Workflow processing method and device, computer equipment, readable storage medium and program product

Abstract

The invention relates to a workflow processing method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: in response to a parameter configuration operation on a current configuration node in a first workflow, determining a current configuration parameter of the current configuration node; acquiring configured parameters of the historical configuration nodes from the second workflow; determining a parameter processing type of the current configuration node based on the current configuration parameter, the configured parameter and respective node attributes of the current configuration node and the historical configuration node; determining a shared node and a non-shared node of the current configuration node in each to-be-configured node of the third workflow based on the parameter processing type; determining a node parameter of the shared node based on the current configuration parameter, and determining a node parameter of the non-shared node in response to a parameter configuration operation on the non-shared node; and obtaining a target workflow based on the node parameter of each node in the third workflow. By adopting the method, the generation efficiency of the workflow can be improved.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular, to a workflow processing method, apparatus, computer device, computer-readable storage medium, and computer program product. Background Art

[0002] With the development of computer technology, users can design workflows through computers. When completing a complete workflow design, users usually use a canvas to draw connection lines according to selected nodes, complete the graphic drawing in the canvas area, and then set node parameters.

[0003] However, after the user sets the node parameters for the currently selected node, it is usually necessary for the user to continue to select the next node and set the same parameters, resulting in low efficiency in setting node parameters and thus low efficiency in generating workflows. Summary of the Invention

[0004] Based on this, in view of the above technical problems, it is necessary to provide a workflow processing method, apparatus, computer device, computer-readable storage medium, and computer program product that can improve the efficiency of generating workflows.

[0005] In a first aspect, this application provides a workflow processing method, including:

[0006] In response to a parameter configuration operation for a currently configured node in a first workflow, determine the current configuration parameters of the currently configured node;

[0007] Obtain the configured parameters of a historical configured node from a second workflow;

[0008] Based on the current configuration parameters, the configured parameters, and the respective node attributes of the currently configured node and the historical configured node, determine the parameter processing type corresponding to the currently configured node;

[0009] Based on the parameter processing type, determine the shared nodes and non-shared nodes corresponding to the currently configured node among the nodes to be configured in a third workflow;

[0010] Based on the current configuration parameters, determine the node parameters corresponding to the shared nodes, and in response to a parameter configuration operation for the non-shared nodes, determine the node parameters corresponding to the non-shared nodes;

[0011] Based on the node parameters corresponding to each node in the third workflow, obtain a target workflow.

[0012] In a second aspect, this application also provides a workflow processing apparatus, including:

[0013] A parameter input module, configured to determine the current configuration parameters of the current configuration node in response to a parameter configuration operation on the current configuration node in the first workflow;

[0014] A parameter acquisition module, configured to obtain the configured parameters of the historical configuration node from the second workflow;

[0015] A processing type module, configured to determine the parameter processing type corresponding to the current configuration node based on the current configuration parameters, the configured parameters, and the node attributes of the current configuration node and the historical configuration node respectively;

[0016] A shared node module, configured to determine the shared nodes and non-shared nodes corresponding to the current configuration node among the nodes to be configured in the third workflow based on the parameter processing type;

[0017] A parameter determination module, configured to determine the node parameters corresponding to the shared nodes based on the current configuration parameters, and determine the node parameters corresponding to the non-shared nodes in response to a parameter configuration operation on the non-shared nodes;

[0018] A workflow generation module, configured to obtain a target workflow based on the node parameters corresponding to each node in the third workflow.

[0019] In a third aspect, the present application further provides a computer device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0020] Determine the current configuration parameters of the current configuration node in response to a parameter configuration operation on the current configuration node in the first workflow;

[0021] Obtain the configured parameters of the historical configuration node from the second workflow;

[0022] Determine the parameter processing type corresponding to the current configuration node based on the current configuration parameters, the configured parameters, and the node attributes of the current configuration node and the historical configuration node respectively;

[0023] Determine the shared nodes and non-shared nodes corresponding to the current configuration node among the nodes to be configured in the third workflow based on the parameter processing type;

[0024] Determine the node parameters corresponding to the shared nodes based on the current configuration parameters, and determine the node parameters corresponding to the non-shared nodes in response to a parameter configuration operation on the non-shared nodes;

[0025] Obtain a target workflow based on the node parameters corresponding to each node in the third workflow.

[0026] Fourthly, the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0027] In response to a parameter configuration operation on the current configuration node in the first workflow, determine the current configuration parameters of the current configuration node;

[0028] Obtain the configured parameters of the historical configuration node from the second workflow;

[0029] Based on the current configuration parameters, the configured parameters, and the node attributes of the current configuration node and the historical configuration node respectively, determine the parameter processing type corresponding to the current configuration node;

[0030] Based on the parameter processing type, determine the shared nodes and non-shared nodes corresponding to the current configuration node among the to-be-configured nodes in the third workflow;

[0031] Based on the current configuration parameters, determine the node parameters corresponding to the shared nodes, and in response to a parameter configuration operation on the non-shared nodes, determine the node parameters corresponding to the non-shared nodes;

[0032] Based on the node parameters corresponding to each node in the third workflow, obtain the target workflow.

[0033] Fifthly, the present application further provides a computer program product, including a computer program. When the computer program is executed by a processor, the following steps are implemented:

[0034] In response to a parameter configuration operation on the current configuration node in the first workflow, determine the current configuration parameters of the current configuration node;

[0035] Obtain the configured parameters of the historical configuration node from the second workflow;

[0036] Based on the current configuration parameters, the configured parameters, and the node attributes of the current configuration node and the historical configuration node respectively, determine the parameter processing type corresponding to the current configuration node;

[0037] Based on the parameter processing type, determine the shared nodes and non-shared nodes corresponding to the current configuration node among the to-be-configured nodes in the third workflow;

[0038] Based on the current configuration parameters, determine the node parameters corresponding to the shared nodes, and in response to a parameter configuration operation on the non-shared nodes, determine the node parameters corresponding to the non-shared nodes;

[0039] Based on the node parameters corresponding to each node in the third workflow, obtain the target workflow.

