Data processing method, business system and storage medium

Abstract

The invention relates to a data processing method, a business system and a storage medium. The method comprises the steps that in response to an execution request sent by a target object for a target task, a first state machine cluster determines a first class matching state of the target object based on the target object and a plurality of first candidate objects; when the first type of matching state indicates that the object is successfully matched, the second state machine cluster determines a corresponding intention state based on the current operation information of the target object; and when the first type of matching state indicates that the object matching fails, the third state machine cluster determines a second type of matching state of the target object based on an object matching result of the auxiliary server, and when the second type of matching state indicates that the object matching succeeds, the third state machine cluster determines a second type of matching state of the target object. And determining a corresponding intention state based on the current operation information of the target object by using the second state machine cluster. According to the invention, the stability and disaster recovery capability of the service system are improved. The embodiment of the invention can be applied to various scenes such as cloud technology, artificial intelligence, intelligent transportation, intelligent entertainment and the like.

Description

Technical Field

[0001] The present application relates to the field of Internet communication technology, and in particular to a data processing method, a business system and a storage medium. Background Art

[0002] With the development of Internet communication technology, various Internet products emerge in an endless stream, which enrich the user experience. Related Internet products provide some tasks that require the participation of at least two objects to execute. In the related technology, for the task execution request of the object, the node cluster is often responsible for the preparation work before the task execution (such as object matching), and the preparation logic deployed on each node is the same. However, if an exception occurs during the execution of the preparation logic of a node, it will affect the task execution request processed by the node and affect the execution of related tasks by the object that sent the task execution request. Such a setting lacks stability and has poor disaster recovery capabilities. Summary of the invention

[0003] In order to solve at least one of the above-mentioned technical problems, the present application provides a data processing method, a business system and a storage medium:

[0004] According to a first aspect of the present application, a data processing method is provided, which is applied to a business system, wherein the business system includes a first state machine cluster, a second state machine cluster, and a third state machine cluster, and the method includes:

[0005] In response to an execution request for a target task sent by a target object, the first state machine cluster determines a first type of matching state of the target object based on the target object and a plurality of first candidate objects;

[0006] In the case where the first type of matching status indicates that the object is successfully matched, the second state machine cluster determines a corresponding intention state based on the current operation information of the target object, so as to send a response result including the intention state to the target object;

[0007] In the case where the first-class matching status indicates that the object matching fails, the third state machine cluster determines the second-class matching status of the target object based on the object matching result of the auxiliary server, and in the case where the second-class matching status indicates that the object matching succeeds, the second state machine cluster is used to determine the corresponding intention state based on the current operation information of the target object, so as to send a response result including the intention state to the target object;

[0008] The intention state indicates the target object's current intention to perform the target task, and the auxiliary server performs object matching based on the target object and a plurality of second candidate objects.

[0009] According to a second aspect of the present application, a business system is provided, the business system comprising a first state machine cluster, a second state machine cluster and a third state machine cluster;

[0010] The first state machine cluster is configured to determine, in response to an execution request for a target task sent by a target object, a first type of matching state of the target object based on the target object and a plurality of first candidate objects;

[0011] The second state machine cluster is used to determine the corresponding intention state based on the current operation information of the target object when the first type of matching state indicates that the object is successfully matched, so as to send a response result including the intention state to the target object;

[0012] The third state machine cluster is used to determine the second type of matching state of the target object based on the object matching result of the auxiliary server when the first type of matching state indicates that the object matching fails, and to determine the corresponding intention state based on the current operation information of the target object using the second state machine cluster when the second type of matching state indicates that the object matching succeeds, so as to send a response result including the intention state to the target object;

[0013] The intention state indicates the target object's current intention to perform the target task, and the auxiliary server performs object matching based on the target object and a plurality of second candidate objects.

[0014] According to the third aspect of the present application, an electronic device is provided, comprising at least one processor and a memory communicatively connected to the at least one processor; wherein the memory stores at least one instruction or at least one program, and the at least one instruction or at least one program is loaded and executed by the at least one processor to implement the data processing method as described in the first aspect.

[0015] According to the fourth aspect of the present application, a computer-readable storage medium is provided, in which at least one instruction or at least one program is stored, and the at least one instruction or at least one program is loaded and executed by a processor to implement the data processing method as described in the first aspect.

[0016] According to a fifth aspect of the present application, a computer program product is provided, which includes at least one instruction or at least one program, and the at least one instruction or at least one program is loaded and executed by a processor to implement the data processing method as described in the first aspect.

[0017] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application.

[0018] Implementing this application has the following beneficial effects:

[0019] In this application, for the execution request of the target object for the target task, the business system is responsible for the preparatory work before the task is executed. The business system includes three state machine clusters, which can better cope with the processing logic involved in the preparatory work through the collaboration of the three state machine clusters. The collaborative setting of the three state machine clusters provides support for the update of the processing logic (such as expansion). The processing logic in this application includes sub-logics that match two types of objects and sub-logics that capture the object's intent when the object is successfully matched, which enriches the connotation of the preparatory work and helps to improve the object usage experience. At the same time, these sub-logics are executed by different state machine clusters, which is conducive to improving the stability and disaster recovery capabilities of the business system.

[0020] Other features and aspects of the present application will become apparent from the following detailed description of exemplary embodiments with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0022] Figure 1 A schematic diagram of an application environment according to an embodiment of the present application is shown;

[0023] Figure 2 A schematic diagram showing a flow chart of a data processing method according to an embodiment of the present application;

[0024] Figure 3 A schematic diagram showing a flow chart of determining a first type of matching state of a target object based on the target object and a plurality of first candidate objects according to an embodiment of the present application;

[0025] Figure 4 A schematic diagram showing a process of obtaining a corresponding parameter set for a target object according to an embodiment of the present application;

[0026] Figure 5 A schematic diagram showing the architecture of a business system according to an embodiment of the present application is shown;

[0027] Figure 6 A schematic diagram showing a process of executing a first business logic of a first state machine cluster according to an embodiment of the present application;

[0028] Figure 7A schematic diagram showing a flow chart of executing a second business logic of a second state machine cluster according to an embodiment of the present application;

[0029] Figure 8 A schematic diagram showing a process of executing a third business logic of a third state machine cluster according to an embodiment of the present application;

[0030] Fig. 9 A schematic diagram of an electronic device according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0031] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0032] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0033] Various exemplary embodiments, features and aspects of the present application will be described in detail below with reference to the accompanying drawings. The same reference numerals in the accompanying drawings represent elements with the same or similar functions. Although various aspects of the embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless otherwise specified.

