Custom index generation method, index editor and related device

Abstract

The invention provides a user-defined index generation method, an index editor and a related device, and the method comprises the steps: determining at least one first-level operator and a data processing rule corresponding to the first-level operator, the first-level operator being an index of a basic category or a user-defined category; according to the at least one first-level operator and the corresponding data processing rule, generating a user-defined index, the user-defined index being an index of a user-defined category; wherein the user-defined index can serve as a first-level operator, so that a new user-defined index is generated. According to the method, customized self-defined indexes can be created, and data analysis requirements of different industries and fields can be met.

Description

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. Background Art

[0002] In the field of data analysis and statistics, traditional methods are usually limited to a fixed set of parameter indicators, which are used to generate charts and data analysis, but cannot generate further customized indicators based on specific data and needs. This limitation leads to the inflexibility of data analysis because only predefined limited parameter indicators can be selected for analysis. However, in order to meet the requirements of data analysis and statistics in different scenarios, a more flexible approach is needed.

[0003] 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. Summary of the invention

[0004] 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.

[0005] The purpose of this application is achieved by the following technical solutions:

[0006] In a first aspect, the present application provides a method for generating a custom indicator, the method comprising:

[0007] Determine at least one primary operator and its corresponding data processing rule, where the primary operator is an indicator of a basic category or a custom category;

[0008] Generate a custom indicator according to at least one primary operator and its corresponding data processing rule, wherein the custom indicator is an indicator of a custom category;

[0009] The custom indicator can act as a primary operator to generate a new custom indicator.

[0010] In a second aspect, the present application provides an indicator editor, the indicator editor is used to provide a custom indicator generation interface, the custom indicator generation interface includes:

[0011] 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;

[0012] A formula editing box, used to display each primary operator determined by the user in the form of an operator control, and to receive a rule selection operation from the user on the drop-down list box in the operator control to determine the data processing rule corresponding to each primary operator;

[0013] An indicator generation button is used to generate a custom indicator according to at least one primary operator and its corresponding data processing rule after being clicked, wherein the custom indicator is an indicator of a custom category;

[0014] The custom indicator can act as a primary operator to generate a new custom indicator.

[0015] In a third aspect, the present application provides an electronic device, comprising a memory and at least one processor, the memory storing a computer program, and the at least one processor being 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.

[0016] 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.

[0017] 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 calculating different operators, new indicators can be generated for further data analysis and visualization. The advantage of this is that users can select 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The present application is further described below in conjunction with the accompanying drawings and specific implementation methods.

[0019] Figure 1 It is a flowchart of a method for generating a custom indicator provided by an embodiment of the present application.

[0020] Figure 2 It is a schematic diagram of an interface of an indicator editor provided by one embodiment of the present application.

[0021] Figure 3 It is a structural block diagram of an electronic device provided by one embodiment of the present application. DETAILED DESCRIPTION

[0022] 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 described embodiments are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0023] In the description of the embodiments of the present application, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of the present application, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0024] In the data analysis and processing environment, users usually need to process a variety of indicators and data. These data may include different types of indicators such as numerical, character, time, and geography. The relevant technology provides some basic data processing functions, but only supports data processing of existing indicators, and cannot meet the complex customized needs of users. In addition, the configuration interface is complicated, and users need to invest a lot of time and energy to understand and configure indicators. Users cannot create and edit custom indicators according to their specific needs. The configuration interface may be difficult to understand and use, and users need to be trained and learned.

[0025] Therefore, it is necessary to consider how to provide a more flexible, customized and user-friendly method to generate and edit indicators. This application can include the following key elements:

[0026] Primary operators and secondary operators: The concepts of primary operators and secondary operators are introduced to better organize and manage the data processing rules of indicators.

[0027] Data processing rules: Provides a variety of data processing rules to meet various data types and requirements.

[0028] Visual Editing Tool: Creates a user-friendly custom indicator generation interface that allows users to easily configure and edit indicators through elements such as tabs, combo boxes, and formula edit boxes.

[0029] Tooltip control: Added tooltip information to help users better understand and use primary and secondary operators.

[0030] Specifically, the present application can provide a custom indicator generation method, an indicator editor, an electronic device, and a computer-readable storage medium, so that users can create and edit custom indicators according to their specific needs. These custom indicators can include various data processing rules and operation rules, so that users can analyze and process data more flexibly, improving user experience and data processing efficiency.

[0031] It should be noted that although this application takes the patent data analysis scenario in the field of data analysis as an example, this application can be applied to other data analysis scenarios in the field of data analysis, 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 usually requires processing different types of data and indicators. Therefore, the flexibility and customizability of this application enable it to easily adapt to the needs of different fields.

[0032] In the field of patents, 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.

[0033] 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.

[0034] In the healthcare field, medical data usually 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.

[0035] 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.

[0036] In the field of supply chain management, supply chain experts can use this application to create custom indicators to track various aspects of supply chain operations, including inventory management, transportation efficiency, and supplier performance. Since there are multiple links and indicators involved in the supply chain, the customization capabilities of this application can help supply chain managers better adapt to different supply chain situations.

[0037] 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 capability. Specifically, for example, the client may be a desktop computer, a tablet computer, a laptop computer, a smart phone, a digital assistant, a smart wearable device, a shopping guide terminal, a television, a smart speaker, a 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 also be software that can be run in the electronic device. The server may be an electronic device with a certain computing and processing capability, which may have a network communication module, a processor, and a memory, etc. Of course, the server may also refer to software running in 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 collaboration. Alternatively, the server may also 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 implementation method. For example, it may be a new form of "server" based on quantum computing.

[0038] (Custom indicator generation method)

[0039] See also Figure 1 , Figure 1 It is a flowchart of a method for generating a custom indicator provided by an embodiment of the present application.

[0040] The method comprises:

[0041] Step S101: Determine at least one primary operator and its corresponding data processing rule, where the primary operator is an indicator of a basic category or a custom category;

[0042] Step S102: Generate a custom indicator according to at least one primary operator and its corresponding data processing rule, where the custom indicator is an indicator of a custom category;

[0043] The custom indicator can act as a primary operator to generate a new custom indicator.

