Data selection method, device and equipment across datasets, and medium

Abstract

This application provides a method, apparatus, device, and medium for cross-dataset data selection. The method includes: in response to a user's selection operation on any node in any target dataset, determining at least one target node selected by the user; obtaining the user's operation type and a composite identifier for each target node, the composite identifier including a semantic identifier and a dataset identifier; the operation type being selected or deselected, indicating the user's intention to select the node; and updating the selection status of each node in multiple datasets based on the operation type and the composite identifier of each target node. This method is used to efficiently select data across multiple datasets, thereby improving the efficiency of multi-version data configuration and management.

Description

Technical Field

[0001] This application relates to the field of data processing, and in particular to a method, apparatus, device and medium for selecting data across datasets. Background Technology

[0002] In fields such as automotive electronics and industrial IoT, there are often scenarios involving the configuration and management of multiple versions of data. Taking vehicle-to-everything (V2X) signal configuration as an example, in the development of modern automobiles, it is necessary to synchronize the configuration of the same type of signals for different Over-the-Air (OTA) technology versions of the same vehicle model.

[0003] In existing methods, users need to perform signal synchronization selection operations in each version of the template. For example, if a user wants to configure the vehicle speed signal, the user needs to perform the vehicle speed signal selection operation in each version.

[0004] However, this method, which requires repeated user operations, is cumbersome and prone to omissions, seriously affecting the efficiency of multi-version data configuration and management. Summary of the Invention

[0005] This application provides a method, apparatus, device, and medium for cross-dataset data selection, which can efficiently select data among multiple datasets, thereby improving the efficiency of multi-version data configuration and management.

[0006] In a first aspect, embodiments of this application provide a data selection method across datasets, including:

[0007] In response to a user's selection operation on any node in any target dataset, determine at least one target node selected by the user;

[0008] Obtain the user's operation type and the composite identifier of each target node; the composite identifier includes the node's semantic identifier and dataset identifier; the operation type is selected or deselected, used to indicate the user's intention to select a node;

[0009] Based on the operation type and the composite identifier of each target node, the selection status of each node in multiple datasets is updated.

[0010] In one possible implementation, updating the selection state of each node in multiple datasets based on the operation type and the composite identifier of each target node includes:

[0011] An initial semantic identifier set is constructed based on the composite identifier of each historically selected node, the initial semantic identifier set including the semantic identifier of each historically selected node; wherein, the historically selected node is the node whose current selection state is selected in the multiple datasets;

[0012] If the operation type is selected, then for each target node, the semantic identifier in the composite identifier of the target node is added to the initial semantic identifier set to obtain the target semantic identifier set;

[0013] If the operation type is cancellation, then for each target node, the semantic identifier in the composite identifier of the target node is deleted from the initial semantic identifier set to obtain the target semantic identifier set;

[0014] The selection status of each node in the multiple datasets is updated based on the target semantic identifier set.

[0015] In one possible implementation, updating the selection state of each node in the plurality of datasets based on the target semantic identifier set includes:

[0016] For each node in the multiple datasets, if the target semantic identifier set includes the semantic identifier in the composite identifier of the node, then the selection status of the node is updated to selected;

[0017] If the target semantic identifier set does not include the semantic identifier in the composite identifier of the node, then the selection status of the node is updated to unselected.

[0018] In one possible implementation, before updating the selection state of each node in the multiple datasets based on the operation type and the composite identifier of each target node, the method further includes:

[0019] Based on the composite identifier of each node in the multiple datasets, a semantic identifier index list is constructed, which includes the composite identifier of at least one node corresponding to different semantic identifiers;

[0020] Accordingly, updating the selection state of each node in the plurality of datasets based on the target semantic identifier set includes:

[0021] For each semantic identifier in the target semantic identifier set, obtain the composite identifier of at least one node corresponding to the semantic identifier according to the semantic identifier index list;

[0022] For each of the at least one nodes, update the selection status of that node to selected;

[0023] For each node in the plurality of datasets other than at least one node, update the selection status of that node to unselected.

[0024] In one possible implementation, determining at least one target node selected by the user includes:

[0025] If the node selected by the user is any child node in any target dataset, then the child node is determined as the target node;

[0026] If the node selected by the user is any parent node in any target dataset, then each child node under that parent node will be determined as a target node.

[0027] In one possible implementation, the method further includes:

[0028] Obtain the number of nodes in the target dataset whose status is selected, and obtain the dynamic quota of the target dataset;

[0029] The user's selection operation is restricted based on the quantity and the dynamic quota.

