Table data processing method, device, equipment and computer readable storage medium

Abstract

The present invention discloses a method for processing table data, including: when table data is detected, determining the parsing rules corresponding to the table data, and based on the parsing rules, determining the target data in the table data, and the target storage field corresponding to the target data; based on the parsing rules, determining whether the target data needs to extract indicators; if extraction is required, determining the indicator rules associated with the parsing rules, and based on the indicator rules, generating indicator data corresponding to the target data; based on the target storage field, storing the indicator data. The present invention also discloses a table data processing device, equipment and computer-readable storage medium. Compared with the cumbersome development process adopted in the prior art, the present invention only needs to adjust the corresponding parsing rules to automatically identify the target data that needs to be processed, and when the target data needs to be adjusted, it is adjusted through the indicator rules, thereby realizing intelligent processing of data.

Description

Technical Field

[0001] The present invention relates to the technical field of financial technology (Fintech), and in particular to a table data processing method, device, equipment and computer-readable storage medium. Background Art

[0002] In recent years, with the continuous development of financial technology (Fintech), especially Internet finance, data processing technology has been introduced into the daily services of banks and other financial institutions. In the daily service process of banks and other financial institutions, it is often necessary to connect to the data of external institutions, such as customer lists, loan bills and other data, which are generally in the form of tables. When banks and other financial institutions connect to such data, they often adjust due to demand, such as when they need to calculate or filter certain data, they need to perform secondary operations on the data or threshold judgments, and then further obtain results; or because of data source adjustments, such as when a single data is abnormal, resulting in the inability to write other data in the table data or program abnormalities, it is necessary to detect abnormalities on the data source. Therefore, how to process table data is an important task that banks and other financial institutions need to do. Summary of the invention

[0003] The main purpose of the present invention is to provide a table data processing method, device, equipment and computer-readable storage medium, aiming to realize intelligent data processing.

[0004] To achieve the above object, the present invention provides a table data processing method, which comprises the following steps:

[0005] When the table data is detected, determining the parsing rule corresponding to the table data, and based on the parsing rule, determining the target data in the table data and the target storage field corresponding to the target data;

[0006] Based on the parsing rule, determining whether the target data needs to be adjusted;

[0007] If necessary, determining the indicator rule associated with the parsing rule, and generating indicator data corresponding to the target data based on the indicator rule;

[0008] Based on the target storage field, the indicator data is stored.

[0009] Optionally, if the table data is detected, the step of determining the parsing rule corresponding to the table data includes:

[0010] When the table data is detected, identifying the file attribute of the table data;

[0011] Based on the file attributes, a parsing rule corresponding to the table data is determined.

[0012] Optionally, the step of determining the target data in the table data and the target storage field corresponding to the target data based on the parsing rule includes:

[0013] Based on the parsing rule, determine a target cell in the table data, and determine the cell type to which the target cell belongs;

[0014] Based on the target cell and the cell type, the target data in the table data is acquired, and based on the target cell and the cell type, a data model corresponding to the target data and a target storage field corresponding to the target data in the data model are determined.

[0015] Optionally, the step of determining a target cell in the table data based on the parsing rule and determining a cell type to which the target cell belongs includes:

[0016] Read the target file path of the table data, obtain the target form name of the table data, and determine the target cell position of the table data;

[0017] Determine a target cell in the table data based on the file path, the form name and the target cell position;

[0018] The data content of the target cell is identified, and based on the data content, the cell type to which the target cell belongs is determined.

[0019] Optionally, the step of determining an indicator rule associated with the parsing rule if necessary, and generating indicator data corresponding to the target data based on the indicator rule includes:

[0020] If necessary, based on the rule ID of the parsing rule, determine the indicator rule associated with the parsing rule, and determine the operation rule of the indicator rule;

[0021] Based on the operation rules, the target data is converted into index data.

[0022] Optionally, the step of storing the indicator data based on the target storage field includes:

[0023] Based on the target storage field, determine model data belonging to the same data model as the indicator data;

[0024] The model data and the indicator data are stored in a storage location corresponding to the data model.

[0025] Optionally, the step of storing the model data and the indicator data in a storage location corresponding to the data model includes:

[0026] Verifying whether the model data and the indicator data meet preset requirements;

[0027] If they are consistent, the model data and the indicator data are stored in a storage location corresponding to the data model.

