CN113435176A - Method and device for analyzing report data, computer equipment and storage medium - Google Patents
Method and device for analyzing report data, computer equipment and storage medium Download PDFInfo
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- CN113435176A CN113435176A CN202110729383.6A CN202110729383A CN113435176A CN 113435176 A CN113435176 A CN 113435176A CN 202110729383 A CN202110729383 A CN 202110729383A CN 113435176 A CN113435176 A CN 113435176A
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Abstract
The application relates to the field of data analysis and discloses a method, a device, equipment and a medium for analyzing report data, wherein the method comprises the following steps: acquiring a report filled based on a predefined template, wherein hidden symbols are configured in each data column in the predefined template; identifying hidden symbols in the report, and acquiring numerical value information of a data column corresponding to each hidden symbol; determining subject labels mapped by the hidden symbols based on a label library, and acquiring reference values of the subject labels; calculating the score value of each hidden symbol according to the dispersion of the numerical information and the reference numerical value; counting score values of all hidden symbols of the same subject label to generate score values of the subject label; generating a score for the report based on the score values of the respective subject labels. The report can be scored from a plurality of different dimensions, and the report can be analyzed and scored quickly and systematically.
Description
Technical Field
The present application relates to the field of data analysis, and in particular, to a method and an apparatus for analyzing report data, a computer device, and a storage medium.
Background
Some existing platforms in the industry can analyze report data, but all the platforms need to identify a report template first to obtain a report established template, and analyze the report data on the established template, if the report changes or is adjusted, the codes of the analysis template need to be changed, so that the analysis efficiency of the report data is low, and after the report is analyzed usually, the report is scored manually, and scoring rules are not uniform, so that the accuracy of scoring results is low.
Disclosure of Invention
The application mainly aims to provide a report data analysis method, a report data analysis device, a computer device and a storage medium, and aims to solve the problem that a report cannot be analyzed and scored quickly and accurately at present.
In order to achieve the above object, the present application provides a method for analyzing report data, including:
acquiring a report filled based on a predefined template, wherein hidden symbols are configured in each data column in the predefined template; the hidden symbol is used for tracking numerical information filled in the data field;
identifying hidden symbols in the report, and acquiring numerical value information of a data column corresponding to each hidden symbol;
determining subject labels mapped by the hidden symbols based on a label library, and acquiring reference values of the subject labels; the label library comprises a mapping relation between subject labels and hidden symbols;
calculating the score value of each hidden symbol according to the dispersion of the numerical information and the reference numerical value;
counting score values of all hidden symbols of the same subject label to generate score values of the subject label;
generating a score for the report based on the score values of the respective subject labels.
Further, before the calculating the score value of each hidden symbol according to the dispersion between the numerical information and the reference value, the method further includes:
acquiring the project type applied by the report;
determining a scoring rule for the report according to the project type;
and configuring the corresponding relation between the dispersion and the score value according to the scoring rule.
Further, the obtaining a reference value of each subject label includes:
obtaining a target report of the same type based on the big data;
obtaining the score values of the subject labels in the target report;
and determining the reference value of the subject label according to the score value of each subject label in the target report.
Further, the counting score values of the hidden symbols of the same subject label, and generating the score value of the subject label includes:
acquiring the weight of each hidden symbol of the same subject label;
and calculating the score value of the subject label according to the weight of each hidden symbol of the same subject label and the score value of each hidden symbol.
Further, the method further comprises:
receiving modification information of a predefined template;
and modifying the hidden symbol of the corresponding data column in the template according to the modification information to generate a new predefined template.
Further, after generating the score of the report based on the score values of the respective subject labels, the method includes:
sending the report and the score of the report to a blockchain network;
sending the report and the reported score to a block user of the blockchain network to perform uplink on the report and the reported score.
Further, after the sending the report and the score of the report to the tile user of the blockchain network, the method further includes:
receiving modification information of the scoring of the report by any block user;
sending the modification information to other users of the block chain network;
and if the block users of the block chain network agree that the number of the modification information reaches a preset proportional value, modifying the score of the report according to the modification information, and performing uplink on the report and the modified score of the report.
