A data processing method, device and electronic equipment

Abstract

Embodiments of the present application provide a data processing method and device and electronic equipment, which are applied to a data large screen. The data large screen includes view interfaces of multiple components and parameter controls, wherein parameters in the parameter controls are used to filter calculation properties of the components in the data large screen. The method includes: in response to a triggering operation on the parameter controls, obtaining a target parameter corresponding to the triggering operation; according to the target parameter, obtaining a calculation property corresponding to the target parameter from the calculation properties of the multiple components, the calculation property being a to-be-calculated property, calculating the to-be-calculated property, and updating a view interface of a component corresponding to the to-be-calculated property in the data large screen according to a calculation result. The method abstracts calculation properties from each component, and performs linkage based on corresponding parameters in the calculation properties, thereby realizing multi-level linkage, and thus adapting to complex linkage requirements of users.

Description

Technical Field

[0001] The present application relates to the field of data large screen display, and in particular to a data processing method, apparatus and electronic device. Background Art

[0002] The data large screen is a comprehensive display interface based on data visualization technology, which is used to centrally display scattered business data through charts, such as bar charts, line charts, tables and maps, so that users can quickly grasp the global data. When users view data on the data large screen, there is a need to filter, screen and link component data. Filtering and screening refers to screening the data in a single component to hide irrelevant data. The linkage requirement refers to the data of multiple components: when the data of one component is updated, the data of other components are updated in linkage to maintain data consistency.

[0003] Currently, the linkage relationship between components can be established through data fields to realize filtering, screening and linkage. However, when users view data on the data large screen, there will be various linkage requirements, such as one-to-one linkage, one-to-many linkage, many-to-one linkage, and many-to-many linkage. The linkage relationship connected based on data fields often has the problem of being unable to adapt to complex actual linkage requirements due to the complex linkage relationship. Summary of the Invention

[0004] The embodiments of the present application provide a data processing method, apparatus and electronic device, which are used to improve the adaptability to actual linkage requirements.

[0005] In a first aspect, the embodiments of the present application provide a data processing method applied to a data large screen. The data large screen includes a view interface of a plurality of components, and the data large screen further includes a parameter control. The parameters in the parameter control are used to filter the calculation attributes of the components in the data large screen. The method includes: In response to a trigger operation on the parameter control, a target parameter is acquired; the trigger operation indicates an operation of acquiring the target parameter; To-be-calculated attributes are acquired according to the target parameter; the to-be-calculated attributes are the calculation attributes corresponding to the target parameter among all the calculation attributes; The to-be-calculated attributes are calculated, and the view interface of the component corresponding to the to-be-calculated attributes in the data large screen is updated according to the calculation result.

[0006] Optionally, there are N to-be-calculated attributes, where N is an integer greater than 1, and the acquiring to-be-calculated attributes according to the target parameter includes: N target calculation elements are determined according to the target parameter; the target calculation elements are the calculation elements corresponding to the target parameter; one calculation element indicates one calculation attribute; Obtain N attributes to be calculated corresponding to the target calculation element.

[0007] Optionally, the computed element includes a forward pointer and a backward pointer, the forward pointer indicating the preceding dependent element of the computed element, and the backward pointer pointing to the following dependent element of the computed element. Before computing the attribute to be computed, the method further includes: Obtain a computation linked list corresponding to N target computation elements; the computation linked list includes the N target computation elements and the dependencies between the N target computation elements; Based on the computation chain, the computation order of the N attributes to be computed is determined; for the first computation element and the second computation element in the computation chain, and the first computation element is the predecessor dependency element of the second computation element, the computation order includes first executing the attribute to be computed corresponding to the first computation element, and then executing the attribute to be computed corresponding to the second computation element. Calculate N attributes to be calculated according to the calculation order.

[0008] Optionally, determining the calculation order of the N attributes to be calculated based on the calculation linked list includes: Determine whether a loop exists in the computation linked list; If no loop exists, the calculation order of the N attributes to be calculated is determined according to the calculation linked list.

[0009] Optionally, the method further includes: if a loop exists, cutting off the loop and outputting an alarm message indicating a configuration error.

[0010] Optionally, determining the calculation order of the N attributes to be calculated based on the calculation linked list includes: In the calculation chain, if there are multiple preceding dependent elements of the target calculation element, the determined calculation order includes only the attribute to be calculated corresponding to one of the target calculation elements.

[0011] Optionally, obtaining the attribute to be calculated based on the target parameter includes: Based on the target parameters, determine the direct attributes to be calculated that are directly associated with the target parameters; The directly computed attributes are mapped to computed elements and stored in a pre-built processing sequence; Retrieve computational elements from the processing sequence and perform processing operations: the processing operations are to move the retrieved computational elements to a pre-built completion queue and determine whether the retrieved computational elements will cause subsequent linkages; if they will cause subsequent linkages, obtain the indirect computational attributes that affect subsequent computations; map the indirect computational attributes to computational elements and store them in the processing sequence. The process involves repeatedly retrieving computational elements from the processing sequence and performing the processing operation until the processing sequence is empty.