[0040] The above workflow processing method, device, computer device, computer-readable storage medium, and computer program product, after detecting the input of the current configuration parameter to the current configuration node of the first workflow, obtain the configured parameters of the historical configuration node from the second workflow, and determine the parameter processing type corresponding to the current configuration node according to the current configuration parameter, the configured parameter, and the node attributes of the current configuration node and the historical configuration node respectively, can identify the parameter sharing relationship between the current configuration node and the historical configuration node through the parameter processing type; determine the shared node and non-shared node corresponding to the current configuration node among the to-be-configured nodes of the third workflow according to the parameter processing type, can determine the shared node that conforms to the parameter sharing relationship among the to-be-configured nodes, ensuring the accuracy of the shared node, then determine the node parameter of the shared node according to the current configuration parameter, without the need for manual input of the node parameter of the shared node, improving the input efficiency of the node parameter of the shared node, and obtaining the target workflow according to the node parameters of each node in the third workflow, thereby improving the generation efficiency of the workflow. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the drawings required for use in the description of the embodiments of the present application or related technologies. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0042] Figure 1 It is an application environment diagram of the workflow processing method in an embodiment;

[0043] Figure 2 It is a flowchart of the workflow processing method in an embodiment;

[0044] Figure 3 It is a schematic diagram of a parameter sharing scenario of a workflow in an embodiment;

[0045] Figure 4 It is a schematic diagram of a parameter sharing scenario for batch nodes for batch parameters in an embodiment;

[0046] Figure 5 It is a schematic diagram of a parameter sharing scenario for batch parameters in an embodiment;

[0047] Figure 6 It is a schematic diagram of a parameter sharing scenario of a filtering node in an embodiment;

[0048] Figure 7 It is a schematic diagram of a single parameter sharing scenario in an embodiment;

[0049] Figure 8 Schematic diagram of node parameter sharing logic in an embodiment;

[0050] Figure 9 Structural block diagram of a workflow processing device in an embodiment;

[0051] Figure 10 Internal structure diagram of a computer device in an embodiment;

[0052] Figure 11 Internal structure diagram of a computer device in another embodiment. Detailed implementation manners

[0053] In order to make the objectives, technical solutions and advantages of the present application more clear and understandable, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0054] The workflow processing method provided by the embodiments of the present application can be applied to an application environment as Figure 1 shown. Among them, the terminal 102 communicates with the server 104 through a network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or placed in the cloud or other network servers. The terminal 102 determines the current configuration parameters of the current configuration node in the first workflow in response to a parameter configuration operation for the current configuration node; the terminal 102 obtains the configured parameters of the historical configuration node from the second workflow; the terminal 102 determines the parameter processing type corresponding to the current configuration node based on the current configuration parameters, the configured parameters, and the respective node attributes of the current configuration node and the historical configuration node; the terminal 102 determines the shared nodes and non-shared nodes corresponding to the current configuration node among the nodes to be configured in the third workflow based on the parameter processing type; the terminal 102 determines the node parameters corresponding to the shared nodes based on the current configuration parameters, and determines the node parameters corresponding to the non-shared nodes in response to a parameter configuration operation for the non-shared nodes; the terminal 102 obtains a target workflow based on the node parameters corresponding to each node in the third workflow, and the terminal 102 can send the target workflow to the server 104 for storage. Among them, the terminal 102 can be, but is not limited to, various personal computers, laptop computers, smart phones, tablet computers, etc. The server 104 can be an independent physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0055] In an exemplary embodiment, as Figure 2 shown, a workflow processing method is provided, and this method is applied to Figure 1Taking the terminal in as an example, the following steps are included:

[0056] Step 202: In response to a parameter configuration operation on the current configuration node in the first workflow, determine the current configuration parameters of the current configuration node.

[0057] Herein, the first workflow refers to the workflow to which the node where the user is currently configuring node parameters belongs. A workflow is a general description of a work process and the business rules between its various operation steps, including various nodes. A node represents a specific operation or task, and a node can include different functions, such as data processing, decision-making, approval, task assignment, etc. Node parameters are configuration parameters used to configure the corresponding functions of a node. For example, the parameter of "allowing circulation" for the configured approval node. The current configuration node refers to the node that the user has currently filled out. The current configuration parameters refer to the node parameters of the current configuration node, including configuration items and their corresponding configuration parameters.

[0058] Exemplarily, in response to a workflow generation instruction, the terminal displays a view window and a sidebar on the current page. The view window is used to place nodes, and the sidebar is used to provide nodes of different selectable node types and a parameter configuration area. The parameter configuration area can configure node parameters for the nodes in the view window. The user can select the required nodes from the sidebar on the current page and drag them to the view window for placement, and then configure the node parameters for the nodes in the view window in the parameter configuration area. Generally, after the user places the selected nodes in the view window and completes the layout settings of each node, at least one workflow with node parameters to be configured is obtained and displayed in the view window.

[0059] In response to a selection operation on any node in the workflow with at least one node parameter to be configured, the terminal takes this node as the current configuration node and takes the workflow to which the current configuration node belongs as the first workflow. Then, according to the node type of the current configuration node, the corresponding configurable items of the current configuration node are displayed in the node parameter configuration area. The user can perform parameter configuration on the configurable items in the node parameter configuration area. After detecting that the parameter configuration operation on the current configuration node by the user ends, the current configuration parameters corresponding to the current configuration node are obtained. Then, the terminal triggers parameter sharing detection for the current configuration node to detect whether the current configuration node meets the parameter sharing condition. The current configuration parameters can be configuration parameters for the enabled state of the corresponding function of the node. For example, the configuration parameter "on" for the enabled state of the configuration item "allowing circulation" or the configuration parameter "off" for the disabled state; the current configuration parameters can also be text configuration parameters input by the user. For example, the text information input for the configuration item "circulation personnel".

[0060] Step 204: Obtain the configured parameters of the historical configuration node from the second workflow.

[0061] Step 206: Determine the parameter processing type corresponding to the current configuration node based on the current configuration parameters, the configured parameters, and the node attributes of the current configuration node and the historical configuration node respectively.

[0062] Among them, the second workflow refers to a workflow with historical configuration nodes. A historical configuration node is a node that has completed parameter configuration before the current configuration node. The configured parameters refer to the node parameters of the historical configuration node. The node attribute refers to the characteristic attribute of the node, including node type, node name, node hierarchical position, etc. The node hierarchical position refers to the process stage where the node is located in the workflow, such as the submission level, the approval level, etc. The parameter processing type refers to the processing type for the node parameters of the current configuration node, including the processing type for parameter sharing and non - sharing of the current configuration parameters. The processing type for parameter sharing also includes the sharing object type for the current configuration node.