[0030] The dynamic quota number is used to indicate the maximum number of nodes that the user is allowed to select in the target dataset.

[0031] In one possible implementation, obtaining the dynamic quota of the target dataset includes:

[0032] Obtain the total quota and frozen quota of the multiple datasets;

[0033] The difference between the total quota and the frozen quota is taken as the dynamic quota of the target dataset;

[0034] The frozen quota is the number of nodes in the datasets other than the target dataset among the multiple datasets, and the selected status is the number of nodes that have been selected.

[0035] Secondly, embodiments of this application provide a data selection device across datasets, comprising:

[0036] The determination module, in response to a user's selection operation on any node in any target dataset, determines at least one target node selected by the user;

[0037] The first acquisition module is used to acquire the user's operation type and the composite identifier of each target node; the composite identifier includes the semantic identifier of the node and the dataset identifier; the operation type is selection or cancellation, used to indicate the user's intention to select a node;

[0038] The update module is used to update the selection status of each node in multiple datasets based on the operation type and the composite identifier of each target node.

[0039] In one possible implementation, the update module includes:

[0040] The first construction unit is used to construct an initial semantic identifier set based on the composite identifier of each historically selected node. The initial semantic identifier set includes the semantic identifier of each historically selected node. The historically selected node is a node in the plurality of datasets whose current selection state is selected.

[0041] The processing unit is configured to, if the operation type is selected, add the semantic identifier in the composite identifier of the target node to the initial semantic identifier set for each target node, thereby obtaining the target semantic identifier set;

[0042] If the operation type is cancellation, then for each target node, the semantic identifier in the composite identifier of the target node is deleted from the initial semantic identifier set to obtain the target semantic identifier set;

[0043] The update unit is used to update the selection state of each node in the plurality of datasets according to the target semantic identifier set.

[0044] In one possible implementation, the updating unit is specifically used for:

[0045] For each node in the multiple datasets, if the target semantic identifier set includes the semantic identifier in the composite identifier of the node, then the selection status of the node is updated to selected;

[0046] If the target semantic identifier set does not include the semantic identifier in the composite identifier of the node, then the selection status of the node is updated to unselected.

[0047] In one possible implementation, the device further includes:

[0048] The construction module is used to construct a semantic identifier index list based on the composite identifier of each node in the multiple datasets, wherein the semantic identifier index list includes the composite identifier of at least one node corresponding to different semantic identifiers;

[0049] Accordingly, the update unit is specifically used for:

[0050] For each semantic identifier in the target semantic identifier set, obtain the composite identifier of at least one node corresponding to the semantic identifier according to the semantic identifier index list;

[0051] For each of the at least one nodes, update the selection status of that node to selected;

[0052] For each node in the plurality of datasets other than at least one node, update the selection status of that node to unselected.

[0053] In one possible implementation, the determining module is specifically used for:

[0054] If the node selected by the user is any child node in any target dataset, then the child node is determined as the target node;

[0055] If the node selected by the user is any parent node in any target dataset, then each child node under that parent node will be determined as a target node.

[0056] In one possible implementation, the device further includes:

[0057] The second acquisition module is used to acquire the number of nodes in the target dataset whose status is selected, and to acquire the dynamic quota of the target dataset;

[0058] The restriction module is used to restrict the user's selection operation based on the quantity and the dynamic quota.

[0059] The dynamic quota number is used to indicate the maximum number of nodes that the user is allowed to select in the target dataset.

[0060] In one possible implementation, the second acquisition module is specifically used for:

[0061] Obtain the total quota and frozen quota of the multiple datasets;

[0062] The difference between the total quota and the frozen quota is taken as the dynamic quota of the target dataset;

[0063] The frozen quota is the number of nodes in the datasets other than the target dataset among the multiple datasets, and the selected status is the number of nodes that have been selected.

