Data display methods and terminal devices based on indicator trees

Abstract

This disclosure presents a data display method and terminal device based on indicator trees. One specific implementation of the method includes: receiving a target indicator input by a user; determining a calculation rule indicator; determining an analysis method; determining a candidate data sequence based on the target indicator, the calculation rule indicator, and the analysis method; and determining the target data based on the candidate data sequence. This implementation constructs the data indicators into an indicator tree model set, locates the target indicator tree model based on the indicator tree model set, and performs causal analysis, effect-cause analysis, and bidirectional analysis on the target indicator tree model using the determined analysis method. This improves the accuracy of determining the target data and controls the target device to improve the level of action completion.

Description

Technical Field

[0001] The embodiments disclosed herein relate to the field of computer technology, specifically to information generation and display methods and terminal devices. Background Technology

[0002] Data analysis refers to the analysis of large amounts of collected data using appropriate statistical analysis methods, summarizing, understanding, and digesting the data to maximize its functionality and effectiveness. Data analysis is the process of detailed study and summarization of data to extract useful information and draw conclusions. By displaying the results of data analysis, it is possible to control external devices to issue warning messages or perform warning actions. Therefore, the data analysis results can be used to assist subsequent tasks and improve their completion.

[0003] However, when using the above methods for data analysis and display, the following technical problems often arise:

[0004] First, when the amount of data in the database is huge, finding and determining the relationships between data is time-consuming and has low accuracy, resulting in inaccurate judgments about factors that affect the target data and factors that are affected by the target data.

[0005] Second, traditional data analysis methods generally perform causal analysis, which can only find the results affected by the target data, but cannot analyze and judge the relationships between the data in reverse. This makes it difficult to handle complex data analysis, affecting the level of data analysis and resulting in low accuracy in finding the target data. Summary of the Invention

[0006] The summary portion of this disclosure is intended to provide a brief overview of the concepts, which will be described in detail in the detailed description portion. This summary portion is not intended to identify key or essential features of the claimed technical solutions, nor is it intended to limit the scope of the claimed technical solutions.

[0007] Some embodiments of this disclosure propose data display methods, electronic devices, and computer-readable media based on indicator trees to address one or more of the technical problems mentioned in the background section above.

[0008] In a first aspect, some embodiments of this disclosure provide a data display method based on an indicator tree, the method comprising: receiving a target indicator input by a user; determining a calculation rule indicator; determining an analysis method; determining a candidate data sequence based on the target indicator, the calculation rule indicator, and the analysis method; and determining target data based on the candidate data sequence.

[0009] In a second aspect, some embodiments of this disclosure provide a terminal device, including: one or more processors; and a storage device having one or more programs stored thereon, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement any of the methods in the first aspect.

[0010] The above embodiments of this disclosure have the following beneficial effects: The data display method based on indicator trees in some embodiments of this disclosure can construct a set of indicator tree models for the data indicators. Based on the indicator tree model set, a target indicator tree model can be found. Using the determined analysis method, causal analysis, effect-cause analysis, and bidirectional analysis are performed on the target indicator tree model, which can improve the accuracy of determining target data and control the target device to improve the level of action completion. Specifically, the inventors have found that the reason for the poor accuracy of judging target data is that when the data volume is very large, the speed and efficiency of analyzing and finding target data are greatly affected. Furthermore, traditional methods can only perform unidirectional causal analysis, that is, using the target indicator as a cause for analysis and the result of finding the target indicator as the target data. They cannot use the target indicator as a result for analysis and to find the target data affecting the target indicator, thus affecting the accuracy of the target data. Based on this, firstly, some embodiments of this disclosure construct a set of indicator tree models for the indicators in the historical database. Each indicator tree model corresponds to a data topic, thereby establishing a correlation between data of the same topic. Then, the target indicator is matched with the set of indicator tree models to find the target indicator tree model. Secondly, the analytical methods are determined, including causal analysis, effect-cause analysis, and bidirectional analysis, enabling multi-dimensional analysis of the target indicator tree model. Finally, based on the target indicators, calculation rule indicators, and analytical methods, candidate data sequences are determined. Based on these candidate data sequences, the target data is then identified. During the process of determining the candidate data sequences, analysis can be performed according to the indicator tree ensemble model, improving the efficiency and accuracy of the analysis. Furthermore, causal analysis, effect-cause analysis, and bidirectional analysis can be performed as needed to further improve the accuracy of determining the target data, thereby controlling the target equipment to improve its performance. Attached Figure Description

[0011] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and the originals and elements are not necessarily drawn to scale.

