CN115796347A - Education resource investment balance degree prediction method, device, equipment and storage medium - Google Patents
Education resource investment balance degree prediction method, device, equipment and storage medium Download PDFInfo
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Abstract
The application relates to a method, a device, equipment and a storage medium for predicting the input equilibrium degree of educational resources, which relate to the technical field of data processing, and the method comprises the following steps: acquiring the current education resource quantity and the historical year prediction data source of a prediction area; inputting the historical year prediction data source into a preset academic degree analysis model to obtain the number of students in the predicted year; calculating the number of the annual average educational resources based on the current educational resource number and the number of annual students predicted in the prediction area; and if the number of the annual prediction average education resources is larger than the preset standard annual prediction average education resources, calculating the number of the newly added education resources in the predicted years based on the number of the current education resources, the number of students in the predicted years and the number of the standard annual prediction average education resources. The method and the device have the effect of predicting the input balance of the education resources.
Description
Technical Field
The present application relates to the field of data analysis technologies, and in particular, to a method, an apparatus, a device, and a storage medium for predicting an educational resource investment balance.
Background
The education at the present stage has the defects that the problem of unbalanced education resource allocation among areas, cities and countryside, schools and social groups is particularly shown, the development of balanced education resource investment is deeply analyzed, and the tamping foundation of the national education can be better realized.
In the related art, education statistics, assessment and supervision are realized by using systems such as an obligation education quality detection system, an obligation education high-quality balance monitoring system and education development evaluation, and education administration departments, schools and the like are guided to carry out related work.
However, in the above scheme, only the education investment of the historical data can be monitored, but the education resources in the next year cannot be predicted, and further, the situation of shortage of the education resources investment may occur, and meanwhile, the different systems all need to analyze the investment situation of the education resources, so that a large amount of repeated and customized work is brought, and the calculation resources, the storage resources and the capital investment are wasted, so that a technology for predicting the balance degree of the education resources investment is urgently needed.
Disclosure of Invention
In order to realize the prediction of the input equilibrium degree of the education resources, the application provides a method, a device, equipment and a storage medium for predicting the input equilibrium degree of the education resources.
In a first aspect, the present application provides a method for predicting an educational resource investment balance, which adopts the following technical scheme:
a method for predicting the input balance of educational resources comprises the following steps:
acquiring the current education resource quantity and the historical year prediction data source of a prediction area;
inputting the historical year prediction data source into a preset academic degree analysis model to obtain the number of students in the predicted year;
calculating the number of the annual average education resources based on the current education resources and the number of annual students in the prediction area; and if the number of the predicted annual average education resources is larger than the number of the preset standard annual average education resources, calculating the number of the predicted annual newly-added education resources based on the number of the current education resources, the number of the predicted annual students and the number of the standard annual average education resources.
By adopting the technical scheme, the number of students in the predicting area in the future year is predicted through the academic degree analysis model, the current education resource quantity and the historical year prediction data source information in the predicting area are obtained through various modes, the monitoring of the current education resource quantity and the prediction of the education resource balance degree of the predicting year are realized, and the dynamic monitoring and prediction of the education resources in the predicting area are realized.
Optionally, the historical year prediction data source includes student information, population information and economic information corresponding to the prediction region; wherein,
the student information comprises the number of recruits and the number of graduates in the historical year;
the population information comprises the number of household members, the number of resident members, the number of born members and the birth rate of the historical year;
the economic information includes the total economic production value, the urbanization rate, the housing area and the GDP of the historical years.
Optionally, the step of inputting the historical year prediction data source into a preset academic degree analysis model to obtain the number of students in the predicted year includes:
performing data transformation on the historical year prediction data source based on a preset data processing formula to obtain characteristic data information; the data transformation comprises the steps of carrying out logarithm drawing, square calculation and square root calculation on the historical year prediction data source, carrying out window sliding on sequence data, calculating the mean value, calculating the variance, calculating the skewness and calculating the quantile;
inputting the historical annual academic degree information into a correlation coefficient matrix model to obtain weight information of a prediction region;
inputting the weight information of the prediction region into a least square method analysis model to obtain a first prediction student number;
inputting the characteristic data information into a ridge regression fitting model to obtain a second predicted student number;
predicting the number of students in the predicted year based on a Holter linear trend method and a historical year prediction data source to obtain a third predicted number of students;
and carrying out weighted average on the first predicted student number, the second predicted student number and the third predicted student number based on a preset linear weighted average algorithm to obtain the predicted annual student number.