[0012] Optionally, before responding to the trigger operation on the parameter control, the method further includes: initializing the data dashboard; During the initialization of the data dashboard page, the calculated attributes of the multiple components are obtained; Determine the dependencies between the computed properties of the multiple components; The computation order of the computed properties of the multiple components is determined based on the dependencies between them. The calculation order of the calculated attributes of the multiple components is determined, the calculated attributes of the multiple components are calculated, and the view interface of the multiple components is displayed on the data screen according to the calculation results.

[0013] Secondly, embodiments of this application provide a data processing apparatus applied to a large data dashboard. The large data dashboard includes a view interface with multiple components, and also includes a parameter control. Parameters in the parameter control are used to filter the calculated attributes of the components in the large data dashboard. The apparatus includes: A response unit is configured to obtain a target parameter in response to a trigger operation on the parameter control; the trigger operation indicates the operation of obtaining the target parameter. The acquisition unit is used to acquire the attribute to be calculated based on the target parameter; the attribute to be calculated is the calculated attribute corresponding to the target parameter among various calculated attributes; The processing unit is used to calculate the attribute to be calculated and update the view interface of the component corresponding to the attribute to be calculated in the data dashboard according to the calculation result.

[0014] Thirdly, embodiments of this application provide an electronic device, including: Memory, used to store computer programs; A processor for executing the computer program to implement the method as described in any one of the first aspects.

[0015] Fourthly, embodiments of this application provide a computer program that, when run on a computer, causes the computer to perform the method in any of the possible implementations of any of the above aspects.

[0016] Fifthly, embodiments of this application provide a computer-readable storage medium storing a computer program (also referred to as code or instructions) that, when run on a computer, causes the computer to perform the method in any of the possible implementations of any of the above aspects.

[0017] Sixthly, embodiments of this application provide a chip system including one or more processors for calling and executing instructions stored in memory, causing the methods in any of the above aspects or possible implementations to be executed. The chip system may be composed of chips or may include chips and other discrete devices.

[0018] This application provides a data processing method, apparatus, and electronic device applied to a data dashboard. The data dashboard includes view interfaces of multiple components and parameter controls, wherein parameters in the parameter controls are used to filter calculated attributes of the components in the data dashboard. The method includes: in response to a trigger operation on the parameter controls, obtaining a target parameter corresponding to the trigger operation; based on the target parameter, obtaining a calculated attribute corresponding to the target parameter from the calculated attributes of multiple components, the calculated attribute being a to-be-calculated attribute; calculating the to-be-calculated attribute; and updating the view interface of the component corresponding to the to-be-calculated attribute in the data dashboard based on the calculation result. This method achieves multi-level linkage by abstracting calculated attributes for each component and linking them based on the corresponding parameters in the calculated attributes, thereby adapting to complex user linkage needs. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this application 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 only some embodiments of the embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This application provides a schematic diagram of the architecture of a processing system. Figure 2 A schematic diagram of a correspondence provided for an embodiment of this application; Figure 3 A schematic representation of a computational chain provided in an embodiment of this application; Figure 4 This application provides a schematic diagram of the structure of a computational linked list. Figure 5 A flowchart of a data processing method provided in an embodiment of this application; Figure 6 This application provides an illustration of an application scenario for the problem of repetitive computation. Figure 7 This is a schematic diagram of component relationships provided in an embodiment of this application; Figure 8 A flowchart of another data processing method provided in the embodiments of this application; Figure 9 A schematic diagram of a merged computational linked list provided in an embodiment of this application; Figure 10 This is a schematic diagram of the structure of a data processing device provided in an embodiment of this application. Detailed Implementation

[0021] To enable those skilled in the art to better understand the present application, the technical solutions in this embodiment will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0022] In the implementation of this application, the collection and processing of relevant data should strictly comply with the requirements of relevant laws and regulations, obtain the informed consent or separate consent of the personal information subject, and carry out subsequent data use and processing within the scope of laws and regulations and the authorization of the personal information subject.

[0023] As mentioned earlier, users have various interaction needs when viewing data on a data dashboard, such as one-to-one, one-to-many, many-to-one, and many-to-many interactions. Interaction relationships based on data field connections often suffer from complexity and fail to meet actual interaction requirements.