[0028] Optionally, the step of determining whether the target data needs to be adjusted based on the parsing rule includes:

[0029] Determine the rule ID of the parsing rule, and determine whether the rule ID is associated with an indicator ID;

[0030] If so, it is determined that the target data needs to be adjusted.

[0031] In addition, to achieve the above-mentioned purpose, the present invention further provides a table data processing device, the table data processing device comprising:

[0032] A data parsing module, for determining, when detecting table data, a parsing rule corresponding to the table data, and determining, based on the parsing rule, target data in the table data and a target storage field corresponding to the target data;

[0033] An indicator judgment module, used to determine whether the target data needs to extract indicators based on the analysis rules;

[0034] An indicator generation module, used for determining the indicator rule associated with the parsing rule if extraction is required, and generating indicator data corresponding to the target data based on the indicator rule;

[0035] A data storage module is used to store the indicator data based on the target storage field.

[0036] Optionally, the data parsing module is further used for:

[0037] When the table data is detected, identifying the file attribute of the table data;

[0038] Based on the file attributes, a parsing rule corresponding to the table data is determined.

[0039] Optionally, the data parsing module is further used for:

[0040] Based on the parsing rule, determine a target cell in the table data, and determine the cell type to which the target cell belongs;

[0041] Based on the target cell and the cell type, the target data in the table data is acquired, and based on the target cell and the cell type, a data model corresponding to the target data and a target storage field corresponding to the target data in the data model are determined.

[0042] Optionally, the data parsing module is further used for:

[0043] Read the target file path of the table data, obtain the target form name of the table data, and determine the target cell position of the table data;

[0044] Determine a target cell in the table data based on the file path, the form name and the target cell position;

[0045] The data content of the target cell is identified, and based on the data content, the cell type to which the target cell belongs is determined.

[0046] Optionally, the indicator generation module is further used for:

[0047] If necessary, based on the rule ID of the parsing rule, determine the indicator rule associated with the parsing rule, and determine the operation rule of the indicator rule;

[0048] Based on the operation rules, the target data is converted into index data.

[0049] Optionally, the data storage module is further used for:

[0050] Based on the target storage field, determine model data belonging to the same data model as the indicator data;

[0051] The model data and the indicator data are stored in a storage location corresponding to the data model.

[0052] Optionally, the data storage module is further used for:

[0053] Verifying whether the model data and the indicator data meet preset requirements;

[0054] If they are consistent, the model data and the indicator data are stored in a storage location corresponding to the data model.

[0055] Optionally, the indicator judgment module is further used to:

[0056] Determine the rule ID of the parsing rule, and determine whether the rule ID is associated with an indicator ID;

[0057] If so, it is determined that the target data needs to be adjusted.

[0058] In addition, to achieve the above-mentioned purpose, the present invention also provides a table data processing device, which includes: a memory, a processor, and a table data processing program stored in the memory and executable on the processor, and the table data processing program implements the steps of the table data processing method described above when executed by the processor.

[0059] In addition, to achieve the above-mentioned purpose, the present invention also provides a computer-readable storage medium, on which a table data processing program is stored, and when the table data processing program is executed by a processor, the steps of the table data processing method described above are implemented.

[0060] The table data processing method proposed in the present invention determines the parsing rules corresponding to the table data when the table data is detected, and determines the target data in the table data and the target storage field corresponding to the target data based on the parsing rules; determines whether the target data needs to extract indicators based on the parsing rules; if extraction is required, determines the indicator rules associated with the parsing rules, and generates the indicator data corresponding to the target data based on the indicator rules; stores the indicator data based on the target storage field. The present invention automatically identifies the target data that needs to be processed through parsing rules, and adjusts the target data through indicator rules when the target data needs to be adjusted. Compared with the prior art process of evaluating requirements, developing programs based on the evaluation requirements, and then testing, the present invention greatly saves labor costs and reduces the cumbersome development process to only adjusting the corresponding parsing rules and indicator rules through interfaces, thereby realizing intelligent data processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 It is a schematic diagram of the device structure of the hardware operating environment involved in the embodiment of the present invention;

[0062] Figure 2 It is a flowchart of the first embodiment of the table data processing method of the present invention.

[0063] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0064] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0065] like Figure 1 As shown, Figure 1 It is a schematic diagram of the device structure of the hardware operating environment involved in the embodiment of the present invention.

