Virtual index data processing method and system

Abstract

The invention provides a virtual index data processing method, and relates to a data processing technology, after a server sends a virtual index configuration request at a configuration end, a full-function configuration template is called and fed back to the configuration end for interaction, and a functional first configuration template is obtained; the server calls configuration elements of the virtual indexes in the first configuration template, custom processing is conducted on the configuration elements based on the configuration end, and the configuration elements comprise input elements, calculation elements and output elements; the server determines a corresponding real-time engine and a period engine according to the attribute of the virtual index; after the configuration end determines the configuration element of each virtual index, the configuration end carries out data interface establishment on the real-time engine and the periodic engine, de-duplication is carried out after an information extraction table is established, the configured virtual indexes are obtained based on the information extraction table, a corresponding updating mode can be adopted according to the functional requirements corresponding to the virtual indexes, and the updating efficiency is improved. The flexibility of virtual index configuration is improved, and the data processing efficiency is improved while the user can use the method easily.

Description

Technical Field

[0001] The present invention relates to data processing technologies, and particularly to a method and system for processing virtual indicator data. Background Art

[0002] In the fields of enterprise digital management and project evaluation, virtual indicators play a key role as important tools for measuring business performance, resource allocation, and operation status. For example, in the energy industry, virtual indicators such as the operation efficiency of battery swapping stations and the energy storage utilization rate of energy storage stations are the core basis for evaluating enterprise operation levels and making decisions. However, with the complexity and diversity of business scenarios, the configuration and processing of virtual indicators face many challenges. The functional requirements for virtual indicators vary significantly among different business departments. Traditional single data processing methods are difficult to meet personalized needs, and the indicator configuration process is cumbersome, with low data calculation efficiency, seriously restricting the enterprise's ability to quickly analyze and respond to business data. In the prior art, the data update method for virtual indicators in each unit is usually to set a fixed cycle update or real-time update, that is, all indicator data required by the unit is updated periodically or all are updated in real-time, resulting in a lack of diversity in the timeliness of virtual indicator data updates. At the same time, the data correlation between different virtual indicators is not effectively utilized, there will be duplicate calculation work, the data processing efficiency is low, and it causes waste of computing and storage resources.

[0003] Therefore, how to adopt corresponding update methods according to the functional requirements corresponding to virtual indicators, improve the flexibility of virtual indicator configuration, and improve data processing efficiency while being easy for users to use has become an urgent problem to be solved. Summary of the Invention

[0004] The present invention provides a method and system for processing virtual indicator data, which can adopt corresponding update methods according to the functional requirements corresponding to virtual indicators, improve the flexibility of virtual indicator configuration, and improve data processing efficiency while being easy for users to use.

[0005] In a first aspect of the present invention, there is provided a method for processing virtual indicator data, including: After the server receives a configuration request for a virtual indicator sent by the configuration end, it calls a full-function configuration template and feeds it back to the configuration end for interaction to obtain a functional first configuration template; The server retrieves the configuration elements of the virtual indicator in the first configuration template, and customizes the processing of the configuration elements based on the configuration end. The configuration elements include input elements, calculation elements, and output elements; The server determines the corresponding real-time engine and periodic engine according to the attributes of the virtual indicator; After the configuration end determines the configuration elements of each virtual metric, it builds the data interface for the real-time engine and the periodic engine, deduplicates after establishing the information extraction table, and obtains the configured virtual metrics based on the information extraction table.

[0006] Optionally, in a possible implementation manner of the first aspect, after the server receives the configuration request of the virtual metric sent by the configuration end, it calls the full-functional configuration template and feeds it back to the configuration end for interaction to obtain the functional first configuration template, including: If the server determines that the configuration end selects the functional selection method, it generates a function selection module, and determines the corresponding first configuration template based on the function selection module. Each function selection module has a preset virtual metric. If the server determines that the configuration end selects the customized selection method, it directly displays the virtual metrics, and obtains the functional first configuration template based on the virtual metrics respectively selected by the configuration end.

[0007] Optionally, in a possible implementation manner of the first aspect, it further includes: If it is determined that the configuration end applies for custom processing after selecting the function selection module, it copies the virtual metrics corresponding to the function selection module to generate an interaction slot template. If it is determined that the user deletes the virtual metric in the interaction slot, it deletes it from the first configuration template. If it is determined that the user creates a new interaction slot in the interaction slot template, it calls the full-functional configuration template based on the new interaction slot and selects the corresponding virtual metrics to fill into the new interaction slot to obtain the first configuration template.

[0008] Optionally, in a possible implementation manner of the first aspect, the server retrieves the configuration elements of the virtual metrics in the first configuration template, and customizes the processing based on the configuration end for the configuration elements, including: The server sequentially retrieves the virtual metrics in the first configuration template, calculates the metric similarity based on the configuration elements of each virtual metric, and sorts based on the metric similarity to obtain a metric sequence. Sequentially traverse the virtual metrics in the metric sequence and perform structured processing according to the processing structure of the configuration elements to obtain multiple structural sub-slots. Each structural sub-slot corresponds to an input element, a calculation element, or an output element. The configuration end determines the information of the structural sub-slots of each virtual metric, and the server combines the metric sequence order and the information determined by the configuration end to perform parallel processing on the structural sub-slots of the subsequent virtual metrics to obtain the configuration elements of each virtual metric.

[0009] Optionally, in a possible implementation manner of the first aspect, the server sequentially retrieves the virtual metrics in the first configuration template, calculates the metric similarity based on the configuration elements of each virtual metric, and sorts based on the metric similarity to obtain a metric sequence, including: The server calculates the number of configuration elements with exactly the same two virtual metrics to obtain a first quantity, calculates the number of configuration elements with different two virtual metrics to obtain a second quantity, and obtains the total quantity based on the sum of the first quantity and the second quantity; Calculate the ratio of the first quantity to the total quantity to obtain the metric similarity; Based on the metric similarity, all virtual metrics are divided into multiple metric subsets and then sorted to obtain a metric sequence.

[0010] Optionally, in a possible implementation manner of the first aspect, the step of dividing all virtual metrics into multiple metric subsets and then sorting based on the metric similarity to obtain a metric sequence includes: Determine a first virtual metric with the highest similarity and greater than or equal to a preset similarity value for each virtual metric. If two virtual metrics are each other's first virtual metrics, then classify the two virtual metrics into a first metric subset and label the similarity of the metric subset; If it is determined that the virtual metrics in the first metric subset are also the first virtual metrics of other virtual metrics and the similarity is greater than or equal to the preset similarity value, then classify the other virtual metrics into the corresponding first metric subset; If it is determined that the similarity between a virtual metric and other virtual metrics is less than the preset similarity value, then classify it into a second metric subset; Sort all the first metric subsets in descending order based on the labeled similarity to obtain a first sequence, and place the second metric subset after the first sequence to obtain a metric sequence.