[0044] In some possible implementations, when the number of primary operators used to generate the custom indicator is greater than one, the method further includes:

[0045] Determine the operation rules between primary operators;

[0046] The step of generating a custom indicator according to at least one primary operator and its corresponding data processing rule (i.e., step S102) includes:

[0047] The custom indicator is generated according to each primary operator and its corresponding data processing rule and the operation rules between the primary operators.

[0048] This application does not limit the indicators of the basic category. In the field of patent data analysis, for example, they can be 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, 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.

[0049] The first-level operator is the basic building block for building custom indicators. The first-level operator can be an indicator of the basic category (such as applicant, authorization date, etc.) or an indicator of the custom category (such as the average patent value of each inventor). For the first-level operator (whether it is an indicator of the basic category or an indicator of the custom category), a data processing rule needs to be determined to indicate what kind of useful information based on the indicator is extracted from the data set to be processed. In addition, after determining a first-level operator, the second-level operator can also be used to perform secondary 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.

[0050] Data processing rules are the data processing methods corresponding to the first-level operators, which are used to operate and convert indicator data. Different types of indicators can have different data processing rules, such as average, sum, count, etc. As an example, for the first-level operator "patent value", the data processing rule can be summation to determine the total patent value. If you need to convert between currency units (for example, convert US dollars to RMB), you can calculate based on the exchange rate between currencies. The 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.

[0051] Operation rules define the operation mode between first-level operators, including operators and operation priority (expressed as operation level). Operation rules are used to generate custom indicators and combine the results of different first-level operators. For example, if you need to generate a custom indicator to calculate the value score of each patent, you can use arithmetic operators to add or multiply the patent's value score with other indicators.

[0052] 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 value of patents", which is composed of indicators such as the value score and number of citations of the patent. In some possible implementations, the custom indicator corresponds to an indicator formula, which is used to indicate 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-duplicate count), which is used to calculate the average number of patents for each inventor.

[0053] For example, when you need to create a custom indicator to calculate the average patent value of each inventor, you can use the above method. In the initial stage, by constructing a search formula and running it, the patent data of a specific applicant is filtered from the patent database as the first-level filtering result. First, determine two first-level operators and their corresponding data processing rules.

[0054] The first-level operator 1 is "patent value", which is a custom category indicator that represents the value of each patent. Data processing rule 1 is "sum", which calculates the sum of the patent values ​​of all patents corresponding to the first-level filtering results.

[0055] The first-level operator 2 is "inventor", which is a basic category indicator, indicating the inventor of each patent. Data processing rule 2 is "count without duplicates", which adds up the number of inventors of all patents after deduplication to obtain the total number of inventors.

[0056] 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.

[0057] Through the above settings, a custom indicator can be generated, namely "average patent value per inventor". The data processing result of this custom indicator will be calculated based on the first-level filtering result, two first-level operators and the division operator. The indicator formula is: patent value (sum) / inventor (de-duplicate count). This custom indicator can help companies evaluate the average contribution of each inventor in creating patents, so as to have a more comprehensive understanding of the performance of the innovation team.

[0058] Therefore, first of all, it is necessary to determine at least one first-level operator, which 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 original data of the operator. For example, the data processing result of each first-level operator is determined by the known first-level filtering result (e.g., the authorized patent of a specific applicant), the first-level operator (e.g., the patent value) and its data processing rules (e.g., summation). When the number of first-level operators used to generate custom indicators is greater than 1, 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 custom indicators. Next, based on the selected first-level operators and their data processing rules and the operation rules between them (if any), generate custom indicators. These custom indicators belong to the custom category of indicators. Their indicator formulas are determined by each first-level operator and its data processing rules (and possibly the operation rules between first-level operators). In other words, the data processing results of the custom indicators are determined by the data processing results of each first-level operator (and possibly the operation rules between first-level operators). By combining and operating different operators, new indicators can be generated for further data analysis and visualization.

[0059] The advantage of this is that users can choose different primary operators, data processing rules, and operation rules according to their specific needs and analysis purposes, thereby creating 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 method enriches data analysis capabilities, supports multi-scenario data analysis, and improves decision support capabilities by enhancing flexibility and customization.

[0060] 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.

[0061] In some possible implementations, the process of determining at least one primary operator includes:

[0062] Receiving one or more first selection operations of a user on an indicator set through a terminal device to determine one or more primary operators; or,

[0063] Receiving one or more first text input operations of the user through the terminal device, and identifying one or more primary operators according to the received first text input operations; or,

[0064] Import one or more primary operators through the indicator sharing platform; or,

[0065] A first chat robot interaction interface is provided through the terminal device to receive one or more first input information of the user; semantic extraction is performed on each first input information to determine one or more primary operators.

[0066] Terminal devices refer to the devices that users use to interact with the data analysis system, such as personal computers, smartphones, tablets, etc.

[0067] The first selection operation refers to the selection operation performed by the user on the terminal device to select one or more primary operators. These operations can be user interface interaction operations such as clicking, dragging and dropping. As an example, the user clicks the "Application Date" button on the application interface to perform the first selection operation.

[0068] 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 primary operators. As an example, the user enters the text information of "patent value" in the search bar to identify the primary operator "patent value".

[0069] The indicator sharing platform is an online platform for storing and sharing indicator data, from which users can import first-level operators for the generation of custom indicators.

[0070] The first chatbot interaction interface is an interface where a user can perform a first input information operation by talking to the chatbot, thereby identifying and determining a first-level operator. As an example, a user talks to a chatbot and provides instructions on indicator selection. Semantic extraction and reasoning are performed through a large language model to obtain a suitable first-level operator.

[0071] Semantic extraction is the process of identifying and understanding the meaning from the input information provided by the user to determine the first-level operators.

[0072] The first input information is, 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 semantics of "screen European and US patents for me", the corresponding primary operator "accepting office" is matched.

[0073] In the following examples, four methods are used to illustrate how to determine at least one primary operator:

[0074] The first method: Provide a selection interface containing multiple indicators. Users select primary operators by clicking the checkboxes on the interface. For example, if the user selects "Application Date" and "Patent Value" as primary operators, these selections will be used to generate custom indicators.

