optimal rule package switching.

[0097] The first switching processing module 23 is used to perform switching processing on the flow data based on the flow data switching ratio through the online application of the decision engine.

[0098] In this embodiment, in the online application of the decision engine, the traffic data switching ratio and related configuration of different versions of the rule package are obtained in real time to select the rule package to be executed.

[0099] This embodiment configures the traffic data switching ratio corresponding to the push of the rule package through the interaction between the back-end management application and the online application in the decision engine, and switches the traffic data based on the traffic data switching ratio, ensuring that in the scenario of large traffic data transactions, the release and update of the rule package can be smoothly transitioned without affecting the stable operation of the system, thereby improving the flexibility and stability of traffic data management.

[0100] In an optional embodiment, the processing system further includes:

[0101] A push module, used to push the rule package to the remote dictionary service through the backend application of the decision engine, so as to push the rule package to the online application of the decision engine through the remote dictionary service;

[0102] In this embodiment, after clicking on the publish rule request on the backend application page of the decision engine, the rules are compiled into rule packages in the backend application, pushed to redis, and then the deployment status in zookeeper is polled.

[0103] The loading module is used to load the rule package into the memory through the online application of the decision engine and to perform the initial operation on the rule package in the memory.

[0104] In this embodiment, after receiving the push in the online application of the decision engine, the rule package is loaded and hot loaded to speed up the time consumption for the first use;

[0105] In this embodiment, a traffic data preheating and gradual increase mechanism is introduced to gradually increase the traffic data switching ratio of the new rule package in the early stage of rule release until it is completely taken over. The system's operating status and performance indicators (such as response time, throughput, error rate, etc.) during the rule switching process are monitored in real time. Once an abnormality is found, the alarm mechanism is immediately triggered to notify relevant personnel to handle it, so as to ensure a smooth switching process.

[0106] In an optional embodiment, the processing system further includes:

[0107] The verification module is used to perform grayscale verification on the rule package. If the verification fails, the fallback processing module is called; if the verification passes, the second switching processing module is called;

[0108] The rollback processing module is used to roll back the rule packages that fail the verification;

[0109] The second switching processing module is used to switch the traffic data switching ratio corresponding to the old rule package to the traffic data switching ratio corresponding to the new rule package.

[0110] In this embodiment, the grayscale verification function is supported, that is, in the early stage of the new rule launch, only a small part of the traffic is directed to the new rule to evaluate its performance and stability. A fast fallback mechanism is provided. Once a problem is found in the new rule package, the traffic data switching ratio corresponding to the new rule package can be immediately switched back to the traffic data switching ratio corresponding to the old rule package to ensure business continuity.

[0111] In addition, the architecture can increase or decrease the number of online applications at will, which is managed and dynamically loaded by Zookeeper, with good flexibility and scalability; the newly released rule packages can support proportional verification, automatic switching of rules in champion challenges, and automatic offline of rule packages according to specified monitoring rules, which is convenient for business personnel to verify new rules in a timely manner and deal with inappropriate rules.

[0112] In an optional embodiment, the processing system further includes:

[0113] A registration module, used to register the rule package to the registration center through the online application of the decision engine;

[0114] In this embodiment, after all online applications of the decision engine are loaded and preheated, that is, the current rule package can provide services, the current rule package is registered to Zookeeper, that is, the content queried by the above polling. Using Zookeeper to register rule packages can dynamically expand the capacity of online applications without doing additional operations, and the current online rule package can be viewed on the back-end application page.

[0115] The determination module is used to determine the release status of the rule package according to the existence of the rule package corresponding to each online application in the registration center through the back-end management application of the decision engine.

[0116] In this embodiment, after the rule package is published in the back-end application page of the decision engine, the deployment status is polled until all online applications are deployed, and then it can be considered that the current rule package has been fully published.