[0011] Optionally, in a possible implementation manner of the first aspect, the step of sequentially traversing the virtual metrics in the metric sequence and performing structured processing according to the processing structure of the configuration elements to obtain multiple structural sub-slots, where each structural sub-slot corresponds to an input element, a calculation element, or an output element, includes: Construct an input area, a calculation area, and an output area in sequence; Construct and fill slots corresponding to each input element, calculation element, or output element in the input area, calculation area, and output area; Connect the slots in sequence based on the relationship between the input element, calculation element, or output element to obtain a structural sub-slot.

[0012] Optionally, in a possible implementation manner of the first aspect, the configuration end determines the information of the structural sub-slots of each virtual metric, and the server performs parallel processing on the structural sub-slots of subsequent virtual metrics in combination with the metric sequence order and the information determined by the configuration end to obtain the configuration elements of each virtual metric, including: If it is determined that the configuration end adjusts the initial information of the structural sub-slot of a virtual metric, then determine the adjustment information; Retrieve the structural sub - slots of the next virtual indicator in the order of the indicator sequence, adjust them correspondingly based on the adjustment information, and highlight the adjusted information in a preset form; If it is determined that the user confirms the highlighted adjustment information, save it.

[0013] Optionally, in a possible implementation manner of the first aspect, after the configuration end determines the configuration elements of each virtual indicator, it builds data interfaces for the real - time engine and the periodic engine, de - duplicates after establishing an information extraction table, and obtains the configured virtual indicators based on the information extraction table, including: The configuration end sequentially builds the corresponding data interfaces for each structural sub - slot, and counts the corresponding relationships between all structural sub - slots and data interfaces to generate an initial information extraction table; If it is determined that multiple sub - slots correspond to the same data interface, obtain a slot association group, de - duplicate the duplicate data interfaces, and set them corresponding to the slot association group; Retrieve the real - time label or periodic label corresponding to each sub - slot to generate comprehensive extraction information and store it in the extraction period slot to obtain an information extraction table; The server extracts data in the data interface according to the independent extraction information or comprehensive extraction information corresponding to each data interface in the information extraction table and inputs it into the structural sub - slot to obtain a virtual indicator.

[0014] In the second aspect of the present invention, a virtual indicator data processing system is provided, including: An interaction module, configured to enable the server to call a full - function configuration template and feedback it to the configuration end for interaction after receiving a configuration request for a virtual indicator from the configuration end, so as to obtain a functional first configuration template; A retrieval module, configured to enable the server to retrieve the configuration elements of the virtual indicator in the first configuration template and customize the processing of the configuration elements based on the configuration end, where the configuration elements include input elements, calculation elements, and output elements; A determination module, configured to enable the server to determine the corresponding real - time engine and periodic engine according to the attributes of the virtual indicator; An extraction module, configured to enable the configuration end to build data interfaces for the real - time engine and the periodic engine, de - duplicate after establishing an information extraction table, and obtain the configured virtual indicators based on the information extraction table after determining the configuration elements of each virtual indicator.

[0015] In the third aspect of the present invention, a storage medium is provided, in which a computer program is stored, and when the computer program is executed by a processor, it is used to implement the method described in the first aspect of the present invention and various possible designs of the first aspect.

[0016] The beneficial effects of the present invention are as follows: 1. The present invention can adopt corresponding update methods according to the functional requirements corresponding to virtual indicators, improve the flexibility of virtual indicator configuration, and enhance data processing efficiency while being easy for users to use. First, the diverse flexibility of the configuration mode of the present invention meets personalized needs. By combining the full-functional configuration template with various selection methods, flexible configuration of virtual indicators is realized. The server provides a full-functional configuration template containing multiple functional sub-templates, and users can select functional selection methods or customized selection methods according to their needs. In addition, the present invention also supports custom processing based on function selection. By copying virtual indicators to generate interactive slot templates, users can flexibly add, delete, or modify indicator content. Among them, users can delete redundant indicators in the interactive slots, or create new slots and select indicators from the full-functional configuration template to fill them, making the first configuration template fully conform to personalized needs, effectively solving the problem of insufficient flexibility in traditional configuration methods and meeting the needs of complex and diverse business scenarios of enterprises.

[0017] 2. The present invention can perform similarity analysis on virtual indicators and conduct structured processing to improve data processing efficiency. Among them, the present invention significantly improves data processing efficiency by calculating the similarity of configuration elements of virtual indicators and conducting structured processing. The server first calculates the number of identical and different configuration elements between virtual indicators, and then obtains the indicator similarity. Virtual indicators with high similarity are classified into different subsets and sorted to form an indicator sequence. Subsequently, the indicator sequence is traversed in turn, and the virtual indicators are constructed into input, calculation, and output regions according to input elements, calculation elements, and output elements, and corresponding slots are constructed in each region to fill elements. Based on the element relationship, the slots are connected to form structural sub-slots. The structured processing method not only clearly presents the internal structure of virtual indicators but also reduces repeated data retrieval using indicator similarity. When the configuration end determines the information of the structural sub-slots, the server can perform parallel processing on subsequent similar virtual indicators according to the order of the indicator sequence, such as reusing the same configuration element interface, avoiding repeated configuration, greatly improving data processing efficiency, and reducing system resource consumption.

[0018] 3. The present invention can intelligently match a real-time engine and a periodic engine based on virtual index attributes, and optimize the data interface to ensure the efficiency of data processing. Among them, the present invention can match a suitable processing engine for a virtual index according to attributes such as the data update frequency and real-time requirement of the virtual index. For virtual indexes with high real-time requirements, a real-time engine is adopted to ensure timely data update and calculation. For periodically updated indexes, a periodic engine is used to efficiently process them according to a preset period. In terms of building the data interface, the configuration end establishes a corresponding data interface for each structural sub-slot, generates an initial information extraction table by counting the corresponding relationships, removes duplicates from the duplicate data interfaces and sets them corresponding to the slot association group, optimizes the data transmission path. At the same time, the real-time or periodic tags of the sub-slots are retrieved to generate comprehensive extraction information and stored in the extraction period slot to improve the information extraction table. The server accurately extracts data according to the information extraction table and inputs it into the structural sub-slots to ensure that the virtual indexes run according to the configuration requirements, so as to ensure the accuracy and efficiency of data processing, meet the diverse needs of enterprises for virtual index processing, and provide reliable data support for business decisions. Description of the Drawings

[0019] Figure 1 is a flowchart of a method for processing virtual index data provided by the present invention; Figure 2 is a schematic diagram of a structural sub-slot provided by the present invention; Figure 3 is a schematic diagram of the structure of a virtual index data processing system provided by the present invention. Detailed Embodiments

[0020] The technical solutions of the present invention will be 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 repeated in some embodiments.

