Workflow data storage method, workflow data loading method, apparatus, and device

Abstract

Embodiments of the present disclosure provide a workflow data storage method, a workflow data loading method, an apparatus and a device. The method comprises: obtaining a target workflow model, the target workflow model comprising at least one work node, wherein the target workflow model represents a task flow of a target task, and the work node represents a task item in the task flow; determining a first target work node in the target workflow model, wherein the first target work node is a newly configured work node of the target workflow model in a current model version; obtaining a storage parameter of the first target work node, and storing the first target work node and the corresponding storage parameter as target node data, wherein the storage parameter represents a version interval applicable to the work node, and the target node data is used to generate work nodes in the target workflow model of the model version within the version interval corresponding to the storage parameter. The independent storage of each work node in the workflow model is achieved, and the storage space occupation is reduced.

Description

Technical Field

[0001] This disclosure relates to the field of Internet technology, and in particular to a workflow data storage method, a workflow data loading method, an apparatus, and a device. Background Technology

[0002] Workflow is a commonly used task management model in various project development tasks. Creating, assigning, and managing tasks based on a workflow platform can effectively accelerate the progress of development tasks and improve the overall efficiency of the project team. A workflow typically consists of multiple task nodes. By adding, deleting, and modifying task nodes in the workflow, adjustments can be made to the workflow, thereby achieving version changes.

[0003] In existing technologies, when a workflow version changes, all work nodes in the workflow are typically saved in full, and a corresponding workflow file is generated using the workflow version identifier as an index. However, saving all nodes in the workflow in full can lead to wasted storage space and affect the overall performance of the workflow platform. Summary of the Invention

[0004] This disclosure provides a workflow data storage method, a workflow data loading method, an apparatus, and a device to overcome the problem of wasted storage space affecting the overall performance of the workflow platform.

[0005] In a first aspect, embodiments of this disclosure provide a workflow data storage method, including:

[0006] Obtain a target workflow model, the target workflow model including at least one work node, wherein the target workflow model represents the task flow of a target task, and the work node represents a task item in the task flow; determine a first target work node in the target workflow model, wherein the first target work node is a newly configured work node in the current model version of the target workflow model; obtain the storage parameters of the first target work node, and store the first target work node and the corresponding storage parameters as target node data, wherein the storage parameters represent the version range applicable to the work node, and the target node data is used to generate work nodes in the target workflow model within the version range corresponding to the storage parameters.

[0007] Secondly, embodiments of this disclosure provide a workflow data loading method, including:

[0008] Receive a first loading instruction, the first loading instruction including a target model version of the target workflow model, wherein the target workflow model includes at least one worker node, the target workflow model represents the task flow of the target task, and the worker node represents a task item in the task flow; obtain at least one target node data corresponding to the target model version, the target node data including storage parameters, the storage parameters representing the version range applicable to the worker node, the target model version being located within the version range, and the target node data being used to generate worker nodes in the target workflow model corresponding to the target model version;

[0009] Based on the data of each target node, load the target workflow model corresponding to the target model version.

[0010] Thirdly, embodiments of this disclosure provide a workflow data storage device, comprising:

[0011] An acquisition module is used to acquire a target workflow model, the target workflow model including at least one work node, wherein the target workflow model represents the task flow of a target task, and the work node represents a task item in the task flow;

[0012] The determination module is used to determine the first target work node in the target workflow model, wherein the first target work node is a newly configured work node in the current model version of the target workflow model;

[0013] The storage module is used to obtain the storage parameters of the first target worker node and store the first target worker node and the corresponding storage parameters as target node data. The storage parameters represent the version range applicable to the worker node, and the target node data is used to generate worker nodes in the target workflow model whose model version is within the version range corresponding to the storage parameters.

[0014] Fourthly, embodiments of this disclosure provide a workflow data loading apparatus, comprising:

[0015] A receiving module is configured to receive a first loading instruction, the first loading instruction including a target model version of a target workflow model, wherein the target workflow model includes at least one working node, the target workflow model represents the task flow of a target task, and the working node represents a task item in the task flow;

[0016] The acquisition module is used to acquire at least one target node data corresponding to the target model version. The target node data includes storage parameters, which represent the version range applicable to the work node. The target model version is located within the version range. The target node data is used to generate work nodes in the target workflow model corresponding to the target model version.

[0017] The loading module is used to load the target workflow model corresponding to the target model version based on the data of each target node.

[0018] Fifthly, embodiments of this disclosure provide an electronic device, including:

[0019] A processor, and a memory communicatively connected to the processor;

[0020] The memory stores computer-executed instructions;

[0021] The processor executes computer execution instructions stored in the memory to implement the workflow data storage method described in the first aspect and various possible designs of the first aspect, or to implement the workflow data loading method described in the second aspect and various possible designs of the second aspect.

[0022] In a sixth aspect, embodiments of this disclosure provide a computer-readable storage medium storing computer-executable instructions. When a processor executes the computer-executable instructions, it implements the workflow data storage method described in the first aspect and various possible designs of the first aspect, or implements the workflow data loading method described in the second aspect and various possible designs of the second aspect.

[0023] In a seventh aspect, embodiments of this disclosure provide a computer program product, including a computer program that, when executed by a processor, implements the workflow data storage method described in the first aspect and various possible designs of the first aspect, or implements the workflow data loading method described in the second aspect and various possible designs of the second aspect.

[0024] The workflow data storage method, workflow data loading method, apparatus, and device provided in this embodiment obtain a target workflow model, which includes at least one work node. The target workflow model represents the task flow of a target task, and the work node represents a task item within the task flow. A first target work node is determined in the target workflow model, wherein the first target work node is a newly configured work node in the current model version. Storage parameters of the first target work node are obtained, and the first target work node and its corresponding storage parameters are stored as target node data. The storage parameters represent the version range applicable to the work node, and the target node data is used to generate work nodes in the target workflow model whose model version falls within the version range corresponding to the storage parameters. Since storage parameters representing the version range applicable to the work node are generated based on the newly added first target work node in the target workflow model, and corresponding target node data is generated based on the storage parameters, independent storage of each work node in the target workflow model is achieved. When it is necessary to load different historical versions of the target workflow model in the future, it can be done based on the target node data corresponding to each work node, avoiding duplicate storage, reducing storage space occupation, and improving the overall performance of the workflow platform. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 This disclosure provides a schematic diagram of a digital management scenario based on a target workflow model.