[0066] The device in the embodiment of the present invention may be a mobile terminal or a server device.

[0067] like Figure 1 As shown, the device may include: a processor 1001, such as a CPU, a network interface 1004, a user interface 1003, a memory 1005, and a communication bus 1002. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display), an input unit such as a keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface). The memory 1005 may be a high-speed RAM memory, or it may be a stable memory (non-volatile memory), such as a disk memory. The memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0068] Those skilled in the art will understand that Figure 1 The device structure shown in the figure does not constitute a limitation of the device, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0069] like Figure 1 As shown, the memory 1005 as a computer storage medium may include an operating system, a network communication module, a user interface module, and a table data processing program.

[0070] Among them, the operating system is a program that manages and controls table data processing equipment and software resources, and supports the operation of network communication modules, user interface modules, table data processing programs and other programs or software; the network communication module is used to manage and control the network interface 1002; the user interface module is used to manage and control the user interface 1003.

[0071] exist Figure 1 In the table data processing device shown, the table data processing device calls the table data processing program stored in the memory 1005 through the processor 1001, and executes the operations in each embodiment of the table data processing method described below.

[0072] Based on the above hardware structure, an embodiment of the table data processing method of the present invention is proposed.

[0073] Reference Figure 2 , Figure 2 This is a flow chart of a first embodiment of a table data processing method of the present invention, the method comprising:

[0074] Step S10, when the table data is detected, determining the parsing rule corresponding to the table data, and based on the parsing rule, determining the target data in the table data and the target storage field corresponding to the target data;

[0075] Step S20, determining whether the target data needs to be adjusted based on the parsing rule;

[0076] Step S30, if necessary, determining the indicator rule associated with the analysis rule, and generating indicator data corresponding to the target data based on the indicator rule;

[0077] Step S40: storing the indicator data based on the target storage field.

[0078] The table data processing method of this embodiment is applied to a table data processing device. The table data processing device may be a terminal, a robot or a PC device. For the convenience of description, the table data processing device is referred to as a processing device.

[0079] The processing device of this embodiment provides configurable parsing rules. Specifically, the user can design corresponding data parsing rules according to the data model required in the processing device, and expose the parsing interface corresponding to the parsing rule. The parsing rules can be persisted to the database and can be modified at any time through the parsing interface. That is, in this embodiment, at least one data model is provided in the processing device, and each data model corresponds to at least one parsing rule, wherein the parsing rule is used to parse the detected data; the data model is used to store data. It should be noted that the data model includes several model fields, also called storage fields, and the storage field is used to store data corresponding to a certain type of parsing rule. That is, the processing device parses the data through the parsing rules to determine the data the user wants, and stores the parsing results in the storage fields in the data model, thereby changing the storage location of the data.

[0080] In addition, the processing device also provides adjustable calculation indicators, that is, indicator rules. Specifically, users can design general calculation indicators according to the secondary operation needs of the actual parsing rules, and expose the indicator interface corresponding to the calculation indicators to the outside. The corresponding operation rules can be configured in the calculation indicators. The calculation indicators can be persisted to the database and can be modified at any time through the indicator interface. That is, in this embodiment, in the processing device, some or all of the parsing rules correspond to at least one calculation indicator, wherein the calculation indicator is used to perform secondary operations on the parsed data according to the operation rules. That is, the processing device adjusts the parsed data through the calculation indicators, that is, the indicator rules, so that the final result meets the actual needs of the user, thereby realizing intelligent data processing.

[0081] This embodiment automatically identifies the data that needs to be read and processed, that is, the target data, through parsing rules, and adjusts the target data through indicator rules when the target data needs to be adjusted, so that the final data meets the actual needs of the user, thereby realizing intelligent data processing.

[0082] The following is a detailed description of each step:

[0083] Step S10, when the table data is detected, determining the parsing rules corresponding to the table data, and based on the parsing rules, determining the target data in the table data and the target storage field corresponding to the target data.

[0084] In this embodiment, the processing device can run in real time or on a scheduled basis. If tabular data is detected during operation, the parsing rules corresponding to the tabular data are first determined, wherein the tabular data may be external data outside the processing device or internal data within the processing device, and the parsing rules may be preset unified rules. For example, no matter what kind of tabular data is detected, the parsing rule is to process the data in the first row and first column of the tabular data, etc.