[0034] The word “exemplary” is used exclusively herein to mean “serving as an example, example, or illustration.” Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

[0035] The term "and / or" herein is only a description of the association relationship of the associated objects, indicating that there may be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the term "at least one" herein represents any combination of at least two of any one or more of a plurality of. For example, including at least one of A, B, and C can represent including any one or more elements selected from the set consisting of A, B, and C.

[0036] In addition, in order to better illustrate the present application, numerous specific details are given in the following specific embodiments. It should be understood by those skilled in the art that the present application can also be implemented without certain specific details. In some examples, methods, means, components and circuits well known to those skilled in the art are not described in detail in order to highlight the subject matter of the present application.

[0037] Before further describing the embodiments of the present application in detail, the nouns and terms involved in the embodiments of the present application are explained. The nouns and terms involved in the embodiments of the present application are subject to the following interpretations.

[0038] Finite state machine: a mathematical model that represents a finite number of states and a set of behaviors such as transitions and actions between these states.

[0039] Distributed cluster: A complex cluster consisting of different programs running on multiple nodes.

[0040] Maximum matching of bipartite graph: Given a bipartite graph G, if any two edges in the edge set {E} of a subgraph M of G do not intersect at the same node, then M is called a matching, and the matching with the largest number of edges is called the maximum matching of the bipartite graph G.

[0041] PVP: full name player versus player, player versus player.

[0042] Docker: An open source application container engine.

[0043] K8s: Full name Kubernetes, is an open source system for automatically deploying, scaling, and managing containerized applications. The system aims to provide a "platform for automatically deploying, scaling, and running application containers across host clusters." It supports a range of container tools, including Docker.

[0044] See also Figure 1 , Figure 1A schematic diagram of an application environment according to an embodiment of the present application is shown, and the application environment may include a client 10 and a server 20. The client 10 and the server 20 may be directly or indirectly connected via wired or wireless communication. The target object (such as a target user) sends an execution request for a target task to the server 20 via the client 10. The server 20 constructs a first state machine cluster, a second state machine cluster, and a third state machine cluster in the business system. The first state machine cluster determines a first type of matching state of the target object based on the target object and a plurality of first candidate objects. When the first type of matching state indicates that the object is successfully matched, the second state machine cluster determines a corresponding intention state based on the current operation information of the target object, so as to send a response result containing the intention state to the target object. When the first-class matching state indicates that the object matching fails, the third state machine cluster determines the second-class matching state of the target object based on the object matching result of the auxiliary server, and when the second-class matching state indicates that the object matching succeeds, the second state machine cluster is used to determine the corresponding intention state based on the current operation information of the target object, so as to send a response result containing the intention state to the target object; wherein the intention state indicates the intention of the target object to currently perform the target task, and the auxiliary server performs object matching based on the target object and multiple second candidate objects. It should be noted that Figure 1 Just an example.

[0045] The client 10 may be a physical device such as a smart phone, a computer (such as a desktop computer, a tablet computer, a laptop computer), an augmented reality (AR) / virtual reality (VR) device, a digital assistant, an intelligent voice interaction device (such as an intelligent speaker), an intelligent wearable device, an intelligent home appliance, a vehicle-mounted terminal, etc., or may be software running in a physical device, such as a computer program. The operating system of the client 10 may be an Android system (Android system), an iOS system (a mobile operating system developed by Apple), a Linux system (an operating system), a Microsoft Windows system (Microsoft Windows operating system), etc.

[0046] The server end 20 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms. The server can include a network communication unit, a processor, a memory, etc. The server end 20 can provide background services for the corresponding client.

[0047] In practical applications, the data processing method provided in this application can be applied to related game products, and the game types provided by the related game products can be ACT (action), adventure, RPG (Role-playing game), narrative, strategy, FPS (First-person shooting), fighting, puzzle, arcade, science fiction, open world, survival, etc. Related game products include but are not limited to mobile games and terminal games. For example, the data processing method provided in this application can be applied to the competition system of mobile games, and the competition system provides a PVP mode of play.

[0048] It should be noted that for object information, execution requests, etc. that are associated with user information, when the embodiments of the present application are applied to specific products or technologies, user permission or consent is required, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards of relevant countries and regions.

[0049] Figure 2 A flow chart of a data processing method according to an embodiment of the present application is shown as follows: Figure 2 As shown, the method includes:

[0050] S201: In response to an execution request for a target task sent by a target object, the first state machine cluster determines a first type of matching state of the target object based on the target object and a plurality of first candidate objects;

[0051] In an embodiment of the present application, the data processing method provided in the embodiment of the present application is applied to a business system. For the task execution request of the object, the business system is responsible for the preparatory work before the task execution (such as object matching, capturing the object intention, etc.). The business system includes a first state machine cluster, a second state machine cluster and a third state machine cluster, and each state machine cluster executes different preparation sub-logics. Each state machine cluster is composed of multiple finite state machines that execute the same preparation sub-logics. For the task execution request of the object, the distributed collaboration between clusters is processed, and the distributed collaboration between clusters can constitute a complete link for processing the task execution request. The three clusters constitute a distributed cluster, which is conducive to improving the overall throughput of the system and reducing the response time. For the execution request for the target task sent by the target object, the first state machine cluster executes the first class object matching sub-logic. In the process of executing the first class object matching sub-logic, the first state machine cluster matches the target object and multiple first candidate objects. If there is an object matching the target object among the multiple first candidate objects, then the first class matching state indicates that the object matching is successful. If there is no object matching the target object among the multiple first candidate objects, then the first class matching state indicates that the object matching fails.

[0052] The object may be an account, a user, etc. The target object may be a target account, a target user, etc. The relevant Internet products provide some tasks that require the participation of at least two objects to execute. The target task is a task that requires the participation of at least two objects to execute. Such tasks may be competitive tasks, cooperative tasks, etc. For competitive tasks, the object matched with the target object will serve as the opponent of the target object. For cooperative tasks, the object matched with the target object will serve as the teammate of the target object.

