User state management method and device for digital issuing process

Abstract

The invention relates to a user state management method and device for a digital issuing process. The method comprises the following steps: performing feature analysis processing on user information of a target user in a digital issuing process to obtain state feature data corresponding to the target user; a time sequence event set triggered by the target user in the digital issuing process is obtained, state transition modeling processing based on the participation plate is carried out on the time sequence event set according to the state feature data, and a state evolution track of the target user in the digital issuing process is obtained; state labels corresponding to all track nodes of the target user in the state evolution track are determined, mapping binding is carried out on all the state labels and process nodes in the digital issuing process, and a state process binding result is obtained and used for driving a process engine to manage process behaviors according to the participation plate dimension. By adopting the method, the process scheduling and behavior management of the multi-plate process behavior can be flexibly and pertinently realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of process management, in particular to a user state management method and device for a digital publishing process. BACKGROUND

[0002] In the technical field of process management, the process scheduling and behavior management of users in each link of the digital publishing process are involved. However, the related method usually schedules and manages according to the preset unified process path combined with the identity information of the user, and cannot perform differential processing on the process behavior in the plate dimension, thereby reducing the flexibility and pertinence of process scheduling and behavior management in the complex process scene of multi-plate collaborative promotion. SUMMARY

[0003] Therefore, it is necessary to provide a user state management method and device for a digital publishing process, a computer device and a computer readable storage medium, for flexibly and pertinently implementing process scheduling and behavior management of multi-plate process behavior.

[0004] In a first aspect, the present application provides a user state management method for a digital publishing process, comprising: performing feature analysis processing on user information of a target user in a digital publishing process to obtain state feature data corresponding to the target user, the user information including identity attribute information, historical task records and participation plate information corresponding to the target user; obtaining a set of time sequence events triggered by the target user in the digital publishing process, and performing state transition modeling processing on the set of time sequence events based on participation plates according to the state feature data to obtain a state evolution trajectory of the target user in the digital publishing process; determining state labels corresponding to each trajectory node in the state evolution trajectory of the target user, mapping and binding each state label with a process node in the digital publishing process to obtain a state process binding result, and driving the process engine to manage process behavior according to the participation plate dimension.

[0005] In a second aspect, the present application further provides a user state management device for a digital publishing process, comprising: an analysis module configured to perform feature analysis processing on user information of a target user in a digital publishing process to obtain state feature data corresponding to the target user, the user information including identity attribute information, historical task records and participation plate information corresponding to the target user; a modeling module, configured to acquire a set of time-series events triggered by the target user in the digital publishing process, and perform state transition modeling based on participation blocks on the set of time-series events according to the state feature data, to obtain a state evolution track of the target user in the digital publishing process; a mapping module, configured to determine state labels corresponding to respective track nodes in the state evolution track of the target user, and map and bind each state label with a process node in the digital publishing process, to obtain a state-process binding result, for driving the process engine to manage process behaviors according to the participation block dimension.

[0006] In a third aspect, the present application also provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the above steps when executing the computer program.

[0007] In a fourth aspect, the present application also provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the above steps.

[0008] The above user state management method, device, computer device and computer readable storage medium for a digital publishing process first perform feature analysis processing on identity attribute information, historical task records and participation block information in user information, to obtain state feature data in multiple dimensions and in a structured manner. Then, the state transition modeling based on participation blocks is performed on a set of time-series events according to the state feature data, to obtain a state evolution track reflecting the state change of the user block. Then, the state labels marked in the state evolution track are mapped and bound with corresponding process nodes to obtain a state-process binding result, so as to construct a state-process control basis recognizable and callable by the process engine in the participation block dimension. Based on this, the process scheduling and behavior management of the process engine on the multi-block process behavior are realized without introducing redundant process judgment conditions. BRIEF DESCRIPTION OF DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related art, the drawings needed to be used in the embodiments or the related art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0010] Figure 1 a process schematic diagram of a user state management method for a digital publishing process in an embodiment; Figure 2A structural block diagram of a user state management apparatus for a digital issuance process in an embodiment. DETAILED DESCRIPTION

[0011] For the purposes of the present application, the technical solutions and advantages thereof are more clearly apparent, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely intended to explain the present application, and are not intended to limit the present application.

[0012] In one embodiment, as shown in Figure 1 A user state management method for a digital issuance process is provided, and the embodiment is exemplified by the method applied to a server. It should be understood that the method can also be applied to a terminal, and can also be applied to a system including a terminal and a server, and is implemented through the interaction of the terminal and the server. In the embodiment, the method includes the following steps S101 to S103.

[0013] Step S101, performing feature analysis processing on user information of a target user in a digital issuance process to obtain state feature data corresponding to the target user, the user information including identity attribute information, historical task records, and participation panel information corresponding to the target user.

[0014] The digital issuance process refers to an overall business process for completing the issuance of a financial product or a digital asset in a digital manner, for example, a collaborative process system composed of various continuous business stages such as project establishment, approval, pricing, and collection.

[0015] The target user refers to a specific user individual to be analyzed in the digital issuance process, for example, a user participating in tasks such as audit, submission, and subscription in the process of issuing a certain financial product.

[0016] The identity attribute information refers to information describing the basic identity features of the target user, used to distinguish the roles, permission levels, and organizational affiliations of different users, for example, the identity attribute information of the user includes fields such as “underwriting agency personnel”, “has approval authority”, and “belongs to securities company A”.

[0017] The historical task records refer to various tasks and related process information completed by the target user in the past in the digital issuance process, used to describe the task history of the target user in the process, for example, the historical task records of the user include fields such as “has participated in 3 preliminary audits and 2 compliance reviews”.

[0018] The participation panel information refers to the business domain division information involved by the target user in the digital issuance process, used to define the activity area and operation authority of the target user in the process, for example, the user participates in both the content audit panel and the issuance pricing panel.

[0019] Among them, the status feature data represents a structured data set obtained based on the analysis of the user information of the target user, which includes structured data of information dimensions such as identity attribute information, historical task records, and participation section information.