[0012] Figure 1 This is an architecture diagram of an exemplary system to which some embodiments of this disclosure can be applied;

[0013] Figure 2This is a flowchart of some embodiments of the indicator tree-based data display method according to the present disclosure;

[0014] Figure 3 This is an example of the effect of an indicator tree model;

[0015] Figure 4 This is the effect of the organization dimension table in the set of two-dimensional tables corresponding to the exemplary indicator tree model;

[0016] Figure 5 This is a schematic diagram of the structure of a terminal device suitable for implementing some embodiments of the present disclosure. Detailed Implementation

[0017] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0018] It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings. Unless otherwise specified, the embodiments and features described in this disclosure can be combined with each other.

[0019] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0020] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0021] This disclosure will now be described in detail with reference to the accompanying drawings and embodiments.

[0022] Figure 1 An exemplary system architecture 100 is shown, in which embodiments of the indicator tree-based data display method of this disclosure can be applied.

[0023] like Figure 1 As shown, system architecture 100 may include terminal devices 101, 102, and 103, a network 104, and a server 105. Network 104 serves as the medium for providing communication links between terminal devices 101, 102, and 103 and server 105. Network 104 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.

[0024] Users can use terminal devices 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc. Various communication client applications, such as data generation applications, data display applications, and data analysis applications, can be installed on terminal devices 101, 102, and 103.

[0025] Terminal devices 101, 102, and 103 can be either hardware or software. When terminal devices 101, 102, and 103 are hardware, they can be various terminal devices with displays, including but not limited to smartphones, tablets, laptops, and desktop computers. When terminal devices 101, 102, and 103 are software, they can be installed on the terminal devices listed above. They can be implemented as multiple software programs or software modules (e.g., used to provide target indicator input), or as a single software program or software module. No specific limitations are made here.

[0026] Server 105 can be a server that provides various services, such as a server that stores target data input by terminal devices 101, 102, and 103. The server can process the received target metrics and feed back the processing results (such as target data) to the terminal devices.

[0027] It should be noted that the data display method based on indicator trees provided in this embodiment can be executed by the server 105 or by the terminal device.

[0028] It should be noted that the target metrics can also be stored locally on the server 105. The server 105 can directly extract the local data and obtain the target data after processing. In this case, the exemplary system architecture 100 may not include the terminal devices 101, 102, 103 and the network 104.

[0029] It should also be noted that data display applications may also be installed on terminal devices 101, 102, and 103. In this case, the processing method may also be executed by terminal devices 101, 102, and 103. In this case, the exemplary system architecture 100 may not include server 105 and network 104.

[0030] It should be noted that server 105 can be either hardware or software. When server 105 is hardware, it can be implemented as a distributed server cluster consisting of multiple servers, or as a single server. When server 105 is software, it can be implemented as multiple software programs or software modules (e.g., used to provide data display services), or as a single software program or software module. No specific limitations are made here.

[0031] It should be understood that Figure 1The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of electronic devices, networks, and servers can be included.

[0032] Continue to refer to Figure 2 The diagram illustrates a flow 200 of some embodiments of a tree-based data display method according to the present disclosure. This tree-based data display method includes the following steps:

[0033] Step 201: Receive the target metrics input by the user.

[0034] In some embodiments, the entity executing the indicator tree-based data display method (e.g.) Figure 1 The server shown can obtain the target metrics via wired or wireless connection. It should be noted that the aforementioned wireless connection methods may include, but are not limited to, 3G / 4G connections, WiFi connections, Bluetooth connections, WiMAX connections, Zigbee connections, UWB (ultra wideband) connections, and other currently known or future wireless connection methods.

