CN111078870A - Evaluation data processing method, evaluation data processing device, evaluation data processing medium, and computer device - Google Patents
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Abstract
本发明提供了一种评价数据处理方法、装置、计算机存储介质和计算机设备；该方法包括：响应于包含员工标识的评价数据处理指令，获取与所述员工标识对应的评价数据；确定与所述员工标识对应的岗位类型，从预先训练好的多个情感判别模型中确定与所述岗位类型对应的情感判别模型；使用所述确定出的情感判别模型对所述评价数据进行处理，得到情感判别序列；获取与所述员工标识对应的绩效等级对照表，查询所述绩效等级对照表，确定与所述情感判别序列对应的绩效等级信息。通过本发明技术方案，能够更准确地对员工相关的绩效总结文本即评价数据进行判别，有利于提高对员工绩效等级的评估准确性。
The present invention provides an evaluation data processing method, device, computer storage medium and computer equipment; the method includes: in response to an evaluation data processing instruction including an employee identification, obtaining evaluation data corresponding to the employee identification; The employee identifies the corresponding post type, and determines an emotion discrimination model corresponding to the post type from a plurality of pre-trained emotion discrimination models; uses the determined emotion discrimination model to process the evaluation data to obtain an emotion discrimination model sequence; obtaining a performance level comparison table corresponding to the employee identification, querying the performance level comparison table, and determining the performance level information corresponding to the emotion discrimination sequence. Through the technical solution of the present invention, the performance summary text related to the employee, that is, the evaluation data, can be more accurately discriminated, which is beneficial to improve the evaluation accuracy of the employee's performance level.
Description
Technical Field
The invention relates to the field of computers, in particular to an evaluation data processing method, an evaluation data processing device, an evaluation data processing medium and computer equipment.
Background
The enterprise requires the staff to make a middle-of-year or end-of-year summary at the middle-of-year or end-of-year time, and evaluates the performance level of the staff according to the performance summary made by the staff. In order to improve the efficiency of evaluating the performance level of the employee, the inventor thinks that a text classification tool can be used to identify the performance summary text submitted by the employee, so as to determine the corresponding performance level of the employee.
However, the inventor finds that most of the performance summary texts performed by employees are work summaries rather than complete text paragraphs, and the performance summary texts include a lot of descriptions of indicators and completion conditions and the like of the employees, and relate to professional terms and indicators in a plurality of fields.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides an evaluation data processing method, an evaluation data processing device, an evaluation data processing medium and computer equipment.
An embodiment of the present invention provides an evaluation data processing method according to a first aspect, including:
responding to an evaluation data processing instruction containing employee identification, and acquiring evaluation data corresponding to the employee identification;
determining a post type corresponding to the employee identification, and determining an emotion distinguishing model corresponding to the post type from a plurality of emotion distinguishing models trained in advance;
processing the evaluation data by using the determined emotion judging model to obtain an emotion judging sequence;
and acquiring a performance level comparison table corresponding to the employee identification, inquiring the performance level comparison table, and determining performance level information corresponding to the emotion distinguishing sequence.
Further, the evaluation data comprises employee self-evaluation data and superior evaluation data;
the acquiring of the evaluation data corresponding to the employee identifier includes:
inquiring an employee relationship table, and determining a superior employee identifier corresponding to the employee identifier;
and acquiring employee self-evaluation data corresponding to the employee identification and superior evaluation data corresponding to the superior employee identification from an evaluation database.
Further, the processing the evaluation data by using the determined emotion recognition model to obtain an emotion recognition sequence includes:
processing the employee self-evaluation data by using the determined emotion judging model to obtain an employee emotion judging type;
processing the superior evaluation data by using the determined emotion judging model to obtain a superior emotion judging type;
and sequencing the staff emotion judgment types and the superior emotion judgment types according to a preset sequencing format to obtain an emotion judgment sequence.
Further, acquiring a plurality of pieces of historical evaluation data, and determining a post type and a labeling type corresponding to each piece of historical evaluation data;
processing the plurality of pieces of historical evaluation data by using a preset language model to obtain a semantic vector corresponding to each piece of historical evaluation data;
and respectively taking the label types and semantic vectors corresponding to all historical evaluation data belonging to the same post type as training samples for training the emotion distinguishing models to obtain a plurality of trained emotion distinguishing models corresponding to different post types.
