CN111444244B - Big data information management system - Google Patents
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Abstract
The invention discloses a big data information management system, which comprises an information input unit, a data processing unit, a storage module, a chief end, a distribution module, a teacher end, an identity identification module and an attendance notification module, wherein the information input unit is used for inputting student data, the student data comprises a school number, a student name, a sex, a class, scores of various departments, an instructor identity number of various departments and a parent identity number, and the school number, the student name, the sex, the class, the scores of various departments, the instructor identity number of various departments and the parent identity number are transmitted as unified associated data; the invention completes the division and statistics of big data of students in a school, carries out class evaluation processing according to the scores, is more efficient and convenient, can know the score states of all the students in the class according to the average score of the class, and then knows the learning states of the students by comparing with the previous scores of the students, so that the students can see clearly and intuitively when checking the scores, and the information point-to-point pushing is realized.
Description
Technical Field
The invention belongs to the field of big data management, relates to a big data technology, and particularly relates to a big data information management system.
Background
The big data refers to a data set which cannot be captured, managed and processed by a conventional software tool within a certain time range, is a massive, high-growth rate and diversified information asset which can have stronger decision-making power, insight discovery power and process optimization capability only by a new processing mode, technically, the relation between the big data and cloud computing is as inseparable as the front and back surfaces of a coin, the big data cannot be processed by a single computer, a distributed architecture is required, the big data is characterized by being subjected to distributed data mining, but the big data depends on distributed processing, a distributed database, cloud storage and virtualization technology of the cloud computing, the big data comprises structured, semi-structured and unstructured data, the unstructured data increasingly becomes a main part of the data, the current society is a high-speed developing society, the technology is developed, information circulation is realized, communication among people is more and more intimate, life is more and more convenient, big data is a product of the high-technology era, the big data is an expression of the internet in the current stage, the data which is difficult to collect and use originally is easy to utilize under the setback of a technical innovation large screen represented by cloud computing, and the big data can gradually create more value for human beings through continuous innovation of various industries.
However, at present, division of student score information in a school is not careful enough, statistics processing cannot be performed on each department score of each class of each grade, class evaluation processing needs to be performed manually according to the score, current learning states of students cannot be judged according to class score conditions and past scores of the students, point-to-point information pushing cannot be performed, score information of the students and attendance conditions need to be recorded and counted manually by teachers and problem feedback is performed to parents one by one, and further improvement and reinforcement are needed; in order to solve the above-mentioned drawbacks, a solution is now provided.
Disclosure of Invention
The invention aims to provide a big data information management system.
All the technical problems solved by the invention are as follows:
(1): how to realize the statistics of the scores of each class of each grade and the class optimization treatment according to the scores;
(2): judging the qualified state of each department of each student according to the average score of each department under the class, comparing the prior highest subject score of each student with the subject examination score of the current time, and judging the learning state of the student;
(3): how to realize statistics on student problems and finish point-to-point information push.
