Data processing method and engine based on discrete parallel model

Abstract

The present application provides a data processing method based on a discrete parallel model, which is applied to a data processing engine, wherein a request response module pulls a business flow request, obtains business flow parameter information based on the business flow request, and sends it to a flow drive module; the flow drive module receives the business flow parameter information, generates call operation information based on the business flow parameter information, and sends it to a process network module; the process network module receives the call operation information, performs the corresponding operation based on the call operation information, generates operation result information, and sends it to the request response module; the request response module receives the operation result information and sends it to the business party. Based on the colored Petri net model, a data processing engine for a content review process is provided, which can support the dynamic flow of multiple versions of multiple businesses, provide support for business parties to understand and intervene in the flow path of business content, and use a reachability algorithm to predict the risk of stagnation and dead loops in the process network.

Description

Technical Field

[0001] The present application relates to the field of process network orchestration technology, and more particularly to a data processing method based on a discrete parallel model. The present application also relates to a data processing engine based on a discrete parallel model, a computing device, and a computer-readable storage medium. Background Art

[0002] With the development of the internet, users are increasingly sharing information and content online. To enhance user experience and create a positive sharing environment, online platforms are developing review systems to filter out inappropriate content and showcase high-quality content. During the review process, due to the diverse nature of content, the review rules for each piece of content vary. The collaborative relationship between manual and machine review also varies depending on the content. In complex and diverse review scenarios, it's difficult to orchestrate the entire process in a universal manner. Therefore, providing a universal process network for content review systems is a pressing issue. Summary of the Invention

[0003] In view of this, embodiments of the present application provide a data processing method based on a discrete parallel model. This application also relates to a data processing engine based on a discrete parallel model, a computing device, and a computer-readable storage medium to address the existing problems of building multiple audit systems to handle different business contents, resulting in wasted resources, low reuse, and high development costs.

[0004] According to a first aspect of an embodiment of the present application, a data processing method is provided, which is applied to a data processing engine. The engine includes: a request response module, a flow driving module, and a process network module, wherein:

[0005] The request response module pulls the business flow request, obtains business flow parameter information according to the business flow request, and sends the business flow parameter information to the flow driving module;

[0006] The flow driving module receives the business flow parameter information, generates call operation information according to the business flow parameter information, and sends the call operation information to the process network module;

[0007] The process network module receives the call operation information, performs a corresponding operation according to the call operation information and generates operation result information, and sends the operation result information to the request response module;

[0008] The request response module receives the operation result information and sends it to the business party.

[0009] According to a second aspect of an embodiment of the present application, a data processing engine is provided, the engine comprising: a request response module, a flow driving module, and a process network module, wherein:

[0010] The request response module is configured to pull the business flow request, obtain business flow parameter information according to the business flow request, send the business flow parameter information to the flow driving module, receive the operation result information, and send it to the business party;

[0011] The flow driving module is configured to receive the business flow parameter information, generate call operation information based on the business flow parameter information, and send the call operation information to the process network module; the process network module is configured to receive the call operation information, perform corresponding operations based on the call operation information and generate operation result information, and send the operation result information to the request response module.

[0012] According to a third aspect of an embodiment of the present application, a computing device is provided, comprising a memory, a processor, and computer instructions stored in the memory and executable on the processor, wherein the processor implements the steps of the data processing method based on a discrete parallel model when executing the computer instructions.

[0013] According to a fourth aspect of an embodiment of the present application, a computer-readable storage medium is provided, which stores computer instructions. When the computer instructions are executed by a processor, the steps of the data processing method based on the discrete parallel model are implemented.

[0014] The data processing method based on the discrete parallel model provided in the present application is applied to a data processing engine, and the engine includes: a request response module, a flow driving module, and a process network module, wherein the request response module pulls the business flow request, obtains the business flow parameter information according to the business flow request, and sends the business flow parameter information to the flow driving module; the flow driving module receives the business flow parameter information, generates call operation information according to the business flow parameter information, and sends the call operation information to the process network module; the process network module receives the call operation information, performs the corresponding operation according to the call operation information and generates operation result information, and sends the operation result information to the request response module; the request response module receives the operation result information and sends it to the business party. Based on the discrete parallel model, a flow engine for a content review process is provided. The review flow engine supports the dynamic flow of multiple businesses in multiple versions, and by analyzing the reachability of each state, it can predict the risk of stagnation and dead loop in the process network. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1This is a flow chart of a data processing method based on a discrete parallel model provided by an embodiment of the present application;

[0016] Figure 2 This is a processing flow chart of a data processing method based on a discrete parallel model for processing a request to update video service attributes, provided in one embodiment of the present application;

[0017] Figure 3 This is a structural diagram of a data processing engine based on a discrete parallel model provided by an embodiment of the present application;

[0018] Figure 4 This is a structural block diagram of a computing device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0019] The following description sets forth many specific details to facilitate a thorough understanding of the present application. However, the present application can be implemented in many other ways than those described herein, and those skilled in the art can make similar generalizations without violating the scope of the present application. Therefore, the present application is not limited to the specific implementations disclosed below.

