Missing data positioning method, device and equipment and readable storage medium

Abstract

The invention discloses a missing data positioning method, device and equipment and a readable storage medium, and the method comprises the steps: analyzing and determining a multi-level label hierarchical relationship between target data and source data based on a rule engine, and sequentially calculating the values of at least one atomic label and at least one derivative label contained in the target data, according to the method, the target data and the source data are subjected to data verification, the missing source data with abnormities are verified in combination with the composition relation between the atomic tag and the at least one derivative tag, compared with data verification achieved through SQL statements, the rule engine can achieve more convenient rule configuration, meanwhile, the logic relation or mathematical relation between the target data and the source data can be clearer due to the existence of the tags, and the user experience is improved. According to the method, the technical problems of slow adjustment response timeliness, difficulty in maintenance, difficulty in positioning missing data and incapability of intuitively displaying rules in a means for realizing complex logic rule configuration among data by adopting SQL (Structured Query Language) statements are solved.

Description

Technical Field

[0001] The present application relates to the field of data verification technology, and in particular to a missing data locating method, device, equipment and readable storage medium. Background Art

[0002] In the digital age, we are surrounded by massive amounts of data, which are generated by smart devices, transmitted through the network, and finally stored. These data include both structured data, such as table data in the database, and unstructured data, such as text, pictures, videos, etc. The core challenge of big data processing is how to extract valuable information from these complex data and effectively process and analyze this information.

[0003] Traditional data processing methods, such as SQL queries, perform well when processing structured data, but their limitations become apparent when faced with complex business logic and rules. As a declarative language, SQL was originally designed to simplify data queries and operations, rather than to process complex business logic. When business rules become complex, SQL statements become lengthy and difficult to understand, which not only increases the difficulty of development and maintenance, but also increases the requirements for developer skills.

[0004] Taking the certification process as an example, this process may involve multiple types of certificates, different sales teams, completion status of exams or courses, completion time, job training, commitment letters, and other conditions. There may be complex logical relationships between these conditions, which require comprehensive judgment. Using SQL to implement complex logical rules between data may encounter the following problems:

[0005] Slow response time for rule adjustments: Whenever business rules change, developers are required to manually modify SQL statements, which then go through a series of processes such as testing and deployment. This is not only time-consuming but also prone to errors.

[0006] Difficulty in rule maintenance: As the business grows, the rules may become more and more complex, and SQL statements will become more and more difficult to maintain. For non-technical personnel, it is even more difficult to understand these rules.

[0007] Problems with identifying missing data: When data does not meet certain conditions, it is difficult for SQL to accurately identify which necessary data is missing, which makes targeted reminders and data supplementation difficult.

[0008] Lack of intuitive rule display: SQL statements usually exist in text form, which lacks intuitiveness and is not conducive to business personnel's understanding and analysis of the composition of rules. Summary of the invention

[0009] The present application provides a missing data location method, device, equipment and readable storage medium, which solves the technical problems of using SQL statements to implement complex logical rule configuration between data, such as slow adjustment response time, difficult maintenance, difficulty in locating missing data, and inability to display rules intuitively.

[0010] In view of this, the first aspect of the present application provides a missing data location method, the method comprising:

[0011] Acquire target data, where the target data carries at least one atomic tag and at least one derived tag associated with each of the atomic tags based on a preconfigured logical rule;

[0012] Based on the rule engine and the label hierarchy relationship, the values ​​of the derived label and the atomic label are calculated in sequence;

[0013] Parsing the preconfigured logic rule of the atomic tag and at least one of the derived tags based on the rule engine, determining a constitutive relationship between the atomic tag and at least one of the derived tags;

[0014] According to the constituent relationship, the missing source data of the target data is determined in combination with the derived tag and the value of the atomic tag.

[0015] Optionally, before acquiring the target data, the step further includes:

[0016] Configure the atomic tag of the target data, and define the tag level of the atomic tag as the first level;

[0017] According to the logical relationship between the target data and the source data, the preconfigured logical rules between the atomic tag of the target data and at least one derived tag and the label level of each derived label are determined; wherein the preconfigured logical rules include logical expressions and / or mathematical expressions generated according to the logical relationship between the atomic tag and the derived tags, and the derived tags.

[0018] Optionally, the values ​​of the derived tag and the atomic tag are calculated sequentially based on the rule engine and the tag hierarchy relationship as follows:

[0019] Parsing the target data based on a rule engine to determine a label hierarchical relationship between at least one of the atomic labels and at least one of the derived labels contained in the target data;

[0020] According to the label hierarchy, with the first level being the highest, the values ​​of the derived label and the atomic label are calculated from low to high. If the value of the derived label or the atomic label does not exist, a null value is used to represent the derived label or the atomic label.