By adopting the technical scheme, the corresponding predicted student number is obtained through a least square method analysis model, a ridge regression analysis model and a Holter linear trend method, and weighted average is carried out on the corresponding predicted student number to obtain the final predicted annual student number, so that the accuracy of calculating the predicted student number is improved.
Optionally, the least square analysis model is:
wherein,
for the first prediction of the number of students, a 0 a 1 …a n Is the fitting coefficient, μ is the error;
by calculating a first prediction of the number of students
And the actual historical number of students y i Fitting a 0 a 1 …a N So that a is 0 a 1 …a N And the e is satisfied to be minimum, wherein,
a after fitting 0 a 1 …a N Substituting the first prediction student number into a least square method analysis model;
the ridge regression analysis model is as follows:
wherein,
for the second prediction of the number of students, a 0 a 1 …a n Is the fitting coefficient, μ is the error;
by calculating a second predicted number of students
And the actual historical number of students y i Fit a while fitting the error of the best fit 0 a 1 …a N So that a is 0 a 1 …a N And the e is satisfied to be minimum, wherein,
| represents the parameter vector a 0 a 1 …a N Norm of (d);
the Holter linear trend method formula is as follows:
L t ＝αX t +(1-α)(L t-1 -T t-1 )
T t ＝β(L t -T t-1 )+(1-β)T t-1
S t+T ＝L t +T t m
wherein, L represents the level, T represents the trend, X is the number value of the actual history student, alpha is the level smooth coefficient, beta is the trend coefficient, and T represents the period;
predicting a third predicted student population for a time period t using sequence data from historical year prediction data sources
The preset linear weighted average algorithm is as follows:
wherein
Is finalThe predicted value is the value of the predicted value,
in order to first predict the number of students,
in order to second predict the number of students,
for the third prediction of student number, ρ 1 ρ 2 ρ 3 Are weighting coefficients.
Optionally, after calculating the number of new educational resources in the predicted year based on the current number of educational resources, the number of students in the predicted year, and the number of standard average student educational resources, the method further includes:
responding to a report template selection trigger action of a user, and acquiring a report template;
and carrying out visual analysis on the quantity of the newly added education resources in the predicted year based on the report template to generate an education resource analysis report.
By adopting the technical scheme, the number of the education resources newly increased in the predicted year is visualized by using the report template, and a user can configure the education resources for the area or school needing to invest the education resources according to the education resource report.
Optionally, the visually analyzing the number of the newly added education resources in the predicted year based on the report template, and generating an education resource analysis report includes:
establishing a data set based on the number of newly added educational resources in the predicted year, the number of current educational resources and the academic degree information in the historical year;
dividing the data set into an educational data set and an assessment data set;
acquiring a corresponding visualization model based on the report template;
inputting the education data set and the evaluation data set into the visual model for training to obtain an optimal visual model;
generating the educational resource analysis report based on the optimal visualization model.
Optionally, the obtaining of the current educational resource amount and the historical annual degree information of the prediction area includes:
responding to a port calling request of a user, calling a port pointed by the port calling request, and receiving the current educational resource quantity and the historical year prediction data source of the prediction area, wherein the ports comprise a database direct connection port, a file interface docking port, a prepositive library docking port, a report form port, an official website acquisition port and a file sharing port.
By adopting the technical scheme, the number of the current education resources and the historical annual academic degree information are acquired by adopting a plurality of port calling modes, so that more data are acquired, and the accuracy of data analysis is improved.
In a second aspect, the present application provides an educational resource investment balance monitoring device, which adopts the following technical scheme:
an educational resource investment balance monitoring device, comprising:
the acquisition module is used for acquiring the current educational resource quantity and the historical year prediction data source of the prediction area;
the input module is used for inputting the historical year prediction data source into a preset academic degree analysis model to obtain the number of students in the predicted year; a first calculation module for calculating the number of annual average educational resources to be predicted based on the current number of educational resources and the number of annual students to be predicted in the prediction region;
and the second calculation module is used for calculating the quantity of the newly added education resources in the predicted year based on the quantity of the current education resources, the quantity of students in the predicted year and the quantity of the standard average education resources if the quantity of the predicted year average education resources is greater than the preset quantity of the standard average education resources.
By adopting the technical scheme, the number of students in the predicting area in the future year is predicted through the academic degree analysis model, the current education resource quantity and the historical year prediction data source information in the predicting area are obtained through various modes, the monitoring of the current education resource quantity and the prediction of the education resource balance degree of the predicting year are realized, and the dynamic monitoring and prediction of the education resources in the predicting area are realized.