[0035] Optionally, before receiving the target metrics input by the user, the aforementioned executing entity determines a set of metrics based on historical data. Specifically, the historical data can be business data stored in a database. Based on the business data stored in the database, a set of business-related metrics is determined. Specifically, the database can store the company's employee salary data. By analyzing the employee salary data, metrics such as employee level, years of service, and length of service with the company can be determined to form the metric set.

[0036] Optionally, the aforementioned execution entity generates an indicator tree model set based on the indicator set. This indicator tree model set includes a first set of indicator tree models. Each indicator tree model is a tree-structured model generated by retrieving the corresponding set of database table names and field contents from the database based on the indicator set. The root node of each indicator tree model reflects a theme, and the leaf nodes include the database table names and field contents corresponding to the indicators related to that theme. Based on different themes in historical data, the indicator tree model set contains indicator tree models corresponding to a first set of themes. The root node of each indicator tree model is the basic indicator data corresponding to that theme. Specifically, for a personnel salary database, the length of service of personnel can be a theme. The root node can be "10", representing 10 years of service. The root node can include two leaf nodes, "less than 10" and "greater than 10", representing less than 10 years or more of service, respectively. Each leaf node can also correspond to its own child leaf node; the corresponding leaf node is called the parent node, and its corresponding child leaf node is called the child node. Drilling down the root node of the indicator tree model allows you to find child nodes at different levels within the topic.

[0037] Optionally, for each indicator tree model in the indicator tree model set, a set of two-dimensional tables corresponding to that indicator tree model is generated. This set of two-dimensional tables includes organizational dimension tables, regional dimension tables, product dimension tables, indicator dimension tables, and time dimension tables.

[0038] Continue to refer to Figure 3 This demonstrates the effect of an exemplary index tree model.

[0039] exist Figure 3 In this application scenario, the theme corresponding to this indicator tree model is "Business Unit Revenue". The root node of this indicator tree model corresponds to four leaf nodes, namely "Product Revenue", "Operations and Maintenance Revenue", "Pre-sales Revenue", and "Secondary Revenue". In this example, by drilling down the root node, the leaf nodes related to the theme of "Business Unit Revenue" can be found, that is, the relevant indicators, and then the corresponding data can be found.

[0040] Continue to refer to Figure 4 This illustrates the effect of the organization dimension table in a set of two-dimensional tables corresponding to an exemplary indicator tree model.

[0041] exist Figure 4In the application scenario, the organizational dimension table corresponding to the indicator tree model of the "Business Unit Revenue" theme is displayed. The organizational dimensions include "Big Data Business Unit," "Strategic Development Department," "Management Innovation Business Unit," "Beijing Branch," "Zhejiang Subsidiary," and "Qingdao Branch." The first four rows of this two-dimensional table correspond to the leaf nodes of "Secondary Transfer Revenue," "Pre-sales Revenue," "Operations and Maintenance Revenue," and "Product Revenue," respectively, as well as the related leaf nodes corresponding to the root node of the "Business Unit Revenue" theme. These represent the set of database table names and field content sets corresponding to the relevant indicators, thus allowing the retrieval of the corresponding data.

[0042] The optional content in step 201 above, namely, "generating a set of indicator tree models," is an inventive point of this disclosure, solving the technical problem mentioned in the background art: "When the amount of data in the database is huge, searching and determining the relationship between data is time-consuming and has low accuracy, resulting in inaccurate judgment of factors related to the target data and factors affected by the target data, i.e., the target data itself." The factors that cause the time-consuming and inaccurate search and determination of data relationships are often as follows: the amount of data in the database is very huge, and each piece of data corresponds to a set of database table names and a set of field contents. These data are stored in different database tables according to their different sources. Searching each database table one by one during the search process is time-consuming and has low accuracy. If the above factors are solved, the level of searching and determining data relationships can be improved. To achieve this effect, this disclosure constructs an indicator tree model set based on indicators from the historical database. First, based on the historical data, an indicator set is determined. Key indicators affecting the historical data can be determined according to the specific database situation, thereby obtaining an indicator set corresponding to the historical data. Then, based on the indicator set, an indicator tree model set is generated. An indicator tree model is constructed for each topic. The root node of the indicator tree model is the basic indicator data, and its leaf nodes are the child nodes after drill-down processing. Finally, for each indicator tree model in the indicator tree model set, a corresponding two-dimensional table set is generated. Displaying the indicator tree model in the form of a two-dimensional table set improves the display effect. After finding the indicator tree model based on the topic, matching using the tree structure improves the matching efficiency and the accuracy of the search, thus solving the first technical problem.

