Custom indicator generation method, indicator editor, electronic device and storage medium

Abstract

A custom indicator generation method, an indicator editor, an electronic device and a storage medium. The method comprises: determining at least one first-level operator, and determining a data processing rule corresponding to each first-level operator among the at least one first-level operator, wherein each first-level operator is an indicator of a basic category or a custom category; and generating a custom indicator on the basis of the at least one first-level operator and the data processing rule corresponding to each first-level operator, wherein the custom indicator is an indicator of the custom category, and the custom indicator can serve as a first-level operator to generate a new custom indicator. In the present application, customized custom indicators can be created to meet data analysis requirements in different industries and fields.

Description

Custom indicator generation method, indicator editor, electronic device and storage medium Technical Field

[0001] The present application relates to the technical field of data processing and indicator editors, and in particular to a custom indicator generation method, an indicator editor, an electronic device, and a computer-readable storage medium.

[0002] Background of the Invention

[0003] In the field of data analysis and statistics, traditional methods are often limited to a fixed set of parameter indicators for generating charts and data analysis, without the ability to further customize indicators based on specific data and needs. This limitation leads to inflexibility in data analysis, as only a limited set of predefined parameter indicators can be selected for analysis. However, to meet the data analysis and statistics requirements of different scenarios, a more flexible approach is needed.

[0004] Based on this, the present application provides a custom indicator generation method, an indicator editor, an electronic device and a computer-readable storage medium to improve related technologies.

[0005] Summary of the Invention

[0006] The purpose of this application is to provide a custom indicator generation method, an indicator editor, an electronic device and a computer-readable storage medium to create customized custom indicators to meet the data analysis needs of different industries and fields.

[0007] In a first aspect, the present application provides a method for generating a custom indicator, the method comprising: determining at least one first-level operator, and determining a data processing rule corresponding to each of the at least one first-level operator, where the first-level operator is an indicator of a basic category or a custom category; generating a custom indicator based on the at least one first-level operator and the data processing rule corresponding to each first-level operator, where the custom indicator is an indicator of a custom category; wherein the custom indicator can act as a first-level operator to generate a new custom indicator.

[0008] In the second aspect, the present application provides an indicator editor, which is used to provide a custom indicator generation interface, and the custom indicator generation interface includes: a combo box for receiving a user's first text input operation and / or a first selection operation to determine at least one first-level operator; a formula editing box for displaying each of the at least one first-level operator determined by the user in the form of an operator control, and receiving the user's rule selection operation for the drop-down list box in the operator control to determine the data processing rules corresponding to each first-level operator; an indicator generation button for generating a custom indicator according to at least one first-level operator and the data processing rules corresponding to each first-level operator after being clicked, and the custom indicator is an indicator of a custom category; wherein the custom indicator can act as a first-level operator to generate a new custom indicator.

[0009] In a third aspect, the present application provides an electronic device comprising a memory and at least one processor, wherein the memory stores a computer program, and the at least one processor is configured to implement the steps of any of the above methods or the functions of any of the above indicator editors when executing the computer program.

[0010] In a fourth aspect, the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by at least one processor, it implements the steps of any of the above methods or implements the functions of any of the above indicator editors.

[0011] The custom indicator generation method, indicator editor, electronic device and computer-readable storage medium provided in this application create customized custom indicators to meet the data analysis needs of different industries and fields. By combining and operating different operators, new indicators can be generated for further data analysis and visualization. The advantage of this is that users can choose different primary operators according to their specific needs and analysis purposes to create fully customized custom indicators, which improves the flexibility of the analysis tool and enables it to adapt to the data analysis needs of different industries and fields; by generating custom indicators, users can obtain more data analysis results at different levels and dimensions, rather than being limited to predefined fixed parameter indicators, which helps to dig deeper into the data. In summary, this application enriches data analysis capabilities and supports multi-scenario data analysis by enhancing flexibility and customization.

[0012] BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The present application is further described below with reference to the accompanying drawings and specific implementation methods.

[0014] FIG1 is a flow chart of a method for generating a custom indicator provided in one embodiment of the present application.

[0015] FIG2 is a schematic diagram of an interface of an indicator editor provided in one embodiment of the present application.

[0016] FIG3 is a structural block diagram of an electronic device provided in one embodiment of the present application.

[0017] Modes for Carrying Out the Invention

[0018] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without making any creative work are within the scope of protection of this application.

[0019] In the description of the embodiments of this application, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly indicate the number of technical features indicated. Therefore, features defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of this application, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0020] In data analysis and processing environments, users often need to work with a wide variety of metrics and data. This data may include numerical, textual, temporal, geographic, and other types of metrics. Related technologies provide some basic data processing capabilities, but they only support data processing for existing metrics and cannot meet complex, customized user requirements. Furthermore, the configuration interface can be complex, requiring users to invest significant time and effort to understand and configure metrics. Users cannot create and edit custom metrics based on their specific needs, and the configuration interface can be difficult to understand and use, requiring user training and learning.

[0021] Therefore, it is necessary to consider how to provide a more flexible, customized, and user-friendly method for generating and editing indicators. This application may include the following key elements:

[0022] First-level operators and second-level operators: The concepts of first-level operators and second-level operators are introduced to better organize and manage the data processing rules of indicators.

[0023] Data processing rules: Provides a variety of data processing rules to meet various data types and requirements.

[0024] Visual Editing Tool: A user-friendly interface for generating custom indicators has been created, allowing users to easily configure and edit indicators through elements such as tabs, combo boxes, and formula edit boxes.

[0025] Tooltip control: Added tooltip information to help users better understand and use primary and secondary operators.

[0026] Specifically, this application provides a custom indicator generation method, an indicator editor, an electronic device, and a computer-readable storage medium, allowing users to create and edit custom indicators based on their specific needs. These custom indicators can include various data processing rules and calculation rules, allowing users to perform data analysis and processing more flexibly, improving user experience and data processing efficiency.

[0027] It should be noted that although this application uses patent data analysis scenarios in the data analysis field as an example, this application can be applied to other data analysis scenarios in the data analysis field, such as financial analysis, medical and health data analysis, marketing analysis, supply chain management, and other fields, and this application is not limited to this. Data analysis in different fields generally requires processing different types of data and indicators. Therefore, the flexibility and customizability of this application make it easy to adapt to the needs of different fields.

[0028] In the patent field, powerful data analysis tools are provided to R&D personnel and patent agents to create a variety of customized indicators based on the specific requirements of patent data, which helps to predict patent trends, risk assessment and intellectual property management.

[0029] In the financial field, it can help financial analysts create various financial indicators based on market conditions and customer needs for predicting market trends, risk assessment and asset allocation.

[0030] In the healthcare sector, medical data typically includes patients' physiological indicators, medical records, and medical imaging data. Doctors and R&D personnel can create custom indicators for disease diagnosis, treatment monitoring, and clinical research.

[0031] In the marketing field, market analysts can use this application to create various market indicators for customer segmentation, advertising effectiveness analysis, and market trend forecasting.

[0032] In the supply chain management field, supply chain experts can use this application to create custom metrics to track various aspects of supply chain operations, including inventory management, transportation efficiency, and supplier performance. Because supply chains involve multiple links and metrics, this customizable capability can help supply chain managers better adapt to different supply chain situations.

[0033] One embodiment of the present application provides a data analysis system. The data analysis system may include a client and a server. The client may be an electronic device with network access capabilities. Specifically, for example, the client may be a desktop computer, tablet computer, laptop computer, smartphone, digital assistant, smart wearable device, shopping guide terminal, television, smart speaker, microphone, etc. Among them, smart wearable devices include but are not limited to smart bracelets, smart watches, smart glasses, smart helmets, smart necklaces, etc. Alternatively, the client may be software that can run on the electronic device. The server may be an electronic device with certain computing and processing capabilities, which may have a network communication module, a processor, and memory. Of course, the server may also refer to software running on the electronic device. The server may also be a distributed server, which may be a system with multiple processors, memories, network communication modules, etc. operating in coordination. Alternatively, the server may be a server cluster formed by several servers. Alternatively, with the development of science and technology, the server may also be a new technical means that can realize the corresponding functions of the application embodiment. For example, it may be a new form of "server" based on quantum computing.

[0034] Referring to Figure 1, Figure 1 is a flow chart of a method for generating a custom indicator according to one embodiment of the present application. The method includes the following contents.

[0035] Step S101: determining at least one first-level operator and determining a data processing rule corresponding to each of the at least one first-level operator, wherein the first-level operator is an indicator of a basic category or a user-defined category.

[0036] Step S102: Generate a custom indicator based on at least one first-level operator and the data processing rules corresponding to each first-level operator, where the custom indicator is an indicator of a custom category.

[0037] The custom indicator can act as a first-level operator to generate a new custom indicator.

[0038] In some possible implementations, when at least one first-level operator includes multiple first-level operators (i.e., the number of first-level operators used to generate the custom indicator is greater than 1), the method further includes: determining the operation rules between the multiple first-level operators; wherein, generating the custom indicator based on the at least one first-level operator and the data processing rules corresponding to each first-level operator (i.e., step S102) includes: generating the custom indicator based on the multiple first-level operators, the data processing rules corresponding to each first-level operator, and the operation rules between the multiple first-level operators.