[0021] Optionally, the determining of the constitutive relationship between the atomic tag and the at least one derived tag based on the preconfigured logic rule of parsing the atomic tag and the at least one derived tag by the rule engine is specifically:

[0022] Parsing the preconfigured logic rule of the target data based on the rule engine, determining a logic expression and / or a mathematical expression included in the preconfigured logic rule;

[0023] According to the logical expression and / or mathematical expression, the constituent relationship between the atomic label and at least one of the derived labels is obtained by parsing.

[0024] Optionally, the determining of the missing source data of the target data according to the constituent relationship in combination with the derived tag and the value of the atomic tag is specifically:

[0025] According to the composition relationship, starting from the atomic tag, the values ​​of the atomic tag and the derived tag are checked level by level;

[0026] If the value of the derived tag or the atomic tag is a null value, the derived tag or the atomic tag corresponding to the null value is marked as an abnormal tag, and the source data corresponding to the abnormal tag is located to determine the missing source data of the target data.

[0027] Optionally, the determining the missing source data of the target data according to the constituent relationship in combination with the derived tag and the value of the atomic tag further includes:

[0028] Call the preset visualization template;

[0029] The composition relationship between the atomic tag and at least one of the derived tags of the target data and the values ​​of the atomic tag and at least one of the derived tags are filled into the preset visualization template to generate a graphic visualization interface.

[0030] Optionally, after determining the missing source data of the target data, the method further includes:

[0031] In the graphical visualization interface, marking the abnormal label in a highlighted form or in a first target color;

[0032] In the graphic visualization interface, a label association line between the target data and the missing source data is marked in a highlighted form or in a second target color.

[0033] A second aspect of the present application provides a missing data locating device, the device comprising:

[0034] an acquisition unit, configured to acquire target data, wherein the target data carries at least one atomic tag and at least one derived tag associated with each of the atomic tags based on a preconfigured logical rule;

[0035] A calculation unit, used for calculating the values ​​of the derived tag and the atomic tag in sequence based on a rule engine and a tag hierarchy relationship;

[0036] A parsing unit, configured to parse the preconfigured logic rules of the atomic tag and at least one of the derived tags based on the rule engine, and determine a constitutive relationship between the atomic tag and at least one of the derived tags;

[0037] A positioning unit is used to determine the missing source data of the target data according to the composition relationship in combination with the derived tag and the value of the atomic tag.

[0038] Optionally, a configuration unit is further included, for:

[0039] Configure the atomic tag of the target data, and define the tag level of the atomic tag as the first level;

[0040] According to the logical relationship between the target data and the source data, the preconfigured logical rules between the atomic tag of the target data and at least one derived tag and the label level of each derived label are determined; wherein the preconfigured logical rules include logical expressions and / or mathematical expressions generated according to the logical relationship between the atomic tag and the derived tags, and the derived tags.

[0041] Optionally, the computing unit is specifically used for:

[0042] Parsing the target data based on a rule engine to determine a label hierarchical relationship between at least one of the atomic labels and at least one of the derived labels contained in the target data;

[0043] According to the label hierarchy, with the first level being the highest, the values ​​of the derived label and the atomic label are calculated from low to high. If the value of the derived label or the atomic label does not exist, a null value is used to represent the derived label or the atomic label.

[0044] Optionally, the parsing unit is specifically used for:

[0045] Parsing the preconfigured logic rule of the target data based on the rule engine, determining a logic expression and / or a mathematical expression included in the preconfigured logic rule;

[0046] According to the logical expression and / or mathematical expression, the constituent relationship between the atomic label and at least one of the derived labels is obtained by parsing.

[0047] Optionally, the positioning unit is specifically used for:

[0048] According to the composition relationship, starting from the atomic tag, the values ​​of the atomic tag and the derived tag are checked level by level;

[0049] If the value of the derived tag or the atomic tag is a null value, the derived tag or the atomic tag corresponding to the null value is marked as an abnormal tag, and the source data corresponding to the abnormal tag is located to determine the missing source data of the target data.

[0050] Optionally, the positioning unit further includes a visualization subunit, configured to:

[0051] A graphic visualization interface is generated, wherein the graphic visualization interface includes a composition relationship between the atomic tag and at least one of the derived tags of the target data, and values ​​of the atomic tag and at least one of the derived tags.

[0052] Optionally, the visualization subunit is further used for:

[0053] In the graphical visualization interface, marking the abnormal label in a highlighted form or in a first target color;

[0054] In the graphic visualization interface, a label association line between the target data and the missing source data is marked in a highlighted form or in a second target color.

[0055] A third aspect of the present application provides a missing data locating device, the device comprising a processor and a memory:

[0056] The memory is used to store program code and transmit the program code to the processor;

[0057] The processor is used to execute the steps of the missing data locating method as described in the first aspect according to the instructions in the program code.

[0058] A fourth aspect of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium is used to store program code, and the program code is used to execute the method described in the first aspect above.

