CN108985618B - Grading system for improving skill level of people - Google Patents
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Abstract
The invention discloses a scoring system for improving the skill level of a person, which comprises: the system comprises a user registration module, a user login module, an input module, a big data storage module, a planning module, a comparison module and an information output module; the big data storage module comprises a user basic information storage sub-module, an evaluation project storage sub-module, a rating standard storage sub-module, an efficiency detection content storage sub-module, a plan scheme storage sub-module and an intelligent plan and node rating standard storage sub-module, and the input module comprises a state detection input sub-module, an efficiency detection input sub-module, a target input sub-module and a plan execution input sub-module. Through the scoring system, the user can accurately evaluate the user and clearly determine the target of the user so as to make a scientific solution to achieve the ideal improvement and target realization of the user.
Description
Technical Field
The invention relates to scoring software for various factors of things, in particular to a scoring system for improving the skill level of people.
Background
The scoring software is a multi-aspect, multi-level and all-around scoring tool, analyzes and evaluates scoring objects, and provides a corresponding scheme according to user requirements. Is an assessment of the real, mental aspects throughout its various cycles. The existing scoring software (such as Huazhi sports health software) is relatively single in scoring, not comprehensive enough, small in promotion effect on things and relatively less in adaptive groups. With the increasingly fierce competition in the information age, things are continuously advanced, and a full-period and all-around grading software is urgently needed to analyze and position the things they do. At present, information in the information age is messy, and many evaluation software or systems are not reasonably analyzed, so that the ideal effect required by a user cannot be achieved. Therefore, the improvement of the score and essence of things is a problem to be solved. In addition, when people use software, the information amount is huge, the definition is lacked, the software is easily misled by messy information, a proper method is not found, the target of the software is separated, and the software cannot improve the software. Therefore, a software system for evaluating things, defining the goals of things and making scientific solutions is urgently needed.
Disclosure of Invention
Aiming at the technical defects, the invention provides a scoring system for improving the skill level of people.
The technical scheme adopted by the invention is as follows: a scoring system for improving a skill level of a person, comprising: the system comprises an input module, a big data storage module, a planning module, a comparison module and an information output module;
the input module comprises a state detection input sub-module, an efficiency detection input sub-module, a target input sub-module and a plan execution input sub-module; the system comprises a state detection and entry submodule, an evaluation item storage submodule, an efficiency detection and entry submodule, a target entry submodule and a plan execution and entry submodule, wherein the state detection and entry submodule is connected with an evaluation item storage submodule and is used for entering an initial skill level of a user, the efficiency detection and entry submodule is used for evaluating and detecting the self efficiency of a detection item selected by the user, the detection mode is an answer mode, the entry information of the target entry submodule comprises a final target required to be completed by the user and a final completion date, and the plan execution and entry submodule is used for entering the information of the task completion condition of the user at each plan node;
the big data storage module comprises a user basic information storage submodule, an evaluation project storage submodule, a grading standard storage submodule, an efficiency detection content storage submodule, a plan scheme storage submodule and an intelligent plan and node grading standard storage submodule, wherein the efficiency detection content storage submodule stores the question of work efficiency information of a detection project, and the storage information of the plan scheme storage submodule comprises an expert recommendation plan scheme;
the user basic information storage submodule is used for storing information input by the state detection input submodule, the efficiency detection input submodule, the target input submodule and the plan execution input submodule, the user basic information storage submodule is connected with the cloud database through the Internet according to the user ID,
the planning module is used for comparing an initial state detection value obtained by the state detection input sub-module, an efficiency value obtained by the efficiency detection input sub-module and information input by the target input sub-module with an expert recommended planning scheme of the planning scheme storage sub-module, giving a planning scheme and statically inputting the planning scheme into the planning scheme storage sub-module; the planning scheme is divided into a plurality of nodes in sequence by time and corresponding node targets are formulated;