In a third aspect, the present application provides an electronic device, which adopts the following technical solutions:
an electronic device comprising a memory and a processor, the memory having stored thereon a computer program that can be loaded by the processor and that executes the method of predicting an educational resource engagement balance of any of the first aspects.
In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solutions:
a computer-readable storage medium storing a computer program that can be loaded by a processor and executes the method for predicting the equilibrium degree of investment in educational resources according to any one of the first aspect.
In summary, the present application includes at least one of the following beneficial technical effects:
1. the number of students in the prediction area in the future year is predicted through the academic degree analysis model, the current education resource quantity and the historical year prediction data source information in the prediction area are obtained through various modes, the monitoring of the current education resource quantity and the prediction of the education resource balance degree of the prediction year are realized, and therefore dynamic monitoring and prediction of the education resources in the prediction area are achieved.
2. And respectively obtaining corresponding predicted student numbers through a least square method analysis model, a ridge regression analysis model and a Holter linear trend method, and carrying out weighted average on the corresponding predicted student numbers to obtain the final predicted annual student number, so that the accuracy of calculating and predicting the student numbers is improved.
Drawings
Fig. 1 is a schematic flowchart of a method for predicting educational resource investment balance according to an embodiment of the present application.
Fig. 2 is a schematic flowchart of the substep S102 in the embodiment of the present application.
FIG. 3 is a schematic flow chart of steps S111-S112 according to the embodiment of the present application
FIG. 4 is a schematic flowchart of the substep of step S112 in the embodiment of the present application
Fig. 5 is a block diagram showing a configuration of an educational resource investment balance prediction apparatus 200 according to an embodiment of the present invention.
Fig. 6 is a block diagram of an electronic device 300 according to an embodiment of the present application.
Detailed Description
The present application is described in further detail below with reference to the attached drawings.
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
In addition, the term "and/or" herein is only one kind of association relationship describing an associated object, and means that there may be three kinds of relationships, for example, a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship, unless otherwise specified.
The embodiment of the application provides a method for predicting the educational resource investment balance degree, the method can be executed by electronic equipment, the electronic equipment can be a server or terminal equipment, the server can be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, and a cloud server for providing cloud computing service. The terminal device may be, but is not limited to, a smart phone, a tablet computer, a desktop computer, etc.
The embodiments of the present application will be described in further detail with reference to the drawings attached hereto. As shown in fig. 1, the main flow of the method is described as follows (steps S101 to S104):
step S101, acquiring the current education resource quantity and the historical year prediction data source of a prediction area;
in this embodiment, the prediction area may be a province, an autonomous region, a direct prefecture city, a certain area of a city, for example, a sunny area in beijing, an area divided by streets, or a school district, or of course, the prediction area may be multiple cities or multiple areas of a city, and this embodiment is not particularly limited.
The current educational resource quantity is the educational resource quantity invested in the prediction area in the past year, wherein the current educational resource quantity comprises human, material and financial resources occupied, used and consumed in the educational process. I.e. the sum of educated human, material and financial resources. The human resources comprise administrative staff number, teaching auxiliary staff number, work attendance staff number, production staff number and the like. The material resources comprise fixed assets, materials and low-value consumable articles in the school. The fixed assets are classified into common fixed assets, fixed assets for teaching and scientific research, fixed assets for other general equipment, and the like.
The historical year prediction data source comprises student information, population information, economic information and other related data information corresponding to the prediction area; the student information comprises the number of students, the number of graduates, the number of students at school, the number of students at class, sequence data and the like; the sequence data is the dimension of the number of the recruits in a certain grade of a certain region history; the population information comprises the number of household registers, the number of resident persons, the number of births, the birth rate and the like; the economic information includes economic production total value, urbanization rate, housing area, GDP, and the like.
Specifically, the obtaining of the current educational resource amount and the historical year prediction data source of the prediction area includes: responding to a port calling request of a user, calling a port pointed by the port calling request, and receiving the current education resource quantity and the historical annual degree information of a prediction area, wherein the ports comprise a database direct connection port, a file interface docking port, a preposed library docking port, a report form filling port, an official network acquisition port and a file sharing port.
In this embodiment, the user may obtain the port call request by a mechanical key triggering manner, or may send the port call request by a virtual key triggering manner; the mechanical key triggering mode can be that the port calling request is automatically sent after the machine is started by pressing a startup key, or the port calling request is sent by pressing a corresponding trigger key again after the machine is started; the virtual key triggering mode can realize the transmission of the port calling request by pressing the related virtual triggering key in the interface of the corresponding software. The electronic device receives a current educational resource amount and historical annual degree information for the prediction region in response to the call port request instruction.