[0064] Thirdly, embodiments of this application provide a computer device, including: a memory and a processor;

[0065] The memory stores computer-executed instructions;

[0066] The processor executes computer execution instructions stored in the memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0067] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0068] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0069] The data selection method, apparatus, device, and medium across datasets provided in this application first respond to a user's selection operation on any node in any target dataset, determine at least one target node selected by the user, and obtain the user's operation type and a composite identifier for each target node, which includes the semantic identifier of the node and the dataset identifier. Then, based on the operation type and the composite identifier of each target node, the selection status of each node in multiple datasets is updated. This method is used to efficiently synchronize data selection across multiple datasets, thereby improving the efficiency of multi-version data configuration and management. Attached Figure Description

[0070] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0071] Figure 1 A flowchart illustrating a cross-dataset data selection method provided in Embodiment 1 of this application;

[0072] Figure 2 This is a flowchart illustrating a cross-dataset data selection method provided in Embodiment 3 of this application;

[0073] Figure 3 This is a schematic diagram of the structure of a cross-dataset data selection device provided in Embodiment 4 of this application;

[0074] Figure 4 This is a schematic diagram of the structure of a cross-dataset data selection device provided in Embodiment 5 of this application;

[0075] Figure 5 A schematic diagram of the structure of the computer device provided in this application.

[0076] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0077] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0078] To facilitate understanding of the technical content of this solution, the background technology is described in detail below:

[0079] In existing data selection technologies, tree-structured selection components (such as Ant Design Tree and ElementPlus Tree front-end component libraries) are typically used to manage the selection state of nodes. Specifically, existing tree-structured selection components usually manage the selection state based on the unique identifier (i.e., key value) of the node. When data is distributed across multiple independent datasets (such as multiple templates), it is impossible to achieve automatic synchronization of the selection state of nodes of the same type across datasets.

[0080] Taking vehicle-to-everything (V2X) signal configuration as an example, a certain vehicle model has three OTA-version signal templates: A, B, and C. Each signal template corresponds to an independent configuration dataset, and each onboard signal (such as vehicle speed, temperature, tire pressure, etc.) within the template serves as a node in the corresponding dataset. Given that each signal template includes a speed signal, the user needs to select the speed signal for all versions during signal configuration. However, in existing methods, the user must repeatedly select the same signal for each version, which is not only cumbersome but also prone to overlooking the selection of a particular version. The workload of the selection process is proportional to the number of versions, resulting in low configuration and management efficiency.

[0081] To address the aforementioned problems in the background technology, the inventors discovered during their research that a composite identifier for a node can be constructed based on both the dataset identifier and the node's semantics. The dataset identifier ensures the uniqueness of the composite identifier, while adding the node's semantics to the composite identifier facilitates the synchronous selection of nodes with the same semantics. Thus, when a user performs a selection operation on a certain node, all nodes in the dataset with the same semantics as that node can be automatically selected synchronously, achieving synchronous configuration of signals of the same type.

[0082] It should be noted that this solution can be applied to any multi-version data configuration scenario, such as vehicle network signal template management, industrial IoT parameter setting, software version control, etc. This application does not impose specific restrictions on the specific application scenarios of the solution.

[0083] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0084] Figure 1 This is a flowchart illustrating a cross-dataset data selection method provided in Embodiment 1 of this application, as shown below. Figure 1 As shown, the method includes:

[0085] S101. In response to a user's selection operation on any node in any target dataset, determine at least one target node selected by the user.

[0086] In this scheme, a user can select any dataset as the target dataset from multiple datasets and perform a select / deselect operation on any node in the target dataset. Accordingly, this step will determine at least one target node that the user intends to select in the target dataset in response to the user's selection operation.

[0087] It should be understood that in this solution, the data for each version is stored in a dataset in the form of tree-structured nodes. In practical applications, the nodes in the target dataset include parent nodes and child nodes under the parent nodes. Accordingly, when performing a selection operation, the user can choose to select / deselect the parent node or directly select / deselect the child node.

[0088] Accordingly, in one possible implementation, the method for determining the target node varies depending on the node level selected by the user, and can be categorized into the following two types:

[0089] Case 1: If the node selected by the user is any child node in any target dataset, then that child node is determined as the target node.

[0090] Specifically, if the user selects any child node in the target dataset, then only that child node will be designated as the target node, and the number of target nodes will be 1.

[0091] Case 2: If the node selected by the user is any parent node in any target dataset, then each child node under that parent node is determined as the target node.

[0092] Specifically, if a user selects any parent node in the target dataset, it means the user expects to perform the same selection operation on all child nodes under that parent node. Therefore, each child node under that parent node needs to be identified as a target node. It should be understood that by identifying all child nodes under a parent node as target nodes, cascading propagation across datasets can be achieved, thereby improving the comprehensiveness of the solution.

[0093] The method provided in this implementation precisely determines the target nodes by formulating differentiated processing methods for child nodes and parent nodes in the target dataset. This ensures that the operational needs of nodes at different levels are adapted without discrimination, eliminating blind spots in node operation coverage and improving the comprehensiveness and applicability of the solution.