the comparison module is used for comparing the task completion condition information of the user in the plan node, which is input by the plan execution input sub-module, with the node target in the plan scheme stored in the plan scheme storage sub-module; if the node target is reached, the node task is completed successfully, the comparison module sends a comparison result to the information output module, and the information output module outputs information for entering the next node to learn; if the node target is not reached, the node task is failed to complete, and the comparison module sends information for re-planning to the planning module; the planning module calls the plan execution entry sub-module to obtain task completion condition information of a node of which the target is not completed by a user after receiving the information of the re-designated plan, calls the target entry sub-module to obtain target information, prompts the client to enter the state detection entry sub-module and the efficiency detection entry sub-module again so as to obtain an initial state detection value and an efficiency value, compares the initial state detection value and the efficiency value with an expert recommended plan scheme of the plan scheme storage sub-module again, and gives a new plan scheme again; the user learns according to the new plan scheme, and after reaching a plan node of the new plan scheme, the task completion condition is recorded through the plan execution recording sub-module; the comparison module compares the data and determines to enter the learning of the next node or reformulate a plan scheme according to the comparison result until the final completion date is reached, and judges whether the final target is reached; if the final target is reached, the information output module outputs the information that the task is successful, the process of the user executing the plan scheme is recorded and forms a plan execution success scheme, and the plan execution success scheme is stored in the plan scheme storage sub-module; and uploading the information to a cloud database through the Internet, and if the final target is not reached, outputting information of task failure by an information output module.
Preferably, the system further comprises a cloud database information calling module, when the system provides an expert recommendation plan scheme, the cloud database information calling module calls plans uploaded by other users and stored in the cloud database to execute a successful scheme, the entry submodule and the target entry submodule are matched with the closest plan execution successful scheme according to the state detection, and the entry submodule and the target entry submodule are matched with the closest plan execution successful scheme, and the plan execution successful scheme is output through the information output module and is selected by the user.
Preferably, the content of the comparison information between the task completion condition of the planning node generated by the comparison module and the node target in the planning scheme stored in the planning scheme storage sub-module includes whether the target is completed, and the comparison information is stored in the user basic information storage sub-module.
Preferably, after the intelligent plan and node score standard storage submodule analyzes the target input by the user, one or more different plan execution success schemes are provided for the user to select, and the requirements of different users on the target plan are met.
Preferably, the system further comprises a user registration module and a user login module, wherein the user registration module is used for registering a user account, the user registration module records the basic information of the user, and the basic information of the user is stored in the user basic information storage submodule;
the user login module is connected with the input module and calls data of the user basic information storage submodule.
The invention has the beneficial effects that: through the scoring system, the user can accurately evaluate the user and clearly determine the target of the user so as to make a scientific solution to achieve the ideal improvement and target realization of the user. In the using process of a user, if the user reaches a final target, the information output module outputs information that the task is successful, the process of the user executing the plan scheme is recorded and forms a plan execution success scheme, and the plan execution success scheme is stored in the plan scheme storage sub-module; and upload to the high in the clouds database through the internet, therefore this grading system is an intelligent system that can carry out self growth and evolution, is favorable to giving big data to give the scheme that the more reasonable realization of user set the target.
Drawings
FIG. 1 is a diagram of the logical relationship between modules of the present invention.
Fig. 2 is a flowchart of an application of the scoring system of the present invention.
Detailed description of the preferred embodiments
In order to make the technical means, innovative features, objectives and effects of the present invention easily understood, the present invention will be further described with reference to the following detailed drawings.