[0059] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages:

[0060] In the present application, a missing data location method, apparatus, device and readable storage medium are provided. The multi-level label hierarchy relationship between the target data and the source data is determined based on the rule engine analysis, and the values ​​of at least one atomic label and at least one derived label contained in the target data are calculated in turn. In combination with the composition relationship between the atomic label and the at least one derived label, the missing source data with abnormalities is verified. Compared with SQL statements to implement data verification, the rule engine can implement more convenient rule configuration. At the same time, the existence of labels makes the logical relationship or mathematical relationship between the target data and the source data clearer, which solves the technical problems of using SQL statements to implement complex logical rule configuration between data, such as slow adjustment response time, difficult maintenance, difficulty in locating missing data, and inability to display rules intuitively. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 This is a method flow chart of the missing data location method in an embodiment of the present application;

[0062] Figure 2 This is a schematic diagram of the structure of a missing data locating device in an embodiment of the present application;

[0063] Figure 3 Schematic diagram of the structure of the missing data locating device in the embodiment of the present application. DETAILED DESCRIPTION

[0064] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0065] The present application designs a missing data location method, device, equipment and readable storage medium, which solves the technical problems of using SQL statements to implement complex logical rule configuration between data, such as slow adjustment response time, difficult maintenance, difficulty in locating missing data, and inability to display rules intuitively.

[0066] For easier understanding, see Figure 1 , Figure 1 is a flow chart of the method for locating missing data in an embodiment of the present application, such as Figure 1 As shown, specifically:

[0067] Step 101: Acquire target data, where the target data carries at least one atomic tag and at least one derived tag associated with each atomic tag based on a preconfigured logical rule;

[0068] It should be noted that the data or data set to be processed are loaded and identified, and these data or data set contain at least one atomic label and at least one derivative label.

[0069] Extract data from a database or data warehouse.

[0070] Make sure that the dataset contains predefined atom labels and derived labels.

[0071] Example:

[0072] Target data: employee performance records.

[0073] Atomic labels: whether the training has been passed, whether the assessment has been passed.

[0074] Derivative tags: Valid qualifications, Excellent performance.

[0075] Step 102: Based on the rule engine and the label hierarchy, the values ​​of the derived labels and the atomic labels are calculated in sequence;

[0076] It should be noted that the value of each tag is calculated using a rule engine, which may be Aviator.

[0077] Aviator is a Java library that provides a lightweight and high-performance way to execute script expressions. It is particularly suitable for scenarios where expressions need to be dynamically calculated in the application, such as dynamically calculating values ​​based on user input or rules in configuration files. Aviator supports Java primitive types, Java Bean properties, method calls, ternary operators, etc., and can be easily extended to support custom functions and operators.

[0078] Features

[0079] High performance: Aviator optimizes the parsing and execution process of expressions, making its execution speed close to that of directly writing Java code.

[0080] Ease of use: Aviator provides a simple and intuitive API that can be easily integrated into any Java application.

[0081] Flexibility: Supports custom functions and operators, and its functionality can be extended as needed.

[0082] Security: Aviator can limit the context of expression execution to avoid executing unsafe code.

[0083] Usage scenarios

[0084] Dynamic evaluation: The value of an expression is dynamically calculated at run time based on user input or other variables.

[0085] Rule Engine: Implement complex rule evaluation in business logic, such as discount calculation, permission verification, etc.

[0086] Report Generation: Dynamically calculate and display data when generating reports.

[0087] In this example, the calculation starts from the lowest level (atomic label) according to the preset rules and hierarchical relationships.

[0088] The values ​​of derived tags are calculated sequentially upwards.

[0089] Example:

[0090] Calculate whether the training and assessment are passed (atomic labels).

[0091] Calculate the qualifications (derived tags) based on the results: whether the training was passed AND whether the assessment was passed.

[0092] Step 103: Based on the preconfigured logic rule of parsing the atomic tag and at least one derived tag by the rule engine, determine the constitutive relationship between the atomic tag and the at least one derived tag;

[0093] It should be noted that the rule engine is used to parse the logic and mathematical expressions of each tag to determine the dependencies between tags and how each tag is composed of other tags.

[0094] Example:

[0095] Excellent performance depends on both valid qualifications and additional contributions, and the expression may be: valid qualifications AND additional contributions>5.

[0096] Step 104: Determine the missing source data of the target data based on the composition relationship and the values ​​of the derived tags and the atomic tags.

[0097] It should be noted that the missing source data of the target data can be located based on the composition relationship obtained through analysis and the values ​​of each derived label and atomic label, for example:

[0098] If Performance Excellence is empty, check its dependent Qualification Validity and Additional Contribution to find the missing data.

[0099] Furthermore, before obtaining the target data, the following steps are also included:

[0100] Configure the atomic tags of the target data and define the tag level of the atomic tags as the first level;

[0101] According to the logical relationship between the target data and the source data, the preconfigured logical rules between the atomic label of the target data and at least one derived label and the label hierarchy of each derived label are determined; wherein the preconfigured logical rules include logical expressions and / or mathematical expressions generated according to the logical relationship between the atomic label and the derived label, and the derived labels.