[0053] The target object sends an execution request for the target task, indicating that the target object expects to execute the target task. Since the target task requires the participation of at least two objects to execute, the business system's response to the execution request is intended to prepare the target object for the task execution. Based on the nature of the target task, the preparation work needs to match the target object. At the same time, considering that the intention of the target object may change, the preparation work also needs to capture the intention of the target object to avoid affecting the user experience of the target object and other objects. Taking the target object as player A as an example, player A expects to play competitive game task a, and the business system prepares for player A to play competitive game task a. The business system matches player B as an opponent for player A from multiple candidate players. If player A's intention changes, he no longer wants to play competitive game task a. The business system needs to stop preparing for player A to play competitive game task a based on this, and also needs to release the pending opponent relationship between player A and player B, otherwise it may affect player B's experience of participating in competitive game task a.

[0054] S202: When the first type of matching status indicates that the object is successfully matched, the second state machine cluster determines a corresponding intention state based on the current operation information of the target object, so as to send a response result including the intention state to the target object;

[0055] In an embodiment of the present application, when the first type of matching state indicates that the object match is successful, the second state machine cluster determines the corresponding intention state based on the current operation information of the target object to send a response result containing the intention state to the target object. Combined with the example in the aforementioned step S201, considering that the intention of the target object may change, the preparatory work also needs to capture the intention of the target object to avoid affecting the user experience of the target object and other objects. The object intention capture sub-logic is executed by the second state machine cluster. In the process of executing the object intention capture sub-logic, the second state machine cluster determines the corresponding intention state based on the current operation information of the target object, and the intention state indicates the target object's current intention to perform the target task. The current operation information can be compared with the preset rules to determine whether the target object currently still expects to perform the target task, or to determine whether the target object's intention to perform the target task has changed.

[0056] The current operation information can reflect whether the target object currently has an operation behavior, and what the operation behavior is if there is an operation behavior. The preset operation rule can record at least one preset operation, and the representational meaning of the preset operation being executed. The representational meaning of at least one preset operation being executed is that the relevant object still expects to perform the target task, and the intention of the relevant object to perform the relevant task has not changed. The preset operation can be click, browse, etc., and the preset operation can also be an empty operation. Therefore, if the current operation information reflects that the current operation behavior of the target object is to execute the preset operation, then the intention state indicates that the target object currently still expects to perform the target task, and the intention of the target object to perform the target task has not changed; if the current operation information reflects that the current operation behavior of the target object is not to execute the preset operation, or the target object currently has no operation behavior, then the intention state indicates that the target object does not currently expect to perform the target task, and the intention of the target object to perform the target task has changed.

[0057] The second state machine cluster sends the response result including the intention state to the target object. If the intention state indicates that the target object currently still expects to perform the target task, the response result including the intention state can inform the target object that the preparation work has been completed and the target object is about to perform the target task. If the intention state indicates that the target object currently does not expect to perform the target task, the response result including the intention state can inform the target object that the preparation work has been canceled and the target object does not need to perform the target task later.

[0058] S203: when the first-class matching status indicates that the object matching fails, the third state machine cluster determines the second-class matching status of the target object based on the object matching result of the auxiliary server, and when the second-class matching status indicates that the object matching succeeds, the second state machine cluster is used to determine the corresponding intention state based on the current operation information of the target object, so as to send a response result including the intention state to the target object;

[0059] In an embodiment of the present application, when the first type of matching status indicates that the object match fails, the third state machine cluster determines the second type of matching status of the target object based on the object matching result of the auxiliary server, and when the second type of matching status indicates that the object match is successful, the second state machine cluster determines the corresponding intention state based on the current operation information of the target object to send a response result including the intention state to the target object.

[0060] First, the third state machine cluster executes the second-class object matching sub-logic. In the process of executing the second-class object matching sub-logic, the third state machine cluster guides the auxiliary server to match the target object with multiple second candidate objects. If there is an object that matches the target object among the multiple second candidate objects, then the second-class matching state indicates that the object matching is successful. If there is no object that matches the target object among the multiple second candidate objects, then the second-class matching state indicates that the object matching fails. Then, when the second-class matching state indicates that the object matching is successful, the second state machine cluster executes the object intent capture sub-logic. Reference may be made to the relevant records in the aforementioned step S202, which will not be repeated here.

[0061] The second candidate object is different from the first candidate object. It is the auxiliary server that determines the matching object for the target object from the multiple second candidate objects. Compared with the first state machine cluster determining the matching object for the target object from the multiple first candidate objects, the execution of the second type of object matching sub-logic expands the object scope for finding objects that match the target object and introduces a new substantial search party (i.e., the auxiliary server). Such a second type of object matching sub-logic setting reflects the extensibility brought by the second type of object matching sub-logic, which is conducive to improving the success rate of determining matching objects for the target object, and thus is conducive to improving the user experience of the target object.

[0062] The business system breaks down the preparatory work before the execution of the task it is responsible for into multiple business functions. The setting of the three state machine clusters provides support for the realization of logical isolation and decoupling between different business functions. On this basis, if it is necessary to add, delete or replace the content in a certain business function, it is only necessary to adjust the corresponding state machine cluster without adjusting the overall adjustment of the business system, which is convenient for updating and maintaining system functions, and can also make the update and maintenance more targeted and fine-grained, which is conducive to ensuring the horizontal expansion capability of clustering. At the same time, logical isolation and decoupling can also reduce the code scale and maintenance burden deployed on the finite state machines in the state machine cluster. In the above practical application, in the above steps S201-S203, the first type of object matching sub-logic executed by the first state machine cluster can be specifically executed by any finite state machine in the first state machine cluster; the object intent capture sub-logic executed by the second state machine cluster can be specifically executed by any finite state machine in the second state machine cluster; the second type of object matching sub-logic executed by the third state machine cluster can be specifically executed by any finite state machine in the third state machine cluster.

[0063] The following is a detailed introduction to the preparatory work before the execution of the tasks that the business system is responsible for.