[0027] Figure 2 A flowchart illustrating the workflow data storage method provided in this disclosure embodiment. Figure 1 ;

[0028] Figure 3 for Figure 2 The flowchart of the specific implementation steps of step S102 in the embodiment shown is as follows;

[0029] Figure 4 This is a schematic diagram illustrating the correspondence between version information and working nodes provided in an embodiment of this disclosure;

[0030] Figure 5 A flowchart outlining the specific steps to obtain the storage parameters of the first target working node;

[0031] Figure 6 This is a schematic diagram illustrating the generation of storage parameters according to an embodiment of the present disclosure;

[0032] Figure 7 A flowchart illustrating the workflow data storage method provided in this embodiment of the disclosure. Figure 2 ;

[0033] Figure 8 This is a schematic diagram illustrating the storage of a workflow model with multiple model versions, provided as an embodiment of this disclosure.

[0034] Figure 9 A flowchart illustrating a workflow data loading method provided in this embodiment of the disclosure;

[0035] Figure 10 A structural block diagram of the workflow data storage device provided in the embodiments of this disclosure;

[0036] Figure 11 A structural block diagram of the workflow data loading device provided in the embodiments of this disclosure;

[0037] Figure 12 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present disclosure;

[0038] Figure 13 This is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of this disclosure. Detailed Implementation

[0039] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0040] The application scenarios of the embodiments of this disclosure are explained below:

[0041] The workflow data storage method and workflow data loading method provided in this disclosure can be applied to application scenarios of digital management of development tasks. Figure 1This disclosure provides a schematic diagram of a digital management scenario based on a target workflow model. The method provided in this disclosure can be applied to a management server, whereby the management server provides a workflow platform for digital management of development tasks to the outside world through a web project. Authorized users can create, delete, and modify target workflow models based on this workflow platform. After loading the target workflow model, users with corresponding permissions can manage tasks for each work node (e.g., development node A, development node B, and review node shown in the image) through corresponding terminal devices (e.g., terminal device A, terminal device B, and terminal device C shown in the image), ultimately achieving digital management of the development project.

[0042] In practical use, the overall task flow of a target development project often needs to be adjusted during project development, such as adding new work nodes or removing certain work nodes, meaning the target workflow model changes. At this time, the entire target workflow model is saved, along with its version information, for subsequent management and reuse. Current technology saves the target workflow model by fully saving all work nodes and generating corresponding workflow files indexed by the workflow version identifier. However, during version updates, only a few work nodes actually change, while most remain unchanged. Therefore, saving all work nodes in the target workflow model leads to wasted storage space. Furthermore, when modifying historical versions of the target workflow model, the large number of work nodes and excessive interface parameters result in poor interface performance. Storage impacts the overall performance of the workflow platform.

[0043] This disclosure provides a workflow data storage method to solve the above-mentioned problems.

[0044] refer to Figure 2 , Figure 2 A flowchart illustrating the workflow data storage method provided in this disclosure embodiment. Figure 1 The method described in this embodiment can be applied to a server. This workflow data storage method includes:

[0045] Step S101: Obtain the target workflow model, which includes at least one work node. The target workflow model represents the task flow of the target task, and the work node represents the task item in the task flow.

[0046] For example, a workflow model is a model data used to describe the task flow of a target task. It has a specific data format and includes multiple task nodes describing the task flow. Each task node can be considered as sub-data within the workflow model and also has a specific data format. Specifically, for example, a workflow model describing a development task might include four task nodes: "Task Startup," "Program Development," "Program Testing," and "Task Completion." These four task nodes are connected in series. The target workflow model, composed of these four task nodes, represents the task flow of the development task. After the workflow model is run, each task node is managed by a user with corresponding permissions, thereby achieving the goal of digital management of the development task.

[0047] Furthermore, for example, a work node, as a type of structured data, may include task information and associated information. The task information is used to characterize information related to the task item, such as a specific task description, task owner, task time, etc. The associated information is used to characterize the child work nodes associated with the work node, that is, the new work node or new workflow model triggered when the work node is executed.

[0048] In one possible implementation, before step S101, the server receives an editing command sent by a user with corresponding permissions via a terminal device, enters the editing mode of the workflow model, and then loads an initial workflow model. This initial workflow model may include at least one preset initial work node, such as a "project start node" and a "project end node," or it may not include any work nodes. Then, based on the configuration command sent by the user via the terminal device, the existing work nodes in the initial workflow model are configured, such as adding task information corresponding to the work nodes, thereby generating the target workflow model. Alternatively, in another possible implementation, after the server enters the editing mode of the workflow model based on the editing command sent by the terminal device, it loads a workflow model (i.e., a historical workflow model) that already contains multiple work nodes and can represent the task flow of the target task, as the target workflow model. Further details will not be elaborated here.

[0049] Step S102: Determine the first target work node in the target workflow model, wherein the first target work node is a newly configured work node in the current model version of the target workflow model.

[0050] For example, after entering the editing mode for the target workflow model, at least one new working node, namely the first target working node, is configured based on the target workflow model. This first target working node can be located anywhere within the target workflow model. This process can be implemented by receiving and responding to a first editing instruction sent by the terminal device. For example, this includes: in response to the first editing instruction, configuring the first target working node in the first target workflow model, wherein the first editing instruction characterizes the task item corresponding to the first target working node and the position of the first target working node within the first target workflow model.

[0051] After adding the first target worker node to the target workflow model, the server can obtain the newly configured first target worker node through detection. For example... Figure 3 As shown, the specific implementation steps of step S102 include:

[0052] Step S1021: Obtain the first version information of the target workflow model. The first version information is used to determine the work nodes included in the previous version of the target workflow model.

[0053] Step S1022: Determine the first target work node based on the first version information and the work nodes included in the target workflow model in the current model version.

