CN112557585A - Learning environment quality evaluation system and method based on Weber-Feishlnut law - Google Patents
Learning environment quality evaluation system and method based on Weber-Feishlnut law Download PDFInfo
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Abstract
The invention provides a learning environment quality evaluation system and a method, wherein the method comprises the following steps: collecting environmental information including measurements indicative of a plurality of parameters associated with learning efficiency in an environment, wherein the plurality of parameters include: illumination, noise, temperature, relative humidity of air (RH), fine Particulate Matter (PM)2.5) Formaldehyde, carbon dioxide (CO)2) And Total Volatile Organic (TVOC); evaluating the environment information through a learning environment quality evaluation model, and determining the level of the environment suitable for learning, wherein the learning environment quality evaluation model is used for evaluating the environment information and determining the level of the environment suitable for learningThe environment quality evaluation model comprises an influence index model, an influence weight coefficient model and a weighting index of each parameter in the plurality of parameters, and the influence index model is established based on the Weber-Fisher-Ohtop law; and displaying the measured values and the ratings of the plurality of parameters on a graphical user interface.
Description
Technical Field
The invention relates to the field of environmental monitoring, in particular to a learning environment quality evaluation system and method based on a Weber-Fisher extension law.
Background
Currently, a large number of studies and reports indicate that the indoor environment in a classroom directly affects the learning efficiency of students. At present, the specifications of indoor environment quality are mainly referred to the indoor air quality standard (GB/T18883-10Particulate matter, colony biological indicators, and radioactivity indicators, totaling 19 indicators, this standard is the pollutant limit requirement that proposes under the building operation condition. Furthermore, aiming at the classroom environment of schools, the classroom air quality standard of primary and secondary schools (T/CAQI 27-2017) and the lighting and lighting health standard of the classroom of primary and secondary schools (GB7793-2010) are provided, wherein the lighting and lighting health standard comprises light, carbon dioxide, ozone and fine particulate matter PM2.5Ammonia gas, formaldehyde and total volatile organic compounds, and the total index is 7, and the standard is the pollutant limit requirement under the operating conditions of classrooms of middle and primary schools.
However, in the actual use process, the current regulation standard does not consider the influence of the indoor environment on the learning efficiency of students. Relevant literature reports at home and abroad show that the learning efficiency of students is influenced by other physical pollution such as illumination, noise and the like besides indoor air pollutants.
Disclosure of Invention
To solve the above problems. The application provides a learning environment quality evaluation system and method based on a Weber-Fisher extension law.
In a first aspect, the present application provides a learning environment quality evaluation system, including: a processor; a computer-readable storage medium communicatively connected to the processor and storing instructions for execution by the processor; and when executed by the processor, the instructions cause the learning environment quality assessment system to:
collecting environmental information including measurements indicative of a plurality of parameters associated with learning efficiency in an environment, wherein the plurality of parameters include: illumination, noise, temperature, relative humidity of air (RH), fine Particulate Matter (PM)2.5) Formaldehyde, carbon dioxide (CO)2) And Total Volatile Organic (TVOC);
evaluating the environment information through a learning environment quality evaluation model, and determining the level suitable for learning of the environment, wherein the learning environment quality evaluation model comprises an influence index model, an influence weight coefficient model and a weighting index of each parameter of the multiple parameters, and the influence index model is established based on a Weber-Fisher extension law; and
displaying the measured values and the ratings for the plurality of parameters on a graphical user interface.
According to the first aspect, evaluating the environmental information by a learning environment quality evaluation model, and determining the level of environmental suitability for learning further comprises:
obtaining an impact index for each of the plurality of parameters based on the impact index model for each of the plurality of parameters; obtaining the weighted index of each parameter according to the influence index of each parameter and the influence weight coefficient of each parameter, wherein the influence weight coefficient is determined based on the influence weight coefficient model; summing the weighted index for each of the plurality of parameters to obtain a learning environment quality assessment index; and comparing the learning environment quality assessment index with a threshold value to determine the grade.