[0085] In addition, the parsing rule may also be a changeable rule adapted to the table data. Specifically, if the table data is detected, the step of determining the parsing rule corresponding to the table data includes:

[0086] Step a1, when table data is detected, identifying the file attribute of the table data;

[0087] In one embodiment, if the processing device detects table data, it first identifies the file attributes of the table data, wherein the file attributes are inherent attributes of the table data, such as file name, file size, etc.

[0088] Further, in one embodiment, when the file attribute of the table data is a file name, step a1 includes:

[0089] Step a11, determining the file type of the table data based on the file name of the table data;

[0090] In one embodiment, the processing device may use text recognition technology to identify the file name of the table data, and then determine the file type of the table data based on the file name. For example, if the file name is XXX New Customer, XXX Contact Form, then its file type is determined to be customer information, etc.

[0091] Step a12, determining a rule ID corresponding to the file type, and based on the rule ID, determining a parsing rule corresponding to the table data.

[0092] Afterwards, determine the corresponding rule ID based on the file type. Specifically, you can build a file type-rule ID mapping table in advance. After determining the file type of the table data, you can determine the rule ID through the mapping table. Finally, you can determine the corresponding parsing rule based on the rule ID, where the rule ID corresponds to the parsing rule one by one.

[0093] Step a2: determining the parsing rules corresponding to the table data based on the file attributes.

[0094] Afterwards, the processing device determines the parsing rules corresponding to the table data based on the identified file attributes. Specifically, a file attribute-parsing rule mapping table is constructed in advance. After determining the file attributes of the current table data, the parsing rules corresponding to the table data can be determined through the file attribute-parsing rule mapping table.

[0095] After determining the parsing rules corresponding to the current table data, the processing device can determine the target data in the table data and the target storage field corresponding to the target data according to the parsing rules. Specifically, the table data is parsed according to the parsing steps in the parsing rules to obtain the target data, and the numerical model and the target storage field corresponding to the target data are determined according to the parsing fields in the parsing rules.

[0096] In one embodiment, the parsing fields in the parsing rules include but are not limited to the following fields:

[0097] Field Name Field Description Example ruleId Parsing rule ID 12345 excelPath Table file path / excel / a.xls excelSheet Table form name Sheet1 excelUnit Table Data Cell A1, B3, C5 excelUnitType Table data cell type Number entityName Model Name RiskInfo entityKey Model fields CorpName

[0098] Therefore, in one embodiment, based on the parsing rule, the step of determining the target data in the table data and the target storage field corresponding to the target data includes:

[0099] Step a3, determining a target cell in the table data based on the parsing rule, and determining the cell type to which the target cell belongs;

[0100] In one embodiment, the target cell in the table data is first determined by parsing rules, that is, the location of the data desired by the user is determined, and then the cell type of the target cell is determined to ensure that the data in the target cell is the data desired by the user.

[0101] Specifically, in one embodiment, the parsing process corresponding to the parsing rule may be:

[0102] Step a31, reading the target file path of the table data, obtaining the target form name of the table data, and determining the target cell position of the table data;

[0103] In one embodiment, the specific process of the processing device parsing according to the parsing rules is: according to the excelPath field in the parsing rules, read the target file path of the table data, according to the excelSheet field, obtain the target form name of the table data, and according to the excelUnit field, determine the target cell position of the table data.

[0104] Step a32, determining a target cell in the table data based on the file path, the form name and the target cell position;

[0105] Then, the unique target cell in the table data is determined by determining the file path, sheet name and target cell position. It is understandable that, according to actual needs, the target cell can be part of the cells in the table data or all the cells in the table data. For example, if the parsing rule is to obtain the data in the target cells A1, B3, and C5 in the Excel file path / excel / a.xls, the sheet name Sheet1, and the positions, the processing device determines the target cells step by step according to the parsing rule.

[0106] Step a33, identifying the data content of the target cell, and determining the cell type to which the target cell belongs based on the data content.

[0107] Next, the data content of the target cell is identified, and the cell type to which the target cell belongs is determined based on the data content, wherein the cell type includes a value, text, and the like.

[0108] That is, in one embodiment, the parsing steps may be determined according to the parsing rules, and then the table data may be parsed according to the parsing steps.