Further, the querying the performance level comparison table to determine performance level information corresponding to the emotion judging sequence, then includes:
acquiring all historical performance grade information corresponding to the employee identification, wherein the evaluation time belongs to a preset time interval;
recording the performance grade information and the historical performance grade information into a preset contrast report template to generate a performance contrast report;
and pushing the performance comparison report to an employee account corresponding to the employee identifier.
An embodiment of the present invention provides an evaluation data processing apparatus according to a second aspect, including:
the evaluation data acquisition module is used for responding to an evaluation data processing instruction containing employee identification and acquiring evaluation data corresponding to the employee identification;
the model determining module is used for determining the post type corresponding to the employee identification and determining an emotion distinguishing model corresponding to the post type from a plurality of emotion distinguishing models trained in advance;
a sequence obtaining module, configured to process the evaluation data using the determined emotion judging model to obtain an emotion judging sequence;
and the performance level determining module is used for acquiring a performance level comparison table corresponding to the employee identification, inquiring the performance level comparison table and determining performance level information corresponding to the emotion judging sequence.
Embodiments of the present invention provide, according to a third aspect, a computer-readable storage medium on which a computer program is stored, the program, when executed by a processor, implementing the above-described evaluation data processing method.
An embodiment of the present invention provides a computer device according to a fourth aspect, the computer device including:
one or more processors;
storage means for storing one or more programs;
when the one or more programs are executed by the one or more processors, the one or more processors implement the above-described evaluation data processing method.
In the embodiment of the invention, the evaluation data corresponding to the employee identification is acquired by responding to the evaluation data processing instruction containing the employee identification, then determining the post type corresponding to the employee identification, determining an emotion distinguishing model corresponding to the post type from a plurality of emotion distinguishing models trained in advance as the emotion distinguishing model used at this time, and the evaluation data is processed by using the determined emotion judging model to obtain an emotion judging sequence, and finally a performance level comparison table corresponding to the employee identification is obtained to inquire the performance level comparison table, and then, the performance grade information corresponding to the emotion distinguishing sequence is determined, so that evaluation data which is a performance summary text related to the staff can be distinguished in a targeted manner, the evaluation data can be distinguished more accurately, and the evaluation accuracy of the staff performance grade can be improved.
Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
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The foregoing and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a schematic flow chart of a method for evaluating data processing according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of an evaluation data processing apparatus according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a computer device according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative only and should not be construed as limiting the invention.
It will be understood by those skilled in the art that, unless otherwise specified, the singular forms "a", "an", "the" and "the" may include the plural forms as well, and the "first" and "second" used herein are only used to distinguish one technical feature from another and are not intended to limit the order, number, etc. of the technical features. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
An embodiment of the present invention provides an evaluation data processing method, and the following describes in detail a specific embodiment of the present invention with reference to the accompanying drawings. As shown in fig. 1, the evaluation data processing method provided in the embodiment of the present invention includes the following steps:
s110: responding to an evaluation data processing instruction containing employee identification, and acquiring evaluation data corresponding to the employee identification;
s120: determining a post type corresponding to the employee identification, and determining an emotion distinguishing model corresponding to the post type from a plurality of emotion distinguishing models trained in advance;
s130: processing the evaluation data by using the determined emotion judging model to obtain an emotion judging sequence;
s140: and acquiring a performance level comparison table corresponding to the employee identification, inquiring the performance level comparison table, and determining performance level information corresponding to the emotion distinguishing sequence.
In this embodiment, when performance summarization is started, an employee can log in an employee account on an intelligent terminal within a preset time interval, and then upload a performance summarization text written by the employee, that is, evaluation data, to a server through the intelligent terminal, and the server stores the received evaluation data in an evaluation database, where the intelligent terminal may be a smart phone, a tablet computer, or a conventional PC computer. The evaluation data processing instruction can be sent to the server by the staff responsible for processing and evaluating the performance level of the staff, or the staff can request the performance level of the performance summary of the staff from the server after logging in the staff account.
