Report data analysis method, device, computer equipment and storage medium

Abstract

The present application relates to the field of data analysis, and discloses a method, device, equipment and medium for analyzing report data, wherein the method includes: obtaining a report filled out based on a predefined template, wherein each data column in the predefined template is configured with a hidden symbol; identifying the hidden symbols in the report, and obtaining the numerical information of the data column corresponding to each of the hidden symbols; determining the subject label mapped by the hidden symbol based on a label library, and obtaining the reference value of each of the subject labels; calculating the score value of each of the hidden symbols according to the discreteness of the numerical information and the reference value; counting the score values ​​of each hidden symbol of the same subject label to generate the score value of the subject label; generating the score of the report based on the score value of each subject label. The present application can score the report from multiple different dimensions, and quickly and systematically parse and score the report.

Description

Technical Field

[0001] The present application relates to the field of data analysis, and in particular to a method, device, computer equipment and storage medium for analyzing report data. Background Art

[0002] There are some platforms in the industry that can parse report data, but they all need to first identify the report template, obtain the established report template, and parse the report data based on the established template. If the report is changed or adjusted, the code of the parsing template needs to be changed, which makes the parsing efficiency of the report data low. After the report is parsed, it is usually scored manually, and the scoring rules are not unified, resulting in low accuracy of the scoring results. Summary of the invention

[0003] The main purpose of this application is to provide a method, device, computer equipment and storage medium for analyzing report data, aiming to solve the current problem of being unable to quickly and accurately analyze and score reports.

[0004] In order to achieve the above-mentioned invention object, the present application proposes a method for analyzing report data, comprising:

[0005] Obtaining a report filled in based on a predefined template, wherein each data column in the predefined template is configured with a hidden symbol; the hidden symbol is used to track the numerical information filled in the data column;

[0006] Identify hidden symbols in the report, and obtain numerical information of data columns corresponding to each hidden symbol;

[0007] Determine the subject label to which the hidden symbol is mapped based on a label library, and obtain reference values ​​of each subject label; the label library contains a mapping relationship between subject labels and hidden symbols;

[0008] Calculating the score value of each hidden symbol according to the discreteness of the numerical value information and the reference numerical value;

[0009] Counting the score values ​​of each hidden symbol of the same subject label to generate the score value of the subject label;

[0010] A score for the report is generated based on the score values ​​of each subject label.

[0011] Furthermore, before calculating the score value of each hidden symbol according to the discreteness of the numerical information and the reference numerical value, the method further includes:

[0012] Obtaining the type of project to which the report applies;

[0013] Determining a scoring rule for the report according to the project type;

[0014] The correspondence between the discreteness and the score value is configured according to the scoring rule.

[0015] Furthermore, the obtaining of reference values ​​of the subject labels includes:

[0016] Get the same type of target reports based on big data;

[0017] Obtain the score value of each subject label in the target report;

[0018] The reference value of the subject label is determined according to the score value of each subject label in the target report.

[0019] Furthermore, the step of counting the scores of the hidden symbols of the same subject label to generate the score of the subject label includes:

[0020] Get the weights of each hidden symbol of the same subject label;

[0021] The score value of the subject label is calculated according to the weight of each hidden symbol of the same subject label and the score value of each hidden symbol.

[0022] Furthermore, the method further comprises:

[0023] Receiving modification information of a predefined template;

[0024] The hidden symbols of the corresponding data column in the template are modified according to the modification information to generate a new predefined template.

[0025] Furthermore, after generating the score of the report based on the score values ​​of each subject label, the following steps are included:

[0026] sending the report and the score of the report to a blockchain network;

[0027] The report and the score of the report are sent to block users of the blockchain network to perform on-chain execution of the report and the score of the report.

[0028] Furthermore, after sending the report and the score of the report to the block users of the blockchain network, the method further includes:

[0029] Receive modification information of the score of the report by any block user;

[0030] Sending the modification information to other block users of the blockchain network;

[0031] If the number of block users of the blockchain network agreeing to the modification information reaches a preset value, the score of the report is modified according to the modification information, and the report and the modified score of the report are uploaded to the chain.