[0039] This application does not limit the indicators of the basic category. In the field of patent data analysis, they can be, for example, receiving office, applicant, inventor, agency, patent agent, application date, authorization date, priority date, application number, publication number (announcement number), patent abstract, patent title, patent type, patent classification number, cited literature, number of claims, first claim, patent legal status, international patent classification, patent examiner, patent priority, patent litigation information, patent transfer information, number of patent citations, number of patent citations to others, patent family members, patent technology field classification, patent application field classification, etc. Generally speaking, the indicators of the basic category are the indicators provided by default in the data analysis system.

[0040] The first-level operator is the basic building block for building custom indicators. The first-level operator can be a basic category indicator (such as applicant, authorization date, etc.) or a custom category indicator (such as the average patent value of each inventor). For the first-level operator (whether it is a basic category indicator or a custom category indicator), a data processing rule needs to be determined to indicate what kind of useful information based on the indicator should be extracted from the data set to be processed. In addition, after determining a first-level operator, a second-level operator can be used to perform a second filtering on the data set to be processed by the first-level operator. In other words, the first-level operator can be used in combination with the second-level operator for more complex data filtering and processing tasks.

[0041] Data processing rules are the data processing methods corresponding to the first-level operators, used to operate and convert indicator data. Different types of indicators can have different data processing rules, such as averaging, summing, counting, etc. As an example, for the first-level operator "Patent Value", the data processing rule can be summing to determine the total patent value. To convert between currency units (for example, converting US dollars to RMB), calculations can be performed based on the exchange rate between the two currencies. This exchange rate can be a user-entered value, a system default value, or an exchange rate value read in real time through the data interface.

[0042] Operation rules define how first-level operators operate on each other, including the operators and their precedence (expressed as operation levels). Operation rules are used to generate custom metrics by combining the results of different first-level operators. For example, if you need to generate a custom metric to calculate the value score of each patent, you can use arithmetic operators to add or multiply the patent's value score with other metrics.

[0043] Custom indicators are new indicators generated based on the primary operator and its data processing rules. These indicators usually reflect the user's specific analysis needs. As an example, a custom indicator can be "comprehensive patent value", which is composed of indicators such as the patent's value score and the number of citations. In some possible implementations, the custom indicator corresponds to an indicator formula that indicates what kind of data processing is performed on the data set to be processed. As an example, the indicator formula is: number of patents (sum) / inventors (de-duplicated count), which is used to calculate the average number of patents for each inventor.

[0044] For example, the above method can be used to create a custom metric to calculate the average patent value of each inventor. Initially, a search query is constructed and executed to filter patent data from the patent database for a specific applicant as the first-level filtering result. First, two first-level operators and their corresponding data processing rules are determined.

[0045] Level 1 operator 1 is "Patent Value," a custom category indicator that represents the value of each patent. Data processing rule 1 is "Sum," which calculates the total patent value of all patents corresponding to the level 1 filtering results.

[0046] Level 2 operator, "Inventor," is a basic indicator representing the inventors of each patent. Data processing rule 2, "Count by Deduplication," adds up the inventor counts of all patents after deduplication to obtain the total number of inventors.

[0047] The operation rule is "division", which is to divide the data processing result of the first-level operator 1 (i.e., patent value) (i.e., the total patent value) by the data processing result of the first-level operator 2 (i.e., inventor) (i.e., the number of inventors) to calculate the average patent value of each inventor.

[0048] The above settings generate a custom metric, "Average Patent Value per Inventor." This custom metric's data processing results are calculated using the first-level filtering results, two first-level operators, and a division operator. The formula is: Patent Value (sum) / Inventors (de-duplicated count). This custom metric can help companies assess the average contribution of each inventor to patent creation, providing a more comprehensive understanding of innovation team performance.

[0049] Therefore, first, it is necessary to determine at least one first-level operator. These operators can be indicators of the basic category (i.e., basic indicators) or user-defined indicators. Each first-level operator has its corresponding data processing rules, which define how to process the operator's raw data. For example, the data processing results of each first-level operator are determined by the known primary filtering results (e.g., the authorized patents of a specific applicant), the first-level operator (e.g., patent value), and its data processing rules (e.g., summation). When the number of first-level operators used to generate a custom indicator is greater than one, it is also necessary to determine the operation rules between the first-level operators. These rules describe how to combine different first-level operators for operation, and can include operation rules such as addition, subtraction, multiplication, division, and operation level. These rules define how to generate the custom indicator. Next, based on the selected first-level operators and their data processing rules, as well as the operation rules between them (if any), the custom indicator is generated. These custom indicators fall into the custom category. Their formulas are determined by each first-level operator and its data processing rules (possibly including the inter-operation rules between first-level operators). In other words, the data processing results of a custom indicator are determined by the data processing results of each first-level operator (possibly including the inter-operation rules between first-level operators). By combining and calculating different operators, new indicators can be generated for further data analysis and visualization.

[0050] The benefit of this approach is that users can select different primary operators, data processing rules, and calculation rules based on their specific needs and analysis objectives, thereby creating fully customized custom indicators. This increases the flexibility of the analysis tool, enabling it to adapt to the data analysis needs of different industries and fields. By generating custom indicators, users can obtain data analysis results at more different levels and dimensions, rather than being limited to predefined fixed parameter indicators, which helps to deeply explore the data. In summary, this method enriches data analysis capabilities by enhancing flexibility and customization, supports multi-scenario data analysis, and improves decision support capabilities.

[0051] It should be noted that when the number of first-level operators is greater than one, the operation rules between the first-level operators need to be determined; when the number of first-level operators is one, the operation rules between the first-level operators are not involved.

[0052] In some possible implementations, the process of determining at least one first-level operator includes: receiving one or more first selection operations of a user for an indicator set through a terminal device to determine one or more first-level operators; or, receiving one or more first text input operations of a user through the terminal device, and identifying one or more first-level operators based on the received one or more first text input operations; or, importing one or more first-level operators through an indicator sharing platform; or, providing a first chat robot interaction interface through the terminal device to receive one or more first input information of the user; and performing semantic extraction on the one or more first input information to determine one or more first-level operators.

[0053] Terminal devices refer to devices used by users to interact with data analysis systems, such as personal computers, smartphones, tablets, etc.

[0054] The first selection operation refers to a selection operation performed by a user on a terminal device to select one or more primary operators. These operations can be user interface interactions such as clicking, dragging, and dropping. For example, a user clicks the "Application Date" button on the application interface to perform the first selection operation.

[0055] The first text input operation refers to the operation of a user inputting text information on a terminal device, which is used to identify and determine one or more first-level operators. For example, a user enters the text information "patent value" in the search bar to identify the first-level operator "patent value".

[0056] The indicator sharing platform is an online platform for storing and sharing indicator data, from which users can import first-level operators for generating custom indicators.

[0057] The first chatbot interaction interface allows users to perform a first input operation by communicating with the chatbot, thereby identifying and determining first-level operators. For example, a user may communicate with a chatbot and provide instructions for selecting indicators. Semantic extraction and reasoning are performed using a large language model to determine appropriate first-level operators.

[0058] Semantic extraction is the process of identifying and understanding meaning from the input information provided by the user to determine the first-level operators.

[0059] The first input information may be, for example, text information, audio information, image information, etc. As an example, natural language processing technology is used to understand the text information input by the user to determine the primary operator. For example, after extracting the semantic meaning of "screen European and US patents for me", the corresponding primary operator "receiving office" is matched.

[0060] In the following examples, four methods are used to illustrate how to determine at least one primary operator.

[0061] The first method is to provide a selection interface containing multiple indicators. Users select primary operators by clicking checkboxes on the interface. For example, if a user selects "Application Date" and "Patent Value" as primary operators, these selections will be used to generate custom indicators.

[0062] The second method: The user enters the following text information in the search box (or combo box): "value". Through keyword matching, it is recognized that the user wants to use "patent value" as the first-level operator.

[0063] The third method: A user imports two custom metrics from the metric sharing platform, such as "Average Patent Value per Inventor" and "Top 10 Applicants by Patent Number." These two imported custom metrics become first-level operators and can be used to generate new custom metrics.

[0064] Method 4: A user opens the first chatbot interface and asks the chatbot: "I need to calculate the total value of all patents." After semantic extraction, the chatbot obtains the first-level operator "Patent Value" and selects "Sum" as the data processing rule for "Patent Value."

[0065] These examples show different ways how the user can determine at least one first-level operator.

[0066] Thus, at least one first-level operator is determined, thus providing a basis for generating custom indicators.

[0067] Specifically, a terminal device can receive a user's first selection of an indicator set. The user can select one or more indicators from the existing indicator set, and these selected indicators will become the basis for the first-level operator. This method allows users to directly select from the list of available indicators and use them as the basis for generating custom indicators.