[0063] Exemplarily, after obtaining the current configuration parameters of the current configuration node, the terminal traverses the parameter configuration of each node in the view window, takes the nodes with configured node parameters other than the current configuration node as historical configuration nodes, takes the workflow to which the historical configuration node belongs as the second workflow, and obtains the configured parameters of the historical configuration node. Among them, the second workflow includes the first workflow, that is, when there are historical configuration nodes in the first workflow, the first workflow also serves as the second workflow.

[0064] Then the terminal obtains the node attributes of the current configuration node and the historical configuration node respectively, and performs parameter sharing detection on the current configuration node according to the current configuration parameters, the configured parameters, and the node attributes of the current configuration node and the historical configuration node respectively. It can be to identify whether there is a shareable relationship between the current configuration node and the historical configuration node according to the current configuration parameters, the configured parameters, and the node attributes of the current configuration node and the historical configuration node respectively. If not, the current configuration node does not meet the parameter sharing condition, and the parameter processing type corresponding to the current configuration node is the parameter non - sharing type, and the parameter sharing process for the current configuration parameters ends; if so, the current configuration node meets the parameter sharing condition, and the sharing object type for the current configuration node is determined according to the node attribute characteristics of the current configuration node and the historical configuration node with a shareable relationship, and the parameter processing type corresponding to the current configuration node is the parameter shareable type.

[0065] In an exemplary embodiment, after obtaining the current configuration parameters, the configured parameters, and the node attributes of the current configuration node and the historical configuration node respectively, the terminal inputs the current configuration parameters, the configured parameters, and the node attributes of the current configuration node and the historical configuration node respectively into a preset parameter processing type prediction model for parameter sharing detection, and outputs the parameter processing type corresponding to the current configuration node through the parameter processing type prediction model.

[0066] In an exemplary embodiment, after obtaining the current configuration parameters, the configured parameters, and the node attributes of the current configuration node and the historical configuration node respectively, the terminal performs parameter sharing detection on the current configuration node according to the preset sharing rules. Specifically, it can determine whether there is a shareable relationship between the historical configuration node and the current configuration node according to the consistency of the node attributes of the current configuration node and the historical configuration node and the consistency between the current configuration parameters and the configured parameters. For example, there is a shareable relationship between the current configuration node and the historical configuration node with the same node type and the same node parameters; for example, there is a shareable relationship between the current configuration node and the historical configuration node with the same node name and the same node hierarchical position; for example, there is a shareable relationship between the current configuration node and the historical configuration node with the same node hierarchical position in multiple branches in the workflow.

[0067] In an exemplary embodiment, after obtaining the current configuration parameters, the configured parameters, and the node attributes of the current configuration node and the historical configuration node respectively, the terminal calculates the feature similarity between the current configuration node and each historical configuration node, and performs parameter sharing detection on the current configuration node according to the feature similarity. Specifically, it can extract features from the current configuration node and each historical configuration node, including converting the node type, node parameters, node name, and node hierarchical position into feature vectors, and then comparing the node vector distances between the current configuration node and the historical configuration node. It can use cosine similarity or Euclidean distance for comparison, and then determine the historical configuration node with a vector distance exceeding the threshold as having a shareable relationship with the current configuration node.

[0068] Step 208: Based on the parameter processing type, determine the shared nodes and non-shared nodes corresponding to the current configuration node among the to-be-configured nodes of the third workflow.

[0069] Among them, the third workflow refers to a workflow with to-be-configured nodes. The to-be-configured node refers to a node whose node parameters have not been configured. The shared node refers to an object that can be shared with node parameters.

[0070] Exemplarily, when the parameter processing type corresponding to the current configuration node is a parameter sharable type, obtain the shared object type corresponding to the current configuration node. The shared object type can be, for example, the same node name type, the same node hierarchical position type, etc. Determine the to-be-configured nodes that conform to the shared object type among the to-be-configured nodes in the third workflow according to the shared object type, obtain the shared nodes corresponding to the current configuration node, and determine the non-shared nodes corresponding to the current configuration node for the to-be-configured nodes that do not conform to the shared object type. Among them, the third workflow includes the first workflow and the second workflow, that is, when there are to-be-configured nodes in the first workflow and the third workflow, the first workflow and the second workflow also serve as the third workflow.

[0071] In an exemplary embodiment, when the first workflow, the second workflow, and the third workflow are the same workflow, parameter sharing of the current configuration node for the same workflow can be achieved; when the first workflow, the second workflow, and the third workflow are different workflows, parameter sharing of the current configuration node for different workflows can be achieved.

[0072] Step 210, determine the node parameters corresponding to the shared nodes based on the current configuration parameters, and determine the node parameters corresponding to the non-shared nodes in response to the parameter configuration operation on the non-shared nodes.

[0073] Step 212, obtain the target workflow based on the node parameters corresponding to each node in the third workflow.

[0074] Among them, the target workflow refers to the workflow in which the node parameters of each node have been configured.

[0075] Exemplarily, after the terminal determines the shared nodes and non-shared nodes in the third workflow, perform parameter configuration on each shared node according to the current configuration parameters of the current configuration node to obtain the node parameters corresponding to each shared node, and then obtain the node parameters corresponding to the non-shared nodes in response to the parameter configuration operation on the non-shared nodes. After the node parameters corresponding to each node in the third workflow have been configured, the target workflow is obtained.

[0076] In an exemplary embodiment, in response to a parameter configuration operation on the current non - shared node in the non - shared nodes, the terminal uses the current non - shared node as the current configuration node, uses the workflow to which the current non - shared node belongs as the first workflow, and returns to execute the step of determining the current configuration parameters of the current configuration node. When it is detected that the parameter processing type is the parameter non - sharable type, the next non - shared node is used as the current configuration node, and it returns to execute the step of determining the current configuration parameters of the current configuration node until it is detected that the parameter processing type is the parameter sharable type. Then, it enters the step of determining the shared node and non - shared node corresponding to the current configuration node among the to - be - configured nodes of the third workflow based on the parameter processing type, and obtains the target workflow based on the node parameters corresponding to each node in the third workflow.

[0077] In the above workflow processing method, after detecting the input of the current configuration parameters for the current configuration node of the first workflow, the configured parameters of the historical configuration node are obtained from the second workflow. According to the current configuration parameters, the configured parameters, and the node attributes of the current configuration node and the historical configuration node respectively, the parameter processing type corresponding to the current configuration node is determined. It can identify the parameter sharing relationship between the current configuration node and the historical configuration node through the parameter processing type. Determining the shared node and non - shared node corresponding to the current configuration node among the to - be - configured nodes of the third workflow according to the parameter processing type can determine the shared node that conforms to the parameter sharing relationship among the to - be - configured nodes, ensuring the accuracy of the shared node. Then, the node parameters of the shared node are determined according to the current configuration parameters, without the need for manual input of the node parameters of the shared node, improving the input efficiency of the node parameters of the shared node. And the target workflow is obtained based on the node parameters of each node in the third workflow, thereby improving the generation efficiency of the workflow.

