Data reporting method and device, electronic equipment and computer readable storage medium

Abstract

The application relates to an artificial intelligence technology and discloses a data reporting method, which comprises the following steps: collecting multi-source data from multiple data sources, standardizing and dividing the collected multi-source data according to a preset field granularity to obtain refined data; obtaining a historical reporting data table, analyzing the historical reporting data table to obtain historical reporting fields; obtaining a data reporting requirement, and constructing a requirement matrix of the data reporting requirement; screening out fields in the historical fields that meet the data reporting requirement as to-be-reported fields according to the requirement matrix; dividing the to-be-reported fields into refined fields according to the field granularity, matching the refined fields to obtain reporting data from the refined data, and reporting the reporting data. In addition, the application also relates to a blockchain technology, and the multi-source data can be stored in a node of the blockchain. The application further discloses a data reporting device, an electronic device and a storage medium. The application can improve the efficiency of data reporting.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence technology, and in particular to a data reporting method, apparatus, electronic device, and computer-readable storage medium. Background Technology

[0002] As the financial regulatory system becomes increasingly sound, the state's requirements for continued supervision of financial data are becoming more and more stringent. Various regulatory agencies may require financial companies to provide reports and fields quickly at any time in order to conduct timely supervision.

[0003] Currently, most regulatory reporting in the industry is done through siloed automated development and reporting. However, this siloed approach has several drawbacks. First, the reported data is diverse, requiring the development of new data collection rules for different regulatory needs. This consumes a lot of development time and makes it impossible to achieve rapid data reporting response. Second, the increasing number of ad-hoc regulatory projects makes it difficult to guarantee development quality. If the reported data is incorrect, it needs to be redeveloped and resubmitted, further reducing the efficiency of data reporting. Summary of the Invention

[0004] This invention provides a data reporting method, apparatus, and computer-readable storage medium, the main purpose of which is to solve the problem of low efficiency in data reporting.

[0005] To achieve the above objectives, the present invention provides a data reporting method, comprising:

[0006] Multiple data sources are collected, and the collected data is standardized and segmented according to the preset field granularity to obtain refined data;

[0007] Obtain the historical data report table, parse the historical data report table, and obtain the historical data report fields;

[0008] Obtain data reporting requirements and construct a requirement matrix for those requirements;

[0009] Based on the demand matrix, the fields in the historical fields that meet the data reporting requirements are selected as fields to be reported;

[0010] The field to be reported is divided into refined fields according to the field granularity. The reported data is obtained by matching the refined data from the refined data according to the refined fields, and the reported data is reported.

[0011] Optionally, the multi-source data collection from multiple data sources includes:

[0012] Call the data interface of each data source;

[0013] Extract the interface parameters of the data interface;

[0014] Construct an aggregated data interface based on the interface parameters;

[0015] The aggregated data interface is used to collect multi-source data from the multiple data sources.

[0016] Optionally, the step of standardizing and segmenting the collected multi-source data according to a preset field granularity to obtain refined data includes:

[0017] Select one data point from each of the multi-source data sources as the target data;

[0018] Determine whether the data granularity of the target data is less than or equal to the preset field granularity;

[0019] If the data granularity of the target data is less than or equal to the field granularity, then return to the step of selecting one data from the multi-source data as the target data one by one;

[0020] If the data granularity of the target data is greater than the field granularity, then the target data is segmented according to the field granularity to obtain the refined data corresponding to the target data.

[0021] Optionally, constructing the demand matrix for the data reporting requirements includes:

[0022] The data reporting request is processed by word segmentation to obtain the request word segmentation;

[0023] Calculate the similarity between each of the demand segments and multiple preset demand intent tags, and select the demand intent tags with similarity greater than a preset similarity threshold as target tags;

[0024] The demand matrix is ​​constructed using the target labels.

[0025] Optionally, the step of selecting fields from the historical fields that meet the data reporting requirements based on the demand matrix as fields to be reported includes:

[0026] Each row vector in the demand matrix is ​​selected as the target vector;

[0027] The target features are used as parameters to assign values ​​to a preset decision function, and the assigned decision function is used as a decision condition to generate a decision tree.