[0032] The present application also provides a report data parsing device, comprising:

[0033] A report acquisition module, used to acquire a report filled in based on a predefined template, wherein each data column in the predefined template is configured with a hidden symbol; the hidden symbol is used to track the numerical information filled in the data column;

[0034] A symbol recognition module, used to recognize hidden symbols in the report and obtain numerical information of data columns corresponding to each hidden symbol;

[0035] A label identification module, used to determine the subject label mapped by the hidden symbol based on a label library, and to obtain reference values ​​of each subject label; the label library contains mapping relationships between subject labels and hidden symbols;

[0036] A symbol scoring module, used for calculating the score value of each hidden symbol according to the discreteness of the numerical information and the reference numerical value;

[0037] A label scoring module is used to count the scores of each hidden symbol of the same subject label to generate the score of the subject label;

[0038] The parsing and scoring module is used to generate a score for the report based on the score values ​​of each subject label.

[0039] The present application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any of the above-mentioned report data parsing methods when executing the computer program.

[0040] The present application also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of any of the above-mentioned report data parsing methods.

[0041] The present application example provides a method for parsing and scoring a report, by obtaining a report filled in based on a predefined template, wherein each data column in the predefined template is configured with a hidden symbol, and the hidden symbol is used to track the numerical information filled in the data column; then the hidden symbols in the report are identified, and the numerical information of the data column corresponding to each of the hidden symbols is obtained, and then the subject label mapped by the hidden symbol is determined based on a label library, and the reference numerical value of each of the subject labels is obtained, and the score value of each of the hidden symbols is calculated according to the discreteness of the numerical information and the reference numerical value. After the score value of each hidden symbol is obtained, the subject label and the hidden symbol are in a one-to-many mapping relationship, that is, there are multiple different hidden symbols with the same subject label in the report, and then the score values ​​of each hidden symbol of the same subject label are counted to generate the score value of the subject label, and then the hidden symbols of the same type are classified and counted, so as to determine the score of the report under this type, and the score of the report is generated based on the score value of each subject label, thereby completing the parsing of each data in the report and the scoring of the report, scoring the report from multiple different dimensions, and parsing and scoring the report quickly and systematically. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 A flowchart of an embodiment of a method for analyzing report data of this application;

[0043] Figure 2 A flowchart of another embodiment of the method for analyzing report data of the present application;

[0044] Figure 3 A schematic diagram of the structure of an embodiment of a device for analyzing report data of this application;

[0045] Figure 4 This is a schematic block diagram of the structure of an embodiment of a computer device of the present application.

[0046] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0048] Reference Figure 1 An embodiment of the present application provides a method for analyzing report data, including steps S10-S60. The detailed description of each step of the method for analyzing report data is as follows.

[0049] S10, obtaining a report filled in based on a predefined template, wherein each data column in the predefined template is configured with a hidden symbol; the hidden symbol is used to track the numerical information filled in the data column.

[0050] This embodiment is applied to various report parsing scenarios, such as the target company's financial report parsing scenario, by providing a predefined template for filling in, and each data column in the predefined template is configured with a hidden symbol. When the target company fills in the information, a corresponding report is generated, thereby obtaining a report filled in based on the predefined template, and each data column in the predefined template is configured with a hidden symbol, and the hidden symbol is used to track the numerical information filled in the data column; that is, a hidden symbol is marked on the data column to be filled in the template, for example, the template is a word template, and different hidden symbols are configured for different data columns at the bottom layer of the word template, and the information filled in the data column can be associated through the hidden symbol, for example, "net profit" is configured with a hidden symbol &netprofits&, and the "depreciation" data column is configured with a hidden symbol &depreciation&, and the hidden symbol will not be displayed in the template.

[0051] S20, identifying hidden symbols in the report, and obtaining numerical information of data columns corresponding to each of the hidden symbols.

[0052] In this embodiment, after obtaining a report filled out based on a predefined template, the report is identified to identify hidden symbols in the report. After the hidden symbols in the report are identified, the numerical information associated with the hidden symbols is identified to obtain the numerical information of the data column corresponding to the hidden symbol. The numerical values ​​filled in the data columns in the report can be known, thereby obtaining the numerical information of the data columns corresponding to each hidden symbol.

[0053] S30, determining the subject label to which the hidden symbol is mapped based on a label library, and obtaining a reference value of each subject label; the label library contains a mapping relationship between the subject label and the hidden symbol.