[0054] For example, version information is stored on the server to represent the model version of the workflow model. More specifically, the version information can be a version identifier of the workflow model, such as v1.0, v2.0, etc. When the workflow model version changes, at least one work node in the workflow model changes (added or deleted). Therefore, different version information corresponds to different sets of work nodes. Figure 4 This is a schematic diagram illustrating the correspondence between version information and working nodes provided in an embodiment of this disclosure, such as... Figure 4 As shown, when the version information is version_1, the corresponding workflow model under this model version includes worker node A, worker node B, and worker node C; after the version is updated, the version information is version_2, and the corresponding workflow model under this model version includes worker node A, worker node B, worker node C, and worker node D; when the version information is version_3, the corresponding workflow model under this model version includes worker node A, worker node C, and worker node D.

[0055] For the target workflow model in this embodiment, the version identifier of the previous model version, i.e., the first version information, can be obtained based on the version identifier of the current model version of the target workflow model. Then, based on the work node corresponding to the first version information, it can be compared with the work nodes included in the current model version of the target workflow model to determine the first target work node added in the current model version.

[0056] Step S103: Obtain the storage parameters of the first target working node, and store the first target working node and the corresponding storage parameters as target node data. The storage parameters represent the version range applicable to the working node, and the target node data is used to generate working nodes in the target workflow model where the model version is within the version range corresponding to the storage parameters.

[0057] For example, after obtaining the first target worker node, the storage parameters stored in the first target worker node are further read. These storage parameters characterize the version range to which the worker node applies; more specifically, they refer to the lifecycle of the worker node in different versions of the workflow model. For instance, workflow model M1 has 10 model versions, from version 1 to version 10. During the aforementioned version update process of workflow model M1, worker node A appears when workflow model M1 is updated to version 2 and is deleted when workflow model M1 is updated to version 4. Therefore, the version range corresponding to worker node A is from version 2 to version 4.

[0058] In one possible implementation, the storage parameters include a minimum version identifier and a maximum version identifier. The minimum version identifier represents the lowest version applicable to the worker node, and the maximum version identifier represents the higher or lower version of the version applicable to the worker node. Specifically, for example, the storage parameter of the first target worker is Para1, where Para1 = [min, max]. min is the minimum version identifier, and max is the maximum version identifier.

[0059] For example, the storage parameters of the first target worker node are generated after the first target worker node is created and stored as attribute parameters of the first target worker node. In one possible implementation, such as Figure 5 As shown, the specific steps for obtaining the storage parameters of the first target worker node include:

[0060] Step S1031: Obtain the version identifier of the current model version of the target workflow model.

[0061] Step S1032: Based on the version identifier of the current model version, obtain the minimum version identifier of the target working node.

[0062] Step S1033: Based on the preset maximum identifier, obtain the maximum version identifier of the target working node.

[0063] Figure 6 This is a schematic diagram illustrating the generation of storage parameters provided in an embodiment of the present disclosure, such as... Figure 6 As shown, in the target workflow model with version identifier v1.0 (shown as version v1.0 in the figure), there are worker nodes A and B. Worker nodes A and B are the initial worker nodes, and their corresponding storage parameters are both [1, 1000]. That is, the minimum version identifier is 1, corresponding to the initial version (e.g., version v1.0), and the maximum version identifier is the preset maximum identifier 1000. Then, the target workflow model is modified (i.e., version updated). After worker node B, a new worker node C is configured. At this time, the version identifier of the target workflow model is v2.0 (shown as version v2.0 in the figure). The storage parameters for generating worker node C are para1 = [2, 1000]. The minimum version identifier in the storage parameters is obtained by extracting the version identifier of the target workflow model, and the maximum version identifier in the storage parameters is the preset maximum identifier 1000.

[0064] Furthermore, after obtaining the storage parameters, the storage parameters and the corresponding first target working node are stored as a set of data that can be independently called and loaded, namely, target node data. The target node data has a detection interface; by calling the retrieval interface, the storage parameters in the target node data, i.e., the version range applicable to the first target working node, can be obtained. For example, the target node data has a data identifier corresponding to the first target working node. More specifically, for example, the target node data is a structured file, and the filename of the target node data corresponds one-to-one with the first target working node. By using the filename of the target node data, the target node data can be loaded, thereby obtaining the corresponding first target working node.

[0065] Furthermore, when generating a historical version of the workflow model, several target node data can be determined based on the version identifier corresponding to the historical version and the retrieval results of each target node data. These target node data are then used to obtain the corresponding target work nodes. Finally, by combining these target work nodes, the historical version of the workflow model is generated. Moreover, when modifications to the historical version of the workflow model are needed, only the target node data needs to be modified, resulting in higher efficiency.

[0066] In this embodiment, a target workflow model is obtained, which includes at least one work node. The target workflow model represents the task flow of a target task, and the work node represents a task item within that flow. A first target work node is determined within the target workflow model, where the first target work node is a newly configured work node in the current model version. Storage parameters of the first target work node are obtained, and the first target work node and its corresponding storage parameters are stored as target node data. The storage parameters represent the version range applicable to the work node, and the target node data is used to generate work nodes in the target workflow model within the version range corresponding to the storage parameters. Because storage parameters representing the version range applicable to the work node are generated based on the newly added first target work node in the target workflow model, and corresponding target node data is generated based on these storage parameters, independent storage of each work node in the target workflow model is achieved. When different historical versions of the target workflow model need to be loaded subsequently, this can be done based on the target node data corresponding to each work node, avoiding redundant storage, reducing storage space usage, and improving the overall performance of the workflow platform.

[0067] refer to Figure 7 , Figure 7 A flowchart illustrating the workflow data storage method provided in this embodiment of the disclosure. Figure 2 This embodiment is in Figure 2 Based on the illustrated embodiment, the workflow data storage method includes the following steps for processing the deleted second target work node:

[0068] Step S201: Obtain the target workflow model, which includes at least one work node. The target workflow model represents the task flow of the target task, and the work node represents the task item in the task flow.

[0069] Step S202: Determine the first target work node in the target workflow model, wherein the first target work node is a newly configured work node in the current model version of the target workflow model.

[0070] Step S203: Determine the second target working node. The second target working node is the working node that has been newly removed in the current version of the target workflow model.