[0109] In another embodiment, the processing device may also record the execution information during the parsing process, specifically the execution information of each parsing step, wherein the execution information includes exception information. If the parsing process encounters an exception or error, the processing device may record the processing node, that is, the exception information of which step, and may choose to continue or re-execute the next time the processing is performed; at the same time, it may also choose to ignore a preset number of exceptions or errors when encountering an exception or error.

[0110] Step a4, based on the target cell and the cell type, obtain the target data in the table data, and based on the target cell and the cell type, determine the data model corresponding to the target data and the target storage field corresponding to the target data in the data model.

[0111] Afterwards, the target data in the table data, that is, the data the user wants, is obtained according to the target cell and cell type. Then, based on the target cell and cell type, the data model corresponding to the target data and the target storage field corresponding to the target data in the data model are determined.

[0112] In one embodiment, the entityName field and the entityKey field can be jointly determined by the ruleId field, the excelPath field, the excelSheet field, the excelUnit field and the excelUnitType field. In addition, in another embodiment, the entityName field and the entityKey field, that is, the data model and the storage field, can also be determined by other parsing fields.

[0113] It should be explained that one table data can correspond to multiple parsing rules, and therefore one table data can correspond to at least one data model and at least one storage field. If there are two parsing rules corresponding to the current table data, the corresponding target data is obtained according to the parsing process of the two parsing rules respectively, and then the corresponding target data can be stored in different storage fields of the same data model, or in different storage fields of different data models, etc.

[0114] Step S20: determining whether the target data needs to be adjusted based on the parsing rule.

[0115] In this embodiment, after the table data is parsed to obtain the target data, it is determined whether the target data needs to be adjusted through the parsing rules.

[0116] Specifically, step S20 includes:

[0117] Step b, determining the rule ID of the parsing rule, and determining whether the rule ID is associated with the indicator ID;

[0118] If so, it is determined that the target data needs to be adjusted.

[0119] That is, the parsing rule and the indicator rule are associated and bound through the rule ID and the indicator ID. Therefore, whether the parsing rule is associated with the indicator rule can be determined by determining the rule ID of the parsing rule and determining whether there is an indicator ID associated with the rule ID. If so, it means that the target data parsed by the parsing rule needs to be operated secondary, that is, it needs to be adjusted.

[0120] It should be noted that if the rule ID of the current parsing rule is not associated with the indicator ID, it means that the target data parsed by the current parsing rule does not need to be subjected to secondary calculation, that is, does not need to be adjusted.

[0121] Step S30: if necessary, determine the indicator rule associated with the analysis rule, and generate indicator data corresponding to the target data based on the indicator rule.

[0122] In this embodiment, if it is determined that the parsed target data needs to be adjusted, the processing device determines the indicator rule associated with the parsing rule, wherein, in one embodiment, the indicator rule can be a fixed and unified indicator rule, that is, all parsing rules correspond to the same indicator rule, and then, according to the indicator rule, the indicator data corresponding to the target data is generated, wherein, in one embodiment, the indicator data includes the target data, that is, while the processing device adjusts the target data according to the indicator rule, it retains the original target data, thereby obtaining the data before and after the adjustment, collectively referred to as indicator data.

[0123] Further, in one embodiment, step S30 includes:

[0124] Step c1, if necessary, determining the indicator rule associated with the analysis rule based on the rule ID of the analysis rule, and determining the operation rule of the indicator rule;

[0125] In one embodiment, if it is determined that the parsed target data needs to be adjusted, the processing device determines the rule ID of the current parsing rule, and determines the indicator rule associated with the current parsing rule through the rule ID. Specifically, the associated indicator ID is determined through the rule ID, and then the indicator rule is determined through the indicator ID.

[0126] In one embodiment, the indicator fields in the indicator rule include but are not limited to the following fields:

[0127] Field Name Field Description Example indexId Indicator Id 123 indexName Indicator name Indicator A indexRule Indicator calculation rules ≥10 indexScore Indicator calculation score 10 ruleId Associated rule ID 12345

[0128] The indexScore field is used to indicate the score of the target data satisfying the index rule. For example, in the above example, the operation rule is ≥10, and the target data is greater than 10, so the calculated score is 10 points (full score), which is used to indicate the degree of adjustment of the target data. The higher the score, the smaller the degree of adjustment.

[0129] Step c2: generating index data from the target data based on the operation rules.