And after receiving the evaluation data processing instruction containing the employee identification, the server acquires the evaluation data corresponding to the employee identification from the evaluation database. The employee identifier is a unique identity for distinguishing each employee, and may be an identifier such as an employee number (e.g., 050809), a combination of an employee name and post information (e.g., zhang san-ministry of finance-chief finance officer), and the like.
Considering that in practical situations, performance summary texts written by different employees may include specific vocabularies from different fields, such as financial fields, communication fields, IT fields, and the like, and even the same vocabulary may represent different meanings in different fields, if only an emotion judgment model of one field is used to perform emotion judgment on the performance summary texts, the accuracy of the finally obtained emotion judgment type will be affected. Considering that the content of the Performance summary text written by the employee is mainly KPI (Key Performance Indicator) Indicator description, KPI completion condition description, improvement and the like, wherein the used vocabularies have great relevance to the work content thereof, and the vocabularies used by the employees with the same or similar work content to write the Performance summary text are overlapped to a great extent, therefore, the embodiment trains a plurality of emotion distinguishing models corresponding to different fields in advance, and then selects and processes the emotion distinguishing model of the Performance summary text according to the work content of the employees, so that the emotion distinguishing accuracy of the employee Performance summary text can be improved.
Therefore, the server firstly determines the post type corresponding to the employee identification, and then determines the emotion distinguishing model corresponding to the post type from the plurality of emotion distinguishing models trained in advance. The post type is used for representing the working content of the staff, a plurality of post types corresponding to different working contents are preset by a developer, and specific setting can be determined according to an actual application scene, for example, the working contents of the staff of the same department or the same group in an enterprise are the same or similar, so that one department or group can be used as one post type, such as setting a manpower resource type, a product test type, a product development type and the like. The developer also needs to pre-configure the post type corresponding to each employee, for example, the developer pre-configures a mapping table between the post type and the employee identifier, and then uploads the mapping table to the server, and the server can determine the post type corresponding to each employee identifier according to the mapping table.
After the server determines the emotion distinguishing model used at this time from a plurality of emotion distinguishing models trained in advance, the server uses the determined emotion distinguishing model to carry out emotion distinguishing on the evaluation data so as to obtain an emotion distinguishing sequence, finally, a performance level comparison table corresponding to the employee identification is obtained, the performance level comparison table is further inquired, and performance level information corresponding to the emotion distinguishing sequence is determined, wherein the performance level comparison table is configured in advance by a developer and uploaded to the server, and the server can determine the performance level information corresponding to the emotion distinguishing sequence according to the mapping table. For example, if the emotion discrimination sequence is merit-merit, the corresponding performance level information is excellent; if it is an advantage-deficiency, the corresponding performance level information is good; if it is not sufficient-advantageous, the corresponding performance level information is medium; if the performance level information is insufficient, the corresponding performance level information is not good.
The embodiment can specifically judge the evaluation data which is the performance summary text related to the staff, can judge the evaluation data more accurately, and is beneficial to improving the accuracy of evaluation of the staff performance level.
Specifically, in one embodiment, the evaluation data includes employee self-evaluation data and superior evaluation data. In step S110, the step of obtaining evaluation data corresponding to the employee identifier includes:
s111: inquiring an employee relationship table, and determining a superior employee identifier corresponding to the employee identifier;
s112: and acquiring employee self-evaluation data corresponding to the employee identification and superior evaluation data corresponding to the superior employee identification from an evaluation database.
In this embodiment, the relationship between each employee and the upper-level employee to be evaluated is recorded in the employee relationship table. The employee relationship table may be uploaded to the server by an administrator, or an employee responsible for processing employee performance level assessment transactions, such as an employee of the human resources department.
When the performance summarization is carried out, all the employees need to self-evaluate the working conditions of the employees in the time interval of the performance summarization, the made performance summarization texts are uploaded to the server, and the server carries out emotion judgment on the performance summarization texts. The staff with the subordinate staff also needs to evaluate the working conditions of the subordinate staff, and uploads a performance summary text of the evaluated subordinate staff to the server. That is, for a certain employee, the employee self-evaluation data refers to a performance summary text that the employee evaluates for himself, and the upper-level evaluation data refers to a performance summary text that the upper-level employee corresponding to the employee evaluates for himself.