[0078] In an exemplary embodiment, step 206, determining the parameter processing type corresponding to the current configuration node based on the current configuration parameters, the configured parameters, and the node attributes of the current configuration node and the historical configuration node respectively, includes:

[0079] Input the current configuration parameters, the configured parameters, and the node attributes of the current configuration node and the historical configuration node respectively into the parameter processing type prediction model;

[0080] Through the parameter processing type prediction model, based on the current configuration parameters and the configured parameters, matching nodes are determined among the historical configuration nodes. Based on the node attributes of the current configuration node and the matching nodes, the similar attribute features between the current configuration node and the matching nodes are extracted, and the parameter processing type corresponding to the current configuration node is output based on the similar attribute features.

[0081] Among them, a matching node refers to a historical configuration node whose configured parameters match the current configuration parameters. Similar attribute features refer to the features of node attributes that are similar between the current configuration node and the matching node.

[0082] Exemplarily, the terminal inputs the current configuration parameters, the configured parameters, and the node attribute input parameter processing type prediction model of the current configuration node and the historical configuration node respectively. The parameter processing type prediction model matches the current configuration parameters with the configured parameters of each historical configuration node, and takes the historical configuration node with successful node parameter matching as the matching node corresponding to the current configuration node. Generally, the historical configuration node with consistent node parameters is taken as the matching node corresponding to the current configuration node. Then, the node attributes of the matching node are obtained, and the similar attribute features between the current configuration node and the matching node are extracted through the feature extraction network of the parameter processing type prediction model. For example, the similar attribute features between the node name "Approval Node A" and "Review Node B", and then the similar attribute features are input into the classification layer of the parameter processing type prediction model, and the parameter processing type corresponding to the current configuration node is output through the classification layer. Among them, the similar attribute features can be characterized as a shareable relationship, and the parameter processing type prediction model can be understood as identifying the shareable relationship between the matching node with consistent node parameters and the current configuration node for the input node parameters and node attributes of the current configuration node and the historical configuration node, and learning the parameter sharing habit of the user to set the same node parameters for a certain type of node according to the identified shareable relationship, so as to predict the user intention for the current configuration node, that is, the parameter sharing type that meets the user's expectations.

[0083] In an exemplary embodiment, assume there is a workflow diagram, which includes the following three nodes: Node A: Approval node, parameter is B1, name is Node A, located in Branch 1; Node B: Approval node, parameter is B1, name is Node B, located in Branch 1; Node C: Approval node, parameter is B2, name is Node C, located in Branch 2. After the user sets the parameter of Node A to B1, the parameter processing type prediction model can be used to determine whether there is a shareable relationship between the parameters of Node B and Node C and Node A, and if not, output the parameter processing type of non-shareable parameters to determine whether further inspection or grouping processing is required.

[0084] Specifically, the parameter processing type prediction model includes a data preprocessing module, a neural network model module, and an inference module.

[0085] Data preprocessing module: Use BERT Tokenizer (a basic tool for cutting the input text into individual units) to vectorize the relevant text features of the node and encode the numerical features.

[0086] Neural network model module: It determines the shareable relationship between nodes to predict user intent, and constructs a multi-layer perceptron (MLP) model based on TensorFlow.js (a JavaScript library for machine learning development in browsers and Node.js). The model structure includes:

[0087] Input layer: It receives the feature vectors of nodes, and the input dimension is N, representing the length of the feature vectors of each node.

[0088] Hidden layer: A multi-layer fully connected network, including two hidden layers (128 neurons in the first layer and 64 neurons in the second layer), activation function (using ReLU (Rectified Linear Unit) to enhance the non-linear expression ability of the model), and regularization (preventing overfitting through Dropout), which is used to capture the relationship of node features, that is, similar attribute features.

[0089] Output layer: It uses the Softmax activation function to classify the output into three types of user intents (share parameters, do not share parameters, further check).

[0090] Inference module: According to the classification result of the neural network model, it determines the type of parameter sharing that meets the user's expectations.

[0091] In an exemplary embodiment, the original data is collected to train the parameter processing prediction model. The original data contains the node information of each node in the workflow: node type (type), such as approval node, task node, etc.; node parameter (parameter), such as the parameter value set manually by the user, for example, "B1"; node name (name), such as "Node A", "Node B"; hierarchical information (level), such as whether the node is at the same level of a certain branch; intent label (label), which indicates whether the node shares parameters (such as "share parameters", "do not share parameters", etc.). Sharing parameters means that the node has a shareable relationship where parameters can be shared with other nodes, and not sharing parameters means that the node does not have a shareable relationship where parameters can be shared with other nodes.

[0092] Then, the original data is preprocessed by the data preprocessing module, and each field in the original data is converted into numerical features, including encoding the node type, vectorizing the parameter value, calculating the length of the node name, encoding the hierarchical information, and encoding the intent label.

[0093] Encoding of node type: Convert the node type into a numerical value. For example, approval node → 1, non-approval node → 0. For complex types, the text can also be vectorized through BERT Tokenizer.

[0094] Parameter value vectorization: Extract the numerical part from the parameter value. For example, "B1" is extracted as 1. If the parameter value contains multiple parts (such as "B1.2"), it can be disassembled and standardized.

[0095] Node name length calculation: Calculate the character length of the node name or convert it into a word vector using word embedding technology.

[0096] Hierarchical information encoding: Convert whether a node is at a specific level of a certain branch into a binary label. For example, located in "Branch 1" → 1, not in Branch 1 → 0.

[0097] Intention label encoding: Convert the intention label into a one-hot encoding. For example, "Shared parameter" → [1, 0, 0], "Non-shared parameter" → [0, 1, 0], "Further inspection" → [0, 0, 1].

[0098] Then, perform standardization on the numerical features. For the parameter value and name length, use Min-Max Scaling to scale the data to the range [0, 1], and for features with uneven distribution, Z-score standardization can be used; for text data such as node type and node name, use BERT Tokenizer to extract context-related embedding vectors, which can capture richer semantic information. By defining the input text to be encoded, the array of word vector IDs corresponding to the text, initializing BERT Tokenizer, specifying the vocabulary file path and whether to lowercase, convert the input text into tokens (text units) of BERT Tokenizer, convert the tokens into word vector IDs, and return. For example, the input text: "Approval node" → output word vector [101, 2345, 756] (index values in the vocabulary). The length of the word vector can be controlled to a fixed length (such as 10) by padding or truncation.