[0075] The second method: the user enters the following text information in the search box (or combo box): "value". Through keyword matching, it is identified that the user wants to use "patent value" as the primary operator.

[0076] The third method: The user imports two custom indicators from the indicator sharing platform, such as "average patent value per inventor" and "top 10 applicants by number of patents". These two imported custom indicators become first-level operators and can be used to generate new custom indicators.

[0077] The fourth method: The user opens the first chatbot interface and asks the chatbot: "I need to calculate the sum of patent values." After semantic extraction, the first-level operator "patent value" is obtained, and "sum" is selected as the data processing rule corresponding to "patent value."

[0078] These examples show different ways how a user can determine at least one primary operator.

[0079] Thus, at least one primary operator is determined, thus providing a basis for generating custom indicators.

[0080] Specifically, through the terminal device, the user's first selection operation of the indicator set can be received. The user can select one or more indicators from the existing indicator set, and these selected indicators will become the basis of the first-level operator. This method allows the user to directly select from the available indicator list and use it as the basis for generating a custom indicator.

[0081] Another method is to receive the user's first text input operation through the terminal device. One or more first-level operators are identified and generated according to the text input by the user. The user can enter a specific operator name or description, and the corresponding first-level operator is identified and generated according to the input content. This method allows the user to describe the required first-level operator in text form, which is convenient for quickly locking the corresponding first-level operator.

[0082] 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, and users can choose to import these first-level operators and use them for custom indicator generation.

[0083] In addition, a first chatbot interaction interface may be provided to receive first input information from the user. Such 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 method allows the user to interact with the system in the form of natural language, which is more intelligent and user-friendly.

[0084] 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.

[0085] In some possible implementations, the process of determining the data processing rule for each primary operator includes:

[0086] For each first-level operator, perform the following processing:

[0087] According to a data processing rule set corresponding to the data type of the primary operator, one of the data processing rules is determined as the data processing rule of the primary operator; the data type of the primary operator is numeric value, character, time or geography.

[0088] Each first-level operator has a specific data type and needs to specify a data processing rule (or use the default value) to generate a custom indicator.

[0089] Data types are classifications that describe the nature of the information represented by the primary operator, which can be numerical, character, time, or geographic. Numerical values ​​represent information that can be represented by numbers, such as patent value. Character representation includes text or string information, such as applicant and classification number. Time represents date and time information, such as application date. Geographic information represents information related to geographic location, such as receiving office and applicant address.

[0090] Data processing rules are specifications that define how to process and calculate the data of the first-level operators, depending on the data type of the first-level operators.

[0091] For example, suppose you want to generate a custom metric "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.

[0092] First, select “Number of Patents” and “Inventor” as the first-level operators.

[0093] The data type of "Number of Patents" is numeric. Select "Count" as the data processing rule to obtain the number of patents.

[0094] The data type of "Inventor" is character. Select "Duplicate Count" as the data processing rule to obtain the number of inventors.

[0095] 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".

[0096] Thus, 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 numeric value, a character, a time, or a geography (also known as a 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 rule sets 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.

[0097] The advantage of this is that the corresponding data processing rules are selected according to the data type of the first-level operator, ensuring the type of data 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 the 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.

[0098] In some possible implementations, after determining at least one primary operator and its corresponding data processing rule, the method further includes:

[0099] For one or more primary operators, perform the following processing:

[0100] Determine at least one secondary operator corresponding to the primary operator and its corresponding secondary filtering rule; the secondary operator is an indicator of a basic category or a custom category;

[0101] The step of generating a custom indicator according to at least one primary operator and its corresponding data processing rule includes:

[0102] The custom indicator is generated according to at least one primary operator and its corresponding data processing rule, at least one secondary operator and its corresponding secondary filtering rule.

[0103] For some or all of the first-level operators, apply the second-level operator to them for secondary filtering. The second-level operator is a data operation unit that performs further processing or screening based on the first-level operator and is used for data filtering or processing. As an example, the second-level operator can be used to screen out the number of patents under a specific classification number, screen out products with sales greater than 1,000 in a certain city, obtain the stock price of a company in a specific time period, etc.

[0104] Secondary filtering rules are rules used to determine which data will be retained or deleted, and are associated with secondary operators to further accurately filter data. As an example, secondary filtering rules may include filtering out data under a specific classification number, filtering out data with sales greater than 1,000, filtering out stock price data within a specific time period, etc.

[0105] Among them, secondary filtering is the process of further screening or processing the to-be-processed data set corresponding to the primary operator according to the secondary operator and its secondary filtering rules. As an example, the to-be-processed data set corresponding to the primary operator includes patent application data corresponding to all classification numbers. Through secondary filtering, a data subset under a specific classification number can be obtained.

[0106] After the introduction of secondary operators, custom indicators are determined by the primary operators and their data processing rules, the secondary operators and their secondary filtering rules. Among them, some or all of the primary operators correspond to secondary operators. In other words, each secondary operator performs secondary filtering on one of the primary operators.

[0107] 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 primary operator and its data processing rules and the operation rules between the primary operators can be displayed. In other embodiments, when displaying the indicator formula, the primary operator and its data processing rules, the secondary operator corresponding to the primary operator and its secondary filtering rules, and the operation rules between the primary operators can be displayed. As an example, an indicator formula is: [Number of patents (sum) & <Accepting Office: United Kingdom, United States>] / Inventor (de-duplication count). Among them, the number of patents and inventors are primary operators, and the accepting office is a secondary operator.

[0108] Thus, after determining the primary operator and its 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 the corresponding secondary filtering rule. 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 operator, and are used to further screen, 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 screen 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 data set to be processed is screened or processed using the secondary operator and the secondary filtering rule, 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.

[0109] 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 control the analysis scope of data more finely, 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.