It should be noted that the port call may be called in the following manners:
(1) Directly connecting the databases: the education resource investment balance degree prediction device is directly connected with the database to acquire and monitor the education resource investment;
(2) Pre-library access: the system is in butt joint with an education service system, and the education service system pushes data in real time through a prepositive database;
(3) File interface docking: the education service system calls a data reflux interface of the platform to submit the number of education resources and historical year prediction data source data, and the interface server writes the number of the education resources and the historical year prediction data source data into a preposed database;
(4) Filling a report form: the education resource input balance degree prediction device creates a report form, and a user reports data;
(5) File sharing: providing data files such as execl, csv, txt, sql and the like at regular time, and manually analyzing, cleaning and writing the data files into a preposed database;
(6) Collecting by using an official website: and compiling a crawling program, implementing acquisition of public data of the official website, and writing the crawling data into a preposed database.
It should be noted that, in this embodiment, the port calling method may be adopted, or other port calling methods may also be adopted, which is not specifically limited in this embodiment, and in addition, after receiving the current educational resource quantity of the prediction area and the historical year prediction data source, data needs to be preprocessed, so as to implement unification of formats, units, and the like.
Step S102, inputting a historical year prediction data source into a academic degree analysis model to obtain the number of students in the prediction year;
optionally, as shown in fig. 2, step S102 specifically includes the following sub-steps:
step S1021, performing data transformation on the historical year prediction data source based on a preset data processing formula to obtain characteristic data information; the data transformation comprises the steps of carrying out logarithm drawing, square calculation and square root calculation on the historical year prediction data source, carrying out window sliding on sequence data, calculating the mean value, calculating the variance, calculating the skewness and calculating the quantile;
in this embodiment, the historical year prediction data source is statistics of data at each stage of the last seven years.
Step S1022, inputting the characteristic data information into a least square method analysis model to obtain a first predicted student number; specifically, the least square analysis model is as follows:
wherein,
for the first prediction of the number of students, a 0 a 1 …a n Is the fitting coefficient, μ is the error;
by calculating a first prediction of the number of students
And the actual historical number of students y i Fitting a 0 a 1 …a N So that a is 0 a 1 …a N And the ∈ minimum is satisfied, wherein,
a after fitting 0 a 1 …a N And substituting the model into a least square method analysis model to obtain a first predicted student number.
Step S1023, inputting the characteristic data information into a ridge regression fitting model to obtain a second predicted student number; the calculation method of the predicted value of the ridge regression is consistent with the least square method, the target function is added with a regular term on the basis of general linear regression, the best fitting error is guaranteed, meanwhile, the selection of the coefficient not only needs to obtain a good prediction result on training data, but also needs to fit additional constraint, the coefficient is expected to be as small as possible, and the generalization capability of the model is strong.
Specifically, the ridge regression analysis model is as follows:
wherein,
for the second prediction of the number of students, a 0 a 1 …a n μ is the error for the fitting coefficient;
by calculating a second predicted number of students
And the actual historical number of students y i Fit a while fitting the error of the best fit 0 a 1 …a N So that a is 0 a 1 …a N And the e is satisfied to be minimum, wherein,
| represents the parameter vector a 0 a 1 …a N Norm of (d).
Step S1024, predicting the number of students in the predicted year based on a Holter linear trend method and a historical year prediction data source to obtain a third predicted number of students;
the data source of the Holter linear trend method is different from the regression analysis, the data source is historical sequence data of a prediction object, for example, if the prediction target is student enrollment data of a grade of a primary school in 2024 years in a certain region, the data source is the data of the number of students enrollment of the grade of the historical primary school in the region;
specifically, the holter linear trend method formula is as follows:
L t ＝αX t +(1-α)(L t-1 -T t-1 )
T t ＝β(L t -T t-1 )+(1-β)T t-1
S t+T ＝L t +T t m
wherein, L represents the level, T represents the trend, X is the number value of the actual history student, alpha is the level smooth coefficient, beta is the trend coefficient, and T represents the period;
predicting a third predicted student population for a time period t using sequence data from historical year prediction data sources
And S1025, carrying out weighted average on the first predicted student number, the second predicted student number and the third predicted student number based on a preset linear weighted average algorithm to obtain the predicted annual student number.
Specifically, the preset linear weighted average algorithm is as follows:
wherein
In order to obtain the final predicted value,
in order to first predict the number of students,
in order to second predict the number of students,
for the third prediction of student number, ρ 1 ρ 2 ρ 3 Are weighting coefficients.