According to a first aspect, the influence index model further comprises:
wherein HPRepresenting the influence index corresponding to each parameter in the multiple parameters, wherein the influence index is a dimensionless representation of the influence on the human body, P represents any one of the multiple parameters, C is an influence constant, n is a monitoring value0Are comparative values.
According to the first aspect, the influence weight coefficient model further comprises:
wherein, KPRepresenting the weight coefficient corresponding to each parameter in the plurality of parameters, P representing any one of the plurality of parameters, SPAnd the efficiency factors corresponding to the various parameters are represented, and N represents the number of the various parameters.
According to the first aspect, the weighted index further comprises:
IP＝HP×KP
wherein, IPRepresents a weighted index, H, corresponding to each of the plurality of parametersPExpressing the influence index, K, corresponding to each of the plurality of parametersPAnd representing the weight coefficient corresponding to each parameter in the plurality of parameters, wherein P represents any one of the plurality of parameters.
According to the first aspect, the learning environment quality evaluation model further includes:
wherein I represents a learning environment quality evaluation index, IPRepresenting a weighting index corresponding to each of the plurality of parameters, P representing any of the plurality of parameters, N representing the plurality of parametersThe number of parameters.
According to a first aspect, the system further comprises a monitoring device communicatively connected with the processor, the monitoring device comprising:
one or more sensors for detecting the plurality of parameters in the environmental information;
a transceiving unit to transmit the plurality of parameters for the measurement of the environment to the system via a wireless network.
According to a first aspect, the system further comprises: one or more display devices to present the graphical user interface.
In a second aspect, the present application provides a learning environment quality assessment method, including:
collecting environmental information including measurements indicative of a plurality of parameters associated with learning efficiency in an environment, wherein the plurality of parameters include: illumination, noise, temperature, relative humidity of air (RH), fine Particulate Matter (PM)2.5) Formaldehyde, carbon dioxide (CO)2) And Total Volatile Organic (TVOC);
evaluating the environment information through a learning environment quality evaluation model, and determining the level suitable for learning of the environment, wherein the learning environment quality evaluation model comprises an influence index model, an influence weight coefficient model and a weighting index of each parameter of the multiple parameters, and the influence index model is established based on a Weber-Fisher extension law; and
displaying the measured values and the ratings for the plurality of parameters on a graphical user interface.
According to the second aspect, evaluating the environmental information by a learning environment quality evaluation model, and determining the level of environmental suitability for learning further comprises:
obtaining an impact index for each of the plurality of parameters based on the impact index model for each of the plurality of parameters; obtaining the weighted index of each parameter according to the influence index of each parameter and the influence weight coefficient of each parameter, wherein the influence weight coefficient is determined based on the influence weight coefficient model; summing the weighted index for each of the plurality of parameters to obtain a learning environment quality assessment index; and comparing the learning environment quality assessment index with a threshold value to determine the grade.
Compared with the prior art, the implementation mode of the application has the main differences and the effects that: the online sensor monitoring system is used for testing indoor environment parameters, indoor environment quality is evaluated in real time, the influence level of the environmental factors on the learning efficiency of students is effectively indicated and identified according to the correlation between the environment parameters and the learning efficiency on the basis of a Weber-Fischna (W-F) law model algorithm, and a basis is provided for implementing environmental pollution prevention and control measures.
Drawings
FIG. 1 shows a schematic diagram of an example system according to an embodiment of the invention.
Fig. 2 shows a schematic view of a monitoring device according to an embodiment of the invention.
Fig. 3 shows a flowchart of a learning environment quality evaluation system according to an embodiment of the present invention.
Detailed Description
In order to make the purpose and technical solution of the embodiments of the present invention clearer, the technical solution of the embodiments of the present invention will be clearly and completely described below with reference to the drawings of the embodiments of the present invention. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the described embodiments of the invention without any inventive step, are within the scope of protection of the invention.
In accordance with an embodiment of the present invention, there is provided an embodiment of a learning environment quality assessment system, it is noted that the steps illustrated in the flowchart of the drawings may be performed in a computer system such as a set of computer executable instructions, and that while a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different than here.