[0054] In this embodiment, the label library contains a mapping relationship between subject labels and hidden symbols. The label library classifies and stores different hidden symbols, classifies different hidden symbols into different subjects, and assigns corresponding labels to different subjects to obtain subject labels. That is, the label library contains a mapping relationship between subject labels and hidden symbols. The subject labels and hidden symbols are a one-to-many mapping relationship, so that the subject labels mapped by the hidden symbols are determined based on the label library, and the reference values ​​of each subject label are obtained. The reference value can be the value of the subject label determined by big data.

[0055] S40: Calculate the score value of each hidden symbol according to the discreteness of the numerical value information and the reference numerical value.

[0056] In this embodiment, after obtaining the numerical information of the data column corresponding to each hidden symbol and the reference numerical value of each subject label, the score value of each hidden symbol is calculated according to the discreteness of the numerical information and the reference numerical value, that is, by calculating the difference between the numerical information and the reference numerical value, the discreteness of the numerical information and the reference numerical value is determined according to the difference, thereby determining the score value of each hidden symbol. Specifically, the ratio of the difference to the reference numerical value is used as the discreteness, and then the corresponding score value is matched according to the discreteness. For example, the rule for matching the corresponding score value according to the discreteness is as follows: The dispersion of the numerical information and the reference numerical value is defined as an expected IRR. Different expected IRRs match different ranges of score values. The range of score values ​​is (0, 1]: expected IRR ≦ 10%; the range of score values ​​is (1, 2]: 10% < expected IRR ≦ 15%; the range of score values ​​is (2, 3]: 15% < expected IRR ≦ 20%; the range of score values ​​is (3, 4]: 20% < expected IRR ≦ 30%; the range of score values ​​is (4, 5]: expected IRR> 30%. Then, a score value is matched from the range of score values ​​according to the size of the dispersion as the score value of the hidden symbol.

[0057] S50: Count the score values ​​of the hidden symbols of the same subject label to generate the score value of the subject label.

[0058] In this embodiment, after obtaining the score value of each hidden symbol, the subject label and the hidden symbol are in a one-to-many mapping relationship, that is, there are multiple different hidden symbols with the same subject label in the report, and then the score values ​​of each hidden symbol with the same subject label are counted to generate the score value of the subject label. One implementation method is to calculate the average of the score values ​​of each hidden symbol with the same subject label as the score value of the subject label.

[0059] S60: Generate a score for the report based on the score values ​​of each subject label.

[0060] In this embodiment, after the score values ​​of each hidden symbol are statistically classified into the score values ​​of the corresponding subject labels, the score of the report is generated based on the score values ​​of each subject label. The content reflected by each subject label is the detailed information of the company corresponding to the report. Scoring the report is also scoring the company in the report. Specifically, each content of the company reflected in the report is scored according to the score values ​​of each label to generate a multi-dimensional vector as the score of the report, thereby completing the analysis of each data in the report and the scoring of the report. By classifying and counting the hidden symbols of the same type, the score of the report under this type is determined, such as the score of the report under the subject label "Company Analysis" or the score of the report under the subject label "Financial Quality". In this way, the report is analyzed and scored from multiple different dimensions, and the report is analyzed and scored quickly and systematically.

[0061] The present embodiment provides a method for parsing and scoring a report, by obtaining a report filled in based on a predefined template, wherein each data column in the predefined template is configured with a hidden symbol, and the hidden symbol is used to track the numerical information filled in the data column; then the hidden symbols in the report are identified, and the numerical information of the data column corresponding to each of the hidden symbols is obtained, and then the subject label mapped by the hidden symbol is determined based on a label library, and the reference numerical value of each of the subject labels is obtained, and the score value of each of the hidden symbols is calculated according to the discreteness of the numerical information and the reference numerical value. After the score value of each hidden symbol is obtained, the subject label and the hidden symbol are in a one-to-many mapping relationship, that is, there are multiple different hidden symbols with the same subject label in the report, and then the score values ​​of each hidden symbol with the same subject label are counted to generate the score value of the subject label, and then the hidden symbols of the same type are classified and counted, so as to determine the score of the report under this type, and the score of the report is generated based on the score value of each subject label, so as to complete the parsing of each data in the report and the scoring of the report, score the report from multiple different dimensions, and parse and score the report quickly and systematically.