The purpose of the invention can be realized by the following technical scheme:
a big data information management system comprises an information input unit, a data processing unit, a storage module, a family terminal, a distribution module, a teacher terminal, an identity recognition module and an attendance notification module;
the information input unit is used for inputting student data, and student data includes school number, student name, sex, class, each branch of academic or vocational study mark, each tutor identity serial number, head of a family identity serial number to with school number, student name, sex, class, each branch of academic or vocational study mark, each tutor identity serial number, head of a family identity serial number transmit as unified associated data, storage module is used for saving student data, adds the information of academic or vocational study when the storage, data processing unit is used for carrying out the calculation according to the student mark of school and handles, and specific calculation processing step shows:
s01: taking the school number of the student as associated retrieval data, taking the school number as a variable and marking the number as ST;
SS 01: when the ST is 1101, calling all examination scores of the students with the study number information of 1101, wherein the examination scores comprise Chinese examination scores, mathematic examination scores, English examination scores, political examination scores, historical examination scores, geographical examination scores, physical examination scores, chemical examination scores and biological examination scores;
SS 02: performing cumulative superposition on Chinese examination scores, mathematical examination scores, English examination scores, political examination scores, historical examination scores, geographic examination scores, physical examination scores, chemical examination scores and biological examination scores to obtain a total score of a student with an academic number of 1101;
SS 03: when the ST is 1102, calling all examination scores of students with the study number information of 1102, and specifically processing the test scores with SS01 and SS 02;
SS 04: when the ST is 1103, calling all examination scores of the student with the number information of 1103, wherein the specific processing contents are SS01 and SS 02;
s04: acquiring score information of students under each school number, including scores of all departments and total scores, and performing shift judgment processing on the students according to the school number;
when the school number is 1101, the classmate belongs to a grade one class, and score information of the classmate with the school number of 1101 is added to the grade one class;
when the school number is 1102, the classmate belongs to a grade one class, and the score information of the classmate with the school number of 1102 is added into the grade one class;
when the school number is 1201, the classmate belongs to the grade one and the grade two, and the score information of the classmate with the school number of 1201 is added into the grade one and the grade two;
when the school number is 1301, the classmate belongs to grade three, and the score information of the classmate with the school number of 1301 is added to grade three;
when the school number is 1401, the classmate belongs to a grade four class, the score information of the classmate with the school number of 1401 is added into the grade four class, and the rest is done;
s05: performing score ranking on students in the same class, wherein the ranking comprises ranking of each department score and ranking of a total score, and then performing the ranking of the total score of the same grade and ranking of each department score of the same grade;
s06: acquiring Chinese scores, mathematic scores, English scores, political scores, historical scores, geographic scores, physical scores, chemical scores and biological scores of different students in the same class;
s06: adding the Chinese scores of different students and dividing the Chinese scores by the total number of the classes to obtain class Chinese average scores, adding the mathematic scores of different students and dividing the mathematic scores by the total number of the classes to obtain class mathematic average scores, adding the English scores of different students and dividing the English scores of different students by the total number of the classes to obtain class English average scores, adding the political scores of different students and dividing the political scores of different students by the total number of the classes to obtain class history average scores, adding the geographical scores of different students and dividing the geographical scores of different students by the total number of the classes to obtain class geographical average scores, adding the physical scores of different students and dividing the physical scores of different students by the total number of the classes to obtain class physical average scores, and performing analogization to obtain all branch average scores in the classes;
s07: judging and processing the scores of all the departments of each student according to the average scores of all the departments in the class:
when the school number is 1101, comparing the scores of all subjects with the school number of 1101 with the scores of all the subjects in the class;
when the Chinese achievement of the student with the school number of 1101 is more than or equal to the average time division of the class Chinese, the student is marked as green;
when the Chinese score of the student with the school number of 1101 is smaller than the average time division of the class Chinese, marking the score as red;
when the mathematic score of the student with the school number of 1101 is more than or equal to the average score of the class mathematics, the student is marked as green;
when the math score of the student with the school number 1101 is smaller than the average score of the class math, the student is marked with red, and the like, and the comparison processing of the scores of all the departments of the student is the same as S07;
s08: the number of the student is used as associated retrieval data, and the past examination score information of the student corresponding to the number of the student is called through a storage module;
s09: comparing the previous Chinese scores to obtain personal Chinese highest scores, comparing the previous mathematic scores to obtain personal mathematic highest scores, comparing the previous English scores to obtain personal English highest scores, and analogizing the same, wherein the highest scores of other subjects are obtained as above;
s10: subtracting the current Chinese score, mathematic score, English score, political score, historical score, geographical score, physical score, chemical score and biological score of the student from the previous Chinese highest score, mathematic highest score, English highest score, political highest score, historical highest score, geographical highest score, physical highest score, chemical highest score and biological highest score in a one-to-one correspondence manner to obtain a Chinese score difference, a mathematic score difference, an English score difference, a political score difference, a historical score difference, a geographical score difference, a physical score difference, a chemical score difference and a biological score difference in sequence;
s11: when the difference of the Chinese scores is more than or equal to-10, judging that the student is in a backstepping state;
when the difference value of the Chinese scores is more than or equal to 0 and less than-10, judging that the student is in a stable state;
and when the difference value of the Chinese scores is greater than 0, judging that the student is in a progress state.