[0110] In some possible implementations, the process of determining at least one secondary operator corresponding to the primary operator includes:

[0111] 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,

[0112] Receiving one or more second text input operations of the user through the terminal device, and identifying one or more secondary operators according to the received second text input operations;

[0113] The process of determining the secondary filtering rules corresponding to each secondary operator includes:

[0114] (Static filtering) receiving a third text input operation and / or a third selection operation of the user through the terminal device to determine a secondary filtering rule corresponding to the secondary operator; or

[0115] (Dynamic filtering) Receive, through the terminal device, a user's type selection operation for a judgment type set to determine the judgment type corresponding to the secondary operator; receive, through the terminal device, a user's fourth text input operation and / or a fourth selection operation, in combination with the judgment type, to determine a secondary filtering rule corresponding to the secondary operator.

[0116] In some possible implementations, 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:

[0117] 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;

[0118] A type selection operation of a user for one of the judgment types is received, and the judgment type selected by the user is used as the judgment type corresponding to the secondary operator.

[0119] In some possible implementations, the judgment type set corresponding to the data type (secondary operator) of the value includes one or more of the following: greater than, less than, equal to, not greater than, not less than; and / or,

[0120] The judgment type set corresponding to the time data type (secondary operator) includes one or more of the following: before, after; and / or,

[0121] The judgment type set corresponding to the character data type (secondary operator) includes one or more of the following: contains, does not contain; and / or,

[0122] The judgment type set corresponding to the geography data type (secondary operator) includes one or more of the following: contains, does not contain.

[0123] 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; according to 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.

[0124] 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 according to the multiple secondary operators corresponding to the primary operator and 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, NOT.

[0125] Secondary filtration may include static filtration and / or dynamic filtration.

[0126] To illustrate static filtering, suppose that an e-commerce website's sales data analysis is performed. First, the sales data for 2022 is filtered out as the data set to be processed (also called the data set to be analyzed). Next, the user selects a first-level operator, namely "product sales". The user continues to select the data processing rule "sum", which can sum the sales of each product to obtain the total sales. The user performs a second selection operation and selects a second-level operator, namely "sales region". After that, the user performs a third selection operation and selects "China" and "Germany" to implement the following secondary filtering rules: from the sales data for 2022, filter out the sales data for China and Germany in 2022. After the second-level operator and its secondary filtering rules are applied to the first-level operator, when the data processing process corresponding to the first-level operator is executed, the original data set to be processed corresponding to the first-level operator "product sales" will be updated to include only the sales data for China and Germany, and the data processing result corresponding to the first-level operator (i.e., the sum result corresponding to "product sales") is no longer the total sales of all regions, but the total sales of China and Germany.

[0127] To illustrate dynamic filtering, assume that patent data analysis is performed, and first filter out the authorized invention patent data for 2022 as the data set to be processed. Next, the user selects a primary operator, namely "patent value". The user continues to select the data processing rule "sum". After that, the user selects "number of patents" and "applicants" as secondary operators from the indicator list, and selects "and (or, AND)" as the operator between the two. For the secondary 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 secondary operator: filter data with a number of patents greater than 100. For the secondary 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 secondary operator: filter the data of the top 10 applicants. Finally, according to the two secondary operators (i.e., "number of patents" and "applicants") corresponding to the primary operator (i.e., "patent value") and their corresponding secondary filtering rules, the to-be-processed data set corresponding to the primary operator is updated by secondary filtering: from the authorized invention patent data of 2022, the data corresponding to the top 10 applicants with a number of patents greater than 100 are screened 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 a number of patents greater than 100.

[0128] 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.

[0129] First, you need to determine at least one secondary operator, which can be achieved in different ways:

[0130] The first method: the user performs a second selection operation through a terminal device to select one or more secondary operators.

[0131] The second method: the user performs a second text input operation through a terminal device, and one or more secondary operators are automatically identified from the text.

[0132] Next, determine the secondary filtering rules corresponding to each secondary operator, which can also be achieved in different ways:

[0133] The first method (static filtering): The user performs a third text input operation and / or a third selection operation through the terminal device to determine the secondary filtering rules. For example, enter a specific value or select an existing option from a list. This static filtering method allows the user to statically filter based on the attributes of the parameter value, for example, the number of patents of a specific applicant in a specific country. In this case, the first-level operator is "number of patents", the second-level operator is "accepting office", and the secondary filtering rule is to filter out data with the accepting office being Europe and the United States.

[0134] The second method (dynamic filtering): The user performs a type selection operation and selects a judgment type in 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.

[0135] Finally, the determined secondary filtering rules are applied to update the data set to be processed corresponding to the primary operator in a secondary filtering manner. That is, the secondary operator and the secondary filtering rules are used to further screen or process the data set to be processed corresponding to the primary operator (for example, the patent application data of a specific applicant) to update the data set to be processed. Since the data set to be processed is updated, if the data processing process corresponding to the primary operator is executed, its data processing result will also change accordingly.

[0136] 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.

[0137] In some possible implementations, the process of determining the operation rules between primary operators includes:

[0138] receiving a fifth text input operation and / or a fifth selection operation of the user to determine an operation rule between primary operators; or,

[0139] A second chat robot interaction interface is provided through the terminal device to receive one or more second input information of the user; semantic extraction is performed on each second input information to determine the operation rules between the primary operators.

[0140] The process of determining the operation rules between the secondary operators is similar to the process of determining the operation rules between the primary operators, and will not be repeated here.

[0141] 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.

[0142] A text input operation is an operation or instruction that a user enters through a text input box. A selection operation is an operation or parameter that a user selects through a drop-down list or a tab. For example, a user selects "division" from a drop-down list as an operator between two primary operators.

[0143] The second chatbot interaction interface is an interface provided by a terminal device, in which the user can have a dialogue and interact with the virtual assistant or chatbot. For example, the user can put forward specific data analysis requirements to the chatbot, and obtain appropriate operation rules through semantic extraction and reasoning. The second input information is, for example, text information, voice information, and image information.

[0144] Therefore, when determining the operation rules between the primary 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.