[0099] Convert the standardized numerical features into feature vectors, and concatenate the features of all fields into a complete input vector: node type encoding + parameter value vectorization + node name length + hierarchical information to obtain the preprocessed data.

[0100] Divide the preprocessed data into a feature array features and a label array labels for training and testing: features: model input (two-dimensional array, each row is a feature vector of a node); labels: model output labels (one-hot encoding). Examples of preprocessed samples are as follows:

[0101] "feature": [101, 2345, 756, 1, 5, 1] / / Feature vector of node 1

[0102] [101, 2345, 789, 0, 2, 0] / / Feature vector of node 2

[0103] "labels": [1, 0, 1] / / Labels of node 1 (shared parameters)

[0104] [0, 1, 0] / / Labels of node 2 (non-shared parameters).

[0105] Before training the neural network model, model compilation is required, including specifying the loss function (using the Categorical Crossentropy loss function, suitable for multi-classification tasks), the optimizer (Adam optimizer), and the evaluation metric (classification accuracy (Accuracy)). After the model is compiled, the preprocessed data is input into the model for training. The Categorical Crossentropy loss function is used as the objective function, and the Adam optimizer is used for gradient optimization. The dataset is divided into a training set (80%, for model learning), a validation set (10%, for monitoring performance during training (such as overfitting)), and a test set (10%, for evaluating the final performance of the model). Then, the feature and label data are converted into the tensor format supported by TensorFlow.js. For example, features: two-dimensional tensor, labels: two-dimensional tensor (one-hot encoding).

[0106] Then, the model is trained. During the training process, training parameters need to be set, including: number of epochs: determines the number of times the model is optimized (such as 50 times), batch size: the number of samples used in each training (such as 8 samples), validation split: the proportion of data used for validation (such as 20%), and early stopping mechanism: terminates training early when the validation loss no longer decreases to prevent overfitting. The model training is completed through multiple rounds of optimization iterations, including model fitting: fitting the model using the training set data, and calculating the performance metrics (loss and accuracy) on the validation set after each training epoch; real-time monitoring: printing the loss value and accuracy at the end of each training epoch, and using callback functions to record the training process.

[0107] After training is completed, the test set is used to evaluate the model performance, which can be represented by evaluation metrics. The evaluation metrics include the loss value (Loss, indicating the error between the model prediction and the actual label) and the accuracy (Accuracy, representing the proportion of correctly predicted samples). After the model performance meets the standard, the trained model is stored in the local file system. Among them, during the training process, the model performance can be optimized by adjusting the learning rate (using a learning rate scheduler to dynamically reduce the learning rate according to the training progress; in TensorFlow.js, tf.train.exponentialDecay can be used to apply exponential decay to the learning rate learning_rate), increasing the training data (expanding the scale of the training set to improve the generalization ability of the model; using data augmentation techniques such as adding noise and randomly adjusting feature values), and regularization techniques (using Dropout to randomly discard some neurons to reduce overfitting).

[0108] In an exemplary embodiment, in response to a user's parameter configuration operation on the current configuration node in the first workflow, the current configuration parameters of the current configuration node are obtained, and the loaded parameter processing type prediction model is called. The current configuration parameters and the configured parameters are input into the parameter processing type prediction model. The input data is preprocessed by the data preprocessing module and transformed into a feature vector. Then, the neural network model module performs prediction calculations on the feature vector and outputs the classification result corresponding to the current configuration node, such as shared parameters (i.e., parameter sharable types), non-shared parameters (i.e., parameter non-sharable types), and further inspection (requiring manual confirmation or other complex rule analysis). The inference module determines the parameter sharing type that meets the user's expectations.

[0109] In this embodiment, the parameter processing type prediction model is used to determine which nodes can share parameters and automatically execute parameter synchronization, which can avoid the user from manually checking and setting the consistency of node parameters, thereby improving the generation efficiency of the workflow.

[0110] In an exemplary embodiment, the workflow processing method further includes:

[0111] Displaying a shared node control;

[0112] In response to a trigger operation on the shared node control, entering the step of obtaining the configured parameters of the historical configuration node from the second workflow for execution;

[0113] After determining the shared nodes and non-shared nodes corresponding to the current configuration node, displaying the shared nodes and parameter sharing controls;

[0114] In response to a trigger operation on the parameter sharing control, entering the step of determining the node parameters corresponding to the shared node based on the current configuration parameters for execution.

[0115] Among them, the shared node control is a control provided to the user to select whether to enable the parameter sharing detection function. The parameter sharing control is a control provided to the user to select whether to perform parameter sharing on the shared node.

[0116] Exemplarily, after the terminal detects that the user has completed the parameter configuration operation of the pre-configured node in the first workflow of the view window, it displays the shared node control in the sidebar, and in response to the trigger operation on the shared node control, triggers the terminal to perform parameter sharing detection on the current configured node, and enters the step of obtaining the configured parameters of the historical configured node from the second workflow. After determining the shared nodes and non-shared nodes corresponding to the current configured node, it displays the parameter sharing control in the view window, and differently displays the shared nodes in the third workflow of the view window, and in response to the trigger operation on the parameter sharing space, enters the step of determining the node parameters corresponding to the shared node according to the current configured parameters, and performs batch parameter configuration on each shared node according to the current configured parameters.

[0117] In an exemplary embodiment, as Figure 3 shown, a schematic diagram of a scenario for parameter sharing of a workflow is provided. Figure 3 The area containing the workflow is the view window, which shows at least one workflow with the node parameters to be configured that the user has completed the layout. This workflow can be a reimbursement workflow, such as reimbursement workflows for labor costs, business entertainment expenses, hotel expenses for business trips, etc. The areas on both sides of the view window are the sidebars. The left area provides nodes of different selectable node types, such as start nodes, end nodes, approval nodes, etc., and the right area is the parameter configuration area. Figure 3 The control pointed by the arrow in the box in the right parameter configuration area in it is the shared node control, and it also includes the explanation of the shared node control "This parameter can be quickly shared to other nodes". The user can activate the "Intelligent Batch Share Parameters" state by clicking the shared node control.