For example, in the performance summary of the current round, performance summary in the end of 2018 years is performed, zhang is a financial chief reason of a financial department, and its subordinate employee is a financial li four of the financial department, and in the performance summary of the round, zhang needs to self-evaluate the working condition of the self-subordinate employee in 2018 years, and also needs to evaluate the working condition of the self-subordinate employee in 2018 years; for the employee lie four, the self-evaluation data corresponding to the employee lie four is a performance summary text of Zhang Sanli evaluating the employee lie four by self, and the corresponding upper-level evaluation data is a performance summary text of Zhang Sanli evaluating the employee lie III at the upper level.
Further, in one embodiment, step S130: processing the evaluation data by using the determined emotion judging model to obtain an emotion judging sequence, wherein the method comprises the following steps:
s131: processing the employee self-evaluation data by using the determined emotion judging model to obtain an employee emotion judging type;
s132: processing the superior evaluation data by using the determined emotion judging model to obtain a superior emotion judging type;
s133: and sequencing the staff emotion judgment types and the superior emotion judgment types according to a preset sequencing format to obtain an emotion judgment sequence.
In the present embodiment, the emotion discrimination type can be obtained by processing the performance summary text using the determined emotion discrimination model. Wherein, the emotion judgment types comprise an advantage type and a deficiency type; if the emotion judgment type is an advantage, the staff is satisfied with the working condition summarized in the performance of the staff, otherwise, the staff is not satisfied with the working condition summarized in the performance of the staff, and the staff is considered to be not well enough.
Specifically, the performance summary text is input into the emotion judgment model, a series of calculations are performed on the input performance summary text by the emotion judgment model to obtain a value, the value is compared with a preset contrast value, and the emotion judgment type of the input performance summary text can be determined according to the comparison result, for example, if the calculated value is larger than the preset contrast value, the emotion judgment type of the performance summary text is determined to be superior, and otherwise, the emotion judgment type of the performance summary text is determined to be deficient.
For convenience of distinguishing, the emotion judgment type obtained by processing the employee self-evaluation data by using the emotion judgment model is called an employee emotion judgment type, and the emotion judgment type obtained by processing the superior evaluation data by using the emotion judgment model is called a superior emotion judgment type.
After the employee emotion judgment type corresponding to the employee self-evaluation data and the superior emotion judgment type corresponding to the superior evaluation data are obtained through the emotion judgment model, the employee emotion judgment type and the superior emotion judgment type are sequenced according to a preset sequencing format to obtain an emotion judgment sequence. The preset sorting format is a superior emotion judgment type-employee emotion judgment type, namely, the superior emotion judgment type is arranged in front of the preset sorting format, and the employee emotion judgment type is arranged behind the preset sorting format, for example, the superior emotion judgment type is an advantage, the employee emotion judgment type is an advantage, and the obtained emotion judgment sequence is an advantage-advantage.
In one embodiment, S210: acquiring a plurality of pieces of historical evaluation data, and determining a post type and a labeling type corresponding to each piece of historical evaluation data;
s220: processing the plurality of pieces of historical evaluation data by using a preset language model to obtain a semantic vector corresponding to each piece of historical evaluation data;
s230: and respectively taking the label types and semantic vectors corresponding to all historical evaluation data belonging to the same post type as training samples for training the emotion distinguishing models to obtain a plurality of trained emotion distinguishing models corresponding to different post types.
In this embodiment, the developer needs to collect a large amount of historical evaluation data in advance, that is, the performance summary text submitted by the employee during performance summary in the past includes the performance summary text for evaluating the developer and the performance summary text for evaluating the employee below. The mark type is the emotion type corresponding to the performance summary text and corresponds to the emotion judgment type, and the mark type comprises an advantage mark type and a deficiency mark type.