[0021] As Figure 1 shown, the present invention provides a flowchart of a method for processing virtual index data. The method for processing virtual index data includes: S1. After the server sends a configuration request for a virtual index to the configuration end, it calls a full-function configuration template and feeds it back to the configuration end for interaction to obtain a functional first configuration template.

[0022] It can be understood that after the configuration terminal sends a configuration request for virtual metrics to the server, the server will respond by invoking its own full-functional configuration template. The full-functional configuration template contains various possible virtual metric configuration options and related function settings. The server feeds back this template to the configuration terminal, enabling the configuration terminal to interact with the server. Through the interaction, the configuration terminal can select and adjust the content in the template according to its own needs, and finally obtain a first configuration template that meets specific functional requirements, so as to subsequently configure the corresponding virtual metrics specifically.

[0023] Among them, the configuration terminal is the information terminal of the person who configures the metric information. For example, it can be a mobile phone or a computer. The virtual metric is a digital measurement tool, similar to the actual collected data metrics, and is a description of the model instance, with the characteristics of data metrics, such as data quality, data attribute extension, data occurrence events, etc., including the storage characteristics of data, and is used to evaluate the metrics related to enterprise or project information. The configuration request is the request operation information for data configuration of virtual metrics. The full-functional configuration template is a configuration template that contains all function-corresponding metrics. The first configuration template is the configuration template selected by the configuration terminal that meets the required functions.

[0024] It is not difficult to understand that the full-functional configuration template contains sub-templates corresponding to various functions. For example, the function configuration template corresponding to the battery swapping station and the function configuration template corresponding to the energy storage station. Since the required functions are different, the corresponding metrics also vary. Therefore, the full-functional configuration template can be displayed to the configuration terminal for interactive selection, so as to obtain the first configuration template, so that the virtual metrics that meet the functions can be quickly determined subsequently and data configuration can be performed to improve data processing efficiency.

[0025] In some embodiments, the specific implementation manner in step S1 (after the server receives the configuration request for virtual metrics from the configuration terminal, invokes the full-functional configuration template and feeds it back to the configuration terminal for interaction to obtain a functional first configuration template) includes: S11, if the server determines that the configuration terminal selects the functional selection method, it generates a function selection module, and determines the corresponding first configuration template based on the function selection module. Each function selection module has preset virtual metrics.

[0026] It should be noted that the full-functional configuration template has function selection modules corresponding to each function, and also has a customized configuration template to meet the configuration needs of different users. Among them, when it is determined that the configuration terminal selects the functional selection method, a function selection module can be generated according to the selection interaction information of the configuration terminal, so as to determine the corresponding first configuration template. Moreover, each function selection module has preset some virtual metrics for subsequent quick configuration by the configuration terminal.

[0027] It is understandable that virtual indicators are combined according to a certain functional logic. The configuration terminal can select from these function selection modules, and the server determines the corresponding first configuration template according to the function selection module selected by the configuration terminal.

[0028] For example, when the configuration terminal selects a function selection module related to a substation, the server will generate a first configuration template containing virtual indicators related to the substation according to the virtual indicators preset in the module. This method provides a way for the configuration terminal to quickly select virtual indicators of common functions and improves the configuration efficiency.

[0029] S12, if the server determines that the configuration terminal selects the customized selection method, it directly displays the virtual indicators, and obtains a functional first configuration template based on the virtual indicators separately selected by the configuration terminal.

[0030] It is understandable that when the server determines that the configuration terminal selects the customized selection method, the server directly displays the virtual indicators to the configuration terminal. Different from the functional selection method, the customized selection method gives the configuration terminal greater autonomy. The configuration terminal can select the displayed virtual indicators respectively according to its own specific needs, and the server will generate a functional first configuration template according to these virtual indicators selected by the configuration terminal. This method is applicable to the situation where the configuration terminal has special and non-standard functional requirements for virtual indicators, can more accurately meet the personalized needs of the configuration terminal, and makes the generated first configuration template meet the expectations and actual application scenarios of the configuration terminal.

[0031] After some embodiments, it further includes: A1, if it is determined that the configuration terminal applies for custom processing after selecting a function selection module, copy the virtual indicators corresponding to the function selection module to generate an interactive slot template.

[0032] It is understandable that when the configuration terminal applies for custom processing after selecting a function selection module, the server can copy the virtual indicators corresponding to the function selection module to generate an interactive slot template, so as to provide a new operation space for the subsequent configuration terminal.

[0033] Among them, the interactive slot template is a slot template that the configuration terminal can perform interactive configuration on. The interactive slot template allows the configuration terminal to further perform personalized settings on the virtual indicators. By copying the virtual indicators to generate an interactive slot template, it provides the possibility for the configuration terminal to make more detailed adjustments on the basis of the function selection module, meets the diverse requirements of the configuration terminal for virtual indicator configuration, and makes the configuration more flexible.

[0034] A2, if it is determined that the user deletes the virtual indicator in the interactive slot, delete it from the first configuration template.

[0035] It is understandable that when it is determined that the user deletes a virtual indicator in the interaction slot, to ensure the consistency between the first configuration template and the user's operation, the server deletes the virtual indicator from the first configuration template. This operation ensures the accuracy of the first configuration template, reflects the user's latest configuration intention. Deleting the virtual indicator in the interaction slot may be an adjustment made by the user according to actual needs. The server executes the deletion operation to update the first configuration template in real time, avoiding the existence of invalid or unnecessary virtual indicators in the configuration template, thereby improving the quality and usability of the configuration template.

[0036] A3. If it is determined that the user creates a new interaction slot in the interaction slot template, then based on the newly created interaction slot, call the full-function configuration template and select the corresponding virtual indicators to fill into the newly created interaction slot to obtain the first configuration template.

[0037] It is understandable that if it is determined that the user creates a new interaction slot in the interaction slot template, the server calls the full-function configuration template based on the newly created interaction slot, and selects the corresponding virtual indicators from the full-function configuration template to fill into the newly created interaction slot, and finally obtains the first configuration template. Creating a new interaction slot is an operation for the user to expand the configuration according to the actual situation. The server calls the full-function configuration template to select virtual indicators for filling, enriching the content of the first configuration template, so that the configuration side can flexibly expand the interaction slot and add appropriate virtual indicators according to its own needs, enhancing the scalability of the configuration, ensuring that the first configuration template can better meet the user's usage needs in different scenarios, and improving the perfection and accuracy of the virtual indicator configuration.