[0024] In view of this, embodiments of this application provide a data processing method. This method is applied to a processing system connected to a data dashboard. The data dashboard includes view interfaces of multiple components and parameter controls. Parameters in the parameter controls are used to filter calculated attributes of components in the data dashboard. Specifically, the processing system performs the following operations: in response to a trigger operation on the parameter controls, it obtains the target parameter corresponding to the trigger operation; based on the target parameter, it obtains the calculated attribute corresponding to the target parameter from the calculated attributes of multiple components. This calculated attribute is the attribute to be calculated; it calculates the attribute to be calculated; and based on the calculation result, it updates the view interface of the component corresponding to the attribute to be calculated in the data dashboard. This method abstracts calculated attributes for each component and links them based on the corresponding parameters in the calculated attributes, thereby achieving multi-level linkage and adapting to complex user linkage needs.

[0025] In practical applications, the processing system may include a software system, which can be provided to the user as a software package for deployment, such as on a local physical server or in a private cloud. In some possible implementations, the processing system may also be deployed in a public cloud and provided to the user as a cloud service. For example, a cloud service provider could offer a one-stop system service integrating the functions of the aforementioned core components.

[0026] For example, Appendix Figure 1 This is a schematic diagram of the architecture of a processing system provided in an embodiment of this application. The processing system 10 is a software system and includes: a presentation layer, a data layer, a computing layer, and a service layer.

[0027] The presentation layer includes various components that need to be displayed on data dashboards. For example, the presentation layer includes title components, image components, icon components, and parameter controls.

[0028] The data layer is used to abstract the data of each component into computed properties, preparing for independent calculation of these properties. The component's data is its underlying implementation data, including but not limited to the component's business data, rendering data, and related data. A computed property corresponds to a set of data, and a component can have one or more computed properties. This allows for understanding the computed properties of different components.

[0029] In one specific implementation, for ease of management, the computed properties of components can be categorized, such as formula-based computed properties, dataset-based computed properties, and database table-based computed properties.

[0030] The calculation layer comprises a calculation center and a parameter center. The calculation center performs the following actions: in response to a trigger operation on a parameter control, it retrieves the target parameter corresponding to the trigger operation based on the parameters in the parameter center. Based on the target parameter, it retrieves the corresponding calculated attribute from the calculated properties of multiple components, designating this calculated attribute as the attribute to be calculated. It then calculates this attribute and updates the view interface of the component corresponding to the attribute in the data dashboard based on the calculation result.

[0031] The parameter center is used to persist the parameters of all components in the current data dashboard.

[0032] In one specific implementation, for each component's computed attribute in the data dashboard, the compute center will have a corresponding computed element.

[0033] For example, such as Figure 2 The diagram shown illustrates a correspondence relationship provided in an embodiment of this application. The view interface in the data dashboard includes view interfaces for component one, component two, and component three. The computational attributes corresponding to component one have corresponding computational elements in the computation center; the computational attributes corresponding to component two have corresponding computational elements in the computation center; and the computational attributes corresponding to component three have corresponding computational elements in the computation center.

[0034] It should be noted that a computing element is an independent logic segment that only performs data operations. The computing element does not focus on views and components, and is only responsible for receiving raw data and parameters and outputting calculation results. A computed property is exclusive view layer data held by a component itself. A computed property is data that the component can directly use to render the interface, and only serves the component to which it belongs.

[0035] Further, as shown in Figure 2 , the parameter center is configured to persist all parameters of the first component, the second component and the third component.

[0036] In a specific implementation, the computing center may include a storage module, a dependency analysis module, a loop detection module, a computing element construction module, a computation merging module, and a computation execution module.

[0037] The storage module is configured to store the computing elements corresponding to the computed properties of the components.

[0038] In a specific implementation, a computing element includes at least one of a forward pointer and a backward pointer. The forward pointer is used to point to the pre-dependent element of the current element, and the backward pointer is used to point to the post-dependent element of the current element.

[0039] In the embodiments of the present application, the computing center may connect all computing elements to form a computing network, that is, a computing linked list, through the forward pointer and / or backward pointer of the computing elements. The computing linked list is a computing dependency network that takes the computing elements as nodes and the forward pointers and backward pointers of the computing elements as directed connecting edges. The computing linked list manages the computing elements and is used to clarify the dependency relationship and computing sequence between the respective computing elements.

[0040] Exemplarily, computing element A and computing element B only include backward pointers, the backward pointers of computing element A and computing element B point to computing element C and computing element D, and the backward pointers of computing element C and computing element D point to computing element E. Then computing elements A to E can form a computing linked list, as shown in Figure 3 the schematic diagram of the computing linked list. When the computed properties corresponding to computing element A and computing element B are updated, the computing center quickly identifies the computing element chain that needs linked computation through the pointers of the computing elements, that is, the chain from computing element C and computing element D to computing element E. The computation is scheduled in combination with the sequence of the computing linked list, that is, the computed property corresponding to computing element C and the computed property corresponding to computing element D are called for computation first, and then the computed property corresponding to computing element E is called for computation.

[0041] The loop detection module is configured to detect whether a computing loop exists in the computing linked list. If a computing loop occurs, the loop detection module cuts off the computing loop in time and notifies the user that the configuration is incorrect.