[0068] Another method is to receive a first text input operation from the user through a terminal device. Based on the text input by the user, one or more first-level operators are identified and generated. The user can enter a specific operator name or description, and the corresponding first-level operator is identified and generated based on the input content. This method allows the user to describe the required first-level operator in text form, making it easy to quickly locate the corresponding first-level operator.

[0069] In addition, users can import one or more first-level operators from indicator sharing platforms. These sharing platforms may contain first-level operators created and shared by other users. Users can choose to import these first-level operators and use them for custom indicator generation.

[0070] In addition, a first chatbot interaction interface can be provided to receive first user input. This information may include a description of the user's needs or a statement of a problem. Semantic extraction is performed to determine one or more primary operators from the user's input. This approach allows users to interact with the system in natural language, making it more intelligent and user-friendly.

[0071] The advantage of this is that users can determine the first-level operators through selection operations, text input, import from the indicator sharing platform, or natural language interaction. This variety of input methods is more user-friendly and meets the needs and habits of different users. Users can determine the first-level operators in various ways according to their own needs and preferences; it lowers the technical threshold, allowing users who are not familiar with the technical field to easily create custom indicators, thereby improving the system's usability and wide applicability; using the first chatbot interaction interface for semantic extraction is more intelligent, can understand the user's natural language input, and extract useful information from it to determine the first-level operator, increasing the convenience of user interaction.

[0072] In some possible implementations, the process of determining the data processing rule corresponding to each first-level operator in at least one first-level operator includes: performing the following processing for each first-level operator: determining a data processing rule in the data processing rule set as the data processing rule for the first-level operator based on the data processing rule set corresponding to the data type of the first-level operator; the data type of the first-level operator is numeric, character, time, or geography.

[0073] Each first-level operator has a specific data type and requires a data processing rule to be specified (or a default value to be used) to generate a custom indicator.

[0074] Data types describe the nature of the information represented by a primary operator. They can be numeric, character, time, or geographic. Numerical data represents information that can be expressed numerically, such as a patent's value. Character data includes text or string information, such as applicant information and classification numbers. Time data represents date and time information, such as the filing date. Geographic data represents information related to a geographic location, such as the receiving office and the applicant's address.

[0075] Data processing rules are specifications that define how to process and calculate the data of the first-level operators, which depends on the data type of the first-level operators.

[0076] For example, suppose you want to generate a custom metric called "Average number of patents per inventor." To calculate the average number of patents per inventor, you need to calculate the number of patents and the number of inventors separately.

[0077] First, select “Number of Patents” and “Inventor” as the first-level operators.

[0078] The data type of "Number of Patents" is numeric. Select "Count" as the data processing rule to obtain the number of patents.

[0079] The data type of "Inventor" is character. Select "Duplicate Count" as the data processing rule to obtain the number of inventors.

[0080] Applying the arithmetic operator "division" between the two first-level operators "number of patents" and "inventors" generates a custom metric "average number of patents per inventor".

[0081] Therefore, the process of determining the data processing rules for each first-level operator includes the following steps: First, determine the data type of each first-level operator, which can be a number, character, time or geography (also known as region). This is to understand what type of data the operator will process. Secondly, for each data type, there is a set of data processing rules that define how to process data of this data type, including various calculation methods, formatting methods, etc. According to the data type of the first-level operator, a data processing rule is selected from the corresponding data processing rule set and determined as the data processing rule of the first-level operator. The way to determine one of the data processing rules from the data processing rule set is, for example, user-specified or using a default data processing rule. This process ensures that each first-level operator has a suitable data processing rule, which is selected according to its data type to ensure that the data is processed and calculated according to the correct rules.

[0082] The advantage of this is that the corresponding data processing rules are selected according to the data type of the first-level operator, ensuring data type matching and avoiding inappropriate data processing; multiple data processing rule options are provided according to the data type, which increases flexibility and can adapt to different types of data analysis needs; the matching of data processing rules ensures the accuracy of data processing, and each first-level operator is calculated according to the appropriate rules, which improves the accuracy of analysis; for users who have custom indicator generation needs, it is easier to select and configure the data processing rules of the first-level operators, which improves user-friendliness.

[0083] In some possible implementations, after determining at least one first-level operator and determining the data processing rules corresponding to each of the at least one first-level operator, the method further includes: performing the following processing for one or more first-level operators in the at least one first-level operator: determining at least one second-level operator corresponding to the first-level operator, and determining the secondary filtering rules corresponding to each of the at least one second-level operator; the second-level operator is an indicator of a basic category or a custom category; wherein, generating a custom indicator based on the data processing rules corresponding to at least one first-level operator and each first-level operator includes: generating the custom indicator based on the data processing rules corresponding to at least one first-level operator and each first-level operator, and the secondary filtering rules corresponding to at least one second-level operator and each second-level operator.

[0084] Apply secondary operators to some or all of the primary operators for secondary filtering. Secondary operators are data manipulation units that perform further processing or filtering based on primary operators, used for data filtering or processing. For example, secondary operators can be used to filter the number of patents under a specific classification code, filter products with sales greater than 1,000 in a certain city, or retrieve a company's stock price for a specific time period.

[0085] Secondary filtering rules are used to determine which data to retain or delete. They are associated with secondary operators to further refine data filtering. For example, secondary filtering rules might include filtering out data under a specific classification code, filtering out data with sales greater than 1,000, or filtering out stock price data within a specific time period.

[0086] Secondary filtering involves further screening or processing the dataset corresponding to the primary operator using the secondary operator and its secondary filtering rules. For example, the dataset corresponding to the primary operator includes patent application data corresponding to all classification codes. Secondary filtering can be used to obtain a subset of data under a specific classification code.

[0087] After the introduction of secondary operators, custom metrics are determined by the primary operators and their data processing rules, and the secondary operators and their secondary filtering rules. Some or all primary operators may have corresponding secondary operators. In other words, each secondary operator performs secondary filtering on one of the primary operators.

[0088] In some embodiments, when a user clicks on an indicator name in the indicator list, or hovers the mouse over an indicator name in the indicator list, the indicator summary is displayed in the form of a card or a floating layer, and the display content of the indicator summary includes the indicator formula. When displaying the indicator formula corresponding to the custom indicator, only the first-level operator and its data processing rules and the operation rules between the first-level operators can be displayed. In other embodiments, when displaying the indicator formula, the first-level operator and its data processing rules, the second-level operator corresponding to the first-level operator and its secondary filtering rules, and the operation rules between the first-level operators can be displayed. As an example, an indicator formula is: [Number of patents (sum) & <Accepting Office: UK, US>] / Inventor (de-duplication count). Among them, the number of patents and inventors are first-level operators, and the accepting office is the second-level operator.

[0089] Therefore, after determining the primary operators and their data processing rules, one or more of the primary operators are further updated, and secondary filtering is performed by adding at least one secondary operator and corresponding secondary filtering rules. Specifically, at least one secondary operator corresponding to one or more determined primary operators is determined. These secondary operators can be generated based on the computing requirements of the primary operators and are used to further filter, process or calculate the data set to be processed. For each determined secondary operator, a corresponding secondary filtering rule is determined. These secondary filtering rules specify how to use the secondary operator to further filter or process the data set to be processed corresponding to the primary operator. By applying the secondary operator and its secondary filtering rule, the data set to be processed corresponding to the primary operator can be updated in a secondary filtering manner, which means that the secondary operator and the secondary filtering rule are used to filter or process the data set to be processed, so that when executing the data processing process corresponding to the primary operator, the data set to be processed used is no longer the original data set to be processed, but the data set to be processed after secondary filtering.

[0090] The advantage of doing this is that by introducing secondary operators and secondary filtering rules through secondary filtering, users can control the range of a single primary operator, which only affects a single primary operator without affecting the analysis of the entire data. Therefore, it provides more refined data screening and analysis capabilities, and users can more finely control the analysis scope of data, thereby improving the accuracy and flexibility of data analysis; more complex data operations can be performed, such as calculating relative indicators, filtering data under specific conditions, etc.; users can add or modify secondary operators and secondary filtering rules at any time as needed to adapt to different data analysis scenarios, which increases the flexibility and customizability of data analysis; through fine data control and customized processing, users can obtain analysis results that meet their needs more quickly, thereby improving the efficiency of data analysis.

[0091] In some possible implementations, the process of determining at least one secondary operator corresponding to the primary operator includes: receiving one or more second selection operations of the user for the indicator set through the terminal device to determine one or more secondary operators; or, receiving one or more second text input operations of the user through the terminal device, and identifying one or more secondary operators based on the received one or more second text input operations; wherein, the process of determining the secondary filtering rule corresponding to each secondary operator in at least one secondary operator includes: (static filtering) receiving the user's third text input operation and / or third selection operation through the terminal device to determine the secondary filtering rule corresponding to the secondary operator; or, (dynamic filtering) receiving the user's type selection operation for the judgment type set through the terminal device to determine the judgment type corresponding to the secondary operator; receiving the user's fourth text input operation and / or fourth selection operation through the terminal device, and combining the judgment type to determine the secondary filtering rule corresponding to the secondary operator.