S12: and adding the total scores of all students in the same class, and dividing the total scores by the number of the students in the class to obtain the average score of the subjects in the class.
Further, the identity identification module is used for identifying fingerprint information of students.
Furthermore, the attendance notification module presets fingerprint information of students and attendance standard time, associates the fingerprint information of the students with corresponding school numbers, is used for normal school-entering records of the students, and specifically includes the following record processing steps:
h01: acquiring fingerprint information of students in real time;
h02: matching the student fingerprint information acquired in real time with preset fingerprint information;
if the pairing is successful, acquiring the current time, and judging the current sign-in state according to the sign-in standard time;
when the check-in time is later than the check-in standard time, the student is judged to be in a late state, a sending instruction is generated, and the student number and late information are sent to the distribution module;
and when the check-in time is not later than the check-in standard time, judging that the student is in a normal school-in state.
Further, the head of a family end has bound head of a family identity serial number for the head of a family with mobile terminal for receive the student data that corresponds the student, teacher end has bound tutor identity serial number for teacher with mobile terminal for receive the student data that corresponds the student in the class, the distribution module is used for carrying out classification data propelling movement according to the head of a family end and teacher's serial number of binding and handles, and concrete classification propelling movement processing step is as follows:
the method comprises the following steps: acquiring a parent identity number of a home agent;
step two: taking the parent identity number as a retrieval basis, retrieving a school number associated with the parent identity number from the storage module, and sending all student data and late arrival information under the school number to the keeper;
step three: acquiring the identity number of a tutor at a teacher end;
step four: and taking the tutor identity number as a retrieval basis, retrieving all school numbers associated with the tutor identity number from the storage module, and sending student data and late information under the school numbers to the teacher end.
Further, the storage module is further configured to perform class optimization processing according to the average score of the total subjects in the current school period, and the optimization processing step includes:
r01: acquiring current school date information, taking the school date information as a retrieval basis, and calling general subject average scores of all classes in the school date;
r02: the ranking was performed according to the average score of the subjects of each class, and the class with the first average score of the subjects was rated as the superior class.
The invention has the following beneficial effects;
(1): according to the invention, class classification statistics is carried out on all students in a school through a data processing unit, the scores of the students in the same class in the same grade are classified in a class area, the scores of all the students in the same grade are obtained, the total scores of all the students in the same grade are added and divided by the number of the class persons to obtain the total subject average score of the grade, the grade evaluation treatment is carried out according to the total subject average score in the current school period, the school period information is used as a retrieval basis, the total subject average scores of all the classes in the school period are called, ranking is carried out according to the total subject average score of each class, the first class of the total subject average score column is evaluated as an excellent class, the division statistics of big data of the students in the school is completed, and the class evaluation treatment is carried out according to the scores, so that the system is more efficient and convenient;
(2): the invention firstly obtains the scores of different students in the same class, adds the Chinese scores of different students and divides the sum of the number of the total number of the class to obtain the average score of class Chinese, processes by analogy to obtain the average score of each class in the class, judges and processes the scores of each class of each student according to the average score of each class in the class, marks the scores as qualified when the Chinese scores of the students are more than or equal to the average score of class Chinese, marks the scores as unqualified when the Chinese scores of the students are less than the average score of class Chinese, further takes the student numbers as associated retrieval data, calls the past score information of the students with the corresponding numbers through a storage module, compares the past Chinese scores to obtain the highest score of individual Chinese, compares the past mathematic scores to obtain the highest score of individual mathematic, subtracts the current scores of the students from the past scores one by one, obtaining the score difference value between the current score and the highest score of each department in the past, when the Chinese score difference value is more than or equal to-10, judging that the student is in a backstepping state, when the Chinese score difference value is more than or equal to 0 and less than-10, judging that the student is in a stable state, when the Chinese score difference value is more than 0, judging that the student is in a progress state, firstly, knowing the score state of each department of the student in the class through the average score of the class, then, knowing the learning state of the student by comparing with the past score of the student, and being clearer and more intuitive when checking;
(3): the invention carries out classified data push processing according to the binding number of the president end and the teacher end through the distribution module, takes the parent identity number as a retrieval basis, retrieves the study number associated with the parent identity number from the storage module, sends all student data and late information under the study number to the president end, takes the tutor identity number as a retrieval basis, retrieves all study numbers associated with the tutor identity number from the storage module, sends the student data and the late information under the study number to the teacher end, and the parent end and the teacher end can receive all information data of corresponding students, including all discipline scores and learning states, thus finishing the point-to-point push of information.