During training, attention is paid to dividing historical evaluation data into a plurality of data sets corresponding to different position types according to the position types, and each data set is used for specially training an emotion judgment model. Before the emotion distinguishing model is trained, NLP (Natural Language Processing) technology and Language model, such as n-gram model, are used to characterize the text description of the collected historical evaluation data, obtain the corresponding semantic vector, and then train the model by using the obtained semantic vector and the label type (advantage or deficiency).
In one embodiment, step S140: determining performance grade information corresponding to the employee identification according to the emotion distinguishing sequence, and then:
s310: acquiring all historical performance grade information corresponding to the employee identification, wherein the evaluation time belongs to a preset time interval;
s320: recording the performance grade information and the historical performance grade information into a preset contrast report template to generate a performance contrast report;
s330: and pushing the performance comparison report to an employee account corresponding to the employee identifier.
In this embodiment, after performance level information corresponding to the employee identifier is determined, evaluation time, which is system time for determining the performance level information, is recorded, and then a mapping relationship is established between the performance level information and the evaluation time.
Further, all historical performance level information corresponding to the employee identification and with the evaluation time within a preset time interval can be called out. Specifically, all historical performance level information corresponding to the employee identification is determined, then evaluation time corresponding to all historical performance level information is determined, whether the evaluation time belongs to a preset time interval is judged, and the historical performance level information of which the evaluation time belongs to the preset time interval is determined. The preset time interval may be preset by a developer.
And then, recording the performance grade information of the performance summary and the historical performance grade information determined through the steps into a preset comparison report template to generate a performance comparison report, and then pushing the performance comparison report to the employee account corresponding to the employee identifier.
For example, in the summary of the first quarter in 2019, it is determined that the performance level obtained by the third employee in this performance summary is excellent, so that the performance levels of the third employee in the fourth quarter in 2018 are excellent, good and excellent respectively, and then the information of the five performance levels is entered into a preset contrast report template, and a performance contrast report is generated and pushed to the account number of the third employee.
In order to better understand the technical solution of the present invention, the present invention further provides an evaluation data processing apparatus, including:
an evaluation data obtaining module 110, configured to respond to an evaluation data processing instruction including an employee identifier, and obtain evaluation data corresponding to the employee identifier;
a model determining module 120, configured to determine a post type corresponding to the employee identifier, and determine an emotion distinguishing model corresponding to the post type from a plurality of emotion distinguishing models trained in advance;
a sequence obtaining module 130, configured to process the evaluation data using the determined emotion distinguishing model to obtain an emotion distinguishing sequence;
and the performance level determining module 140 is configured to acquire a performance level comparison table corresponding to the employee identifier, query the performance level comparison table, and determine performance level information corresponding to the emotion distinguishing sequence.
In one embodiment, the evaluation data comprises employee self-evaluation data and superior evaluation data;
the evaluation data obtaining module 110 includes:
a superior identification determining sub-module 111, configured to query the employee relationship table, and determine a superior employee identification corresponding to the employee identification;
and the evaluation data obtaining sub-module 112 is configured to obtain employee self-evaluation data corresponding to the employee identifier and superior evaluation data corresponding to the superior employee identifier from an evaluation database.
In one embodiment, the sequence obtaining module 130 includes:
the employee type obtaining submodule 131 is used for processing the employee self-evaluation data by using the determined emotion judging model to obtain an employee emotion judging type;
a superior type obtaining submodule 132, configured to process the superior evaluation data by using the determined emotion recognition model, so as to obtain a superior emotion recognition type;
the sequence obtaining submodule 133 is configured to sequence the employee emotion recognition types and the upper emotion recognition types according to a preset sequencing format to obtain an emotion recognition sequence.
In an embodiment, the type determining module 210 is configured to obtain a plurality of pieces of historical evaluation data, and determine a post type and a label type corresponding to each piece of historical evaluation data;
a semantic vector obtaining module 220, configured to process the multiple pieces of historical evaluation data by using a preset language model, and obtain a semantic vector corresponding to each piece of historical evaluation data;
and the model training module 230 is configured to use the label types and semantic vectors corresponding to all historical evaluation data belonging to the same post type as training samples for training the emotion recognition model, so as to obtain a plurality of trained emotion recognition models corresponding to different post types.