[0145] The user performs the fifth text input operation and / or the fifth selection operation through the terminal device, and can input and / or select one or more operation rules for defining the operation relationship between the primary operators. These operation rules can include operation levels (e.g., brackets) and arithmetic operators (such as addition, subtraction, multiplication, division, etc.), comparison operators (such as greater than, less than, equal to, etc.) and logical operators (such as and, or, not, etc.). The user's input and selection will be used to construct the operation rules.

[0146] Alternatively, a second chatbot interaction interface is provided through the terminal device, through which the user can provide second input information. Each second input information is subjected to semantic extraction to identify the user's intention and operation requirements. Semantic extraction can understand the user's words and convert them into specific operators and operation levels in order to construct operation rules.

[0147] Operators and operation levels are used to construct operation rules to define the operation relationship between first-level operators. These rules will be used for further processing and calculation of custom indicators.

[0148] The benefits of this are that users can define the operational relationships 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 operational rules, improving the user experience; users can choose from a variety of operational rules, including arithmetic, comparison and logical operators to meet different operational needs, increasing the diversity and flexibility of operations; users do not need to have an in-depth understanding of programming or complex operational rule syntax, and can define operational rules in an intuitive way, lowering the threshold for use; the operational rules and rules determined through user text input, selection or semantic extraction are usually more accurate and controllable, avoiding human errors or unclear definitions.

[0149] In some possible implementations, the data processing rules corresponding to the data type (primary operator) of the value include one or more of the following: average, sum, count, duplicate count removal, maximum value, minimum value, value proportion and accumulation; and / or,

[0150] The data processing rules corresponding to the character data type (the primary operator) include one or more of the following: counting, deduplication counting; and / or,

[0151] The data processing rules corresponding to the time data type (first-level operator) include one or more of the following: counting, deduplication counting; and / or,

[0152] The data processing rules corresponding to the geography data type (first-level operator) include one or more of the following: counting, and deduplication counting.

[0153] In some possible implementations, the operation rules between the primary 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,

[0154] The operation rules between the secondary operators include logical operators.

[0155] 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.

[0156] The metric represented by a particular first-level operator is of numeric type. This means that the metric contains numeric data, such as integers or decimals. For numeric type metrics, a range of data processing rules are provided. These rules include:

[0157] Average: Calculates the average of the values.

[0158] Sum: Add the values ​​together to get the total.

[0159] Count: Counts the number of values.

[0160] Duplicate count: After removing duplicate values, calculate the number of remaining values.

[0161] Maximum: Find the largest value among the values.

[0162] Minimum: Find the smallest value among the numbers.

[0163] Value Proportion: Calculates the value's proportion in the total value.

[0164] Accumulation: Perform cumulative calculations on the values ​​to obtain the cumulative value.

[0165] When the primary operator is a data type such as character, time, or geography, 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.

[0166] In addition, the selection of operation rules between primary operators and secondary operators is also provided.

[0167] Operation level: For example, parentheses are used to determine the priority of operations.

[0168] Arithmetic operators: such as addition, subtraction, multiplication, division, power, and remainder, are used to perform basic mathematical operations on numerical values.

[0169] 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.

[0170] Logical operators: such as AND, OR, and NOT, are used to perform logical operations.

[0171] Users can select appropriate data processing rules and calculation rules according to their specific analysis needs to generate custom indicators for numerical data analysis.

[0172] 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; it 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, counting, etc., 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 customized indicators to meet data analysis requirements in different scenarios.

[0173] In a specific application scenario, an embodiment of the present application provides a method for generating a custom indicator, the method comprising:

[0174] Determine at least one first-level operator and its corresponding data processing rule and the operation rules between the first-level operators; the first-level operator is an indicator of a basic category or a custom category; the operation rules between the 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;

[0175] For one or more primary operators, the following processing is performed: determining at least one secondary operator corresponding to the primary operator and its corresponding secondary filtering rule; the secondary operator is an indicator of a basic category or a custom category;

[0176] Generate a custom indicator according to at least one primary operator and its corresponding data processing rule, at least one secondary operator and its corresponding secondary filtering rule, wherein the custom indicator is an indicator of a custom category;

[0177] The custom indicator can act as a primary operator to generate a new custom indicator.

[0178] 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.

[0179] The process of determining the data processing rule of 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 numerical value, character, time or geography; the data processing rule corresponding to the numerical data type includes one or more of the following: average, sum, count, deduplication count, maximum value, minimum value, numerical proportion and accumulation;

[0180] 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.

[0181] 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, included and not included.

[0182] 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 numeric, 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.

[0183] 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 a 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.

[0184] For example, in the field of patent data analysis, after selecting "applicant" as the first-level operator, you can further filter the first-level operator "applicant" and further select a variety of different second-level operators. For example, further selecting "specific IPC classification number" as the second-level operator means that after filtering, the meaning is: 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 for function operations with other operators.

[0185] After selecting a primary operator (for example, patent value), it can be calculated with another indicator (for example, inventor) through operation rules. Specifically, "patent value" / "inventor" can represent the average patent value produced by each inventor. Furthermore, the above operation can be divided by the primary operator "application year", that is, "patent value" / "inventor" / "application year", which can represent the average patent value produced by each inventor each year.

[0186] By supporting operatorization through basic indicators and custom indicators, people with data analysis needs (for example, patent analysts) can flexibly perform data statistics and analysis according to 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 be quickly and accurately implemented through the indicator editor, which can improve the efficiency of data analysis. 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.

[0187] Taking the patent data analysis scenario as an example, user A is a patent analyst. In the initial stage, after one filtering (for example, building a search formula and executing it), user A obtains the data set to be processed, namely, the global authorized invention patent data from 2012 to 2022.

[0188] 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 primary operator. User A then viewed the custom indicator list and selected "number of patents" (an indicator of the custom category) as the primary operator.

[0189] In addition, user A also inputs "validity period", and the indicator editor recognizes and matches the indicator "patent validity period" as the third first-level operator.

[0190] For “Number of Patents”, User A selects “Count” as the data processing rule.

[0191] For “Citations”, User A selected “Average” to assess the impact of these patents.

[0192] For “Patent Validity Period”, User A selects “Average” to view the average validity period of patents in the field.