Drawings
In order to facilitate understanding for those skilled in the art, the present invention will be further described with reference to the accompanying drawings.
FIG. 1 is a block diagram of the system of the present invention.
Detailed Description
As shown in fig. 1, a big data information management system includes an information entry unit, a data processing unit, a storage module, a family terminal, a distribution module, a teacher terminal, an identity recognition module, and an attendance notification module;
the information input unit is used for inputting student data, the student data include school number, student name, sex, class, each branch mark, each branch tutor identity number, head of a family identity number, and with the school number, student name, sex, class, each branch mark, each branch tutor identity number, head of a family identity number as unified associated data and transmit, storage module is used for storing student data, add the information of the period of a school when the storage, data processing unit is used for carrying out calculation processing according to the student mark of school, concrete calculation processing step performance is:
s01: taking the school number of the student as associated retrieval data, taking the school number as a variable and marking the number as ST;
SS 01: when the ST is 1101, calling all examination scores of the students with the study number information of 1101, wherein the examination scores comprise Chinese examination scores, mathematic examination scores, English examination scores, political examination scores, historical examination scores, geographical examination scores, physical examination scores, chemical examination scores and biological examination scores;
SS 02: performing accumulative superposition on Chinese examination scores, mathematical examination scores, English examination scores, political examination scores, historical examination scores, geographic examination scores, physical examination scores, chemical examination scores and biological examination scores to obtain a general score of a student with a student number of 1101;
SS 03: when the ST is 1102, calling all examination scores of students with the study number information of 1102, and specifically processing the test scores with SS01 and SS 02;
SS 04: when the ST is 1103, calling all examination scores of the student with the number information of 1103, wherein the specific processing contents are SS01 and SS 02;
s04: acquiring score information of students under each school code, including scores of all subjects and total scores, and performing class-dividing judgment processing on the students according to the school codes;
when the school number is 1101, the classmate belongs to a grade one class, and the score information of the classmate with the school number of 1101 is added to the grade one class;
when the school number is 1102, the classmate belongs to a grade one class, and the score information of the classmate with the school number of 1102 is added into the grade one class;
when the school number is 1201, the classmate belongs to the grade one and the grade two, and the score information of the classmate with the school number of 1201 is added into the grade one and the grade two;
when the school number is 1301, the classmate belongs to grade three, and the score information of the classmate with the school number of 1301 is added to grade three;
when the school number is 1401, the classmate belongs to a grade four class, the score information of the classmate with the school number of 1401 is added into the grade four class, and the rest is done;
s05: performing score ranking on students in the same class, wherein the ranking comprises ranking of scores of all departments and ranking of total scores, and then performing ranking of total scores of the same grade and ranking of scores of all departments of the same grade;
s06: acquiring Chinese scores, mathematic scores, English scores, political scores, historical scores, geographic scores, physical scores, chemical scores and biological scores of different students in the same class;
s06: adding the Chinese scores of different students and dividing the Chinese scores by the total number of the classes to obtain class Chinese average scores, adding the mathematic scores of different students and dividing the mathematic scores by the total number of the classes to obtain class mathematic average scores, adding the English scores of different students and dividing the English scores of different students by the total number of the classes to obtain class English average scores, adding the political scores of different students and dividing the political scores of different students by the total number of the classes to obtain class history average scores, adding the geographical scores of different students and dividing the geographical scores of different students by the total number of the classes to obtain class geographical average scores, adding the physical scores of different students and dividing the physical scores of different students by the total number of the classes to obtain class physical average scores, and performing analogization to obtain all branch average scores in the classes;
s07: judging and processing the scores of all the departments of each student according to the average scores of all the departments in the class:
when the school number is 1101, comparing the scores of all subjects with the school number of 1101 with the scores of all the subjects in the class;