[0028] The decision tree generated by aggregating all row vectors of the demand matrix is ​​used as a field filtering model;

[0029] The historical fields are filtered using the field filtering model to obtain the fields to be reported.

[0030] Optionally, the step of using the field filtering model to filter the historical fields to obtain the fields to be reported includes:

[0031] Select one of the historical fields one by one as the input value;

[0032] One decision tree is selected from the decision tree 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.

[0033] The historical fields corresponding to the output results where the input values ​​are the same as the parameters of the target decision tree are collected as fields to be reported.

[0034] Optionally, the step of matching the reported data from the refined data based on the refined field includes:

[0035] Select one field from each of the refined fields as the target refined field;

[0036] Calculate the similarity between the target refinement field and each data point within the refinement data;

[0037] The data with the highest similarity to the target refinement field within the refined data is determined as the reported data for the target refinement field.

[0038] To address the above problems, the present invention also provides a data reporting device, the device comprising:

[0039] The data segmentation module is used to collect multi-source data from multiple data sources and standardize and segment the collected multi-source data according to the preset field granularity to obtain refined data.

[0040] The report parsing module is used to obtain the historical report data table, parse the historical report data table, and obtain the historical report fields;

[0041] The requirements analysis module is used to obtain data reporting requirements and construct a requirements matrix for the data reporting requirements.

[0042] The field filtering module is used to filter out fields from the historical fields that meet the data reporting requirements as fields to be reported based on the requirement matrix;

[0043] The data reporting module is used to divide the field to be reported into refined fields according to the field granularity, match the reported data from the refined data according to the refined fields, and report the reported data.

[0044] To address the above problems, the present invention also provides an electronic device, the electronic device comprising:

[0045] At least one processor; and,

[0046] A memory communicatively connected to the at least one processor; wherein,

[0047] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the data reporting method described above.

[0048] 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 reporting method described above.

[0049] This invention enables multi-source data collection from multiple data sources, improving the efficiency of data acquisition and reporting, and consequently, data reporting efficiency. Simultaneously, it standardizes and segments the collected data, achieving fine-grained uniformity across different data sources. By selecting fields to be reported from historically reported fields according to data reporting requirements, and segmenting these fields with the same fine-grained granularity as the multi-source data, it achieves uniformity between field and data granularity. This allows for the selection of reported data based on these fields, avoiding the need to redevelop data reporting rules due to differences in field and data granularity, thus improving data reporting efficiency. Therefore, the data reporting method, apparatus, electronic device, and computer-readable storage medium proposed in this invention can solve the problem of low efficiency in data reporting. Attached Figure Description

[0050] Figure 1 This is a flowchart illustrating a data reporting method according to an embodiment of the present invention;

[0051] Figure 2 This is a schematic diagram of the process for constructing a demand matrix according to an embodiment of the present invention;

[0052] Figure 3 This is a schematic diagram of a process for obtaining reported data from refined data according to an embodiment of the present invention;

[0053] Figure 4 This is a functional block diagram of a data reporting device provided in an embodiment of the present invention;

[0054] Figure 5 This is a schematic diagram of the structure of an electronic device that implements the data reporting method according to an embodiment of the present invention.

[0055] 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

[0056] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0057] This application provides a data reporting method. The execution entity of the data reporting 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 reporting 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.

[0058] Reference Figure 1 The diagram shown is a flowchart illustrating a data reporting method provided in an embodiment of the present invention.

[0059] In this embodiment, the data reporting method includes:

[0060] S1. Collect data from multiple data sources and standardize and segment the collected data according to the preset field granularity to obtain refined data.

[0061] In this embodiment of the invention, the data source is a data storage area such as a database, blockchain node, or network cache that stores the data that needs to be reported.

[0062] In detail, since the data to be reported may be stored in different data sources, multi-source data collection can be performed on different data sources to obtain the multi-source data that needs to be reported.

[0063] In this embodiment of the invention, the multi-source data acquisition from multiple data sources includes:

[0064] Call the data interface of each data source;

[0065] Extract the interface parameters of the data interface;

[0066] Construct an aggregated data interface based on the interface parameters;

[0067] The aggregated data interface is used to collect multi-source data from the multiple data sources.