[0117] In an optional embodiment, the determining module includes:

[0118] A judgment unit, used to judge whether there is a rule package corresponding to each online application in the registration center through the back-end management application of the decision engine, and if so, call the first determination unit; if not, call the second determination unit;

[0119] A first determining unit, used to determine that the rule package corresponding to the rule publishing request has been published;

[0120] The second determining unit is configured to determine that the rule package corresponding to the rule publishing request has not been published.

[0121] This embodiment integrates big data algorithms and intelligent decision-making engines, communicates through efficient data exchange protocols and interfaces, and collaborates to complete the task of proportional traffic switching, thereby realizing efficient traffic control strategies, including grayscale verification, fast rollback, champion challenge and other functions, ensuring that in high-traffic trading scenarios, the release and update of rule packages can be smoothly transitioned without affecting the stable operation of the trading system.

[0122] In an optional embodiment, there are multiple rule packages, and the processing system further includes:

[0123] The selection module is used to select the best rule package from multiple rule packages as the final rule package.

[0124] In this embodiment, a champion challenge function is introduced to allow multiple candidate rule packages to run in parallel, and the best rule package is automatically selected as the final rule package based on actual performance.

[0125] This embodiment effectively solves the problem of rule publishing and updating in high-traffic transaction scenarios by integrating intelligent decision engines, big data algorithms, and distributed deployment technologies, which not only improves the flexibility and stability of traffic management, but also reduces operation and maintenance costs, providing a strong guarantee for the continuous and stable operation of the business system. Specifically, the decision engine is deployed using distributed architecture and load balancing technology to support traffic processing in high-concurrency scenarios. The scalability and availability of the system in high-concurrency scenarios are ensured, the load balancing of the decision engine is achieved, and resource utilization is optimized, thereby improving the processing efficiency of traffic data; by designing a set of efficient rule package publishing processes, it is ensured that during high-traffic transactions, the release and update of rule packages can minimize the impact on the transaction system, and by supporting multiple types of rule definitions and flexible traffic allocation strategies, the needs of different business scenarios are met. Furthermore, through grayscale verification, fast rollback and other mechanisms, business continuity is ensured during the launch of new rules, and intelligent decision-making for traffic management is achieved by integrating big data algorithms and machine learning technologies.

[0126] As for the system embodiment, since it basically corresponds to the method embodiment, the relevant parts can refer to the partial description of the method embodiment. The system embodiment described above is only illustrative, wherein the units described as separate components may or may not be physically separated, and the components as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the disclosed solution.

[0127] Example 3

[0128] Figure 3 This is a structural diagram of an electronic device shown in Example 3 of the present disclosure, where the electronic device includes a memory, a processor, and a computer program stored in the memory and used to run on the processor, and when the processor executes the computer program, the method for processing traffic data described in any of the above embodiments is implemented. Figure 3 The electronic device 90 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0129] like Figure 3 As shown, the electronic device 90 may be in the form of a general-purpose computing device, for example, it may be a server device. The components of the electronic device 90 may include, but are not limited to: at least one processor 91, at least one memory 92, and a bus 93 connecting different system components (including the memory 92 and the processor 91).

[0130] The bus 93 includes a data bus, an address bus, and a control bus.

[0131] The memory 92 may include a volatile memory, such as a random access memory (RAM) 921 and / or a cache memory 922 , and may further include a read only memory (ROM) 923 .

[0132] The memory 92 may also include a program tool 925 (or utility) having a set (at least one) of program modules 924, such program modules 924 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0133] The processor 91 executes various functional applications and data processing by running the computer program stored in the memory 92, such as the flow data processing method provided in any of the above embodiments.

[0134] The electronic device 90 may also communicate with one or more external devices 94 (e.g., keyboards, pointing devices, etc.). Such communication may be performed via an input / output (I / O) interface 95. Furthermore, the electronic device 90 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 96. Figure 3 As shown, the network adapter 96 communicates with other modules of the electronic device 90 via the bus 93. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with the electronic device 90, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, and data backup storage systems, etc.