when the Chinese score of the student with the school number of 1101 is more than or equal to the average time division of the class Chinese, the student is marked as green;
when the Chinese score of the student with the school number of 1101 is smaller than the average time division of the class Chinese, marking the score as red;
when the mathematic score of the student with the school number of 1101 is more than or equal to the average score of the class mathematics, the student is marked as green;
when the math score of the student with the school number 1101 is smaller than the average score of the class math, the student is marked with red, and the like, and the comparison processing of the scores of all the departments of the student is the same as S07;
s08: the student numbers are used as associated retrieval data, and the past examination score information of students corresponding to the student numbers is called through a storage module;
s09: comparing the previous Chinese scores to obtain personal Chinese highest scores, comparing the previous mathematic scores to obtain personal mathematic highest scores, comparing the previous English scores to obtain personal English highest scores, and analogizing the same, wherein the highest scores of other subjects are obtained as above;
s10: subtracting the current Chinese score, mathematic score, English score, political score, historical score, geographical score, physical score, chemical score and biological score of the student from the previous Chinese highest score, mathematic highest score, English highest score, political highest score, historical highest score, geographical highest score, physical highest score, chemical highest score and biological highest score in a one-to-one correspondence manner to obtain a Chinese score difference, a mathematic score difference, an English score difference, a political score difference, a historical score difference, a geographical score difference, a physical score difference, a chemical score difference and a biological score difference in sequence;
s11: when the difference of the Chinese scores is more than or equal to-10, judging that the student is in a backstepping state;
when the difference value of the Chinese scores is more than or equal to 0 and less than-10, the student is judged to be in a stable state;
and when the difference of the Chinese scores is larger than 0, judging that the student is in a progress state.
S12: adding the total scores of all students in the same class, and dividing the total scores by the number of the students in the class to obtain the average score of the subjects in the class;
the identity recognition module is used for recognizing fingerprint information of students; attendance report module has preset student's fingerprint information and has signed in standard time to fingerprint information and the corresponding academic number of student are correlated with, attendance report module is used for student's normal record of learning up, and specific record processing step includes:
h01: acquiring fingerprint information of students in real time;
h02: matching the student fingerprint information acquired in real time with preset fingerprint information;
if the pairing is successful, acquiring the current time, and judging the current sign-in state according to the sign-in standard time;
when the check-in time is later than the check-in standard time, the student is judged to be in a late state, a sending instruction is generated, and the student number and late information are sent to the distribution module;
when the check-in time is not later than the check-in standard time, the student is judged to be in a normal check-in state;
the family end has bound the head of a family identity serial number for the head of a family with mobile terminal for receive the student data that corresponds the student, the teacher end has bound tutor identity serial number for the teacher with mobile terminal, be used for receiving the student data that corresponds the student in class, the distribution module is used for carrying out classification data propelling movement according to the family end and the serial number of binding of teacher end and handles, specific classification propelling movement processing step is as follows:
the method comprises the following steps: acquiring a parent identity number of a home agent;
step two: taking the parent identity number as a retrieval basis, retrieving a school number associated with the parent identity number from the storage module, and sending all student data and late arrival information under the school number to the keeper;
step three: acquiring the identity number of a tutor at a teacher end;
step four: the tutor identity number is used as a retrieval basis, all school numbers related to the tutor identity number are retrieved from the storage module, and student data and late information under the school numbers are sent to the teacher end;
the storage module is also used for carrying out class optimization evaluation according to the average score of the total subjects in the current school term, and the optimization evaluation processing steps comprise:
r01: acquiring current school date information, taking the school date information as a retrieval basis, and calling the general subject average scores of all classes in the school date;
r02: the ranking was performed according to the average score of the subjects of each class, and the class with the first average score of the subjects was rated as the superior class.