The method embodiments provided in the manner of this application may be executed in a computing system. Fig. 1 is a schematic structural diagram of a computing system for learning environment quality evaluation according to an embodiment of the present invention. As shown in fig. 1, computing system 100 may include one or more (only one shown) processors 101 (processor 101 may include, but is not limited to, a processing device such as a central processing unit CPU, an image processor GPU, a digital signal processor DSP, a microprocessor MCU, or a programmable logic device FPGA), a bus 102, a memory 103 for storing data, a communication interface 104 for communication functions, and a monitoring device 105 and a display device 106 communicatively connected to communication interface 104. It will be understood by those skilled in the art that the structure shown in fig. 1 is only an illustration and is not intended to limit the structure of the electronic device. For example, computing system 100 may also include more or fewer components than shown in FIG. 1, or have a different configuration than shown in FIG. 1.
The memory 103 may be used to store a database, a queue, and software programs and modules of application software, such as program instructions/modules corresponding to the learning environment quality assessment method according to the embodiment of the present invention, and the processor 101 executes various functional applications and data processing by running the software programs and modules stored in the memory 103, so as to implement the learning environment quality assessment method described above. The memory 103 may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 103 may further include memory located remotely from the processor 101, which may be connected to the computing system 100 over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The communication interface 104 is used to receive and transmit data via a network, which may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few. Specific examples of such networks may include the internet provided by a communications provider for computing system 100.
The monitoring device 105 is connected to the communication interface 104 via a wireless network. As shown in fig. 2, the monitoring device 105 includes one or more of the sensors 1051 and 1058 and a transceiver unit 1059. These sensors are used to detect various parameters in the environment associated with learning efficiency, such as light, noise, temperature, air Relative Humidity (RH), fine Particulate Matter (PM), for example2.5) Formaldehyde, carbon dioxide (CO)2) And Total Volatile Organic Compounds (TVOC), and the like. Various embodiments of the present application have been described herein with the above 8 parameters as examples, but the systems and methods described herein may also include other environmental parameters. The transceiving unit 1059 is used to transmit the above-mentioned parameters measured by the monitoring device 105 to the computing system 100 via a wireless network.
The display device 106 may include a monitor, a touch screen with a display and manual input capabilities, and the like. Display device 106 receives display data for system 100 via communication interface 104, which may include graphical user interface configurations and various environmental information.
Fig. 1 and 2 are for illustrative purposes only and are not intended to be limiting. For example, although the various modules of the computing system 100 and the monitoring device 105 are shown, they are not intended to represent the only modules in the computing system 100 and the monitoring device 105, or that the modules be arranged in the manner shown. For example, the sensors 1051-1058 may alternatively or additionally be combined in any manner and are not limited to the manner shown in the figures.
The functionality of the computing system 100 is described in detail below. The system 100 evaluates the learning environment quality based on evaluation based on the extended weber-fisher (W-F) law.
The memory 103 may store a learning environment quality evaluation model further including an influence index model, an influence weight coefficient model, and a weighted index model for each parameter. The memory 103 may also store a threshold level table for environment evaluation and learning efficiency evaluation. The learning environment quality evaluation model determines whether the test environment is suitable for a level of learning based on a threshold level table. The threshold level table may include one or more tables that record evaluation thresholds and levels of all parameters measured by the monitoring device 105, score thresholds and corresponding levels of the learning environment quality assessment index (i.e., learning efficiency environment index), and indoor pollutant learning efficiency factor value division criteria, among others.