The present application further provides an analysis apparatus for report data, including:
the report acquisition module is used for acquiring reports filled based on a predefined template, and hidden symbols are configured in each data column in the predefined template; the hidden symbol is used for tracking numerical information filled in the data field;
the symbol identification module is used for identifying the hidden symbols in the report and acquiring the numerical value information of the data column corresponding to each hidden symbol;
the label identification module is used for determining the subject labels mapped by the hidden symbols based on a label library and acquiring reference values of the subject labels; the label library comprises a mapping relation between subject labels and hidden symbols;
the symbol scoring module is used for calculating the score value of each hidden symbol according to the dispersion of the numerical information and the reference numerical value;
the label scoring module is used for counting the score values of all the hidden symbols of the same subject label and generating the score values of the subject label;
and the analysis scoring module is used for generating the score of the report based on the score value of each subject label.
The application also provides a computer device, which comprises a memory and a processor, wherein the memory stores a computer program, and the processor realizes the steps of the report data analysis method when executing the computer program.
The present application also provides a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the steps of the method for parsing report data according to any one of the above-mentioned items.
The embodiment of the application provides a method for analyzing and scoring a report, which comprises the steps of acquiring 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 for tracking numerical information filled in the data column; then, recognizing hidden symbols in the report, obtaining numerical information of a data column corresponding to each hidden symbol, then determining subject labels mapped by the hidden symbols based on a label library, obtaining reference values of each subject label, calculating score values of each hidden symbol according to dispersion of the numerical information and the reference values, after obtaining the score values of each hidden symbol, enabling the subject labels and the hidden symbols to be in one-to-many mapping relation, namely that different hidden symbols of a plurality of same subject labels exist in the report, then counting the score values of each hidden symbol of the same subject labels, generating the score values of the subject labels, then carrying out statistics and classification on the hidden symbols of the same type, thereby determining the score of the report under the type, and generating the score of the report based on the score values of each subject label, therefore, the analysis of each item of data in the report and the scoring of the report are completed, the report is scored from a plurality of different dimensions, and the report is analyzed and scored quickly and systematically.
Drawings
FIG. 1 is a flowchart illustrating an embodiment of a method for parsing report data according to the present application;
FIG. 2 is a flow chart illustrating another exemplary embodiment of a method for parsing report data according to the present application;
FIG. 3 is a schematic structural diagram of an apparatus for parsing report data according to an embodiment of the present application;
FIG. 4 is a block diagram illustrating a computer device according to an embodiment of the present invention.
The implementation, functional features and advantages of the objectives of the present application will be further explained with reference to the accompanying drawings.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
Referring to fig. 1, an embodiment of the present application provides a method for parsing report data, including steps S10-S60, and the steps of the method for parsing report data are described in detail as follows.
S10, acquiring a report filled based on a predefined template, wherein each data column in the predefined template is configured with a hidden symbol; the hidden symbol is used for tracking numerical information filled in by the data field.
The embodiment is applied to various report analysis scenes, such as a financial and newspaper analysis scene of a target company, and provides a predefined template for filling, wherein hidden symbols are configured in various data columns in the predefined template, and a corresponding report is generated after the target company fills, so that a report filled based on the predefined template is obtained, and the hidden symbols are configured in various data columns in the predefined template and are used for tracking numerical information filled in the data columns; namely, a hidden symbol is marked on a data field to be filled in a template, for example, the template is a word template, different hidden symbols are configured for different data fields at the bottom layer of the word template, information filled in the data fields can be associated through the hidden symbol, for example, the hidden symbol & netmessages & is configured in the "net profit" and the hidden symbol & decoding & is configured in the "depreciation" data field, and the hidden symbol cannot be displayed in the template.
And S20, identifying the hidden symbols in the report, and acquiring the numerical information of the data column corresponding to each hidden symbol.