A big data information management system is characterized in that when in work, fingerprints of students are obtained through identification of an identity identification module, an attendance notification module matches the fingerprint information preset in a database according to the fingerprint information and the obtained fingerprint information, when the matching is successful, the attendance notification module checks the attendance successfully and judges according to the attendance time, when the actual attendance time is later than standard time, the students are in a late arrival state, a sending instruction is generated to send the late arrival information and the student numbers to a distribution module, when the actual attendance time is not later than the standard time, the students are in a normal arrival state, the students record own numbers, the names of the students, the sexes, the grades, the identity numbers of the tutors, the parents and the numbers of the students, the names, the sexes, the grades, the scores of the tutors, the teacher identities and the parents through an information recording unit, the data processing unit carries out statistical processing on the scores of the students according to the school numbers, the scores of all subjects of the students are accumulated together to obtain a total score, the total scores of all the students in the same class are added and divided by the number of the students in the class to obtain the average score of the total subjects of the class, class evaluation processing is carried out according to the average score of the total subjects under the current school time, the information of the school time is used as a retrieval basis, the average score of the total subjects of all the classes under the school time is called, ranking is carried out according to the average score of the total subjects of all the classes, the first class of the total subject score list is evaluated as an excellent class, class classification statistics is carried out on all the students in the school according to the school numbers, the score conditions of the students in the same class and the same class are classified under a class area, and the Chinese scores, the mathematical scores, the English scores, the political scores, the historical scores and the historical scores of the students in different classes in the same class are obtained, Geographic score, physical score, chemical score and biological score, adding the Chinese scores of different students and dividing the number of the class total to obtain class Chinese average score, adding the mathematic scores of different students and dividing the class total number of the class total to obtain class mathematic average score, analogizing to obtain the class Chinese average score, judging the class scores of the students according to the class Chinese average score, marking the class Chinese average score as qualified when the Chinese score of the student is greater than or equal to the class Chinese average score, marking the class Chinese average score as unqualified when the Chinese score of the student is less than the class Chinese average score, further taking the student number as associated retrieval data, calling the past examination score information of the student corresponding to the number through a storage module, comparing the past Chinese scores to obtain the personal Chinese highest score, comparing the past mathematic scores to obtain the personal mathematic highest score, and analogizing, the current Chinese score, the mathematic score, the English score, the political score, the historical score, the geographic score, the physical score, the chemical score and the biological score of the student are correspondingly subtracted from the previous Chinese highest score, the mathematic highest score, the English highest score, the political highest score, the historical highest score, the geographic highest score, the physical highest score, the chemical highest score and the biological highest score one by one to obtain the score difference value of the current score and the previous highest score of each department, when the Chinese score difference value is more than or equal to-10, the student is judged to be in a backstep state, when the Chinese score difference value is more than or equal to 0 and less than-10, the student is judged to be in a stable state, when the Chinese score difference value is more than 0, the student is judged to be in a progress state, each subject judgment mode is the same as above, the score states of each department of the student in each class are known through the average score of the class, and then the score of the student is compared with the previous score, learning states of students per se are known, the distribution module is used for carrying out classified data pushing processing according to binding numbers of the master terminal and the teacher terminal, the parent identity numbers are used as retrieval bases, the student numbers associated with the parent identity numbers are retrieved from the storage module, all student data and late arrival information under the student numbers are sent to the master terminal, the tutor identity numbers are used as retrieval bases, all student numbers associated with the tutor identity numbers are retrieved from the storage module, the student data and the late arrival information under the student numbers are sent to the teacher terminal, and therefore point-to-point pushing of information is completed.
The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the invention as defined in the following claims.