Specifically, in some embodiments, attached table 1 shows a range of values of the learning Efficiency Environment Index (inoor Environment Index for student Efficiency, IEI-SE), and divides the student's appropriate learning Environment quality level. The invention takes 100 as score full points, and sets 5 grades for the indoor quality of the suitable learning environment of the student:
TABLE 1 learning efficiency Environment index IEI-SE rating
The evaluation threshold values and the evaluation levels of all the parameters of the suitability environment index, which have a significant influence on the learning efficiency of the students, measured by the monitoring device 105 can be seen in tables 2 to 9, and tables 2 to 9 respectively show the evaluation threshold values and the evaluation levels of all the parameters of the suitability environment index, which have a significant influence on the learning efficiency of the students, on illumination, noise, temperature, Relative Humidity (RH) of air and Particulate Matter (PM) of the students2.5Formaldehyde, CO2And limiting a numerical range by an evaluation threshold of the TVOC, grading and grading the corresponding subentry environmental index EI, and correspondingly grading the 8 subentry parameters by 5 grades:
TABLE 2 PM2.5Learning efficiency environmental index PiScoring and ranking of
TABLE 3 indoor noise learning efficiency Environment index NiScoring and ranking of
TABLE 4 indoor Formaldehyde learning efficiency Environment index HCHOiScoring and ranking of
TABLE 5 indoor TVOC learning efficiency Environment index OiScoring and ranking of
TABLE 6 environmental index RH for indoor air relative humidity learning efficiencyiScoring and ranking of
TABLE 7 indoor Lighting learning efficiency Environment index IiScoring and ranking of
TABLE 8 indoor temperature learning efficiency Environment index TiScoring and ranking of
TABLE 9 indoor CO2Learning efficiency environmental index CBiScoring and ranking of
According to some embodiments of the present application, an efficiency factor needs to be set for each environmental parameter according to the influence level of each environmental parameter on the learning efficiency of the student, so as to represent the relative weight level of each environmental parameter on the learning efficiency. The efficiency factor may be set according to user requirements. As an example, the learning efficiency factor K is divided into 5 scores, where 5 scores indicate that the influence of the efficiency factor is the largest, and 1 score indicates that the influence of the efficiency factor is the smallest, which is specifically shown in the attached table 10:
TABLE 10 pollutant learning efficiency factor value partitioning
| Learning efficiency factor value | Index of indoor pollutants |
| 5 | Noise, temperature |
| 4 | PM2.5Illumination, HCHO |
| 3 | TVOC |
| 2 | RH |
| 1 | CO2 |
During operation of the system 100, the system 100 may monitor environmental parameters, including PM, in-line in the room via the monitoring device 1052.5RH, illumination, noise, temperature, formaldehyde, CO2And TVOC 8 indicators, the measurements may be real-time measurements or may be averages of measurements of the respective parameters over a predetermined time interval, such as an average every 30 seconds.
After the measured values of the parameters are obtained, the system 100 obtains the influence index of each parameter by using the influence index model of each parameter. In particular fine particulate matter PM2.5For example, consider that harmful substances irritate the body to a similar extent as noise irritates the ears of a human. Therefore, the degree of influence of harmful substances in a classroom on human health is described by using a processing method in acoustics, namely the extension law of Weber-Fisher as follows:
wherein P represents PM2.5Dimensionless representation of the effects on the human body, n representing PM2.5Concentration values monitored in μ g/m3，n0Represents PM2.5Comparison of concentration values in μ g/m3And C is a contamination constant.
Referring to Table 2, n0As PM2.5Limit value of health influence grade, namely scoring of 100 minutes, corresponding PM2.5Concentration of PM at this time2.5The impact dimensionless calculation result is equal to 0; and secondly, defining the dimensionless results of pollution influence of various grades from excellent to severe harm as 1-5 respectively. And a concentration value n when P is 2 (good health grade, score 90 points)2The concentration was determined using the upper limit of the standard. And n is to be0,n2And P2 is substituted for formula 1 to obtain PM2.5Corresponding constant C.
Similarly, referring to tables 3-9, the system utilizes N, respectivelyi、HCHOi、Oi、RHi、Ii、TiAnd CBiModel, noise, formaldehyde, TVOC, relative humidity in air, light, temperature and CO obtained2The respective impact index of the environmental parameter.