As shown in fig. 1-2, after the user registers in the user registration module, the user fills in the relevant basic information, and the basic information is stored in the user basic information storage sub-module in the big data storage module; after a user logs in through the user login module, the state detection entry submodule of the entry module is connected with the evaluation item storage submodule, the initial skill level and the initial skill level of the user are monitored through the state detection entry submodule, the initial skill level is evaluated by an initial expert, the initial data evaluation comprises but is not limited to knowledge reserve evaluation (the current knowledge point is determined in a large range by using the knowledge point to perform score evaluation), the data base evaluation and big data evaluation of the expert master knowledge reserve evaluation (the percentage of the current knowledge type is determined in a large range of the knowledge type to determine the score) are carried out, so that the initial skill level is more reasonably confirmed, the actual initial skill level of the user is evaluated and is a comprehensive test result, the more data are tested, the more accurate result is obtained, and information such as the initial skill level is stored in the user basic information storage submodule, (the system does not need to set a user registration module and a login module, the running program directly stores the user information into a user basic information storage sub-module according to the ID information of the user, but the system recommends to use the user registration module and the user login module so as to facilitate the input and storage of the information), an evaluation item storage sub-module in the big data storage module selects an item to be evaluated, meanwhile, the system is connected with the information of a scoring standard storage submodule and also provides an efficiency detection input submodule, the efficiency detection input sub-module is used for self efficiency evaluation detection of the detection item selected by the user, the detection mode is an answering mode, the questions are stored in the efficiency detection content storage sub-module, the system is used for detecting the work efficiency information of the project, and then the target and the completion date which are required to be reached by the project to be evaluated by the user are input through the target input sub-module of the input module. At this time, the planning module, according to the entered target information, the user basic information and the test items, the user basic information includes: comparing an initial state detection value obtained by the state detection input sub-module and an efficiency value obtained by the efficiency detection input sub-module with an expert recommendation plan scheme of a plan scheme storage sub-module (a specific comparison method is that the expert recommendation plan scheme comprises a target, an applicable crowd, a test item, and corresponding nodes in sequence of time and establishes corresponding node targets, wherein the comparison process comprises the steps of inputting information, matching test item information, matching applicable crowd information, mainly comprising the initial level of the applicable crowd, matching target information, and giving a plan scheme after matching), and the plan scheme is divided into a plurality of nodes in sequence of time and establishes corresponding node targets; the planning scheme is associated to the user basic information storage submodule and is output by the information output module; in different learning periods, a user can input the plan execution condition through the plan execution input sub-module, and the comparison module compares the information input by the user in the plan execution input sub-module with the information of the intelligent plan and the node score standard storage sub-module and outputs the information through the information output module. If the node target is reached, the node task is completed successfully, the comparison module sends a comparison result to the information output module, and the information output module outputs information for entering the next node to learn; if the node target is not reached, the node task is failed to complete, and the comparison module sends information for re-planning to the planning module; simultaneously prompting a client to perform state detection input submodule and efficiency detection input submodule again so as to obtain an initial state detection value and an efficiency value, calling a plan execution input submodule after a plan module receives information of a reassigned plan to obtain task completion condition information of a node of an unfinished target of the user, calling a target input submodule to obtain target information, comparing the target information with an expert recommendation plan scheme of a plan scheme storage submodule again, and giving a new plan scheme again; the user learns according to the new plan scheme, and after reaching a plan node of the new plan scheme, the task completion condition is recorded through the plan execution recording sub-module; the comparison module compares the data and determines to enter the learning of the next node or reformulate a plan scheme according to the comparison result until the final completion date is reached, and judges whether the final target is reached; if the final target is reached, the information output module outputs information that the task is successful, the process of executing the plan scheme by the user is recorded and forms a plan execution success scheme, and the plan execution success scheme is stored in the plan scheme storage submodule and is uploaded to the cloud database through the internet; and if the final target is not reached, the information output module outputs the information of task failure. And when the plan execution success scheme is stored, the recorded information comprises the initial skill level of the user, the information input by the user plan execution input sub-module, the task completion condition of each node and the like.
The system further comprises a cloud database information calling module, when the system gives an expert recommendation plan scheme, the cloud database information calling module calls a plan execution success scheme uploaded by other users and stored in a cloud database, the plan execution success scheme which is matched with the closest plan execution success scheme by the state detection entry sub-module and the target entry sub-module is output through the information output module, and the plan execution success scheme is selected by the user (but the cloud database does not give private information of other users).