[0094] S102. Obtain the user's operation type and the composite identifier of each target node, which includes the semantic identifier of the node and the dataset identifier.

[0095] The operation type is selected or deselected, which is used to indicate the user's intention to select a node.

[0096] In this step, the user's operation type will be determined based on the user's selection operation on the target dataset, and the composite identifier of each target node will be obtained.

[0097] It should be understood that in practical applications, a globally unique composite identifier needs to be pre-assigned to each node in each dataset. This composite identifier consists of the node's dataset identifier and semantic identifier. Specifically, the node's dataset identifier indicates the identification (ID) of the dataset to which the node belongs, while the node's semantic identifier indicates the node's business meaning. Therefore, the composite identifier provided by this solution ensures both the global uniqueness of nodes and preserves their semantic information, supporting semantic parsing across datasets.

[0098] In addition, to facilitate the rapid extraction of semantic identifiers, a delimiter can be used to connect the semantic identifiers and the dataset representation. For example, the composite identifier of the speed signal in template v1.0 is "v1.0-speed".

[0099] S103. Update the selection status of each node in multiple datasets based on the operation type and the composite identifier of each target node.

[0100] Here, multiple datasets refer to all datasets in the global scope; the selection status of each node is either "selected" or "unselected".

[0101] In this step, based on the semantic identifier in the composite identifier of each target node, a selection operation synchronized with the operation type will be performed globally for nodes with the same semantic meaning.

[0102] In detail, this step requires updating the selection status of each node in multiple datasets that shares the same semantic identifier as the target node to the status indicated by the operation type. For example, if the user selects a speed signal (semantic identifier: speed) and the operation type is cancel, then the selection status of all nodes with the semantic identifier "speed" in all versions of the signal template needs to be updated to unselected.

[0103] The cross-dataset data selection method provided in this application first responds to a user's selection operation on any node in any target dataset, determines at least one target node selected by the user, and obtains the user's operation type and the composite identifier of each target node. The composite identifier includes the semantic identifier of the node and the dataset identifier. Then, based on the operation type and the composite identifier of each target node, the selection status of each node in multiple datasets is updated. This method is used to efficiently synchronize data selection across multiple datasets, thereby improving the efficiency of multi-version data configuration and management.

[0104] Furthermore, Embodiment 2 of this application provides a data selection method across datasets. Based on the above embodiments, this embodiment details the specific implementation method of step S103, including the following steps 3.1 to 3.3:

[0105] Step 3.1: Construct an initial semantic identifier set based on the composite identifier of each historical selected node. This initial semantic identifier set includes the semantic identifier of each historical selected node.

[0106] Among them, the historical selected nodes are the nodes in multiple datasets whose current selection status is selected.

[0107] It should be understood that in practical applications, the number of historically selected nodes can be zero.

[0108] In this step, the semantic representation of each historically selected node needs to be extracted from its composite identifier, and then the semantic identifier of each historically selected node is added to an empty set to obtain the initial semantic identifier set.

[0109] Step 3.2: Update the initial semantic identifier set according to the operation type to obtain the target semantic identifier set.

[0110] It should be understood that the initial semantic identifier set is updated differently depending on the type of operation, specifically including the following two cases:

[0111] Case 1: If the operation type is selected, then for each target node, the semantic identifier in the composite identifier of the target node is added to the initial semantic identifier set to obtain the target semantic identifier set.

[0112] Specifically, when the operation type is selection, it is necessary to traverse each target node that the user expects to select in this operation, extract the semantic identifier of the node from the composite identifier of the target node, and add the semantic identifier to the initial semantic identifier set to obtain the target semantic identifier set.

[0113] Case 2: If the operation type is cancellation, then for each target node, the semantic identifier in the composite identifier of the target node is removed from the initial semantic identifier set to obtain the target semantic identifier set.

[0114] Specifically, when the operation type is cancellation, it is necessary to traverse each target node that the user expects to select in this operation, extract the semantic identifier of the node from the composite identifier of the target node, and delete the semantic identifier from the initial semantic identifier set to obtain the target semantic identifier set.

[0115] Step 3.3: Update the selection state of each node in multiple datasets based on the target semantic identifier set.