[0092] In some possible implementations, receiving a user's type selection operation for a judgment type set through the terminal device to determine the judgment type corresponding to the secondary operator includes: displaying the judgment type set corresponding to the data type of the secondary operator through the terminal device; the data type of the secondary operator is a number, character, time or geography; receiving a user's type selection operation for a judgment type in the judgment type set, and using the judgment type selected by the user as the judgment type corresponding to the secondary operator.

[0093] In some possible implementations, the judgment type set corresponding to the numerical data type (its secondary operator) includes one or more of the following: greater than, less than, equal to, not greater than, not less than; and / or, the judgment type set corresponding to the time data type (its secondary operator) includes one or more of the following: before, after; and / or, the judgment type set corresponding to the character data type (its secondary operator) includes one or more of the following: contains, does not contain; and / or, the judgment type set corresponding to the geography data type (its secondary operator) includes one or more of the following: contains, does not contain.

[0094] In some possible implementations, when the data type of the secondary operator is time, the terminal device can also receive the user's unit text input operation and / or unit selection operation to determine the time unit; based on the fourth text input operation and / or the fourth selection operation, combined with the judgment type and time unit, the secondary filtering rule corresponding to the secondary operator is determined.

[0095] In some possible implementations, when the number of secondary operators is greater than one, the to-be-processed data set corresponding to the primary operator is updated in a secondary filtering manner based on the multiple secondary operators corresponding to the primary operator, their corresponding secondary filtering rules, and the operation rules between the secondary operators. The operation rules between the secondary operators may be, for example, AND, OR, or NOT.

[0096] Secondary filtration may include static filtration and / or dynamic filtration.

[0097] To illustrate static filtering, suppose an e-commerce website's sales data is analyzed. First, the 2022 sales data is selected as the dataset to be processed (also called the dataset to be analyzed). Next, the user selects a first-level operator, "Product Sales." The user then selects the data processing rule "Sum," which sums the sales of each product to obtain the total sales. The user then makes a second selection, selecting a second-level operator, "Sales Region." Then, the user makes a third selection, selecting "China" and "Germany," implementing the following secondary filtering rule: From the 2022 sales data, select only the sales data for China and Germany. After applying the second-level operator and its secondary filtering rule to the first-level operator, when the data processing for the first-level operator is executed, the dataset to be processed corresponding to the first-level operator "Product Sales" is updated to include only the sales data for China and Germany. The data processing result for the first-level operator (i.e., the sum result for "Product Sales") is no longer the total sales for all regions, but the total sales for China and Germany.

[0098] To illustrate dynamic filtering, assume that patent data analysis is performed, and first the authorized invention patent data for 2022 is filtered out as the data set to be processed. Next, the user selects a first-level operator, namely "Patent Value". The user continues to select the data processing rule "Sum". After that, the user selects "Number of Patents" and "Applicant" from the indicator list as second-level operators, and selects "AND (or, AND)" as the operator between the two. For the second-level operator "Number of Patents", the user selects "Greater than" as the judgment type of "Number of Patents" from the judgment type list, and enters "100" as the dynamic filtering threshold to implement the secondary filtering rule corresponding to the second-level operator: filter data with a number of patents greater than 100. For the second-level operator "Applicant", the user selects "Top" as the judgment type of "Applicant" from the judgment type list, and enters "10" as the dynamic filtering threshold to implement the secondary filtering rule corresponding to the second-level operator: filter data of the top 10 applicants. Finally, based on the two secondary operators (i.e., "Patent Quantity" and "Applicant") corresponding to the primary operator (i.e., "Patent Value") and their corresponding secondary filtering rules, the processed dataset corresponding to the primary operator is updated using a secondary filtering method: from the 2022 authorized invention patent data, the data corresponding to the top 10 applicants with more than 100 patents are filtered out. The data processing result corresponding to the primary operator (i.e., the summation result corresponding to "Patent Value") is the sum of the patent values ​​of the authorized invention patents in 2022 corresponding to the top 10 applicants with more than 100 patents.

[0099] Therefore, after determining the primary operator, at least one secondary operator and the secondary filtering rule corresponding to each secondary operator are further determined, so as to update the to-be-processed data set corresponding to the primary operator.

[0100] First, you need to determine at least one secondary operator, which can be achieved in different ways:

[0101] The first method: The user performs the second selection operation through the terminal device and selects one or more secondary operators.

[0102] The second method: the user performs a second text input operation through the terminal device, and one or more secondary operators are automatically recognized from the text.

[0103] Next, determine the secondary filtering rules corresponding to each secondary operator, which can also be achieved in different ways:

[0104] The first method (static filtering): The user performs a third text input operation and / or a third selection operation on the terminal device to determine the secondary filtering rules. For example, a specific value may be entered or an existing option may be selected from a list. This static filtering method allows users to statically filter based on the attributes of the parameter values. For example, the number of patents filed by a specific applicant in a specific country. In this case, the primary operator is "Number of Patents" and the secondary operator is "Accepting Office." The secondary filtering rule is to filter out data with the receiving office being Europe and the United States.

[0105] The second method (dynamic filtering): The user performs a type selection operation and selects a judgment type from the judgment type set (for example, greater than, less than, equal to, before, after, included, not included) to determine the judgment type corresponding to the secondary operator. Then, the user performs a fourth text input operation and / or a fourth selection operation through the terminal device, and determines the secondary filtering rules in combination with the judgment type. This dynamic filtering method allows users to dynamically filter according to the attributes of the parameter value, for example, to filter out applicants with more than 100 patents, or to filter out the top 50 merchants in terms of sales.

[0106] Finally, the determined secondary filtering rules are applied to update the dataset to be processed corresponding to the primary operator in a secondary filtering manner. That is, the secondary operator and secondary filtering rules are used to further filter or process the dataset to be processed corresponding to the primary operator (for example, patent application data for a specific applicant) to update the dataset to be processed. Because the dataset to be processed has been updated, the data processing results of the primary operator will also change accordingly if the data processing process is executed.

[0107] The advantage of this is that users can flexibly select or input secondary operators and secondary filtering rules on the terminal device according to their specific needs and preferences, which increases the user's ability to customize analysis; supports multiple input methods, including selection operations and text input operations, which increases the way users interact with the system and makes it more convenient; users can choose secondary filtering methods such as static filtering or dynamic filtering to meet different data analysis needs; introduces a judgment type set, and users can choose different judgment types according to their needs, so as to better control and customize secondary filtering rules; users can add or modify secondary operators and rules at any time as needed to adapt to different data analysis scenarios, which increases the flexibility and customizability of data analysis; through fine data control and customized processing, users can obtain analysis results that meet their needs more quickly, thereby improving the efficiency of data analysis.

[0108] In some possible implementations, the process of determining the operation rules between multiple first-level operators includes: receiving a fifth text input operation and / or a fifth selection operation from the user to determine the operation rules between the multiple first-level operators; or, providing a second chat robot interaction interface through a terminal device to receive one or more second input information from the user; performing semantic extraction on each second input information in the one or more second input information to determine the operation rules between the multiple first-level operators.

[0109] The process of determining the operation rules between the second-level operators is similar to the process of determining the operation rules between the first-level operators, and will not be repeated here.

[0110] Operation rules are used to specify the operation mode and priority between primary operators. They can be arithmetic operations, comparison operations, or logical operations, and are used to process and analyze data.

[0111] A text input operation is an operation or instruction entered by the user through a text input box. A selection operation is an operation or parameter selected by the user through a drop-down list or tab. For example, the user selects "division" from a drop-down list as the operator between two first-level operators.

[0112] The second chatbot interaction interface is provided by a terminal device, allowing users to converse and interact with the virtual assistant or chatbot. For example, users can provide specific data analysis requirements to the chatbot, which then extracts and infers semantic information to generate appropriate operational rules. Secondary input information can be, for example, text, voice, or image information.

[0113] Therefore, when determining the operation rules between the first-level operators, the user's input operation and / or selection operation can be received, or semantic extraction can be performed through the second chat robot interaction interface.

[0114] The user can perform a fifth text input operation and / or a fifth selection operation through the terminal device to input and / or select one or more operation rules for defining the operation relationship between the first-level operators. These operation rules can include operation levels (e.g., brackets) and arithmetic operators (e.g., addition, subtraction, multiplication, division, etc.), comparison operators (e.g., greater than, less than, equal to, etc.), and logical operators (e.g., and, or, not, etc.). The user's input and selection will be used to construct the operation rules.

[0115] Alternatively, a second chatbot interaction interface is provided via the terminal device, through which the user can provide second input information. Each second input information undergoes semantic extraction to identify the user's intent and computational requirements. Semantic extraction understands the user's utterances and converts them into specific operators and computation levels, thereby constructing computational rules.

[0116] Operators and operation levels are used to construct operation rules to define the operation relationships between first-level operators. These rules will be used for further processing and calculation of custom indicators.