[0071] For example, similar to the first target work node, the second target work node is also a work node that has changed in the target workflow model. Specifically, it is a work node that existed in the previous version of the workflow model but does not exist in the current version. Further, the second target work node can be determined after the server removes a work node from the target workflow model in response to the second editing command. The second target work node can be determined directly based on the server's response to the second editing command; alternatively, it can be determined by comparing the work nodes included in the previous version of the target workflow model with those included in the updated target workflow model when saving the updated target workflow model. The specific implementation process is similar to that of determining the first target work node, and will not be elaborated here.

[0072] Step S204: Obtain the current version identifier, which represents the current version of the target workflow model.

[0073] Step S205: Determine the storage parameters of the first target worker node and the second target worker node based on the current version identifier.

[0074] For example, the current version identifier can be a version number representing the current version of the target workflow model, and the storage parameters are the version range applicable to the work node, including the minimum version identifier and the maximum version identifier. Based on the current version identifier, the specific implementation method for determining the storage parameters of the first target work node is determined. Figure 2 The embodiments shown have already been described, and will not be repeated here.

[0075] Regarding the storage parameters of the second target working node, since the second target working node is removed in the current model version, that is, the life cycle of the second target working node ends, the maximum version identifier of the second target working node can be determined based on the current version identifier. At this time, the maximum version identifier of the second target working node is updated from the preset maximum identifier to the current version identifier.

[0076] Step S206: Based on the storage parameters of the first target working node, store the first target working node as first target node data.

[0077] Step S207: Based on the storage parameters of the second target working node, update the storage parameters in the second target node data corresponding to the second target working node.

[0078] Furthermore, after obtaining the storage parameters of the first target working node and the second target working node, the first target working node is a newly added working node in the current model version. Since its content, such as task information and associated information, has not been saved, it is necessary to save the first target working node and its corresponding storage parameters together as storage objects, i.e., storing the first target working node as first target node data. As for the second target working node, its corresponding content (task information, associated information) has already been saved, meaning the second target node data already exists on the server. Therefore, it is only necessary to update the storage parameters in the second target node data; that is, the storage parameters of the second target working node obtained in step S205 are updated to the storage parameters in the second target node data.

[0079] In another possible implementation, the target workflow model of the current model version also includes a third target work node, which is a work node included in both the current and previous model versions of the target workflow model. The method further includes: determining the third target work node and updating the storage parameters corresponding to the third target work node based on the version identifier of the current model version; and updating the storage parameters in the third target node data corresponding to the third target work node based on the storage parameters of the third target work node. Specifically, each time the model version is updated, the maximum version identifier of the third target work node is updated to the version identifier of the current model version based on the version identifier of the current model version, thereby matching the upper limit of the version range of each work node with the version identifier of the current model version, and correspondingly updating the storage parameters in the third target node data corresponding to each third target work node. This allows the maximum version identifier in the storage parameters to change dynamically, reflecting the actual version number instead of continuously storing a preset maximum identifier, thus improving the accuracy of version records. It should be noted that if the third target work node is processed using the implementation method provided in this embodiment, step S207 can be skipped, i.e., it is not necessary to update the storage parameters of the newly removed second target work node in the current model version.

[0080] Figure 8 This is a schematic diagram illustrating the storage of a workflow model with multiple model versions, as provided in an embodiment of this disclosure. Figure 8As shown, after creating the initial workflow model, the model version number of this initial workflow model is v1 (shown as workflow model v1 in the figure). Based on the initial workflow model, work nodes A and B are added, and storage parameters for work nodes A and B are generated according to the current model version, namely Para_A = [1, 1000] and Para_B = [1, 1000], respectively, indicating that the applicable versions of work nodes A and B are v1 to v1000 (v1000 is the preset maximum version number). Then, the content of work node A and its corresponding storage parameter Para_A are stored to generate node data File_A, and the content of work node B and its corresponding storage parameter Para_B are stored to generate node data File_B. File_A and File_B are then saved to the server's storage area. Furthermore, the workflow model of version v1 is updated to generate a workflow model with version number v1 (shown as workflow model v2 in the diagram). This involves adding a worker node C based on version v1. According to the current model version, the storage parameter Para_C = [2, 1000] is generated for worker node C, indicating that worker node C is applicable to versions v2 to v1000. Afterwards, the content of worker node C and its corresponding storage parameter Para_C are stored to generate node data File_C, which is then saved to the server's storage area. Furthermore, the workflow model of version v2 is updated to generate a workflow model of version v3 (shown as workflow model v3 in the figure). That is, based on version v2, worker node B is deleted, and the storage parameter Para_B = [1,3] of worker node B is updated according to the current model version, indicating that the applicable version of worker node B is version v1 to v3. The storage parameter Para_B is then updated to File_B, so that the storage parameter in File_B is consistent with the storage parameter of worker node B updated in the current model version (that is, Para_B = [1,1000] in File_B is updated to Para_B = [1,3]).

[0081] As can be seen from the above embodiments, when updating the workflow model due to changes in specific tasks within a development project, it is only necessary to save the newly added work nodes between different versions. For changes caused by deleting work nodes between different versions, only the storage parameters of the work nodes need to be updated. Figure 8Taking the illustrated embodiment as an example, existing solutions that require full data storage would need to store data from seven worker nodes if storing workflow models for versions v1-v3. However, the method provided in this disclosure only requires storing data from three worker nodes and their corresponding storage parameters to achieve the same storage purpose. Furthermore, the reduced number of worker nodes effectively decreases the number of interface parameters for complex workflow models, thereby improving the interface performance of worker nodes and the overall performance of the workflow platform. Additionally, since the node data for each worker node is stored separately, in the event of abnormal node data, only the information in the worker node corresponding to the abnormal node data will be lost; the information in the worker nodes corresponding to other node data will remain intact, ensuring the data security of historical versions of the workflow model.

[0082] Step S208: Based on the work nodes included in the target workflow model in the current model version, generate version information for the current model version. The version information represents each work node included in the target workflow model and the order between each work node.

[0083] Step S209: Store the mapping relationship between the version identifier of the current model version and the version information of the current model version.