[0020] The terms used in one or more embodiments of the present application are for the purpose of describing specific embodiments only and are not intended to limit one or more embodiments of the present application. The singular forms "a", "the" and "the" used in one or more embodiments of the present application and the appended claims are also intended to include plural forms, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in one or more embodiments of the present application refers to and includes any or all possible combinations of one or more associated listed items.

[0021] It should be understood that although the terms first, second, etc. may be used to describe various information in one or more embodiments of the present application, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from each other. For example, without departing from the scope of one or more embodiments of the present application, the first may also be referred to as the second, and similarly, the second may also be referred to as the first. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0022] First, the terms involved in one or more embodiments of the present application are explained.

[0023] Discrete parallel model: A system model suitable for describing concurrent phenomena. It can be represented by a mesh-like graph, such as a Petri net. State elements and change elements in the net are represented by nodes and transitions, respectively. Each node represents a resource, and a transition represents a change that causes the resource to flow. Flow relationships describe the direction of resource flow. Transitions can only have direct flow relationships with nodes, and a Petri net can only have one isolated node or transition. Individual resource attributes specified on node, transition, and flow relationships are called feature tokens.

[0024] Colored Petri net: Based on the Petri net, resource categories are distinguished. Individuals of the same type of resources are dyed with the same color, and individuals of different types are dyed with different colors.

[0025] At present, there are more and more information and content products that encourage users to actively share. In order to improve user sharing and usage experience, the platform will formulate review rules, filter out bad content, and display high-quality content. Different types of review processes are different. When and how to process which part of the content, serial and parallel execution and other complex and diverse flow scenarios make it difficult to apply a fixed process control method. At present, the commonly used solution is to build a general process flow system based on finite state machine theory. The system pre-arranges a static state process network, which only displays the state change path. The content under review maintains a global status field to describe the latest flow status. The conditional judgment and change details required for each state flow are still implemented in the code.

[0026] Process flow systems implemented based on finite state machine models only meet the needs of simple review status processes. However, complex processes involving non-state flow factors incur high custom development costs. Furthermore, content only has a single global state field, which cannot describe non-state local changes to a piece of content or concurrent scenarios. In a pre-programmed complex state machine process network, the lack of flow information for non-state factors makes it impossible to predict process stalls and infinite loops, and it is difficult to analyze the reachability of each state.

[0027] Based on this, the present invention provides a content review process flow engine and a reachability algorithm based on a colored Petri net model. Functionally, this pre-programmed process network can reduce stagnation and dead loop risks, drive the dynamic flow of content, and support business parties in understanding and intervening in content flow paths.

[0028] In this application, a data processing method based on a discrete parallel model is provided. This application also involves a data processing engine based on a discrete parallel model, a computing device, and a computer-readable storage medium, which are described in detail one by one in the following embodiments.

[0029] Figure 1A flowchart of a data processing method based on a discrete parallel model according to an embodiment of the present application is shown. The method is applied to a data processing engine, which includes: a request response module, a flow driving module, and a process network module. The method specifically includes the following steps:

[0030] Step 102: The request response module pulls the business flow request, obtains business flow parameter information according to the business flow request, and sends the business flow parameter information to the flow driving module.

[0031] Among them, the request response module can be understood as a module that processes business flow requests. It mainly receives external requests from business parties and humans, obtains business flow parameters, calls the flow driver module, packages the response body and returns it. Business flow requests are divided into review, update content attributes without flow, trigger flow, and stop flow according to their functions. The request response module obtains business flow parameter information, such as business number, process network version number, content business unique number, request function information and other fields, and passes it to the flow driver module to obtain the operation result and return the response. Business flow parameters also include all pre-agreed attributes. When repeated review or trigger flow request parameters, some attributes may be missing.

[0032] In practice, when the request-response module receives notifications from the flow driver module, such as callbacks or successful flow, it sends a real-time API to the business side, while the latter asynchronously sends a flow event message to the queue, allowing the business side to understand the content flow status. The message fields include: the content's unique business ID, the process network version number, the nodes and flow status before and after the flow, the enabling conditions before and after the flow, the triggering transition information, and the triggering time.

[0033] In a specific embodiment of the present application, the request response module pulls a request to trigger video service flow transfer. Based on the request, it obtains the video service flow transfer parameter information: "Review video service number 01, process network version number V1.0, video service unique number 001, request function: trigger flow transfer." This video service flow transfer parameter information is sent to the flow drive module for subsequent processing operations.

[0034] To ensure the processing stability of the process engine, you can set up a request queue interface (SDK interface) to obtain business flow requests in the message queue.

[0035] Optionally, the data processing engine further includes a request queue interface, wherein the request queue interface is connected to a service request queue;

[0036] The request response module receives a service transfer request, including:

[0037] The request response module pulls the service flow request in the service request queue through the request queue interface.