[0102] It should be noted that the basic tags are defined to be extracted directly from the data source.

[0104] Identify the key fields in your data source.

[0105] Create rules to map field values ​​to atom labels.

[0106] Example:

[0107] Data source: employee information table.

[0108] Atomic tag: isCertified (whether a certificate has been obtained).

[0109] Rule: If the certificate field in the employee information table exists, isCertified is True.

[0110] Construct composite logic based on atom tags or other derived tags.

[0111] Implementation method:

[0112] Select an existing atom or derivative label.

[0113] Defines logical or mathematical operation rules.

[0114] Example:

[0115] Derivative tag: eligibleForPromotion (eligible for promotion).

[0116] Rule: eligibleForPromotion=isCertified and (yearsOfService>=5).

[0117] Furthermore, based on the rule engine and the label hierarchy, the values ​​of the derived labels and atomic labels are calculated in sequence as follows:

[0118] Parsing the target data based on the rule engine, determining a label hierarchical relationship between at least one atomic label and at least one derived label contained in the target data;

[0119] According to the label hierarchy, the first level is the highest, and the values ​​of derived labels and atomic labels are calculated from low to high. If the value of a derived label or an atomic label does not exist, a null value is used to represent the derived label or atomic label.

[0120] Furthermore, based on the preconfigured logic rule of the atomic tag and the at least one derived tag parsed by the rule engine, the composition relationship between the atomic tag and the at least one derived tag is determined as follows:

[0121] Based on the preconfigured logic rules of the target data parsed by the rule engine, a logical expression and / or a mathematical expression included in the preconfigured logic rules is determined;

[0122] According to the logical expression and / or the mathematical expression, the constituent relationship of the atomic label and at least one derived label is obtained by parsing.

[0123] Furthermore, according to the composition relationship, combined with the values ​​of the derived tags and the atomic tags, the missing source data of the target data is determined as follows:

[0124] Based on the composition relationship, starting from the atomic label, the values ​​of the atomic label and the derived label are checked layer by layer;

[0125] If the value of a derived label or an atomic label is a null value, the derived label or atomic label corresponding to the null value is marked as an abnormal label, and the source data corresponding to the abnormal label is located to determine the missing source data of the target data.

[0126] It should be noted that the tag value is checked to identify tags that are empty or do not meet the conditions.

[0127] Trace back the data sources these labels depend on to identify missing data.

[0128] Example:

[0129] If eligibleForPromotion is False, check isCertified and yearsOfService and find that isCertified is False. Further inspection finds that the certificate field is missing.

[0130] Furthermore, according to the composition relationship, combined with the values ​​of the derived tags and the atomic tags, determining the missing source data of the target data also includes:

[0131] Call the preset visualization template;

[0132] The constituent relationship of the atomic label and at least one derived label of the target data, and the values ​​of the atomic label and at least one derived label are filled into a preset visualization template to generate a graphic visualization interface.

[0133] Furthermore, after determining the missing source data of the target data, the method further includes:

[0134] In the graphical visualization interface, abnormal labels are marked in a highlighted form or in the first target color;

[0135] In the graphical visualization interface, the label association lines between the target data and the missing source data are marked in a highlighted form or a second target color.

[0136] It should be noted that the composition and status of the tags are intuitively displayed through a graphical interface.

[0137] Develop a graphical interface to display the dependencies and values ​​between tags.

[0138] Use different colors or highlighting to display anomaly labels and associated lines.

[0139] Example:

[0140] On the interface, the high-performing tags are highlighted in red, and the additional contributions of the missing tags they depend on are marked in yellow.

[0141] Connect high performance and additional contributions with blue lines, highlighting data flows and dependencies.

[0142] Based on the missing data location method provided in the embodiment of the present application, a specific case is used to illustrate how to use the rule engine to perform complex data labeling and analysis. In this case, we will use the data of a fictitious e-commerce company that hopes to classify users based on their purchasing behavior and user information and provide personalized recommendations.

[0143] Scene Setting

[0144] Company: Fictional e-commerce company "ShopOnline"

[0145] Goal: To label users for market segmentation and personalized recommendations.

[0146] Data sources: user information table, purchase history table, browsing history table.

[0147] Step 1: Define atom labels

[0148] The user information table contains the fields: age, location, member_since.

[0149] The purchase history table contains the fields: purchase_amount, product_category.

[0150] The browsing history table contains the fields: page_views, session_time.

[0151] Atom label definition:

[0152] is_new_customer: True if member_since is current year.

[0153] location_based_tag: Based on the location field, such as is_beijing_customer.

[0154] high_spending_customer: True if purchase_amount exceeds a certain threshold.

[0155] Step 2: Define derived tags

[0156] Derived tag definition:

[0157] loyal_customer: True if purchase_amount is above the threshold and page_views are above average within the past year.

[0158] interested_in_electronics: True if product_category contains "Electronics" more than 50% of the purchases.