[0042] Exemplarily, as shown in Figure 4The diagram illustrates the structure of a computational linked list. When the computed attribute of computed element A is updated, the computed attribute of computed element B is also updated. The update of computed attribute B will trigger an update of computed element C, which in turn will cause an update of computed attribute A. This means that a loop exists in the computational linked list, making it impossible to exit the loop.

[0043] To address this issue, in this embodiment, the loop detection module first checks whether the computation linked list is a computation loop. If so, it cuts off the loop to prevent the computation loop from causing the inability to exit the linkage loop. An alarm message is then output, indicating a configuration error.

[0044] The dependency analysis module is used to analyze the dependencies of each computed property.

[0045] The computed element construction module maps computed attributes to corresponding computed elements based on the computed attribute dependencies held by the dependency analysis module, and then compiles the computed elements into a computed linked list.

[0046] The computation merging module is used to merge computation tasks corresponding to computational lists. By leveraging the browser's maximum concurrency capabilities, these tasks are executed in parallel, thereby improving computational efficiency while making efficient use of network resources. For example, the computational list includes computational elements A and B, whose components originate from the same data source. The query computation tasks for computational element A and B can be merged.

[0047] The calculation execution module performs calculations on the calculated elements and returns the results to the calculated properties. This module also controls the corresponding components to update their view interface on the main view based on the calculation results.

[0048] To enable those skilled in the art to better understand the data processing method provided in the embodiments of this application, a detailed discussion is provided below in conjunction with the accompanying drawings. It should be noted that this data processing method can be applied to... Figure 1 The processing system shown.

[0049] Appendix Figure 5 This application provides a flowchart of a data processing method, which is applied to a data dashboard. The data dashboard includes a view interface with multiple components. In this application embodiment, the data dashboard also includes parameter controls. The method includes steps S10-S30: S10. In response to the trigger operation of the parameter control, obtain the target parameter.

[0050] The trigger operation indicates the action to retrieve target parameters. The target parameter refers to the parameter value or set of parameters that the processing system obtains after the trigger operation is performed on the parameter control, and that has the capability to calculate attribute filtering. For example, the target parameter is a time-dimensional parameter, such as the start time to the end time, or 2023 to 2024.

[0051] A parameter control refers to a component with data entry capabilities. In this embodiment, the parameter control is the control that binds to the parameters of the calculated attributes of components in the data dashboard. That is, the parameters in the parameter control are used to filter the calculated attributes of components in the data dashboard.

[0052] Users can trigger operations on the parameter controls of the data dashboard to enable the processing system to obtain target parameters. These trigger operations include, but are not limited to, modifying parameters through the parameter controls.

[0053] S20. Based on the target parameters, obtain the attributes to be calculated; the attributes to be calculated are the calculated attributes that correspond to the target parameters among the various calculated attributes.

[0054] The processing system can retrieve the calculated attributes of the components in the data dashboard that are affected by the target parameter based on the target parameter. These calculated attributes are the attributes to be calculated.

[0055] For example, if the target parameter is this month, after obtaining the target parameter, the processing system can retrieve the calculated attributes of all components in the data dashboard interface that are associated with this month based on the target parameter. For example, the search results are the real-time business value calculation attributes of the digital dashboard component, the Y-axis value aggregation calculation attributes of the bar chart component, and the time period trend calculation attributes of the line chart component. All three types of calculated attributes are strongly correlated with this month and will be directly affected by the target parameter of this month. Therefore, these three types of calculated attributes are the attributes to be calculated obtained in this screening.

[0056] S30. Calculate the attribute to be calculated, and update the view interface of the component corresponding to the attribute to be calculated in the data dashboard according to the calculation result.

[0057] In this embodiment of the application, the processing system can call the background calculation interface to complete the calculation of the attribute to be calculated and obtain the calculation result.

[0058] The processing system may update the view interface of the component corresponding to the attribute to be calculated on the large data screen according to the calculation result. For example, for a bar chart component, its data source is to display data of all years between parameter A and parameter B. The view interface of the bar chart component on the large data screen can realize different implementations of the view interface of the bar chart component by changing parameter A and parameter B. For example, initially, parameter A is 2023 and parameter B is 2024, the view interface corresponding to the bar chart component on the large data screen will display the results from 2023 to 2024. When parameter A is modified to 2024 and parameter B is modified to 2025, the view interface corresponding to the bar chart component will display the results from 2024 to 2025.