[0038] Among them, the newly created interaction slot is a newly constructed information interaction slot.

[0039] S2. The server retrieves the configuration elements of the virtual indicators in the first configuration template, and customizes the processing of the configuration elements based on the configuration side. The configuration elements include input elements, calculation elements, and output elements.

[0040] It is understandable that the server first retrieves the configuration elements of the virtual indicators from the first configuration template. These configuration elements cover input elements, calculation elements, and output elements, which are the components that make up the virtual indicators. Then, based on the configuration side, custom processing is performed on these configuration elements to meet the specific calculation needs of different users.

[0041] Among them, the configuration element is the index element included in the corresponding calculation of the virtual indicator in the first configuration template. The input element is the input-level element for determining the virtual indicator. The calculation element is the element corresponding to the calculation method for determining the virtual indicator, such as arithmetic operation elements such as addition and subtraction. The output element is the information element corresponding to the output of the virtual indicator.

[0042] In some embodiments, the specific implementation manner of step S2 (wherein the server retrieves the configuration elements of the virtual metrics in the first configuration template and customizes the processing of the configuration elements based on the configuration end) includes: S21. The server sequentially retrieves the virtual metrics in the first configuration template, calculates the metric similarity based on the configuration elements of each virtual metric, and obtains a metric sequence based on the metric similarity ranking.

[0043] It can be understood that after the element configuration of the virtual metrics, when calculating different metrics subsequently, the same element data may be called between different virtual metrics. For example, when obtaining the stored electric energy of a substation, element data such as electric energy and power consumption may be retrieved. At the same time, when calculating the photovoltaic power consumption, electric energy and power consumption are also called. Therefore, there is a possibility that the configuration elements between different virtual metrics are the same. Thus, during subsequent calculation processing, multi-virtual metric processing or sorting processing can be performed simultaneously to reduce the data retrieval path and improve the data processing efficiency. Furthermore, the metric similarity of the configuration elements of each virtual metric can be calculated to obtain a metric sequence, which is convenient for improving the subsequent data processing efficiency.

[0044] Among them, the metric similarity is the similarity degree of the configuration elements between different virtual metrics, the metric sequence is the sequence obtained by sorting the virtual metrics according to the metric similarity, and the metric sequence arranges the similar virtual metrics together, providing a clear processing order for subsequent processing.

[0045] In some embodiments, the specific implementation manner of step S21 (wherein the server sequentially retrieves the virtual metrics in the first configuration template, calculates the metric similarity based on the configuration elements of each virtual metric, and obtains a metric sequence based on the metric similarity ranking) includes: S211. The server calculates the number of configuration elements that are exactly the same for any two virtual metrics to obtain a first quantity, calculates the number of configuration elements that are different between the two virtual metrics to obtain a second quantity, and obtains the total quantity according to the sum of the first quantity and the second quantity.

[0046] It can be understood that the server conducts a detailed comparative analysis of the configuration elements of any two virtual metrics, determines the number of configuration elements that are exactly the same in these two virtual metrics, and records it as the first quantity. At the same time, calculates the number of configuration elements that are different between these two virtual metrics to obtain the second quantity. Then, adds the first quantity and the second quantity to obtain the total quantity, and the total quantity represents the total number of configuration elements of the two virtual metrics.

[0047] Among them, the first quantity is the number of configuration elements that are exactly the same for any two virtual metrics, the second quantity is the number of different configuration elements between the two virtual metrics, and the total quantity is the sum of the first quantity and the second quantity.

[0048] Through the above embodiments, the server can accurately quantify the similarities and differences of configuration elements between two virtual metrics, so as to enhance the accuracy of subsequent calculation of metric similarity.

[0049] S212. Calculate the ratio of the first quantity to the total quantity to obtain the metric similarity.

[0050] It can be understood that the server calculates the ratio of the first quantity to the total quantity, and the result obtained is the metric similarity. Among them, the metric similarity is a value between 0 and 1, which intuitively reflects the similarity degree of two virtual metrics in terms of configuration elements.

[0051] For example, if the metric similarity of two virtual metrics is 0.8, it means that 80% of their configuration elements are the same, and the similarity is relatively high. By calculating the metric similarity, the server can quantitatively evaluate the similarity relationship between all virtual metrics, so as to classify and sort the virtual metrics subsequently.

[0052] S213. Based on the metric similarity, divide all virtual metrics into multiple metric subsets and then sort them to obtain a metric sequence.

[0053] It can be understood that the server first divides all virtual metrics into multiple metric subsets based on the metric similarity. Specifically, virtual metrics with relatively high similarity are grouped into one subset, so that the virtual metrics within each subset have relatively high similarity in terms of configuration elements. Then, these metric subsets are sorted, and finally an ordered metric sequence is obtained. Through this classification and sorting method, the server can organize complex virtual metrics according to similarity, enabling the configuration side to more clearly understand the relationship between different virtual metrics and facilitating subsequent configuration and management operations.

[0054] Among them, the metric subset is a sub-classification set corresponding to the virtual metric, and the metric sequence is a sequence obtained by sorting the metric subsets.

[0055] In some embodiments, the specific implementation manner in step S213 (divide all virtual metrics into multiple metric subsets and then sort them to obtain a metric sequence based on the metric similarity) includes: S2131. Determine the first virtual metric with the highest similarity and greater than or equal to the preset similarity value for each virtual metric. If two virtual metrics are each other's first virtual metrics, classify the two virtual metrics into a first metric subset and label the similarity of the metric subset.

[0056] It can be understood that the server first determines the first virtual metrics with the highest similarity among each virtual metric and greater than or equal to a preset similarity value. The preset similarity value is a standard value set in advance to measure the similarity of metrics. If there are two virtual metrics that are each other's first virtual metrics, that is, the similarity between them is relatively the highest among all virtual metrics and reaches the preset similarity value, then these two virtual metrics are classified into a first metric subset, and the similarity between the virtual metrics included in this metric subset is marked. Thus, virtual metrics with high similarity can be grouped together, facilitating subsequent unified management and configuration of similar virtual metrics. At the same time, marking the similarity can intuitively reflect the similarity degree of the virtual metrics within the metric subset, providing a reference for subsequent sorting.

