Data import template configuration method and device, equipment and storage medium
By constructing an import demand matrix and a rule-based filtering model, standard data import templates that conform to user and business data are generated, solving the problem of low usability of data import templates in existing technologies and realizing personalized and accurate data import.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- PINGAN INT SMART CITY TECH CO LTD
- Filing Date
- 2022-03-15
- Publication Date
- 2026-06-26
AI Technical Summary
Existing data import methods cannot meet users' personalized needs, leading to import errors or failures, and the generated data import templates have low usability.
By constructing an import requirement matrix, calculating the matching degree, identifying business types, building a rule filtering model, and using target import rules to render data import templates, a standard data import template that conforms to user and business data is generated.
It enables the analysis of users' personalized import needs, improves the usability of data import templates, and ensures the accuracy and adaptability of data import.
Smart Images
Figure CN114610807B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of artificial intelligence technology, and in particular to a data import template configuration method, apparatus, electronic device, and computer-readable storage medium. Background Technology
[0002] With the development of the big data era, massive amounts of data surround people's daily lives and work. However, with the explosive growth of data, people increasingly need to organize the data. But the premise of organizing data is to effectively import the data into data tables, data analysis systems, and other places.
[0003] Currently, the most common method for importing data involves backend developers writing SQL in the code, structuring the necessary query conditions in the list, and then returning the data to the frontend via an API for import. However, this method requires different code to transform the data for different types of data, generating a corresponding data list, and then importing the data into that list. The data lists generated by this method are too rigid and cannot take into account the user's personalized import needs or the limitations of the data's import rules. This can easily lead to import errors or even failures when importing different types of data. Summary of the Invention
[0004] This invention provides a data import template configuration method, apparatus, and computer-readable storage medium, the main purpose of which is to solve the problem of low usability of the generated data import template.
[0005] To achieve the above objectives, the present invention provides a data import template configuration method, comprising:
[0006] Obtain data import requirements and construct an import requirement matrix based on those requirements;
[0007] Calculate the matching degree between the import demand matrix and multiple preset import rules respectively, and select the import rules with matching degree greater than the preset matching threshold as the rules to be filtered;
[0008] Obtain the business data corresponding to the data import requirement, identify the business type of the business data, and obtain the data import conditions corresponding to the business type;
[0009] A rule-based filtering model is constructed based on the data import conditions, and the rule-based filtering model is used to filter the rules to be filtered to obtain the target import rules.
[0010] Obtain a blank data import template, and render the data import template using the target import rule to obtain a standard data import template.
[0011] Optionally, constructing the import requirement matrix based on the data import requirements includes:
[0012] The data import requirements are processed by word segmentation to obtain the required word segments;
[0013] Calculate the similarity between each of the aforementioned demand-related word segments and multiple preset operation demand terms, and select the demand-related word segments with similarity greater than a preset similarity threshold as import intent word segments;
[0014] The import demand matrix is constructed using the word segmentation of the import intent.
[0015] Optionally, constructing the import demand matrix using the import intent word segmentation includes:
[0016] Convert the imported intent into word vectors;
[0017] The word vectors are written into a pre-constructed blank matrix to obtain the import requirement matrix.
[0018] Optionally, the business type for identifying the business data includes:
[0019] Extract the business type field from the business data;
[0020] Calculate the distance values between the business type field and various preset type labels;
[0021] The type label with the smallest distance value is determined as the business type of the business data.
[0022] Optionally, the step of constructing a rule-based filtering model based on the data import conditions includes:
[0023] Select one data import condition from the data import conditions one by one as the target condition;
[0024] The target conditions are used as parameters to assign values to a preset decision function, and the assigned decision function is used as the decision conditions to generate a decision tree.
[0025] The decision tree generated by combining all data import conditions is used as a rule-based filtering model.
[0026] Optionally, the step of using the rule-based filtering model to filter out the operable data of the user role within the rules to be filtered includes:
[0027] Select one rule from the list of rules to be filtered as the input value;
[0028] One decision tree is selected from the rule-selection model as the target decision tree, and the input value is input into the target decision tree to obtain the output result of the target decision tree. The output result is either the same as the parameters of the input value and the parameters of the target decision tree, or the input value and the parameters of the target decision tree are different.
[0029] The target import rules are obtained by combining the output results of the rules to be filtered that have the same input value as the parameters of the target decision tree.