[0084] For example, after the modification process of the target workflow model in the current model template is completed, version information is generated according to the order of each work node in the target workflow model. In one possible implementation, the version information is an ordered sequence, which includes multiple unique identifiers of the ordered work nodes. Based on the version information, the work nodes and their order in the target workflow model of the current model version can be determined. Further, the version identifier of the current model version of the target workflow model is mapped to this version information to form a mapping table. Then, based on this mapping table, the work nodes in a specific version of the workflow model can be restored. By forming a mapping between version identifiers and version information, when a historical version of the workflow model needs to be loaded, the work nodes that need to be loaded in the workflow model can be quickly determined, improving the loading speed of historical versions of the workflow model.

[0085] In this embodiment, the implementation of steps S201-S202 is the same as that in this disclosure. Figure 2 The implementation methods of steps S101-S102 in the illustrated embodiment are the same, and will not be described in detail here.

[0086] Figure 9 This is a flowchart illustrating a workflow data loading method provided in an embodiment of this disclosure. The method of this embodiment can be applied in a server, and the workflow data loading method includes:

[0087] Step S301: Receive a first loading instruction, the first loading instruction including a target model version of the target workflow model, wherein the target workflow model includes at least one work node, the target workflow model represents the task flow of the target task, and the work node represents the task item in the task flow.

[0088] The workflow data loading method provided in this embodiment can be used to load historical versions of workflow models, wherein the historical versions of workflow models are based on Figures 2-8 The data is stored using the workflow data storage method provided in any of the corresponding embodiments. The following is in conjunction with... Figures 2-8 This is a corresponding implementation example, which explains the process of loading the workflow model of the historical version described above.

[0089] For example, the first loading instruction is a command sent by the user to the server through a terminal device to load a historical version of the workflow model. The first loading instruction includes a target model version, specifically, for example, a version identifier. After receiving the first loading instruction, the server can determine the corresponding target workflow model, i.e., the historical version of the workflow model mentioned above, based on the target model version. The target workflow model includes multiple ordered connected work nodes to implement the task flow of the target task. The specific implementation method is described in... Figures 2-8 The embodiments shown have been described in detail and will not be repeated here.

[0090] Step S302: Obtain at least one target node data corresponding to the target model version. The target node data includes storage parameters, which represent the version range applicable to the working node. The target model version is located within the version range. The target node data is used to generate the working node in the target workflow model corresponding to the target model version.

[0091] Furthermore, after receiving the first loading instruction, the server, based on the target model version, loads the data through... Figures 2-8 The method provided in any corresponding embodiment detects multiple pre-stored node data to obtain at least one target node data corresponding to the target model version. For example, the specific implementation steps of step S302 include:

[0092] Step S3021: Obtain the preset storage parameters of the node data. The storage parameters include the minimum version identifier and the maximum version identifier. The minimum version identifier represents the lowest version of the working node corresponding to the node data, and the maximum version identifier represents the higher or lower version of the working node corresponding to the node data.

[0093] Step S3022: Based on the target model version and the storage parameters corresponding to each node data, determine the node data that can cover the storage parameters of the target model version as the target node data.

[0094] Each pre-stored node data includes corresponding storage parameters, which characterize the highest and lowest model versions that the node data is compatible with. Specifically, the storage parameters can determine the version range using the maximum and minimum version identifiers. Then, by comparing whether the target model version falls within this version range, it can be determined whether the node data is the target node data matching the target model version. The storage parameters can be a subset of the node data or an attribute. They can be obtained by calling the node data retrieval interface or by reading the attributes representing the storage parameters from the node data. The specific implementation method of the storage parameters is discussed in... Figures 2-8 The corresponding embodiments have been described, and will not be repeated here.

[0095] Step S303: Based on the data of each target node, load the target workflow model corresponding to the target model version.

[0096] Furthermore, after obtaining the target node data, the target working node corresponding to the target node data is obtained by parsing and loading the target node data, thereby realizing the restoration of the target working node. The corresponding target workflow model is generated through the target working node, and the target workflow model is loaded. In this embodiment, the matching target node data is determined by the target model version, and the target workflow model corresponding to the target model version is loaded through the data of each target node. This enables fast loading of historical versions of the workflow model. At the same time, since the node data corresponding to each working node is loaded separately, in the event of abnormal node data, only the abnormal node data needs to be processed (the corresponding working node is regenerated), without the need to rebuild the entire workflow model, reducing system maintenance costs and improving data security.

[0097] Corresponding to the workflow data storage method in the above embodiments, Figure 10 This is a structural block diagram of a workflow data storage device provided for embodiments of the present disclosure. For ease of explanation, only the parts relevant to embodiments of the present disclosure are shown. (Refer to...) Figure 10 The workflow data storage device 4 includes:

[0098] Acquisition module 41 is used to acquire the target workflow model, which includes at least one work node. The target workflow model represents the task flow of the target task, and the work node represents the task item in the task flow.

[0099] The determination module 42 is used to determine the first target work node in the target workflow model, wherein the first target work node is a newly configured work node in the current model version of the target workflow model;

[0100] Storage module 43 is used to obtain the storage parameters of the first target working node and store the first target working node and the corresponding storage parameters as target node data. The storage parameters represent the version range applicable to the working node, and the target node data is used to generate working nodes in the target workflow model where the model version is within the version range corresponding to the storage parameters.

[0101] In one possible implementation, the storage parameters include a minimum version identifier and a maximum version identifier, wherein the minimum version identifier represents the lowest version of the version applicable to the worker node, and the maximum version identifier represents the higher or lower version of the version applicable to the worker node.

[0102] In one possible implementation, the storage module 43 is specifically used to: obtain the version identifier of the current model version of the target workflow model; obtain the minimum version identifier of the target worker node based on the version identifier of the current model version; and obtain the maximum version identifier of the target worker node based on the preset maximum identifier.

[0103] In one possible implementation, the determining module 42 is specifically used to: obtain first version information of the target workflow model, the first version information being used to determine the work nodes included in the previous model version of the target workflow model; and determine the first target work node based on the first version information and the work nodes included in the current model version of the target workflow model.

[0104] In one possible implementation, the determining module 42 is further configured to: determine a second target working node, wherein the second target working node is a working node newly removed in the current model version of the target workflow model; and update the storage parameters in the target node data corresponding to the second target working node based on the version identifier of the current model version of the target workflow model.