[0117] The advantage of this is that users can define the operation relationship between primary operators through intuitive text input operations, selection operations or natural language interaction interfaces, which is more user-friendly; through semantic extraction, the user's needs and intentions can be better understood, so that the user input can be accurately converted into appropriate operation rules, which improves the user experience; users can choose from a variety of operation rules, including arithmetic, comparison and logical operators to meet different operation needs, which increases the diversity and flexibility of operations; users do not need to have an in-depth understanding of programming or complex operation rule syntax, and can define operation rules in an intuitive way, which lowers the threshold for use; through user text input, selection or semantic extraction, the operation rules and rules determined are usually more accurate and controllable, avoiding human errors or unclear definitions.

[0118] In some possible implementations, the data processing rules corresponding to the numerical data type (the first-level operator) include one or more of the following: average, sum, count, deduplication count, maximum value, minimum value, numerical proportion and accumulation; and / or, the data processing rules corresponding to the character data type (the first-level operator) include one or more of the following: count, deduplication count; and / or, the data processing rules corresponding to the time data type (the first-level operator) include one or more of the following: count, deduplication count; and / or, the data processing rules corresponding to the geographic data type (the first-level operator) include one or more of the following: count, deduplication count.

[0119] In some possible implementations, the operation rules between the first-level operators include an operation level and one or more of the following operators: an arithmetic operator, a comparison operator, and a logical operator, where the operation level is used to indicate the priority of the operation; and / or, the operation rules between the second-level operators include a logical operator.

[0120] Thus, data processing rules are determined for the primary operators of each data type, and operation rules are determined for the primary operators and the secondary operators.

[0121] The metric represented by a specific first-level operator is of numeric type. This means that the metric contains numeric data, such as integers or decimals. For numeric metrics, a range of data processing rules are available. These rules include the following.

[0122] Average: Calculates the average of the values.

[0123] Sum: Adds the values ​​to get the total.

[0124] Count: Counts the number of values.

[0125] Duplicate count: After removing duplicate values, calculate the number of remaining values.

[0126] Maximum: Find the largest value among the values.

[0127] Minimum: Find the smallest value among the numbers.

[0128] Value Proportion: Calculates the proportion of a value in the total value.

[0129] Accumulation: Perform cumulative calculations on the values ​​to obtain the cumulative value.

[0130] When the first-level operators are character, time, and geography data types, the data processing rules include counting and deduplication counting. Deduplication is used to calculate the quantity, and deduplication counting is used to calculate the quantity after deduplication.

[0131] In addition, the selection of operation rules between the first-level operators and the second-level operators is also provided.

[0132] Operation level: For example, brackets are used to determine the priority of operations.

[0133] Arithmetic operators: such as addition, subtraction, multiplication, division, exponentiation, and remainder, are used to perform basic mathematical operations on numerical values.

[0134] Comparison operators: such as greater than, less than, equal to, not greater than, and not less than, are used to compare the relationship between different values.

[0135] Logical operators: such as AND, OR, and NOT, are used to perform logical operations.

[0136] Users can select appropriate data processing rules and calculation rules based on their specific analysis needs to generate custom indicators for numerical data analysis.

[0137] The advantage of this is that users can choose appropriate data processing rules and operation rules according to their specific numerical data analysis needs, which increases the diversity of numerical data analysis and enables users to process numerical data more flexibly; provides a variety of mathematical and logical operators to choose from, allowing users to perform complex numerical and logical operations to meet different analysis scenarios; by providing common data processing rules such as averaging, summing, and counting, users can easily perform accurate numerical statistics and analysis without manual calculations; users can select accumulation rules to generate customized cumulative calculation indicators, which is very useful when tracking data trends and cumulative effects; different data processing rules and operation rules are suitable for different analysis needs, allowing users to flexibly customize custom indicators to meet data analysis requirements in different scenarios.

[0138] In a specific application scenario, an embodiment of the present application provides a method for generating a custom indicator, the method comprising: determining at least one first-level operator and its corresponding data processing rules and operation rules between first-level operators; the first-level operator is an indicator of a basic category or a custom category; the operation rules between first-level operators include an operation level and one or more of the following operators: arithmetic operators, comparison operators, and logical operators, and the operation level is used to indicate the priority of the operation; for one or more first-level operators, performing the following processing: determining at least one second-level operator corresponding to the first-level operator and its corresponding secondary filtering rules; the second-level operator is an indicator of a basic category or a custom category; generating a custom indicator based on at least one first-level operator and its corresponding data processing rules, at least one second-level operator and its corresponding secondary filtering rules, the custom indicator being an indicator of a custom category; wherein the custom indicator can act as a first-level operator to generate a new custom indicator.

[0139] Among them, the process of determining at least one first-level operator includes: receiving one or more first selection operations of the user for the indicator set through the terminal device to determine one or more first-level operators; or, receiving one or more first text input operations of the user through the terminal device, and identifying one or more first-level operators based on the received first text input operations; or, importing one or more first-level operators through the indicator sharing platform; or, providing a first chat robot interaction interface through the terminal device to receive one or more first input information of the user; performing semantic extraction on each first input information to determine one or more first-level operators.

[0140] The process of determining the data processing rules for each first-level operator includes: for each first-level operator, according to the data processing rule set corresponding to the data type of the first-level operator, determining one of the data processing rules as the data processing rule of the first-level operator; the data type of the first-level operator is numeric, character, time or geography; the data processing rules corresponding to the numeric data type include one or more of the following: average, sum, count, deduplication count, maximum value, minimum value, numeric proportion and accumulation.

[0141] Among them, the process of determining at least one secondary operator corresponding to the primary operator includes: receiving one or more second selection operations of the user for the indicator set through the terminal device to determine one or more secondary operators; or, receiving one or more second text input operations of the user through the terminal device, and identifying one or more secondary operators based on the received second text input operations.

[0142] Among them, the process of determining the secondary filtering rule corresponding to each secondary operator includes: receiving the user's third text input operation and / or third selection operation through the terminal device to determine the secondary filtering rule corresponding to the secondary operator; or, receiving the user's type selection operation for the judgment type set through the terminal device to determine the judgment type corresponding to the secondary operator; receiving the user's fourth text input operation and / or fourth selection operation through the terminal device, combined with the judgment type, to determine the secondary filtering rule corresponding to the secondary operator; the judgment type includes one or more of greater than, less than, equal to, before, after, contain and not contain.

[0143] Among them, the process of determining the judgment type corresponding to the secondary operator includes: displaying the judgment type set corresponding to the data type of the secondary operator through the terminal device; the data type of the secondary operator is a number, character, time or geography; receiving the user's type selection operation for one of the judgment types, and using the judgment type selected by the user as the judgment type corresponding to the secondary operator.

[0144] Among them, the process of determining the operation rules between the first-level operators includes: receiving the user's fifth text input operation and / or fifth selection operation to determine the operation rules between the first-level operators; or, providing a second chat robot interaction interface through the terminal device to receive one or more second input information of the user; performing semantic extraction on each second input information to determine the operation rules between the first-level operators.

[0145] For example, in the field of patent data analysis, after selecting "Applicant" as the first-level operator, you can further filter this first-level operator and select a variety of different second-level operators. For example, if you further select "Specific IPC Classification Number" as the second-level operator, the filtering means: "The number of patents of the current applicant under the "specific IPC Classification Number"." After filtering, "The number of patents of the "current applicant" under this "specific IPC Classification Number"" is formed into a new operator, which can be used in function operations with other operators.

[0146] After selecting a primary operator (e.g., patent value), it can be calculated with another indicator (e.g., inventor) through the operation rules. Specifically, "patent value" / "inventor" can represent the average patent value produced by each inventor.

[0147] Furthermore, the above calculation can also be divided by the first-level operator "application year", that is, "patent value" / "inventor" / "application year", which can represent the average patent value produced by each inventor each year.

[0148] By supporting operatorization for basic and custom indicators, people with data analysis needs (for example, patent analysts) can flexibly perform data statistics and analysis based on different data types and calculation requirements. Whether it is counting and deduplicating the number of patents, or advanced calculations such as the average, sum, maximum, and minimum values ​​of patent data, they can all be quickly and accurately implemented through the indicator editor, which can improve data analysis efficiency. At the same time, it also provides more analysis dimensions and methods to help patent analysts better understand and apply patent data. In addition, since this method has the characteristics of SQL visualization, patent analysts can use this tool to query, filter, and analyze data. For example, according to specific needs and conditions, through the combination of filtering operations and operators, qualified patent data can be quickly obtained to provide customers with accurate and targeted patent information and analysis reports. For patent analysts, this can greatly improve work efficiency and accuracy, and meet the market demand for efficient patent analysis services.

[0149] Taking the patent data analysis scenario as an example, user A is a patent analyst. In the initial stage, after one filtering (for example, constructing a search formula and executing it), user A obtains the data set to be processed, namely, global authorized invention patent data from 2012 to 2022.

[0150] Through the user graphical interface provided by the indicator editor, user A viewed the basic indicator list and selected "number of citations" (an indicator of the basic category) as the first-level operator. User A then viewed the custom indicator list and selected "number of patents" (an indicator of the custom category) as the first-level operator.

[0151] In addition, user A also entered "validity period", and the indicator editor identified and matched the "patent validity period" indicator as the third first-level operator.