[0059] Embodiments of the present application provide a data processing method, which is applied to a large data screen. The large data screen includes view interfaces of a plurality of components and parameter controls, wherein parameters in the parameter controls are used for filtering calculation attributes of the components in the large data screen. The method includes: in response to a trigger operation on the parameter control, acquiring a target parameter corresponding to the trigger operation; acquiring a calculation attribute corresponding to the target parameter from the calculation attributes of the plurality of components according to the target parameter, where the calculation attribute is an attribute to be calculated, calculating the attribute to be calculated, and updating the view interface of the component corresponding to the attribute to be calculated on the large data screen according to the calculation result. According to the method, the calculation attribute is abstracted for each component, and linkage is performed based on the corresponding parameter in the calculation attribute, so that multi-level linkage is realized, thereby adapting to complex linkage requirements of users.

[0060] In actual application scenarios, there will be multi-level linkage requirements. For example, for the filtering of province, city and district, after the province is modified, the city will change, and the change of the city will automatically lead to the change of the district. This is equivalent to that modifying one parameter will cause a chain of changes. In the changes of multiple chains, repeated calculation of these linked components may exist.

[0061] For example, as Figure 6 shows, it is an application scenario diagram of a repeated calculation problem provided by an embodiment of the present application. As Figure 6 shows, when component A triggers linkage, it will link component B and component C, and component B and component C will both link component D at the same time. In this case, every time component A triggers linkage, two linkage chains will be generated. Among them, chain 1: the linkage triggered by component A causes the calculation attribute of component B to be recalculated, and the recalculation of component B further causes component D to be recalculated. Chain 2: the linkage triggered by component A causes component C to be recalculated, and the recalculation of component C further causes component D to be recalculated. When component A triggers linkage once, component D will be calculated twice.

[0062] In addition, there is a linkage relationship between some components, while there is no relationship between some components, as Figure 7The diagram shown is a schematic representation of a component relationship provided in an embodiment of this application. Figure 7 As shown, component A has a dependency on component B, and components A also have dependencies on components C and D, but no dependency on component E. This causes the computation process of components C and D to be blocked when calculating the computed properties of component B. Component E can only be calculated after the computation processes of components A, B, C, and D have finished. This results in computational blocking.

[0063] To address the aforementioned issues, this application provides another data processing method. In this method, a computing center stores computational elements that correspond one-to-one with computational attributes. Based on these computational elements, the computing center updates the data dashboard by constructing a mesh dependency relationship (i.e., a computational linked list). The following section, in conjunction with the appendix... Figure 8 Explanation will be provided. (Attached) Figure 8 Another data processing method flowchart provided in this application embodiment includes the following steps: S810, in response to a trigger operation on the parameter control, obtains the target parameter.

[0064] The specific implementation is the same as step S10, and will not be repeated here.

[0065] S820: Obtain the target calculation elements based on the target parameters.

[0066] The target computational element refers to the computational element corresponding to the target parameter.

[0067] It should be noted that in this embodiment, the computing center pre-stores the computational elements corresponding to the computational attributes of all components in the data dashboard. These computational elements include forward and backward pointers. The parameter center stores the parameters of all components. The computing center can determine the computational element corresponding to the target parameter, i.e., the target computational element, based on the target parameter in the parameter center.

[0068] In this embodiment, the processing system can use a depth-first search to retrieve the computational elements in the data dashboard that are affected by the target parameter, based on the target parameter. Specifically, this includes the following steps: A1. Construct a processing queue to store the computational elements to be processed.

[0069] The processed sequence is an empty sequence.

[0070] A2. Construct a queue to store processed computational elements.

[0071] The queue is empty.

[0072] It should be noted that A1 and A2 can be performed simultaneously, or A1 can be performed first and then A2, or A2 can be performed first and then A1. This application does not limit the specific implementation.

[0073] A3. Based on the target parameters, determine the attributes to be calculated that are directly related to the target parameters.

[0074] For ease of explanation, the attribute to be calculated that is directly related to the target parameter is called the directly related calculated attribute.

[0075] A4. Map directly associated computed properties to computed elements and store them in a pre-built processing sequence.

[0076] A5. Extract computational elements sequentially from the processing sequence, move each computational element to the completion queue, and determine whether the computational element will trigger subsequent linked computations. If it will trigger subsequent linked computations, determine the computational attribute that affects the subsequent linked computations (this computational attribute is called the indirect computational attribute). Map the indirect computational attribute to a computational element and place it in the processing queue.

[0077] A6. Repeat step A5 until the processing queue is empty.

[0078] At this point, the computed property corresponding to the element in the completed queue is the property to be computed. This depth-first traversal method can fully and completely obtain the property to be computed.

[0079] It should be noted that through the above steps, each discrete computational attribute is mapped to a computational element in the computational network. The forward and backward pointers in the computational element are used to guide the preceding and subsequent computations of that element. Therefore, in subsequent computations, a computation can only be performed after all the preceding computations of a computational element have been completed, and a subsequent element can only be computed after the computation of a computational element has been completed.

[0080] S830, the processing system obtains the computation linked list corresponding to the target computation element.