[0118] Figure 3 In it, the user clicks on the "First-level Approval" node in the workflow of "Business Entertainment Expenses", which is shown in a dark frame, indicating the current configured node that the user is currently clicking. Then, the configurable items corresponding to the current configured node "First-level Approval" are displayed in the parameter configuration area on the right, and approval settings can be performed, such as "Allow Transfer", "Allow Circulation", "Allow Countersign", etc. Among them, the checked configuration items "Allow Circulation" and "Allow Batch Approval" are the configuration items selected by the user for "First-level Approval".

[0119] In an exemplary embodiment, as Figure 4As shown, a schematic diagram of a parameter sharing scenario for batch nodes with respect to batch parameters is provided. In response to a triggering operation by the user on the shared node control, the terminal triggers parameter sharing detection for the current configured node, and after determining the shared nodes among the nodes to be filled in according to the parameter sharing detection node, the shared nodes are displayed differently in the corresponding workflow (the third workflow), such as Figure 4 The "first-level approval" node marked with an asterisk and highlighted as shown in Figure 4 At the same time, a parameter sharing control is displayed in the view window, such as Figure 4 The "Share immediately" control pointed to by the arrow within the box as shown in

[0120] In this embodiment, by providing a shared node control and a parameter sharing node, an interactive scenario of batch sharing of node parameters can be realized, which can avoid the user from manually checking and setting the consistency of node parameters, thereby improving the generation efficiency of the workflow.

[0121] In an exemplary embodiment, the workflow processing method further includes:

[0122] In response to a selection operation on a shared node, the selected shared node is taken as the first target node;

[0123] In response to a parameter sharing operation on the first target node, the node parameters corresponding to the first target node are determined based on the current configured parameters.

[0124] Exemplarily, in response to a selection operation on a shared node, the terminal takes the selected shared node as the first target node, and then in response to a parameter sharing operation on the first target node, it can be in response to the user dragging the selected current configured parameters to the first target node and, through a mouse release operation, configuring the current configured parameters to the first target node to obtain the node parameters corresponding to the first target node. As Figure 5 Shown in the schematic diagram of the parameter sharing scenario for batch parameters, the selected 30 parameters are dragged to the first target node "first-level approval" in the direction of the arrow.

[0125] In this embodiment, through the parameter sharing operation on the first target node, the flexibility of parameter sharing is realized, and the accuracy of node parameters is ensured.

[0126] In an exemplary embodiment, the workflow processing method further includes:

[0127] In response to a selection operation on a non-shared node, the selected non-shared node is taken as the second target node;

[0128] In response to a parameter sharing operation for a second target node, determine the node parameters corresponding to the second target node based on the current configuration parameters.

[0129] Exemplarily, the terminal's response to a selection operation on a non-shared node can be a filtering operation on the non-shared node according to the filtering information input by the user. The filtered non-shared nodes are used as the second shared nodes, and then parameter sharing controls are displayed in the view window. By triggering the parameter sharing controls by the user, the terminal performs a parameter sharing operation for the second target node, configures the current configuration parameters to the second target node, and obtains the node parameters corresponding to the second target node. Among them, after selecting the current configuration parameters, the user can perform node filtering on shared nodes and non-shared nodes, rather than being limited to filtering non-shared nodes. As Figure 6 shown in the schematic diagram of the parameter sharing scenario for filtering nodes, filter the "department manager" nodes according to the node type in the view window. The parameter configuration area on the left shows 30 selected parameters. The terminal filters nodes according to "department manager" in each workflow and differentiates and displays the filtered "department manager" nodes in the workflow, such as Figure 6 the nodes other than the "department manager" shown in the background in the figure, and display parameter sharing controls in the view window, such as Figure 6 the "Share Immediately" control shown in the box in the figure. The user can share the 30 selected parameters to the filtered "department manager" nodes by triggering the "Share Immediately" control.

[0130] In this embodiment, by filtering nodes and realizing batch sharing of parameters to filtered nodes, the flexibility of parameter sharing is achieved, and the accuracy of node parameters is ensured.

[0131] In an exemplary embodiment, the workflow processing method further includes:

[0132] In response to a selection operation on the current configuration parameters, use the sub-parameters selected from the current configuration parameters as the target sub-parameters;

[0133] From each configured node, use the configured nodes whose node parameters include the target sub-parameters as the third target nodes;

[0134] Differentially display the third target nodes with different parameter contents on the target sub-parameters.

[0135] Exemplarily, in response to a selection operation on the current configuration parameters, the terminal uses the sub-parameters selected from the current configuration parameters as target sub-parameters, which are generally the configuration parameters for a certain configuration item, such as the configuration parameter "enabled" for the "allow circulation" configuration item. Then, among the configured nodes, the configured nodes whose node parameters contain the target sub-parameters are used as the third target nodes, and the configured nodes refer to the nodes in the workflow that have been configured. Then, in the view window, the third target nodes with different parameter contents on the target sub-parameters are displayed differently, which can be represented by node frames of different colors in each third target node. As Figure 7 shown in the schematic diagram of the single parameter sharing scenario, the terminal displays a "deselect all" control in the parameter configuration area, such as Figure 7 the control in the box shown in Figure a in the middle. By triggering the "deselect all" control, the user switches the current configuration parameters from the locked state to the selectable state. In response to the user's selection operation on the current configuration parameters, such as Figure 7 the "allow circulation" configuration item and its corresponding configuration parameter "enabled" selected in Figure b in the middle are used as the target sub-parameters, and then the third target nodes with different parameter contents on the target sub-parameters are displayed in the form of different colored frames in the view window, such as Figure 7 the "Business Company / General Manager" nodes pointed to by the arrows of "nodes with this parameter enabled" and "nodes with this parameter disabled" in Figure b in the middle.

[0136] In this embodiment, by selecting a single parameter for parameter sharing, the flexibility of parameter sharing is achieved, and the accuracy of node parameters is ensured.

[0137] In an exemplary embodiment, the parameter contents of the target sub-parameters are divided into two types; the workflow processing method further includes:

[0138] In response to a selection operation on the third target node, update the parameter content of the selected third target node on the target sub-parameter from the current content to another content.

[0139] Exemplarily, in response to a selection operation on the third target node, the terminal updates the parameter content of the selected third target node on the target sub-parameter from the current content to another content, which can be to switch the enabled state of the target sub-parameter on the corresponding configuration item, such as switching the "enabled" state to the "disabled" state.

[0140] In this embodiment, by selecting a single parameter for parameter sharing, the flexibility of parameter sharing is achieved, and the accuracy of node parameters is ensured.