[0130] Afterwards, according to the determined operation rules and the operation logic of the operation rules, the indicator data corresponding to the target data is generated, wherein the indicator data does not include the target data, that is, the target data is generated into the indicator data.

[0131] In one embodiment, the design of the operation rules includes but is not limited to the following:

[0132]

[0133] That is, the operation rules include rule expressions and rule descriptions, and the processing device can generate indicator data from target data according to the rule expressions in the operation rules.

[0134] Step S40: storing the indicator data based on the target storage field.

[0135] In this embodiment, after the indicator data is obtained, the indicator data is stored in the corresponding position through the target storage field obtained before.

[0136] It should be noted that when the processing device is parsing table data and generating indicator data for the parsed target data, it can realize the separation of data reading and writing based on a preset framework, such as the springBatch framework. That is, in a single processing step, such as the parsing process, the processing quantity can be set, a certain amount of data can be read, and the data can be stored in a unified manner after processing, thereby improving data processing and storage efficiency.

[0137] When the present embodiment detects tabular data, it determines the parsing rules corresponding to the tabular data, and based on the parsing rules, determines the target data in the tabular data and the target storage field corresponding to the target data; based on the parsing rules, determines whether the target data needs to extract indicators; if extraction is required, determines the indicator rules associated with the parsing rules, and based on the indicator rules, generates indicator data corresponding to the target data; based on the target storage field, stores the indicator data. The present invention automatically identifies the target data that needs to be processed through parsing rules, and adjusts the target data through indicator rules when the target data needs to be adjusted. Compared with the prior art, the original process of evaluating the needs, developing the program according to the evaluation needs, and then testing is reduced to only adjusting the corresponding parsing rules and indicator rules through the interface, thereby realizing intelligent processing of data.

[0138] Furthermore, based on the first embodiment of the table data processing method of the present invention, a second embodiment of the table data processing method of the present invention is proposed.

[0139] The second embodiment of the table data processing method is different from the first embodiment of the table data processing method in that step S40 includes:

[0140] Step d1, based on the target storage field, determining model data belonging to the same data model as the indicator data;

[0141] Step d2, storing the model data and the indicator data in a storage location corresponding to the data model.

[0142] In the process of generating indicator data, this embodiment stores data of the same data model together by matching model data of the data model, so as to facilitate subsequent complete analysis of the data.

[0143] The following is a detailed description of each step:

[0144] Step d1, based on the target storage field, determine the model data belonging to the same data model as the indicator data.

[0145] In this embodiment, after generating indicator data, the processing device determines the model data that matches the indicator data based on its corresponding target storage field. Specifically, the data model corresponding to the indicator data is determined through the storage field, and then the model data of the same data model as the indicator data is determined.

[0146] It is understandable that, when the current parsing rule is associated with the indicator rule, the processing device generates the indicator data from the target data parsed by the current parsing rule; when the current parsing rule is not associated with the indicator rule, the target data is not adjusted, and the target data is stored in the corresponding storage field of the data model, which is the model data. In the specific implementation process, in most cases, the same table data corresponds to multiple parsing rules. Therefore, during the parsing process, there may be a situation where multiple target data are parsed, and not all target data need to be adjusted.

[0147] Therefore, in order to perform a complete analysis of the table data later, after obtaining the indicator data, it is necessary to determine the corresponding model data through the target storage field corresponding to the current indicator data, and determine other storage fields of the same data model as the target storage field, and the data corresponding to the other storage fields are the model data. In this embodiment, the same table data corresponds to one data model, that is, the target data in the table data are stored in different storage fields of the same data model. By matching the model data, the data that the user wants in the same table data is found, which is convenient for subsequent complete analysis of the data.

[0148] Step d2, storing the model data and the indicator data in a storage location corresponding to the data model.

[0149] In this embodiment, after the indicator data and the model data matching the indicator data are determined, the indicator data and the model data are stored together, specifically in a storage location corresponding to the data model.

[0150] Further, in one embodiment, step d2 includes:

[0151] Step d21, verifying whether the model data and the indicator data meet the preset requirements;

[0152] In one embodiment, during the process of storing the indicator data and the model data, it is also necessary to verify whether the model data and the indicator data meet preset requirements, wherein the preset requirements include a preset data format and a preset value range.