[0020] Step S102: obtain the set of time series events triggered by the target user in the digital distribution process, perform state transition modeling on the time series event set based on the participating sections according to the state feature data, and obtain the state evolution trajectory of the target user in the digital distribution process.

[0021] Among them, the time series event set represents a series of event records with time sequence that are continuously triggered by the target user in the digital distribution process. For example, the user has successively executed a set of events with time sequence characteristics such as "logging in to the system", "submitting approval materials", "modifying distribution parameters", and "confirming quotation".

[0022] Among them, the state evolution trajectory represents the continuous transfer path of the target user's corresponding section status in the digital distribution process over time in the dimension of specific participation sections. It is used to reflect the behavioral stage of the user in the corresponding section process. For example, in the inquiry section, the user may experience multiple section states of "not participating", "submitting application", "qualification confirmation", and "quotation feedback" in sequence.

[0023] Among them, the state evolution trajectory is constructed by multiple trajectory nodes in chronological order, and each trajectory node is used to identify the plate state of the target user in the corresponding plate process at a specific time point.

[0024] For example, first, the state feature data includes multi-dimensional structured data such as the target user's identity attribute information, historical task records, and participation section information, so that the subsequent state transition modeling process only acts on the target user's behavioral data generated within the corresponding section, that is, the modeling scope is focused on the process scope that is directly related to the target user's actual operation scenario. Furthermore, after obtaining the time series event set, the events in the time series event set are subjected to multi-layer screening processing based on the state feature data. Specifically, events that match the user role are screened out in the identity attribute information dimension, and events with a logical connection relationship are screened out in the historical task record dimension, and events belonging to the current participation section are screened out in the participation section information dimension. The resulting screened time series event set has both temporal integrity and is highly relevant to the subsequent state transition modeling process.

[0025] Further, in the screened time sequence event set, the logical connection between the events is analyzed to identify the cause-effect relationship and stage change between the events, and the plate state progression process of the target user is deduced based on the same; specifically, the plate state progression process is based on event triggering to gradually form the state evolution chain of the target user under the current participating plate, i.e., the state evolution track; each transition process in the state evolution track is driven by a specific event and reflects the plate state of the target user at different process stages.

[0026] In step S103, the state labels corresponding to each track node in the state evolution track of the target user are determined, each state label is mapped and bound with a process node in the digital publishing process, and a state process binding result is obtained to drive the process engine to manage the process behavior according to the participating plate dimension.

[0027] Among them, the state label represents a semantic identifier for representing a certain specific plate state of the target user in a certain plate, such as the label fields of "to be approved", "completed", "to be fed back", etc.

[0028] Among them, the process node in the digital publishing process represents a processing unit organized in the digital publishing process according to the business logic order, such as "qualification examination", "submission confirmation", "feedback notification", etc. Process nodes with unique business functions and upstream and downstream logical positions.

[0029] Among them, the state process binding result represents a mapping set for structurally expressing the logical linkage relationship between the plate state of the user and the process behavior in the participating plate dimension, to represent whether the calling of the plate-in process node is controlled by the state evolution position of the current user in the corresponding plate; for example, if the state label of the user in the content review plate is "to be approved", the binding relationship between the plate state and the "qualification examination" process node is recorded in the state process binding result.

[0030] Among them, the process engine represents an execution module for performing process logic scheduling and control according to the current plate state of the target user in each participating plate and the corresponding state process binding result, for triggering or shielding the calling logic of a specific process node in actual operation; for example, in the data publishing plate, if the current plate state of a user is "to be archived", the process engine activates the "archiving review" process node according to the corresponding state process binding result, thereby ensuring the consistency between the process behavior and the user plate state.

[0031] Exemplarily, firstly, in the state evolution trajectory, each trajectory node corresponds to a state label respectively, and each state label is mapped and bound to each process node in the digital publishing process, that is, it is realized according to the preset mapping rule, that is, the mapping rule sets the position, role and input condition of the process node in the digital publishing process, which is used to judge whether it is consistent with the semantics of a certain state label, so as to determine whether to build the mapping relationship between the two.

[0032] Further, when the mapping relationship between a certain state label and a specific process node is established, it means that the user board state represented by the state label meets the execution requirements of the process node, so the mapping relationship between the two can be written into the state process binding result; the state process binding result will be used as the basis for configuring the process control entrance, and input into the preset process engine, so that the process engine can parse the process node corresponding to different state labels according to the dimension of the participating board, and accordingly manage the process behavior of the target user by board; Specifically, in the process running phase, when a certain state label is identified, the process engine can trigger the corresponding process node execution logic accordingly, and complete the process response action consistent with the current board state of the target user.

[0033] In the above-mentioned user state management method for digital publishing process, firstly, the state feature data is obtained in multiple dimensions and in a structured manner according to the feature analysis processing of the identity attribute information, historical task record and participating board information in the user information; Further, the state evolution trajectory reflecting the change of the user board state is obtained by performing state transition modeling processing on the time sequence event set based on the participating board according to the state feature data; Further, the state process binding result is obtained by mapping and binding the state label marked in each trajectory node in the state evolution trajectory with the corresponding process node, so as to build the state process control basis that can be recognized and called by the process engine under the participating board dimension; Based on this, without introducing redundant process judgment conditions, the process scheduling and behavior management of the process engine on the multi-board process behavior are realized.

[0034] In one exemplary embodiment, the state evolution trajectory of the target user in the digital publishing process is obtained by performing state transition modeling processing on the time sequence event set based on the participating board according to the state feature data, including steps S201 to S203.

[0035] Step S201, obtain a preset state modeling parameter template, and index match the state modeling parameter template according to the state feature data to obtain a set of modeling parameters for driving the state transition modeling process.

[0036] The state modeling parameter template represents a preset rule set for providing structured, reusable, and matchable modeling reference basis for state transition modeling processes of different target users in a digital publishing process.

[0037] The modeling parameter set represents a group of parameter entities that are matched with the state feature data of the target user and can be used to control the state transition modeling process, to specifically guide the filtering logic of the timing event and the state transition judgment logic in the state transition modeling process.