[0038] In a specific embodiment of the present application, following the above example, the request response module pulls the business flow requests in the business request queue one by one through the SDK interface, thereby ensuring the normal operation of each business flow request.

[0039] Step 104: The flow driving module receives the business flow parameter information, generates calling operation information according to the business flow parameter information, and sends the calling operation information to the process network module.

[0040] The flow driver module can be understood as the module that drives the operation of process nodes in the process network. After receiving the business flow parameter information sent by the request response module, the flow driver module generates different call operation information based on the requested function type and sends it to the process network module. The call operation information can be understood as a message that calls the process network module to perform the corresponding operation.

[0041] In actual applications, the flow driver module will generate different call operation information according to the request function in the business flow parameter information and send it to the process network module, thereby calling the process network module to perform the corresponding operation.

[0042] In a specific embodiment of the present application, following the above example, after the flow driving module receives the business flow parameter information sent by the request response module, it will generate trigger flow operation information based on the business flow parameter information, and send the trigger flow operation information to the process network module for subsequent processing operations.

[0043] The flow driving module generates call operation information based on the request function information and corresponding business attributes contained in the business flow parameter information. For example, the call operation information can be information containing other fields such as "calling the process network module to execute the trigger flow operation".

[0044] Specifically, the flow driving module generates the calling operation information according to the business flow parameter information, including:

[0045] The flow driving module obtains the request function information in the business flow parameter information, obtains the corresponding business attribute information according to the request function information, and generates the call operation information according to the request function information and the business attribute information, wherein the request function information includes at least one of the following: review request information, update business attribute request information, trigger flow request information, and stop flow request information.

[0046] The request function information is the request function field in the business flow parameter information, and the business attribute information is the business number, process network version number, unique number of content business and other fields in the business flow parameter information.

[0047] In a specific embodiment of the present application, following the above example, after the flow driving module receives the business flow parameter information sent by the request response module, it first obtains the request function information "trigger flow" and the business attribute information "review video business number 01, process network version number V1.0, video business unique number 001", thereby generating a call operation information "call process network module to execute video 001 review business 01 trigger flow operation".

[0048] Step 106: The process network module receives the call operation information, performs a corresponding operation according to the call operation information and generates operation result information, and sends the operation result information to the request response module.

[0049] The process network module can be understood as the module that executes business flow requests. It can locate the process network corresponding to the target business and use it to execute the target business flow request. Operation result information can be understood as the result information generated after the process network module executes the corresponding operation.

[0050] In actual applications, after the process network module receives the call operation information sent by the flow driving module, it will perform the corresponding operation according to the call operation information. For example, when the call operation is to stop the flow, the process network module will immediately stop the process node that is flowing in the current process network, and generate the operation result information that the current process node has stopped flowing, and send the operation result information to the request response module.

[0051] In a specific embodiment of the present application, following the above example, the process network module receives the call operation information "Call the process network module to execute the video 001 review business 01 to trigger the flow operation", and determines the corresponding process network and triggers the flow node based on the call operation information.

[0052] In practical applications, the process network module includes a rule submodule and a static process network submodule, and corresponding operations are performed according to the rule submodule and the static process network submodule.

[0053] Specifically, the process network module performs corresponding operations according to the call operation information and generates operation result information, including:

[0054] Determining a target process network from the static process network submodule according to the calling operation information;

[0055] Determine operation rule information from the rule submodule according to the target process network, process the business flow request according to the operation rule information, and generate operation result information.

[0056] The operation rule information may be understood as rule information for processing a service transfer request, for example, rule information composed of one or more tokens.

[0057] The static process network submodule can be understood as a module that determines the process network corresponding to the target business. After the static process network submodule receives the business attribute information in the call operation information, it will load the pre-arranged process network into the memory to form a netGraph object. A netGraph object contains: a version object, a flow map, a transition map, a global direction map (allDirectionsmap), a synchronization direction array (syncDirections), a feature token map (tokens map), and a color map (colors map). Among them, a version object contains the specified version attributes and the starting node; a flows map contains the basic business attributes, binding relationship attributes, and direct directed line numbers of the node; a transitions map contains the basic business attributes, binding relationship attributes, and direct directed line numbers of the transition; an allDirections map contains all directed line attributes; a syncDirections array contains the directed line number paths that can be executed concurrently; a tokens map contains the feature token attributes; and a colors map contains the color attributes of the feature token.