[0159] Step 3: Use the rules engine to calculate the value of the tag.

[0160] Step 4: Analyze the relationship between tags

[0161] The rule engine will parse the expression of each derived tag to determine the atomic tags they depend on.

[0162] Step 5: Analyze missing tags

[0163] If a user does not have a loyal_customer tag, the system will check to see if they are missing purchase_amount or page_views data.

[0164] Step 6: Graphical display

[0165] The following information can be displayed on the user interface:

[0166] The user's loyal_customer status.

[0167] Clicking the tag displays the dependent purchase_amount and page_views data.

[0168] If interested_in_electronics is False, highlight and provide the option to drill down to specific purchase and browsing records.

[0169] See also Figure 2 , Figure 2 Schematic diagram of the structure of the missing data locating device in the embodiment of the present application. Figure 2 As shown, specifically:

[0170] An acquisition unit 201 is used to acquire target data, where the target data carries at least one atomic tag and at least one derived tag associated with each atomic tag based on a preconfigured logical rule;

[0171] A calculation unit 202, used to calculate the values ​​of the derived tags and the atomic tags in sequence based on the rule engine and the tag hierarchy relationship;

[0172] A parsing unit 203, configured to parse the atomic tag and the preconfigured logic rule of the at least one derived tag based on the rule engine, and determine the constitutive relationship between the atomic tag and the at least one derived tag;

[0173] The positioning unit 204 is used to determine the missing source data of the target data according to the composition relationship and the values ​​of the derived tags and the atomic tags.

[0174] Furthermore, it also includes a configuration unit, which is used to:

[0175] Configure the atomic tags of the target data and define the tag level of the atomic tags as the first level;

[0176] According to the logical relationship between the target data and the source data, the preconfigured logical rules between the atomic label of the target data and at least one derived label and the label hierarchy of each derived label are determined; wherein the preconfigured logical rules include logical expressions and / or mathematical expressions generated according to the logical relationship between the atomic label and the derived label, and the derived labels.

[0177] Further, the calculation unit 202 is specifically used for:

[0178] Parsing the target data based on the rule engine, determining a label hierarchical relationship between at least one atomic label and at least one derived label contained in the target data;

[0179] According to the label hierarchy, the first level is the highest, and the values ​​of derived labels and atomic labels are calculated from low to high. If the value of a derived label or an atomic label does not exist, a null value is used to represent the derived label or atomic label.

[0180] Furthermore, the parsing unit 203 is specifically used for:

[0181] Based on the preconfigured logic rules of the target data parsed by the rule engine, a logical expression and / or a mathematical expression included in the preconfigured logic rules is determined;

[0182] According to the logical expression and / or the mathematical expression, the constituent relationship of the atomic label and at least one derived label is obtained by parsing.

[0183] Furthermore, the positioning unit 204 is specifically used for:

[0184] Based on the composition relationship, starting from the atomic label, the values ​​of the atomic label and the derived label are checked layer by layer;

[0185] If the value of a derived label or an atomic label is a null value, the derived label or atomic label corresponding to the null value is marked as an abnormal label, and the source data corresponding to the abnormal label is located to determine the missing source data of the target data.

[0186] Furthermore, the positioning unit 204 further includes a visualization subunit, which is used to:

[0187] Call the preset visualization template;

[0188] The constituent relationship of the atomic label and at least one derived label of the target data, and the values ​​of the atomic label and at least one derived label are filled into a preset visualization template to generate a graphic visualization interface.

[0189] Furthermore, the visualization subunit is also used to:

[0190] In the graphical visualization interface, abnormal labels are marked in a highlighted form or in the first target color;

[0191] In the graphical visualization interface, the label association lines between the target data and the missing source data are marked in a highlighted form or a second target color.

[0192] The present application also provides another missing data location device, such as Figure 3 For the sake of convenience, only the parts related to the embodiments of the present application are shown. For specific technical details not disclosed, please refer to the method part of the embodiments of the present application. The terminal can be any terminal device including a mobile phone, a tablet computer, a personal digital assistant (English full name: Personal Digital Assistant, English abbreviation: PDA), a sales terminal (English full name: Point of Sales, English abbreviation: POS), a car computer, etc., taking the terminal as a mobile phone as an example:

[0193] Figure 3 FIG. 1 is a block diagram showing a partial structure of a mobile phone related to a terminal provided in an embodiment of the present application. Figure 3The mobile phone includes: a radio frequency (RF) circuit 1010, a memory 1020, an input unit 1030, a display unit 1040, a sensor 1050, an audio circuit 1060, a wireless fidelity (WiFi) module 1070, a processor 1080, and a power supply 1090. Those skilled in the art can understand that Figure 3 The mobile phone structure shown in the figure does not constitute a limitation on the mobile phone, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0194] Combine the following Figure 3 A detailed introduction to the various components of the mobile phone:

[0195] The RF circuit 1010 can be used for receiving and sending signals during information transmission or calls. In particular, after receiving the downlink information of the base station, it is sent to the processor 1080 for processing; in addition, the designed uplink data is sent to the base station. Generally, the RF circuit 1010 includes but is not limited to an antenna, at least one amplifier, a transceiver, a coupler, a low noise amplifier (full name: Low Noise Amplifier, English abbreviation: LNA), a duplexer, etc. In addition, the RF circuit 1010 can also communicate with the network and other devices through wireless communication. The above-mentioned wireless communications can use any communication standard or protocol, including but not limited to Global System of Mobile communications (Global System of Mobile communication, English abbreviation: GSM), General Packet Radio Service (General Packet Radio Service, GPRS), Code Division Multiple Access (Code Division Multiple Access, English abbreviation: CDMA), Wideband Code Division Multiple Access (WCDMA), Long Term Evolution (Long Term Evolution, English abbreviation: LTE), e-mail, Short Messaging Service (SMS), etc.

[0196] The memory 1020 can be used to store software programs and modules. The processor 1080 executes various functional applications and data processing of the mobile phone by running the software programs and modules stored in the memory 1020. The memory 1020 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory 1020 can include a high-speed random access memory, and can also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0197] The input unit 1030 can be used to receive input digital or character information, and to generate key signal input related to the user settings and function control of the mobile phone. Specifically, the input unit 1030 may include a touch panel 1031 and other input devices 1032. The touch panel 1031, also known as a touch screen, can collect the user's touch operation on or near it (such as the user's operation on the touch panel 1031 or near the touch panel 1031 using any suitable object or accessory such as a finger, stylus, etc.), and drive the corresponding connection device according to a pre-set program. Optionally, the touch panel 1031 may include two parts: a touch detection device and a touch controller. Among them, the touch detection device detects the user's touch orientation, detects the signal brought by the touch operation, and transmits the signal to the touch controller; the touch controller receives the touch information from the touch detection device, converts it into contact coordinates, and then sends it to the processor 1080, and can receive and execute commands sent by the processor 1080. In addition, the touch panel 1031 can be implemented in various types such as resistive, capacitive, infrared, and surface acoustic waves. In addition to the touch panel 1031, the input unit 1030 may further include other input devices 1032. Specifically, the other input devices 1032 may include but are not limited to one or more of a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a trackball, a mouse, a joystick, and the like.

[0198] The display unit 1040 can be used to display information input by the user or information provided to the user and various menus of the mobile phone. The display unit 1040 may include a display panel 1041. Optionally, the display panel 1041 may be configured in the form of a liquid crystal display (full name in English: Liquid Crystal Display, English abbreviation: LCD), an organic light-emitting diode (full name in English: Organic Light-Emitting Diode, English abbreviation: OLED), etc. Further, the touch panel 1031 may cover the display panel 1041. When the touch panel 1031 detects a touch operation on or near it, it is transmitted to the processor 1080 to determine the type of touch event. Subsequently, the processor 1080 provides a corresponding visual output on the display panel 1041 according to the type of touch event. Although in Figure 3 In the embodiment, the touch panel 1031 and the display panel 1041 are used as two independent components to realize the input and output functions of the mobile phone, but in some embodiments, the touch panel 1031 and the display panel 1041 can be integrated to realize the input and output functions of the mobile phone.

[0199] The mobile phone may also include at least one sensor 1050, such as a light sensor, a motion sensor, and other sensors. Specifically, the light sensor may include an ambient light sensor and a proximity sensor, wherein the ambient light sensor may adjust the brightness of the display panel 1041 according to the brightness of the ambient light, and the proximity sensor may turn off the display panel 1041 and / or the backlight when the mobile phone is moved to the ear. As a type of motion sensor, the accelerometer sensor can detect the magnitude of acceleration in all directions (generally three axes), and can detect the magnitude and direction of gravity when stationary. It can be used for applications that identify the posture of the mobile phone (such as horizontal and vertical screen switching, related games, magnetometer posture calibration), vibration recognition related functions (such as pedometer, tapping), etc.; as for other sensors that can be configured in the mobile phone, such as gyroscopes, barometers, hygrometers, thermometers, infrared sensors, etc., they will not be repeated here.

[0200] The audio circuit 1060, the speaker 1061, and the microphone 1062 can provide an audio interface between the user and the mobile phone. The audio circuit 1060 can transmit the received audio data to the speaker 1061 after converting the received audio data into an electrical signal, which is converted into a sound signal for output; on the other hand, the microphone 1062 converts the collected sound signal into an electrical signal, which is received by the audio circuit 1060 and converted into audio data, and then the audio data is output to the processor 1080 for processing, and then sent to another mobile phone through the RF circuit 1010, or the audio data is output to the memory 1020 for further processing.