[0038] Exemplarily, first, a set of state modeling parameter templates for matching modeling parameters is pre-set, and the state modeling parameter templates include a plurality of modeling parameter configuration items for different combinations of identity attributes and behavior types, to cover the state definition range, event trigger condition, and state transition condition in different participation platform scenarios. Then, the state modeling parameter templates are indexed and matched according to the state feature data of the target user, to identify the configuration range in which the target user falls in the state modeling parameter templates in multiple dimensions such as identity attributes, historical task records, and participation platforms. Specifically, the corresponding modeling parameter configuration items in the state modeling parameter templates can be compared item by item based on a multi-field joint matching manner, so as to select a group of parameter configuration items that best match the multi-dimensional state feature data of the target user, and finally construct the modeling parameter set corresponding to the target user. The modeling parameter set is used to determine the state definition range, event trigger condition, and state transition condition that need to be referred to in the subsequent modeling process.

[0039] Among the determined modeling parameter set, the state definition range represents a set of platform states in which the target user is located in each participation platform in the digital publishing process; the event trigger condition represents a rule set for conditionally judging and filtering the original timing events generated by the target user in the digital publishing process; and the state transition condition is used to determine whether the transition between any two platform states in the state definition range is allowed, and the conditions that need to be met for the transition.

[0040] Step S202: Multi-dimensional filtering processing of each timing event in the timing event set is performed according to the modeling parameter set, to obtain an effective timing event set.

[0041] The effective timing event set represents a set of events that are retained after filtering according to the filtering conditions set in the modeling parameter set, from all original timing events triggered by the target user in the digital publishing process.

[0042] Exemplarily, first, according to the event trigger condition defined in the modeling parameter set, the matching degree of each time sequence event in the original time sequence event set in terms of type, trigger position, belonging plate, user behavior context and the like is judged item by item under multiple screening dimensions such as event type restriction, plate adaptation condition, user identity attribute requirement and the like. Thus, instead of directly filtering by a single condition, the relevance and validity of the event are judged according to the joint condition judgment rule of multiple screening dimensions set in the modeling parameter set. On this basis, the redundant events that do not satisfy the event trigger condition of any screening dimension are eliminated from the original time sequence event set, and the events that satisfy the event type restriction, meet the participation plate requirement and are highly associated with the target user identity attribute are retained as the effective time sequence event set.

[0043] In step S203, the effective time sequence event set is subjected to participation plate-based state transition modeling processing according to the plate state transition path in the modeling parameter set, so as to obtain the state evolution track of the target user in the digital publishing process.

[0044] The plate state transition path represents a change path set of user plate state evolution under events for a specific participation plate scenario, and is used to indicate that the current plate state of the target user can be transferred to what new state under what event.

[0045] Exemplarily, first, the effective time sequence event set is sorted in the order of event trigger time to ensure that the state driving can be performed event by event according to the real interaction behavior flow of the target user in the modeling process. Then, according to the plate state transition path reflected by the state transition condition in the modeling parameter set, the action path of each event under the corresponding plate state is analyzed in the path dimension. Specifically, at each moment, according to the current plate state and the event type corresponding to the event, the state transition condition defined in the modeling parameter set is searched to determine whether the event satisfies the trigger premise of state transition. If yes, the current plate state is updated to the corresponding target state, and this state change is recorded as a track node in the state evolution track, and each track node contains event related information, previous state, target state and trigger time. In this way, the subsequent events are processed in sequence, and the current state and the state evolution track are iterated until all the effective events are traversed. Finally, the state evolution track formed is a track node sequence arranged in chronological order, which completely describes the state change logic of the target user in the corresponding participation plate under the event driving.

[0046] In this embodiment, firstly, the state modeling parameter template is indexed and matched according to the state feature data to obtain a modeling parameter set, so that the corresponding state modeling control logic can be quickly extracted for the target user; secondly, the multi-dimensional screening processing is performed on the time sequence event set according to the modeling parameter set, so that the noise events irrelevant to the current modeling are effectively eliminated, and the effective time sequence event set with state driving significance is extracted; thirdly, the state transition modeling processing is performed on the effective time sequence event set according to the plate state transition path in the modeling parameter set, so that the state evolution trajectory reflecting the behavior evolution process of the target user under the current participation plate is obtained; based on this, the state dynamic modeling facing the participation plate dimension can be realized, and the structural clarity and response accuracy of the process management are enhanced.

[0047] In one exemplary embodiment, the state transition modeling processing based on the participation plate is performed on the effective time sequence event set according to the plate state transition path in the modeling parameter set, to obtain the state evolution trajectory of the target user in the digital publishing process, including steps S301 to S302.

[0048] Step S301, according to the plate state transition path in the modeling parameter set, the path matching processing is performed on the effective time sequence event set according to the event trigger sequence, to obtain the effective path set in the plate state transition path, and each effective path in the effective path set corresponds to the combination of the corresponding event type and the corresponding plate state.

[0049] Among them, the effective path set represents the path set that meets the plate state transition path constraint defined in the modeling parameter set.

[0050] Exemplarily, firstly, each event in the effective time sequence event set is sorted, and then the event type corresponding to each sorted event and the plate state associated with the event occurrence are extracted, so as to construct the combination description of the event type and the plate state for each event. Then, combined with the combination description and the plate state transition path, it is judged in turn whether the corresponding event meets the joint requirements of the state transition starting point and the transition condition in a transition path in the plate state transition path, if yes, the event is included in the corresponding transition path, thereby forming an effective path corresponding to the corresponding event type and the corresponding plate state. Based on this, by performing the above path matching operation on all events in the effective time sequence event set, a set of effective paths that can drive state transition can be finally formed, providing a traceable, matchable and traceable path basis for the subsequent state transition process.

[0051] Step S302, according to each effective path in the effective path set, the state transition processing is sequentially performed on the corresponding plate state, to obtain the state evolution trajectory of the target user in the digital publishing process.

[0052] Exemplarily, according to the sequence of event triggering in each valid path, the corresponding plate state is driven to perform state transition processing one by one, specifically: first, in all valid paths, the plate state node of the earliest triggered event in the corresponding valid path is selected as the starting point of the state evolution track; then, starting from the starting point, the corresponding target node of the plate state node of each event in the corresponding valid path after state transition is analyzed in time sequence, and the node relationship before and after transition is connected in turn to form a complete state migration chain; based on this, through the sequential state transition processing of all valid paths, a plate state node sequence from the initial state to the terminal state, i.e. the state evolution track, can be finally constructed, which is used to describe the state change process of the target user in the digital publishing process driven by a series of events in the plate dimension.