[0068] In detail, embodiments of the present invention can utilize the SQL interface creation method to compile the interface parameters into aggregate parameters, use SQL to create an initial aggregate interface that includes only initialization parameters, and use the aggregate parameters to assign parameter values ​​to the initial aggregate interface to obtain the aggregate data interface.

[0069] Specifically, the interface parameters include: interface address, interface request method, interface request field name and rules, and interface response field name and rules, etc.

[0070] This invention, through the construction of an aggregated data interface, enables the collection of data from multiple data sources. This allows for the use of a unified interface to access data from multiple data sources, thereby improving the efficiency of data retrieval.

[0071] Furthermore, since the data granularity is inconsistent when data from different data sources is stored, after collecting multi-source data from the multiple data sources, the multi-source data can be standardized and segmented according to a preset field granularity to obtain refined data, thereby achieving uniformity of the granularity of each data in the multi-source data.

[0072] In this embodiment of the invention, the step of standardizing and segmenting the collected multi-source data according to a preset field granularity to obtain refined data includes:

[0073] Select one data point from each of the multi-source data sources as the target data;

[0074] Determine whether the data granularity of the target data is less than or equal to the preset field granularity;

[0075] If the data granularity of the target data is less than or equal to the field granularity, then return to the step of selecting one data from the multi-source data as the target data one by one;

[0076] If the data granularity of the target data is greater than the field granularity, then the target data is segmented according to the field granularity to obtain the refined data corresponding to the target data.

[0077] In detail, the data granularity of each data point in the multi-source data can be compared with the preset field granularity to determine whether the data granularity of each data point in the multi-source data is less than or equal to the preset field granularity. When it is less than or equal to the field granularity, the data granularity is determined to be small enough that no further granularity division is needed. When it is greater than the field granularity, the data granularity is determined to be large, and the data can be segmented according to the field granularity to achieve refinement of the data granularity.

[0078] In this embodiment of the invention, the collected multi-source data is standardized and segmented according to a preset field granularity, which can unify the granularity of data in different data sources, thereby avoiding the need to differentiate data of different granularities when reporting data, which is conducive to improving the efficiency of data reporting.

[0079] S2. Obtain the historical data table, parse the historical data table, and obtain the historical data fields.

[0080] In this embodiment of the invention, the historical reporting data table is a data table that has been reported in a historical period. The historical reporting data table records multiple data fields that have been reported in the past (i.e., historical reporting fields) and the detailed content of each data field.

[0081] In detail, the historical data table can be retrieved from a predetermined data storage for storing the historical data table using computer statements with data retrieval capabilities (such as Java statements, Python statements, etc.).

[0082] Specifically, the historical reporting data table can be parsed using built-in parsers such as SQL, Hive, and JSON to obtain the historical reporting fields contained in the historical reporting data table.

[0083] S3. Obtain data reporting requirements and construct a requirement matrix for the data reporting requirements.

[0084] In this embodiment of the invention, the data reporting requirement is data that is uploaded in advance and describes the field types, content, and other related requirements that need to be uploaded.

[0085] In detail, to improve the accuracy of data reporting, the data reporting requirements can be analyzed to generate a requirement matrix for the data reporting requirements.

[0086] In this embodiment of the invention, the reference Figure 2 As shown, constructing the requirement matrix for the data reporting requirement includes:

[0087] S21. Perform word segmentation on the data reporting request to obtain the request word segmentation;

[0088] S22. Calculate the similarity between each of the demand segmented words and multiple preset demand intent tags, and select the demand intent tags with similarity greater than a preset similarity threshold as target tags;

[0089] S23. Construct the demand matrix using the target labels.

[0090] In this embodiment of the application, the data reporting requirement is split into requirement word segments so that each requirement word segment can be analyzed and processed separately, which can reduce the computational burden during analysis and improve analysis efficiency.

[0091] Specifically, the data reporting requests are retrieved in a preset standard dictionary according to different lengths, and the content that can be retrieved in the standard dictionary is collected into request word segments, wherein the standard dictionary contains multiple standard word segments.

[0092] In this embodiment, algorithms with similarity calculation functions, such as Euclidean distance and cosine distance, can be used to calculate the similarity between each demand segmentation word and multiple preset demand intent tags. Demand intent tags with similarity greater than a preset similarity threshold are then selected as target tags. Tags that may represent demand intent are filtered out based on the similarity, achieving fuzzy filtering of demand intent based on demand segmentation. This avoids the situation where some data reporting demands are missed during filtering due to differences in user descriptions of the data reporting demands, thus improving the accuracy of the selected target tags.