[0105] In one possible implementation, when determining the storage parameters in the target node data corresponding to the second target worker node based on the version identifier of the current model version of the target workflow model, the determining module 42 is specifically used to: update the maximum version identifier of the second target worker node based on the version identifier of the current model version.

[0106] In one possible implementation, the storage module 43 is further configured to: generate version information of the current model version based on the work nodes included in the target workflow model in the current model version, wherein the version information represents each work node included in the target workflow model and the order between the work nodes; and store the mapping relationship between the version identifier of the current model version and the version information of the current model version.

[0107] In one possible implementation, the determining module 42 is further configured to: determine a third target working node, wherein the third target working node is a working node included in both the current model version and the previous model version of the target workflow model; and update the storage parameters in the target node data corresponding to the third target working node based on the version identifier of the current model version.

[0108] The acquisition module 41, the determination module 42, and the storage module 43 are connected in sequence. The workflow data storage device 5 provided in this embodiment can execute the technical solution of the above method embodiment, and its implementation principle and technical effect are similar, so it will not be described again here.

[0109] Corresponding to the workflow data loading method in the above embodiment, Figure 11 This is a structural block diagram of a workflow data loading apparatus provided in an embodiment of this disclosure. For ease of explanation, only the parts relevant to the embodiments of this disclosure are shown. (Refer to...) Figure 11 The workflow data loading device 5 includes:

[0110] The receiving module 51 is used to receive a first loading instruction, which includes a target model version of the target workflow model. The target workflow model includes at least one work node, and the target workflow model represents the task flow of the target task. The work node represents the task item in the task flow.

[0111] The acquisition module 52 is used to acquire at least one target node data corresponding to the target model version. The target node data includes storage parameters, which represent the version range applicable to the work node. The target model version is located within the version range. The target node data is used to generate the work node in the target workflow model corresponding to the target model version.

[0112] Loading module 53 is used to load the target workflow model corresponding to the target model version based on the data of each target node.

[0113] In one possible implementation, the acquisition module 52 is specifically used to: acquire the storage parameters of the preset node data, the storage parameters including the minimum version identifier and the maximum version identifier, the minimum version identifier representing the lowest version of the working node corresponding to the node data, and the maximum version identifier representing the higher or lower version of the working node corresponding to the node data; and determine the node data corresponding to the storage parameters that can cover the target model version as the target node data based on the target model version and the storage parameters corresponding to each node data.

[0114] Figure 12 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present disclosure, such as... Figure 12 As shown, the electronic device 6 includes:

[0115] Processor 61, and memory 62 communicatively connected to processor 61;

[0116] Memory 62 stores instructions executed by the computer;

[0117] The processor 61 executes computer-executable instructions stored in the memory 62 to achieve, for example, Figures 2-9 The workflow data storage method or workflow data loading method in the illustrated embodiments.

[0118] Optionally, the processor 61 and the memory 62 are connected via a bus 63.

[0119] For relevant instructions, please refer to the corresponding text. Figures 2-9 The relevant descriptions and effects of the steps in the corresponding embodiments are understood, and will not be elaborated on here.

[0120] refer to Figure 13 The diagram illustrates a structural schematic of an electronic device 900 suitable for implementing embodiments of the present disclosure. The electronic device 900 can be a terminal device or a server. The terminal device can include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, personal digital assistants (PDAs), portable Android devices (PADs), portable media players (PMPs), and in-vehicle terminals (e.g., in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 13 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments disclosed herein.

[0121] like Figure 13 As shown, the electronic device 900 may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 901, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 902 or a program loaded from a storage device 908 into a random access memory (RAM) 903. The RAM 903 also stores various programs and data required for the operation of the electronic device 900. The processing unit 901, ROM 902, and RAM 903 are interconnected via a bus 904. An input / output (I / O) interface 905 is also connected to the bus 904.

[0122] Typically, the following devices can be connected to I / O interface 905: input devices 906 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 907 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 908 including, for example, magnetic tapes, hard disks, etc.; and communication devices 909. Communication device 909 allows electronic device 900 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 13 An electronic device 900 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively.

[0123] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 909, or installed from a storage device 908, or installed from a ROM 902. When the computer program is executed by a processing device 901, it performs the functions defined in the methods of embodiments of this disclosure.

[0124] It should be noted that the computer-readable medium described in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0125] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0126] The aforementioned computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform the methods shown in the above embodiments.

[0127] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0128] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0129] The units described in the embodiments of this disclosure can be implemented in software or in hardware. The name of a unit does not necessarily limit the unit itself; for example, the first acquisition unit can also be described as "a unit that acquires at least two Internet Protocol addresses".

[0130] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0131] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0132] In a first aspect, according to one or more embodiments of this disclosure, a workflow data storage method is provided, comprising:

[0133] Obtain a target workflow model, the target workflow model including at least one work node, wherein the target workflow model represents the task flow of a target task, and the work node represents a task item in the task flow;

[0134] Determine the first target work node in the target workflow model, wherein the first target work node is a newly configured work node in the current model version of the target workflow model;

[0135] Obtain the storage parameters of the first target worker node, and store the first target worker node and the corresponding storage parameters as target node data. The storage parameters represent the version range applicable to the worker node, and the target node data is used to generate worker nodes in the target workflow model where the model version is within the version range corresponding to the storage parameters.

[0136] According to one or more embodiments of this disclosure, the storage parameters include a minimum version identifier and a maximum version identifier, wherein the minimum version identifier represents the lowest version of the version to which the working node is applicable, and the maximum version identifier represents the higher or lower version of the version to which the working node is applicable.

[0137] According to one or more embodiments of this disclosure, obtaining the storage parameters of the first target worker node includes: obtaining the version identifier of the current model version of the target workflow model; obtaining the minimum version identifier of the target worker node based on the version identifier of the current model version; and obtaining the maximum version identifier of the target worker node based on a preset maximum identifier.

[0138] According to one or more embodiments of this disclosure, determining the first target work node in the target workflow model includes: obtaining first version information of the target workflow model, wherein the first version information is used to determine the work nodes included in the previous model version of the target workflow model; and determining the first target work node based on the first version information and the work nodes included in the current model version of the target workflow model.