[0058] The rule submodule can be understood as a module that calculates flow conditions and flow transitions on a feature-based process network. Features include content attributes. Each content has a basic attribute and multiple flow attributes. The former includes: unique number, business number, unique number under the business, other flow characteristics, etc. The latter includes: unique number of basic attribute of content, node version number, version number, flow status, and flow status is divided into: stop flow, serial flow, parallel flow. If the content flows normally, the directed line where the latest node is located is in syncDirections, then the flow status is "parallel flow", and in other cases it is "serial flow". For each content, the input parameter of the rule module is a rule detail (ruleDetail) consisting of several feature token numbers and a feature token tokens map. The calculation result of the flow condition is a Boolean value, and ruleDetail comes from: the feature constraint of the current node, the enabling condition of the directed line from the current node to the transition, the enabling condition of the directed line from the transition to the result node, and the feature constraint of the result node. The calculation results of a transition are updated to the content attributes. After a successful transition, the calculation results are permanently stored. RuleDetails are derived from the transition operation item, the output changes of the directed line from the transition to the result node, and the feature changes of the result node. A lock is acquired before concurrent transitions and released after the concurrent transitions complete. Content in the stopped transition state cannot update the transition attributes of the same version.

[0059] In a specific embodiment of the present application, following the above example, after the process network module receives the call operation information "Call the process network module to execute the video 001 review business 01 to trigger the flow transfer operation," the process network module determines the process network corresponding to the video 001 review business 01 from the process network library of the static process network submodule based on the call operation information. The version number of this process network is V1.0. After determining the process network, the process network module determines the operation rule information from the rule library of the rule submodule based on the process network and processes the business flow request based on the operation rule information.

[0060] Specifically, determining the target process network from the static process network submodule according to the calling operation information includes:

[0061] The target process network is determined from the static process network submodule according to the business attribute information in the calling operation information.

[0062] Determining operation rule information from the rule submodule according to the target process network includes:

[0063] The operation rule information is determined from the rule submodule according to the request function information in the calling operation information.

[0064] Among them, if the business flow request is a stop flow request, the rule submodule is called to determine whether the video content has been reviewed. If it has not been reviewed, it will return directly. If it has been reviewed, the flow status of the video content in the specified version or all versions will be updated to "stop flow" based on the business attribute information, and then the flow process in progress on that version will be interrupted. The interruption occurs the next time the rule module is called based on the same content. If the latest flow status in the memory = "stop flow", the operation will be interrupted. The stop flow of a content on a certain version will be broken only when the next review request of the same version is made, the flow request is triggered, and the parameter is forced flow.

[0065] If the business flow request is a request to update content attributes, the rule submodule is called to update the video content attribute information.

[0066] If the business flow request is a trigger flow request, it supports on-demand updates of basic content attributes. The rule submodule is called to obtain the video content attribute feature f0. If the parameter contains basic content attributes, the content attributes are updated on the f0 replica, and f1 is obtained as the latest feature f2. If the parameter does not contain basic content attributes, f0 is used as the latest feature f2. The node where f0 is located is used as the flow starting point, and the change flags in the request parameters, such as the operation item number and message name, are filtered to trigger the flow. If the flow is interrupted, the transition configuration policy determines whether to stay at the latest node or return to the flow starting point.

[0067] If the business flow request is for review, the static process network module collects the downstream directed line enable conditions of the specified node and, based on f0, calls the rule module to calculate the enable conditions. A true result indicates that the directed line direct connection transition is enabled; otherwise, the flow is interrupted. The enable transition flag is checked to see if it matches the incoming transition flag. If not, the flow is interrupted. The rule module updates or adds content attributes, with the node of the flow attribute set to the initial node and the flow state set to "serial flow."

[0068] In actual applications, the process network module obtains business attribute information in the call operation information to determine the target process network, obtains request function information in the call operation information, and determines the operation rule information corresponding to the target process network from the rule submodule.

[0069] In a specific embodiment of the present application, following the above example, after the process network module receives the call operation information "Call the process network module to execute the video 001 review business 01 trigger flow operation," the process network module determines the process network corresponding to the video 001 review business 01 from the process network library of the static process network submodule based on the business attribute information "video 001 review business 01" in the call operation information. The version number of this process network is V1.0. After determining the process network, the process network module determines the operation rule information from the rule library of the rule submodule based on the request function information "trigger flow" and processes the business flow request based on the operation rule information.

[0070] After the operation rule information is determined, the business flow request can be executed in the target process network according to the operation rule information. Specifically, the business flow request is processed according to the operation rule information and operation result information is generated, including:

[0071] Determining a target subnode of the business flow request in the target process network;

[0072] Determining target sub-operation rule information corresponding to the target sub-node in the operation rule information;

[0073] The service transfer request at the target sub-node is processed based on the target sub-node operation rule information, and operation result information of the service transfer request at the target sub-node is generated.

[0074] The target subnode can be understood as one of multiple nodes in the target process network. There are multiple nodes in the process network, and one of the process nodes needs to be determined based on the operation rule information so that the business flow request can be executed at that process node. The target sub-operation rule information can be understood as one of the multiple rule information in the operation rule information. A sub-operation rule corresponding to the target subnode is determined from the multiple operation rule information. The business flow request at the target subnode is then processed, and the operation result information of the business flow request at the target subnode is generated.