[0062] In one embodiment, before calculating the score value of each hidden symbol according to the discreteness of the numerical information and the reference numerical value, the method further includes:

[0063] Obtaining the type of project to which the report applies;

[0064] Determining a scoring rule for the report according to the project type;

[0065] The correspondence between the discreteness and the score value is configured according to the scoring rule.

[0066] In this embodiment, the score value of each hidden symbol is calculated according to the discreteness of the numerical information and the reference numerical value. In order to make the reports of the same type have a unified scoring rule, the project type to which the report is applied is first obtained, such as a report applied to a valuation type, a report applied to a financing type, or a report applied to a listing type. Then, the scoring rule of the report is determined according to the project type, that is, different scoring rules are configured for different types of reports, and the accuracy of the discreteness is configured according to the scoring rule. For example, under project type A, when the discreteness is 10%, the score value of the hidden symbol is 0.1; under project type B, when the discreteness is 10%, the score value of the hidden symbol is 0.01. By configuring the scoring rules of different project types, the accuracy of parsing and scoring of reports in different scenarios is improved.

[0067] In one embodiment, Figure 2 As shown, the step of obtaining the reference value of each subject label includes:

[0068] S31: Obtain the same type of target report based on big data;

[0069] S32: Obtain the score value of each subject label in the target report;

[0070] S33: Determine the reference value of the subject label according to the score value of each subject label in the target report.

[0071] In this embodiment, when obtaining the reference value of each subject label, the reference value of each subject label is determined based on the historical data of big data statistics. Specifically, the same type of target report is obtained based on big data. One implementation method is to obtain the company type of the report, and then obtain the report of the same type of company as the target report, and then obtain the score value of each subject label of the target report, and determine the reference value of the subject label according to the score value of each subject label in the target report, and calculate the average value according to the score value of each subject label in multiple target reports, and determine it as the reference value of the subject label. By drawing on historical data, the scoring of the report has more comprehensive practical significance.

[0072] In one embodiment, the step of counting the scores of the hidden symbols of the same subject label to generate the score of the subject label includes:

[0073] Get the weights of each hidden symbol of the same subject label;

[0074] The score value of the subject label is calculated according to the weight of each hidden symbol of the same subject label and the score value of each hidden symbol.

[0075] In this embodiment, when the score values ​​of each hidden symbol of the same subject label are counted to generate the score value of the subject label, the weights of each hidden symbol of the same subject label are obtained. For example, the weight of hidden symbol I for project A in the report is Q1, and the weight of hidden symbol I for project B is Q2. Then, the score value of the subject label is calculated based on the weights of each hidden symbol of the same subject label and the score value of each hidden symbol. In one embodiment, a weighted average is taken based on the weights of each hidden symbol of the same subject label and the score value of each hidden symbol to obtain the score value of the subject label. By configuring the influence weights of different hidden symbols on the same subject label, the score value of the subject label in different scenarios is reasonably calculated.

[0076] In one embodiment, the report data parsing method further includes:

[0077] Receiving modification information of a predefined template;

[0078] The hidden symbols of the corresponding data column in the template are modified according to the modification information to generate a new predefined template.

[0079] In this embodiment, when the predefined template needs to be modified, modification information of the predefined template is received, the modification information includes adding a data column to the predefined template and configuring a hidden symbol for the data column, or changing the hidden symbol of a data column in the predefined template, or deleting a data column in the predefined template, together with deleting and modifying its hidden symbol. The hidden symbol of the corresponding data column in the template is modified according to the modification information to generate a new predefined template. Updating the template only requires a relatively short time to add hidden symbols to each data column in the template, thereby realizing background template update. The user downloads the new template on the front-end page, fills in the data in the data column on the new template and uploads it to realize automatic parsing of the report, thereby improving the template maintenance efficiency and the report data parsing efficiency.

[0080] In one embodiment, after generating the score of the report based on the score values ​​of each subject label, the following steps are performed:

[0081] sending the report and the score of the report to a blockchain network;

[0082] The report and the score of the report are sent to block users of the blockchain network to perform on-chain execution of the report and the score of the report.