In some embodiments, the system 100 obtains the influence weight coefficient for each parameter according to the influence weight coefficient model for each parameter. The model is that the model is as follows,
wherein the weight coefficient KPAnd N represents the quantity value of the measured parameter, wherein the ratio of the efficiency factor corresponding to any one of the monitored parameters P to the sum of the efficiency factors of all the monitored parameters is shown.
After obtaining the weighting coefficients of the respective parameters, their respective weighting indices IEI-SE can be obtained accordinglyPAs in formula (3):
IEI-SEP＝HP×KP (3)
wherein HPIndicating the corresponding impact index of the parameter P. Weighted index IEI-SE according to each parameter in environment informationPThe system 100 obtains the comprehensive learning efficiency environmental index IEI-SE of the current environment by using a learning environment quality evaluation model as shown in the following formula (4),
the corresponding learning efficiency level is then determined according to table 1 above.
The learning efficiency environmental index IEI-SE calculated by the above model and the corresponding quality grade of the suitable learning environment in table 1 are output by the system 100 to the display device 106 in real time to characterize whether the monitoring environment is suitable for learning within the full monitoring period.
According to the implementation mode of the application, the learning environment quality evaluation system represents whether the current environment is suitable for learning in real time on the basis of the monitored environment information through the online monitoring equipment, so that the simple identifiability of the learning environment quality is realized, and an effective technical support is provided for students to improve the learning efficiency environment.
The learning efficiency environmental index can qualitatively describe the suitability level of the room environmental quality to learning, and can further implement corresponding intervention measures for indoor environmental purification under the displayed bad air condition, thereby effectively improving the learning efficiency of students.
Under the above operating environment, the present invention provides a learning environment quality evaluation method as shown in fig. 3. The method may be applied in the system 100, executed by the processor 101 in the system 100.
Fig. 3 shows a flow diagram of a learning environment quality assessment method 300 according to an embodiment of the invention. As shown in fig. 3, the process flow of the method is as follows:
310. collecting environmental information, the environmental information including measurements indicative of a plurality of parameters associated with learning efficiency in an environment;
320. evaluating the environmental information through a learning environment quality evaluation model to determine the level of environment suitable for learning;
330. the measured values of the various parameters and the ratings are displayed on a graphical user interface.
According to some embodiments of the invention, the indoor environment parameters are tested by using the online monitoring equipment, the indoor environment quality is evaluated in real time, the quantitative influence of the environment quality on the learning efficiency of students is obtained based on a Weber-Fisher-Tropsch law model algorithm, and an IEI-SE real-time value is obtained, so that the suitability level of the influence of the indoor environment on the learning efficiency of the students is described.
The method embodiments of the present invention may be implemented in software, magnetic, firmware, etc. Whether implemented in software, magnetic, or firmware, the instruction code may be stored in any type of computer-accessible memory (e.g., permanent or modifiable, volatile or non-volatile, solid or non-solid, fixed or removable media, etc.). Also, the Memory may be, for example, Programmable Array Logic (PAL), Random Access Memory (RAM), Programmable Read Only Memory (PROM), Read-Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), a magnetic disk, an optical disk, a Digital Versatile Disk (DVD), or the like.
In the description provided herein, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the invention and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that the invention as claimed requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the claims, any of the claimed embodiments may be used in any combination.
The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed terminal device. In the unit claims enumerating several terminal devices, several of these terminal devices may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.
Claims (10)
1. A learning environment quality evaluation system, comprising:
a processor;
a computer-readable storage medium communicatively connected to the processor and storing instructions for execution by the processor; and
the instructions, when executed by the processor, cause the learning environment quality assessment system to:
collecting environmental information including measurements indicative of a plurality of parameters associated with learning efficiency in an environment, wherein the plurality of parameters include: illumination, noise, temperature, airGas phase Relative Humidity (RH), fine Particulate Matter (PM)2.5) Formaldehyde, carbon dioxide (CO)2) And Total Volatile Organic (TVOC);
evaluating the environment information through a learning environment quality evaluation model, and determining the level suitable for learning of the environment, wherein the learning environment quality evaluation model comprises an influence index model, an influence weight coefficient model and a weighting index of each parameter of the multiple parameters, and the influence index model is established based on a Weber-Fisher extension law; and
displaying the measured values and the ratings for the plurality of parameters on a graphical user interface.