[0030] Optionally, rendering the data import template using the target import rule to obtain a standard data import template includes:
[0031] The rendering area is determined by importing the template based on the blank data.
[0032] Components are constructed in the rendering area to obtain a standard data import template.
[0033] To address the above problems, the present invention also provides a data import template configuration device, the device comprising:
[0034] The matrix construction module is used to obtain data import requirements and construct an import requirement matrix based on the data import requirements.
[0035] The first filtering module is used to calculate the matching degree between the import demand matrix and multiple preset import rules respectively, and select the import rules with matching degree greater than the preset matching threshold as the rules to be filtered.
[0036] The condition acquisition module is used to acquire the business data corresponding to the data import requirement, identify the business type of the business data, and acquire the data import conditions corresponding to the business type.
[0037] The second filtering module is used to construct a rule filtering model based on the data import conditions, and use the rule filtering model to filter the rules to be filtered to obtain the target import rules.
[0038] The data import module is used to obtain a blank data import template, and to render the data import template using the target import rules to obtain a standard data import template.
[0039] To address the above problems, the present invention also provides an electronic device, the electronic device comprising:
[0040] At least one processor; and,
[0041] A memory communicatively connected to the at least one processor; wherein,
[0042] The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the data import template configuration method described above.
[0043] To address the aforementioned problems, the present invention also provides a computer-readable storage medium storing at least one computer program, which is executed by a processor in an electronic device to implement the data import template configuration method described above.
[0044] This invention analyzes users' data import needs to obtain corresponding data import rules, achieving personalized import requirement analysis. Simultaneously, based on the business type of the data to be imported, the data import rules are further filtered to account for the differences in importing data of different business types. Finally, the filtered data import rules are used to generate a data import template, improving its usability. Therefore, the data import template configuration method, apparatus, electronic device, and computer-readable storage medium proposed in this invention can solve the problem of low usability of generated data import templates. Attached Figure Description
[0045] Figure 1 A flowchart illustrating a data import template configuration method according to an embodiment of the present invention;
[0046] Figure 2 This is a schematic diagram of a process for identifying service types according to an embodiment of the present invention;
[0047] Figure 3 This is a flowchart illustrating the construction of a rule-based filtering model according to an embodiment of the present invention.
[0048] Figure 4 This is a functional block diagram of a data import template configuration device provided in an embodiment of the present invention;
[0049] Figure 5 This is a schematic diagram of the structure of an electronic device that implements the data import template configuration method according to an embodiment of the present invention.
[0050] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation
[0051] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
[0052] This application provides a data import template configuration method. The execution subject of the data import template configuration method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the data import template configuration method can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.
[0053] Reference Figure 1 The diagram shown is a flowchart illustrating a data import template configuration method according to an embodiment of the present invention. In this embodiment, the data import template configuration method includes:
[0054] S1. Obtain data import requirements and construct an import requirement matrix based on the data import requirements.
[0055] In this embodiment of the invention, the data import requirement refers to the user's desired content, time, method, and other requirements when importing data into data tables, databases, and other data storage facilities.
[0056] In detail, the data import request can be uploaded by the user in advance, or the data import request can be retrieved by computer statements with data scraping capabilities (such as Java statements, Python statements, etc.) that are pre-stored in databases, blockchain stages, or network caches.
[0057] In this embodiment of the invention, since the data import requirement may contain a large amount of content, but not all of it is an operation that the user needs to perform, the data import requirement can be analyzed to construct an import requirement matrix based on the data import requirement. The import requirement matrix contains the content of the data import requirement used to perform specific operations related to the data import function.
[0058] In this embodiment of the invention, the content representing the user's import needs is first filtered from the content of the data import needs, and then the import needs matrix of the control voice is constructed based on the filtered content. Compared with directly searching for the user's import needs from the content of the data import needs, the accuracy of analyzing data import needs can be improved.
[0059] In this embodiment of the invention, constructing the import requirement matrix based on the data import requirements includes:
[0060] The data import requirements are processed by word segmentation to obtain the required word segments;
[0061] Calculate the similarity between each of the aforementioned demand-related word segments and multiple preset operation demand terms, and select the demand-related word segments with similarity greater than a preset similarity threshold as import intent word segments;
[0062] The import demand matrix is constructed using the word segmentation of the import intent.
[0063] In this embodiment of the invention, the data import requirement is split into requirement word segments, and each requirement word segment is analyzed and processed separately, which can reduce the computational burden during analysis and improve analysis efficiency.