[0043] Step 202: Determine the calculation rule indicators.

[0044] In some embodiments, the execution entity determines calculation rule indicators. Specifically, calculation rule indicators include addition, subtraction, multiplication, division, and remainder.

[0045] Step 203: Determine the analysis method.

[0046] In some embodiments, the execution entity determines the analysis method. Optionally, the analysis method includes a causal analysis method, an effect-cause analysis method, and a bidirectional analysis method. The causal analysis method determines the value of the leaf node based on the root node. The effect-cause analysis method determines the value of the root node based on the set of leaf nodes. The bidirectional analysis method determines the value of the leaf node based on both the root node and the set of leaf nodes.

[0047] Step 204: Based on the target indicators, calculation rule indicators, and analysis methods, determine the candidate data sequence.

[0048] In some embodiments, the execution entity matches the target metric with the set of root nodes in the metric tree model set to find the target metric tree model. The target metric tree model includes a second number of leaf nodes.

[0049] Optionally, the target indicator tree model is analyzed according to the calculation rules and analysis methods to obtain candidate data sequences. Assuming the analysis method is causal analysis, for each leaf node in the target indicator tree model, the indicator score for that leaf node is generated using the following formula according to the calculation rules to obtain the indicator score set:

[0050] b = -w T m,

[0051] Where m is the numerical mean of the leaf node. x i Let be the i-th value in the database field corresponding to this leaf node, where i is the index. Let n be the total number of values ​​in the database field corresponding to this leaf node. w satisfies... λ is the control parameter, which can be any integer; w is the control matrix; and b is the index score.

[0052] For each indicator score in the indicator score set, reorder the scores from largest to smallest to generate an indicator score sequence. The set of leaf nodes corresponding to the third-largest number of indicator scores in the indicator score sequence is then selected as the candidate data sequence.

[0053] Specifically, causal analysis can determine the outcome based on the cause. According to the calculation rules, the results calculated by the child nodes of the target indicator tree model are assigned to the parent node. A set of indicator scores corresponding to each child node that can be assigned to the parent node is generated, thus determining the candidate data sequence.

[0054] Optionally, in response to the causal analysis method, for each leaf node in the target indicator tree model, the influence score of that leaf node is generated according to the calculation rules using the following formula to obtain the influence score set:

[0055] y i =w T x i+γ,

[0056] Where, x i Let be the i-th value in the database field corresponding to this leaf node, where i is the index. w satisfies... n is the total number of values ​​in the database field corresponding to the leaf node, λ is the control parameter (λ can be any integer), w is the control matrix, γ represents the bias parameter (γ can be any integer), and y i To affect the score.

[0057] For each influence score in the influence score set, reorder them from largest to smallest to generate an influence score sequence. The set of leaf nodes corresponding to the third-largest number of influence scores in the influence score sequence is then selected as the candidate data sequence.

[0058] Specifically, cause-effect analysis can determine the cause based on the result. According to the calculation rules, the value of the root node of the target indicator tree model is proportionally allocated to the child nodes, generating an influence score set corresponding to each child node, and determining the candidate data sequence.

[0059] Optionally, the response analysis method is a two-way analysis method. The causal analysis method is executed to obtain the indicator score sequence. The causal analysis method is then executed to obtain the influence score sequence. The sum of the set of leaf nodes corresponding to the first three index scores in the indicator score sequence and the set of leaf nodes corresponding to the first three influence scores in the influence score sequence is determined as the candidate data sequence.