2. The learning environment quality assessment system according to claim 1, wherein the environment information is assessed by a learning environment quality assessment model, and determining the level of environment suitability for learning further comprises:
obtaining an impact index for each of the plurality of parameters based on the impact index model for each of the plurality of parameters;
obtaining the weighted index of each parameter according to the influence index of each parameter and the influence weight coefficient of each parameter, wherein the influence weight coefficient is determined based on the influence weight coefficient model;
summing the weighted index for each of the plurality of parameters to obtain a learning environment quality assessment index; and
and comparing the learning environment quality evaluation index with a threshold value to determine the grade.
3. The learning environment quality assessment system according to any one of claims 1-2, wherein said influence index model further comprises:
wherein HPRepresenting the influence index corresponding to each parameter in the multiple parameters, wherein the influence index is a dimensionless representation of the influence on the human body, P represents any one of the multiple parameters, C is an influence constant, n is a monitoring value0Are comparative values.
4. The learning environment quality evaluation system according to any one of claims 1 to 3, wherein the influence weight coefficient model further includes:
wherein, KPRepresenting the weight coefficient corresponding to each parameter in the plurality of parameters, P representing any one of the plurality of parameters, SPAnd the efficiency factors corresponding to the various parameters are represented, and N represents the number of the various parameters.
5. The learning environment quality assessment system according to any one of claims 1 to 4, wherein said weighted index further comprises:
IP＝HP×KP
wherein, IPRepresents a weighted index, H, corresponding to each of the plurality of parametersPExpressing the influence index, K, corresponding to each of the plurality of parametersPAnd representing the weight coefficient corresponding to each parameter in the plurality of parameters, wherein P represents any one of the plurality of parameters.
6. The learning environment quality assessment system according to any one of claims 1 to 4, wherein the learning environment quality assessment model further comprises:
wherein I represents a learning environment quality evaluation index, IPAnd the weighting indexes corresponding to the parameters in the plurality of parameters are represented, P represents any one of the parameters, and N represents the number of the parameters.
7. The learning environment quality assessment system according to any one of claims 1-6, further comprising a monitoring device communicatively connected with the processor, the monitoring device comprising:
one or more sensors for detecting the plurality of parameters in the environmental information;
a transceiving unit to transmit the plurality of parameters for the measurement of the environment to the system via a wireless network.
8. The learning environment quality assessment system according to any one of claims 1 to 7, further comprising: one or more display devices to present the graphical user interface.
9. A learning environment quality evaluation method, the method comprising:
collecting environmental information including measurements indicative of a plurality of parameters associated with learning efficiency in an environment, wherein the plurality of parameters include: illumination, noise, temperature, relative humidity of air (RH), fine Particulate Matter (PM)2.5) Formaldehyde, carbon dioxide (CO)2) And Total Volatile Organic (TVOC);
evaluating the environment information through a learning environment quality evaluation model, and determining the level suitable for learning of the environment, wherein the learning environment quality evaluation model comprises an influence index model, an influence weight coefficient model and a weighting index of each parameter of the multiple parameters, and the influence index model is established based on a Weber-Fisher extension law; and
displaying the measured values and the ratings for the plurality of parameters on a graphical user interface.
10. The learning environment quality evaluation method of claim 9, wherein the environment information is evaluated by a learning environment quality evaluation model, and determining the level of environment suitability for learning further comprises:
obtaining an impact index for each of the plurality of parameters based on the impact index model for each of the plurality of parameters;
obtaining the weighted index of each parameter according to the influence index of each parameter and the influence weight coefficient of each parameter, wherein the influence weight coefficient is determined based on the influence weight coefficient model;
summing the weighted index for each of the plurality of parameters to obtain a learning environment quality assessment index; and
and comparing the learning environment quality evaluation index with a threshold value to determine the grade.
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