[0201] WiFi is a short-range wireless transmission technology. The mobile phone can help users send and receive emails, browse web pages and access streaming media through the WiFi module 1070. It provides users with wireless broadband Internet access. Figure 3 A WiFi module 1070 is shown, but it is understandable that it is not an essential component of the mobile phone and can be omitted as needed without changing the essence of the invention.

[0202] The processor 1080 is the control center of the mobile phone. It uses various interfaces and lines to connect various parts of the entire mobile phone. By running or executing software programs and / or modules stored in the memory 1020 and calling data stored in the memory 1020, it executes various functions of the mobile phone and processes data, thereby monitoring the mobile phone as a whole. Optionally, the processor 1080 may include one or more processing units; preferably, the processor 1080 may integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user interface and application programs, etc., and the modem processor mainly processes wireless communications. It is understandable that the above-mentioned modem processor may not be integrated into the processor 1080.

[0203] The mobile phone also includes a power supply 1090 (such as a battery) for supplying power to various components. Preferably, the power supply can be logically connected to the processor 1080 through a power management system, so that the power management system can manage functions such as charging, discharging, and power consumption.

[0204] Although not shown, the mobile phone may also include a camera, a Bluetooth module, etc., which will not be described in detail here.

[0205] In the embodiment of the present application, the processor 1080 included in the terminal also has the following functions:

[0206] Acquire target data, where the target data carries at least one atomic tag and at least one derived tag associated with each atomic tag based on a preconfigured logical rule;

[0207] Based on the rule engine and the label hierarchy, the values ​​of the derived labels and atomic labels are calculated in sequence;

[0208] Based on the preconfigured logic rule of parsing the atomic tag and the at least one derived tag by the rule engine, determining the constitutive relationship between the atomic tag and the at least one derived tag;

[0209] According to the composition relationship, the missing source data of the target data is determined in combination with the values ​​of the derived labels and the atomic labels.

[0210] Furthermore, before obtaining the target data, the following steps are also included:

[0211] Configure the atomic tags of the target data and define the tag level of the atomic tags as the first level;

[0212] According to the logical relationship between the target data and the source data, the preconfigured logical rules between the atomic label of the target data and at least one derived label and the label hierarchy of each derived label are determined; wherein the preconfigured logical rules include logical expressions and / or mathematical expressions generated according to the logical relationship between the atomic label and the derived label, and the derived labels.

[0213] Furthermore, based on the rule engine and the label hierarchy, the values ​​of the derived labels and atomic labels are calculated in sequence as follows:

[0214] Parsing the target data based on the rule engine, determining a label hierarchical relationship between at least one atomic label and at least one derived label contained in the target data;

[0215] According to the label hierarchy, the first level is the highest, and the values ​​of derived labels and atomic labels are calculated from low to high. If the value of a derived label or an atomic label does not exist, a null value is used to represent the derived label or atomic label.

[0216] Furthermore, based on the preconfigured logic rule of the atomic tag and the at least one derived tag parsed by the rule engine, the composition relationship between the atomic tag and the at least one derived tag is determined as follows:

[0217] Based on the preconfigured logic rules of the target data parsed by the rule engine, a logical expression and / or a mathematical expression included in the preconfigured logic rules is determined;

[0218] According to the logical expression and / or the mathematical expression, the constituent relationship of the atomic label and at least one derived label is obtained by parsing.

[0219] Furthermore, according to the composition relationship, combined with the values ​​of the derived tags and the atomic tags, the missing source data of the target data is determined as follows:

[0220] Based on the composition relationship, starting from the atomic label, the values ​​of the atomic label and the derived label are checked layer by layer;

[0221] If the value of a derived label or an atomic label is a null value, the derived label or atomic label corresponding to the null value is marked as an abnormal label, and the source data corresponding to the abnormal label is located to determine the missing source data of the target data.

[0222] Furthermore, according to the composition relationship, combined with the values ​​of the derived tags and the atomic tags, determining the missing source data of the target data also includes:

[0223] Call the preset visualization template;

[0224] The constituent relationship of the atomic label and at least one derived label of the target data, and the values ​​of the atomic label and at least one derived label are filled into a preset visualization template to generate a graphic visualization interface.

[0225] Furthermore, after determining the missing source data of the target data, the method further includes:

[0226] In the graphical visualization interface, abnormal labels are marked in a highlighted form or in the first target color;

[0227] In the graphical visualization interface, the label association lines between the target data and the missing source data are marked in a highlighted form or a second target color.

[0228] An embodiment of the present application further provides a computer-readable storage medium for storing program code, wherein the program code is used to execute any one of the implementation methods of a missing data location method described in the aforementioned embodiments.

[0229] In an embodiment of the present application, a missing data location method, apparatus, device and readable storage medium are provided. The multi-level label hierarchy relationship between the target data and the source data is determined based on the rule engine analysis, and the values ​​of at least one atomic label and at least one derived label contained in the target data are calculated in turn. In combination with the structural relationship between the atomic label and the at least one derived label, the missing source data with abnormalities is verified. Compared with SQL statements to implement data verification, the rule engine can implement more convenient rule configuration. At the same time, the existence of labels makes the logical relationship or mathematical relationship between the target data and the source data clearer, which solves the technical problems of using SQL statements to implement complex logical rule configuration between data, such as slow adjustment response time, difficult maintenance, difficulty in locating missing data, and inability to display rules intuitively.