[0153] That is, the processing device needs to verify whether the data format of the currently obtained indicator data matches the preset data format, whether the value of the indicator data satisfies the preset value range, and similarly, whether the data format of the model data matches the preset data format, whether the value of the model data satisfies the preset value range, etc.

[0154] Step d22: If the conditions are met, the model data and the indicator data are stored in a storage location corresponding to the data model.

[0155] In one embodiment, if it is determined that both the indicator data and the model data meet the preset requirements, the model data and the indicator data are stored in a storage location corresponding to the data model. Specifically, a blank table can be used as the storage location, and the model data and the indicator data are stored in corresponding cells according to their respective storage fields.

[0156] In one embodiment, if it is determined that there is data that does not meet the preset requirements, corresponding exception information is recorded, wherein the exception information includes an exception storage field and abnormal target data.

[0157] Afterwards, determine the number of times the abnormal information appears. If it is lower than the preset number, ignore the abnormality and update the number of times the abnormal information appears. Store the indicator data and model data normally in the storage location corresponding to the data model. If the number of times the abnormal information appears is not lower than the preset number, report an error.

[0158] That is, in one embodiment, if there is a single data anomaly, a certain number of anomalies can be chosen to be ignored, while corresponding anomaly information is recorded to facilitate subsequent data analysis.

[0159] In order to conduct a complete analysis of the data, this embodiment stores the data of the same data model together by matching the model data of the data model during the process of generating indicator data, thereby effectively helping users to adjust the storage path at any time and meet the user's needs for secondary data processing, thereby realizing intelligent data processing.

[0160] The present invention also provides a table data processing device. The table data processing device of the present invention comprises:

[0161] A data parsing module, for determining, when detecting table data, a parsing rule corresponding to the table data, and determining, based on the parsing rule, target data in the table data and a target storage field corresponding to the target data;

[0162] An indicator judgment module, used to determine whether the target data needs to extract indicators based on the analysis rules;

[0163] An indicator generation module, used for determining the indicator rule associated with the parsing rule if extraction is required, and generating indicator data corresponding to the target data based on the indicator rule;

[0164] A data storage module is used to store the indicator data based on the target storage field.

[0165] Optionally, the data parsing module is further used for:

[0166] When the table data is detected, identifying the file attribute of the table data;

[0167] Based on the file attributes, a parsing rule corresponding to the table data is determined.

[0168] Optionally, the data parsing module is further used for:

[0169] Based on the parsing rule, determine a target cell in the table data, and determine the cell type to which the target cell belongs;

[0170] Based on the target cell and the cell type, the target data in the table data is acquired, and based on the target cell and the cell type, a data model corresponding to the target data and a target storage field corresponding to the target data in the data model are determined.

[0171] Optionally, the data parsing module is further used for:

[0172] Read the target file path of the table data, obtain the target form name of the table data, and determine the target cell position of the table data;

[0173] Determine a target cell in the table data based on the file path, the form name and the target cell position;

[0174] The data content of the target cell is identified, and based on the data content, the cell type to which the target cell belongs is determined.

[0175] Optionally, the indicator generation module is further used for:

[0176] If necessary, based on the rule ID of the parsing rule, determine the indicator rule associated with the parsing rule, and determine the operation rule of the indicator rule;

[0177] Based on the operation rules, the target data is converted into index data.

[0178] Optionally, the data storage module is further used for:

[0179] Based on the target storage field, determine model data belonging to the same data model as the indicator data;

[0180] The model data and the indicator data are stored in a storage location corresponding to the data model.

[0181] Optionally, the data storage module is further used for:

[0182] Verifying whether the model data and the indicator data meet preset requirements;

[0183] If they are consistent, the model data and the indicator data are stored in a storage location corresponding to the data model.

[0184] Optionally, the indicator judgment module is further used to:

[0185] Determine the rule ID of the parsing rule, and determine whether the rule ID is associated with an indicator ID;

[0186] If so, it is determined that the target data needs to be adjusted.

[0187] The present invention also provides a computer-readable storage medium.

[0188] The computer-readable storage medium of the present invention stores a table data processing program, and when the table data processing program is executed by a processor, the steps of the table data processing method described above are implemented.

[0189] The method implemented when the table data processing program running on the processor is executed can refer to the various embodiments of the table data processing method of the present invention, and will not be described in detail here.

[0190] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or system including the element.