[0053] In this embodiment, first, the valid time sequence event set is processed according to the event triggering order based on the plate state transition path, so as to filter out the valid path set that can be used to drive state transition based on the state transition rule; second, the corresponding plate state is sequentially processed by state transition based on the valid path set to obtain the state evolution track, so as to ensure that the state evolution process conforms to the time sequence causality and transition logic consistency in the participation plate dimension, based on which the time sequence evolution modeling of the plate state experienced by the target user in the digital publishing process can be realized.

[0054] In one exemplary embodiment, each state label is mapped and bound to a process node in the digital publishing process to obtain a state process binding result, including steps S401 to S402.

[0055] Step S401, according to each state label, the feature extraction processing of the process node in the digital publishing process is performed, and the node rule description set corresponding to each process node is obtained.

[0056] Among them, the node rule description set represents a set of structured parameters for describing the characteristics of each process node in the digital publishing process, such as the control conditions depended by the process node, the behavior triggering mode of the process node, the logical structure position of the upstream and downstream of the process node and other information.

[0057] Exemplarily, firstly, according to the state labels corresponding to each trajectory node in the state evolution trajectory, the stage in which each state label is located in the digital publishing process is identified, and the set of process nodes associated with the state label is located according to the stage. Then, the identified process nodes are subjected to structured feature extraction processing. Specifically, the execution role of each process node in the overall structure of the digital publishing process is identified, such as whether it is located in the starting stage, the intermediate transition stage or the termination stage, or whether it belongs to the main control link of a specific board, so as to obtain the hierarchical position information of the process node in the structure. In addition, the operation trigger features corresponding to each process node need to be extracted, such as including the requirements of the process node on the input state, the acceptable behavior event categories, and the response ability to the user behavior trigger frequency and execution conditions. Finally, through the above extraction process, a set of rule parameters for describing the characteristics of each process node can be constructed, that is, the node rule description set.

[0058] In step S402, according to the mapping rule between each state label and the node rule description set, the node matching processing of each state label is performed, the mapping binding relationship between each state label and the corresponding process node is constructed, and the state process binding result is obtained.

[0059] Exemplarily, firstly, for each state label, the state semantics, behavior driving intention and board attribution information associated with the process structure contained in the state label need to be parsed, and these contents are further encoded into structured matching parameters. Subsequently, the rule parameters represented by each process node in the node rule description set are matched one by one according to the preset mapping rule in combination with the matching parameters. Specifically, the mapping rule includes the feature consistency judgment between the matching parameters corresponding to the state label and the rule parameters in the node rule description set, and the logical alignment degree between the upstream and downstream dependence relationship of the process node in the digital publishing process structure and the stage in which the state label is located. Based on this, through the above mapping matching process, one or more process nodes with the highest logical consistency with each state label can be identified, and the mapping binding relationship between the state label and the corresponding process node is constructed. Finally, after completing the node matching operation of all state labels, the mapping binding relationship between the state labels and the process nodes obtained by summarizing will constitute the complete state process binding result.

[0060] In this embodiment, firstly, feature extraction processing is performed on the process nodes according to the state labels, to obtain a node rule description set corresponding to each process node, so that the process nodes have structured rule parameters available for matching; secondly, node matching processing is performed according to the mapping rule between the state labels and the node rule description set, to construct a mapping and binding relationship between the state labels and the process nodes, and to realize effective mapping of the state to the process; based on this, by constructing an accurate mapping mechanism of the state labels and the process nodes, the process behavior control effect based on the user board state can be accurately realized.

[0061] In an exemplary embodiment, feature extraction processing is performed on the process nodes in the digital publishing process according to each state label, to obtain a node rule description set corresponding to each process node, including steps S501 to S504.

[0062] Step S501, in the digital publishing process, according to the execution role and upstream and downstream position relationship of each process node in the process structure, identify the process type identifier and node structure attribute information respectively corresponding to each process node.

[0063] The process type identifier represents the function category assumed by a certain process node in the digital publishing process, such as an audit type node, a release type node, or an interaction type node, etc.

[0064] The node structure attribute information represents the connection relationship between the process node and other nodes in the process structure, the number of input and output logical interfaces, and whether there is a parallel or convergence path, etc.

[0065] Exemplarily, firstly, according to the overall process structure of the digital publishing process, the connection relationship and arrangement order of each process node in the process structure are determined one by one, and the logical dependency and connection mode between each process node and its upstream and downstream nodes are identified; through this process, the node structure attribute information of each process node can be determined, such as whether it is located in a parallel execution path, whether it has convergence or branching function, whether it assumes a transfer logic or trigger behavior, etc., to provide clear context positioning. In addition, the process type identifier of each process node needs to be further divided in combination with the operation responsibility assumed by each process node in the process; the process type identifier is an abstract expression of the logical function embodied by the process node, and this type division process needs to analyze and classify the information such as the timing of the process node being triggered in the entire process execution, the logical type executed, and the driving effect on the subsequent nodes. Based on this, the combined description of the execution role and structure characteristics can be constructed for each process node through the above joint processing, i.e., the process type identifier and the node structure attribute information respectively corresponding to each process node are identified.

[0066] Step S502, according to the respective process node corresponding to the process type identifier and the node structure attribute information, respectively, each process node is subjected to node behavior parameter extraction processing, and the node behavior parameter set is obtained.

[0067] Among them, the node behavior parameter set represents a multi-dimensional feature set for describing the behavior trigger path, logic response mode, state evolution direction and other information of each process node in the digital publishing process execution logic.