[0060] Step 205: Determine the target data based on the candidate data sequence.

[0061] In some embodiments, the execution entity determines the target data based on a candidate data sequence. Specifically, the first candidate data in the candidate data sequence can be determined as the target data.

[0062] Optionally, the aforementioned executing entity controls the target device to perform target operations based on the target data. The target device can be a device communicatively connected to the executing entity, capable of performing operations corresponding to the received data. For example, if the target data received by the target device is "product revenue," it can be determined that the main indicator affecting the business unit's revenue is product revenue. The target device can display "product revenue" on its screen to issue a warning. This automated operation method emphasizes the status of the target data, thereby improving the accuracy and convenience for users to make judgments and decisions regarding the target data.

[0063] The optional content in steps 203-205 above, namely, "determining the target data," is an inventive point of this disclosure, solving the second technical problem mentioned in the background: "Traditional data analysis methods generally perform causal analysis, that is, they can only find the results affected by the target data, but cannot analyze and judge the relationships between the data in reverse, cannot handle complex data analysis, affect the level of data analysis, and result in low accuracy in finding the target data." Factors leading to low data analysis level and low accuracy in finding target data are often as follows: a single causal analysis method cannot accurately determine the target data. If the above factors are solved, the effect of improving the data analysis level and the accuracy of determining the target data can be achieved. To achieve this effect, this disclosure uses causal analysis, effect-cause analysis, and bidirectional analysis methods for data analysis. First, the specific method used for data analysis is determined. Then, causal, effect-cause, and bidirectional data analysis is performed on the target indicator according to different analysis methods. Responding to the analysis method being effect-cause analysis, the cause is determined based on the result. According to the calculation rules, the value of the root node of the target indicator tree model is proportionally allocated to the child nodes, generating an influence score set corresponding to each child node. The response analysis method is a causal analysis method, determining the result based on the cause. According to the calculation rules, the results calculated from the child nodes of the target indicator tree model are assigned to the parent node. A set of indicator scores corresponding to each child node that can be assigned to the parent node is generated. The response analysis method is a bidirectional analysis method, simultaneously determining the cause based on the result and determining the result based on the cause. The scores of the child nodes are predicted twice. Through this complex data analysis, the factors affecting the target indicator, various calculation rules, and results can be accurately identified, thereby improving the level of data analysis and increasing the accuracy of determining the target data, thus solving technical problem two.

[0064] Figure 2 One embodiment provides the following beneficial effects: receiving target indicators input by the user; determining calculation rule indicators; determining analysis methods; determining candidate data sequences based on the target indicators, calculation rule indicators, and analysis methods; and determining target data based on the candidate data sequences. This implementation constructs the data indicators into an indicator tree model set, finds the target indicator tree model based on the indicator tree model set, and uses the determined analysis methods to perform causal analysis, effect-cause analysis, and bidirectional analysis on the target indicator tree model. This improves the accuracy of determining target data and controls the target device to improve the level of action completion.

[0065] The following is for reference. Figure 5 It shows a schematic diagram of the structure of a computer system 500 suitable for implementing the server of the present disclosure embodiments. Figure 5 The server shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments disclosed herein.

[0066] like Figure 5 As shown, the computer system 500 includes a central processing unit (CPU) 501, which can perform various appropriate actions and processes based on programs stored in read-only memory (ROM) 502 or programs loaded from storage section 508 into random access memory (RAM) 503. The RAM 503 also stores various programs and data required for the operation of the system 500. The CPU 501, ROM 502, and RAM 503 are interconnected via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0067] The following components are connected to I / O interface 505: storage section 506, including hard disks, etc.; and communication section 507, including network interface cards such as LAN (Local Area Network) cards, modems, etc. Communication section 507 performs communication processing via a network such as the Internet. Drive 508 is also connected to I / O interface 505 as needed. Removable media 509, such as disks, optical disks, magneto-optical disks, semiconductor memories, etc., are installed on drive 508 as needed so that computer programs read from them can be installed into storage section 506 as needed.