Claims (5)
1. A big data information management system is characterized by comprising an information input unit, a data processing unit, a storage module, a family terminal, a distribution module, a teacher terminal, an identity recognition module and an attendance notification module;
the information input unit is used for inputting student data, and student data includes school number, student name, sex, class, each branch of academic or vocational study mark, each tutor identity serial number, head of a family identity serial number to with school number, student name, sex, class, each branch of academic or vocational study mark, each tutor identity serial number, head of a family identity serial number transmit as unified associated data, storage module is used for saving student data, adds the information of academic or vocational study when the storage, data processing unit is used for carrying out the calculation according to the student mark of school and handles, and specific calculation processing step shows:
s01: taking the school number of the student as associated retrieval data, taking the school number as a variable and marking the number as ST;
SS 01: when the ST is 1101, calling all examination scores of the students with the study number information of 1101, wherein the examination scores comprise Chinese examination scores, mathematical examination scores, English examination scores, political examination scores, historical examination scores, geographic examination scores, physical examination scores, chemical examination scores and biological examination scores;
SS 02: performing cumulative superposition on Chinese examination scores, mathematical examination scores, English examination scores, political examination scores, historical examination scores, geographic examination scores, physical examination scores, chemical examination scores and biological examination scores to obtain a total score of a student with an academic number of 1101;
SS 03: when the ST is 1102, calling all examination scores of students with the study number information of 1102, and specifically processing the test scores with SS01 and SS 02;
SS 04: when the ST is 1103, calling all examination scores of the student with the number information of 1103, wherein the specific processing contents are SS01 and SS 02;
s04: acquiring score information of students under each school code, including scores of all subjects and total scores, and performing class-dividing judgment processing on the students according to the school codes;
when the school number is 1101, the classmate belongs to a grade one class, and the score information of the classmate with the school number of 1101 is added to the grade one class;
when the school number is 1102, the classmate belongs to a grade one class, and the score information of the classmate with the school number of 1102 is added into the grade one class;
when the school number is 1201, the classmate belongs to the grade one and the grade two, and the score information of the classmate with the school number of 1201 is added into the grade one and the grade two;
when the school number is 1301, the classmate belongs to grade three, and the score information of the classmate with the school number of 1301 is added to grade three;
when the school number is 1401, the classmate belongs to a grade four class, the score information of the classmate with the school number of 1401 is added into the grade four class, and the rest is done;
s05: performing score ranking on students in the same class, wherein the ranking comprises ranking of scores of all departments and ranking of total scores, and then performing ranking of total scores of the same grade and ranking of scores of all departments of the same grade;
s06: acquiring Chinese scores, mathematic scores, English scores, political scores, historical scores, geographic scores, physical scores, chemical scores and biological scores of different students in the same class;
s06: adding the Chinese scores of different students and dividing the Chinese scores by the total number of the classes to obtain class Chinese average scores, adding the mathematic scores of different students and dividing the mathematic scores by the total number of the classes to obtain class mathematic average scores, adding the English scores of different students and dividing the English scores of different students by the total number of the classes to obtain class English average scores, adding the political scores of different students and dividing the political scores of different students by the total number of the classes to obtain class history average scores, adding the geographical scores of different students and dividing the geographical scores of different students by the total number of the classes to obtain class geographical average scores, adding the physical scores of different students and dividing the physical scores of different students by the total number of the classes to obtain class physical average scores, and performing analogization to obtain all branch average scores in the classes;
s07: judging and processing the scores of all the departments of each student according to the average scores of all the departments in the class:
when the school number is 1101, comparing the scores of all subjects with the school number of 1101 with the scores of all the subjects in the class;
when the Chinese score of the student with the school number of 1101 is more than or equal to the average time division of the class Chinese, the student is marked as green;
when the Chinese score of the student with the school number of 1101 is smaller than the average time division of the class Chinese, marking the score as red;
when the mathematic score of the student with the school number of 1101 is more than or equal to the average score of the class mathematics, the student is marked as green;