[0068] Exemplarily, first, by retrieving the typical operation behavior of the process nodes with the same process type identifier involved in the actual process execution, the behavior dimension of the process node in the execution logic is preliminarily extracted, such as input data characteristics, trigger condition characteristics, execution feedback mechanism and behavior coupling relationship with other nodes. At the same time, according to the node structure attribute information, the role of the process node in the process structure is further parameterized modeling, and the node behavior parameters of the structure dimension are obtained, such as whether the process node has a multi-entry or multi-exit structure, whether it is a conditional judgment node, whether it depends on the state of the upstream or downstream node, and other logic characteristics. Subsequently, the node behavior parameters of the above behavior dimension and structure dimension are integrated, and they are uniformly represented as a parameterized structure with behavior trigger path, logic response mode, state evolution direction and other semantic elements, so as to obtain the node behavior parameter set for all process nodes in the entire digital publishing process; The node behavior parameter set can not only express the behavior driving mode of the process node in the process, but also reflect its cooperation logic in the user interaction or system internal scheduling process.

[0069] Step S503, the user identity attribute and behavior category reflected by each state label are encoded and aggregated to obtain a user behavior mode set.

[0070] Among them, the user behavior mode set represents a multi-dimensional feature set for describing the specific behavior preference, role characteristics and operation habits of the target user in the digital publishing process.

[0071] Exemplarily, first, according to the identity categories, registration attributes, permission levels and other user identity attributes reflected by the state labels, and the participation task types, interaction action sequences, process trigger frequencies and other behavior categories, a raw input basis for depicting user behavior characteristics is constituted. Then, the data related to the user identity attributes and the behavior categories is constructed according to a preset semantic dimension to form a unified coding structure, so that the coding characteristics corresponding to each state label are obtained, so as to eliminate the heterogeneity and dispersion of the original features, and to enable subsequent unified aggregation analysis. Further, after the coding operation is completed, the coding characteristics corresponding to each state label are collected and integrated, and based on the association relationship between the user identity attribute dimension and the behavior category dimension, a user behavior mode set is constructed, which embodies the typical behavior combination of the target user in the process, and can unify the behavior preferences of the target user in multiple plateaus and multiple task stages and the evolution trend in multiple dimensions.

[0072] In step S504, each process node is structurally modeled according to the structural association relationship between the node behavior parameter set and the user behavior mode set, and a node rule description set corresponding to each process node is obtained.

[0073] Exemplarily, each node behavior parameter in the node behavior parameter set and each user behavior mode in the user behavior mode set are structurally arranged and paired according to a logical correspondence relationship, and the similarity, response direction and parameter dependency between different node behavior parameters and user behavior modes are analyzed, so that each process node is structurally modeled, and thus a node rule description set corresponding to each process node is obtained, so that the execution logic of the process node has analyzability and configurability. In addition, in the structural modeling process, the upstream and downstream dependency relationship of each process node in the process structure also needs to be considered to ensure that the execution logic semantics of the process node remain consistent and coordinated in the overall process.

[0074] In this embodiment, first, the process type identifier and the node structure attribute information are extracted according to the execution role and the upstream and downstream position relationship of the process node in the process structure, so as to provide a structural positioning basis for subsequent modeling; further, the node behavior parameter set is constructed according to the process type identifier and the node structure attribute information, so as to establish the expression basis of the node behavior characteristics; further, the user behavior mode set is constructed according to the user identity attributes and the behavior categories reflected by the state labels, so as to establish the expression basis of the user behavior characteristics; further, the process node modeling processing is performed according to the structural association relationship between the node behavior parameter set and the user behavior mode set, and a node rule description set corresponding to each process node is obtained. Based on this, the system modeling is performed through multiple dimensions such as node behavior characteristics and user behavior characteristics, the accurate expression of the process node behavior response rule is realized, and the matching and executable rule support is provided for the process control logic under the state driving.

[0075] In an exemplary embodiment, according to the structural association between the node behavior parameter set and the user behavior pattern set, each process node is structurally modeled to obtain a node rule description set corresponding to each process node, including steps S601 to S603.

[0076] Step S601 : constructing a joint response structure between node behavior parameters in the node behavior parameter set and user behavior patterns in the user behavior pattern set according to the structural association relationship between the node behavior parameter set and the user behavior pattern set.

[0077] Among them, the joint response structure represents a data structure constructed based on the structural mapping relationship between node behavior parameters and user behavior patterns, which is used to describe the response association between the two; for example, if the node behavior parameter is "high review frequency" and the user behavior pattern is "frequent jumping between sections", then the corresponding joint response structure of the two is used to describe whether the node behavior parameter is sensitive, inhibitory or enhancing to this type of user behavior.

[0078] For example, a structural connection is established between the node behavior parameter set and the user behavior pattern set. Specifically, the expression of each process node in the node behavior parameter dimension is semantically expanded and matched with the corresponding user behavior pattern, thereby forming a logical response combination between the node behavior parameter and the user behavior pattern. Based on this, in this process, it is necessary to map all node behavior parameters and user behavior patterns that may interact in pairs, and organize and integrate the mapping relationship in a multi-dimensional response description method, ultimately obtaining a set of joint response structures. In this joint response structure, each response entry clearly indicates the response characteristics of a specific node behavior parameter under a specific user behavior pattern.

[0079] Step S602 : performing semantic clustering processing on the joint response structure to obtain a multi-dimensional semantic structure model for characterizing the logical response relationship between the node behavior characteristics and the user behavior preferences.

[0080] Among them, the multidimensional semantic structure model represents a data model used to characterize the response logic between the behavioral characteristics of process nodes and user behavioral preferences, so as to establish a multidimensional mapping relationship between multiple behavioral characteristic dimensions of process nodes (such as response time, activation conditions, etc.) and user behavioral preference dimensions (such as operation frequency, task transfer path, etc.).

[0081] Exemplarily, the semantic association degree of the response combination between the node behavior parameters and the user behavior patterns in the joint response structure is mined, thereby constructing a multi-dimensional semantic structure model. Specifically, the attribute space of each response entry in the joint response structure is modeled, so that it can be classified and expressed under a unified feature dimension system, thereby identifying the similar response features between the specific node behavior parameters and the specific user behavior patterns in a quantitative manner. On this basis, all the response entries are clustered and divided in accordance with the preset semantic similarity measurement manner, so that the response entries belonging to the same category have consistency and continuity in the node behavior features and the user behavior preferences. Furthermore, after the clustering and division processing is completed, the corresponding semantic labels are formed by abstract extraction of each category of response entries, and a response structure matrix covering multiple dimensions is constructed based on these semantic labels, which is used as the final generated multi-dimensional semantic structure model. The multi-dimensional semantic structure model not only reflects the response relationship of the process node to the user behavior preference under different node behavior features, but also can inversely reveal the node behavior features relied on by various user behavior preferences.