[0093] Furthermore, to facilitate subsequent analysis of the selected target tags, the target tags can be converted into word vectors.

[0094] In detail, the character vector of each character in the target tag can be queried from a preset character vector table, and the character vectors are concatenated into the word vector of the target tag according to the order of each character in the target tag. 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 of the target tag, and the character vectors are concatenated into the word vector of the target tag according to the order of each character in the target tag. 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.

[0095] For example, the target tag includes the three characters "precious metal". By querying the character vector table for each of the three characters, the character vector corresponding to "precious" is {A}, the character vector corresponding to "gold" is {B}, and the character vector corresponding to "belongs" is {C}. Then, the three character vectors can be concatenated according to the order of the three characters in the target tag "precious metal" to form the word vector for the required word segmentation: {ABC}.

[0096] In other embodiments of this application, models with word vector conversion functions, such as word2vec model, NLP (Natural Language Processing) model, and BERT model, can be used to convert the target label into word vectors.

[0097] In this embodiment of the application, constructing the demand matrix using the target tags includes:

[0098] Convert the target label into word vectors;

[0099] The word vectors are written into a pre-constructed blank matrix to obtain the demand matrix.

[0100] 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.

[0101] In this embodiment of the application, the word vectors can be filled into the blank matrix one by one in the form of row vectors to obtain a demand matrix containing the word vectors.

[0102] S4. Based on the requirement matrix, select the fields in the historical fields that meet the data reporting requirements as fields to be reported.

[0103] In this embodiment of the invention, since the historical fields contain a large number of different fields, but the fields that need to be reported may be few, the historical fields can be filtered using the demand matrix to select the fields in the historical fields that meet the data reporting requirements.

[0104] In this embodiment of the invention, the step of selecting fields from the historical fields that meet the data reporting requirements as fields to be reported based on the demand matrix includes:

[0105] Each row vector in the demand matrix is ​​selected as the target vector;

[0106] The target features are used as parameters to assign values ​​to a preset decision function, and the assigned decision function is used as a decision condition to generate a decision tree.

[0107] The decision tree generated by aggregating all row vectors of the demand matrix is ​​used as a field filtering model;

[0108] The historical fields are filtered using the field filtering model to obtain the fields to be reported.

[0109] For example, the decision function can be:

[0110]

[0111] 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.

[0112] In detail, one feature can be selected from each of the product features as the target feature. The parameter x of the decision function is then assigned a value using this target feature, and the assigned decision function is used as the decision condition to generate the following decision tree:

[0113] 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;

[0114] 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.

[0115] In this embodiment of the invention, the decision trees corresponding to each feature in the row vector can be aggregated in parallel or in series to obtain a field filtering model.

[0116] Furthermore, the process of using the field filtering model to filter the historical fields to obtain the fields to be reported includes:

[0117] Select one of the historical fields one by one as the input value;

[0118] One decision tree is selected from the decision tree 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.

[0119] The historical fields corresponding to the output results where the input values ​​are the same as the parameters of the target decision tree are collected as fields to be reported.

[0120] For example, if the historical fields include fields A and B, and field A is selected as the input, after inputting field A into the field filtering model, if the output of at least one target decision tree in the field filtering model shows that field A has the same parameters as the target decision tree, then field A can be determined as a field to be reported. Then, if field B is selected as the input again, and after inputting field B into the field filtering model, if the output of no target decision tree in the field filtering model shows that field A has the same parameters as the target decision tree, then field B can be confirmed as not a field to be reported.

[0121] S5. Divide the field to be reported into refined fields according to the field granularity, obtain the reporting data from the refined data according to the refined fields, and report the reporting data.

[0122] In this embodiment of the invention, since the field to be reported is a field selected based on the data reporting requirements, the granularity of the field to be reported may be inconsistent. Therefore, the field to be reported can be divided into refined fields according to the field granularity to obtain data with the same field granularity as the refined data.