[0083] In this embodiment, after the score of the report is generated based on the score value of each subject label, in order to ensure that the report and the score of the report are not easily tampered with, the report and the score of the report are sent to the blockchain network, and the traceability and non-tamperability of the blockchain network are used to monitor the report and the score of the report. After the report and the score of the report are sent to the blockchain network, the report and the score of the report need to be uploaded to the chain to ensure that the report and the score of the report are traceable and cannot be easily tampered with. At this time, the report and the score of the report are sent to the block user of the blockchain network. The block user of the blockchain network can be a user who manually reviews the report. The block user approves the report and the score. When the block user completes the approval, the report and the score of the report are uploaded to the chain, thereby ensuring that the report and the score of the report are traceable and cannot be easily tampered with.

[0084] In one embodiment, after sending the report and the score of the report to the block users of the blockchain network, the method further includes:

[0085] Receive modification information of the score of the report by any block user;

[0086] Sending the modification information to other block users of the blockchain network;

[0087] If the number of block users of the blockchain network agreeing to the modification information reaches a preset value, the score of the report is modified according to the modification information, and the report and the modified score of the report are uploaded to the chain.

[0088] In this embodiment, after sending the report and the score of the report to the block users of the blockchain network, if any block user has doubts about the report or the score of the report, he or she may propose to modify the score of the report. At this time, the modification information of the score of the report by any block user is received, and then the modification information is sent to other block users of the blockchain network to notify other block users of the blockchain network to view the modification information. If the number of block users of the blockchain network who agree to the modification information reaches a preset value, the score of the report is modified according to the modification information, and the report and the modified score of the report are executed on the chain. When the score of the report needs to be modified, sufficient reasons and support from enough other users are required to modify it, thereby improving the reliability of the score of the report.

[0089] Reference Figure 3 The present application also provides a report data parsing device, comprising:

[0090] The report acquisition module 10 is used to acquire a report filled in based on a predefined template, wherein each data column in the predefined template is configured with a hidden symbol; the hidden symbol is used to track the numerical information filled in the data column;

[0091] A symbol recognition module 20, used to recognize hidden symbols in the report and obtain numerical information of data columns corresponding to each hidden symbol;

[0092] The label identification module 30 is used to determine the subject label to which the hidden symbol is mapped based on a label library, and to obtain reference values ​​of each subject label; the label library contains mapping relationships between subject labels and hidden symbols;

[0093] A symbol scoring module 40, configured to calculate a score value of each of the hidden symbols according to a discreteness between the numerical value information and the reference numerical value;

[0094] The label scoring module 50 is used to count the scores of the hidden symbols of the same subject label to generate the score of the subject label;

[0095] The parsing and scoring module 60 is used to generate a score for the report based on the score values ​​of each subject label.

[0096] As described above, it can be understood that the various components of the report data analysis device proposed in the present application can realize the functions of any one of the report data analysis methods described above.

[0097] In one embodiment, the apparatus further comprises a relationship determination module, configured to execute:

[0098] Get the project type to which the report applies;

[0099] Determining a scoring rule for the report according to the project type;

[0100] The correspondence between the discreteness and the score value is configured according to the scoring rule.

[0101] In one embodiment, the tag identification module 30 further includes a module for executing:

[0102] Get the same type of target reports based on big data;

[0103] Obtain the score value of each subject label in the target report;

[0104] The reference value of the subject label is determined according to the score value of each subject label in the target report.

[0105] In one embodiment, the tag scoring module 50 further includes a module for executing:

[0106] Get the weights of each hidden symbol of the same subject label;

[0107] The score value of the subject label is calculated according to the weight of each hidden symbol of the same subject label and the score value of each hidden symbol.

[0108] In one embodiment, the device further comprises a template updating module, configured to execute:

[0109] Receiving modification information of a predefined template;

[0110] The hidden symbols of the corresponding data column in the template are modified according to the modification information to generate a new predefined template.

[0111] In one embodiment, the device further includes a block chaining module, configured to execute:

[0112] sending the report and the score of the report to a blockchain network;

[0113] The report and the score of the report are sent to block users of the blockchain network to perform on-chain execution of the report and the score of the report.