In this embodiment, after a report filled based on a predefined template is obtained, the report is identified to identify hidden symbols in the report, and after the hidden symbols in the report are identified, numerical value information filled in association with the hidden symbols is identified, so as to obtain numerical value information of a data field corresponding to the hidden symbols, that is, numerical values filled in each data field in the report can be known, so as to obtain numerical value information of the data field corresponding to each hidden symbol.
S30, determining the subject label mapped by the hidden symbol based on a label library, and obtaining a reference value of each subject label; the label library comprises a mapping relation between the subject label and the hidden symbol.
In this embodiment, the tag library includes a mapping relationship between a subject tag and a hidden symbol, the tag library classifies and stores different hidden symbols, classifies different hidden symbols into different subjects, and assigns corresponding tags to different subjects to obtain subject tags, that is, the tag library includes a mapping relationship between a subject tag and a hidden symbol, where the subject tag and the hidden symbol are in a one-to-many mapping relationship, so as to determine the subject tag mapped by the hidden symbol based on the tag library and obtain a reference value of each subject tag, where the reference value may be a value of the subject tag determined by big data.
And S40, calculating the score value of each hidden symbol according to the dispersion of the numerical information and the reference numerical value.
In this embodiment, after obtaining the numerical information of the data field corresponding to each hidden symbol and the reference value of each subject label, the score of each hidden symbol is calculated according to the dispersion between the numerical information and the reference value, that is, by calculating the difference between the numerical information and the reference value, and determining the dispersion between the numerical information and the reference value according to the difference, the score of each hidden symbol is determined, specifically, the ratio of the difference to the reference value is used as the dispersion, and then the corresponding score is matched according to the dispersion, for example, the rule of matching the corresponding score according to the dispersion is as follows: and defining the dispersion degree of the numerical information and the reference value as predicted IRR, wherein the ranges of the score values matched by different predicted IRR are different, the range (0, 1) of the score value is less than or equal to 10%, the range (1, 2) of the score value is 10% < predicted IRR is less than or equal to 15%, the range (2, 3) of the score value is 15% < predicted IRR is less than or equal to 20%, the range (3, 4) of the score value is 20% < predicted IRR is less than or equal to 30%, the range (4, 5) of the score value is greater than or equal to 30%, and then matching a score value from the range of the score value according to the size of the dispersion degree to be used as the score value of the hidden symbol.
And S50, counting the score values of all the hidden symbols of the same subject label, and generating the score values of the subject label.
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 a plurality of different hidden symbols of the same subject label in the report, and then the score values of the hidden symbols of the same subject label are counted to generate the score value of the subject label.
And S60, generating the score of the report based on the score value of each subject label.
In this embodiment, after the score values of the hidden symbols 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 the subject labels, the content reflected by the subject labels is the details of the company corresponding to the report, and scoring the report is also performed on the company of the report, specifically, each item of content reflected by the report is scored according to the score value of each label, a multi-dimensional vector is generated as the score of the report, so as to complete analysis of each item of data in the report and the score of the report, and the hidden symbols of the same type are classified and counted, so as to determine the score of the report in the type, for example, the score of the report in the "company analysis" of the subject label, or the score of the report in the "financial quality" of the subject label, therefore, the report is analyzed and scored from a plurality of different dimensions, and the report is analyzed and scored quickly and systematically.
The embodiment provides a method for analyzing and scoring a report, which includes acquiring a report filled based on a predefined template, where each data field in the predefined template is configured with a hidden symbol, and the hidden symbol is used for tracking numerical information filled in the data field; then, recognizing hidden symbols in the report, obtaining numerical information of a data column corresponding to each hidden symbol, then determining subject labels mapped by the hidden symbols based on a label library, obtaining reference values of each subject label, calculating score values of each hidden symbol according to dispersion of the numerical information and the reference values, after obtaining the score values of each hidden symbol, enabling the subject labels and the hidden symbols to be in one-to-many mapping relation, namely that different hidden symbols of a plurality of same subject labels exist in the report, then counting the score values of each hidden symbol of the same subject labels, generating the score values of the subject labels, then carrying out statistics and classification on the hidden symbols of the same type, thereby determining the score of the report under the type, and generating the score of the report based on the score values of each subject label, therefore, the analysis of each item of data in the report and the scoring of the report are completed, the report is scored from a plurality of different dimensions, and the report is analyzed and scored quickly and systematically.