[0075] In actual applications, the process network module determines the target sub-node of the business flow request in the target process network, determines the target sub-operation rule information corresponding to the target sub-node in the operation rule information, processes the business flow request located at the target sub-node based on the target sub-operation rule information, and generates the operation result information of the business flow request at the target sub-node.

[0076] In a specific embodiment of the present application, following the above example, the process network module determines the node of the business flow request in the process network with version number V1.0, which is the starting node, and determines in the operation rule information that the operation rule information of the starting node is "the video type is animation". The business flow request in the starting node is processed according to the operation rule information. If the operation rule information is met, the business flow is successful, the rule sub-module updates the result, and sends a callback notification.

[0077] This application can also provide a reachability algorithm based on the data processing engine to predict whether the process network has the risk of stagnation or dead loop.

[0078] Specifically, the process network module also includes:

[0079] The process network module collects network attribute set information of the target process network, and determines the flow status of the target process network according to the network attribute set information.

[0080] The network attribute set can be understood as the set of all process nodes and feature tokens in the process network. The flow state can be understood as the correctness of the process network, for example, the process network is normal, the process network is stagnant.

[0081] In practical applications, to predict whether a process network is experiencing flow stagnation, the static process network submodule can be called to collect all node sets and feature token sets in the network. The node set, starting with the initial node f0, is sorted by node version number to form the array flowList. The feature token set is grouped by variable feature paths, values, and colors, resulting in a colorless variable feature path and value array tokenPlainList and a color array tokenColorList. This matrix M0 is composed of rows = [flowList] and columns = [tokenPlainList tokenColorList]. The initial value of M0 is 1 in rows = [f0] and columns = [tokenPlainList], and n in columns = [tokenColorList]. Starting from the initial node, the engine traverses every path to the leaf node. This traversal includes triggering each action item if the transition has one. If the directed line type is AND split, the paths after this and before the AND merge directed line are considered concurrent paths, and the transitions on all concurrent paths are triggered in different orders. The engine maintains a flowCount map, which records the number of flows to each node, as well as each triggered flow condition and flow change. The trigger involves the starting node fstart and the result node fend. The flow change change is applied to the M matrix to obtain Mi. Change is the value of row = [fstart] minus 1, and the value of row = [fend] plus 1. The feature tokens of the flow condition are grouped into conditionPlainList and conditionColorList according to the variable feature path, value, and color. If the value of row = [fstart] and column = [conditionPlainList] is 1, and the value of column = [conditionColorList] is between [n, n + flowCount[fstart]] and the flow condition is met, the calculation condition of the condition is true; otherwise, the flow is stagnant.

[0082] To predict whether a process network will experience a dead loop, the node numbers associated with the directed lines are written into a map, starting from the initial node. If a node already exists in the map before writing, it indicates a loop in the network and a possible dead loop. The process engine traverses the path from the initial node. If the sample content enters a loop and is triggered multiple times, it will repeatedly flow to the same node. If the flow condition at the junction of the loop and the non-loop appears to be 0 in the specified row and column = [conditionPlainList], or the value in the column = [conditionColorList] is less than n, it indicates an dead loop.

[0083] In a specific embodiment of the present application, following the above example, the process network module collects all network attribute set information in the process network, and writes the node numbers associated with the directed lines in the network attribute set information into the map. Before writing, it is found that the node already exists in the map, indicating that there is a ring path in the network and an infinite loop may occur.

[0084] Step 108: The request response module receives the operation result information and sends it to the business party.

[0085] In actual applications, the request response module can receive operation result information in real time, thereby obtaining the path and status of business flow, and sending it to the business party in real time in the form of an API.

[0086] In a specific embodiment of the present application, following the above example, the rule submodule successfully processes the business flow request in the starting node, and the request response module receives an operation result information, which shows that the business content starting node is successfully transferred.

[0087] The present application provides a data processing method based on a discrete parallel model, which is applied to a data processing engine. The engine includes: a request response module, a flow drive module, and a process network module, wherein the request response module pulls a business flow request, obtains business flow parameter information according to the business flow request, and sends the business flow parameter information to the flow drive module; the flow drive module receives the business flow parameter information, generates call operation information according to the business flow parameter information, and sends the call operation information to the process network module; the process network module receives the call operation information, performs a corresponding operation according to the call operation information and generates operation result information, and sends the operation result information to the request response module; the request response module receives the operation result information and sends it to the business party. Based on the colored Petri net model, a data processing engine for content review process is provided, which can support the dynamic flow of multiple versions of multiple businesses, provide support for business parties to understand and intervene in the flow path of business content, and use the reachability algorithm to predict the risk of stagnation and dead loop in the process network.

