CN105653688A - Data statistic method - Google Patents
Data statistic method Download PDFInfo
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- CN105653688A CN105653688A CN201511021627.6A CN201511021627A CN105653688A CN 105653688 A CN105653688 A CN 105653688A CN 201511021627 A CN201511021627 A CN 201511021627A CN 105653688 A CN105653688 A CN 105653688A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2462—Approximate or statistical queries
Abstract
Embodiments of the invention relate to a data statistic method. The method comprises the following steps: receiving first dot matrix image data, sent by a dot matrix recognition device, of a first recognition area; recognizing the first dot matrix image data into first recognition data; obtaining user identity information according to the first recognition data; receiving second dot matrix image data, sent by the dot matrix recognition device, of a second recognition area; recognizing the second dot matrix image data into second recognition data; matching the second recognition data with standard recognition data to obtain first threshold data; receiving third dot matrix image data, sent by the dot matrix recognition device, of a third recognition area; and recognizing the third dot matrix image data into third recognition data, and increasing corresponding threshold data to obtain second threshold data. According to the data statistic method provided by the embodiments of the invention, the automatic high-efficiency test score statistic is realized by utilizing a dot matrix recognition technology.
Description
Technical field
The present invention relates to technical field of data processing, relate in particular to a kind of data statistical approach.
Background technology
Along with the raising of social informatization degree, informationization has been penetrated into each of routine work and lifeAspect.
In education process, teacher wants to understand student to mastery of knowledge situation, often need to be to learningRaw taking an exam. Traditional teaching method, is distributed to student by paper after teacher sets a question, when student answersAfter submit paper, teacher reads and makes comments accounting mark one by one, in the time that paper is a lot, statisticsThe number waste plenty of time estranged, and easily make mistakes. Therefore, informationization need to be introduced in traditional education,Realize the high efficiency fractional statistics that takes an exam of automation by computer.
Summary of the invention
The object of this invention is to provide a kind of data statistical approach, utilize dot matrix recognition technology to realize automationThe high efficiency fractional statistics that takes an exam.
For achieving the above object, the invention provides a kind of data statistical approach, described method comprises:
Receive the first dot matrix image data of the first identified region of dot matrix recognition device transmission;
Be the first identification data by described the first dot matrix image data identification;
Obtain subscriber identity information according to described the first identification data;
Receive the second dot matrix image data of the second identified region of dot matrix recognition device transmission;
Be the second identification data by described the second dot matrix image data identification;
Described the second identification data and standard identification data are carried out to matching treatment, obtain first threshold data;
Receive the thirdly battle array view data of the 3rd identified region of dot matrix recognition device transmission;
Be the 3rd identification data by the described thirdly system of battle formations as data identification, increase corresponding threshold data,To Second Threshold data.
Further, described described the second identification data and standard identification data are carried out to matching treatment,Specifically comprise to first threshold data:
In the time that described the second identification data is identical with standard identification data, increase this standard identification data instituteCorresponding threshold data, obtains first threshold data.
Further, described method also comprises:
Described standard identification data has corresponding threshold data.
Further, described is the 3rd identification data by the described thirdly system of battle formations as data identification, increases correspondingThreshold data, obtain Second Threshold data and specifically comprise:
In the time that the described thirdly system of battle formations is the first symbol as data identification, increase corresponding threshold data,Obtain Second Threshold data.
Further, described is the 3rd identification data by the described thirdly system of battle formations as data identification, increases correspondingThreshold data specifically comprise:
When the described thirdly system of battle formations is second symbol as data identification, do not increase corresponding threshold data.
Further, described is the 3rd identification data by the described thirdly system of battle formations as data identification, increases correspondingThreshold data specifically comprise:
When the described thirdly system of battle formations is the 3rd symbol as data identification, increase described the 3rd symbol quantityThreshold data.
Further, described is the 3rd identification data by the described thirdly system of battle formations as data identification, increases correspondingThreshold data specifically comprise:
When the described thirdly system of battle formations is the 4th symbol as data identification, by corresponding threshold data with described inThe threshold data of the 4th symbol quantity is added, and obtains the threshold data in this region.