[0139] According to one or more embodiments of this disclosure, the method further includes: determining a second target working node, wherein the second target working node is a working node newly removed in the current model version of the target workflow model; and updating the storage parameters in the target node data corresponding to the second target working node based on the version identifier of the current model version of the target workflow model.

[0140] According to one or more embodiments of this disclosure, updating the storage parameters in the target node data corresponding to the second target worker node based on the version identifier of the current model version of the target workflow model includes: updating the maximum version identifier of the second target worker node based on the version identifier of the current model version.

[0141] According to one or more embodiments of this disclosure, the method further includes: generating version information of the current model version based on the work nodes included in the target workflow model in the current model version, wherein the version information represents each work node included in the target workflow model and the order between the work nodes; and storing the mapping relationship between the version identifier of the current model version and the version information of the current model version.

[0142] According to one or more embodiments of this disclosure, the method further includes: determining a third target working node, the third target working node being a working node included in both the current model version and the previous model version of the target workflow model; and updating the storage parameters in the target node data corresponding to the third target working node based on the version identifier of the current model version.

[0143] Secondly, according to one or more embodiments of this disclosure, a workflow data loading method is provided, comprising:

[0144] Receive a first loading instruction, the first loading instruction including a target model version of the target workflow model, wherein the target workflow model includes at least one worker node, the target workflow model represents the task flow of the target task, and the worker node represents a task item in the task flow; obtain at least one target node data corresponding to the target model version, the target node data including storage parameters, the storage parameters representing the version range applicable to the worker node, the target model version being located within the version range, the target node data being used to generate worker nodes in the target workflow model corresponding to the target model version; load the target workflow model corresponding to the target model version according to each of the target node data.

[0145] According to one or more embodiments of this disclosure, obtaining at least one target node data corresponding to the target model version includes: obtaining preset storage parameters for node data, the storage parameters including a minimum version identifier and a maximum version identifier, the minimum version identifier representing the lowest version applicable to the working node corresponding to the node data, and the maximum version identifier representing the higher or lower version applicable to the working node corresponding to the node data; and determining the node data corresponding to the storage parameters that can cover the target model version as target node data based on the target model version and the storage parameters corresponding to each node data.

[0146] Thirdly, according to one or more embodiments of this disclosure, a workflow data storage device is provided, comprising:

[0147] An acquisition module is used to acquire a target workflow model, the target workflow model including at least one work node, wherein the target workflow model represents the task flow of a target task, and the work node represents a task item in the task flow;

[0148] The determination module is used to determine the first target work node in the target workflow model, wherein the first target work node is a newly configured work node in the current model version of the target workflow model;

[0149] The storage module is used to obtain the storage parameters of the first target worker node and store the first target worker node and the corresponding storage parameters as target node data. The storage parameters represent the version range applicable to the worker node, and the target node data is used to generate worker nodes in the target workflow model whose model version is within the version range corresponding to the storage parameters.

[0150] According to one or more embodiments of this disclosure, the storage parameters include a minimum version identifier and a maximum version identifier, wherein the minimum version identifier represents the lowest version of the version to which the working node is applicable, and the maximum version identifier represents the higher or lower version of the version to which the working node is applicable.

[0151] According to one or more embodiments of this disclosure, the storage module is specifically configured to: obtain the version identifier of the current model version of the target workflow model; obtain the minimum version identifier of the target worker node based on the version identifier of the current model version; and obtain the maximum version identifier of the target worker node based on a preset maximum identifier.

[0152] According to one or more embodiments of this disclosure, the determining module is specifically configured to: obtain first version information of the target workflow model, wherein the first version information is used to determine the work nodes included in the previous model version of the target workflow model; and determine the first target work node based on the first version information and the work nodes included in the current model version of the target workflow model.

[0153] According to one or more embodiments of this disclosure, the determining module is further configured to: determine a second target working node, wherein the second target working node is a working node newly removed in the current model version of the target workflow model; and update the storage parameters in the target node data corresponding to the second target working node based on the version identifier of the current model version of the target workflow model.

[0154] According to one or more embodiments of this disclosure, when the determining module updates the storage parameters in the target node data corresponding to the second target worker node based on the version identifier of the current model version of the target workflow model, it is specifically used to: update the maximum version identifier of the second target worker node based on the version identifier of the current model version.

[0155] According to one or more embodiments of this disclosure, the storage module is further configured to: generate version information of the current model version based on the work nodes included in the target workflow model in the current model version, wherein the version information represents each work node included in the target workflow model and the order between the work nodes; and store the mapping relationship between the version identifier of the current model version and the version information of the current model version.

[0156] According to one or more embodiments of this disclosure, the determining module is further configured to: determine a third target working node, wherein the third target working node is a working node included in both the current model version and the previous model version of the target workflow model; and update the storage parameters in the target node data corresponding to the third target working node based on the version identifier of the current model version.

[0157] Fourthly, according to one or more embodiments of this disclosure, a workflow data loading apparatus is provided, comprising:

[0158] A receiving module is configured to receive a first loading instruction, the first loading instruction including a target model version of a target workflow model, wherein the target workflow model includes at least one working node, the target workflow model represents the task flow of a target task, and the working node represents a task item in the task flow;

[0159] The acquisition module is used to acquire at least one target node data corresponding to the target model version. The target node data includes storage parameters, which represent the version range applicable to the work node. The target model version is located within the version range. The target node data is used to generate work nodes in the target workflow model corresponding to the target model version.

[0160] The loading module is used to load the target workflow model corresponding to the target model version based on the data of each target node.

[0161] According to one or more embodiments of this disclosure, the acquisition module is specifically configured to: acquire preset storage parameters of node data, the storage parameters including a minimum version identifier and a maximum version identifier, the minimum version identifier representing the lowest version applicable to the working node corresponding to the node data, and the maximum version identifier representing the higher or lower version applicable to the working node corresponding to the node data; and determine the node data corresponding to the storage parameters that can cover the target model version as target node data based on the target model version and the storage parameters corresponding to each node data.

[0162] Fifthly, according to one or more embodiments of the present disclosure, an electronic device is provided, including: a processor, and a memory communicatively connected to the processor;

[0163] The memory stores computer-executed instructions;

[0164] The processor executes computer execution instructions stored in the memory to implement the workflow data storage method described in the first aspect and various possible designs of the first aspect, or to implement the workflow data loading method described in the second aspect and various possible designs of the second aspect.