[0064] Specifically, the data is imported into the requirements and retrieved in a preset standard dictionary according to different lengths. The content that can be retrieved in the standard dictionary is then compiled into requirement word segments, wherein the standard dictionary contains multiple standard word segments.
[0065] In this embodiment of the invention, algorithms with similarity calculation functions, such as Euclidean distance and cosine distance, can be used to calculate the similarity between each demand word segment and multiple preset operation demand terms. Demand words with similarity greater than a preset similarity threshold are selected as import intent words. Based on the similarity, words that may be used to represent import demands are filtered out from the demand words, achieving fuzzy filtering of demand words. This avoids the situation where words representing import demands are missed during filtering due to differences in user expressions, and helps improve the accuracy of the filtered import intent words.
[0066] Furthermore, to facilitate subsequent analysis of the selected import intent word segmentation, the import intent word segmentation can be converted into word vectors.
[0067] In detail, the character vector of each character in the imported intent segmentation can be queried from a preset character vector table, and the character vectors are concatenated into the word vector of the imported intent segmentation according to the order of each character in the imported intent segmentation. The character vector table contains multiple characters and the character vector corresponding to each character. The character vector corresponding to each character can be obtained by searching the character vector table for each character in the imported intent segmentation, and the character vectors are concatenated into the word vector of the imported intent segmentation according to the order of each character in the imported intent segmentation. The character vector table is similar to the standard dictionary and is a pre-constructed data table containing character vectors corresponding to multiple single characters.
[0068] For example, the imported intent word segmentation includes the three characters "time point". The three characters are queried in the character vector table respectively, and the character vector corresponding to the character "time" is {A}, the character vector corresponding to the character "few" is {B}, and the character vector corresponding to the character "point" is {C}. Then, the three character vectors can be concatenated into the word vector of the required word segmentation: {ABC}, according to the order of the three characters in the imported intent word segmentation "teenagers".
[0069] In other embodiments of the present invention, models with word vector conversion functions, such as word2vec model, NLP (Natural Language Processing) model, and BERT model, can be used to convert the imported intent into word vectors.
[0070] In this embodiment of the invention, constructing the import demand matrix using the import intent word segmentation includes:
[0071] Convert the imported intent into word vectors;
[0072] The word vectors are written into a pre-constructed blank matrix to obtain the import requirement matrix.
[0073] Specifically, the blank matrix is a matrix whose elements are all 0, and can be created using the B = zeros(m,n) function in the R library, which has m rows and n columns.
[0074] In this embodiment of the invention, the word vectors can be filled into the blank matrix one by one in the form of row vectors to obtain an import requirement matrix containing the word vectors.
[0075] S2. Calculate the matching degree between the import requirement matrix and multiple preset import rules respectively, and select the import rules with matching degree greater than the preset matching threshold as the rules to be filtered.
[0076] In this embodiment of the invention, since the import demand matrix contains multiple word vectors that may represent the user's import demand, the user's data import demand can be analyzed using the import demand matrix.
[0077] In this embodiment of the invention, calculating the matching degree between the import requirement matrix and multiple preset import rules includes:
[0078] The following matching algorithms are used to calculate the matching degree between the import requirement matrix and multiple preset import rules:
[0079]
[0080] Among them, D iLet P be the matching degree between the import requirement matrix and the i-th import requirement, and Q be the import requirement matrix. i This is the i-th import rule.
[0081] Furthermore, import rules with a matching degree greater than a preset matching threshold can be selected, and the selected import rules can be determined as rules to be filtered, wherein the rules to be filtered are the import rules that match the data import rules corresponding to the import requirement matrix among the plurality of preset import rules.
[0082] S3. Obtain the business data corresponding to the data import requirement, identify the business type of the business data, and obtain the data import conditions corresponding to the business type.
[0083] In this embodiment of the invention, the business data is the data that the data import requirement wants to import.
[0084] In detail, the steps for obtaining the business data corresponding to the data import requirement are the same as the steps for obtaining the data import requirement in S1, and will not be repeated here.
[0085] In one practical application scenario of this invention, since the import requirement data is the user's desired content, time, method, etc., the import requirement data represents the user's import intention. However, for different business data, the import rules may differ from the user's import intention.
[0086] For example, user import request data is used to identify whether a user wants to import business data in audio format, but in practice, this business data only supports import in text format.