Further, described method also comprises:
Described subscriber identity information is set up with described first threshold data, described Second Threshold data respectivelyAssociated;
Respectively described first threshold data, Second Threshold data are carried out to statistical summaries;
By subscriber identity information and corresponding first threshold data combined data, Second Threshold data total amountAccording to showing.
The data statistical approach that the embodiment of the present application provides, utilizes dot matrix recognition technology, can realize automaticallyChange the high efficiency fractional statistics that takes an exam and gather, be difficult for makeing mistakes, accuracy rate is high.
Brief description of the drawings
The flow chart of the data statistical approach that Fig. 1 provides for the embodiment of the present invention;
The schematic diagram of the dot matrix that Fig. 2 provides for the embodiment of the present invention;
The schematic diagram that Fig. 3 encodes for the dot matrix that the embodiment of the present invention provides.
Detailed description of the invention
Below by drawings and Examples, technical scheme of the present invention is described in further detail.
Executive agent in the present invention is terminal device, specifically comprises mobile phone, panel computer, notebook electricityBrain, desktop computer etc. have the terminal device of disposal ability.
The flow chart of the data statistical approach that Fig. 1 provides for the embodiment of the present invention. As shown in Figure 1, thisThe data statistical approach of bright embodiment comprises:
Step 101, the first dot matrix image data of the first identified region that reception dot matrix recognition device sends.
Particularly, the paper with lattice information is divided into three identified regions: the first identified region is examineeIdentification region; The second identified region is objective item identified region; The 3rd identified region is subjective item identificationRegion. Terminal device receives the dot matrix image data that dot matrix recognition device gathers in examinee's identification region.
Wherein, dot matrix is made up of several points, according to the regularly arranged composition of particular algorithm, as shown in Figure 2.For example, every 36 points, permutation and combination becomes a dot matrix. Average distance between 2 is 0.3mm. OftenThe size of individual dot matrix is 1.8mm × 1.8mm. Dot matrix, by special coded system, is representing special seatCursor position information. After the each some coding in dot matrix, obtain dot matrix coded sequence, as shown in Figure 3.The effect of dot matrix is available to coordinate parameters information of dot matrix recognition device, ensures that dot matrix recognition device existsWhile moving in dot matrix region, can record accurately movement locus.
Dot matrix recognition device is specially the dot matrix digital pen with dot matrix recognition function, is built-in with pressure sensingDevice, processor, camera, memory and communication module etc.
When the pressure sensor of dot matrix digital pen detects pressure signal, start camera, nib is passed throughDot matrix region take pictures, obtain dot matrix image data.
Wherein, camera is high-speed camera head, can with the speed of 100 per second to nib processDot matrix is taken pictures.
Dot matrix recognition device sends data by data-interface to terminal device by the view data collecting to be hadTwo kinds of modes, one is real-time transmission, the built-in processor of dot matrix recognition device is in real time by view data solutionAnalyse as data signal, and become standard transmission packet with the pressure sensitivity data real-time coding of pressure sensor transmission,In real time transmission packet is transferred to terminal device by wired or wireless mode by data-interface. In additionOne is non real-time transmission, and processor is data signal by image data analyzing in real time, and and pressure sensingThe pressure sensitivity data encoding that device sends becomes standard transmission packet to be stored in memory, at setting-up time orOther non real-time modes are transmitted the transmission packet of storing in memory by data-interface to terminal device.
Wherein, data-interface is cable data interface or wireless data interface; Cable data interface is speciallyUSB interface, MiniUSB interface, MicroUSB interface, parallel port, serial ports; Wireless data interface is concreteFor blue tooth interface, infrared interface, Wifi interface, 2.4-5.0GHz wave band interface or radio communication connectMouthful.
Step 102 is the first identification data by described the first dot matrix image data identification.
Particularly, terminal device, by after the first dot matrix image Data Analysis, obtains dot array data, according to pointBattle array coding, what confirm the transmission of dot matrix recognition device is the identification data of the first identified region.