[0116] In this step, since the target semantic identifier set clarifies the semantic scope that must ultimately take effect (i.e., the selection status of the corresponding node is updated to "selected"), the selection status of each node in the multiple datasets is updated based on the target semantic identifier set and the semantic representation of each node in the multiple datasets.

[0117] In one possible implementation, this step can be implemented as follows: for each node in multiple datasets, if the target semantic identifier set includes the semantic identifier in the composite identifier of the node, then the selection status of the node is updated to selected; if the target semantic identifier set does not include the semantic identifier in the composite identifier of the node, then the selection status of the node is updated to unselected.

[0118] In detail, this implementation requires traversing each node in multiple datasets to determine whether the semantic identifier of the node belongs to the set, thereby accurately defining the selection state that the node should have (set as selected if it is in the set, and set as unselected if it is not).

[0119] This implementation uses the target semantic identifier set as a unified judgment benchmark and performs bidirectional precise updates of the selection state of all nodes in multiple datasets. It achieves a high degree of consistency and synchronization of the selection state of nodes across datasets, effectively ensuring that the selection state of all nodes is completely consistent with the target semantic range, and significantly improving the accuracy of multi-version data configuration.

[0120] To further reduce the time complexity of state update operations based on the target semantic set, this embodiment also provides another implementation method:

[0121] In the specific implementation of this method, a semantic identifier index list needs to be constructed in advance based on the composite identifier of each node in multiple datasets. This semantic identifier index list includes the composite identifier of at least one node corresponding to different semantic identifiers.

[0122] Specifically, a semantic identifier index list needs to be pre-built. This list should include semantic identifiers involved in multiple datasets, as well as an index for each semantic identifier. This index is used to indicate the composite identifier of the nodes with that semantic identifier in multiple datasets.

[0123] For example, the index list includes, for instance, “speed: [v1.0-speed, v2.0-speed, v3.0-speed] and “temperature: [v1.0-temperature, v2.0-temperature, v3.0-temperature].

[0124] Accordingly, based on this semantic identifier index list, step 3.3 can be implemented using the methods described in steps 3.3.1 to 3.3.2:

[0125] Step 3.3.1: For each semantic identifier in the target semantic identifier set, obtain the composite identifier of at least one node corresponding to the semantic identifier according to the semantic identifier index list.

[0126] In this step, for each semantic identifier in the target semantic identifier set, it is necessary to retrieve the index corresponding to that semantic identifier from the semantic identifier index list, and obtain the composite identifier of each node indicated by the index.

[0127] Step 3.3.2: For each node in at least one node, update the selection status of that node to selected.

[0128] In this step, the selection status of the node corresponding to the obtained composite identifier will be updated to "selected".

[0129] Step 3.3.3: For each node in multiple datasets other than the at least one node, update the selection status of that node to unselected.

[0130] In this step, for nodes corresponding to composite identifiers other than the composite identifiers in step 3.3.2 in multiple databases, the selection status of the nodes can be directly updated to unselected.

[0131] It should be understood that, compared to the previous implementation, the method provided in this implementation does not require performing semantically identifier-based membership judgment for each node, which can further improve the configuration efficiency of node selection state.

[0132] The cross-dataset data selection method provided in this application first constructs an initial semantic identifier set based on the composite identifier of historically selected nodes, converging the scattered historically selected node states into a unified semantic dimension set. Then, by performing semantic identifier addition or deletion operations on this set, a target semantic identifier set is quickly generated. Finally, all node states are updated using the target semantic identifier set as the sole benchmark. By performing cross-dataset node operations from the semantic dimension, the method achieves the effect of efficiently updating the synchronous state of nodes across datasets.

[0133] Furthermore, due to limitations in data processing resources, practical configuration scenarios typically require limiting the number of nodes a user can select globally (i.e., setting a total quota). Current methods for implementing selection quantity limits usually employ "global counting" or "group counting," which makes it impossible to limit the quota for a single dataset when a user performs selection operations across multiple datasets, leading to the problem of exceeding the selection limit.

[0134] To address this issue, Embodiment 3 of this application provides a data selection method across datasets. Figure 2 This is a flowchart illustrating a cross-dataset data selection method provided in Embodiment 3 of this application. Based on the above embodiments, the method provided in this embodiment further includes a restriction operation based on dynamic quotas, specifically including:

[0135] S201. Obtain the number of nodes in the target dataset whose status is selected, and obtain the dynamic quota of the target dataset.

[0136] The dynamic quota number is used to indicate the maximum number of nodes that a user is allowed to select in the target dataset.