In one embodiment, before calculating the score value of each hidden symbol according to the dispersion of the numerical information and the reference value, the method further includes:
acquiring the project type applied by the report;
determining a scoring rule for the report according to the project type;
and configuring the corresponding relation between the dispersion and the score value according to the scoring rule.
In this embodiment, in order to enable reports of the same type to have a uniform scoring rule, first obtain a project type to which the report is applied, for example, a report applied to an evaluation type, a report applied to a financing type, or a report applied to a marketing type, then determine the scoring rule of the report according to the project type, that is, configure different scoring rules for different types of reports, configure the precision of the dispersion according to the scoring rules, for example, under the project type a, when the dispersion is 10%, the score of a hidden symbol is 0.1; under the item type B, when the dispersion is 10%, the score value of the hidden symbol is 0.01, and the analysis and scoring accuracy of the report under different scenes is improved by configuring scoring rules of different item types.
In an embodiment, as shown in fig. 2, the obtaining the reference value of each subject label includes:
s31: obtaining a target report of the same type based on the big data;
s32: obtaining the score values of the subject labels in the target report;
s33: and determining the reference value of the subject label according to the score value of each subject label in the target report.
In this embodiment, when obtaining the reference value of each subject label, the reference value of each subject label is determined based on historical data of big data statistics, specifically, a target report of the same type is obtained based on big data, in one implementation, a company type of the report is obtained, then the report of the same type of company is obtained as the target report, then score values of each subject label of the target report are obtained, the reference value of each subject label is determined according to the score values of each subject label in the target report, an average value is obtained according to the score values of each subject label in multiple target reports, the average value is determined as the reference value of each subject label, and the score of the report has more comprehensive practical significance by referring to historical data.
In one embodiment, the counting score values of the hidden symbols of the same subject label, and generating the score value of the subject label includes:
acquiring the weight of each hidden symbol of the same subject label;
and calculating the score value of the subject label according to the weight of each hidden symbol of the same subject label and the score value of each hidden symbol.
In this embodiment, when the score values of the hidden symbols of the same subject label are counted and generated, the weight of each hidden symbol of the same subject label is obtained, for example, the weight of the hidden symbol I of the a item in the report is Q1, and the weight of the hidden symbol I of the B item is Q2, and then 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.
In one embodiment, the method for parsing report data further includes:
receiving modification information of a predefined template;
and modifying the hidden symbol of the corresponding data column in the template according to the modification information to generate a new predefined template.
In the embodiment, when the predefined template needs to be modified, the modification information of the predefined template is received, the modification information comprises adding a data field to the predefined template and configuring a hidden symbol of the data field, or change its hidden symbol for a certain data field in the predefined template, or delete a certain data field in the predefined template, together with deleting its hidden symbol, modifying the hidden symbols of the corresponding data fields in the template according to the modification information to generate a new predefined template, updating the template only by spending less time to print the hidden symbols on each data field in the template to realize background updating of the template, downloading the new template by a user on a front-end page, after the data in the data column is filled in the new template and uploaded, the report can be automatically analyzed, and the maintenance efficiency of the template and the analysis efficiency of the report data are improved.
In one embodiment, after generating the score of the report based on the score values of the respective subject labels, the method further comprises:
sending the report and the score of the report to a blockchain network;
sending the report and the reported score to a block user of the blockchain network to perform uplink on the report and the reported score.