[0081] For example, the processing system obtains N target computational elements, where N is an integer greater than 1, such as N=3. Based on the N target computational elements and the dependencies between them, a computational linked list corresponding to each target computational element can be obtained. For example, if the N target computational elements include computational elements A, B, C, and D, then the computational linked list is as follows: Figure 9 As shown, in this computational linked list, the backward pointer of computational element A includes computational elements B and C, and the backward pointer of computational element C is computational element D.

[0082] S840 processes computational tasks in the system's merged computation list.

[0083] In this embodiment, the processing system merges identical computational tasks in the computation list. For example, if the computation list includes query task 1 and query task 2, both of which query data from the same data source, query task 1 and query task 2 can be merged, performing only one query task. This approach fully utilizes the browser's maximum concurrency limit for concurrent computation, minimizing computational tasks.

[0084] It is understandable that the merged computation list contains no duplicate computation tasks. All computation tasks originating from the same source have been merged into a single-execution task, and the computation list retains only a unique target computation element.

[0085] S850, the processing system determines the calculation order of the attributes to be calculated based on the merged calculation list.

[0086] Here, the attribute to be calculated refers to the attribute to be calculated corresponding to the target computed element. One target computed element corresponds to one attribute to be calculated, and N target computed elements correspond to N attributes to be calculated.

[0087] Specifically, the computation chain includes a first computation element and a second computation element, wherein the first computation element is a prerequisite dependency of the second computation element. The computation order includes executing the attribute to be computed corresponding to the first computation element first, and then executing the attribute to be computed corresponding to the second computation element. That is, in this embodiment, all computations of the prerequisite computation elements must be completed before the computation of the current attribute to be computed can be started.

[0088] For example, such as Figure 9 The diagram shown is a schematic of a merged computational linked list provided in an embodiment of this application. The target computational element includes computational element A and computational element B. Computational element A is a prerequisite dependency of computational element B. Therefore, the computational order obtained by the processing system is to first execute the computational attribute corresponding to computational element A, and then execute the computational attribute corresponding to computational element B.

[0089] In this embodiment, if the target element has multiple preceding dependent elements in the computation chain, the determined computation order only includes the attribute to be computed corresponding to one target computation element. This method avoids duplicate computation of attributes. Figure 6 As shown, when determining the calculation order, the calculated attribute corresponding to the calculated element D is calculated only once.

[0090] against Figure 7 To address the computational blocking problem illustrated, in this embodiment, the computational attributes corresponding to components B and C are calculated in parallel. After the computational attributes of component A are calculated, the computational attributes of components B and C are calculated simultaneously. By using components without dependent attributes to perform parallel computation of the computational attributes corresponding to that component, computational blocking is reduced.

[0091] If a computed element has no forward or backward pointers, it is an independent element to be computed, for example... Figure 7 The computational element E shown is an independent component whose corresponding computational attribute is not included in the overall computation order and is unaffected by the computation of any other attribute. It can be activated at any time as needed. This ensures that component E is not affected by other components.

[0092] Furthermore, in the embodiments of this application, the following operations may be performed before determining the calculation order: Step 1: Determine if a computation loop exists in the computation linked list.

[0093] Step 2: If there is no computation loop, determine the computation order based on the computation linked list.

[0094] Step 3: If a computation loop exists, switch the loop and notify the user of the configuration error.

[0095] This method can avoid the problem of being unable to exit the linkage loop due to calculation loops.

[0096] The S860 processing system calculates N attributes to be calculated according to the calculation order.

[0097] S870, the processing system sends the calculation results to the calculation component corresponding to the target calculation attribute based on the target calculation attribute.

[0098] S890 handles the view interface of the calculation component corresponding to the target calculation attribute in the data dashboard when the system updates.

[0099] The specific steps are the same as in step S30, so they will not be repeated here.

[0100] Furthermore, before performing the data update operation in this embodiment, a data dashboard can be initialized. During the initialization of the data dashboard page, the computed attributes of multiple components are obtained; the dependencies between the computed attributes of multiple components are determined; the calculation order between the computed attributes of multiple components is determined according to the dependencies between the computed attributes of multiple components; the computed attributes of multiple components are calculated according to the calculation order between the computed attributes of multiple components, and the view interface of multiple components is displayed on the data dashboard according to the calculation results.

[0101] This application provides a data processing method that maps computed attributes to computed elements, obtains a computed linked list based on the forward and backward pointers of the computed elements, determines the computed order based on the computed linked list, and uses the computed order to perform computed attribute calculations. This approach can reduce problems such as repeated calculations and computational blocking in parameter-based multi-level linkages, ensuring that while meeting users' complex linkage filtering needs, linkage calculations are fast and accurate, providing users with a better interactive experience.