[0137] In this step, it is necessary to count the number of nodes in the target dataset that are currently selected, and obtain the maximum number of nodes that the user is allowed to select in the target dataset (i.e., the dynamic quota).

[0138] In one possible implementation, the dynamic quota of the target dataset can be obtained using the methods described in steps 1.1 to 1.2 below:

[0139] Step 1.1: Obtain the total quota and frozen quota for the multiple datasets.

[0140] The total quota is the pre-set total number of nodes that can be selected globally, i.e., the total number of nodes in multiple datasets that are simultaneously selected; the frozen quota is the number of nodes in other datasets besides the target dataset that are selected.

[0141] In this step, the preset total quota will be obtained, and the number of nodes currently selected in the other datasets outside the target dataset will be counted to obtain the frozen quota.

[0142] Step 1.2: Use the difference between the total quota and the frozen quota as the dynamic quota for the target dataset.

[0143] Specifically, the dynamic quota of the target dataset = total quota - frozen quota.

[0144] The method provided by this implementation calculates the dynamic quota of the target dataset based on the preset total quota and the statistically obtained frozen quota, so that the available quota of the target dataset accurately excludes the proportion of frozen items that have been selected but are not in the current dataset, thus providing a reliable basis for accurately controlling the number of nodes selected in the target dataset.

[0145] S202. Based on this quantity and dynamic quota, restrict the user's selection operations.

[0146] In this step, the user's selection operation needs to be restricted based on the number of nodes in the target dataset that are selected and the dynamic quota of the target dataset. This is to ensure that after the user makes a selection operation, the number of nodes in the target dataset that are selected does not exceed the dynamic quota, thereby limiting the number of nodes in the target dataset that are selected across multiple datasets to not exceed the total quota.

[0147] Specifically, if the number is less than or equal to the dynamic quota, or if the number is equal to the dynamic quota and the user's current operation type is cancellation, then the system will respond to the user's selection operation on any node in the target dataset and perform a subsequent update operation on the selection status of all nodes based on the semantic identifier of the target node; if the number is equal to the dynamic quota and the user's current operation type is selection, then the system will not respond to the user's selection operation on any node in any target dataset, in order to avoid exceeding the limit for the total number of nodes selected by the user.

[0148] Optionally, the number of nodes in the target dataset that are selected and the dynamic quota of the target dataset can also be displayed on the operation interface to prompt the user's selection operation, thereby achieving quota transparency and optimizing the user experience. For example, the operation interface may display the following: "Number of nodes selected in the target dataset / Dynamic quota of the target dataset".

[0149] It should be understood that in practical applications, the number of frozen quotas and the total quota should be updated in real time as the number of nodes selected in other datasets outside the target dataset changes.

[0150] The cross-dataset data selection method provided in this application replaces the traditional extensive management method by accurately counting the actual number of selected nodes in the target dataset and combining it with the dynamic quota as the core restriction basis for the selection operation. This achieves refined and precise management of the user's selection operation in the target dataset, effectively ensuring that the number of selections strictly matches the actual available quota of the target dataset.

[0151] Furthermore, in the field of multi-version data configuration and management, scenarios often involve filtering before selection. However, in existing methods, when users clear search criteria after filtering, the selection status may become abnormal. For example, the signals currently selected by the user only include signals A, B, and C; after the user searches for "speed," only signal C is displayed in the view; after the user clears the search criteria, the global selection status is mistakenly deleted, meaning that the user has not selected any signals at present.

[0152] To address this technical issue, the cross-dataset data selection method provided in this solution also includes: simultaneously maintaining the global selection state and the view state.

[0153] Specifically, a dual-state maintenance mechanism is adopted, where the global selection state is independent of the view display state. During search filtering, only matching items are displayed, but all selected items are retained in the global selection state. The global selection state is the set of all selected nodes; the view display state is the set of selection signals under the current filtering conditions.

[0154] As a specific example, we first construct a global selection state set and a view state set: if the user is not currently performing a search operation, the view state set is the same as the global selection state set; if the user is currently performing a search operation, the view state set only includes nodes that meet the search criteria from the global selection state set; the global selection state set is used to store the composite identifiers of nodes whose selection state is selected in multiple datasets, and the view state set includes the composite identifiers of nodes that need to be displayed in the view, indicating the nodes that need to be displayed.