[0114] In one embodiment, the apparatus further comprises a block modification module, configured to execute:

[0115] Receive modification information of the score of the report by any block user;

[0116] Sending the modification information to other block users of the blockchain network;

[0117] If the number of block users of the blockchain network agrees that the modification information reaches a preset ratio, the score of the report is modified according to the modification information, and the report and the modified score of the report are uploaded to the chain.

[0118] Reference Figure 4 In the embodiment of the present application, a computer device is also provided. The computer device may be a mobile terminal, and its internal structure may be as follows: Figure 4 As shown. The computer device includes a processor, a memory, a network interface, a display device and an input device connected through a system bus. Among them, the network interface of the computer device is used to communicate with an external terminal through a network connection. The input device of the computer device is used to receive user input. The processor designed by the computer is used to provide computing and control capabilities. The memory of the computer device includes a storage medium. The storage medium stores an operating system, a computer program and a database. The database of the computer device is used to store data. When the computer program is executed by the processor, a method for parsing report data is implemented.

[0119] The above-mentioned processor executes the above-mentioned report data parsing method, including: obtaining a report filled out based on a predefined template, in which each data column in the predefined template is configured with hidden symbols; identifying the hidden symbols in the report and obtaining the numerical information corresponding to each of the hidden symbols; determining the subject labels mapped by the hidden symbols based on the label library, and obtaining the reference numerical values ​​of each of the subject labels; calculating the score value of each of the hidden symbols based on the discreteness of the numerical information and the reference numerical value; counting the score values ​​of each hidden symbol with the same subject label to generate the score value of the subject label; generating the score of the report based on the score value of each subject label.

[0120] The computer device provides a method for parsing and scoring a report, by obtaining a report filled out based on a predefined template, wherein each data column in the predefined template is configured with hidden symbols, then identifying the hidden symbols in the report, and obtaining the numerical information corresponding to each of the hidden symbols, then determining the subject label mapped by the hidden symbol based on a label library, and obtaining the reference numerical value of each of the subject labels, and calculating the score value of each of the hidden symbols according to the discreteness of the numerical information and the reference numerical value. After obtaining the score value of each hidden symbol, the subject label and the hidden symbol are in a one-to-many mapping relationship, that is, there are multiple different hidden symbols with the same subject label in the report, and then the score values ​​of each hidden symbol with the same subject label are counted to generate the score value of the subject label, and then the hidden symbols of the same type are classified and counted, so as to determine the score of the report under this type, and the score of the report is generated based on the score value of each subject label, thereby completing the parsing of each data in the report and the scoring of the report, scoring the report from multiple different dimensions, and parsing and scoring the report quickly and systematically.

[0121] An embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by the processor, a method for parsing report data is implemented, including the following steps: obtaining a report filled out based on a predefined template, in which each data column in the predefined template is configured with hidden symbols; identifying the hidden symbols in the report and obtaining numerical information corresponding to each of the hidden symbols; determining the subject labels mapped to the hidden symbols based on a label library, and obtaining reference numerical values ​​for each of the subject labels; calculating the score value of each of the hidden symbols based on the discreteness of the numerical information and the reference numerical value; counting the score values ​​of each hidden symbol with the same subject label to generate the score value of the subject label; and generating a score for the report based on the score value of each subject label.

[0122] The computer-readable storage medium provides a method for parsing and scoring a report, by obtaining a report filled out based on a predefined template, wherein each data column in the predefined template is configured with hidden symbols, then identifying the hidden symbols in the report, and obtaining the numerical information corresponding to each of the hidden symbols, then determining the subject label mapped by the hidden symbol based on a label library, and obtaining the reference numerical value of each of the subject labels, and calculating the score value of each of the hidden symbols according to the discreteness of the numerical information and the reference numerical value. After obtaining the score value of each hidden symbol, the subject label and the hidden symbol are in a one-to-many mapping relationship, that is, there are multiple different hidden symbols of the same subject label in the report, and then the score values ​​of each hidden symbol of the same subject label are counted to generate the score value of the subject label, and then the hidden symbols of the same type are classified and counted, so as to determine the score of the report under this type, and the score of the report is generated based on the score value of each subject label, thereby completing the parsing of each data in the report and the scoring of the report, scoring the report from multiple different dimensions, and parsing and scoring the report quickly and systematically.