[0102] against Figures 1-9 The data processing method shown in this application is further illustrated by a data processing apparatus. (See attached document.) Figure 10 This is a schematic diagram of the structure of a data processing apparatus provided in an embodiment of this application. The apparatus 100 includes: The response unit 101 is configured to obtain a target parameter in response to a trigger operation on the parameter control; the trigger operation indicates the operation of obtaining the target parameter. The acquisition unit 102 is used to acquire the attribute to be calculated based on the target parameter; the attribute to be calculated is the calculated attribute corresponding to the target parameter among various calculated attributes; The processing unit 103 is used to calculate the attribute to be calculated and update the view interface of the component corresponding to the attribute to be calculated in the data screen according to the calculation result.

[0103] Optionally, the number of attributes to be calculated is N, where N is an integer greater than 1. Obtaining the attributes to be calculated based on the target parameter includes: Based on the target parameters, N target computational elements are determined; each target computational element is a computational element corresponding to the target parameters; one computational element indicates one computational attribute. Obtain N attributes to be calculated corresponding to the target calculation element.

[0104] Optionally, the computed element includes a forward pointer and a backward pointer, the forward pointer indicating the preceding dependent element of the computed element, and the backward pointer pointing to the following dependent element of the computed element. Before computeding the attribute to be computed, the method further includes: Obtain a computation linked list corresponding to N target computation elements; the computation linked list includes the N target computation elements and the dependencies between the N target computation elements; Based on the computation chain, the computation order of the N attributes to be computed is determined; for the first computation element and the second computation element in the computation chain, and the first computation element is the predecessor dependency element of the second computation element, the computation order includes first executing the attribute to be computed corresponding to the first computation element, and then executing the attribute to be computed corresponding to the second computation element. Calculate N attributes to be calculated according to the calculation order.

[0105] Optionally, determining the calculation order of the N attributes to be calculated based on the calculation linked list includes: Determine whether a loop exists in the computation linked list; If no loop exists, the calculation order of the N attributes to be calculated is determined according to the calculation linked list.

[0106] Furthermore, if a loop exists, the loop is cut off, and an alarm message is output, indicating a configuration error.

[0107] Optionally, determining the calculation order of the N attributes to be calculated based on the calculation linked list includes: In the calculation chain, if there are multiple preceding dependent elements of the target calculation element, the determined calculation order includes only the attribute to be calculated corresponding to one of the target calculation elements.

[0108] Optionally, obtaining the attribute to be calculated based on the target parameter includes: Based on the target parameters, determine the attribute to be calculated that is directly associated with the target parameters; The attribute to be computed is mapped to the target computed element and stored in a pre-built processing sequence; Retrieve computational elements from the processing sequence and perform processing operations: the processing operations are to move the retrieved computational elements to a pre-built completion queue and determine whether the retrieved computational elements will cause subsequent linkages; if they will cause subsequent linkages, obtain the indirect computational attributes that affect subsequent computations; map the indirect computational attributes to computational elements and store them in the processing sequence. The process involves repeatedly retrieving computational elements from the processing sequence and performing the processing operation until the processing sequence is empty.

[0109] Optionally, the data dashboard is initialized before responding to a trigger operation on the parameter control; During the initialization of the data dashboard page, the calculated attributes of the multiple components are obtained; Determine the dependencies between the computed properties of the multiple components; The computation order of the computed properties of the multiple components is determined based on the dependencies between them. The calculation order of the calculated attributes of the multiple components is determined, the calculated attributes of the multiple components are calculated, and the view interface of the multiple components is displayed on the data screen according to the calculation results.

[0110] This application provides a data processing apparatus for: responding to a trigger operation on a parameter control, obtaining a target parameter corresponding to the trigger operation; obtaining a calculated attribute corresponding to the target parameter from calculated attributes of multiple components based on the target parameter, wherein the calculated attribute is a to-be-calculated attribute; calculating the to-be-calculated attribute; and updating the view interface of the component corresponding to the to-be-calculated attribute in a data dashboard based on the calculation result. This apparatus achieves multi-level linkage by abstracting calculated attributes for each component and linking them based on the corresponding parameters in the calculated attributes, thereby adapting to complex user linkage needs.

[0111] According to the method provided in the embodiments of this application, this application also provides a chip system, which includes one or more processors for calling and executing instructions stored in memory, thereby causing the method described in the embodiments of this application to be executed. The chip system may be composed of chips or may include chips and other discrete devices.

[0112] The chip system may include input circuits or interfaces for transmitting information or data, and output circuits or interfaces for receiving information or data.

[0113] According to the method provided in the embodiments of this application, this application also provides a computer program product, which includes: computer program code, which, when run on a computer, causes the computer to execute the various steps or processes executed by the network device or terminal device in any of the foregoing method embodiments.

[0114] According to the method provided in the embodiments of this application, this application also provides a computer-readable storage medium storing program code, which, when run on a computer, causes the computer to execute the various steps or processes executed by the network device or terminal device in any of the foregoing method embodiments.