And respectively obtaining the corresponding predicted student number through a least square method analysis model, a ridge regression analysis model and a Holter linear trend method, and carrying out weighted average on the corresponding predicted student number to obtain the final predicted annual student number, so that the accuracy of calculating the predicted student number is improved.
Of course, the least square analysis model and the ridge regression analysis model in the above academic degree analysis model may be other prediction models, such as a convolutional neural network model, a random forest model, and the like.
Step S103, calculating the number of annual average education resources based on the current education resources in the prediction area and the number of students in the prediction year;
in the present embodiment, the predicted annual average number of educational resources is the ratio of the current number of educational resources to the predicted annual number of students; and step S104, if the number of the annual average education resources is greater than the preset standard number of the annual average education resources, calculating the number of the annual newly increased education resources based on the current number of the education resources, the number of annual students and the standard number of the annual average education resources.
In this embodiment, the number of newly added educational resources in the predicted year is the difference between the product of the number of students in the predicted year and the number of standard student-average educational resources and the current number of educational resources.
And if the number of the annual average educational resources to be predicted is not more than the preset standard number of the annual average educational resources, the number of the current educational resources in the prediction region meets the demand of the year to be predicted on the educational resources, and the number of the educational resources in the prediction region is in a balanced state.
According to the method, the number of students in the future year in the prediction region is predicted through the academic degree analysis model, the current education resource quantity and the historical annual academic degree information in the prediction region are obtained through various modes, the monitoring of the current education resource quantity and the prediction of the education resource balance degree condition of the prediction year are achieved, and therefore dynamic monitoring and prediction of the education resources in the prediction region are achieved.
As a further embodiment of the method for predicting the balance of input of educational resources, after calculating the number of newly added educational resources for the predicted year based on the number of current educational resources, the number of students for the predicted year, and the number of standard student-average educational resources, as shown in FIG. 3, the method further comprises (steps S111 to S112)
Step S111, responding to the report template selection trigger action of the user, and acquiring a report template;
in this embodiment, the report template may visually display the number of newly added educational resources in the predicted year by means of a bar chart, a pie chart, a line chart, a scatter chart, a bubble chart, a radar chart, and the like. For example, a line graph of the notification template may indicate the number of the newly added education resources required for the prediction area and information related to the number of the newly added education resources.
And S106, visually analyzing the quantity of the newly added education resources in the predicted year based on the report template to generate an education resource analysis report.
The user can configure educational resources for an area or school that requires investment of educational resources according to the educational resource report.
Optionally, as shown in fig. 4, step S112 specifically includes the following sub-steps:
step S1061, establishing a data set based on the number of newly added educational resources in the predicted year, the number of current educational resources and the academic degree information in the historical year;
step S1062, dividing the data set into an educational data set and an evaluation data set;
in this embodiment, the number of newly added educational resources and the number of current educational resources are education data sets, and the data in the degree information of the historical years is an evaluation data set.
Step S1063, acquiring a corresponding visualization model based on the report template;
step S1064, inputting the education data set and the evaluation data set into a visual model for training to obtain an optimal visual model; and step S1065, generating an educational resource analysis report based on the optimal visualization model.
In this embodiment, a user may select a report template according to a demand, the report template is mapped with the visualization model to obtain a visualization model corresponding to the report template, the visualization model is trained through the education data set and the evaluation data set and visual model parameters are modified to obtain an optimal visualization model, and an education resource analysis report corresponding to the newly added education resource number, the current education resource number and the historical annual degree information establishment data set is generated through the optimal visualization model, so that the user can view the education resource analysis report conveniently.
Fig. 2 is a block diagram illustrating a configuration of an educational resource investment balance prediction apparatus 200 according to an embodiment of the present invention.
As shown in fig. 2, the educational resource investment balance degree prediction apparatus 200 mainly includes:
an obtaining module 201, configured to obtain a current educational resource amount and a historical year prediction data source of a prediction area;
the input module 202 is used for inputting the historical year prediction data source into a preset academic degree analysis model to obtain the number of students in the predicted year;
a first calculating module 203, configured to calculate the predicted annual average number of educational resources based on the current number of educational resources in the predicted area and the predicted annual student number;
a second calculating module 204, configured to calculate, if the predicted annual average educational resource number is greater than the preset standard annual average educational resource number, the predicted annual number of students and the standard annual average educational resource number, the predicted annual newly-added educational resource number.