[0082] In step S603, rule extraction and logic coding processing are performed on each process node according to the multi-dimensional semantic structure model, to obtain a node rule description set corresponding to each process node.

[0083] Exemplarily, from the logical response relationship between the node behavior features and the user behavior preferences reflected by the multi-dimensional semantic structure model, a rule expression form capable of describing the decision mechanism of the process node is extracted, and is coded in a structured logic expression form to form a node rule description set. Specifically, the semantic response dimension most relevant to the node behavior parameters of a process node in the multi-dimensional semantic structure model is first identified, and the response conditions of the process node under different user behavior patterns are determined by aggregating the response features reflected in the semantic response dimension. On this basis, the matching relationship between the response conditions and the user behavior patterns is arranged into expressible rule entries, and these rule entries are logically coded in accordance with the execution role of the process node in the entire process structure and the upstream and downstream logical relationship, so that each rule entry can be accurately identified and called in the actual operation process of the process node, while maintaining the process connection relationship with other nodes. Based on this, the finally formed node rule description set can clearly express how each process node should respond when facing specific user identity attributes and behavior preferences, trigger which control logic, and pass what state control signal to the subsequent nodes.

[0084] In this embodiment, firstly, a joint response structure is constructed according to the structural association relationship between the node behavior parameter set and the user behavior mode set, so that the response dependence between the node behavior and the user behavior can be logically abstractly expressed; secondly, a multi-dimensional semantic structure model is obtained by performing semantic clustering processing on the joint response structure, so that the complex behavior interaction relationship can be presented in a dimensional form, and the extraction capability of the node behavior response features is enhanced; and thirdly, the multi-dimensional semantic structure model is used for rule extraction and logical coding processing of the process nodes, so that each process node has a clear response rule and logical expression, based on which the process nodes in the digital publishing process have a personalized decision basis, and the structural response capability of the process management and the fine degree of behavior control are enhanced.

[0085] In one exemplary embodiment, after obtaining the state process binding result, the method further includes steps S701 to S703.

[0086] Step S701: According to the participation block dimension corresponding to the target user, a control unit aggregation processing is performed on the state process binding result, to obtain a control unit set for block-level process management.

[0087] The control unit set represents a group of basic execution units for uniformly managing the process behavior of the user in the digital publishing process at the block level, and each control unit in the control unit set corresponds to a specific block range.

[0088] Exemplarily, in the state process binding result, by analyzing the state tags involved in each participation block of the target user and the corresponding process nodes, a control unit set with complete execution semantics is constructed in the logical structure, each control unit in the control unit set corresponds to a specific block range, and covers all related state tags and process nodes under the block. In addition, not only is it required that the constructed control unit meets the logical integrity requirement in structure, but also the upstream and downstream dependence relationship between states and the function scheduling relationship between process nodes are considered, so as to ensure that each control unit has the basis for independent running and linkage scheduling in actual process execution. Finally, through the above aggregation processing, a control unit set for supporting block-level process scheduling and execution is obtained, and each control unit takes the participation block of the target user as the boundary to form a basic execution unit for block-oriented process management.

[0089] Step S702: Obtain the interaction context information of the target user in the current time period, perform process scheduling analysis on each control unit in the control unit set according to the interaction context information, and obtain a scheduling instruction set for describing the calling sequence and control condition of each control unit.

[0090] The interaction context information represents a data set reflecting the system interaction behavior background of the target user in the current time period in the digital publishing process, such as the current operation time, access source, historical task completion status, interaction frequency, and the like.

[0091] Exemplarily, for each control unit in the control unit set, the context analysis and the call dependency analysis are sequentially performed in combination with the interaction context information of the target user in the current period to determine whether each control unit has the condition of being scheduled to be executed in the current environment. Specifically, the execution conditions of the flow nodes contained in the control unit need to be discriminated one by one to determine whether they meet the constraint requirements of the user behavior, the panel task progress, or the resource state in the current context, in addition, the calling sequence of the control unit in the entire process and the execution dependency logic between other control units need to be determined. Finally, after the above context analysis and call dependency analysis operations, a scheduling sequence is constructed for all control units that meet the conditions, and a clear scheduling instruction is formed for each control unit. The content of the scheduling instruction includes the calling sequence, control condition, execution timing, and dependency relationship of the control unit; based on this, a scheduling instruction set is integrated to describe the scheduling process logic.

[0092] In step S703, according to the scheduling instruction set, the state control logic of the corresponding control unit of the target user in each participating panel is executed in the preset process engine, and a process behavior response result responding to the corresponding state control logic is obtained.

[0093] The state control logic of the control unit represents the state-driven logic configured for the control unit constructed for a specific participating panel, which is used to dynamically transfer and logically respond to the panel state according to the current scheduling instruction of the user, for example, if a user has completed the content uploading task in the review panel and meets the approval condition triggering requirement, the state control logic will convert the panel state from “to upload” to “to review”.

[0094] The process behavior response result represents a set of panel state change records obtained after the process engine executes the state control logic of the control unit, which is used to clearly show the latest panel state performance or stage processing feedback of the target user in each participating panel.

[0095] Exemplarily, based on the set of scheduling instructions, the control units corresponding to each scheduling instruction are scheduled and executed one by one; specifically, in the execution process, the corresponding flow nodes are loaded one by one according to the calling order, control condition, execution opportunity and dependency relationship and other information defined in each scheduling instruction, and it is judged whether the state control logic required for execution is met; wherein, the state control logic is based on the state of the board, according to the state label of the target user in the participation board, the control path, trigger condition and constraint parameter of the flow behavior are judged and combined, so as to limit the execution mode of the control unit.