[0193] User A wants to further analyze the proportion of technical contributions of different applicants in specific technical fields, so he sets two secondary operators for each of the three primary operators mentioned above. User A selects "Applicant" as the secondary operator, and adds "Classification Number" as the second secondary operator through text input.

[0194] For "Applicant", user A selected the judgment type of "Includes" and entered keywords of specific applicants to filter specific patents.

[0195] For "Classification Number", User A selected the "Include" judgment type and entered a specific patent classification number.

[0196] 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 multiplies it by the "patent validity period" to evaluate the market acceptance of the technology field.

[0197] Finally, the indicator editor generates a custom indicator based on the selection and input of user A, which user A named "market acceptance". This custom indicator will help user A evaluate the market trend, R&D activity and market potential of a certain technology field. The above custom indicator generation method enables patent analysts like user A to deeply and meticulously evaluate the trends and potential of a technology field without the need for complex program writing.

[0198] Taking the e-commerce data analysis scenario as an example, user B is an e-commerce data analyst who uses the indicator data editor of the e-commerce data analysis system to generate custom indicators to analyze the sales performance of his online store. In the initial stage, after one filtration, user B obtains the data set to be processed, that is, the e-commerce sales data of the online store in 2022.

[0199] User B selects "Sales Amount" and "Purchase Quantity" as the primary operators.

[0200] In addition, user B also entered "refund amount", and the indicator editor recognized and added the "refund amount" indicator as the third first-level operator.

[0201] For the three numerical indicators of "sales amount", "refund amount" and "purchase quantity", user B selected "sum" as the data processing rule.

[0202] 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.

[0203] For Product Category, User B selects the Include rule and enters Electronic Products as the filter condition.

[0204] User B inputs the following through interaction with the second chatbot: sales amount minus refund amount divided by sales amount. The second chatbot identifies and sets the corresponding calculation rule: sales amount minus refund amount divided by purchase amount.

[0205] After all these steps are completed, the indicator editor will generate a custom indicator "Average Sales Price" based on the selection and input of User B, which represents the purchasing power level of the customers of the online store.

[0206] In some embodiments, the method further comprises:

[0207] 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.

[0208] Alternatively, the formula test result indicates a pass or a fail.

[0209] 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 advantage of doing 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 and 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.

[0210] The present application does not limit the method for performing formula detection, for example, a formula detection model based on deep learning can be used.

[0211] In some embodiments, the method further comprises:

[0212] When the formula is wrong, improvement prompt information corresponding to the custom indicator is generated and displayed on the terminal device.

[0213] Therefore, the real-time feedback mechanism provides users with instant guidance, which helps users 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, so as to avoid 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.

[0214] (Indicator Editor)

[0215] See also Figure 2 , Figure 2 It is a schematic diagram of an interface of an indicator editor provided by one embodiment of the present application.

[0216] The present application provides an indicator editor, which is used to provide a custom indicator generation interface, and the custom indicator generation interface includes:

[0217] 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;

[0218] A formula editing box, used to display each primary operator determined by the user in the form of an operator control, and to receive a rule selection operation from the user on the drop-down list box in the operator control to determine the data processing rule corresponding to each primary operator;

[0219] An indicator generation button is used to generate a custom indicator according to at least one primary operator and its corresponding data processing rule after being clicked, wherein the custom indicator is an indicator of a custom category;

[0220] The custom indicator can act as a primary operator to generate a new custom indicator.

[0221] In some possible implementations, the operator control further includes:

[0222] The secondary filter is used to determine at least one secondary operator corresponding to the primary operator and its corresponding secondary filtering rule.

[0223] In some possible implementations, the custom indicator generation interface further includes:

[0224] A tooltip control is used to display the prompt information corresponding to the formula edit box when a mouse hover operation is received.

[0225] A combo box is a composite control in a computer graphical user interface that consists of a list or a drop-down list and a single-line editable text box, allowing the user to directly type in a value 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 a custom indicator. The combo box may include, for example, a text box and a list box, which supports 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.

[0226] The formula editing box is an interface element used to display each primary operator selected by the user and its corresponding data processing rules and operation rules. The user can set the rules and operation methods of each primary operator here. As an example, the user can set a custom formula such as "sales amount - refund amount" in the formula editing box.

[0227] The formula edit box displays each primary operator determined by the user in the form of an operator control. In the operator control, the user can select the data processing rule corresponding to each primary operator, which instructs the data analysis system how to perform the data processing process corresponding to the selected operator. Different data processing rules can be used for different types of indicators, such as numerical, character, time or geographical indicators.

[0228] The formula editing box is also used to receive the user's fifth text input operation and / or fifth selection operation to determine the operation rules between the primary operators. The user can select different operation rules, such as operation level and arithmetic operator, comparison operator or logical operator, to define the data relationship, operation order, operation priority, etc. between the primary operators.

[0229] Operator controls refer to the interface elements used to display and manage each primary operator in the formula edit box, including a drop-down list box. Users can set the data processing rules of the operator in the operator control. As an example, users can select "Sum" as the data processing rule in the operator control.

[0230] The operator control can also include a secondary filter for users to further refine data processing and filtering. Through the secondary filter, users can determine at least one secondary operator corresponding to the primary operator and the corresponding secondary filtering rule to help users perform secondary filtering on the processed data set. As an example, the user 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.

[0231] The data set to be processed refers to the raw data that the user wants to analyze and process. As an example, the data set to be processed includes the e-commerce sales data of online stores in 2022, including information such as sales volume, purchase quantity, and refund amount.

[0232] A tooltip control is an interface element that displays additional information or instructions when the user hovers the mouse over the tooltip control. In the indicator editor, the tooltip control is used to provide relevant prompt information for the first-level operator to help users better understand and select the operator. As an example, when the user hovers the mouse over the "Sales" operator in the operator control, the tooltip control will display a message such as "Sales represents the sales amount of the online store." As an example, the icon of the tooltip control is "i" (such as Figure 2 shown).