In this embodiment, after generating the score of the report 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, the report and the score of the report are sent to a blockchain network, the score of the report and the score of the report are monitored by using traceability and non-easy-tampering of the blockchain network, after the report and the score of the report are sent to the blockchain network, uplink is required to be performed on the report and the score of the report to ensure that the report and the score of the report are traceable and not easily tampered, at this time, the report and the score of the report are sent to a block user of the blockchain network, the block user of the blockchain network can be a user who manually reviews the report, and the block user approves the report and the score, after the block user finishes the approval, the report and the score of the report are uplink-executed, so that the report and the score of the report are guaranteed to have traceability and not easy to tamper.
In one embodiment, after the sending the report and the score of the report to the tile user of the blockchain network, the method further includes:
receiving modification information of the scoring of the report by any block user;
sending the modification information to other users of the block chain network;
and if the block users of the block chain network agree that the number of the modification information reaches a preset proportional value, modifying the score of the report according to the modification information, and performing uplink on the report and the modified score of the report.
In this embodiment, after sending the report and the score of the report to the users in the blocks of the blockchain network, if any user in the blocks is suspicious of the report or the score of the report, a modification may be proposed for the score of the report, at this time, modification information of the score of the report by any user in the blocks is received, and then the modification information is sent to other users in the blocks of the blockchain network to notify other users in the blocks of the blockchain network to view the modification information, if the users in the blocks of the blockchain network agree that the number of the modification information reaches a preset proportional value, the score of the report is modified according to the modification information, and the report and the modified score of the report are uplink-linked, when the score of the report needs to be modified, sufficient reasons and sufficient support of other users can be modified, the reliability of the reported score is improved.
Referring to fig. 3, the present application further provides an apparatus for parsing report data, including:
a report acquisition module 10, configured to acquire a report filled based on a predefined template, where each data field in the predefined template is configured with a hidden symbol; the hidden symbol is used for tracking numerical information filled in the data field;
a symbol recognition module 20, configured to recognize hidden symbols in the report, and obtain numerical information of a data field corresponding to each hidden symbol;
the tag identification module 30 is configured to determine, based on a tag library, subject tags mapped by the hidden symbol, and obtain a reference value of each subject tag; the label library comprises a mapping relation between subject labels and hidden symbols;
a symbol scoring module 40, configured to calculate a score value of each hidden symbol according to a dispersion of the numerical information and the reference numerical value;
a tag scoring module 50, configured to count score values of the hidden symbols of the same subject tag, and generate a score value of the subject tag;
and a resolution scoring module 60 for generating a score for the report based on the score value of each subject label.
As described above, it is understood that the respective components of the report data analysis device proposed in the present application can realize the functions of any of the above-described report data analysis methods.
In one embodiment, the apparatus further comprises a relationship determination module configured to perform:
acquiring the project type applied by the report;
determining a scoring rule for the report according to the project type;
and configuring the corresponding relation between the dispersion and the score value according to the scoring rule.
In one embodiment, the tag identification module 30 further comprises a processor for performing:
obtaining a target report of the same type based on the big data;
obtaining the score values of the subject labels in the target report;
and determining the reference value of the subject label according to the score value of each subject label in the target report.
In one embodiment, the tag scoring module 50 further comprises means for performing:
acquiring the weight of each hidden symbol of the same subject label;
and calculating the score value of the subject label according to the weight of each hidden symbol of the same subject label and the score value of each hidden symbol.
In one embodiment, the apparatus further comprises a template update module configured to perform:
receiving modification information of a predefined template;
and modifying the hidden symbol of the corresponding data column in the template according to the modification information to generate a new predefined template.
In one embodiment, the apparatus further comprises a block uplink module configured to perform:
sending the report and the score of the report to a blockchain network;
sending the report and the reported score to a block user of the blockchain network to perform uplink on the report and the reported score.
In one embodiment, the apparatus further comprises a block modification module configured to perform:
receiving modification information of the scoring of the report by any block user;
sending the modification information to other users of the block chain network;
and if the block users of the block chain network agree that the number of the modification information reaches a preset proportional value, modifying the score of the report according to the modification information, and performing uplink on the report and the modified score of the report.