[0088] The following combined Figure 2 Taking the application of the data processing method based on the discrete parallel model provided by the present application in processing a request to update video service attributes as an example, the data processing method based on the discrete parallel model is further explained. Figure 2A processing flow chart of a data processing method based on a discrete parallel model for processing a request to update video service attributes provided by an embodiment of the present application is shown. The method is applied to a data processing engine, which includes: a request response module, a flow driving module, and a process network module, and specifically includes the following steps:

[0089] Step 202: The request response module pulls the business flow request in the business request queue through the request queue interface, obtains business flow parameter information according to the business flow request, and sends the business flow parameter information to the flow driving module.

[0090] In a specific embodiment of the present application, the request response module pulls a business flow request from the business request queue through the request queue interface. The request is a request to update the video business attributes. According to the request, the business flow parameter information "Review video business number 02, process network version number V2.0, video business unique number 002, request function: update business attributes" is obtained, and the business flow parameter information is sent to the flow driving module.

[0091] Step 204: The flow driving module receives the business flow parameter information, obtains the request function information in the business flow parameter information, obtains the corresponding business attribute information according to the request function information, generates the call operation information according to the request function information and the business attribute information, and sends the call operation information to the process network module.

[0092] In a specific embodiment of the present application, following the above example, after the flow driving module receives the business flow parameter information sent by the request response module, it first obtains the request function information "update business attributes" and the business attribute information "review video business number 02, process network version number V2.0, video business unique number 002", thereby generating a call operation information "call the process network module to execute the video 002 review business 02 update business attribute operation".

[0093] Step 206: The process network module receives the call operation information, determines the target process network from the static process network sub-module based on the call operation information; determines the operation rule information from the rule sub-module based on the target process network, processes the business flow request based on the operation rule information and generates operation result information, and sends the operation result information to the request response module.

[0094] In a specific embodiment of the present application, following the above example, after the process network module receives the call operation information "Call the process network module to execute the video 002 review business 02 update business attribute operation," the process network module determines the process network corresponding to the video 002 review business 02 from the process network library of the static process network submodule based on the business attribute information "video 002 review business 02" in the call operation information. The version number of this process network is V2.0. After determining the process network, the process network module determines the operation rule information from the rule library of the rule submodule based on the request function information "update business attributes" and processes the business flow request based on the operation rule information.

[0095] Step 208: The request response module receives the operation result information and sends it to the business party.

[0096] In a specific embodiment of the present application, following the above example, the rule submodule successfully updates the video service attribute, and the request response module receives an operation result message, which indicates that the service content starting node is successfully transferred.

[0097] This application provides a data processing method based on a discrete parallel model for processing requests to update video service attributes. The method comprises: a request response module pulls a service flow request from a service request queue through a request queue interface, obtains service flow parameter information based on the service flow request, and sends the service flow parameter information to a flow driver module. The flow driver module receives the service flow parameter information, obtains request function information from the service flow parameter information, obtains corresponding service attribute information based on the request function information, generates call operation information based on the request function information and the service attribute information, and sends the call operation information to a process network module. The process network module receives the call operation information, determines a target process network from a static process network submodule based on the call operation information, determines operation rule information from a rule submodule based on the target process network, processes the service flow request based on the operation rule information, generates operation result information, and sends the operation result information to a request response module. The request response module receives the operation result information and sends it to the service provider. A flow engine based on the content review process of a colored Petri net model is used to drive the dynamic flow of content and provide support for the service provider to understand and intervene in the content flow path.

[0098] Corresponding to the above method embodiment, the present application also provides a data processing engine embodiment based on a discrete parallel model, Figure 3 FIG. 1 shows a schematic diagram of a data processing engine based on a discrete parallel model provided by an embodiment of the present application. Figure 3 As shown, the engine includes: a request response module 302, a flow drive module 304, and a process network module 306, wherein:

[0099] The request response module 302 is configured to pull the business flow request, obtain business flow parameter information according to the business flow request, send the business flow parameter information to the flow driving module, receive the operation result information, and send it to the business party;

[0100] The flow driving module 304 is configured to receive the business flow parameter information, generate call operation information according to the business flow parameter information, and send the call operation information to the process network module;

[0101] The process network module 306 is configured to receive the call operation information, perform a corresponding operation according to the call operation information and generate operation result information, and send the operation result information to the request response module.

[0102] Optionally, the data processing engine further includes: a request queue interface;

[0103] Furthermore, the request response module 302 is further configured to:

[0104] Pull the service flow request in the service request queue through the request queue interface.

[0105] Furthermore, the request flow driving module 304 is further configured to:

[0106] Obtain the request function information in the business flow parameter information, obtain the corresponding business attribute information according to the request function information, and generate the call operation information according to the request function information and the business attribute information, wherein the request function information includes at least one of the following: review request information, update business attribute request information, trigger flow request information, and stop flow request information.

[0107] Furthermore, the process network module 306 includes a rule submodule and a static process network submodule; and is further configured to:

[0108] Determining a target process network from the static process network submodule according to the calling operation information;

[0109] Determine operation rule information from the rule submodule according to the target process network, process the business flow request according to the operation rule information, and generate operation result information.