[0123] In detail, the step of dividing the field to be reported into refined fields according to the field granularity is the same as the step in S1 of standardizing and dividing the collected multi-source data according to the preset field granularity to obtain refined data, and will not be repeated here.

[0124] Furthermore, data can be selected from the refined data based on the refined fields, and then the data can be reported to avoid redeveloping data scraping rules to scrape the reported data, which helps to improve the efficiency of data reporting.

[0125] In this embodiment of the invention, the reference Figure 3 As shown, the step of matching the reported data from the refined data based on the refined fields includes:

[0126] S31. Select one of the refined fields as the target refined field one by one;

[0127] S32. Calculate the similarity between the target refinement field and each data point in the refinement data;

[0128] S33. Determine the data with the highest similarity to the target refinement field within the refined data as the reported data of the target refinement field.

[0129] In detail, the step of calculating the similarity between the target refined field and each data in the refined data is the same as the step in S2 of calculating the similarity between each demand segment and multiple preset demand intent tags, and will not be repeated here.

[0130] This invention enables multi-source data collection from multiple data sources, improving the efficiency of data acquisition and reporting, and consequently, data reporting efficiency. Simultaneously, it standardizes and segments the collected data, achieving fine-grained uniformity across different data sources. Based on data reporting requirements, it selects fields to be reported from historically reported fields and segments them with the same fine-grained granularity as the multi-source data, achieving uniformity between field and data granularity. This allows for the selection of reported data based on these fields, avoiding the need to redevelop data reporting rules due to differences in field and data granularity, thus improving data reporting efficiency. Therefore, the data reporting method proposed in this invention can solve the problem of low efficiency in data reporting.

[0131] like Figure 4 The diagram shown is a functional block diagram of a data reporting device provided in an embodiment of the present invention.

[0132] The data reporting device 100 of this invention can be installed in an electronic device. Depending on the functions implemented, the data reporting device 100 may include a data segmentation module 101, a report parsing module 102, a demand analysis module 103, a field filtering module 104, and a data reporting 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 are stored in the memory of the electronic device.

[0133] In this embodiment, the functions of each module / unit are as follows:

[0134] The data segmentation module 101 is used to collect multi-source data from multiple data sources and to standardize and segment the collected multi-source data according to a preset field granularity to obtain refined data.

[0135] The report parsing module 102 is used to obtain a historical report data table, parse the historical report data table, and obtain the historical report fields.

[0136] The demand analysis module 103 is used to obtain data reporting requirements and construct a demand matrix for the data reporting requirements;

[0137] The field filtering module 104 is used to filter out fields from the historical fields that meet the data reporting requirements as fields to be reported based on the requirement matrix;

[0138] The data reporting module 105 is used to divide the field to be reported into refined fields according to the field granularity, match the reported data from the refined data according to the refined fields, and report the reported data.

[0139] In detail, each module in the data reporting device 100 described in this embodiment of the invention adopts the same usage as described above. Figures 1 to 3 The data reporting method described herein uses the same technical means and can produce the same technical effect, so it will not be repeated here.

[0140] like Figure 5 The diagram shown is a structural schematic of an electronic device for implementing a data reporting method according to an embodiment of the present invention.

[0141] 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, such as a data reporting program, stored in the memory 11 and capable of running on the processor 10.

[0142] 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 reporting programs) and calls data stored in the memory 11 to perform various functions of the electronic device and process data.

[0143] 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 reporting program, but also to temporarily store data that has been output or will be output.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] The data reporting 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:

[0150] Multiple data sources are collected, and the collected data is standardized and segmented according to the preset field granularity to obtain refined data;

[0151] Obtain the historical data report table, parse the historical data report table, and obtain the historical data report fields;

[0152] Obtain data reporting requirements and construct a requirement matrix for those requirements;

[0153] Based on the demand matrix, the fields in the historical fields that meet the data reporting requirements are selected as fields to be reported;

[0154] The field to be reported is divided into refined fields according to the field granularity. The reported data is obtained by matching the refined data from the refined data according to the refined fields, and the reported data is reported.

[0155] 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.

[0156] 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).