when the math score of the student with the school number 1101 is smaller than the average score of the class math, the student is marked with red, and the like, and the comparison processing of the scores of all the departments of the student is the same as S07;
s08: the number of the student is used as associated retrieval data, and the past examination score information of the student corresponding to the number of the student is called through a storage module;
s09: comparing the previous Chinese scores to obtain personal Chinese highest scores, comparing the previous mathematic scores to obtain personal mathematic highest scores, comparing the previous English scores to obtain personal English highest scores, and analogizing the same, wherein the highest scores of other subjects are obtained as above;
s10: subtracting the current Chinese score, mathematic score, English score, political score, historical score, geographical score, physical score, chemical score and biological score of the student from the previous Chinese highest score, mathematic highest score, English highest score, political highest score, historical highest score, geographical highest score, physical highest score, chemical highest score and biological highest score in a one-to-one correspondence manner to obtain a Chinese score difference, a mathematic score difference, an English score difference, a political score difference, a historical score difference, a geographical score difference, a physical score difference, a chemical score difference and a biological score difference in sequence;
s11: when the difference value of the Chinese scores is more than or equal to-10, judging that the student is in a backstepping state;
when the difference value of the Chinese scores is more than or equal to 0 and less than-10, the student is judged to be in a stable state;
when the difference value of the Chinese scores is larger than 0, judging that the student is in a progress state;
s12: and adding the total scores of all students in the same class, and dividing the total scores by the number of the students in the class to obtain the average score of the subjects in the class.
2. The big data information management system according to claim 1, wherein the identification module is configured to identify fingerprint information of a student.
3. The big data information management system according to claim 1, wherein the attendance notification module presets fingerprint information of students and check-in standard time and associates the fingerprint information of students with corresponding school numbers, and is used for normal school-on records of students, and the specific record processing steps include:
h01: acquiring fingerprint information of students in real time;
h02: matching the student fingerprint information acquired in real time with preset fingerprint information;
if the pairing is successful, acquiring the current time, and judging the current sign-in state according to the sign-in standard time;
when the check-in time is later than the check-in standard time, the student is judged to be in a late state, a sending instruction is generated, and the student number and late information are sent to the distribution module;
and when the check-in time is not later than the check-in standard time, judging that the student is in a normal school-in state.
4. The big data information management system according to claim 1, wherein the parental terminal binds a parental identity number to the parental mobile terminal for receiving student data of a corresponding student, the teacher terminal binds a tutor identity number to the teacher mobile terminal for receiving student data of a corresponding student in a class, the assigning module is configured to perform classification data pushing processing according to the binding number of the parental terminal and the tutor terminal, and the specific classification pushing processing steps are as follows:
the method comprises the following steps: acquiring a parent identity number of a home agent;
step two: taking the parent identity number as a retrieval basis, retrieving a school number associated with the parent identity number from the storage module, and sending all student data and late arrival information under the school number to the keeper;
step three: acquiring the identity number of a tutor at a teacher end;
step four: and taking the tutor identity number as a retrieval basis, retrieving all school numbers associated with the tutor identity number from the storage module, and sending student data and late information under the school numbers to the teacher end.
5. The big data information management system according to claim 1, wherein the storage module is further configured to perform a class optimization process according to the average total subject score in the current academic period, and the optimization process step includes:
r01: acquiring current school date information, taking the school date information as a retrieval basis, and calling the general subject average scores of all classes in the school date;
r02: the ranking was performed according to the average score of the subjects of each class, and the class with the first average score of the subjects was rated as the superior class.
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| CN102810198A (en) * | 2011-06-02 | 2012-12-05 | 西安理工大学 | System for compiling fresh classes and arranging student numbers in regular institutions of higher learning |
| CN108648123A (en) * | 2018-07-13 | 2018-10-12 | 江苏开放大学（江苏城市职业学院） | A method of its management network teaching process of the network teaching platform and utilization based on big data |
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| CN102810198A (en) * | 2011-06-02 | 2012-12-05 | 西安理工大学 | System for compiling fresh classes and arranging student numbers in regular institutions of higher learning |
| CN108648123A (en) * | 2018-07-13 | 2018-10-12 | 江苏开放大学（江苏城市职业学院） | A method of its management network teaching process of the network teaching platform and utilization based on big data |
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