[0152] For “Number of Patents”, User A selects “Count” as the data processing rule.

[0153] For "Number of citations", User A selected "Average" to assess the influence of these patents.

[0154] For "Patent Validity Period", User A selects "Average" to view the average validity period of patents in the field.

[0155] User A wants to further analyze the technological contributions of different applicants in a specific technical field. Therefore, for each of the three primary operators, user A sets two secondary operators. User A selects "Applicant" as the secondary operator and adds "Classification Number" as the second secondary operator through text input.

[0156] For "Applicant", user A selected the judgment type of "Include" and entered keywords of a specific applicant to filter specific patents.

[0157] For "Classification Number", User A selected the judgment type of "Include" and entered a specific patent classification number.

[0158] User A chooses through the interface to take the logarithm of the "number of patents" (with the natural number e as the base), then multiplies it by the "number of citations" and then by the "patent validity period" to evaluate the market acceptance of the technology field.

[0159] Ultimately, the indicator editor generates a custom indicator based on User A's selections and inputs. User A names it "Market Acceptance." This custom indicator will help User A assess market trends, R&D activity, and market potential in a specific technology field. This custom indicator generation method enables patent analysts like User A to conduct in-depth and detailed assessments of trends and potential in a technology field without the need for complex programming.

[0160] For example, in the e-commerce data analysis scenario, User B, an e-commerce data analyst, uses the e-commerce data analysis system's indicator data editor to generate custom indicators to analyze the sales performance of their online store. Initially, after a single filter, User B obtains the dataset to be processed: the online store's e-commerce sales data for 2022.

[0161] User B selects "Sales Amount" and "Purchase Quantity" as the first-level operators.

[0162] In addition, user B also entered "refund amount". The indicator editor recognized and added the "refund amount" indicator as the third first-level operator.

[0163] For the three numerical indicators "sales amount", "refund amount" and "purchase quantity", user B selected "sum" as the data processing rule.

[0164] User B decides to further analyze which product categories have a higher sales ratio in total sales, and selects "Product Category" as the second-level operator of the three first-level operators.

[0165] For Product Category, User B selects the Include rule and enters Electronic Products as the filter condition.

[0166] User B enters the following input through interaction with the second chatbot: sales minus refunds divided by sales volume. The second chatbot recognizes and sets the corresponding calculation rule: sales minus refunds divided by purchase volume.

[0167] After all these steps are completed, the indicator editor will generate a custom indicator "Average Sales Price" based on User B's selections and inputs, which represents the purchasing power level of the online store's customers.

[0168] In some embodiments, the method further includes: performing a formula test on the indicator formula corresponding to the custom indicator to obtain a formula test result, wherein the formula test result indicates whether the formula is correct or incorrect, or indicates a pass or fail.

[0169] Therefore, formula detection is performed on the indicator formula corresponding to the custom indicator, which not only ensures the accuracy of the data, but also ensures the stability and reliability of the data processing process. Specifically, the formula detection process will conduct an in-depth analysis of the indicator formula corresponding to the custom indicator to ensure that the indicator formula avoids mathematical errors such as division by 0 and other uncalculatable problems (for example, logical errors). When such problems exist in the indicator formula, the formula detection result will immediately feedback the formula error. The benefit of this is that it not only improves the accuracy of data analysis, but also greatly enhances the user-friendliness of the platform; through timely formula detection, users can quickly correct potential errors, thereby ensuring that the obtained analysis results are highly reliable, saving a lot of time and resources; in addition, the overall stability of the data analysis platform is improved, so that it can still maintain efficient and stable operation when facing complex data processing tasks.

[0170] This application does not limit the method of performing formula detection, for example, a formula detection model based on deep learning can be used.

[0171] In some embodiments, the method further includes: when the formula is incorrect, generating improvement prompt information corresponding to the custom indicator and displaying it on the terminal device.

[0172] Therefore, the real-time feedback mechanism provides users with immediate guidance, helping them to correct and optimize custom indicator formulas more quickly and accurately. In addition, these prompts are not limited to simple error indications, but also include specific modification suggestions, common error cases and related mathematical principle explanations, which enhance users' understanding of data and formula logic, thereby avoiding making the same mistakes again in the future. The advantage of this is that it improves the user experience. Even users who are not familiar with complex mathematical formulas can customize and adjust formulas in the indicator editor; at the same time, through intelligent prompts, users are spared the trouble of frequently consulting manuals or seeking help from support personnel; this real-time feedback and suggestion mechanism not only enhances the platform's ease of use, but also helps to cultivate users' data analysis capabilities, further expanding the use scenarios and audience range of the indicator editor.

[0173] See FIG2 , which is a schematic diagram of an interface of an indicator editor provided in one embodiment of the present application.

[0174] The present application provides an indicator editor, which is used to provide a custom indicator generation interface. The custom indicator generation interface includes: a combo box, which is used to receive a user's first text input operation and / or a first selection operation to determine at least one first-level operator; a formula editing box, which is used to display each first-level operator determined by the user in the form of an operator control, and receive a user's rule selection operation for a drop-down list box in the operator control to determine the data processing rule corresponding to each first-level operator; an indicator generation button, which is used to generate a custom indicator according to at least one first-level operator and its corresponding data processing rule after being clicked, wherein the custom indicator is an indicator of a custom category; wherein the custom indicator can act as a first-level operator to generate a new custom indicator.

[0175] In some possible implementations, the operator control further includes: a secondary filter, configured to determine at least one secondary operator corresponding to the primary operator and its corresponding secondary filtering rule.

[0176] In some possible implementations, the custom indicator generation interface further includes: a tooltip control, configured to display prompt information corresponding to the formula editing box when a mouse hover operation is received.

[0177] A combo box is a composite control in a computer graphical user interface that consists of a list or drop-down list and a single-line editable text box. It allows users to enter a value directly by typing on the keyboard or select a value from a list. The combo box allows the user to perform a first text input operation and / or a first selection operation to determine at least one primary operator. The user can select one or more primary operators from the available basic category or custom category indicators as the basis for constructing the custom indicator. A combo box can, for example, include a text box and a list box, supporting both the functions of a list box and user input of text. As an example, the user can enter or select "applicant" in the combo box.

[0178] The formula edit box displays each user-selected first-level operator and its corresponding data processing and calculation rules. Here, users can set the rules and calculation methods for each first-level operator. For example, a user could set a custom formula like "Sales Amount - Refund Amount" in the formula edit box.

[0179] The formula edit box displays each user-defined first-level operator as an operator control. Within the operator control, users can select the corresponding data processing rule for each first-level operator. This data processing rule instructs the data analysis system how to perform the data processing associated with the selected operator. Different data processing rules can be used for different types of metrics, such as numerical, character, time, or geographic metrics.

[0180] The formula edit box is also used to receive a fifth text input operation and / or a fifth selection operation from the user to determine the operation rules between the first-level operators. The user can select different operation rules, such as the operation level and arithmetic operators, comparison operators, or logical operators, to define the data relationship, operation order, and operation priority between the first-level operators.

[0181] An operator control is an interface element within a formula edit box that displays and manages each primary operator, including a drop-down list. Users can set data processing rules for operators in an operator control. For example, users can select "Sum" as the data processing rule in an operator control.

[0182] Operator controls can also include secondary filters for users to further refine data processing and filtering. Through secondary filters, users can determine at least one secondary operator corresponding to the primary operator and the corresponding secondary filtering rules to help users perform secondary filtering on the processed data set. As an example, users can select "Product Category" as the secondary operator in the secondary filter and set the secondary filtering rule to "Include" + "Mobile Phone OR Computer OR Tablet OR Watch OR Bracelet" to filter records related to "Electronic Products" in the sales data.

[0183] The dataset to be processed is the raw data that users want to analyze and process. For example, the dataset to be processed includes e-commerce sales data from online stores in 2022, including information such as sales volume, purchase quantity, and refund amount.

[0184] A tooltip is an interface element that displays additional information or instructions when the user hovers over it. In the indicator editor, tooltips are used to provide relevant information for primary operators, helping users better understand and select operators. For example, when a user hovers over the "Sales" operator in the operator control, the tooltip displays a message such as "Sales represents the sales amount of an online store." For example, the tooltip icon is an "i" (as shown in Figure 2).

[0185] The advantage of this is that it provides an intuitive interface where users can easily select first-level operators, define data processing rules and operation rules without programming or complex operations; the formula editing box displays the first-level operators and rules selected by the user in the form of operator controls, so that users can clearly see how the custom indicator is constructed; users can select the appropriate data processing rule corresponding to the corresponding data type from the drop-down list box to ensure that the selected data processing rule is applicable to the data type of the selected first-level operator, thereby improving the accuracy of data processing; users can select different operation rules according to their needs to define the data relationship between first-level operators and explore different data analysis methods; users can instantly see how their choices affect the generation of custom indicators, so as to quickly understand whether the results meet their actual needs; by providing a user-friendly interface, the chance of user-defined errors is reduced and the accuracy of data analysis is improved; the creation and editing of custom indicators becomes faster and easier, which improves work efficiency and enables users to conduct data analysis and decision-making more quickly.