Example 1
The application of the scoring system of the present invention is illustrated by taking a second-level builder examination as an example. General learning software only performs simple question brushing or exercise, and different learning schemes cannot be formulated for different users. When the scoring system is used, after a user registers and logs in, after an evaluation item storage submodule selects a secondary builder examination, relevant information of the user and the secondary builder examination is input, wherein the relevant information comprises information such as a state detection input submodule of the input module connected with the evaluation item storage submodule, an initial skill level of the user is monitored through the state detection input submodule, the information such as the initial skill level is stored in a user basic information storage submodule, the system also provides an efficiency detection input submodule, the efficiency detection input submodule is used for self efficiency evaluation detection of a detection item selected by the user, the detection mode is an answer mode, questions are stored in an efficiency detection content storage submodule and are used for detecting work efficiency information of the item, an initial state detection value, a secondary builder examination value and the like obtained by the state detection input submodule, The efficiency detection and input submodule obtains an efficiency value, and then an input module inputs a target which a user wants to reach, such as 70 scores of regulations and 80 scores of management, 75 scores of management and real objects, and the scoring standard storage submodule establishes an initial scoring standard according to related project information input by the user; when the system provides an expert recommendation plan scheme, a plan module detects information input by an input sub-module and a target input sub-module according to states, compares the information with the expert recommendation plan scheme of a plan scheme storage sub-module, provides a plan scheme, and statically inputs the plan scheme into a plan scheme storage sub-module, meanwhile, a cloud database module detects a plan execution success scheme which is most closely matched with information of the input sub-module and the target input sub-module according to the states recorded by other users and is finally qualified, a cloud database information calling module calls the plan execution success scheme uploaded by other users stored in a cloud database, the plan execution success scheme is output through an information output module according to the state detection input sub-module and the target input sub-module, and is selected by the users (when the plan execution success scheme is stored, the recorded information comprises the initial skill level of a user, information input by the user plan execution input sub-module, the task completion condition of each node and the like, the information can be matched with the information of the user state detection input sub-module and the information of the target input sub-module to give the closest and output associated information, but the cloud database does not give the private information of other users), and the plan scheme is associated to the user basic information storage sub-module and output by the information output module.
In different learning periods, the intelligent plan and node score standard storage submodule outputs the learning plan and node score standard of the node through a comparison module and an information output module according to the learning condition of a secondary constructor in the learning period; comparing the current learning condition and score with the node score standard through a comparison module, judging whether the learning condition reaches the target established by the comparison module, if the learning condition reaches the target standard, the node target is qualified, otherwise, returning to the learning of the content of the evaluation item storage submodule of the previous node, simultaneously prompting a client to perform the status detection and the efficiency detection again according to the status information of the previous node and the information input by a target input submodule so as to obtain an initial status detection value and an efficiency value, comparing the obtained information with an expert recommendation plan scheme (or a plan execution success scheme stored in a cloud database) of a plan scheme storage submodule, re-providing a recommendation plan scheme, and if the target criterion is reached when the date node is finally completed, the node is finally qualified, otherwise, outputting a failure result, and the comparison information generated in the system operation process is stored in the user basic information storage submodule of the big data storage module and uploaded to the cloud database module, and finally the qualified execution scheme is collected as the cloud database module recommendation plan scheme.
Similarly, the invention can be used to improve the spoken English or expressive ability of a person.
The embodiments described herein are merely illustrative of the spirit of the invention and various modifications, additions and substitutions may be made by those skilled in the art without departing from the spirit of the invention or exceeding the scope of the invention as defined in the accompanying claims.