As an optional implementation manner of this embodiment, the obtaining module 201 is specifically configured to: responding to a port calling request of a user, calling a port pointed by the port calling request, and receiving the current education resource quantity and the historical annual degree information of a prediction area, wherein the ports comprise a database direct connection port, a file interface docking port, a preposed library docking port, a report form filling port, an official network acquisition port and a file sharing port.
As an optional implementation manner of this embodiment, the input module 202 is specifically configured to:
performing data transformation on the historical year prediction data source based on a preset data processing formula to obtain characteristic data information; the data transformation comprises the steps of carrying out logarithm taking, square solving and square root solving on the historical year prediction data source, carrying out window sliding on sequence data, carrying out mean solving, carrying out variance solving, carrying out skewness solving and carrying out quantile solving;
inputting the characteristic data information into a least square method analysis model to obtain a first predicted student number;
inputting the feature data information into a ridge regression fitting model to obtain a second predicted student number;
predicting the number of students in the predicted year based on a Holter linear trend method and a historical year prediction data source to obtain a third predicted number of students;
and carrying out weighted average on the first predicted student number, the second predicted student number and the third predicted student number based on a preset linear weighted average algorithm to obtain the predicted year student number.
As an optional implementation manner of this embodiment, the apparatus for predicting an educational resource input balance further includes a generation module, after calculating the predicted annual newly-added educational resource amount based on the current educational resource amount, the predicted annual student number, and the standard average educational resource amount, the generation module includes:
the acquisition submodule is used for responding to a report template selection trigger action of a user and acquiring a report template;
and the generation submodule is used for carrying out visual analysis on the quantity of the newly added education resources in the predicted year based on the report template to generate an education resource analysis report.
In this optional embodiment, the generation submodule is specifically configured to establish a data set based on the number of new educational resources in the predicted year, the number of current educational resources, and the academic degree information in the historical year; dividing the data set into an educational data set and an assessment data set; acquiring a corresponding visualization model based on the report template; inputting the education data set and the evaluation data set into a visual model for training to obtain an optimal visual model; and generating an educational resource analysis report based on the optimal visualization model.
Furthermore, the historical year prediction data source comprises student information, population information and economic information corresponding to the prediction region; wherein,
the student information comprises the number of recruits, the number of graduates and sequence data of the historical years;
the population information comprises the number of household registers, the number of resident persons, the number of born persons and the birth rate of the historical years;
the economic information includes the total economic production value, the urbanization rate, the housing area and the GDP of the historical year.
Further, the least square analysis model is as follows:
wherein,
for the first prediction of the number of students, a 0 a 1 …a n Is the fitting coefficient, μ is the error;
by calculating a first prediction of the number of students
And the actual historical number of students y i Fitting a 0 a 1 …a N So that a is 0 a 1 …a N And the e is satisfied to be minimum, wherein,
a after fitting 0 a 1 …a N Substituting the first prediction student number into a least square method analysis model;
the ridge regression analysis model is as follows:
wherein,
for the second prediction of the number of students, a 0 a 1 …a n Is the fitting coefficient, μ is the error;
by calculating a second predicted number of students
And the actual historical number of students y i Fit a while fitting the error of the best fit 0 a 1 …a N So that a is 0 a 1 …a N And the ∈ minimum is satisfied, wherein,
| represents the parameter vector a 0 a 1 …a N Norm of (d);
the Hall linear trend method formula is as follows:
L t ＝αX t +(1-α)(L t-1 -T t-1 )
T t ＝β(L t -T t-1 )+(1-β)T t-1
S t+T ＝L t +T t m
wherein, L represents the level, T represents the trend, X is the number value of the actual history student, alpha is the level smooth coefficient, beta is the trend coefficient, and T represents the period;
predicting a third predicted student population for a time period t using sequence data from historical year prediction data sources
The preset linear weighted average algorithm is as follows:
wherein
In order to be the final predicted value,
in order to first predict the number of students,
to second predict the number of students,
for the third prediction of student number, ρ 1 ρ 2 ρ 3 Are weighting coefficients.
In one example, the modules in any of the above apparatus may be one or more integrated circuits configured to implement the above methods, such as: one or more Application Specific Integrated Circuits (ASICs), or one or more Digital Signal Processors (DSPs), or one or more Field Programmable Gate Arrays (FPGAs), or a combination of at least two of these integrated circuit forms.
For another example, when a module in a device may be implemented in the form of a processing element scheduler, the processing element may be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor capable of invoking programs. As another example, these modules may be integrated together and implemented in the form of a system-on-a-chip (SOC).