[0123] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing related hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods.

[0124] Any reference to memory, storage, database, or other media provided in this application and used in the embodiments may include non-volatile and / or volatile memory.

[0125] Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double-speed data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0126] 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, device, article or method 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, device, article or method. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the existence of other identical elements in the process, device, article or method including the element.

[0127] The above description is only a preferred embodiment of the present application and does not limit the patent scope of the present application.

[0128] Any equivalent structure or equivalent process transformation made using the contents of the description and drawings of this application, or directly or indirectly applied in other related technical fields, are similarly included in the scope of patent protection of this application.

Claims

1. A method for analyzing report data, characterized in that: include: Obtaining a report filled in based on a predefined template, wherein each data column in the predefined template is configured with a hidden symbol; the hidden symbol is used to track the numerical information filled in the data column; Identify hidden symbols in the report, and obtain numerical information of data columns corresponding to each hidden symbol; Determine the subject label to which the hidden symbol is mapped based on a label library, and obtain reference values ​​of each subject label; the label library contains a mapping relationship between subject labels and hidden symbols; Calculating the score value of each hidden symbol according to the discreteness of the numerical value information and the reference numerical value; That is, by calculating the difference between the numerical information and the reference numerical value, the discreteness of the numerical information and the reference numerical value is determined according to the difference, thereby determining the score value of each hidden symbol; Counting the score values ​​of each hidden symbol of the same subject label to generate the score value of the subject label; Generating a score for the report based on the score values ​​of each subject label; The step of counting the score values ​​of the hidden symbols of the same subject label to generate the score value of the subject label includes: Get the weights of each hidden symbol of the same subject label; The score value of the subject label is calculated according to the weight of each hidden symbol of the same subject label and the score value of each hidden symbol.

2. The method for analyzing report data according to claim 1, characterized in that: Before calculating the score value of each hidden symbol according to the discreteness of the numerical information and the reference numerical value, the method further includes: Get the project type to which the report applies; Determining a scoring rule for the report according to the project type; The correspondence between the discreteness and the score value is configured according to the scoring rule.

3. The method for analyzing report data according to claim 1, characterized in that: The obtaining of reference values ​​of each subject label includes: Get the same type of target reports based on big data; Obtain the score value of each subject label in the target report; The reference value of the subject label is determined according to the score value of each subject label in the target report.

4. The method for analyzing report data according to claim 1, characterized in that: The method further comprises: Receiving modification information of a predefined template; The hidden symbols of the corresponding data column in the template are modified according to the modification information to generate a new predefined template.

5. The method for analyzing report data according to claim 1, characterized in that: After generating the score of the report based on the score values ​​of each subject label, the following steps are included: sending the report and the score of the report to a blockchain network; The report and the score of the report are sent to block users of the blockchain network to perform on-chain execution of the report and the score of the report.

6. The method for analyzing report data according to claim 5, characterized in that: After sending the report and the score of the report to the block users of the blockchain network, the process further includes: Receive modification information of the score of the report by any block user; Sending the modification information to other block users of the blockchain network; If the number of block users of the blockchain network agreeing to the modification information reaches a preset value, the score of the report is modified according to the modification information, and the report and the modified score of the report are uploaded to the chain.

7. A report data analysis device, characterized in that: A method for implementing the report data analysis method according to any one of claims 1 to 6, comprising: A report acquisition module, used to acquire a report filled in based on a predefined template, wherein each data column in the predefined template is configured with a hidden symbol; the hidden symbol is used to track the numerical information filled in the data column; A symbol recognition module, used to recognize hidden symbols in the report and obtain numerical information of data columns corresponding to each hidden symbol; A label identification module, used to determine the subject label to which the hidden symbol is mapped based on a label library, and to obtain reference values ​​of each of the subject labels; the label library contains a mapping relationship between the subject label and the hidden symbol; A symbol scoring module, used for calculating the score value of each hidden symbol according to the discreteness of the numerical value information and the reference numerical value; A label scoring module, used to count the scores of each hidden symbol of the same subject label to generate the score of the subject label; The parsing and scoring module is used to generate a score for the report based on the score values ​​of each subject label.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the processor implements the steps of the report data analysis method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the report data analysis method according to any one of claims 1 to 6 are implemented.

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