Claims (4)
1. A scoring system for improving a skill level of a person, comprising: the system comprises an input module, a big data storage module, a planning module, a comparison module, an information output module and a cloud database information calling module;
the input module comprises a state detection input sub-module, an efficiency detection input sub-module, a target input sub-module and a plan execution input sub-module; the system comprises a state detection and entry submodule, an evaluation item storage submodule, an efficiency detection and entry submodule, a target entry submodule and a plan execution and entry submodule, wherein the state detection and entry submodule is connected with an evaluation item storage submodule and is used for entering an initial skill level of a user, the efficiency detection and entry submodule is used for evaluating and detecting the self efficiency of a detection item selected by the user, the detection mode is an answer mode, the entry information of the target entry submodule comprises a final target required to be completed by the user and a final completion date, and the plan execution and entry submodule is used for entering the information of the task completion condition of the user at each plan node;
the big data storage module comprises a user basic information storage submodule, an evaluation project storage submodule, a grading standard storage submodule, an efficiency detection content storage submodule, a plan scheme storage submodule and an intelligent plan and node grading standard storage submodule, wherein the efficiency detection content storage submodule stores the question of work efficiency information of a detection project, and the storage information of the plan scheme storage submodule comprises an expert recommendation plan scheme;
the user basic information storage submodule is used for storing information input by the state detection input submodule, the efficiency detection input submodule, the target input submodule and the plan execution input submodule, the user basic information storage submodule is connected with the cloud database through the Internet according to the user ID,
the planning module is used for comparing an initial state detection value obtained by the state detection input sub-module, an efficiency value obtained by the efficiency detection input sub-module and information input by the target input sub-module with an expert recommended planning scheme of the planning scheme storage sub-module, giving a planning scheme and statically inputting the planning scheme into the planning scheme storage sub-module; the planning scheme is divided into a plurality of nodes in sequence by time and corresponding node targets are formulated;
the comparison module is used for comparing the task completion condition information of the user in the plan node, which is input by the plan execution input sub-module, with the node target in the plan scheme stored in the plan scheme storage sub-module; if the node target is reached, the node task is completed successfully, the comparison module sends a comparison result to the information output module, and the information output module outputs information for entering the next node to learn; if the node target is not reached, the node task is failed to complete, and the comparison module sends information for re-planning to the planning module; the planning module calls the plan execution entry sub-module to obtain task completion condition information of a node of which the target is not completed by a user after receiving the information of the re-designated plan, calls the target entry sub-module to obtain target information, prompts the client to enter the state detection entry sub-module and the efficiency detection entry sub-module again so as to obtain an initial state detection value and an efficiency value, compares the initial state detection value and the efficiency value with an expert recommended plan scheme of the plan scheme storage sub-module again, and gives a new plan scheme again; the user learns according to the new plan scheme, and after reaching a plan node of the new plan scheme, the task completion condition is recorded through the plan execution recording sub-module; the comparison module compares the data and determines to enter the learning of the next node or reformulate a plan scheme according to the comparison result until the final completion date is reached, and judges whether the final target is reached; if the final target is reached, the information output module outputs the information that the task is successful, the process of the user executing the plan scheme is recorded and forms a plan execution success scheme, and the plan execution success scheme is stored in the plan scheme storage sub-module; uploading the information to a cloud database through the Internet, and if the final target is not reached, outputting information of task failure by an information output module;
when the system provides an expert recommendation plan scheme, the cloud database information calling module calls plan execution success schemes uploaded by other users and stored in the cloud database, the plan execution success schemes which are matched with the closest plan execution success scheme are detected according to the state detection input sub-module and the target input sub-module, and the plan execution success schemes are output through the information output module and are provided for the users to select.
2. A scoring system for improving a skill level of a person according to claim 1, wherein: the content of the comparison information of the task completion condition of the planning node generated by the comparison module and the node target in the planning scheme stored in the planning scheme storage submodule comprises whether the target is completed or not, and the comparison information is stored in the user basic information storage submodule.
3. A scoring system for improving a skill level of a person according to claim 1, wherein: after the intelligent plan and node score standard storage submodule analyzes the target input by the user, one or more different plan execution success schemes are provided for the user to select, and the requirements of different users on the target plan are met.
4. A scoring system for improving a skill level of a person according to any one of claims 1-3, wherein: the system also comprises a user registration module and a user login module, wherein the user registration module is used for registering a user account, the user registration module records the basic information of the user, and the basic information of the user is stored in the basic information storage submodule of the user;
the user login module is connected with the input module and calls data of the user basic information storage submodule.
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