Various objects such as various messages/information/devices/network elements/systems/devices/actions/operations/procedures/concepts may be named in the present application, it is to be understood that these specific names do not constitute limitations on related objects, and the named names may vary according to circumstances, contexts, or usage habits, and the understanding of the technical meaning of the technical terms in the present application should be mainly determined by the functions and technical effects embodied/performed in the technical solutions.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the system, the apparatus and the module described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
Those of ordinary skill in the art will appreciate that the various illustrative modules and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
Fig. 3 is a block diagram of an electronic device 300 according to an embodiment of the present disclosure.
As shown in fig. 3, the electronic device 300 includes a processor 301 and a memory 302, and may further include one or more of an information input/information output (I/O) interface 303 and a communication component 304.
The processor 301 is configured to control the overall operation of the electronic device 300 to complete all or part of the steps of the above-mentioned method for adjusting the indoor humidity; the memory 302 is used to store various types of data to support operation at the electronic device 300, such data may include, for example, instructions for any application or method operating on the electronic device 300, as well as application-related data. The Memory 302 may be implemented by any type or combination of volatile and non-volatile Memory devices, such as one or more of Static Random Access Memory (SRAM), electrically Erasable Programmable Read-Only Memory (EEPROM), erasable Programmable Read-Only Memory (EPROM), programmable Read-Only Memory (PROM), read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic or optical disk.
The I/O interface 303 provides an interface between the processor 301 and other interface modules, such as a keyboard, mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 304 is used for testing wired or wireless communication between the electronic device 300 and other devices. Wireless Communication, such as Wi-Fi, bluetooth, near Field Communication (NFC), 2G, 3G, or 4G, or a combination of one or more of them, so that the corresponding Communication component 104 may include: wi-Fi part, bluetooth part, NFC part.
The communication bus 305 may include a path to transfer information between the aforementioned components. The communication bus 305 may be a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The communication bus 305 may be divided into an address bus, a data bus, a control bus, and the like.
The electronic Device 300 may be implemented by one or more Application Specific Integrated Circuits (ASICs), digital Signal Processors (DSPs), digital Signal Processing Devices (DSPDs), programmable Logic Devices (PLDs), field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components, and is configured to perform the method for predicting the educational resource input balance given in the above embodiments.
The electronic device 300 may include, but is not limited to, a digital broadcast receiver, a mobile terminal such as a PDA (personal digital assistant), a PMP (portable multimedia player), and the like, and a fixed terminal such as a digital TV, a desktop computer, and the like, and may also be a server, and the like.
The following describes a computer-readable storage medium provided in an embodiment of the present application, and the computer-readable storage medium described below and the above-described prediction of the educational resource investment balance may be referred to in correspondence.
The application also provides a computer readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the method for predicting the educational resource investment balance are realized.
The computer-readable storage medium may include: various media capable of storing program codes, such as a usb disk, a removable hard disk, a read-only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
The foregoing description is only exemplary of the preferred embodiments of the application and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the application referred to in the present application is not limited to the embodiments with a particular combination of the above-mentioned features, but also encompasses other embodiments with any combination of the above-mentioned features or their equivalents without departing from the spirit of the application. For example, the above features and the technical features (but not limited to) having similar functions in the present application are mutually replaced to form the technical solution.
Claims (10)
1. A method for predicting the input balance of educational resources is characterized by comprising the following steps:
acquiring the current education resource quantity and the historical year prediction data source of a prediction area;
inputting the historical year prediction data source into a preset academic degree analysis model to obtain the number of students in the predicted year;
calculating the number of the annual average educational resources based on the current educational resource number and the number of annual students predicted in the prediction area; and if the number of the annual prediction average education resources is larger than the preset standard annual prediction average education resources, calculating the number of the newly added education resources in the predicted years based on the number of the current education resources, the number of students in the predicted years and the number of the standard annual prediction average education resources.
2. The method of claim 1, wherein the historical year prediction data sources include student information, demographic information, and economic information corresponding to a prediction region; wherein,
the student information comprises the number of recruits, the number of graduates and sequence data of the historical year;
the population information comprises the number of household registers, the number of resident persons, the number of born persons and the birth rate of the historical years;
the economic information includes the total economic production value, the urbanization rate, the housing area and the GDP of the historical year.