[0064] In one embodiment, Figure 3 As shown, the first business logic records the conversion relationship between multiple first preset business states, the multiple first preset business states include multiple process-type states and multiple terminal states, and the first state machine cluster determines the first type of matching state of the target object based on the target object and multiple first candidate objects, including:

[0065] S301: Based on the first business logic, the first state machine cluster processes the object information of the target object to obtain the to-be-matched parameters of the target object, and converts the business state;

[0066] S302: The first state machine cluster obtains a corresponding matching rule and a parameter set for the target object, and converts the service state, wherein the parameter set is composed of to-be-matched parameters corresponding to each of the plurality of first candidate objects;

[0067] S303: the first state machine cluster queries in the parameter set whether there is a parameter matching the to-be-matched parameter of the target object according to the matching rule;

[0068] S304: In the case where there are parameters that match the parameters to be matched of the target object, the first state machine cluster determines a first target state indicating successful object matching from the multiple end states for transition, and determines the first type of matching state based on the target business state;

[0069] S305: In the absence of parameters matching the to-be-matched parameters of the target object, the first state machine cluster determines a first target state indicating object matching failure from the multiple endpoint states for transition, and determines the first type of matching state based on the target business state.

[0070] The first state machine cluster can be in different first preset business states and can be converted to the next first preset business state due to the triggering of different conversion conditions. Through at least one state conversion, the first state machine cluster can determine the first type of matching state of the target object.

[0071] The first business logic can correspond to the above-mentioned first type of object matching sub-logic, and the first business logic serves as the execution basis of the first state machine cluster. Here, the main logic line involved in the execution of the first business logic by the first state machine cluster is provided, and an execution path is given with the object information of the target object being processed as the starting point and the first type of matching state of the target object being determined as the end point. Each node of the execution path corresponds to the first preset business state of the first state machine cluster, and the conversion between adjacent nodes and the conversion between adjacent business states correspond to the triggering of relevant conversion conditions and the execution of relevant steps. The first state machine cluster determines the first type of matching state of the target object based on the end state, and provides a main line logic for determining the object matching state, which is conducive to achieving the stability of determining the object matching state. At the same time, the setting of multiple process-class states is also convenient for timely determining the progress of object matching and capturing anomalies that occur during the object matching process.

[0072] Exemplarily, in state 11, the first state machine cluster processes the object information of the target object. When the object information of the target object is processed to obtain the parameters to be matched of the target object, the state 11 is converted to state 12. In state 12, the first state machine cluster performs preparatory work before object matching. When the corresponding matching rules and parameter sets are obtained for the target object, the state 12 is converted to state 13. In state 13, the first state machine cluster performs object matching, that is, in accordance with the matching rules, it is queried in the parameter set whether there are parameters that match the parameters to be matched of the target object. If the parameter set has parameters that match the parameters to be matched of the target object (that is, there is an object that matches the target object), the state 13 is converted to state 14. At the same time, based on state 14, it can be determined that the first type of matching state of the target object indicates that the object matching is successful. If the parameter set does not have parameters that match the parameters to be matched of the target object (that is, there is no object that matches the target object), the state 13 is converted to state 15. At the same time, based on state 15, it can be determined that the first type of matching state of the target object indicates that the object matching fails. Wherein. State 11, state 12 and state 13 are the above-mentioned process-type states, and state 14 and state 15 are the above-mentioned terminal states.

[0073] In addition, (1) the method may further include the following steps: in the process of executing the first business logic, in response to the cancellation operation of the target object for the target task, the first state machine cluster determines a target business state indicating object matching cancellation from the multiple endpoint states for transition, and determines a corresponding third-class matching state based on the target business state.

[0074] Compared with the logical main line provided by the aforementioned steps S301-S305, a logical branch line involved in the execution of the first business logic by the first state machine cluster is provided here, and a flexible exit mechanism is provided. In the process of the first state cluster executing the above main line logic, the cancellation operation of the target object for the target task can be regarded as a conversion condition being triggered, thereby converting the current business state to the target business state indicating the cancellation of the object match. At the same time, the third type of matching state of the target object is determined to indicate the cancellation of the object match. Compared with the situation in which the triggering of the conversion condition in the aforementioned steps S301-S305 depends on the execution of the relevant steps by the first state cluster, the triggering of the conversion condition here depends on the operational behavior of the target object, and a new type of conversion condition is constructed. Objects outside the first state cluster can affect the triggering of the conversion condition, which is more adaptable.

[0075] Following the example provided by the aforementioned steps S301-S305, when in state 11, state 12 or state 13, the first state machine cluster detects that the target object has performed a cancel operation on the target task, then the state 11, state 12 or state 13 is converted to state 16. State 16 is the above-mentioned terminal state. The cancel operation can indicate the operation behavior of closing or leaving the current page, or the operation behavior of clicking the cancel button in the current page.

[0076] 2) If Figure 4 As shown, the object information of the target object includes a timestamp when the target object sends the execution request, and the first state machine cluster obtains a corresponding parameter set for the target object, including:

[0077] S401: The first state machine cluster determines a plurality of objects to be selected from an object set, wherein the request sending time of the objects to be selected and the timestamp indicate the same preset time interval;

[0078] S402: When the total number of the plurality of objects to be selected is greater than a preset number, the first state machine cluster determines the plurality of first candidate objects that meet the preset number from the plurality of objects to be selected;

[0079] S403: The first state machine cluster obtains the parameter set based on the multiple first candidate objects.

[0080] Here, the source of the first candidate objects and the quantity requirement for the first candidate objects are introduced, so as to set a suitable object range for finding objects matching the target object, which is beneficial for taking into account the efficiency and matching degree of the subsequent determination of matching objects, and further beneficial for the experience of the target object in performing the target task.

[0081] The object to be selected is an object that is also expected to execute the target task within the preset time interval. The object information of the target object includes the timestamp when the target object sends the execution request, and the timestamp points to a preset time interval. The preset time interval can be fixed. For example, 1 minute is divided into 6 intervals of 10 seconds, namely 1-10s, 11-20s, 21-30s, 31-40s, 41-50s, and 51-60s. If the timestamp when the target object sends the execution request is 12:05:35, then the preset time interval pointed to by the timestamp is 12:05:31-12:05:40. The object whose sending time for the execution request of the target task falls between 12:05:31-12:05:40 is the object to be selected. The preset time interval can be variable. If the timestamp when the target object sends the execution request is 12:05:32, a preset time interval with a fixed duration can be constructed with the timestamp as the intermediate time. If the fixed duration of the preset time interval is 6 seconds, then this preset time interval is 12:05:29-12:05:35. The objects whose execution request for the target task is sent between 12:05:29-12:05:35 are the candidates. By filtering the candidates based on the request sending time dimension, it can be ensured that the candidates also have the intention to execute the target task like the target object.