[0155] The method provided in this implementation maintains the global selection state and the view state separately, so that in the search filtering scenario, it can not only retain the real record of all user selection operations without loss, but also make the current view accurately display only the selected items that are visible after filtering. This achieves the effect of eliminating the confusion of the selection state display, improving the accuracy of multi-version data configuration operations and user experience.

[0156] Figure 3 This is a schematic diagram of the structure of a cross-dataset data selection device provided in Embodiment 4 of this application, as shown below. Figure 3 As shown, the cross-dataset data selection device 30 provided in this embodiment includes:

[0157] The determination module 301, in response to the user's selection operation on any node in any target dataset, determines at least one target node selected by the user;

[0158] The first acquisition module 302 is used to acquire the user's operation type and the composite identifier of each target node; the composite identifier includes the semantic identifier of the node and the dataset identifier; the operation type is selected or canceled, which is used to indicate the user's intention to select the node;

[0159] The update module 303 is used to update the selection status of each node in multiple datasets based on the operation type and the composite identifier of each target node.

[0160] The cross-dataset data selection device 30 provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0161] Figure 4 This is a schematic diagram of the structure of a cross-dataset data selection device provided in Embodiment 5 of this application, as shown below. Figure 4 As shown, based on the above embodiments, the cross-dataset data selection device 30 provided in this embodiment further includes:

[0162] Module 304 is used to construct a semantic identifier index list based on the composite identifier of each node in multiple datasets. The semantic identifier index list includes the composite identifier of at least one node corresponding to different semantic identifiers.

[0163] The second acquisition module 305 is used to acquire the number of nodes in the target dataset whose status is selected, and to acquire the dynamic quota of the target dataset;

[0164] The restriction module 306 is used to restrict the user's selection operation based on the quantity and dynamic quota.

[0165] The dynamic quota number is used to indicate the maximum number of nodes that a user is allowed to select in the target dataset.

[0166] In one possible implementation, the update module 303 includes:

[0167] The first construction unit is used to construct an initial semantic identifier set based on the composite identifier of each historically selected node. The initial semantic identifier set includes the semantic identifier of each historically selected node. The historically selected node is a node in multiple datasets whose current selection state is selected.

[0168] The processing unit is used to add the semantic identifier in the composite identifier of the target node to the initial semantic identifier set for each target node if the operation type is selected, so as to obtain the target semantic identifier set.

[0169] If the operation type is cancellation, then for each target node, the semantic identifier in the composite identifier of the target node is removed from the initial semantic identifier set to obtain the target semantic identifier set;

[0170] The update unit is used to update the selection state of each node in multiple datasets based on the target semantic identifier set.

[0171] In one possible implementation, the updating unit is specifically used for:

[0172] For each node in multiple datasets, if the target semantic identifier set includes the semantic identifier in the composite identifier of the node, then the selection status of the node is updated to selected.

[0173] If the target semantic identifier set does not include the semantic identifier in the composite identifier of the node, then the selection status of the node is updated to unselected.

[0174] In one possible implementation, the update unit is specifically used for:

[0175] For each semantic identifier in the target semantic identifier set, obtain the composite identifier of at least one node corresponding to the semantic identifier based on the semantic identifier index list;

[0176] For each node in at least one node, update the node's selection status to selected;

[0177] For each node in multiple datasets except for at least one node, update the node's selection status to unselected.

[0178] In one possible implementation, the determining module 301 is specifically used for:

[0179] If the node selected by the user is any child node in any target dataset, then the child node will be designated as the target node.

[0180] If the node selected by the user is any parent node in any target dataset, then each child node under the parent node will be determined as the target node.

[0181] In one possible implementation, the second acquisition module 305 is specifically used for:

[0182] Retrieve the total quota and frozen quota for multiple datasets;

[0183] The difference between the total quota and the frozen quota is used as the dynamic quota for the target dataset.

[0184] Among them, the frozen quota is the number of nodes selected in the datasets other than the target dataset in the multiple datasets.

[0185] The cross-dataset data selection device 30 provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0186] Figure 5 A schematic diagram of the structure of the computer device provided in this application. Figure 5 As shown, the computer device 40 provided in this embodiment includes at least one processor 401 and a memory 402. Optionally, the device 40 further includes a communication component 403. The processor 401, memory 402, and communication component 403 are connected via a bus 404.

[0187] In a specific implementation, at least one processor 401 executes computer execution instructions stored in memory 402, causing at least one processor 401 to perform the above-described method.