[0141] In an exemplary embodiment, such as Figure 8As shown in the figure, a schematic diagram of node parameter sharing logic is provided. After the user completes parameter configuration on the current configured node, for example, if the user manually sets parameter B1 of node A, parameter sharing detection is performed on node A, including detecting whether other nodes in the workflow use the same setting change, that is, whether it is consistent with the parameter configuration of node A; if not, the current process is terminated and parameter sharing detection is stopped; if so, it is detected whether the qualified nodes are of the same node type C, if not, the current process is terminated; if so, it is detected whether the parameters manually set by the user in the same type of nodes are exactly the same as the parameters of node A, if so, it is determined that the parameter sharing type expected by the user is: the user hopes that nodes of "node type C" share the same parameter B1; if not, it is detected whether there is a node group D with the same node name in nodes of node type C, if not, the current process is terminated; if so, it is detected whether the parameters manually set by the user in node group D are exactly the same as the parameters of node A, if so, it is determined that the parameter sharing type expected by the user is: the user hopes that "node type C" and "node group D with the same node name" share the same parameter; if not, it is detected whether node group D is distributed on multiple branch lines at the same level E, if not, the current process is terminated; if so, it is detected whether the parameters manually set by the qualified nodes are exactly the same as the parameters of node A, if not, the current process is terminated; if so, it is determined that the parameter sharing type expected by the user is: the user hopes that nodes of "node type C", "node group with the same node name and located at the same level E" share the same parameter.

[0142] After the user sets parameter B1 of node A, a bubble prompt appears at the intelligent sharing entry (i.e., the specified position in the view window) indicating that the current node A can be quickly shared. Specifically, the user can click the function entry to enter the intelligent sharing mode (for parameter sharing detection and shared node recommendation). Through the above parameter sharing logic, according to the behavior path of node parameter settings in the user's current workflow, combined with conditions such as the attributes of the nodes, such as classification and naming, intelligent node parameter sharing suggestions are generated, prompting the user to recommend sharing the parameters of node A to node X. The user can switch from the intelligent sharing mode to the single-parameter view of intelligent sharing and default to select the first parameter in the parameter list row and column, and share a single parameter to the recommended shared node.

[0143] In an exemplary embodiment, the user can activate the intelligent batch sharing parameter function by triggering the node sharing control, providing interaction process flows for intelligent suggestion batch sharing scenarios, single-node drag-and-drop sharing scenarios, manual filtering batch sharing scenarios, and single-parameter sharing view function interaction processes. Intelligent suggestion batch sharing scenario: Click "Share Immediately" to share the selected parameters on the right to all the intelligent recommended shared nodes in the view (such as Figure 4 the nodes highlighted in the background in the figure).

[0144] Single-node drag-and-drop sharing scenario: Drag the selected total card on the right to share the selected parameters with the node at the mouse release position.

[0145] Manual filtering and batch sharing scenario: The user filters out nodes through input and highlights them, and clicks "Share Immediately" to share the selected parameters on the right with all the filtered and highlighted nodes in the view (such as Figure 6 the node named "Department Manager" in the example).

[0146] Single-parameter sharing view function: After clicking "Deselect All", the user can select a parameter individually, and the enabled state of the selected parameter in all stages is represented by the node style in the left view.

[0147] In this embodiment, parameter sharing based on nodes as the dimension does not require redefining and configuring parameters. Only the parameter attributes of a certain node need to be passed through to another node. Only the parameters under their respective nodes need to be concerned, which can avoid the risk of accidentally modifying parameters caused by an overly large sharing scope and ensure the accuracy of workflow generation.

[0148] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are shown in sequence according to the arrows, these steps do not necessarily need to be executed in the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages do not necessarily need to be executed at the same time, but can be executed at different times. The execution order of these steps or stages does not necessarily need to be sequential, but can be executed alternately or alternately with at least a part of other steps or steps in other steps.

[0149] Based on the same inventive concept, the embodiments of the present application also provide a workflow processing device for implementing the workflow processing method involved above. The implementation solutions provided by this device to solve problems are similar to the implementation solutions described in the above method. Therefore, the specific limitations in one or more embodiments of the following workflow processing devices can refer to the limitations on the workflow processing method in the above text and will not be repeated here.

[0150] In an exemplary embodiment, as Figure 9 shown, a workflow processing device 900 is provided, including: a parameter input module 902, a parameter acquisition module 904, a processing type module 906, a shared node module 908, a parameter determination module 910, and a workflow generation module 912, where:

[0151] A parameter input module 902, configured to determine the current configuration parameters of the current configuration node in the first workflow in response to a parameter configuration operation on the current configuration node;

[0152] A parameter acquisition module 904, configured to obtain the configured parameters of the historical configuration node from the second workflow;

[0153] A processing type module 906, configured to determine the parameter processing type corresponding to the current configuration node based on the current configuration parameters, the configured parameters, and the node attributes of the current configuration node and the historical configuration node respectively;

[0154] A shared node module 908, configured to determine the shared nodes and non-shared nodes corresponding to the current configuration node among the nodes to be configured in the third workflow based on the parameter processing type;

[0155] A parameter determination module 910, configured to determine the node parameters corresponding to the shared nodes based on the current configuration parameters, and determine the node parameters corresponding to the non-shared nodes in response to a parameter configuration operation on the non-shared nodes;

[0156] A workflow generation module 912, configured to obtain a target workflow based on the node parameters corresponding to each node in the third workflow.

[0157] In an exemplary embodiment, the processing type module 906 is further configured to input the current configuration parameters, the configured parameters, and the node attributes of the current configuration node and the historical configuration node respectively into a parameter processing type prediction model; through the parameter processing type prediction model, based on the current configuration parameters and the configured parameters, determine matching nodes among the historical configuration nodes, extract the similar attribute features between the current configuration node and the matching nodes based on the node attributes of the current configuration node and the matching nodes, and output the parameter processing type corresponding to the current configuration node based on the similar attribute features.

[0158] In an exemplary embodiment, the workflow processing device 900 is further configured to display a shared node control; in response to a trigger operation on the shared node control, enter the step of obtaining the configured parameters of the historical configuration node from the second workflow; after determining the shared nodes and non-shared nodes corresponding to the current configuration node, display the shared nodes and a parameter sharing control; in response to a trigger operation on the parameter sharing control, enter the step of determining the node parameters corresponding to the shared nodes based on the current configuration parameters.