[0191] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0192] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present invention.

[0193] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A table data processing method, characterized in that: The table data processing method is applied to a table data processing device, and the method comprises the following steps: When table data is detected, a parsing rule corresponding to the table data is determined, and based on the parsing rule, target data in the table data and a target storage field corresponding to the target data are determined, wherein the parsing rule is a parsing rule designed by a user according to a data model required in a table data processing device, and a parsing interface corresponding to the parsing rule is exposed externally, and the parsing rule can be modified through the parsing interface; Based on the parsing rule, determining whether the target data needs to be adjusted; If necessary, determining the indicator rule associated with the parsing rule, and generating indicator data corresponding to the target data based on the indicator rule; Based on the target storage field, storing the indicator data; The step of determining the target data in the table data and the target storage field corresponding to the target data based on the parsing rule includes: Based on the parsing rule, determine a target cell in the table data, and determine the cell type to which the target cell belongs; Based on the target cell and the cell type, the target data in the table data is acquired, and based on the target cell and the cell type, a data model corresponding to the target data and a target storage field corresponding to the target data in the data model are determined.

2. The table data processing method according to claim 1, characterized in that: When the table data is detected, the step of determining the parsing rule corresponding to the table data includes: When the table data is detected, identifying the file attribute of the table data; Based on the file attributes, a parsing rule corresponding to the table data is determined.

3. The table data processing method according to claim 1, characterized in that: The step of determining the target cell in the table data based on the parsing rule and determining the cell type to which the target cell belongs includes: Read the target file path of the table data, obtain the target form name of the table data, and determine the target cell position of the table data; Determine a target cell in the table data based on the file path, the form name and the target cell position; The data content of the target cell is identified, and based on the data content, the cell type to which the target cell belongs is determined.

4. The table data processing method according to claim 1, characterized in that: The step of determining the indicator rule associated with the parsing rule if necessary, and generating the indicator data corresponding to the target data based on the indicator rule comprises: If necessary, based on the rule ID of the parsing rule, determine the indicator rule associated with the parsing rule, and determine the operation rule of the indicator rule; Based on the operation rules, the target data is converted into index data.

5. The table data processing method according to claim 1, characterized in that: The step of storing the indicator data based on the target storage field comprises: Based on the target storage field, determine model data belonging to the same data model as the indicator data; The model data and the indicator data are stored in a storage location corresponding to the data model.

6. The table data processing method according to claim 5, characterized in that: The step of storing the model data and the indicator data in the storage location corresponding to the data model comprises: Verifying whether the model data and the indicator data meet preset requirements; If they are consistent, the model data and the indicator data are stored in a storage location corresponding to the data model.

7. The table data processing method according to any one of claims 1 to 6, characterized in that: The step of determining whether the target data needs to be adjusted based on the parsing rule includes: Determine the rule ID of the parsing rule, and determine whether the rule ID is associated with an indicator ID; If so, it is determined that the target data needs to be adjusted.

8. A table data processing device, characterized in that: The table data processing device comprises: A data parsing module, for determining, when table data is detected, a parsing rule corresponding to the table data, and based on the parsing rule, determining target data in the table data, and a target storage field corresponding to the target data, wherein the parsing rule is a parsing rule designed by a user according to a data model required in a table data processing device, and a parsing interface corresponding to the parsing rule is exposed externally, and the parsing rule can be modified through the parsing interface; the data parsing module is also used to determine, based on the parsing rule, a target cell in the table data, and determine a cell type to which the target cell belongs; based on the target cell and the cell type, obtain the target data in the table data, and based on the target cell and the cell type, determine a data model corresponding to the target data, and a target storage field corresponding to the target data in the data model; An indicator judgment module, used to determine whether the target data needs to extract indicators based on the analysis rules; An indicator generation module, used for determining the indicator rule associated with the parsing rule if extraction is required, and generating indicator data corresponding to the target data based on the indicator rule; A data storage module is used to store the indicator data based on the target storage field.

9. A table data processing device, characterized in that: The table data processing device comprises: a memory, a processor and a table data processing program stored in the memory and executable on the processor, wherein the table data processing program implements the steps of the table data processing method according to any one of claims 1 to 7 when executed by the processor.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a table data processing program, and when the table data processing program is executed by a processor, the steps of the table data processing method according to any one of claims 1 to 7 are implemented.

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