[0057] Among them, the preset similarity value is a similarity value set in advance, such as 50%, 80%, etc. The first virtual metric is a virtual metric related to the virtual metric and with the highest similarity and greater than or equal to the preset similarity value. For example, when there are virtual metrics A, B, C, and D, and the server calculates the virtual metric similarity between any two virtual metrics respectively, and the three virtual metric similarities related to A are 90%, 85%, and 50% respectively, and the preset similarity value is 80%, then it can be determined that the similarity between virtual metric B and virtual metric A is 90%, so that virtual metric B can be used as the first virtual metric, and the first metric subset is a set containing the first virtual metric.

[0058] S2132. If it is determined that the virtual metrics within the first metric subset are also the first virtual metrics of other virtual metrics and the similarity is greater than or equal to the preset similarity value, then the other virtual metrics are classified into the corresponding first metric subset.

[0059] It can be understood that after determining some first metric subsets, it is possible to further determine whether the virtual metrics within the first metric subset are also the first virtual metrics of other virtual metrics, and the similarity between them is greater than or equal to the preset similarity value. When this condition is met, the other virtual metrics are classified into the corresponding first metric subset, thereby expanding the scope of the first metric subset, ensuring that virtual metrics with similar characteristics can be classified into appropriate subsets, and making the first metric subset more complete and accurate.

[0060] Through the above implementation methods, similar virtual metrics can be classified more comprehensively, improving the accuracy and integrity of virtual metric classification, and contributing to better management and configuration of virtual metrics.

[0061] S2133. If it is determined that the similarity between the virtual metric and other virtual metrics is less than the preset similarity value, then it is classified into the second metric subset.

[0062] It can be understood that when the similarity of a virtual indicator to all other virtual indicators is less than a preset similarity value, the virtual indicator is classified into the second indicator subset. This second indicator subset contains virtual indicators with relatively low similarity to other virtual indicators. By classifying these virtual indicators separately, the differences between them and other similar virtual indicators can be clearly distinguished, facilitating targeted processing and configuration of different types of virtual indicators, and making the management of virtual indicators more meticulous and effective.

[0063] S2134, perform a descending order sorting on all the first indicator subsets based on the annotation similarity to obtain a first sequence, and place the second indicator subset after the first sequence to obtain an indicator sequence.

[0064] It can be understood that after completing the classification of virtual indicators, all the first indicator subsets can be sorted in descending order according to the annotation similarity, that is, the first indicator subsets are arranged in the order from high to low similarity to obtain a first sequence. Then, place the second indicator subset behind the first sequence to finally obtain a complete indicator sequence.

[0065] Through the above implementation method, the first indicator subsets where the virtual indicators with higher similarity are located can be placed in the front, and the second indicator subsets with lower similarity are placed in the back, forming an ordered indicator sequence. The indicator sequence provides a clear arrangement order for subsequent structured processing of the configuration elements of the virtual indicators, facilitating the configuration side to operate and manage the virtual indicators according to the indicator sequence, and improving the efficiency and accuracy of virtual indicator configuration.

[0066] S22, sequentially traverse the virtual indicators in the indicator sequence and perform structured processing according to the processing structure of the configuration elements to obtain multiple structural sub-slots, and each structural sub-slot corresponds to an input element, a calculation element, or an output element.

[0067] It can be understood that perform structured processing on the virtual indicators sorted by similarity, convert the virtual indicators into multiple structural sub-slots according to the processing structure of the configuration elements, and clarify the corresponding relationship between each structural sub-slot and the input element, the calculation element, or the output element, so as to facilitate the subsequent configuration of the virtual indicators by the configuration side.

[0068] Among them, the processing result is the analysis structure of the configuration element corresponding to the virtual indicator, and the structural sub-slot is the corresponding element node slot in the processing structure tree.

[0069] Through this structured processing, the complex virtual indicators are split into relatively independent and clear sub-parts, enabling each configuration element to have a corresponding specific position and representation form, facilitating subsequent detailed setting and management of each configuration element, and also providing a clearer operation object for the custom processing of the configuration elements by the configuration side.

[0070] In some embodiments, the specific implementation manner in step S22 (wherein the virtual metrics in the index sequence are traversed in sequence and structured processing is performed according to the processing structure of the configuration elements to obtain a plurality of structural sub-slots, and each structural sub-slot corresponds to an input element, a calculation element, or an output element) includes: S221, construct an input area, a calculation area, and an output area in sequence.

[0071] It can be understood that, as Figure 2 shown, the server constructs an input area, a calculation area, and an output area in sequence, and these three areas are divided according to the configuration elements (input elements, calculation elements, and output elements) of the virtual metrics.

[0072] Among them, the input area is the area for processing the input data of the virtual metrics, the calculation area is the element area responsible for performing corresponding calculation operations on the input data, and the output area is the element area for outputting the calculation results.

[0073] Through the above implementation manner, an input area, a calculation area, and an output area are constructed in sequence, providing a framework for subsequent structured processing of the configuration elements of the virtual metrics, making the different functional parts of the virtual metrics clearly divided, and facilitating targeted operations and management of each part.

[0074] S222, construct and fill slots corresponding to each input element, calculation element, or output element in the input area, the calculation area, and the output area.

[0075] It can be understood that after the input area, the calculation area, and the output area are constructed, slots corresponding to each input element, calculation element, or output element are constructed in these areas and filled. Each slot corresponds to a specific configuration element. By constructing the slots and filling the corresponding configuration elements, each configuration element has a specific position and representation form in the corresponding area.

[0076] For example, in the input area, each input element has a corresponding slot to receive input data. In the calculation area, the slot corresponding to the calculation element is used to perform calculation operations. In the output area, the slot corresponding to the output element is used to output the calculation results. Such operations make the configuration elements of the virtual metrics more intuitive and specific, providing a clear structure for subsequent processing.

[0077] S223, connect the slots in sequence based on the relationships between the input elements, calculation elements, or output elements to obtain structural sub-slots.

[0078] It can be understood that considering the logical relationships between different configuration elements, by connecting the slots, the configuration elements of the virtual metrics form an organic whole.

[0079] For example, as Figure 2 shown, after the slot corresponding to the input element receives the input data, the data flows to the slot corresponding to the calculation element for calculation, and the calculation result then flows to the slot corresponding to the output element for output. By connecting the slots in sequence, a structural sub-slot is obtained, which clarifies the information transfer path and operation sequence between the configuration elements of the virtual indicator, making the function implementation of the virtual indicator more orderly and efficient, and providing an accurate structural basis for the subsequent determination of the structural sub-slot information of the virtual indicator by the configuration end and the parallel processing of the server.