[0068] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 507, and / or installed from removable medium 509. When the computer program is executed by central processing unit (CPU) 501, it performs the functions defined in the methods of this disclosure. It should be noted that the computer-readable medium described in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which can transmit, propagate, or transfer a program for use by or in connection with an instruction execution system, apparatus, or device. Program code contained on a computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0069] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0070] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0071] The above description is merely a preferred embodiment of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features disclosed in this disclosure that have similar functions.

Claims

1. A data display method based on indicator trees, comprising: Determine the set of indicators based on historical data; Based on the set of indicators, a set of indicator tree models is generated, wherein the root node of the indicator tree model reflects a theme, and the leaf nodes of the indicator tree model include the database table name and field content of the indicators related to the theme in the database. Target metrics received from user input; Determine the calculation rules and indicators; The analysis method is determined, wherein the analysis method includes a causal analysis method, an effect-cause analysis method, and a bidirectional analysis method, wherein the causal analysis method determines the value of the leaf node based on the root node, the effect-cause analysis method determines the value of the root node based on the set of leaf nodes, and the bidirectional analysis method determines the value of the leaf node based on the root node and the value of the root node based on the set of leaf nodes; Based on the target indicator, the calculation rule indicator, and the analysis method, the candidate data sequence is determined, including: matching the target indicator with the root node set in the indicator tree model set to find the target indicator tree model, wherein the target indicator tree model includes a second number of leaf nodes; and analyzing the target indicator tree model according to the calculation rule and the analysis method to obtain the candidate data sequence. The response analysis method is a causal analysis method. For each leaf node in the target indicator tree model, the indicator score of that leaf node is generated according to the calculation rules using the following formula to obtain the indicator score set: ， wherein m is a numerical average of the leaf node, ; is the i-th numerical value in the database field corresponding to the leaf node, i is a serial number; and n is the total number of numerical values in the database field corresponding to the leaf node; satisfies ; is a control parameter, is any integer, is a control matrix, and b is an index score; The response analysis method is a cause-and-effect analysis method. For each leaf node in the target indicator tree model, the influence score of that leaf node is generated according to the calculation rules using the following formula to obtain the influence score set: ， in, This is the i-th value in the database field corresponding to the leaf node, where i is the index; satisfy ; n is the total number of values ​​in the database field corresponding to this leaf node. For control parameters, It is any integer. For the control matrix, Indicates the bias parameter. For any integer, To affect the score; Based on the set of indicator scores and / or the set of influence scores, a candidate data sequence is generated; Based on the candidate data sequence, the target data is determined.

2. The method according to claim 1, wherein, The indicator tree model set includes a first number of indicator tree models, which are generated by retrieving the corresponding set of database table names and field content from the database based on the indicator set.

3. The method according to claim 2, wherein, Before receiving the target metric input by the user, the method further includes: For each indicator tree model in the indicator tree model set, a set of two-dimensional tables corresponding to that indicator tree model is generated, wherein the set of two-dimensional tables includes an organization dimension table, a region dimension table, a product dimension table, an indicator dimension table, and a time dimension table.

4. The method according to claim 3, wherein, The step of generating a candidate data sequence based on the set of indicator scores and / or the set of influence scores includes: For each indicator score in the set of indicator scores, reorder the scores from largest to smallest to generate an indicator score sequence. The set of leaf nodes corresponding to the third-highest number of index scores in the index score sequence is determined as the candidate data sequence.

5. The method according to claim 4, wherein, The step of generating a candidate data sequence based on the set of indicator scores and / or the set of influence scores includes: For each influence score in the influence score set, reorder them from largest to smallest to generate an influence score sequence; The set of leaf nodes corresponding to the third-highest number of influence scores in the influence score sequence is determined as the candidate data sequence.

6. The method according to any one of claims 1-5, wherein, The method further includes: Based on the target data, control the target device to perform the target operation.

7. A first terminal device, comprising: One or more processors; A storage device on which one or more programs are stored; When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1-6.

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