[0165] Sixthly, according to one or more embodiments of the present disclosure, a computer-readable storage medium is provided, wherein computer-executable instructions are stored therein, which, when executed by a processor, implement the workflow data storage method described in the first aspect and various possible designs of the first aspect, or implement the workflow data loading method described in the second aspect and various possible designs of the second aspect.

[0166] In a seventh aspect, embodiments of this disclosure provide a computer program product, including a computer program that, when executed by a processor, implements the workflow data storage method described in the first aspect and various possible designs of the first aspect, or implements the workflow data loading method described in the second aspect and various possible designs of the second aspect.

[0167] The above description is merely a preferred embodiment of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features disclosed in this disclosure that have similar functions.

[0168] Furthermore, while the operations are described in a specific order, this should not be construed as requiring these operations to be performed in the specific order shown or in a sequential order. In certain environments, multitasking and parallel processing may be advantageous. Similarly, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of this disclosure. Certain features described in the context of individual embodiments may also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may also be implemented individually or in any suitable sub-combination in multiple embodiments.

[0169] Although the subject matter has been described using language specific to structural features and / or methodological logic, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are merely illustrative examples of implementing the claims.

Claims

1. A workflow data storage method, characterized in that, include: Obtain a target workflow model, the target workflow model including at least one work node, wherein the target workflow model represents the task flow of a target task, and the work node represents a task item in the task flow; Determine the first target work node in the target workflow model, wherein the first target work node is a newly configured work node in the current model version of the target workflow model; Obtain the storage parameters of the first target worker node, and store the first target worker node and the corresponding storage parameters as target node data. The storage parameters represent the version range applicable to the worker node, and the target node data is used to generate worker nodes in the target workflow model whose model version is within the version range corresponding to the storage parameters.

2. The method according to claim 1, characterized in that, The storage parameters include a minimum version identifier and a maximum version identifier, wherein the minimum version identifier represents the lowest version to which the working node is applicable, and the maximum version identifier represents the highest version to which the working node is applicable.

3. The method according to claim 2, characterized in that, The step of obtaining the storage parameters of the first target working node includes: Obtain the version identifier of the current model version of the target workflow model; Based on the version identifier of the current model version, the minimum version identifier of the target working node is obtained; Based on the preset maximum identifier, the maximum version identifier of the target working node is obtained.

4. The method according to claim 1, characterized in that, Determining the first target workflow node in the target workflow model includes: Obtain the first version information of the target workflow model, wherein the first version information is used to determine the work nodes included in the previous model version of the target workflow model; The first target work node is determined based on the first version information and the work nodes included in the target workflow model in the current model version.

5. The method according to claim 1, characterized in that, The method further includes: Determine a second target working node, which is a working node that has been newly removed from the target workflow model in the current model version; Based on the version identifier of the current model version of the target workflow model, update the storage parameters in the target node data corresponding to the second target work node.

6. The method according to claim 5, characterized in that, The step of updating the storage parameters in the target node data corresponding to the second target worker node based on the version identifier of the current model version of the target workflow model includes: Based on the version identifier of the current model version, update the maximum version identifier of the second target working node.

7. The method according to any one of claims 1-6, characterized in that, The method further includes: Based on the work nodes included in the target workflow model in the current model version, version information of the current model version is generated. The version information represents each work node included in the target workflow model and the order between each work node. Store the mapping relationship between the version identifier of the current model version and the version information of the current model version.

8. The method according to any one of claims 1-6, characterized in that, The method further includes: Determine the third target working node, which is a working node included in both the current model version and the previous model version of the target workflow model; Based on the version identifier of the current model version, update the storage parameters in the target node data corresponding to the third target working node.

9. A workflow data loading method, characterized in that, include: Receive a first loading instruction, the first loading instruction including a target model version of the target workflow model, wherein the target workflow model includes at least one work node, the target workflow model represents the task flow of the target task, and the work node represents the task item in the task flow; Obtain at least one target node data corresponding to the target model version. The target node data includes storage parameters, which represent the version range applicable to the work node. The target model version is located within the version range. The target node data is used to generate work nodes in the target workflow model corresponding to the target model version. Based on the data of each target node, load the target workflow model corresponding to the target model version.

10. The method according to claim 9, characterized in that, The step of obtaining at least one target node data corresponding to the target model version includes: Obtain the storage parameters of the preset node data. The storage parameters include a minimum version identifier and a maximum version identifier. The minimum version identifier represents the lowest version of the working node corresponding to the node data, and the maximum version identifier represents the highest version of the working node corresponding to the node data. Based on the target model version and the storage parameters corresponding to each node data, the node data that can cover the storage parameters of the target model version is determined as the target node data.

11. A workflow data storage device, characterized in that, include: An acquisition module is used to acquire a target workflow model, the target workflow model including at least one work node, wherein the target workflow model represents the task flow of a target task, and the work node represents a task item in the task flow; The determination module is used to determine the first target work node in the target workflow model, wherein the first target work node is a newly configured work node in the current model version of the target workflow model; The storage module is used to obtain the storage parameters of the first target worker node and store the first target worker node and the corresponding storage parameters as target node data. The storage parameters represent the version range applicable to the worker node, and the target node data is used to generate worker nodes in the target workflow model whose model version is within the version range corresponding to the storage parameters.

12. A workflow data loading device, characterized in that, include: A receiving module is configured to receive a first loading instruction, the first loading instruction including a target model version of a target workflow model, wherein the target workflow model includes at least one working node, the target workflow model represents the task flow of a target task, and the working node represents a task item in the task flow; The acquisition module is used to acquire at least one target node data corresponding to the target model version. The target node data includes storage parameters, which represent the version range applicable to the work node. The target model version is located within the version range. The target node data is used to generate work nodes in the target workflow model corresponding to the target model version. The loading module is used to load the target workflow model corresponding to the target model version based on the data of each target node.

13. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1 to 10.

14. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, implement the method as described in any one of claims 1 to 10.

15. A computer program product comprising a computer program that, when executed by a processor, implements the method of any one of claims 1 to 10.

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