[0115] The computer-readable storage medium may be the aforementioned volatile memory or non-volatile memory, or it may include both volatile memory and non-volatile memory.

[0116] In the embodiments of this application, the terms and English abbreviations are exemplary examples given for ease of description and should not be construed as limiting the application in any way. This application does not preclude the possibility of defining other terms that can achieve the same or similar functions in existing or future agreements.

[0117] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When these computer instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated.

[0118] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and 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 through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

Claims

1. A data processing method applied to a data dashboard, the data dashboard comprising a view interface with multiple components, characterized in that, The data dashboard also includes parameter controls, where parameters are used to filter the calculated properties of components within the data dashboard. The method includes: In response to a trigger operation on the parameter control, a target parameter is obtained; the trigger operation indicates the operation of obtaining the target parameter. Based on the target parameters, the attribute to be calculated is obtained; the attribute to be calculated is the calculated attribute that corresponds to the target parameters among the various calculated attributes. Calculate the attribute to be calculated, and update the view interface of the component corresponding to the attribute to be calculated in the data dashboard based on the calculation result.

2. The method according to claim 1, characterized in that, The number of attributes to be calculated is N, where N is an integer greater than 1. Obtaining the attributes to be calculated based on the target parameter includes: Based on the target parameters, N target computational elements are determined; each target computational element is a computational element corresponding to the target parameters; one computational element indicates one computational attribute. Obtain N attributes to be calculated corresponding to the target calculation element.

3. The method according to claim 2, characterized in that, The computed element includes a forward pointer and a backward pointer. The forward pointer indicates the preceding dependent element of the computed element, and the backward pointer points to the following dependent element of the computed element. Before computing the attribute to be computed, the method further includes: Obtain a computation linked list corresponding to N target computation elements; the computation linked list includes the N target computation elements and the dependencies between the N target computation elements; Based on the computation chain, the computation order of the N attributes to be computed is determined; for the first computation element and the second computation element in the computation chain, and the first computation element is the predecessor dependency element of the second computation element, the computation order includes first executing the attribute to be computed corresponding to the first computation element, and then executing the attribute to be computed corresponding to the second computation element. Calculate N attributes to be calculated according to the calculation order.

4. The method according to claim 3, characterized in that, Determining the calculation order of the N attributes to be calculated based on the calculation linked list includes: Determine whether a loop exists in the computation linked list; If no loop exists, the calculation order of the N attributes to be calculated is determined according to the calculation linked list.

5. The method according to claim 4, characterized in that, The method further includes: If a loop exists, disconnect the loop and output an alarm message indicating a configuration error.

6. The method according to claim 3, characterized in that, Determining the calculation order of the N attributes to be calculated based on the calculation linked list includes: In the calculation chain, if there are multiple preceding dependent elements of the target calculation element, the determined calculation order includes only the attribute to be calculated corresponding to one of the target calculation elements.

7. The method according to claim 1, characterized in that, The step of obtaining the attribute to be calculated based on the target parameter includes: Based on the target parameters, determine the direct attributes to be calculated that are directly associated with the target parameters; The directly computed attributes are mapped to computed elements and stored in a pre-built processing sequence; Retrieve computational elements from the processing sequence and perform processing operations: the processing operations are to move the retrieved computational elements to a pre-built completion queue and determine whether the retrieved computational elements will cause subsequent linkages; if they will cause subsequent linkages, obtain the indirect computational attributes that affect subsequent computations; map the indirect computational attributes to computational elements and store them in the processing sequence. The process involves repeatedly retrieving computational elements from the processing sequence and performing the processing operation until the processing sequence is empty.

8. The method according to claim 1, characterized in that, Before responding to a trigger operation on the parameter control, the method further includes: initializing the data dashboard; During the initialization of the data dashboard page, the calculated attributes of the multiple components are obtained; Determine the dependencies between the computed properties of the multiple components; The computation order of the computed properties of the multiple components is determined based on the dependencies between them. The calculation order of the calculated attributes of the multiple components is determined, the calculated attributes of the multiple components are calculated, and the view interface of the multiple components is displayed on the data screen according to the calculation results.

9. A data processing device applied to a large data screen, the large data screen comprising a view interface with multiple components, characterized in that, The data dashboard also includes parameter controls, where parameters are used to filter the calculated attributes of components in the data dashboard. The device includes: A response unit is configured to obtain a target parameter in response to a trigger operation on the parameter control; the trigger operation indicates the operation of obtaining the target parameter. The acquisition unit is used to acquire the attribute to be calculated based on the target parameter; the attribute to be calculated is the calculated attribute corresponding to the target parameter among various calculated attributes; The processing unit is used to calculate the attribute to be calculated and update the view interface of the component corresponding to the attribute to be calculated in the data dashboard according to the calculation result.

10. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor for executing the computer program to implement the method according to any one of claims 1-8.

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