[0082] Considering the object matching efficiency and the resource consumption of object matching, a plurality of candidates are extracted by taking a preset number as an upper limit to obtain a plurality of first candidate objects. For example, the preset number is 1000. If the total number of the plurality of candidates is greater than 1000, 1000 candidates can be randomly extracted from the plurality of candidates as 1000 first candidate objects. If the total number of the plurality of candidates is less than or equal to 1000, the plurality of candidates can be directly used as a plurality of first candidate objects.

[0083] Since the parameter set is composed of the parameters to be matched corresponding to each of the multiple first candidate objects. The process of obtaining the parameters to be matched of the first candidate object using the object information of the first candidate object is the same as the process of obtaining the parameters to be matched of the target object using the object information of the target object. Taking the object information of the target object as an example, the object information can come from the execution request, and the data in the execution request is parsed. The parsing result is checked against the blacklist. If the parsing result does not contain any content that falls into the blacklist, the parameters to be matched corresponding to the parsing result are generated according to the matching index.

[0084] like Figure 5 , 6 As shown, taking the target task as a competitive game task as an example, State1 corresponds to the first state machine cluster. Each finite state machine in the first state machine cluster converges several upstream states of the complete link of processing task execution requests into one, and the finite state machine will decide to interact with the second state machine cluster or the third state machine cluster according to the end state.

[0085] The first state machine cluster may receive an execution request (request) sent by the target object through the client, and the first state machine cluster enters the initial state (corresponding to Figure 6 In the initial state, the first state machine cluster processes the object information of the target object, which may come from the execution request. The process may include: parsing the data in the execution request. Blacklist checking the parsing result. If the parsing result does not contain any content that falls into the blacklist, then generating the matching parameters corresponding to the parsing result according to the matching index. Of course, additional rules can also be added to the process. When the object information of the target object is processed to obtain the matching parameters of the target object, the first state machine cluster enters the matching start state (corresponding to Figure 6 In the preparation state, the first state machine cluster performs preparation work before object matching. During this period, the target object's to-be-matched parameters will be put into the matching pool for polling matching, and a multi-level inverted index will be established for the to-be-matched parameters to speed up the query. Then, the first state machine cluster enters the rule selection state (corresponding to Figure 6 In the rule selection state, the first state machine cluster obtains the corresponding matching rule for the target object, and then the first state machine cluster enters the player search state (corresponding to Figure 6 In the player search state, the first state machine cluster performs object matching, that is, searches for an object matching the target object in the matching pool according to the matching rules. The first state machine cluster can repeatedly switch between the rule selection state and the player search state until any action such as timeout, cancellation of matching, and finding a matching object occurs, and jumps to the corresponding state (that is, Figure 6 match timeout, matchcancel, match okey), complete the corresponding operation. Figure 6 In the match okey state, it indicates that the first type of match state of the target object indicates that the object is successfully matched, and the first state machine cluster sends information to any finite state machine of the second state machine. Figure 6In the match timeout), it is indicated that the first type of matching state of the target object indicates that the object matching fails, and the first state machine cluster sends information to any finite state machine of the third state machine.

[0086] In one embodiment, the second business logic records the conversion relationship between multiple second preset business states, and the second state machine cluster determines the corresponding intention state based on the current operation information of the target object, which may include the following steps: 1) Based on the second business logic, the second state machine cluster sets a preset time period for the target operation for the target object and converts the business state; 2.1) When the target object is monitored to perform the target operation within the preset time period, the second state machine cluster obtains the current operation information indicating a positive intention, determines a second target state indicating to continue to perform the target task from the multiple second preset business states for conversion, and determines the intention state based on the second target state; 2.2) When the target object is not monitored to perform the target operation within the preset time period, the second state machine cluster obtains the current operation information indicating a reverse intention, determines a second target state indicating to abandon the execution of the target task from the multiple second preset business states for conversion, and determines the intention state based on the second target state.

[0087] The second state machine cluster can be converted to the next second preset business state when it is in a different second preset business state due to the triggering of different conversion conditions. Through at least one state transition, the second state machine cluster can determine the intention state of the target object. The second business logic can correspond to the above-mentioned object intention capture sub-logic, and the second business logic serves as the execution basis of the second state machine cluster. Two logical branches involved in the execution of the second business logic by the second state machine cluster are provided here, and the two logical branches correspond to two execution paths. The starting points of the two execution paths are the same, and the end points of the two execution paths are different. The starting point and the two end points correspond to the second preset business state of the second state machine cluster. The conversion from the starting point to the end point is the conversion between adjacent business states. Such a conversion corresponds to the triggering of relevant conversion conditions and the execution of relevant steps. The second state machine cluster determines the intention state of the target object based on the end point business state, and provides an execution logic for capturing different intentions of the object, which is conducive to achieving the effectiveness and adaptability of determining the intention state of the object.

[0088] Exemplarily, in state 21, the second state machine cluster performs preparatory work before operation monitoring. When a preset time period for the target operation is set for the target object, state 21 is converted to state 22. In state 22, the second state machine cluster performs operation monitoring, that is, monitors whether the target object performs the target operation within the preset time period. If the target object is monitored to have performed the target operation, state 22 is converted to state 23. At the same time, based on state 23, it can be determined that the intention state of the target object indicates that it is currently expected to perform the target task. If the target object is not monitored to have performed the target operation, state 22 is converted to state 24. At the same time, based on state 24, it can be determined that the intention state of the target object indicates that it is currently not expected to perform the target task. Among them, the target operation is an operation with a positive intention to perform the target task.