[0233] The advantage of this is that it provides an intuitive interface where users can easily select primary operators, define data processing rules and calculation rules without programming or complex operations; the formula editing box displays the primary 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 primary operator, thereby improving the accuracy of data processing; users can select different calculation rules according to their needs to define the data relationship between primary 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 definition 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.

[0234] Set up secondary filters in the operator control, and users can use them to manage primary operators and their related 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, which instruct the system how to use the secondary operators to process the data of primary operators. Secondary filtering rules can include conditions, operations, and parameters. After determining at least one secondary operator and the corresponding secondary filtering rules, the system will update the to-be-processed data set corresponding to the selected primary operator in a secondary filtering manner, and the to-be-processed data set corresponding to the primary operator will be affected by the secondary operators and 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; the 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.

[0235] The tooltip control is designed to provide users with an informative way to understand the usage and functions of the formula edit box. When the user hovers the mouse over the tooltip control, the tooltip information is triggered and displayed. The tooltip information contains text about the description, purpose and function of the primary operator (or, its corresponding secondary operator) in the formula edit box, which helps users better understand the meaning of each link and component of indicator editing, so that they can make wise decisions when selecting and configuring operators. The tooltip control is triggered to display when the user hovers the mouse over the tooltip control, so that users can get information about the formula edit box when needed without leaving the current interface or consulting the documentation. The benefits of this are that by displaying the tooltip information, users can understand the functions and usage of the formula edit box, so that they can better select appropriate operators and rules; reduce the risk that users may make wrong choices or configurations due to not understanding the operators, thereby reducing potential errors; by providing clear information, users' understanding of the primary operators and their trust in the results of data analysis are enhanced, making them more willing to use and explore data processing tools; allowing users to quickly obtain information when hovering without additional steps or queries; and new users can more easily understand and use the system.

[0236] In a specific application scenario, an embodiment of the present application provides an indicator editor, which is used to provide a custom indicator generation interface, and the custom indicator generation interface includes:

[0237] 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;

[0238] A formula editing box, used to display each primary operator determined by the user in the form of an operator control, and to receive a rule selection operation from the user on the drop-down list box in the operator control to determine the data processing rule corresponding to each primary operator;

[0239] An indicator generation button is used to generate a custom indicator according to at least one primary operator and its corresponding data processing rule after being clicked, wherein the custom indicator is an indicator of a custom category;

[0240] A tooltip control, used to display the prompt information corresponding to the formula edit box when receiving a mouse hover operation;

[0241] The custom indicator can act as a primary operator to generate a new custom indicator.

[0242] 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. At this time, the indicator generation button is used to generate the custom indicator according to at least one primary operator and its corresponding data processing rule, at least one secondary operator and its corresponding secondary filtering rule after being clicked.

[0243] For example, user C needs to evaluate the R&D activity in a certain technology field. User C logs in to the data analysis platform and clicks "Create custom indicator" to enter the custom indicator generation interface provided by the indicator editor. The custom indicator generation interface includes the indicator name text input box, combo box, formula edit box, indicator generation button, tooltip control, and 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 description information.

[0244] The combo box includes a search text input box and a list box. The list box uses a hierarchical list, including three sublists, namely "My Favorites", "Basic Indicators", and "Custom Indicators". When user C clicks on the name of each sublist, the corresponding sublist is displayed.

[0245] User C enters "Patent Value" as the first text input operation in the search text input box, and determines "Patent Value" as a primary operator. The first-level operator "Patent Value (USD)" just determined is displayed in the formula edit box on the right side of the combo box. In this operator control, a drop-down list box is provided. User C clicks on the drop-down list box, and a series of data processing rules appear, such as "Average", "Sum", "Count", etc. User C selects the "Sum" rule, and enters "*7.2" on the right side of the first-level operator, indicating the conversion of USD into RMB.

[0246] User C continues to input “ / ”, which represents the division operator.

[0247] User C selects "Inventor" as the second first-level operator through the combo box, and selects "Deduplication Count" as the data processing rule of the first-level operator in the operator control corresponding to "Inventor".

[0248] User C continues to input “ / ”, which represents the division operator.

[0249] User C uses the combo box to select "Application Year" as the third primary operator, and selects "Duplicate Count" as the data processing rule of the primary operator.

[0250] Next, user C enters in the indicator name text input box: annual per capita inventor output value (RMB).

[0251] A secondary filter icon is also set in each operator control. User C clicks the secondary filter of the primary operator "Patent Value (USD)", sets a secondary operator "Receiving Office", and selects "Russia", "UK", and "Europe", implementing the following secondary filtering rules: filtering out data with receiving offices in Russia, the UK, and Europe.

[0252] Afterwards, user C enters in the description text input box: This indicator describes the value of patents produced by each inventor each year.

[0253] In the formula editing box, the position (or order) of each primary operator in the formula can be adjusted by dragging.

[0254] In addition, in the lower left corner of the formula editing box, the calculation time (for example, 3.9 seconds) required to analyze the indicator can be displayed in real time.

[0255] At the same time, when user C hovers the mouse over the tooltip control, the prompt information corresponding to the formula edit box is displayed, such as: "Click the indicator on the left to create a formula."

[0256] After completing the above operations, user C clicks the "Generate Indicator" (or "Save", "Submit") button on the interface. Based on the primary operator selected by user C and its data processing rules as well as the operation rules between primary operators, a new custom indicator is generated - "Annual per capita inventor output value (RMB)".

[0257] The edited custom indicator will appear at the top of the sublist of custom indicators. When user C hovers the mouse over the custom indicator, a summary of the custom indicator (i.e., indicator summary) is displayed on the right side of the custom indicator in the form of a floating window, including the indicator name, calculation formula, indicator description, update date, creation date, etc. The edited custom indicator can be edited and deleted again.

[0258] (Electronic equipment)

[0259] One embodiment of the present application provides an electronic device, and its specific implementation is similar to the implementation described in the above method implementation and the technical effects achieved, and some contents are not repeated here.

[0260] 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.

[0261] 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.

[0262] See also Figure 3 , Figure 3 It is a structural block diagram of an electronic device 10 provided in one embodiment of the present application.

[0263] The electronic device 10 may include, for example, at least one memory 11 , at least one processor 12 , and a bus 13 connecting different platform systems.