[0186] Secondary filters are set in the operator control, allowing users to manage primary operators and their associated secondary operators and secondary filtering rules. Secondary operators are used to further refine or filter the data of primary operators. Users define secondary filtering rules for each secondary operator through secondary filters. These secondary filtering rules instruct the system how to use the secondary operator to process the data of the primary operator. Secondary filtering rules can include conditions, operations, and parameters. After determining at least one secondary operator and corresponding secondary filtering rule, the system will update the processed data set corresponding to the selected primary operator in a secondary filtering manner. The processed data set corresponding to the primary operator will be affected by the secondary operator and the secondary filtering rules. The advantage of this is that it allows users to use secondary operators and secondary filtering rules to further accurately process the data of the primary operators without affecting the global analysis data, thereby obtaining more specific and useful analysis results; users can filter out unnecessary data by defining secondary filtering rules, thereby reducing the noise and complexity of the data and improving the quality of data analysis; data can be further refined to gain a deeper understanding of the data situation in specific aspects and provide more dimensional support for decision-making; by providing a user-friendly interface, the chance of user-defined errors is reduced and the accuracy of data processing is improved; users are allowed to manage primary and secondary operators and corresponding rules on one interface, making data analysis and processing faster and easier.

[0187] The tooltip control is designed to provide users with an informative tooltip to help them understand the usage and functionality of the formula edit box. Hovering the mouse over the tooltip control triggers and displays a tooltip. The tooltip contains text describing the description, purpose, and function of the primary operator (or its corresponding secondary operator) in the formula edit box. This information helps users better understand the meaning of each step and component of indicator editing, enabling them to make informed decisions when selecting and configuring operators. Hovering the mouse over the tooltip control triggers the display of the tooltip, allowing users to obtain information about the formula edit box when needed, without having to leave the current interface or consult documentation. The benefits of this are: by displaying tooltips, users understand the function and usage of the formula edit box, enabling them to better select appropriate operators and rules; by reducing the risk of users making incorrect selections or configurations due to a lack of understanding of operators, thereby reducing potential errors; by providing clear information, users enhance their understanding of primary operators and their confidence in data analysis results, thereby increasing their willingness to use and explore data processing tools; by allowing users to quickly obtain information when hovering, without requiring additional steps or queries, making it easier for new users to understand and use the system.

[0188] In a specific application scenario, one embodiment of the present application provides an indicator editor for providing a custom indicator generation interface. The custom indicator generation interface includes: a combo box for receiving a first text input operation and / or a first selection operation from a user to determine at least one primary operator; a formula edit box for displaying each primary operator determined by the user as an operator control, and receiving a rule selection operation from a drop-down list box in the operator control to determine the data processing rule corresponding to each primary operator; an indicator generation button for, when clicked, generating a custom indicator based on at least one primary operator and its corresponding data processing rule, wherein the custom indicator is an indicator of a custom category; and a tooltip control for displaying a prompt corresponding to the formula edit box when a mouse hover operation is received. The custom indicator can act as a primary operator to generate a new custom indicator. In addition to the drop-down list box, the operator control also includes a secondary filter. The secondary filter is used to determine at least one secondary operator corresponding to the primary operator and its corresponding secondary filtering rule. In this case, when clicked, the indicator generation button is used to generate the custom indicator based on at least one primary operator and its corresponding data processing rule, and at least one secondary operator and its corresponding secondary filtering rule.

[0189] For example, User C needs to assess the R&D activity in a specific technology area. User C logs in to the data analysis platform and clicks "Create Custom Indicator" to enter the custom indicator creation interface provided by the indicator editor. The custom indicator creation interface includes an indicator name text input box, a combo box, a formula edit box, an indicator creation button, a tooltip control, and a description text input box. The indicator name text input box is used to enter the indicator name. The description text input box is used to enter a description.

[0190] The combo box includes a search text input box and a list box. The list box uses a hierarchical list and includes three sublists: "My Favorites," "Basic Indicators," and "Custom Indicators." When user C clicks the name of a sublist, the corresponding sublist is displayed.

[0191] User C enters "Patent Value" as the first text input in the search text input box, defining "Patent Value" as a primary operator. The formula edit box to the right of the combo box displays the previously defined primary operator, "Patent Value (USD)." This operator control contains a drop-down list. User C clicks the drop-down list, and a series of data processing rules appear, such as "Average," "Sum," and "Count." User C selects the "Sum" rule and enters "*7.2" to the right of the primary operator, indicating a conversion from USD to RMB.

[0192] User C continues to input “ / ”, which represents the division operator.

[0193] User C selects "Inventor" as the second first-level operator through the combo box, and selects "Duplicate Count" as the data processing rule of the first-level operator in the operator control corresponding to "Inventor".

[0194] User C continues to input “ / ”, which represents the division operator.

[0195] User C then uses the combo box to select "Application Year" as the third first-level operator and selects "Duplicate Count" as the data processing rule for the first-level operator.

[0196] Next, user C enters the indicator name text input box: Annual per capita inventor output value (RMB).

[0197] Each operator control also has a secondary filter icon. User C clicks the secondary filter for the primary operator "Patent Value (USD)", sets a secondary operator "Receiving Office", and selects "Russia", "UK", and "Europe", implementing the following secondary filtering rule: filtering out data with receiving offices in Russia, the UK, and Europe.

[0198] Afterwards, user C enters in the description text input box: This indicator describes the value of patents produced by each inventor each year.

[0199] In the formula editing box, the position (or order) of each primary operator in the formula can be adjusted by dragging it.

[0200] In addition, the calculation time required to analyze the indicator (for example, 3.9 seconds) can be displayed in real time in the lower left corner of the formula editing box.

[0201] At the same time, when user C hovers the mouse over the tooltip control, the prompt information corresponding to the formula editing box is displayed, such as: "Click the indicator on the left to create a formula."

[0202] After completing the above steps, User C clicks the "Generate Metric" (or "Save," "Submit") button on the interface. Based on the primary operators selected by User C, their data processing rules, and the inter-operational rules between the primary operators, a new custom metric—"Annual Per Capita Inventor Output Value (RMB)"—is generated.

[0203] The edited custom indicator will appear first in the sublist of custom indicators. When user C hovers the mouse over the custom indicator, a summary of the custom indicator (i.e., the indicator summary) will be displayed in a floating window to the right of the custom indicator, including the indicator name, calculation formula, indicator description, update date, creation date, etc. Edited custom indicators can be edited again and deleted.

[0204] One embodiment of the present application provides an electronic device, the specific implementation of which is similar to the implementation described in the above method implementation and the technical effects achieved, and some contents are not repeated here.

[0205] The electronic device includes a memory and at least one processor, the memory stores a computer program, and the at least one processor is configured to implement the steps of any of the above methods or implement the functions of any of the above indicator editors when executing the computer program.

[0206] In some embodiments, the electronic device may include a processor, a non-volatile storage medium, an internal memory, a communication interface, a display device, and an input device connected by a system bus. The non-volatile storage medium may store an operating system and related computer programs.

[0207] Referring to FIG. 3 , FIG. 3 is a structural block diagram of an electronic device 10 provided in one embodiment of the present application.

[0208] The electronic device 10 may include at least one memory 11 , at least one processor 12 , and a bus 13 connecting different platform systems.

[0209] It will be understood that the memory 11 in this specification may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM) 113, a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM) 111. It should be noted that the memory 11 of the products and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory. As an example, the memory 11 includes a random access memory (RAM) 111, a cache memory 112, and a read-only memory (ROM) 113. Among them, the memory 11 also stores a computer program, which can be executed by the processor 12 so that the processor 12 implements the steps of any of the above methods. The memory 11 may also include a utility 114 having at least one program module 115, such program module 115 including but not limited to: an operating system, one or more application programs, other program modules and program data, each of which or some combination may include an implementation of a network environment.

[0210] Accordingly, the processor 12 can execute the aforementioned computer program and can execute the utility 114. It will be understood that the processor 12 in this specification can be an integrated circuit chip having signal processing capabilities. During implementation, each step of the aforementioned method implementation can be completed by hardware integrated logic circuits in the processor 12 or by software instructions. The aforementioned processor 12 can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), a programmable logic controller (PLC), a complex programmable logic device (CPLD), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components, and can implement or execute the various methods, steps, and logic block diagrams disclosed in this specification. The general-purpose processor can be a microprocessor, or any conventional processor, etc. The steps of the methods disclosed in this specification can be directly implemented and executed by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium known in the art, such as random access memory (RAM) 111, flash memory, read-only memory (ROM) 113, programmable read-only memory, electrically erasable programmable memory, or registers. The storage medium is located in memory 11, and processor 12 reads information from memory 11 and, in conjunction with its hardware, completes the steps of the above method.

[0211] The bus 13 may represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures.