[0089] In one embodiment, the third business logic records the conversion relationship between multiple third preset business states, the second candidate object is an artificial intelligence (AI) object, and the third state machine cluster determines the second type of matching state of the target object based on the object matching result of the auxiliary server, which may include the following steps: 1) based on the third business logic, the third state machine cluster sends the object information of the target object to the auxiliary server and converts the business state; 2.1) when the object matching result indicates a successful match, the third state machine cluster determines a third target state indicating a successful object match from the multiple third preset business states for conversion, and determines the second type of matching state based on the target business state; 2.2) when the object matching result indicates a failed match, the third state machine cluster determines a third target state indicating a successful object match from the multiple third preset business states for conversion, and determines the second type of matching state based on the target business state.

[0090] The third state machine cluster can be converted to the next third preset business state due to the triggering of different conversion conditions when it is in different third preset business states. Through at least one state conversion, the third state machine cluster can determine the second type of matching state of the target object. The third business logic can correspond to the above-mentioned second type of object matching sub-logic, and the third business logic serves as the execution basis of the third state machine cluster. Two logical branches involved in the execution of the third business logic by the third state machine cluster are provided here, and the two logical branches correspond to two execution paths. The starting points of the two execution paths are the same, and the end points of the two execution paths are different. The starting point and the two end points correspond to the third preset business state of the third state machine cluster. The conversion from the starting point to the end point is the conversion between adjacent business states. Such a conversion corresponds to the triggering of relevant conversion conditions and the execution of relevant steps. The third state machine cluster determines the second type of matching state of the target object based on the end point business state, and provides a bottom-up logic for determining the object matching state, which is conducive to improving the success rate of determining matching objects for the target object.

[0091] Exemplarily, in state 31, the third state machine cluster interacts with the auxiliary server. When sending the object information of the target object to the auxiliary server, the state 31 is converted to state 32. In state 32, the third state machine cluster waits to receive the object matching result returned by the auxiliary server. If the object matching result indicates a successful match, the state 32 is converted to state 33. At the same time, based on state 33, it can be determined that the second-class matching state of the target object indicates a successful object match. If the object matching result indicates a failed match, the state 32 is converted to state 34. At the same time, based on state 34, it can be determined that the second-class matching state of the target object indicates a failed object match.

[0092] like Figure 5 , 7 As shown in , 8, taking the target task as a competitive game task as an example, State2 corresponds to the second state machine cluster. When the information reaches the second state machine cluster, it means that the match has been successful, and it is necessary to wait for the target object to confirm that it is ready (corresponding to the above monitoring of whether the target object performs the target operation within the preset time period) to enter the game server, and generate a matching result (corresponding to the above response result) and return it to the client. This is the last stage of the entire link. The matching result can be referred to in the following code example:

[0093]

[0094] State2 corresponds to the third state machine cluster. The third state machine cluster mainly completes the AI ​​allocation process after the match timeout. The third state machine cluster can receive the information containing the match timeout status sent by the third state machine cluster, and then send it to the auxiliary server (corresponding to Figure 8The AI ​​Server in the middle requests the AI ​​object with the specified parameters to participate in the game, and the third state machine cluster enters the waiting state (corresponding to Figure 8 If insufficient resources cause AI object allocation to fail, the third state machine cluster enters the allocation failure state (corresponding to Figure 8 If the AI ​​object is successfully allocated, the third state machine cluster enters the allocation success state (corresponding to Figure 8 In the apply success), it is indicated that the second type of matching status of the target object indicates that the object is matched successfully, and the allocation success status and other necessary information are packaged and sent to the second state machine cluster.

[0095] The competitive game task can be a qualifying match in a mobile game's event system. There is a high throughput requirement for qualifying match matching. It is necessary to follow the matching rules to find a suitable opponent within a limited time, and at the same time, to find an opponent based on the maximum matching of the bipartite graph as much as possible. The matching rules are complex and diverse, and have the characteristics of changing over time. There are many boundary conditions and branch judgments, which can easily cause serious logical coupling. In the embodiment of the present application, the business system uses a three-cluster architecture to form a complete link for processing task execution requests, which is conducive to coping with the above-mentioned needs and problems, and can effectively adapt to the continuous iteration of game versions and the addition of special event mechanisms. Because the continuous iteration of game versions and the addition of special event mechanisms can easily lead to a large number of branch judgments and patch codes, various modules will expose interfaces to each other to form a meshed calling relationship, and non-linear calling relationships will increase system complexity and drag down the overall performance of the system.

[0096] In one embodiment, the cluster group is composed of the first state machine cluster, the second state machine cluster and the third state machine cluster, and the business system also includes a resource usage monitoring module, which is used to monitor the resource usage of the cluster group. The method may also include the following steps: when the resource usage monitoring module monitors that the current usage of the target cluster is greater than the preset resource upper limit, a risk control reminder is sent to the remaining clusters in the cluster group except the target cluster, and / or resource expansion guidance information is generated for the cluster group; wherein, the target cluster is any cluster in the cluster group.

[0097] The first state machine cluster, the second state machine cluster, and the third state machine cluster all need computing resources to execute their respective preparation sub-logics. Due to the task execution request for the object, the clusters are processed by distributed collaboration to form a complete link for processing the task execution request. From the link dimension, the resource usage of a cluster can be used as a reference for the resource usage of other clusters. In the embodiment of the present application, the business system introduces a resource usage monitoring module, which can monitor the resource usage of each of the three clusters. If the current usage resources of a cluster are greater than the preset resource upper limit, other clusters can be reminded of risk control. The risk control reminder can guide the relevant clusters to take defensive measures such as current limiting, or guide the relevant clusters to expand resources. If the current usage resources of a cluster are greater than the preset resource upper limit, resource expansion guidance information for the three clusters as a whole can also be generated, and the expansion guidance information can guide the business system to expand resources for the three clusters. Resource usage monitoring of the three clusters, and the resulting active or passive adjustment of resource dimensions, is conducive to the smooth development of the preparatory work before the execution of the task that the business system is responsible for.

[0098] In actual applications, 1) the resource expansion guided by the risk control reminder can be local and temporary. The resource expansion guided by the expansion guidance information can be global and phased. 2) The resources used by the three clusters can be cloud resources managed by K8s. The monitoring status of the resource usage monitoring module can guide each cluster to perform adaptive resource scheduling. Of course, clusters can also exchange information about resource usage without using the resource usage monitoring module. In this way, when the pressure of a certain link suddenly changes, the upstream and downstream clusters can take defensive measures such as current limiting through information exchange, and at the same time apply to K8s for more resources to deploy pods, and pods can correspond to finite state machines.