In one embodiment, the evaluation data processing apparatus of the present embodiment, after executing the function corresponding to the performance level determination module 140, further executes the functions corresponding to the following modules:
the historical performance obtaining module 310 is configured to obtain all historical performance level information corresponding to the employee identifier, where the evaluation time belongs to a preset time interval;
the comparison report generating module 320 is configured to enter the performance level information and the historical performance level information into a preset comparison report template, and generate a performance comparison report;
and the comparison report pushing module 330 is configured to push the performance comparison report to an employee account corresponding to the employee identifier.
It should be noted that the evaluation data processing apparatus provided in the embodiment of the present invention can implement the functions implemented by the above evaluation data processing method, and specific implementation of the functions refers to the description in the above evaluation data processing method, which is not described herein again.
An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the above-described evaluation data processing method. The storage medium includes, but is not limited to, any type of disk (including floppy disks, hard disks, optical disks, CD-ROMs, and magneto-optical disks), ROMs (Read-Only memories), RAMs (Random AcceSS memories), EPROMs (EraSable Programmable Read-Only memories), EEPROMs (Electrically EraSable Programmable Read-Only memories), flash memories, magnetic cards, or optical cards. That is, a storage medium includes any medium that stores or transmits information in a form readable by a device (e.g., a computer). Which may be a read-only memory, magnetic or optical disk, or the like.
An embodiment of the present invention further provides a computer device, where the computer device includes:
one or more processors 410;
a storage 420 for storing one or more programs 400,
when the one or more programs 400 are executed by the one or more processors 410, the one or more processors 410 implement the above-described evaluation data processing method.
Fig. 3 is a schematic structural diagram of the computer device of the present invention, which includes a processor 410, a storage device 420, an input unit 430, a display unit 440, and other components. Those skilled in the art will appreciate that the structural elements shown in fig. 3 do not constitute a limitation of all computer devices and may include more or fewer components than those shown, or some of the components may be combined. The storage 420 may be used to store the application program 400 and various functional modules, and the processor 410 executes the application program 400 stored in the storage 420, thereby performing various functional applications of the device and data processing. The storage 420 may be an internal memory or an external memory, or include both internal and external memories. The memory may comprise read-only memory, Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), flash memory, or random access memory. The external memory may include a hard disk, a floppy disk, a ZIP disk, a usb-disk, a magnetic tape, etc. The disclosed memory devices include, but are not limited to, these types of memory devices. The memory device 420 disclosed herein is provided by way of example only and not by way of limitation.
The input unit 430 is used for receiving input of signals and receiving related requests of selecting voice files and the like input by users. The input unit 430 may include a touch panel and other input devices. The touch panel can collect touch operations of a user on or near the touch panel (for example, operations of the user on or near the touch panel by using any suitable object or accessory such as a finger, a stylus and the like) and drive the corresponding connecting device according to a preset program; other input devices may include, but are not limited to, one or more of a physical keyboard, function keys (e.g., play control keys, switch keys, etc.), a trackball, a mouse, a joystick, and the like. The display unit 440 may be used to display information input by a user or information provided to a user and various menus of the computer device. The display unit 440 may take the form of a liquid crystal display, an organic light emitting diode, or the like. The processor 410 is a control center of the computer device, connects various parts of the entire computer using various interfaces and lines, performs various functions and processes data by operating or executing software programs and/or modules stored in the storage device 420 and calling data stored in the storage device.
In one embodiment, the computer device includes one or more processors 410, and one or more storage 420, one or more applications 400, wherein the one or more applications 400 are stored in the storage 420 and configured to be executed by the one or more processors 410, the one or more applications 400 configured to perform the evaluation data processing method described in the above embodiments.
It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and may be performed in other orders unless explicitly stated herein. Moreover, at least a portion of the steps in the flow chart of the figure may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed alternately or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
It should be understood that each functional unit in the embodiments of the present invention may be integrated into one processing module, each unit may exist alone physically, or two or more units may be integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode.
The foregoing is only a partial embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (10)
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