[0110] Furthermore, the process network module 306 is further configured to:

[0111] The target process network is determined from the static process network submodule according to the business attribute information in the calling operation information.

[0112] Furthermore, the process network module 306 is further configured to:

[0113] The operation rule information is determined from the rule submodule according to the request function information in the calling operation information.

[0114] Furthermore, the process network module 306 is further configured to:

[0115] Determining a target subnode of the business flow request in the target process network;

[0116] Determining target sub-operation rule information corresponding to the target sub-node in the operation rule information;

[0117] The service transfer request at the target sub-node is processed based on the target sub-node operation rule information, and operation result information of the service transfer request at the target sub-node is generated.

[0118] Furthermore, the process network module 306 is further configured to:

[0119] The process network module collects network attribute set information of the target process network, and determines the flow status of the target process network according to the network attribute set information.

[0120] The present application provides a data processing engine based on a discrete parallel model, which includes: a request response module 302, a flow drive module 304, and a process network module 306, wherein the request response module 302 is configured to pull a business flow request, obtain business flow parameter information according to the business flow request, send the business flow parameter information to the flow drive module, receive the operation result information, and send it to the business party; the flow drive module 304 is configured to receive the business flow parameter information, generate call operation information according to the business flow parameter information, and send the call operation information to the process network module; the process network module 306 is configured to receive the call operation information, perform corresponding operations according to the call operation information and generate operation result information, and send the operation result information to the request response module. Through a data processing engine based on colored Petri nets, complex and diverse edge flow scenarios can be executed, and support for business parties to understand and intervene in the flow path of business audits is provided.

[0121] The above is a schematic diagram of a data processing engine based on a discrete parallel model according to this embodiment. It should be noted that the technical solution of the data processing engine based on a discrete parallel model and the technical solution of the data processing method based on a discrete parallel model are based on the same concept. For details not described in detail in the technical solution of the data processing engine based on a discrete parallel model, please refer to the description of the technical solution of the data processing engine based on a discrete parallel model.

[0122] Figure 4 4 shows a block diagram of a computing device 400 according to an embodiment of the present application. Components of the computing device 400 include, but are not limited to, a memory 410 and a processor 420. The processor 420 is connected to the memory 410 via a bus 430, and a database 450 is used to store data.

[0123] The computing device 400 also includes an access device 440 that enables the computing device 400 to communicate via one or more networks 460. Examples of these networks include a public switched telephone network (PSTN), a local area network (LAN), a wide area network (WAN), a personal area network (PAN), or a combination of communication networks such as the Internet. The access device 440 may include one or more of any type of network interface (e.g., a network interface card (NIC)) whether wired or wireless, such as an IEEE 802.11 wireless local area network (WLAN) wireless interface, a Worldwide Interoperability for Microwave Access (Wi-MAX) interface, an Ethernet interface, a universal serial bus (USB) interface, a cellular network interface, a Bluetooth interface, a near field communication (NFC) interface, and the like.

[0124] In one embodiment of the present application, the above components of the computing device 400 and Figure 4 Other components not shown in the figure may also be connected to each other, for example, via a bus. Figure 4 The computing device structure block diagram shown is for illustrative purposes only and is not intended to limit the scope of the present application. Those skilled in the art may add or replace other components as needed.

[0125] Computing device 400 can be any type of stationary or mobile computing device, including a mobile computer or mobile computing device (e.g., a tablet computer, a personal digital assistant, a laptop computer, a notebook computer, a netbook computer, etc.), a mobile phone (e.g., a smartphone), a wearable computing device (e.g., a smartwatch, smart glasses, etc.), or other types of mobile devices, or a stationary computing device such as a desktop computer or PC. Computing device 400 can also be a mobile or stationary server.

[0126] When the processor 420 executes the computer instructions, the steps of the data processing method based on the discrete parallel model are implemented.

[0127] The above is a schematic diagram of a computing device according to this embodiment. It should be noted that the technical solution of this computing device and the technical solution of the data processing method based on a discrete parallel model are based on the same concept. For details not described in detail in the technical solution of the computing device, please refer to the description of the technical solution of the data processing method based on a discrete parallel model.

[0128] An embodiment of the present application further provides a computer-readable storage medium storing computer instructions, which, when executed by a processor, implement the steps of the data processing method based on the discrete parallel model as described above.

[0129] The above is a schematic diagram of a computer-readable storage medium according to this embodiment. It should be noted that the technical solution of this storage medium is based on the same concept as the technical solution of the data processing method based on a discrete parallel model. For details not described in detail in the technical solution of the storage medium, please refer to the description of the technical solution of the data processing method based on a discrete parallel model.

[0130] The foregoing description describes specific embodiments of the present application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in an order different from that described in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order shown or the sequential order to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0131] The computer instructions include computer program code, which may be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0132] It should be noted that for the aforementioned method embodiments, for ease of description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in this specification are all preferred embodiments, and the actions and modules involved are not necessarily required by this application.