[0099] It can be seen from the technical solution provided by the above embodiment of the present application that, for the execution request of the target object for the target task, the business system is responsible for the preparatory work before the task is executed. The business system includes three state machine clusters, which can better cope with the processing logic involved in the preparatory work through the collaboration of the three state machine clusters. The collaborative setting of the three state machine clusters provides support for the update (such as expansion) of the processing logic. In the embodiment of the present application, the processing logic includes sub-logics that match two types of objects and sub-logics that capture the object's intention when the object is successfully matched, which enriches the connotation of the preparatory work and helps to improve the object's usage experience. At the same time, these sub-logics are executed by different state machine clusters, which is conducive to improving the stability and disaster recovery capability of the business system. Even if an exception occurs, the exception will be limited to the finite state machine of a certain state machine cluster, and will not cause the services of the business system to be fully unavailable.

[0100] The present application also provides a business system, such as Figure 5As shown, the business system includes a first state machine cluster, a second state machine cluster and a third state machine cluster;

[0101] The first state machine cluster is configured to determine, in response to an execution request for a target task sent by a target object, a first type of matching state of the target object based on the target object and a plurality of first candidate objects;

[0102] The second state machine cluster is used to determine the corresponding intention state based on the current operation information of the target object when the first type of matching state indicates that the object is successfully matched, so as to send a response result including the intention state to the target object;

[0103] The third state machine cluster is used to determine the second type of matching state of the target object based on the object matching result of the auxiliary server when the first type of matching state indicates that the object matching fails, and to determine the corresponding intention state based on the current operation information of the target object using the second state machine cluster when the second type of matching state indicates that the object matching succeeds, so as to send a response result including the intention state to the target object;

[0104] The intention state indicates the target object's current intention to perform the target task, and the auxiliary server performs object matching based on the target object and a plurality of second candidate objects.

[0105] In one embodiment, the cluster group is composed of the first state machine cluster, the second state machine cluster and the third state machine cluster. Information is transmitted between two clusters in the cluster group by remote procedure call (RPC). The transmitted information adopts a data exchange format (such as proto). Please refer to the following code example:

[0106]

[0107] Since the task execution request for the object is processed by distributed collaboration between clusters to form a complete link for processing the task execution request. The transmitted information carries fields, which can be used to indicate the progress of the task execution request, such as the current state and the next action in the expected link. The state here can adopt the preset business state involved in the execution of the respective preparation sub-logic by the first state machine cluster, the second state machine cluster, and the third state machine cluster as described above.

[0108] It should be noted that the system in the system embodiment here is based on the same inventive concept as the above method embodiment. The first state machine cluster, the second state machine cluster and the third state machine cluster in the system embodiment here execute corresponding preparation sub-logic according to the above method embodiment.

[0109] In some embodiments, the functions or modules included in the device provided in the embodiments of the present application can be used to execute the method described in the above method embodiments. The specific implementation can refer to the description of the above method embodiments. For the sake of brevity, it will not be repeated here.

[0110] The embodiment of the present application also provides a computer-readable storage medium, wherein at least one instruction or at least one program is stored in the computer-readable storage medium, and the at least one instruction or at least one program is loaded and executed by a processor to implement the above method. The computer-readable storage medium may be a non-volatile computer-readable storage medium.

[0111] An embodiment of the present application also provides an electronic device, which includes at least one processor and a memory communicatively connected to the at least one processor; wherein the memory stores at least one instruction or at least one program, and the at least one instruction or at least one program is loaded and executed by the at least one processor to implement the above method.

[0112] The electronic device may be provided as a terminal, a server, or a device in other forms.

[0113] Fig. 9 1 shows a block diagram of an electronic device according to an embodiment of the present application. For example, the electronic device 1900 can be provided as a server. Fig. 9 , the electronic device 1900 includes a processing component 1922, which further includes one or more processors, and a memory resource represented by a memory 1932 for storing instructions executable by the processing component 1922, such as an application. The application stored in the memory 1932 may include one or more modules, each corresponding to a set of instructions. In addition, the processing component 1922 is configured to execute instructions to perform the above method.

[0114] The electronic device 1900 may also include a power supply component 1926 configured to perform power management of the electronic device 1900, a wired or wireless network interface 1950 configured to connect the electronic device 1900 to a network, and an input / output (I / O) interface 1958. The electronic device 1900 may operate based on an operating system stored in the memory 1932, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™ or the like.

[0115] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 1932 including computer program instructions, which can be executed by the processing component 1922 of the electronic device 1900 to perform the above method.

[0116] The present application may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present application.

[0117] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples of computer-readable storage media (a non-exhaustive list) include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium is not to be interpreted as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0118] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0119] The computer program instructions for performing the operation of the present application can be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C+, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions can be executed completely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer, partially on the remote computer, or completely on the remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect through the Internet). In some embodiments, by using the state information of computer-readable program instructions to personalize electronic circuits, such as programmable logic circuits, field programmable gate arrays (FPGAs) or programmable logic arrays (PLAs), the electronic circuits can execute computer-readable program instructions, thereby realizing various aspects of the present application.

[0120] Various aspects of the present application are described herein with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer-readable program instructions.

[0121] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine, so that when these instructions are executed by the processor of the computer or other programmable data processing device, a device that implements the functions / actions specified in one or more boxes in the flowchart and / or block diagram is generated. These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions cause the computer, programmable data processing device, and / or other equipment to work in a specific manner, so that the computer-readable medium storing the instructions includes a manufactured product, which includes instructions for implementing various aspects of the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0122] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operating steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0123] The flow chart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to multiple embodiments of the present application. In this regard, each square frame in the flow chart or block diagram can represent a part of a module, program segment or instruction, and a part of the above-mentioned module, program segment or instruction includes one or more executable instructions for realizing the specified logical function. In some alternative implementations, the standard function in the square frame can also occur in a sequence different from the standard in the accompanying drawings. For example, two continuous square frames can actually be executed substantially in parallel, and they can sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each square frame in the block diagram and / or flow chart, and the combination of the square frames in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs the specified function or action, or can be implemented with a combination of special hardware and computer instructions.