[0080] S23. The configuration end determines the information of the structural sub-slot of each virtual indicator, and the server performs parallel processing on the structural sub-slots of the subsequent virtual indicators in combination with the index sequence order and the information determined by the configuration end to obtain the configuration elements of each virtual indicator.

[0081] It should be noted that since different users have corresponding identity tags, such as electricity consumption membership and non-membership identities, there may also be differences in the data configuration for the same index element between members and non-members. For example, the electricity price per unit for membership can be discounted, so the electricity prices for membership and non-membership identities are different, that is, the data information corresponding to the same element can be different. Therefore, corresponding interfaces can be called for data configuration according to the information determined by the configuration end. Conversely, when the configuration elements are the same, the same interface can be retrieved so that the configuration end can achieve fast configuration without re-retrieving the configuration for the same index, improving the data processing efficiency.

[0082] It can be understood that, first, the configuration end determines the information of the structural sub-slot of each virtual indicator, and this information includes the specific settings and parameters of each structural sub-slot, etc. Then, the server combines the order of the index sequence and the information determined by the configuration end to perform parallel processing on the structural sub-slots of the subsequent virtual indicators. The purpose of the parallel processing is to, on the basis of maintaining the index sequence order, make corresponding adjustments and optimizations to the structural sub-slots of the subsequent virtual indicators according to the configuration information of the previous virtual indicators, so that the configuration elements of all virtual indicators can be coordinated and unified with each other, and finally obtain the complete and reasonable configuration elements of each virtual indicator, thereby completing the further refinement and improvement of the virtual indicator configuration.

[0083] In some embodiments, the specific implementation manner in step S23 (where the configuration end determines the information of the structural sub-slot of each virtual indicator, and the server performs parallel processing on the structural sub-slots of the subsequent virtual indicators in combination with the index sequence order and the information determined by the configuration end to obtain the configuration elements of each virtual indicator) includes: S231. If it is determined that the initial information of the structural sub-slot of the virtual indicator by the configuration end is adjusted, the adjustment information is determined.

[0084] It is understandable that when the configuration end adjusts the initial information of the structural sub-slot of the virtual indicator, the server can determine the specific adjustment information, which may involve changes in the value range of the input element, modifications to the calculation formula of the calculation element, or adjustments to the display form of the output element, etc., so as to ensure the accuracy of the subsequent adjustment information.

[0085] The initial information is the configured data information in the structural sub-slot, and the adjustment information is the information for adjusting and modifying the structural sub-slot. For example, the unit price of electricity can be adjusted from 0.8 to 0.7.

[0086] S232, retrieve the structural subslot of the next virtual indicator in the indicator sequence order, make corresponding adjustments based on the adjustment information, and highlight the adjusted information in a preset format.

[0087] It is understandable that after determining the adjustment information, the server will call the structural sub-slot of the next virtual indicator, and then make corresponding adjustments to the structural sub-slot based on the previously determined adjustment information. In order to allow users to clearly see these adjustments, the server will highlight the adjusted information in a preset format.

[0088] The preset format is a pre-set display format, which may be changing the font color, adding a background color, or setting a special border style.

[0089] Through the above implementation, the user can intuitively understand the adjustments made to the sub-slots of the virtual indicator structure, which facilitates the evaluation and confirmation of the adjustment results.

[0090] S233: If it is determined that the user is sure about the highlighted adjustment information, save it.

[0091] It is understandable that when the server determines that the user has confirmed the adjustment information, it will save the adjustment information. Saving the adjustment information means that these modifications will be applied to the configuration of the virtual indicator and become part of the final configuration elements, ensuring that the user's adjustment intentions can be accurately recorded and executed, so as to improve the accuracy and effectiveness of the virtual indicator configuration.

[0092] S3: The server determines the corresponding real-time engine and periodic engine according to the attributes of the virtual indicator.

[0093] It is understandable that, due to the different properties of the virtual indicators, the corresponding engines are also different. Therefore, the corresponding engine mode can be selected according to the properties of the virtual indicators for the convenience of users.

[0094] Among them, the attributes of virtual metrics may include data update frequency, real-time requirements of data sources, etc. The real-time engine is used to process virtual metrics that need to be updated and calculated in real time, and can respond to data changes in a timely manner and perform corresponding calculations and processing. The periodic engine is applicable to virtual metrics that are processed and updated according to a certain period. By matching appropriate engines for virtual metrics with different attributes, the efficiency and accuracy of virtual metric processing can be improved, ensuring that virtual metrics can operate normally as expected.

[0095] S4. After the configuration end determines the configuration elements of each virtual metric, it builds data interfaces for the real-time engine and the periodic engine, creates an information extraction table and then removes duplicates, and obtains the configured virtual metrics based on the information extraction table.

[0096] It can be understood that after the configuration end determines the configuration elements of each virtual metric, it builds data interfaces for the real-time engine and the periodic engine, creates an information extraction table and removes duplicates, and finally obtains the configured virtual metrics based on the information extraction table, so that the virtual metrics can accurately interact with the engine for data, realize effective extraction and input of data, enable the virtual metrics to be put into use with the correct configuration, and ensure the normal operation of the entire virtual metric system.

[0097] In some embodiments, the specific implementation manner in step S4 (after the configuration end determines the configuration elements of each virtual metric, it builds data interfaces for the real-time engine and the periodic engine, creates an information extraction table and then removes duplicates, and obtains the configured virtual metrics based on the information extraction table) includes: S41. The configuration end sequentially builds the corresponding data interfaces for each structural sub-slot, and counts the corresponding relationships between all structural sub-slots and data interfaces to generate an initial information extraction table.

[0098] It can be understood that for each structural sub-slot, the configuration end sequentially builds its corresponding data interface. Each structural sub-slot carries input elements, calculation elements or output elements. Building the data interface enables these elements to transmit data with the real-time engine and the periodic engine. At the same time, the configuration end counts the corresponding relationships between all structural sub-slots and data interfaces, and organizes these corresponding relationships to generate an initial information extraction table for facilitating subsequent information extraction.

[0099] Among them, the data interface is an interface for data transmission corresponding to the structural sub-slot, which is associated with each data information. For example, when calculating the electricity price of a user, one of the structural sub-slots needs to retrieve electrical energy. That is, this structural sub-slot may be connected to a voltage sensor, so as to construct a corresponding data interface to retrieve the voltage sensor value. At the same time, since there may be a certain difference in the electricity unit prices of electricity users and non-users, the corresponding second structural sub-slot needs to retrieve the corresponding user database, so that a new data interface can be constructed to retrieve the user database through the data interface corresponding to the second structural sub-slot and transmit it to the structural sub-slot in the calculation area for data calculation. The information extraction table is a form containing the corresponding relationship between the structural sub-slot and the data interface. For example, the information extraction table is composed of the correspondence between structural sub-slot A and data interface 1, structural sub-slot B and data interface 2, etc.