[0087] Therefore, when there is a difference between the actual import rules of business data and the user's import intention, if the business data is imported only according to the filtering rules determined by the data import requirements, it is easy to cause data errors or even fail to complete the import.
[0088] In this embodiment of the invention, the business data can be analyzed to identify the business type of the business data, and then data import conditions corresponding to the business type of the business data can be obtained.
[0089] In this embodiment of the invention, the reference Figure 2 As shown, the business type for identifying the business data includes:
[0090] S21. Extract the business type field from the business data;
[0091] S22. Calculate the distance value between the business type field and various preset type labels;
[0092] S23. Determine the type label with the smallest distance value as the business type of the business data.
[0093] In detail, the business type field is a field used to identify the type of business data. It is generally marked in a fixed position within the business data and the data format is relatively fixed. Therefore, the business type field in the business data can be extracted using rule expressions with specific data format extraction functions.
[0094] Specifically, algorithms with distance calculation functions, such as Euclidean distance algorithm and cosine distance algorithm, can be used to calculate the distance value between the business type field and various preset type labels.
[0095] Furthermore, the CREATE INDEX statement in the SQL database can be used to query the data import conditions corresponding to the business type from a pre-built type-condition table. The type-condition table includes multiple business types and the data import conditions corresponding to each business type.
[0096] S4. Construct a rule filtering model based on the data import conditions, and use the rule filtering model to filter the rules to be filtered to obtain the target import rules.
[0097] In this embodiment of the invention, in order to further filter the rules to be filtered, a rule filtering model can be constructed based on the data import conditions, and then the rule filtering model can be used to filter out the target import rules that meet the data import conditions from the rules to be filtered.
[0098] In this embodiment of the invention, the reference Figure 3 As shown, the step of constructing a rule-based filtering model based on the data import conditions includes:
[0099] S31. Select one of the data import conditions from the data import conditions as the target condition;
[0100] S32. Assign values to the preset decision function using the target conditions as parameters, and use the assigned decision function as decision conditions to generate a decision tree;
[0101] S33. The decision tree generated by combining all data import conditions is used as a rule-based filtering model.
[0102] For example, the decision function can be:
[0103]
[0104] Where f(x) is the output value of the decision function, x is the parameter of the decision function, and g(y) is the input value of the decision function.
[0105] In detail, one data import condition can be selected from the data import conditions of the object data table and the data import conditions of the operable data table as the target condition. The parameter x of the decision function is assigned a value using the target condition, and the assigned decision function is used as the decision condition to generate the following decision tree:
[0106] When the input value g(y) of the decision tree is the same as the parameter x of the decision tree, the output value f(x) of the decision tree is α;
[0107] When the input g(y) of the decision tree is not the same as the parameter x of the decision tree, the output value of the decision tree is f(x) = β.
[0108] In this embodiment of the invention, the decision trees corresponding to each feature in the data import conditions can be aggregated in parallel or in series to obtain a rule-based filtering model.
[0109] In this embodiment of the invention, the rule filtering model can be used to filter the rules to be filtered, so as to filter out the rules that meet the data import conditions, which helps to improve the accuracy of the final generated data import template.
[0110] In this embodiment of the invention, the step of using the rule-based filtering model to filter out the operable data of the user role within the rules to be filtered includes:
[0111] Select one rule from the list of rules to be filtered as the input value;
[0112] One decision tree is selected from the rule-selection model as the target decision tree, and the input value is input into the target decision tree to obtain the output result of the target decision tree. The output result is either the same as the parameters of the input value and the parameters of the target decision tree, or the input value and the parameters of the target decision tree are different.
[0113] The target import rules are obtained by combining the output results of the rules to be filtered that have the same input value as the parameters of the target decision tree.
[0114] For example, the rule filtering model includes decision trees a1, a2, b1, and b2. Decision tree a1 is selected as the target decision tree. One of the rules to be filtered is selected as the input value. The input value is input to decision tree a1, resulting in an output result where the input value is the same as the parameters of decision tree a1. The input value is input to decision tree a2, resulting in an output result where the input value is different from the parameters of decision tree a2. The input value is input to decision tree b1, resulting in an output result where the input value is different from the parameters of decision tree b1. The input value is input to decision tree b2, resulting in an output result where the input value is different from the parameters of decision tree b2.