[0188] The specific implementation process of processor 401 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0189] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0190] The memory may include read-only memory and random access memory. The memory may be volatile or non-volatile, or may include both. Non-volatile memory may include read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory may include random access memory (RAM), which serves as an external cache. Many forms of RAM are available by way of example, but not limitation. Examples include Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDR SDRAM), Enhanced Synchronous DRAM (ESDRAM), Sync Link DRAM (SLDRAM), and Direct Rambus RAM (DR RAM).

[0191] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0192] This application also provides a computer program product, including a computer program that, when executed, implements the above-described method.

[0193] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed, implement the above-described method.

[0194] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as SRAM, EEPROM, EPROM, PROM, ROM, magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0195] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside within an ASIC. Alternatively, the processor and the readable storage medium can exist as discrete components in a device.

[0196] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0197] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0198] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0199] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0200] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0201] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A data selection method across datasets, characterized in that, include: In response to a user's selection operation on any node in any target dataset, determine at least one target node selected by the user; Obtain the user's operation type and the composite identifier of each target node; the composite identifier includes the node's semantic identifier and dataset identifier; the operation type is selected or deselected, used to indicate the user's intention to select a node; Based on the operation type and the composite identifier of each target node, the selection status of each node in multiple datasets is updated.

2. The method according to claim 1, characterized in that, The step of updating the selection state of each node in multiple datasets based on the operation type and the composite identifier of each target node includes: An initial semantic identifier set is constructed based on the composite identifier of each historically selected node, the initial semantic identifier set including the semantic identifier of each historically selected node; wherein, the historically selected node is the node whose current selection state is selected in the multiple datasets; If the operation type is selected, then for each target node, the semantic identifier in the composite identifier of the target node is added to the initial semantic identifier set; or, if the operation type is canceled, then for each target node, the semantic identifier in the composite identifier of the target node is deleted from the initial semantic identifier set to obtain the target semantic identifier set. The selection status of each node in the multiple datasets is updated based on the target semantic identifier set.

3. The method according to claim 2, characterized in that, The step of updating the selection state of each node in the plurality of datasets according to the target semantic identifier set includes: For each node in the multiple datasets, if the target semantic identifier set includes the semantic identifier in the composite identifier of the node, then the selection status of the node is updated to selected; If the target semantic identifier set does not include the semantic identifier in the composite identifier of the node, then the selection status of the node is updated to unselected.

4. The method according to claim 2, characterized in that, Before updating the selection state of each node in multiple datasets based on the operation type and the composite identifier of each target node, the method further includes: Based on the composite identifier of each node in the multiple datasets, a semantic identifier index list is constructed, which includes the composite identifier of at least one node corresponding to different semantic identifiers; Accordingly, updating the selection state of each node in the plurality of datasets based on the target semantic identifier set includes: For each semantic identifier in the target semantic identifier set, obtain the composite identifier of at least one node corresponding to the semantic identifier according to the semantic identifier index list; For each of the at least one nodes, update the selection status of that node to selected; For each node in the plurality of datasets other than at least one node, update the selection status of that node to unselected.

5. The method according to any one of claims 1 to 4, characterized in that, Determining at least one target node selected by the user includes: If the node selected by the user is any child node in any target dataset, then the child node is determined as the target node; If the node selected by the user is any parent node in any target dataset, then each child node under that parent node will be determined as a target node.

6. The method according to any one of claims 1 to 4, characterized in that, The method further includes: Obtain the number of nodes in the target dataset whose status is selected, and obtain the dynamic quota of the target dataset; The user's selection operation is restricted based on the quantity and the dynamic quota. The dynamic quota number is used to indicate the maximum number of nodes that the user is allowed to select in the target dataset.

7. The method according to claim 6, characterized in that, The step of obtaining the dynamic quota of the target dataset includes: Obtain the total quota and frozen quota of the multiple datasets; The difference between the total quota and the frozen quota is taken as the dynamic quota of the target dataset; The frozen quota is the number of nodes in the datasets other than the target dataset among the multiple datasets, and the selected status is the number of nodes that have been selected.

8. A data selection device across datasets, characterized in that, include: The determination module, in response to a user's selection operation on any node in any target dataset, determines at least one target node selected by the user; The first acquisition module is used to acquire the user's operation type and the composite identifier of each target node; the composite identifier includes the semantic identifier of the node and the dataset identifier; the operation type is selection or cancellation, used to indicate the user's intention to select a node; The update module is used to update the selection status of each node in multiple datasets based on the operation type and the composite identifier of each target node.

9. A computer device, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-7.

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