Referring to fig. 4, a computer device, which may be a mobile terminal and whose internal structure may be as shown in fig. 4, is also provided in the embodiment of the present application. The computer equipment comprises a processor, a memory, a network interface, a display device and an input device which are connected through a system bus. Wherein, the network interface of the computer equipment is used for communicating with an external terminal through network connection. The input means of the computer device is for receiving input from a user. The computer designed processor is used to provide computational 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 for storing data. The computer program is executed by a processor to implement a method of parsing report data.
The processor executes the method for analyzing the report data, and the method comprises the following steps: acquiring a report filled based on a predefined template, wherein hidden symbols are configured in each data column in the predefined template; identifying hidden symbols in the report, and acquiring numerical value information corresponding to each hidden symbol; determining subject labels mapped by the hidden symbols based on a label library, and acquiring reference values of the subject labels; calculating the score value of each hidden symbol according to the dispersion of the numerical information and the reference numerical value; counting score values of all hidden symbols of the same subject label to generate score values of the subject label; generating a score for the report based on the score values of the respective subject labels.
The computer equipment provides a method for analyzing and scoring a report, which comprises the steps of acquiring a report filled based on a predefined template, configuring hidden symbols in each data column in the predefined template, identifying the hidden symbols in the report, acquiring numerical value information corresponding to each hidden symbol, determining subject labels mapped by the hidden symbols based on a label library, acquiring reference values of each subject label, calculating score values of each hidden symbol according to the dispersion degree of the numerical value information and the reference values, and counting the score values of each hidden symbol of the same subject label after the score values of each hidden symbol are obtained, wherein the subject labels and the hidden symbols are in one-to-many mapping relation, namely different hidden symbols of a plurality of same subject labels exist in the report, generating score values of the subject labels, then carrying out classification statistics on the hidden symbols of the same type so as to determine scores of the reports under the type, generating scores of the reports based on the score values of the subject labels, thus completing analysis of various data in the reports and the scoring of the reports, scoring the reports from multiple different dimensions, and rapidly and systematically analyzing and scoring the reports.
An embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored, and the computer program, when executed by the processor, implements a method for parsing report data, including: acquiring a report filled based on a predefined template, wherein hidden symbols are configured in each data column in the predefined template; identifying hidden symbols in the report, and acquiring numerical value information corresponding to each hidden symbol; determining subject labels mapped by the hidden symbols based on a label library, and acquiring reference values of the subject labels; calculating the score value of each hidden symbol according to the dispersion of the numerical information and the reference numerical value; counting score values of all hidden symbols of the same subject label to generate score values of the subject label; generating a score for the report based on the score values of the respective subject labels.
The computer-readable storage medium provides a method for parsing and scoring a report, the method includes acquiring a report filled based on a predefined template, configuring hidden symbols in each data column of the predefined template, then identifying the hidden symbols in the report, acquiring numerical information corresponding to each hidden symbol, then determining subject labels mapped by the hidden symbols based on a label library, acquiring reference values of each subject label, calculating score values of each hidden symbol according to dispersion of the numerical information and the reference values, after the score values of each hidden symbol are obtained, the subject labels and the hidden symbols are in one-to-many mapping relation, namely, different hidden symbols of a plurality of same subject labels exist in the report, and then counting the score values of each hidden symbol of the same subject labels, generating score values of the subject labels, then carrying out classification statistics on the hidden symbols of the same type so as to determine scores of the reports under the type, generating scores of the reports based on the score values of the subject labels, thus completing analysis of various data in the reports and the scoring of the reports, scoring the reports from multiple different dimensions, and rapidly and systematically analyzing and scoring the reports.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above.
Any reference to memory, storage, database, or other medium provided herein and used in the embodiments may include non-volatile and/or volatile memory.
Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), double-rate SDRAM (SSRSDRAM), Enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and bus dynamic RAM (RDRAM).
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, apparatus, article, or method. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, apparatus, article, or method that includes the element.
The above description is only a preferred embodiment of the present application and is not intended to limit the scope of the present application.