Step 103, obtains subscriber identity information according to described the first identification data.
Particularly, the dot matrix coded data obtaining according to step 102, obtains examinee's identity information, exampleAs, name, school, class, the number of examining etc.
Step 104, the second dot matrix image data of the second identified region that reception dot matrix recognition device sends.
Particularly, terminal device receives the dot matrix image of the objective item identified region of dot matrix recognition device transmissionData. The dot array data corresponding to the respective option of objective item, dot matrix recognition device gathers dot matrix imageAfter data, send to terminal device.
Step 105 is the second identification data by described the second dot matrix image data identification.
Particularly, terminal device, by after the second dot matrix image Data Analysis, obtains dot array data, according to pointBattle array coding, what confirm the transmission of dot matrix recognition device is the identification data of the second identified region.
Step 106, carries out matching treatment by described the second identification data and standard identification data, obtainsOne threshold data.
Particularly, standard identification data has corresponding threshold data. When the second identification data and standard knowledgeWhen other data are identical, increase the corresponding threshold data of this standard identification data, obtain first threshold numberAccording to.
For example, the correct option of first topic multiple-choice question the 1st little topic is A option, and full marks are 2 points. WarpIdentification, this topic of examinee " Zhang San " has been selected A option, mates, examinee " Zhang San " with correct optionAdd 2 points.
Step 107, the thirdly battle array view data of the 3rd identified region that reception dot matrix recognition device sends.
Particularly, terminal device receives the dot matrix image of the subjective item identified region of dot matrix recognition device transmissionData. Dot matrix digital pen collection when thirdly battle array view data utilizes dot matrix digital pen to read and make comments paper for teacherDot matrix image data.
Step 108, is the 3rd identification data by the described thirdly system of battle formations as data identification, increases corresponding thresholdValue Data, obtains Second Threshold data.
Particularly, thirdly the system of battle formations is the 3rd identification data as data identification, comprises following several situation:
In the time that the dot array data of image data analyzing is identified as to " √ ", be that the exercise question in this region increasesCorresponding mark, now, the first symbol is specially " √ ".
In the time that the dot array data of image data analyzing is identified as to "×", do not increase this region exercise question pairThe mark of answering, now, second symbol is specially "×".
In the time that the dot array data of image data analyzing is identified as to " 3 ", be that the exercise question in this region increases by 3Point, now, second symbol is specially " 3 ".
In the time that the dot array data of image data analyzing is identified as to " 2 ", it is the exercise question deduction in this region2 points, now, second symbol is specially " 2 ". If these exercise question full marks are " 5 points ", final obtainingBe divided into 3 points.
Finally, each objective item score of student name and this student, subjective item score are set up associated, dividedDo not carry out, after statistical summaries, generating this student's fractional statistics table. In addition, can also be by class or gradeDistribution map is added up and generated to each examinee's score value; The accuracy of each topic is added up, formDistribution map; For each examinee, the similar examination question of examination question high error rate is pushed to student, by wrong instituteThe similar examination question of the examination question that mistake rate is high is pushed to the full classmate of class.
The data statistical approach that the embodiment of the present application provides, utilizes dot matrix recognition technology, can realize automaticallyChange the high efficiency fractional statistics that takes an exam and gather, be difficult for makeing mistakes, accuracy rate is high.
Professional should further recognize, describes in conjunction with embodiment disclosed hereinThe unit of each example and algorithm steps, can come with electronic hardware, computer software or the combination of the twoRealize, for the interchangeability of hardware and software is clearly described, in the above description according to functionComposition and the step of each example have been described in general manner. These functions are come with hardware or software mode actuallyCarry out, depend on application-specific and the design constraint of technical scheme. Professional and technical personnel can be to oftenIndividual specifically should being used for realized described function with distinct methods, but this realization should not be thoughtExceed scope of the present invention.
The method of describing in conjunction with embodiment disclosed herein or the step of algorithm can be used hardware, processingThe software module that device is carried out, or the combination of the two is implemented. Software module can be placed in random access memory(RAM), internal memory, read-only storage (ROM), electrically programmable ROM, electrically erasable ROM,Known any other form in register, hard disk, moveable magnetic disc, CD-ROM or technical fieldStorage medium in.