[0124] The embodiments of the present application have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or technical improvements in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A data processing method, characterized in that: Applied to a business system, the business system includes a first state machine cluster, a second state machine cluster and a third state machine cluster, and the method includes: In response to an execution request for a target task sent by a target object, the first state machine cluster determines a first type of matching state of the target object based on the target object and a plurality of first candidate objects; In the case where the first type of matching status indicates that the object is successfully matched, the second state machine cluster determines a corresponding intention state based on the current operation information of the target object, so as to send a response result including the intention state to the target object; In the case where the first-class matching status indicates that the object matching fails, the third state machine cluster determines the second-class matching status of the target object based on the object matching result of the auxiliary server, and in the case where the second-class matching status indicates that the object matching succeeds, the second state machine cluster is used to determine the corresponding intention state based on the current operation information of the target object, so as to send a response result including the intention state to the target object; The intention state indicates the target object's current intention to perform the target task, and the auxiliary server performs object matching based on the target object and a plurality of second candidate objects.

2. The method according to claim 1, characterized in that The first business logic records the conversion relationship between a plurality of first preset business states, wherein the plurality of first preset business states include a plurality of process-type states and a plurality of terminal states, and the first state machine cluster determines a first type of matching state of the target object based on the target object and a plurality of first candidate objects, including: Based on the first business logic, the first state machine cluster processes the object information of the target object to obtain the to-be-matched parameters of the target object, and converts the business state; The first state machine cluster obtains a corresponding matching rule and a parameter set for the target object and converts the service state, wherein the parameter set is composed of to-be-matched parameters corresponding to each of the plurality of first candidate objects; The first state machine cluster queries, in accordance with the matching rule, whether there is a parameter in the parameter set that matches the to-be-matched parameter of the target object; In the case where there are parameters that match the parameters to be matched of the target object, the first state machine cluster determines a first target state indicating successful object matching from the multiple endpoint states to transition to, and determines the first type of matching state based on the target business state; In the absence of parameters matching the to-be-matched parameters of the target object, the first state machine cluster determines a first target state indicating object matching failure from the multiple endpoint states for transition, and determines the first type of matching state based on the target business state.

3. The method according to claim 2, characterized in that The method further comprises: During the execution of the first business logic, in response to the cancellation operation of the target object on the target task, the first state machine cluster determines a target business state indicating object matching cancellation from the multiple endpoint states for transition, and determines a corresponding third-class matching state based on the target business state.

4. The method according to claim 2, characterized in that: The object information of the target object includes a timestamp of when the target object sends the execution request, and the first state machine cluster obtains a corresponding parameter set for the target object, including: The first state machine cluster determines a plurality of objects to be selected from the object set, wherein the request sending time of the objects to be selected and the timestamp indicate the same preset time interval; In a case where the total number of the plurality of objects to be selected is greater than a preset number, the first state machine cluster determines the plurality of first candidate objects that meet the preset number from the plurality of objects to be selected; The first state machine cluster obtains the parameter set based on the plurality of first candidate objects.

5. The method according to claim 1 or 2, characterized in that: The second business logic records the conversion relationship between multiple second preset business states, and the second state machine cluster determines the corresponding intention state based on the current operation information of the target object, including: Based on the second business logic, the second state machine cluster sets a preset time period for the target operation for the target object and converts the business state; In the case where the target object is monitored to perform the target operation within the preset time period, the second state machine cluster obtains the current operation information indicating a positive intention, determines a second target state indicating continued execution of the target task from the plurality of second preset business states for transition, and determines the intention state based on the second target state; When the target object is not monitored to perform the target operation within the preset time period, the second state machine cluster obtains the current operation information indicating a reverse intention, determines a second target state indicating abandonment of the target task from the multiple second preset business states for transition, and determines the intention state based on the second target state.

6. The method according to claim 1 or 2, characterized in that: The third business logic records the conversion relationship between multiple third preset business states, the second candidate object is an artificial intelligence object, and the third state machine cluster determines the second type of matching state of the target object based on the object matching result of the auxiliary server, including: Based on the third business logic, the third state machine cluster sends the object information of the target object to the auxiliary server and converts the business state; In the case where the object matching result indicates a successful match, the third state machine cluster determines a third target state indicating a successful object match from the plurality of third preset business states for transition, and determines the second type of matching state based on the target business state; When the object matching result indicates a matching failure, the third state machine cluster determines a third target state indicating a successful object matching from the plurality of third preset business states for transition, and determines the second type of matching state based on the target business state.

7. The method according to claim 1, characterized in that The cluster group is composed of the first state machine cluster, the second state machine cluster and the third state machine cluster. The business system also includes a resource usage monitoring module, which is used to monitor the resource usage of the cluster group. The method also includes: When the resource usage monitoring module detects that the current usage resources of the target cluster are greater than the preset resource upper limit, a risk control reminder is sent to the remaining clusters in the cluster group except the target cluster, and / or resource expansion guidance information is generated for the cluster group; The target cluster is any cluster in the cluster group.

8. A business system, characterized in that: The business system includes a first state machine cluster, a second state machine cluster and a third state machine cluster; The first state machine cluster is configured to determine, in response to an execution request for a target task sent by a target object, a first type of matching state of the target object based on the target object and a plurality of first candidate objects; The second state machine cluster is used to determine the corresponding intention state based on the current operation information of the target object when the first type of matching state indicates that the object is successfully matched, so as to send a response result including the intention state to the target object; The third state machine cluster is used to determine the second type of matching state of the target object based on the object matching result of the auxiliary server when the first type of matching state indicates that the object matching fails, and to determine the corresponding intention state based on the current operation information of the target object using the second state machine cluster when the second type of matching state indicates that the object matching succeeds, so as to send a response result including the intention state to the target object; The intention state indicates the target object's current intention to perform the target task, and the auxiliary server performs object matching based on the target object and a plurality of second candidate objects.

9. The system according to claim 8, characterized in that The cluster group is composed of the first state machine cluster, the second state machine cluster and the third state machine cluster. Information is transmitted between any two clusters in the cluster group by remote procedure calls, and the transmitted information is in a data exchange format.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one instruction or at least one program, and the at least one instruction or at least one program is loaded and executed by a processor to implement the data processing method according to any one of claims 1 to 7.

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