[0157] 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:

[0158] Multiple data sources are collected, and the collected data is standardized and segmented according to the preset field granularity to obtain refined data;

[0159] Obtain the historical data report table, parse the historical data report table, and obtain the historical data report fields;

[0160] Obtain data reporting requirements and construct a requirement matrix for those requirements;

[0161] Based on the demand matrix, the fields in the historical fields that meet the data reporting requirements are selected as fields to be reported;

[0162] The field to be reported is divided into refined fields according to the field granularity. The reported data is obtained by matching the refined data from the refined data according to the refined fields, and the reported data is reported.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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 data reporting method, characterized in that, The method includes: Multiple data sources are collected, and the collected data is standardized and segmented according to the preset field granularity to obtain refined data; Obtain the historical data report table, parse the historical data report table, and obtain the historical data report fields; Obtain data reporting requirements, perform word segmentation on the data reporting requirements to obtain requirement word segments, and construct a requirement matrix for the data reporting requirements based on the requirement word segments. Row vectors are selected one by one from the demand matrix as target vectors. The target vectors are used as parameters to assign values ​​to a preset decision function. The assigned decision function is used as a decision condition to generate a decision tree. The decision trees generated from all row vectors of the demand matrix are collected to form a field filtering model. The field filtering model is used to filter the historically reported fields to obtain the fields to be reported. The field to be reported is divided into refined fields according to the field granularity. The reported data is obtained by matching the refined data from the refined data according to the refined fields, and the reported data is reported.

2. The data reporting method as described in claim 1, characterized in that, The multi-source data collection from multiple data sources includes: Call the data interface of each data source; Extract the interface parameters of the data interface; Construct an aggregated data interface based on the interface parameters; The aggregated data interface is used to collect multi-source data from the multiple data sources.

3. The data reporting method as described in claim 1, characterized in that, The process of standardizing and segmenting the collected multi-source data according to a preset field granularity to obtain refined data includes: Select one data point from each of the multi-source data sources as the target data; Determine whether the data granularity of the target data is less than or equal to the preset field granularity; If the data granularity of the target data is less than or equal to the field granularity, then return to the step of selecting one data from the multi-source data as the target data one by one; If the data granularity of the target data is greater than the field granularity, then the target data is segmented according to the field granularity to obtain the refined data corresponding to the target data.

4. The data reporting method as described in claim 1, characterized in that, The step of constructing the demand matrix for the data reporting demand based on the demand segmentation includes: Calculate the similarity between each of the demand segments and multiple preset demand intent tags, and select the demand intent tags with similarity greater than a preset similarity threshold as target tags; The demand matrix is ​​constructed using the target labels.

5. The data reporting method as described in claim 1, characterized in that, The process of using the field filtering model to filter the historically reported fields to obtain the fields to be reported includes: Select one field from the historically reported fields as the input value; One decision tree is selected from the decision tree 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. The historical fields corresponding to the output results where the input values ​​are the same as the parameters of the target decision tree are collected as fields to be reported.

6. The data reporting method according to any one of claims 1 to 5, characterized in that, The step of matching the reported data from the refined data based on the refined fields includes: Select one field from each of the refined fields as the target refined field; Calculate the similarity between the target refinement field and each data point within the refinement data; The data with the highest similarity to the target refinement field within the refined data is determined as the reported data for the target refinement field.

7. A data reporting device, characterized in that, The device includes: The data segmentation module is used to collect multi-source data from multiple data sources and standardize and segment the collected multi-source data according to the preset field granularity to obtain refined data. The report parsing module is used to obtain the historical report data table, parse the historical report data table, and obtain the historical report fields; The requirement analysis module is used to obtain data reporting requirements, perform word segmentation on the data reporting requirements to obtain requirement words, and construct a requirement matrix of the data reporting requirements based on the requirement words. The field filtering module is used to select row vectors from the demand matrix one by one as target vectors, assign values ​​to a preset decision function as parameters, and use the assigned decision function as decision conditions to generate a decision tree. The decision trees generated by all row vectors of the demand matrix are collected into a field filtering model. The field filtering model is used to filter the historically reported fields to obtain the fields to be reported. The data reporting module is used to divide the field to be reported into refined fields according to the field granularity, match the reported data from the refined data according to the refined fields, and report the reported data.

8. 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 reporting method as described in any one of claims 1 to 6.

9. 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 reporting method as described in any one of claims 1 to 6.

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