[0264] It is 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 (programmable ROM, PROM), an erasable programmable read-only memory (erasable PROM, 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 the implementation of a network environment.

[0265] Accordingly, the processor 12 can execute the above computer program, and can execute the utility 114. It can be understood that the processor 12 in this specification can be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method implementation can be completed by the hardware integrated logic circuit or software instructions in the processor 12. The above processor 12 can be a general processor, a digital signal processor (Digital Signal Processor, DSP), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), a field programmable gate array (Field Programmable Gate Array, FPGA), a programmable logic device (Programmable Logic Controller, PLC), a complex programmable logic device (Complex Programmable Logic Device, CPLD) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, and can implement or execute the various methods, steps and logic block diagrams disclosed in this specification. The general processor can be a microprocessor, or it can also be any conventional processor, etc. The steps of the method disclosed in this specification can be directly embodied as a hardware decoding processor to execute, or it can be executed by a combination of hardware and software modules in the decoding processor. The software module may be located in a mature storage medium in the art, such as a random access memory (RAM) 111, a flash memory, a read-only memory (ROM) 113, a programmable read-only memory, an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 11, and the processor 12 reads the information in the memory 11 and completes the steps of the above method in combination with its hardware.

[0266] The bus 13 may be a local bus representing 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 any of a variety of bus architectures.

[0267] 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 that can interact with the electronic device 10, and / or communicate with any device (such as a router, a modem, etc.) that enables the electronic device 10 to communicate with one or more other computing devices. Such communication may be performed through an input / output interface 14. In addition, the electronic device 10 may also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN) and / or a public network, such as the Internet) through a network adapter 15. The network adapter 15 may communicate with other modules of the electronic device 10 through the bus 13. It should be understood that, although not shown in the figure, 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.

[0268] (Computer readable storage medium)

[0269] One embodiment of the present application provides a computer-readable storage medium, and its specific implementation is similar to the implementation described in the above method implementation and the technical effects achieved, and some contents are not repeated here.

[0270] 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 any of the above methods or implements the functions of any of the above indicator editors.

[0271] (Computer Program Product)

[0272] The implementation method of the present application also provides a computer program product, and its specific implementation method is similar to the implementation method described in the above method implementation method and the technical effects achieved, and some contents will not be repeated here.

[0273] The computer program product comprises a computer program, and when the computer program is executed by at least one processor, the computer program implements the steps of any of the above methods or implements the functions of any of the above indicator editors.

[0274] The computer program product may be a portable compact disk 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.

[0275] 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 provide corresponding operation entrances for users to choose to authorize or refuse.

[0276] It should be understood that the specific examples in this specification are only intended to help those skilled in the art to better understand the implementation methods of the present application, rather than to limit the scope of the present invention.

[0277] It is understood that in the various implementations of this specification, the size of the sequence 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.

[0278] It can be understood that the various implementation modes described in this specification can be implemented individually or in combination, and this application is not limited to this.

[0279] Unless otherwise specified, all technical and scientific terms used in this specification have the same meaning as those generally understood by those skilled in the art of 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 related listed items. The singular forms of "a", "above", and "the" used in this specification and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings.

[0280] 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. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this specification.

[0281] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method implementation methods and will not be repeated here.

[0282] 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 implementation described above is only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, 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.

[0283] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0284] In addition, each functional unit in each embodiment of the present 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.

[0285] 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, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of this specification. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., various media that can store program codes.

[0286] The above is only a specific implementation of this specification, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in this specification, which should be included in the protection scope of this specification. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for generating a custom indicator, characterized in that: The method comprises: Determine at least one primary operator and its corresponding data processing rule, where the primary operator is an indicator of a basic category or a custom category; Generate a custom indicator according to at least one primary operator and its corresponding data processing rule, 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 process of determining at least one primary operator includes: Receiving one or more first selection operations of a user on an indicator set through a terminal device to determine one or more primary operators; or, Receiving one or more first text input operations of the user through the terminal device, and identifying one or more primary operators according to the received 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 the user; semantic extraction is performed on each first input information to determine one or more primary operators.

3. The method for generating a custom indicator according to claim 1, characterized in that: The process of determining the data processing rules for each primary operator includes: For each first-level operator, perform the following processing: According to the data processing rule set corresponding to the data type of the first-level operator, one of the data processing rules is determined 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 claim 1, characterized in that: After determining at least one primary operator and its corresponding data processing rule, the method further includes: For one or more primary operators, perform the following processing: Determine at least one secondary operator corresponding to the primary operator and its corresponding secondary filtering rule; 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 its corresponding data processing rule includes: The custom indicator is generated according to at least one primary operator and its corresponding data processing rule, at least one secondary operator and its corresponding secondary filtering rule.

5. The method for generating a custom indicator according to claim 4, characterized in that: 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 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 the user through the terminal device, and identifying one or more secondary operators according to the received second text input operations; The process of determining the secondary filtering rules corresponding to each secondary operator includes: receiving a third text input operation and / or a third selection operation of the user through the terminal device 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 one of the judgment types 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 claim 1, characterized in that: When the number of primary operators used to generate the custom indicator is greater than one, the method further includes: Determine the operation rules between primary operators; The step of generating a custom indicator according to at least one primary operator and its corresponding data processing rule includes: The custom indicator is generated according to each primary operator and its corresponding data processing rule and the operation rules between the primary operators.

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

9. 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 primary operator determined by the user in the form of an operator control, and to receive a rule selection operation from the user on the drop-down list box in the operator control to determine the data processing rule corresponding to each primary operator; An indicator generation button is used to generate a custom indicator according to at least one primary operator and its corresponding data processing rule 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.

10. The indicator editor according to claim 9, 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 its corresponding secondary filtering rule.

11. The indicator editor according to claim 9, 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.

12. 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 8 or implement the functions of the indicator editor according to any one of claims 9 to 11 when executing the computer program.

13. 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 steps of the method according to any one of claims 1 to 8 or the functions of the indicator editor according to any one of claims 9 to 11 are implemented.

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