[0230] 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 embodiments and will not be repeated here.

[0231] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein, for example. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0232] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0233] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are 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 an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0234] 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 solution of this embodiment.

[0235] In addition, each functional unit in each embodiment of the present application 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. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0236] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (full name in English: Read-Only Memory, English abbreviation: ROM), random access memory (full name in English: Random Access Memory, English abbreviation: RAM), disk or optical disk and other media that can store program codes.

[0237] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A missing data location method, characterized in that: include: Acquire target data, where the target data carries at least one atomic tag and at least one derived tag associated with each of the atomic tags based on a preconfigured logical rule; Based on the rule engine and the label hierarchy relationship, the values ​​of the derived label and the atomic label are calculated in sequence; Parsing the preconfigured logic rule of the atomic tag and at least one of the derived tags based on the rule engine, determining a constitutive relationship between the atomic tag and at least one of the derived tags; According to the constituent relationship, the missing source data of the target data is determined in combination with the derived tag and the value of the atomic tag.

2. The missing data location method according to claim 1, characterized in that: Before acquiring the target data, the method further includes: Configure the atomic tag of the target data, and define the tag level of the atomic tag as the first level; According to the logical relationship between the target data and the source data, the preconfigured logical rules between the atomic tag of the target data and at least one derived tag and the label level of each derived label are determined; wherein the preconfigured logical rules include logical expressions and / or mathematical expressions generated according to the logical relationship between the atomic tag and the derived tags, and the derived tags.

3. The missing data location method according to claim 2, characterized in that: The specific steps of calculating the values ​​of the derived tag and the atomic tag in sequence based on the rule engine and the tag hierarchy are as follows: Parsing the target data based on a rule engine to determine a label hierarchical relationship between at least one of the atomic labels and at least one of the derived labels contained in the target data; According to the label hierarchy, with the first level being the highest, the values ​​of the derived label and the atomic label are calculated from low to high. If the value of the derived label or the atomic label does not exist, a null value is used to represent the derived label or the atomic label.

4. The missing data location method according to claim 3, characterized in that: The determining of the constitutive relationship between the atomic tag and the at least one derived tag based on the preconfigured logic rule of the atomic tag and the at least one derived tag by the rule engine is specifically: Parsing the preconfigured logic rule of the target data based on the rule engine, determining a logic expression and / or a mathematical expression included in the preconfigured logic rule; According to the logical expression and / or mathematical expression, the constituent relationship between the atomic label and at least one of the derived labels is obtained by parsing.

5. The missing data location method according to claim 4, characterized in that: The missing source data of the target data is determined according to the composition relationship in combination with the derived tag and the value of the atomic tag: According to the composition relationship, starting from the atomic tag, the values ​​of the atomic tag and the derived tag are checked level by level; If the value of the derived tag or the atomic tag is a null value, the derived tag or the atomic tag corresponding to the null value is marked as an abnormal tag, and the source data corresponding to the abnormal tag is located to determine the missing source data of the target data.

6. The missing data location method according to claim 5, characterized in that: The determining the missing source data of the target data according to the composition relationship and in combination with the derived tag and the value of the atomic tag further comprises: Call the preset visualization template; The composition relationship between the atomic tag and at least one of the derived tags of the target data and the values ​​of the atomic tag and at least one of the derived tags are filled into the preset visualization template to generate a graphic visualization interface.

7. The missing data locating method according to claim 6, characterized in that: After determining the missing source data of the target data, the method further includes: In the graphical visualization interface, marking the abnormal label in a highlighted form or in a first target color; In the graphic visualization interface, a label association line between the target data and the missing source data is marked in a highlighted form or in a second target color.

8. A missing data locating device, characterized in that: include: an acquisition unit, configured to acquire target data, wherein the target data carries at least one atomic tag and at least one derived tag associated with each of the atomic tags based on a preconfigured logical rule; A calculation unit, used for calculating the values ​​of the derived tag and the atomic tag in sequence based on a rule engine and a tag hierarchy relationship; A parsing unit, configured to parse the preconfigured logic rules of the atomic tag and at least one of the derived tags based on the rule engine, and determine a constitutive relationship between the atomic tag and at least one of the derived tags; A positioning unit is used to determine the missing source data of the target data according to the composition relationship in combination with the derived tag and the value of the atomic tag.

9. A missing data locating device, characterized in that: The device comprises a processor and a memory: The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the missing data locating method according to any one of claims 1 to 7 according to the instructions in the program code.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium is used to store program code, and the program code is used to execute the missing data locating method according to any one of claims 1 to 7.

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