[0115] Since the input value output by decision tree a1 is the same as the output result of the parameters of decision tree a1, the input value (the selected rule to be filtered) can be determined as the target import rule.
[0116] In detail, the target import rules can be obtained by aggregating the output results of the filter rules that have the same input values as the parameters of the target decision tree.
[0117] S5. Obtain a blank data import template, and render the data import template using the target import rule to obtain a standard data import template.
[0118] In this embodiment of the invention, the blank data import template is a blank template that does not contain any data import rules. The target import rule can be used to render the data import template to obtain a standard data import template containing the target import rule.
[0119] In detail, the steps for obtaining a blank data import template are the same as those for obtaining data import requirements in S1, and will not be repeated here.
[0120] In this embodiment of the invention, rendering the data import template using the target import rule to obtain a standard data import template includes:
[0121] The rendering area is determined by importing the template based on the blank data.
[0122] Components are constructed in the rendering area to obtain a standard data import template.
[0123] In this embodiment of the invention, the rendering area is determined according to the blank data import template. For example, the blank data import template includes a query interface for importing data. The upper part of the query interface is the rendering area for the title of the query function, the middle part is the rendering area for entering the query content, and the lower part is the rendering area for the query button.
[0124] In detail, components are constructed in the rendering area to obtain a standard data import template. That is, pop-up components are created in the rendering area according to the content that needs to be rendered. For example, text boxes are created in the rendering area of the function title and the rendering area of the query content in the query interface to display and input the function title and query content. The function title and the entered query content are written in the text box. A query button is created in the rendering area of the query button so that when the query button is clicked, the query can be performed according to the entered query content.
[0125] Specifically, in this embodiment of the invention, text boxes are created in the rendering area of the title of the query function and the rendering area of the entered query content in the query interface using methods such as setMessage, setItems, and setSingleChoiceItems in Java; and query buttons are created in the rendering area of the query button using methods such as setPositiveButton, setNegativeButton, and setNeutralButton.
[0126] In this embodiment of the invention, a standard data import template is generated by rendering the data import template using target import rules, thereby meeting both the user's data import requirements and the data import conditions of the business data.
[0127] This invention analyzes users' data import needs to obtain corresponding data import rules, achieving personalized import requirement analysis. Simultaneously, based on the business type of the data to be imported, the data import rules are further filtered to account for the differences in importing data of different business types. Finally, the filtered data import rules are used to generate a data import template, improving its usability. Therefore, the data import template configuration method proposed in this invention solves the problem of low usability of generated data import templates.
[0128] like Figure 4 The diagram shown is a functional block diagram of a data import template configuration device provided in an embodiment of the present invention.
[0129] The data import template configuration device 100 of this invention can be installed in an electronic device. Depending on the functions implemented, the data import template configuration device 100 may include a matrix construction module 101, a first filtering module 102, a condition acquisition module 103, a second filtering module 104, and a data import module 105. The module described in this invention can also be referred to as a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, and which are stored in the memory of the electronic device.
[0130] In this embodiment, the functions of each module / unit are as follows:
[0131] The matrix construction module 101 is used to obtain data import requirements and construct an import requirement matrix based on the data import requirements.
[0132] The first filtering module 102 is used to calculate the matching degree between the import demand matrix and a plurality of preset import rules respectively, and select the import rules with matching degree greater than a preset matching threshold as the rules to be filtered;
[0133] The condition acquisition module 103 is used to acquire business data corresponding to the data import requirement, identify the business type of the business data, and acquire data import conditions corresponding to the business type.
[0134] The second filtering module 104 is used to construct a rule filtering model based on the data import conditions, and use the rule filtering model to filter the rules to be filtered to obtain the target import rules;
[0135] The data import module 105 is used to obtain a blank data import template, and to render the data import template using the target import rule to obtain a standard data import template.
[0136] In detail, the modules in the data import template configuration device 100 described in this embodiment of the invention adopt the same usage as described above. Figures 1 to 3 The data import template configuration method described herein uses the same technical means and can produce the same technical effect, so it will not be repeated here.
[0137] like Figure 5 The diagram shown is a structural schematic of an electronic device that implements a data import template configuration method according to an embodiment of the present invention.
[0138] The electronic device 1 may include a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may also include a computer program stored in the memory 11 and capable of running on the processor 10, such as a data import template configuration program.