[0100] It is not difficult to understand that the data interface is related to the associated data content, so that the database of corresponding indicators can be retrieved according to the corresponding data interface in the future, improving the data processing efficiency.

[0101] By establishing a data interface and generating an initial information extraction table, a clear structure and data foundation are provided for subsequent data processing and virtual indicator configuration, ensuring the accuracy and orderliness of data transmission.

[0102] S42, if it is determined that multiple sub-slots correspond to the same data interface, a slot association group is obtained, and after removing duplicates from the duplicate data interfaces, they are correspondingly set with the slot association group.

[0103] It should be noted that when the data required for calculating the data of multiple slots is the same data, the corresponding data interface can send the associated data to multiple indicators for data calculation. And when the data called by multiple data slots within the same indicator is the same, the corresponding multiple identical data interfaces can be de-duplicated, so as to obtain a slot association group corresponding to the data interface.

[0104] It can be understood that when it is determined that multiple sub-slots correspond to the same data interface, these sub-slots are grouped into a slot association group. This is because there may be data redundancy or repetition when multiple sub-slots correspond to the same data interface. By grouping them into a slot association group, the data interface can be managed more effectively. Then, the duplicate data interfaces are de-duplicated to remove redundant data interfaces, making the data interface more concise and efficient. Finally, the de-duplicated data interfaces are correspondingly set with the slot association group to ensure that each slot association group has an accurate and non-duplicate data interface corresponding to it, optimizing the data transmission path and improving the data processing efficiency.

[0105] Among them, the slot association group is a combination of multiple different sub-slots corresponding to the data interface.

[0106] S43. Retrieve the real-time tags or periodic tags corresponding to each sub-slot, generate comprehensive extraction information, and store it in the extraction period slot to obtain an information extraction table.

[0107] It can be understood that since different virtual metrics have corresponding periodic tags or real-time tags, the element sub-slots corresponding to the virtual metrics also have corresponding tag information. Therefore, the configuration end can retrieve the real-time tags or periodic tags corresponding to each sub-slot. These tags contain time characteristic information related to the sub-slot. By retrieving these tags, comprehensive extraction information is generated. The comprehensive extraction information integrates the data characteristics and time characteristics of the sub-slot. Then, the comprehensive extraction information is stored in the extraction period slot. The extraction period slot provides a specific space for storing the comprehensive extraction information. Finally, through this series of operations, an information extraction table is obtained. Among them, the information extraction table comprehensively records the relationships among the sub-slot, data interface, real-time tag or periodic tag, and comprehensive extraction information, providing detailed information support for subsequent data extraction and virtual metric configuration.

[0108] Among them, the real-time tag is an information tag for which the data needs to be updated in real time, the periodic tag is an information tag for which the data needs to be updated periodically, the comprehensive extraction information is the information of all the extracted data corresponding to the sub-slot, the extraction period slot is the slot for storing the data extraction information, and the information extraction table is a form with comprehensive extraction information.

[0109] S44. The server extracts the data transmitted in the data interface according to the independent extraction information or comprehensive extraction information corresponding to each data interface in the information extraction table and inputs it into the structural sub-slot to obtain a virtual metric.

[0110] It can be understood that the server extracts the data transmitted in the data interface according to the independent extraction information or comprehensive extraction information corresponding to each data interface in the information extraction table. The independent extraction information and comprehensive extraction information clarify the specific content and characteristics of the data in the data interface. Based on these information, the server accurately extracts the data from the data interface and inputs the extracted data into the corresponding structural sub-slot. Through this process, the extracted data is accurately input into each structural sub-slot of the virtual metric, obtaining a configured virtual metric, enabling the virtual metric to operate normally according to the configured elements and interface requirements, and completing the entire virtual metric configuration and data processing process.

[0111] As Figure 3 shown, the present invention provides a schematic structural diagram of a virtual metric data processing system. The virtual metric data processing system includes: An interaction module, configured to enable the server to, after receiving a configuration request for a virtual metric from the configuration end, call a full-function configuration template and feedback it to the configuration end for interaction to obtain a functional first configuration template.

[0112] A retrieval module, configured to enable the server to retrieve configuration elements of virtual metrics in the first configuration template, and perform custom processing on the configuration elements based on the configuration terminal, where the configuration elements include input elements, calculation elements, and output elements.

[0113] A determination module, configured to enable the server to determine corresponding real-time engines and periodic engines according to the attributes of the virtual metrics.

[0114] An extraction module, configured to enable the configuration terminal to build data interfaces for the real-time engine and the periodic engine after determining the configuration elements of each virtual metric, deduplicate after establishing an information extraction table, and obtain the configured virtual metrics based on the information extraction table.

[0115] The present invention further provides a storage medium, in which a computer program is stored, and when the computer program is executed by a processor, it is used to implement the methods provided by the above various embodiments.

[0116] Among them, the storage medium can be a computer storage medium or a communication medium. The communication medium includes any medium that facilitates the transmission of a computer program from one place to another. The computer storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer. For example, the storage medium is coupled to the processor, so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an application specific integrated circuit (ASIC). In addition, the ASIC can be located in the user equipment. Of course, the processor and the storage medium can also exist as discrete components in the communication device. The storage medium can be a read-only memory (ROM), a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0117] The present invention further provides a program product, which includes execution instructions stored in a storage medium. At least one processor of the device can read the execution instructions from the storage medium, and the execution of the execution instructions by at least one processor enables the device to implement the methods provided by the above various embodiments.

[0118] In the above embodiments of the terminal or the server, it should be understood that the processor may be a central processing unit (CPU for short), or may also be other general-purpose processors, digital signal processors (DSP for short), application specific integrated circuits (ASIC for short), etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in conjunction with the present invention may be directly embodied as being executed by a hardware processor, or may be executed by a combination of hardware and software modules in the processor.

[0119] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for processing virtual index data, characterized in that Including: After the server sends a configuration request for virtual metrics at the configuration end, it calls the full-functional configuration template and feeds it back to the configuration end for interaction to obtain a functional first configuration template; The server retrieves the configuration elements of the virtual metrics in the first configuration template, and customizes the processing of the configuration elements based on the configuration end. The configuration elements include input elements, calculation elements, and output elements; The server determines the corresponding real-time engine and periodic engine according to the attributes of the virtual metrics; After the configuration end determines the configuration elements of each virtual metric, it builds data interfaces for the real-time engine and the periodic engine, removes duplicates after establishing an information extraction table, and obtains the configured virtual metrics based on the information extraction table.