[0159] In an exemplary embodiment, the workflow processing device 900 is further configured to, in response to a selection operation on a shared node, use the selected shared node as a first target node; in response to a parameter sharing operation on the first target node, determine the node parameters corresponding to the first target node based on the current configuration parameters.

[0160] In an exemplary embodiment, the workflow processing device 900 is further configured to, in response to a selection operation on a non-shared node, use the selected non-shared node as a second target node; and in response to a parameter sharing operation on the second target node, determine the node parameters corresponding to the second target node based on the current configuration parameters.

[0161] In an exemplary embodiment, the workflow processing device 900 is further configured to, in response to a selection operation on the current configuration parameters, use the sub-parameters selected from the current configuration parameters as target sub-parameters; from each of the configured nodes, use the configured nodes whose node parameters include the target sub-parameters as third target nodes; and distinguish and display the third target nodes with different parameter contents on the target sub-parameters.

[0162] In an exemplary embodiment, the parameter content of the target sub-parameters is divided into two types; the workflow processing device 900 is further configured to, in response to a selection operation on the third target node, update the parameter content of the selected third target node on the target sub-parameters from the current content to another content.

[0163] Each module in the above workflow processing device can be implemented in whole or in part by software, hardware, and their combination. The above modules can be embedded in the processor in the computer device in hardware form or be independent of it, or can be stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to the above modules.

[0164] In an exemplary embodiment, a computer device is provided. The computer device can be a server, and its internal structure diagram can be as Figure 10 shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store relevant data such as the target workflow. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals through a network connection. When the computer program is executed by the processor, a workflow processing method is implemented.

[0165] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in Figure 11 shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface, the display unit, and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner. The wireless manner can be implemented through WIFI, a mobile cellular network, near field communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a workflow processing method. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the computer device housing, or an external keyboard, touchpad, or mouse, etc.

[0166] Those skilled in the art can understand that Figures 10 - 11 the structure shown in is only a block diagram of some structures related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0167] In an exemplary embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above method embodiments are implemented.

[0168] In an embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by the processor, the steps in the above method embodiments are implemented.

[0169] In an embodiment, a computer program product is provided, including a computer program. When the computer program is executed by the processor, the steps in the above method embodiments are implemented.

[0170] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0171] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in this application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in this application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logic devices, data processing logics based on quantum computing, artificial intelligence (AI) processors, etc., without limitation.

[0172] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this application.

[0173] The above-described embodiments merely represent several implementation manners of this application. The description is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of this application. It should be noted that for those of ordinary skill in the art, without departing from the concept of this application, several modifications and improvements can still be made, and these all belong to the protection scope of this application. Therefore, the protection scope of this application shall be subject to the appended claims.

Claims

1. A workflow processing method, characterized in that: The method comprises: In response to a parameter configuration operation on a current configuration node in the first workflow, determining a current configuration parameter of the current configuration node; Obtain configured parameters of the historical configuration node from the second workflow; Determine a parameter processing type corresponding to the current configuration node based on the current configuration parameters, the configured parameters, and node attributes of the current configuration node and the historical configuration node; Based on the parameter processing type, determining, among the nodes to be configured in the third workflow, a shared node and a non-shared node corresponding to the currently configured node; Determine the node parameters corresponding to the shared node based on the current configuration parameters, and determine the node parameters corresponding to the non-shared node in response to the parameter configuration operation on the non-shared node; Based on the node parameters respectively corresponding to the nodes in the third workflow, a target workflow is obtained.

2. The method according to claim 1, characterized in that The determining, based on the current configuration parameters, the configured parameters, and respective node attributes of the current configuration node and the historical configuration node, the parameter processing type corresponding to the current configuration node includes: Inputting the current configuration parameters, the configured parameters, and the node attributes of the current configuration node and the historical configuration node into a parameter processing type prediction model; Through the parameter processing type prediction model, based on the current configuration parameters and the configured parameters, a matching node is determined in each historical configuration node, based on the node attributes of the current configuration node and the node attributes of the matching node, similar attribute features between the current configuration node and the matching node are extracted, and based on the similar attribute features, the parameter processing type corresponding to the current configuration node is output.

3. The method according to claim 1, characterized in that The method further comprises: Display shared node controls; In response to a trigger operation on the shared node control, the step of acquiring configured parameters of the historical configuration node from the second workflow is executed; After determining the shared nodes and non-shared nodes corresponding to the current configuration node, displaying the shared nodes and parameter sharing controls; In response to a trigger operation on the parameter sharing control, the step of determining the node parameters corresponding to the shared node based on the current configuration parameters is executed.

4. The method according to claim 1, characterized in that: The method further comprises: In response to a selection operation on the shared node, taking the selected shared node as a first target node; In response to the parameter sharing operation for the first target node, a node parameter corresponding to the first target node is determined based on the current configuration parameter.

5. The method according to claim 1, characterized in that The method further comprises: In response to a selection operation on the non-shared node, taking the selected non-shared node as a second target node; In response to the parameter sharing operation for the second target node, a node parameter corresponding to the second target node is determined based on the current configuration parameter.

6. The method according to claim 1, characterized in that The method further comprises: In response to a selection operation on the current configuration parameter, taking a sub-parameter selected from the current configuration parameter as a target sub-parameter; From each configured node, a configured node whose node parameter includes the target sub-parameter is selected as a third target node; The third target nodes having different parameter contents on the target sub-parameters are displayed distinctively.

7. The method according to claim 6, characterized in that The parameter content of the target sub-parameter is divided into two contents; the method further includes: In response to a selection operation on the third target node, the parameter content of the selected third target node on the target sub-parameter is updated from the current content to another content.

8. A workflow processing device, characterized in that: The device comprises: A parameter input module, configured to determine a current configuration parameter of a current configuration node in response to a parameter configuration operation on the current configuration node in the first workflow; A parameter acquisition module, used to acquire configured parameters of the historical configuration node from the second workflow; A processing type module, configured to determine a parameter processing type corresponding to the current configuration node based on the current configuration parameters, the configured parameters, and node attributes of the current configuration node and the historical configuration node; A shared node module, configured to determine, based on the parameter processing type, shared nodes and non-shared nodes corresponding to the currently configured node in each to-be-configured node of the third workflow; a parameter determination module, configured to determine the node parameters corresponding to the shared node based on the current configuration parameters, and to determine the node parameters corresponding to the non-shared node in response to a parameter configuration operation on the non-shared node; The workflow generation module is used to obtain a target workflow based on the node parameters corresponding to each node in the third workflow.

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.

11. 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 7 are implemented.

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