[0139] In some embodiments, the processor 10 may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 10 is the control unit of the electronic device, connecting various components of the entire electronic device through various interfaces and lines. It executes programs or modules stored in the memory 11 (e.g., executing data import template configuration programs) and calls data stored in the memory 11 to perform various functions of the electronic device and process data.
[0140] The memory 11 includes at least one type of readable storage medium, including flash memory, portable hard drive, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of an electronic device, such as a portable hard drive. In other embodiments, the memory 11 can be an external storage device of the electronic device, such as a plug-in portable hard drive, Smart Media Card (SMC), Secure Digital (SD) card, Flash Card, etc. Furthermore, the memory 11 can include both internal and external storage units of the electronic device. The memory 11 can be used not only to store application software and various types of data installed on the electronic device, such as the code of a data import template configuration program, but also to temporarily store data that has been output or will be output.
[0141] The communication bus 12 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. This bus can be divided into an address bus, a data bus, a control bus, etc. The bus is configured to enable communication between the memory 11 and at least one processor 10, etc.
[0142] The communication interface 13 is used for communication between the aforementioned electronic device and other devices, including a network interface and a user interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a Wi-Fi interface, Bluetooth interface, etc.), typically used to establish communication connections between the electronic device and other electronic devices. The user interface may be a display, an input unit (such as a keyboard), or, optionally, a standard wired or wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen, etc. The display may also be appropriately referred to as a screen or display unit, used to display information processed in the electronic device and to display a visual user interface.
[0143] The figure only shows an electronic device with components. Those skilled in the art will understand that the structure shown in the figure does not constitute a limitation on the electronic device and may include fewer or more components than shown, or combine certain components, or have different component arrangements.
[0144] For example, although not shown, the electronic device may also include a power supply (such as a battery) to power the various components. Preferably, the power supply can be logically connected to the at least one processor 10 through a power management device, thereby enabling functions such as charging management, discharging management, and power consumption management. The power supply may also include one or more DC or AC power supplies, recharging devices, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail here.
[0145] It should be understood that the embodiments described are for illustrative purposes only and are not limited to this structure in the scope of the patent application.
[0146] The data import template configuration program stored in the memory 11 of the electronic device 1 is a combination of multiple instructions, which, when run in the processor 10, can achieve the following:
[0147] Obtain data import requirements and construct an import requirement matrix based on those requirements;
[0148] Calculate the matching degree between the import demand matrix and multiple preset import rules respectively, and select the import rules with matching degree greater than the preset matching threshold as the rules to be filtered;
[0149] Obtain the business data corresponding to the data import requirement, identify the business type of the business data, and obtain the data import conditions corresponding to the business type;
[0150] A rule-based filtering model is constructed based on the data import conditions, and the rule-based filtering model is used to filter the rules to be filtered to obtain the target import rules.
[0151] Obtain a blank data import template, and render the data import template using the target import rule to obtain a standard data import template.
[0152] Specifically, the specific implementation method of the processor 10 for the above instructions can be referred to the description of the relevant steps in the corresponding embodiment of the accompanying drawings, and will not be repeated here.
[0153] Furthermore, if the modules / units integrated in the electronic device 1 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM).
[0154] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor of an electronic device, can perform the following:
[0155] Obtain data import requirements and construct an import requirement matrix based on those requirements;
[0156] Calculate the matching degree between the import demand matrix and multiple preset import rules respectively, and select the import rules with matching degree greater than the preset matching threshold as the rules to be filtered;
[0157] Obtain the business data corresponding to the data import requirement, identify the business type of the business data, and obtain the data import conditions corresponding to the business type;
[0158] A rule-based filtering model is constructed based on the data import conditions, and the rule-based filtering model is used to filter the rules to be filtered to obtain the target import rules.
[0159] Obtain a blank data import template, and render the data import template using the target import rule to obtain a standard data import template.
[0160] In the several embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.
[0161] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.
[0162] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.
[0163] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.
[0164] Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be embraced within the invention. No appended diagram markings in the claims should be construed as limiting the scope of the claims.
[0165] The blockchain referred to in this invention is a novel application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanisms, and encryption algorithms. Essentially, a blockchain is a decentralized database, a chain of data blocks linked together using cryptographic methods. Each data block contains information about a batch of network transactions, used to verify the validity of the information (anti-counterfeiting) and generate the next block. A blockchain can include an underlying blockchain platform, a platform product service layer, and an application service layer.