2. The virtual metric data processing method according to claim 1, wherein After the server sends a configuration request for virtual metrics at the configuration end, it calls the full-functional configuration template and feeds it back to the configuration end for interaction to obtain a functional first configuration template, including: If the server determines that the configuration end selects the functional selection method, it generates a function selection module, determines the corresponding first configuration template based on the function selection module, and each function selection module has a preset virtual metric; If the server determines that the configuration end selects the customized selection method, it directly displays the virtual metrics, and obtains a functional first configuration template based on the virtual metrics separately selected by the configuration end.

3. The virtual index data processing method according to claim 2, wherein It also includes: If it is determined that the configuration end applies for custom processing after selecting the function selection module, a interactive slot template is generated by copying the virtual metrics corresponding to the function selection module; If it is determined that the user deletes the virtual metrics in the interactive slot, they are deleted from the first configuration template; If it is determined that the user creates a new interactive slot in the interactive slot template, the full-functional configuration template is called based on the new interactive slot, and the corresponding virtual metrics are selected and filled into the new interactive slot to obtain the first configuration template.

4. The virtual metric data processing method according to claim 1, wherein The server retrieves the configuration elements of the virtual metrics in the first configuration template, and customizes the processing of the configuration elements based on the configuration end, including: The server sequentially retrieves the virtual metrics in the first configuration template, calculates the metric similarity based on the configuration elements of each virtual metric, and obtains a metric sequence by sorting based on the metric similarity; Sequentially traverse the virtual metrics in the metric sequence and perform structured processing according to the processing structure of the configuration elements to obtain multiple structural sub-slots, and each structural sub-slot corresponds to an input element, a calculation element, or an output element; The configuration end determines the information of the structural sub-slots of each virtual metric, and the server performs parallel processing on the structural sub-slots of the subsequent virtual metrics in combination with the metric sequence order and the information determined by the configuration end to obtain the configuration elements of each virtual metric.

5. The virtual metric data processing method according to claim 4, wherein The server sequentially retrieves the virtual metrics in the first configuration template, calculates the metric similarity based on the configuration elements of each virtual metric, and obtains a metric sequence by sorting based on the metric similarity, including: The server calculates the number of configuration elements with exactly the same two virtual metrics to obtain a first quantity, calculates the number of configuration elements with different two virtual metrics to obtain a second quantity, and obtains the total quantity according to the sum of the first quantity and the second quantity; Calculate the ratio of the first quantity to the total quantity to obtain the metric similarity; Based on the metric similarity, all virtual metrics are divided into multiple metric subsets and sorted to obtain a metric sequence.

6. The virtual metric data processing method according to claim 5, wherein The step of dividing all virtual metrics into multiple metric subsets based on the metric similarity and then sorting to obtain a metric sequence includes: Determine a first virtual metric with the highest similarity and greater than or equal to a preset similarity value for each virtual metric. If two virtual metrics are each other's first virtual metrics, classify the two virtual metrics into a first metric subset and label the similarity of the metric subset; If it is determined that the virtual metrics in the first metric subset are also the first virtual metrics of other virtual metrics and the similarity is greater than or equal to the preset similarity value, classify the other virtual metrics into the corresponding first metric subset; If it is determined that the similarity of the virtual metric with other virtual metrics is less than the preset similarity value, classify it into the second metric subset; Based on the labeled similarity, sort all the first metric subsets in descending order to obtain a first sequence, and place the second metric subset after the first sequence to obtain a metric sequence.

7. The virtual metric data processing method according to claim 4, wherein The step of sequentially traversing the virtual metrics in the metric sequence and performing structured processing according to the processing structure of the configuration elements to obtain multiple structural sub-slots, each structural sub-slot corresponding to an input element, a calculation element or an output element, includes: Construct an input area, a calculation area and an output area in sequence; Construct slots corresponding to each input element, calculation element or output element in the input area, calculation area and output area and fill them; Based on the relationship between the input element, the calculation element or the output element, connect the slots in sequence to obtain a structural sub-slot.

8. The virtual metric data processing method according to claim 4, wherein The configuration end determines the information of the structural sub-slot of each virtual metric, and the server performs parallel processing on the structural sub-slots of the subsequent virtual metrics in combination with the metric sequence order and the information determined by the configuration end to obtain the configuration elements of each virtual metric, including: If it is determined that the configuration end adjusts the initial information of the structural sub-slot of the virtual metric, determine the adjustment information; Retrieve the structural sub-slot of the next virtual metric in the metric sequence order and adjust it correspondingly based on the adjustment information, and highlight the adjusted information in a preset form; If it is determined that the user confirms the highlighted adjustment information, save it.

9. The virtual metric data processing method according to claim 7, wherein After the configuration end determines the configuration elements of each virtual metric, it builds data interfaces for the real-time engine and the periodic engine, establishes an information extraction table and then removes duplicates, and obtains the configured virtual metrics based on the information extraction table, including: The configuration end establishes the corresponding data interface for each structural sub-slot in turn, and counts the correspondence between all structural sub-slots and data interfaces to generate an initial information extraction table; If it is determined that multiple sub-slots correspond to the same data interface, a slot association group is obtained, and duplicate data interfaces are removed and then set corresponding to the slot association group; Retrieve the real-time label or periodic label corresponding to each sub-slot to generate comprehensive extraction information and store it in the extraction periodic slot to obtain an information extraction table; The server extracts the data transmitted in the data interface according to the independent extraction information or the comprehensive extraction information corresponding to each data interface in the information extraction table and inputs the data into the structural subslot to obtain the virtual indicator.

10. Virtual index data processing system, characterized in that, include: The interactive module is used to enable the server to call the full-function configuration template to feedback to the configuration end for interaction after the configuration end sends the configuration request of the virtual indicator, so as to obtain the functional first configuration template; A calling module, used to enable the server to call the configuration elements of the virtual indicators in the first configuration template, and customize the configuration elements based on the configuration end, wherein the configuration elements include input elements, calculation elements and output elements; A determination module, used to enable the server to determine the corresponding real-time engine and periodic engine according to the attributes of the virtual indicator; The extraction module is used to enable the configuration end to determine the configuration elements of each virtual indicator, build the data interface for the real-time engine and the periodic engine, establish the information extraction table and then remove duplicates, and obtain the configured virtual indicator based on the information extraction table.

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