[0096] Based on this, the flow engine will read the execution mode specified by the state control logic, and make a state response judgment on the flow node, for example, if the current board state matches the state control logic, the flow node is activated to generate the preset flow behavior; otherwise, the original state is maintained or the waiting state is entered; thus, a state-driven dynamic flow management mechanism is formed, through which the flow engine can schedule and execute each control unit according to the current board state of the target user without relying on fixed flow execution templates, and realize personalized flow response at the board level. Finally, the flow engine will output the flow behavior response result matched with the state control logic of each board after the execution of each control unit, to reflect the task execution, path transfer behavior or node activation state of the target user in the participation board, and serve as the basis for subsequent state adjustment, task scheduling and flow tracking.

[0097] In this embodiment, first, the control units are aggregated according to the state flow binding result to obtain a control unit set for board-level flow management, realizing the logical unification of flow granularity and board granularity; second, the control unit set is analyzed for flow scheduling according to the interactive context information of the target user, thereby generating a set of scheduling instructions to enhance the context adaptability of flow scheduling; third, the state control logic of the control unit is executed according to the set of scheduling instructions, thereby driving the flow behavior according to the board state to generate a flow behavior response result, realizing a state-driven dynamic flow management mechanism; based on this, the digital publishing process has the ability of multi-board parallel control and state response, improving the flow scheduling accuracy and behavior control flexibility of the flow engine under multiple boards.

[0098] It should be understood that although each step in the flowchart involved in the embodiments described above is shown in sequence according to the direction of the arrow, these steps are not necessarily executed in the order indicated by the arrow. Unless otherwise specified herein, there is no strict order limitation for the execution of these steps, and these steps can be executed in other orders. Moreover, at least some of the steps in the flowchart involved in the embodiments described above can include multiple steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily sequential, but can be alternately or alternately executed with at least part of other steps or steps or stages in other steps.

[0099] Based on the same inventive concept, the embodiments of the present application also provide a user state management apparatus for a digital publishing process for implementing the user state management method for a digital publishing process described above. The problem-solving implementation scheme provided by the apparatus is similar to the implementation scheme described in the above method, so the specific limitations in one or more user state management apparatus embodiments for a digital publishing process provided below can refer to the limitations of the user state management method for a digital publishing process described above, which will not be repeated here.

[0100] In one exemplary embodiment, as shown in Figure 2 A user state management apparatus for a digital publishing process is provided, comprising: a parsing module 201, a modeling module 202, and a mapping module 203, wherein: The parsing module 201 is configured to perform feature analysis processing on user information of a target user in a digital publishing process to obtain state feature data corresponding to the target user, wherein the user information includes identity attribute information, historical task records, and participation panel information corresponding to the target user. The modeling module 202 is configured to obtain a set of time sequence events triggered by the target user in the digital publishing process, and perform state transition modeling processing on the set of time sequence events based on the participation panel according to the state feature data, to obtain a state evolution trajectory of the target user in the digital publishing process. The mapping module 203 is configured to determine state labels corresponding to each trajectory node in the state evolution trajectory of the target user, map and bind each state label with a process node in the digital publishing process, and obtain a state process binding result, to drive a process engine to manage process behavior according to the participation panel dimension.

[0101] In an example embodiment, the modeling module 202 is further configured to: obtain a preset state modeling parameter template, perform index matching on the state modeling parameter template according to the state feature data, and obtain a modeling parameter set used to drive a state transition modeling process; perform multi-dimensional screening processing on each time sequence event in the time sequence event set according to the modeling parameter set, and obtain an effective time sequence event set; and perform state transition modeling processing based on a participating plate according to a plate state transition path in the modeling parameter set, and obtain a state evolution track of the target user in the digital issuance process.

[0102] In an example embodiment, the modeling module 202 is further configured to: perform path matching processing on the effective time sequence event set according to an event trigger sequence according to the plate state transition path in the modeling parameter set, obtain an effective path set in the plate state transition path, and each effective path in the effective path set corresponds to a combination of a corresponding event type and a corresponding plate state; and perform state transition processing on the corresponding plate state in sequence according to each effective path in the effective path set, and obtain the state evolution track of the target user in the digital issuance process.

[0103] In an example embodiment, the mapping module 203 is further configured to: perform feature extraction processing on each process node in the digital issuance process according to each state label, obtain a node rule description set corresponding to each process node; perform node matching processing on each state label according to a mapping rule between each state label and the node rule description set, and construct a mapping binding relationship between each state label and a corresponding process node to obtain a state process binding result.

[0104] In an example embodiment, the mapping module 203 is further configured to: in the digital issuance process, identify a process type identifier and node structure attribute information corresponding to each process node according to an execution role and an upstream and downstream position relationship of each process node in the process structure; perform node behavior parameter extraction processing on each process node according to the process type identifier and the node structure attribute information corresponding to each process node, and obtain a node behavior parameter set; perform encoding and aggregation processing on a user identity attribute and a behavior category reflected by each state label, and obtain a user behavior mode set; and perform structure modeling on each process node according to a structure association relationship between the node behavior parameter set and the user behavior mode set, and obtain a node rule description set corresponding to each process node.

[0105] In an example embodiment, the mapping module 203 is further configured to: construct a joint response structure between the node behavior parameters in the node behavior parameter set and the user behavior patterns in the user behavior pattern set according to the structural association relationship between the node behavior parameter set and the user behavior pattern set; perform semantic clustering processing on the joint response structure to obtain a multi-dimensional semantic structure model, so as to represent the logical response relationship between the node behavior characteristics and the user behavior preferences; and perform rule extraction and logical coding processing on each process node according to the multi-dimensional semantic structure model, to obtain a node rule description set corresponding to each process node.

[0106] In an example embodiment, the apparatus further includes an execution module configured to: perform control unit aggregation processing on the state flow binding result according to the participation block dimension corresponding to the target user, to obtain a control unit set for block-level process management; obtain the interaction context information of the target user in a current time period, and perform process scheduling analysis on each control unit in the control unit set according to the interaction context information, to obtain a scheduling instruction set for describing the calling sequence and control condition of each control unit; and execute the state control logic of the corresponding control unit of the target user in each participation block in a preset process engine according to the scheduling instruction set, to obtain a process behavior response result in response to the corresponding state control logic.