All the equivalent structures or equivalent processes performed by using the contents of the specification and the drawings of the present application, or directly or indirectly applied to other related technical fields, are included in the scope of protection of the present application.
Claims (10)
1. A method for parsing report data, comprising:
acquiring a report filled based on a predefined template, wherein hidden symbols are configured in each data column in the predefined template; the hidden symbol is used for tracking numerical information filled in the data field;
identifying hidden symbols in the report, and acquiring numerical value information of a data column corresponding to each hidden symbol;
determining subject labels mapped by the hidden symbols based on a label library, and acquiring reference values of the subject labels; the label library comprises a mapping relation between subject labels and hidden symbols;
calculating the score value of each hidden symbol according to the dispersion of the numerical information and the reference numerical value;
counting score values of all hidden symbols of the same subject label to generate score values of the subject label;
generating a score for the report based on the score values of the respective subject labels.
2. The method of claim 1, wherein before calculating the score of each hidden symbol according to the dispersion of the numerical information and the reference value, the method further comprises:
acquiring the project type applied by the report;
determining a scoring rule for the report according to the project type;
and configuring the corresponding relation between the dispersion and the score value according to the scoring rule.
3. The method for parsing report data according to claim 1, wherein said obtaining a reference value of each of said subject labels comprises:
obtaining a target report of the same type based on the big data;
obtaining the score values of the subject labels in the target report;
and determining the reference value of the subject label according to the score value of each subject label in the target report.
4. The method of claim 1, wherein the counting score values of hidden symbols of the same subject label to generate the score value of the subject label comprises:
acquiring the weight of each hidden symbol of the same subject label;
and calculating the score value of the subject label according to the weight of each hidden symbol of the same subject label and the score value of each hidden symbol.
5. The method for parsing report data according to claim 1, further comprising:
receiving modification information of a predefined template;
and modifying the hidden symbol of the corresponding data column in the template according to the modification information to generate a new predefined template.
6. The method for parsing report data according to claim 1, wherein after generating the score of the report based on the score value of each subject label, the method comprises:
sending the report and the score of the report to a blockchain network;
sending the report and the reported score to a block user of the blockchain network to perform uplink on the report and the reported score.
7. The method for parsing report data according to claim 6, wherein after the sending the report and the score of the report to the users of the blocks in the blockchain network, further comprising:
receiving modification information of the scoring of the report by any block user;
sending the modification information to other users of the block chain network;
and if the block users of the block chain network agree that the number of the modification information reaches a preset proportional value, modifying the score of the report according to the modification information, and performing uplink on the report and the modified score of the report.
8. An apparatus for parsing report data, comprising:
the report acquisition module is used for acquiring reports filled based on a predefined template, and hidden symbols are configured in each data column in the predefined template; the hidden symbol is used for tracking numerical information filled in the data field;
the symbol identification module is used for identifying the hidden symbols in the report and acquiring the numerical value information of the data column corresponding to each hidden symbol;
the label identification module is used for determining the subject labels mapped by the hidden symbols based on a label library and acquiring reference values of the subject labels; the label library comprises a mapping relation between subject labels and hidden symbols;
the symbol scoring module is used for calculating the score value of each hidden symbol according to the dispersion of the numerical information and the reference numerical value;
the label scoring module is used for counting the score values of all the hidden symbols of the same subject label and generating the score values of the subject label;
and the analysis scoring module is used for generating the score of the report based on the score value of each subject label.
9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor when executing the computer program implements the steps of the method of parsing report data according to any of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of parsing report data according to any one of claims 1 to 7.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117151053A (en) * | 2023-11-01 | 2023-12-01 | 东莞信宝电子产品检测有限公司 | Report automation realization method, system and medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117151053A (en) * | 2023-11-01 | 2023-12-01 | 东莞信宝电子产品检测有限公司 | Report automation realization method, system and medium |
| CN117151053B (en) * | 2023-11-01 | 2024-02-13 | 东莞信宝电子产品检测有限公司 | Report automation realization method, system and medium |
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