[0135] It should be noted that although several units / modules or sub-units / modules of the electronic device are mentioned in the above detailed description, this division is merely exemplary and not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functions of one unit / module described above can be further divided into multiple units / modules to be embodied.

[0136] Example 4

[0137] Embodiment 4 of the present disclosure further provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the method for processing traffic data provided by any of the above embodiments is implemented.

[0138] The readable storage medium may include but is not limited to: a portable disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, an optical storage device, a magnetic storage device or any suitable combination of the above.

[0139] Example 5

[0140] Embodiment 5 of the present disclosure further provides a computer program product, including a computer program, which, when executed by a processor, implements any of the above-mentioned methods for processing traffic data.

[0141] Among them, the program code for executing the computer program product of the present disclosure can be written in any combination of one or more programming languages, and the program code can be executed completely on the user device, partially on the user device, as an independent software package, partially on the user device and partially on a remote device, or completely on the remote device.

[0142] Although the specific embodiments of the present disclosure are described above, those skilled in the art should understand that this is only an example, and the protection scope of the present disclosure is defined by the appended claims. Those skilled in the art may make various changes or modifications to these embodiments without departing from the principles and essence of the present disclosure, but these changes and modifications all fall within the protection scope of the present disclosure.

Claims

1. A method for processing flow data, characterized in that: The processing method is applied to a decision engine, and the processing method comprises: Obtaining a rule package based on a received publishing rule request through a back-end management application of the decision engine; The flow data switching ratio corresponding to the rule package push is configured through the back-end management application of the decision engine, and the flow data switching ratio is pushed to the online application of the decision engine; The flow data is switched based on the flow data switching ratio through the online application of the decision engine.

2. The method for processing flow data according to claim 1, characterized in that: The processing method also includes: Pushing the rule package to a remote dictionary service through a backend application of the decision engine, so as to push the rule package to an online application of the decision engine through the remote dictionary service; The rule package is loaded into the memory through the online application of the decision engine, and the rule package is initially run in the memory.

3. The method for processing flow data according to claim 1, characterized in that: The processing method also includes: Grayscale verification is performed on the rule package. If the verification fails, the rule package that fails the verification is rolled back; if the verification passes, the traffic data switching ratio corresponding to the old rule package is switched to the traffic data switching ratio corresponding to the new rule package.

4. The method for processing flow data according to claim 1, characterized in that: The processing method also includes: registering the rule package to a registration center through an online application of the decision engine; The post-management application of the decision engine determines the publishing status of the rule package according to the existence of the rule package corresponding to each online application in the registration center.

5. The method for processing flow data according to claim 4, characterized in that: The step of determining the publishing status of the rule package according to the existence of the rule package corresponding to each online application in the registration center through the back-end management application of the decision engine includes: The back-end application of the decision engine determines whether there is a rule package corresponding to each online application in the registration center. If so, it is determined that the rule package corresponding to the rule publishing request has been published; if not, it is determined that the rule package corresponding to the rule publishing request has not been published.

6. The method for processing flow data according to claim 1, characterized in that: The number of the rule packages is multiple, and the processing method further includes: Select the best rule package from multiple rule packages as the final rule package; and / or, The rule package includes at least one of a routing rule package, a weight allocation rule package, and a condition judgment rule package.

7. A flow data processing system, characterized in that: The processing system is applied to a decision engine, and the processing system comprises: An acquisition module, used for acquiring a rule package based on a received publishing rule request through a back-end management application of a decision engine; A configuration module, configured to configure the flow data switching ratio corresponding to the push of the rule package through the back-end application of the decision engine, and push the flow data switching ratio to the online application of the decision engine; A switching processing module is used to perform switching processing on the flow data based on the flow data switching ratio through the online application of the decision engine.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and used to run on the processor, characterized in that: When the processor executes the computer program, the method for processing flow data according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for processing flow data according to any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for processing flow data according to any one of claims 1 to 6 is implemented.

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