[0107] The above modules in the user state management apparatus for the digital issuance process can be implemented in whole or in part by software, hardware, and combinations thereof. The above modules can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory in the computer device in software form, so as to be called and executed by the processor to perform the operations corresponding to the above modules.

[0108] In an example embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, and the processor implementing the steps in any of the above embodiments when executing the computer program.

[0109] In an embodiment, a computer readable storage medium is provided, storing a computer program, and the computer program is executed by a processor to implement the steps in any of the above embodiments.

[0110] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when the computer program is executed, the processes of the above-mentioned embodiments of the methods can be included. Any reference to memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without being limited thereto.

[0111] Any combination of the technical features of the above embodiments can be made. In order to make the description simple, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combination of the technical features does not exist, it should be considered as the scope of the present application.

[0112] The above embodiments only express several implementation manners of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the patent of the present application. It should be pointed out that for ordinary skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are within the scope of protection of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A user status management method for a digital distribution process, characterized in that: The method comprises: Perform feature analysis on the target user's user information in the digital distribution process to obtain the state feature data corresponding to the target user, the user information including the target user's corresponding identity attribute information, historical task records, and participation section information; Obtaining a set of time series events triggered by the target user in the digital distribution process, performing state transition modeling processing on the time series event set based on the participating sectors according to the state feature data, and obtaining a state evolution trajectory of the target user in the digital distribution process; Determine the state labels corresponding to each trajectory node of the target user in the state evolution trajectory, map and bind each state label with the process node in the digital distribution process, and obtain the state process binding result to drive the process engine to manage the process behavior according to the participating section dimension.

2. The method according to claim 1, characterized in that The step of performing state transition modeling processing on the set of time series events based on the state feature data and obtaining the state evolution trajectory of the target user in the digital distribution process includes: Obtaining a preset state modeling parameter template, performing index matching on the state modeling parameter template according to the state feature data, and obtaining a modeling parameter set for driving the state transition modeling process; Performing multi-dimensional screening processing on each time series event in the time series event set according to the modeling parameter set to obtain a valid time series event set; According to the plate state transition path in the modeling parameter set, the effective time series event set is subjected to state transition modeling processing based on the participating plates to obtain the state evolution trajectory of the target user in the digital distribution process.

3. The method according to claim 2, characterized in that The step of performing state transition modeling on the set of valid time series events based on the state transition path of the plate in the set of modeling parameters to obtain the state evolution trajectory of the target user in the digital distribution process includes: According to the plate state transition path in the modeling parameter set, path matching processing is performed on the valid time series event set according to the event triggering order to obtain a valid path set in the plate state transition path, each valid path in the valid path set corresponds to a combination of a corresponding event type and a corresponding plate state; According to each valid path in the valid path set, the state transition processing is performed on the corresponding plate state in sequence to obtain the state evolution trajectory of the target user in the digital distribution process.

4. The method according to claim 1, wherein Mapping and binding each state tag with a process node in the digital issuance process to obtain a state process binding result includes: Performing feature extraction processing on the process nodes in the digital issuance process according to each status label to obtain a node rule description set corresponding to each process node; According to the mapping rules between each state label and the node rule description set, node matching processing is performed on each state label, and a mapping binding relationship between each state label and the corresponding process node is constructed to obtain a state process binding result.

5. The method according to claim 4, characterized in that The feature extraction process is performed on the process nodes in the digital issuance process according to each status label to obtain a node rule description set corresponding to each process node, including: In the digital distribution process, according to the execution role and upstream and downstream position relationship of each process node in the process structure, the process type identifier and node structure attribute information corresponding to each process node are identified; According to the process type identification and node structure attribute information corresponding to each process node, node behavior parameter extraction processing is performed on each process node to obtain a node behavior parameter set; Encode and aggregate the user identity attributes and behavior categories reflected by each status tag to obtain a set of user behavior patterns; According to the structural association relationship between the node behavior parameter set and the user behavior pattern set, each process node is structurally modeled to obtain a node rule description set corresponding to each process node.

6. The method according to claim 5, characterized in that The structural association relationship between the node behavior parameter set and the user behavior pattern set is used to perform structural modeling on each process node to obtain a node rule description set corresponding to each process node, including: constructing a joint response structure between the node behavior parameters in the node behavior parameter set and the user behavior patterns in the user behavior pattern set according to the structural association relationship between the node behavior parameter set and the user behavior pattern set; Performing semantic clustering on the joint response structure to obtain a multi-dimensional semantic structure model for characterizing the logical response relationship between node behavior characteristics and user behavior preferences; Rule extraction and logic coding are performed on each process node according to the multi-dimensional semantic structure model to obtain a node rule description set corresponding to each process node.

7. The method according to claim 1, characterized in that After obtaining the state process binding result, the method further includes: According to the participation section dimension corresponding to the target user, control unit aggregation processing is performed on the state process binding result to obtain a control unit set for section-level process management; Obtaining interaction context information of the target user in the current time period, performing process scheduling analysis on each control unit in the control unit set based on the interaction context information, and obtaining a scheduling instruction set for describing the calling sequence and control conditions of each control unit; According to the scheduling instruction set, the state control logic of the corresponding control unit of the target user in each participating module is executed in a preset process engine to obtain a process behavior response result responsive to the corresponding state control logic.

8. A user status management device for a digital distribution process, characterized in that: The device comprises: The parsing module is used to perform feature parsing on the target user's user information in the digital distribution process to obtain the state feature data corresponding to the target user. The user information includes the target user's corresponding identity attribute information, historical task records, and participation section information; a modeling module for obtaining a set of time series events triggered by the target user in the digital distribution process, performing state transition modeling processing on the set of time series events based on the state feature data and the participating sectors, and obtaining a state evolution trajectory of the target user in the digital distribution process; A mapping module is used to determine the state labels corresponding to each trajectory node of the target user in the state evolution trajectory, map and bind each state label with the process node in the digital distribution process, and obtain the state process binding result to drive the process engine to manage the process behavior according to the participating section dimension.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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