Above-described detailed description of the invention, carries out object of the present invention, technical scheme and beneficial effectFurther description, institute it should be understood that the foregoing is only the specific embodiment of the present invention and, the protection domain being not intended to limit the present invention, within the spirit and principles in the present invention all, institute doesAny amendment, be equal to replacement, improvement etc., within all should being included in protection scope of the present invention.
Claims (8)
1. a data statistical approach, is characterized in that, described method comprises:
Receive the first dot matrix image data of the first identified region of dot matrix recognition device transmission;
Be the first identification data by described the first dot matrix image data identification;
Obtain subscriber identity information according to described the first identification data;
Receive the second dot matrix image data of the second identified region of dot matrix recognition device transmission;
Be the second identification data by described the second dot matrix image data identification;
Described the second identification data and standard identification data are carried out to matching treatment, obtain first threshold data;
Receive the thirdly battle array view data of the 3rd identified region of dot matrix recognition device transmission;
Be the 3rd identification data by the described thirdly system of battle formations as data identification, increase corresponding threshold data,To Second Threshold data.
2. data statistical approach according to claim 1, is characterized in that, described by described secondIdentification data and standard identification data are carried out matching treatment, obtain first threshold data and specifically comprise:
In the time that described the second identification data is identical with standard identification data, increase this standard identification data instituteCorresponding threshold data, obtains first threshold data.
3. data statistical approach according to claim 1, is characterized in that, described standard identification numberAccording to thering is corresponding threshold data.
4. data statistical approach according to claim 1, is characterized in that, described by the described the 3rdDot matrix image data identification is the 3rd identification data, increases corresponding threshold data, obtains Second Threshold dataSpecifically comprise:
In the time that the described thirdly system of battle formations is the first symbol as data identification, increase corresponding threshold data,Obtain Second Threshold data.
5. data statistical approach according to claim 1, is characterized in that, described by the described the 3rdDot matrix image data identification is the 3rd identification data, increases corresponding threshold data and specifically comprises:
When the described thirdly system of battle formations is second symbol as data identification, do not increase corresponding threshold data.
6. data statistical approach according to claim 1, is characterized in that, described by the described the 3rdDot matrix image data identification is the 3rd identification data, increases corresponding threshold data and specifically comprises:
When the described thirdly system of battle formations is the 3rd symbol as data identification, increase described the 3rd symbol quantityThreshold data.
7. data statistical approach according to claim 1, is characterized in that, described by the described the 3rdDot matrix image data identification is the 3rd identification data, increases corresponding threshold data and specifically comprises:
When the described thirdly system of battle formations is the 4th symbol as data identification, by corresponding threshold data with described inThe threshold data of the 4th symbol quantity is added, and obtains the threshold data in this region.
8. data statistical approach according to claim 1, is characterized in that, described method also comprises:
Described subscriber identity information is set up with described first threshold data, described Second Threshold data respectivelyAssociated;
Respectively described first threshold data, Second Threshold data are carried out to statistical summaries;
By subscriber identity information and corresponding first threshold data combined data, Second Threshold data total amountAccording to showing.
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| CN201511021627.6A CN105653688A (en) | 2015-12-31 | 2015-12-31 | Data statistic method |
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| CN201511021627.6A CN105653688A (en) | 2015-12-31 | 2015-12-31 | Data statistic method |
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Cited By (1)
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|---|---|---|---|---|
| CN108664202A (en) * | 2017-04-02 | 2018-10-16 | 田雪松 | A kind of record generation method, medium and input equipment |
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Effective date of registration: 20180522 Address after: 100029 Beijing Chaoyang District Beichen West Road 69, Junfeng Huating D block 1012 Applicant after: Beijing Chi Lu Management Consulting Co., Ltd. Address before: 100098 Haidian District, Beijing, North Third Ring Road 18, 2, building 803-3. Applicant before: Tian Xuesong |
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