3. The method of claim 1 or 2, wherein inputting the historical year prediction data source into a preset degree analysis model to obtain a predicted year student number comprises:
performing data transformation on the historical year prediction data source based on a preset data processing formula to obtain characteristic data information; the data transformation comprises the steps of carrying out logarithm taking, square solving and square root solving on the historical year prediction data source, carrying out window sliding on sequence data, carrying out mean solving, carrying out variance solving, carrying out skewness solving and carrying out quantile solving;
inputting the characteristic data information into a least square method analysis model to obtain a first predicted student number;
inputting the characteristic data information into a ridge regression fitting model to obtain a second predicted student number;
predicting the number of students in the predicted year based on a Holter linear trend method and a historical year prediction data source to obtain a third predicted number of students;
and carrying out weighted average on the first predicted student number, the second predicted student number and the third predicted student number based on a preset linear weighted average algorithm to obtain the predicted annual student number.
4. The method of claim 3, wherein the least squares analysis model is:
wherein,
for the first prediction of the number of students, a 0 a 1 …a n μ is the error for the fitting coefficient;
by calculating a first prediction of the number of students
And the actual historical number of students y i Fitting a of 0 a 1 …a N So that a is 0 a 1 …a N And the e is satisfied to be minimum, wherein,
a after fitting 0 a 1 …a N Substituting the number of students into a least square method analysis model to obtain a first predicted student number;
the ridge regression analysis model is as follows:
wherein,
for the second prediction of the number of students, a 0 a 1 …a n Is the fitting coefficient, μ is the error;
by calculating a second predicted number of students
And the actual historical number of students y i Fit a while fitting the error of the best fit 0 a 1 …a N So that a is 0 a 1 …a N And the e is satisfied to be minimum, wherein,
| represents the parameter vector a 0 a 1 …a N Norm of (d);
the Hall linear trend method formula is as follows:
L t ＝αX t +(1-α)(L t-1 -T t-1 )
T t ＝β(L t -T t-1 )+(1-β)T t-1
S t+T ＝L t +T t m
wherein, L represents the level, T represents the trend, X is the number value of the actual history student, alpha is the level smooth coefficient, beta is the trend coefficient, and T represents the period;
predicting a third predicted student population for a time period t using sequence data from historical year prediction data sources
The preset linear weighted average algorithm is as follows:
wherein
In order to be the final predicted value,
in order to first predict the number of students,
to second predict the number of students,
for the third prediction of student number, ρ 1 ρ 2 ρ 3 Are weighting coefficients.
5. The method of claim 1, further comprising, after said calculating a predicted annual number of new educational resources based on a current number of educational resources, a predicted annual number of students, and a standard average number of student educational resources:
responding to a report template selection trigger action of a user, and acquiring a report template;
and carrying out visual analysis on the quantity of the newly added education resources in the predicted year based on the report template to generate an education resource analysis report.
6. The method of claim 5, wherein the visually analyzing the predicted annual number of new educational resources based on the report template, and wherein generating an educational resource analysis report comprises:
establishing a data set based on the number of newly added educational resources in the predicted year, the number of current educational resources and the academic degree information in the historical year;
dividing the data set into an educational data set and an assessment data set;
acquiring a corresponding visualization model based on the report template;
inputting the education data set and the evaluation data set into the visual model for training to obtain an optimal visual model;
generating the educational resource analysis report based on the optimal visualization model.
7. The method of claim 1, wherein the obtaining of the current educational resource amount and historical annual degree information for the prediction area comprises:
responding to a port calling request of a user, calling a port pointed by the port calling request, and receiving the current educational resource quantity and the historical year prediction data source of the prediction area, wherein the ports comprise a database direct connection port, a file interface docking port, a prepositive library docking port, a report form port, an official website acquisition port and a file sharing port.
8. An educational resource investment balance degree prediction device, comprising,
the acquisition module is used for acquiring the current education resource quantity and the historical year prediction data source of the prediction area;
the input module is used for inputting the historical year prediction data source into a preset academic degree analysis model to obtain the number of students in the predicted year;
a first calculation module for calculating the number of annual-to-average predicted educational resources based on the current number of educational resources and the number of annual-to-student predicted numbers in the predicted area;
and the second calculation module is used for calculating the quantity of the newly added education resources in the predicted year based on the quantity of the current education resources, the quantity of students in the predicted year and the quantity of the standard average education resources if the quantity of the predicted year average education resources is greater than the preset quantity of the standard average education resources.
9. An electronic device comprising a memory and a processor, the memory having stored thereon a computer program that can be loaded by the processor and that executes the method according to any of claims 1 to 7.
10. A computer-readable storage medium, in which a computer program is stored which can be loaded by a processor and which executes the method of any one of claims 1 to 7.
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| CN115222081A (en) * | 2021-04-15 | 2022-10-21 | 腾讯科技（深圳）有限公司 | Academic resource prediction method and device and computer equipment |
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