[0133] In the above embodiments, the descriptions of each embodiment have their own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments. The preferred embodiments of the present application disclosed above are only used to help illustrate the present application. The optional embodiments do not describe all the details in detail, nor do they limit the invention to only the specific implementation methods described. Obviously, many modifications and changes can be made based on the content of the present application. This application selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present application, so that technical personnel in the relevant technical field can well understand and use the present application. This application is only limited by the claims and their full scope and equivalents.

Claims

1. A data processing method based on a discrete parallel model, characterized in that: Applied to the data processing engine, the engine includes: a request response module, a flow drive module, and a process network module, wherein: The request response module pulls the business flow request, obtains business flow parameter information according to the business flow request, and sends the business flow parameter information to the flow driving module; The flow driving module receives the business flow parameter information, generates call operation information according to the business flow parameter information, and sends the call operation information to the process network module; The process network module receives the call operation information, performs a corresponding operation according to the call operation information and generates operation result information, and sends the operation result information to the request response module; The request response module receives the operation result information and sends it to the business party; Wherein, the process network module includes a rule submodule and a static process network submodule; The process network module performs corresponding operations according to the call operation information and generates operation result information, including: Determining a target process network from the static process network submodule according to the call operation information, wherein the static process network submodule is configured with multiple process networks, and the target process network is one of the multiple process networks; According to the target process network, the operation rule information is determined from the rule submodule, the business flow request is processed according to the operation rule information and operation result information is generated. The rule submodule is configured with multiple operation rules, and the operation rule information is one of the multiple operation rules.

2. The data processing method according to claim 1, wherein: The data processing engine further includes a request queue interface, wherein the request queue interface is connected to a service request queue; The request response module receives a service transfer request, including: The request response module pulls the service flow request in the service request queue through the request queue interface.

3. The data processing method according to claim 1, wherein: The flow driving module generates call operation information according to the business flow parameter information, including: The flow driving module obtains the request function information in the business flow parameter information, obtains the corresponding business attribute information according to the request function information, and generates the call operation information according to the request function information and the business attribute information, wherein the request function information includes at least one of the review request information, the update business attribute request information, the trigger flow request information, and the stop flow request information, and the business attribute information includes at least one of the business number, the process network version number, and the unique number of the content business in the business flow parameter information.

4. The data processing method according to claim 3, wherein: Determining a target process network from the static process network submodule according to the calling operation information includes: The target process network is determined from the static process network submodule according to the business attribute information in the calling operation information.

5. The data processing method according to claim 3, wherein: Determining operation rule information from the rule submodule according to the target process network includes: The operation rule information is determined from the rule submodule according to the request function information in the calling operation information.

6. The data processing method according to claim 3, wherein: Processing the service transfer request according to the operation rule information and generating operation result information includes: Determining a target subnode of the business flow request in the target process network; Determining target sub-operation rule information corresponding to the target sub-node in the operation rule information; The service transfer request at the target sub-node is processed based on the target sub-node operation rule information, and operation result information of the service transfer request at the target sub-node is generated.

7. The data processing method according to any one of claims 3 to 6, wherein: The process network module also includes: The process network module collects network attribute set information of the target process network, and determines the flow status of the target process network according to the network attribute set information.

8. The data processing method according to claim 1, wherein: The service flow parameter information includes at least one of a service number, a process network version number, a service unique number of the content, and request function information; The calling operation information includes a message for calling a process network module to perform an operation; The operation result information includes result information generated after the process network module performs the operation.

9. A data processing engine based on a discrete parallel model, characterized in that: The engine includes: a request response module, a flow drive module, and a process network module, wherein: The request response module is configured to pull the business flow request, obtain business flow parameter information according to the business flow request, and send the business flow parameter information to the flow driving module; The flow driving module is configured to receive the business flow parameter information, generate call operation information according to the business flow parameter information, and send the call operation information to the process network module; The process network module is configured to receive the call operation information, perform a corresponding operation according to the call operation information and generate operation result information, and send the operation result information to the request response module; The request response module is further configured to receive the operation result information and send it to the business party; Wherein, the process network module includes a rule submodule and a static process network submodule; The process network module performs corresponding operations according to the call operation information and generates operation result information, including: Determining a target process network from the static process network submodule according to the call operation information, wherein the static process network submodule is configured with multiple process networks, and the target process network is one of the multiple process networks; According to the target process network, the operation rule information is determined from the rule submodule, the business flow request is processed according to the operation rule information and operation result information is generated. The rule submodule is configured with multiple operation rules, and the operation rule information is one of the multiple operation rules.

10. A computing device comprising a memory, a processor, and computer instructions stored in the memory and executable on the processor, wherein: When the processor executes the computer instructions, the steps of the method according to any one of claims 1 to 8 are implemented.

11. A computer-readable storage medium storing computer instructions, characterized in that: When the computer instructions are executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

12. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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