[0166] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence (AI) refers to the theories, methods, technologies, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.
[0167] Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices recited in a system claim may also be implemented by a single unit or device through software or hardware. The terms "first," "second," etc., are used to indicate names and do not indicate any specific order.
[0168] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims
1. A method for configuring a data import template, characterized in that, The method includes: Obtain data import requirements and construct an import requirement matrix based on those requirements; Calculate the matching degree between the import demand matrix and multiple preset import rules respectively, and select the import rules with matching degree greater than the preset matching threshold as the rules to be filtered; Obtain the business data corresponding to the data import requirement, identify the business type of the business data, and obtain the data import conditions corresponding to the business type; The process involves constructing a rule-based filtering model based on the data import conditions, and using this model to filter the rules to be filtered to obtain target import rules. This includes: selecting one data import condition from each of the data import conditions as a target condition; assigning the target condition as a parameter to a preset decision function; generating a decision tree using the assigned decision function as a decision condition; determining the output value of the decision tree based on whether the input value and parameter are the same; combining all decision trees generated by the data import conditions in parallel or series connection to form a rule-based filtering model; selecting one rule from each of the rules to be filtered as an input value; selecting one decision tree from each of the rule-based filtering models as a target decision tree; inputting the input value into the target decision tree to obtain the output result of the target decision tree, wherein the output result is either the same as the parameter of the target decision tree or different from the parameter of the target decision tree; and combining the rules to be filtered where the output result is the same as the parameter of the target decision tree to obtain the target import rules. Obtain a blank data import template, and render the data import template using the target import rule to obtain a standard data import template.
2. The data import template configuration method as described in claim 1, characterized in that, The step of constructing an import requirement matrix based on the data import requirements includes: The data import requirements are processed by word segmentation to obtain the required word segments; Calculate the similarity between each of the aforementioned demand-related word segments and multiple preset operation demand terms, and select the demand-related word segments with similarity greater than a preset similarity threshold as import intent word segments; The import demand matrix is constructed using the word segmentation of the import intent.
3. The data import template configuration method as described in claim 2, characterized in that, The step of constructing the import demand matrix using the imported intent word segmentation includes: Convert the imported intent into word vectors; The word vectors are written into a pre-constructed blank matrix to obtain the import requirement matrix.
4. The data import template configuration method as described in claim 1, characterized in that, The business type for identifying the business data includes: Extract the business type field from the business data; Calculate the distance values between the business type field and various preset type labels; The type label with the smallest distance value is determined as the business type of the business data.
5. The data import template configuration method as described in any one of claims 1 to 4, characterized in that, The step of rendering the data import template using the target import rule to obtain a standard data import template includes: The rendering area is determined by importing the template based on the blank data. Components are constructed in the rendering area to obtain a standard data import template.
6. A data import template configuration device, characterized in that, The device includes: The matrix construction module is used to obtain data import requirements and construct an import requirement matrix based on the data import requirements. The first filtering module is used to calculate the matching degree between the import demand matrix and multiple preset import rules respectively, and select the import rules with matching degree greater than the preset matching threshold as the rules to be filtered. The condition acquisition module is used to acquire the business data corresponding to the data import requirement, identify the business type of the business data, and acquire the data import conditions corresponding to the business type. The second filtering module is used to construct a rule filtering model based on the data import conditions, and to filter the rules to be filtered using the rule filtering model to obtain target import rules. This includes: selecting one data import condition from each of the data import conditions as a target condition; assigning the target condition as a parameter to a preset decision function; generating a decision tree using the assigned decision function as a decision condition; determining the output value of the decision tree based on whether the input value and the parameter are the same; combining all decision trees generated by the data import conditions in parallel or series form to form a rule filtering model; selecting one rule from each of the rules to be filtered as an input value; selecting one decision tree from each of the rule filtering models as a target decision tree; inputting the input value into the target decision tree; obtaining the output result of the target decision tree; wherein the output result is either the same as the parameter of the target decision tree or different from the parameter of the target decision tree; and combining the rules to be filtered where the output result is the same as the parameter of the target decision tree to obtain the target import rules. The data import module is used to obtain a blank data import template, and to render the data import template using the target import rules to obtain a standard data import template.
7. An electronic device, characterized in that, The electronic device includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the data import template configuration method as described in any one of claims 1 to 5.
8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the data import template configuration method as described in any one of claims 1 to 5.