[0212] The electronic device 10 may also communicate with one or more external devices, such as a keyboard, a mouse, a pointing device, a Bluetooth device, etc., and may also communicate with one or more devices capable of interacting with the electronic device 10, and / or any device that enables the electronic device 10 to communicate with one or more other computing devices (e.g., a router, a modem, etc.). Such communication may be performed via an input / output interface 14. Furthermore, the electronic device 10 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 15. The network adapter 15 may communicate with other modules of the electronic device 10 via the bus 13. It should be understood that, although not shown in the figures, other hardware and / or software modules may be used in conjunction with the electronic device 10 in actual applications, including but not limited to microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms.

[0213] One embodiment of the present application provides a computer-readable storage medium, the specific implementation of which is similar to the implementation described in the above method implementation and the technical effects achieved, and some contents are not repeated here.

[0214] The computer-readable storage medium stores a computer program, and when the computer program is executed by at least one processor, it implements the steps of any of the above methods or implements the functions of any of the above indicator editors.

[0215] The embodiments of the present application also provide a computer program product, the specific implementation of which is similar to the implementation described in the above method implementation and the technical effects achieved, and some contents will not be repeated here.

[0216] The computer program product comprises a computer program, which, when executed by at least one processor, implements the steps of any of the above methods or implements the functions of any of the above indicator editors.

[0217] The computer program product may be a portable compact disc read-only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the computer program product of the present application is not limited thereto, and the computer program product may be any combination of one or more computer-readable media.

[0218] The user information or user account information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, etc.) involved in multiple implementation methods of this specification are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws and standards of relevant countries and regions, and corresponding operation entrances are provided for users to choose to authorize or refuse.

[0219] It should be understood that the specific examples in this specification are only intended to help those skilled in the art better understand the implementation methods of the present application, rather than to limit the scope of the present application.

[0220] It can be understood that in the various implementations of this specification, the size of the serial number of each process does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of this application.

[0221] It can be understood that the various implementation methods described in this specification can be implemented individually or in combination, and this application is not limited to this.

[0222] Unless otherwise indicated, all technical and scientific terms used in this specification have the same meaning as commonly understood by those skilled in the art in the technical field of this specification. The terms used in this specification are only for the purpose of describing specific embodiments and are not intended to limit the scope of this specification. The term "and / or" used in this specification includes any and all combinations of one or more of the relevant listed items. The singular forms "a", "above", and "the" used in this specification and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0223] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this specification.

[0224] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, devices and units can refer to the corresponding processes in the aforementioned method implementation methods and will not be repeated here.

[0225] In the several embodiments provided in this specification, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0226] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of this embodiment.

[0227] In addition, each functional unit in each embodiment of this specification may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0228] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this specification, or the part that contributes to the prior art, or the part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of this specification. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0229] The above description is merely a specific embodiment of this specification, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this specification should be included within the scope of protection of this specification. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A method for generating a custom indicator, characterized in that: include: Determine at least one primary operator, and determine a data processing rule corresponding to each of the at least one primary operator, where the primary operator is an indicator of a basic category or a custom category; Generate a custom indicator according to the at least one primary operator and the data processing rule corresponding to each primary operator, wherein the custom indicator is an indicator of a custom category; The custom indicator can act as a primary operator to generate a new custom indicator.

2. The method for generating a custom indicator according to claim 1, characterized in that: The determining of at least one primary operator comprises: Receiving, through a terminal device, one or more first selection operations of a user on an indicator set to determine one or more primary operators; or Receiving one or more first text input operations of a user through the terminal device, and identifying and obtaining one or more primary operators according to the received one or more first text input operations; or Import one or more primary operators through the indicator sharing platform; or A first chat robot interaction interface is provided through the terminal device to receive one or more first input information of a user; semantic extraction is performed on the one or more first input information to determine one or more primary operators.

3. The method for generating a custom indicator according to claim 1 or 2, characterized in that: The determining of the data processing rule corresponding to each first-level operator in the at least one first-level operator includes: For each primary operator, the following processing is performed: According to the data processing rule set corresponding to the data type of the first-level operator, determine a data processing rule in the data processing rule set as the data processing rule of the first-level operator; the data type of the first-level operator is numeric, character, time or geography; the data processing rules corresponding to the numeric data type include one or more of the following: average, sum, count, deduplication count, maximum value, minimum value, numeric proportion and accumulation.

4. The method for generating a custom indicator according to any one of claims 1 to 3, characterized in that: After determining at least one primary operator and determining a data processing rule corresponding to each of the at least one primary operator, the method further includes: For one or more primary operators in the at least one primary operator, the following processing is performed: Determine at least one secondary operator corresponding to the primary operator, and determine a secondary filtering rule corresponding to each secondary operator in the at least one secondary operator; the secondary operator is an indicator of a basic category or a custom category; The step of generating a custom indicator according to at least one primary operator and a data processing rule corresponding to each primary operator includes: The custom indicator is generated according to the at least one primary operator and the data processing rule corresponding to each primary operator, and the at least one secondary operator and the secondary filtering rule corresponding to each secondary operator.

5. The method for generating a custom indicator according to claim 4, characterized in that: The determining of at least one secondary operator corresponding to the primary operator includes: Receiving one or more second selection operations of the user on the indicator set through the terminal device to determine one or more secondary operators; or Receiving one or more second text input operations of a user through the terminal device, and identifying one or more secondary operators according to the received one or more second text input operations; The step of determining the secondary filtering rule corresponding to each secondary operator in the at least one secondary operator includes: receiving, through the terminal device, a third text input operation and / or a third selection operation of the user, to determine a secondary filtering rule corresponding to the secondary operator; or The terminal device receives a user's type selection operation for a judgment type set to determine the judgment type corresponding to the secondary operator; the terminal device receives a user's fourth text input operation and / or a fourth selection operation, combined with the judgment type, to determine a secondary filtering rule corresponding to the secondary operator.

6. The method for generating a custom indicator according to claim 5, characterized in that: The receiving, by the terminal device, a type selection operation of a user for a judgment type set to determine the judgment type corresponding to the secondary operator includes: Displaying a judgment type set corresponding to the data type of the secondary operator through the terminal device; the data type of the secondary operator is a value, a character, a time or a geography; A type selection operation of a user for a judgment type in the judgment type set is received, and the judgment type selected by the user is used as the judgment type corresponding to the secondary operator.

7. The method for generating a custom indicator according to any one of claims 1 to 6, characterized in that: When the at least one primary operator includes a plurality of primary operators, the method further includes: Determining operation rules between the plurality of primary operators; The generating of a custom indicator according to the at least one primary operator and the data processing rule corresponding to each primary operator includes: The custom indicator is generated according to the multiple first-level operators, the data processing rules corresponding to each of the first-level operators, and the operation rules between the multiple first-level operators.

8. The method for generating a custom indicator according to claim 7, characterized in that: The operation rules between the plurality of primary operators include an operation level and one or more of the following operators: an arithmetic operator, a comparison operator, and a logical operator, wherein the operation level is used to indicate the priority of the operation; Wherein, determining the operation rules between the multiple primary operators includes: receiving a fifth text input operation and / or a fifth selection operation of the user to determine an operation rule between the plurality of primary operators; or A second chat robot interaction interface is provided through the terminal device to receive one or more second input information from the user; semantic extraction is performed on each of the one or more second input information to determine the operation rules between the multiple primary operators.

9. The method for generating a custom indicator according to any one of claims 1 to 8, characterized in that: Also includes: A formula detection is performed on the indicator formula corresponding to the custom indicator to obtain a formula detection result, wherein the formula detection result indicates whether the formula is correct or incorrect.

10. The method for generating a custom indicator according to claim 9, characterized in that: Also includes: When the formula is wrong, improvement prompt information corresponding to the custom indicator is generated and displayed on the terminal device.

11. An indicator editor, characterized in that: The indicator editor is used to provide a custom indicator generation interface, and the custom indicator generation interface includes: A combo box, used to receive a first text input operation and / or a first selection operation of a user to determine at least one primary operator; A formula editing box, used to display each of the at least one first-level operator determined by the user in the form of an operator control, and receive a rule selection operation of the user on the drop-down list box in the operator control to determine the data processing rule corresponding to each first-level operator; An indicator generation button, which is used to generate a custom indicator according to the at least one first-level operator and the data processing rules corresponding to each first-level operator after being clicked, wherein the custom indicator is an indicator of a custom category; The custom indicator can act as a primary operator to generate a new custom indicator.

12. The indicator editor according to claim 11, characterized in that: The operator control also includes: The secondary filter is used to determine at least one secondary operator corresponding to the primary operator and a secondary filtering rule corresponding to each secondary operator in the at least one secondary operator.

13. The indicator editor according to claim 11 or 12, characterized in that: The custom indicator generation interface also includes: A tooltip control is used to display the prompt information corresponding to the formula edit box when a mouse hover operation is received.

14. An electronic device, characterized in that: The electronic device includes a memory and at least one processor, the memory stores a computer program, and the at least one processor is configured to implement the steps of the method according to any one of claims 1 to 10 or implement the functions of the indicator editor according to any one of claims 11 to 13 when executing the computer program.

15. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by at least one processor, the computer program implements the steps of the method according to any one of claims 1 to 10 or implements the functions of the indicator editor according to any one of claims 11 to 13.

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