CN114723151A - Internet of things environment information prediction method and device - Google Patents
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Abstract
The application discloses a method and a device for predicting environmental information of the Internet of things, and relates to the technical field of environmental monitoring of the Internet of things. The method comprises the following steps: acquiring environmental information acquired by a sensor, wherein the environmental information at least comprises one of temperature information, humidity information, harmful gas concentration, smoke concentration, illumination intensity and soil pH value; preprocessing the environment information; establishing a data prediction model according to the environmental information obtained after the pretreatment; and predicting the environmental information according to the data prediction model. When the communication module is in power failure or failure, the established data prediction model can predict the unfinished transmission environment information through the data prediction model, so that the missing data can be supplemented, and the prediction real-time performance and accuracy are realized. The condition that corresponding data is lacked when the transmission of the communication module fault environment information is not finished is avoided.
Description
Technical Field
The application relates to the technical field of environmental monitoring of the Internet of things, in particular to a method and a device for predicting environmental information of the Internet of things.
Background
The internet of things is a huge information network System formed by cooperatively monitoring, perceiving and acquiring information of various environments or monitored objects in a network distribution area in real time by utilizing various different devices such as Radio Frequency IDentification (RFID), various sensors, Global Positioning System (GPS), laser scanners and the like, embedded software and hardware systems, and technologies such as modern networks, wireless communication, distributed data processing and the like, and realizing interconnection between objects, including objects and persons, and between persons and objects, and combining with the internet.
When an existing Internet of things environment monitoring system is arranged, different monitoring nodes are arranged on different monitoring targets. In the operation process of the system, once a communication module of the monitoring node breaks down, the condition of transmission interruption of the environmental information occurs, the real-time problem of feedback processing may be caused, certain potential safety hazards exist, property loss or resource waste phenomena are caused, and the decision feedback processing of the environmental information is influenced and delayed.
In view of the above problems, it is an endeavor of those skilled in the art to find a method for predicting environmental information accurately and timely.
Disclosure of Invention
The application aims to provide a method for predicting the environmental information of the Internet of things, which is used for realizing the real-time performance and accuracy of prediction and avoiding the condition that corresponding data is lacked when the failure environmental information of a communication module is not transmitted.
In order to solve the technical problem, the present application provides a method for predicting environmental information of an internet of things, including:
acquiring environmental information acquired by a sensor, wherein the environmental information at least comprises one of temperature information, humidity information, harmful gas concentration, smoke concentration, illumination intensity and soil pH value;
preprocessing environmental information;
establishing a data prediction model according to the environmental information obtained after preprocessing;
and predicting the environmental information according to the data prediction model.
Preferably, the establishing of the data prediction model according to the environmental information obtained after the preprocessing comprises:
acquiring an environment information array of a node to be predicted, wherein each data in the environment information array is sequentially environment information acquired according to a time sequence;
acquiring an average value of the first n-3 data in the environment information array, wherein n is a serial number of data to be predicted in the environment information array and is greater than 3;
determining a first intermediate value from the average value and the first coefficient;
acquiring the (n-2) th data in the environment information array;
determining a second intermediate value according to the (n-2) th data and the second coefficient;
acquiring the (n-1) th data in the environment information array;
determining a third intermediate value according to the (n-1) th data and the third coefficient;
and superposing the first intermediate value, the second intermediate value and the third intermediate value to obtain a data prediction model.
Preferably, after the environmental information is preprocessed and before the data prediction model is built according to the environmental information obtained after the preprocessing, the method further includes:
and denoising the environmental information obtained after preprocessing.
Preferably, the preprocessing the environment information includes:
acquiring a sample array of the environmental information, wherein the sample array contains a plurality of sample measurement data;
determining an arithmetic mean of all sample measurement data in the sample array and a residual error of all sample measurement data;
determining a standard deviation according to the arithmetic mean and the residual error;
judging whether the residual error is larger than the integral multiple of the standard deviation;
if yes, determining the sample measurement data as an abnormal value and deleting the abnormal value;
if not, retaining the sample measurement data.
Preferably, the denoising processing of the environmental information obtained after the preprocessing includes:
decomposing the environment information to obtain a plurality of components;
processing the components according to the noise superposition times;
and reconstructing the processed components to realize denoising processing.
Preferably, predicting the environmental information according to the data prediction model comprises:
judging whether the predicted value determined according to the data prediction model is larger than the threshold value of the node to be predicted or not;
if so, outputting a decision scheme and regulating and controlling the environmental information according to the decision scheme;
if not, returning to the step of acquiring the environmental information acquired by the sensor.
Preferably, the environment information is predicted by using a sliding window algorithm.
Preferably, the context information is pre-processed using the Lauda criterion.
Preferably, the environmental information is denoised by EMD.
In order to solve the above technical problem, the present application further provides a prediction apparatus for environmental information of the internet of things, including:
the acquisition module is used for acquiring environmental information acquired by the sensor, wherein the environmental information at least comprises one of temperature information, humidity information, harmful gas concentration, smoke concentration, illumination intensity and soil pH value;
the preprocessing module is used for preprocessing the environmental information;
the establishing module is used for establishing a data prediction model according to the environment information obtained after preprocessing;
and the prediction module is used for predicting the environmental information according to the data prediction model.
The prediction method for the environmental information of the internet of things comprises the following steps: acquiring environmental information acquired by a sensor, wherein the environmental information at least comprises one of temperature information, humidity information, harmful gas concentration, smoke concentration, illumination intensity and soil pH value; preprocessing the environment information; establishing a data prediction model according to the environment information obtained after the preprocessing; and predicting the environmental information according to the data prediction model. When the communication module is in power failure or failure, the established data prediction model can predict the unfinished transmission environment information through the data prediction model, so that the missing data can be supplemented, and the prediction real-time performance and accuracy are realized. The condition that corresponding data is lacked when the transmission of the communication module fault environment information is not finished is avoided.
The application also provides a prediction device of the environmental information of the Internet of things, and the effect is the same as the above.
Drawings
In order to more clearly illustrate the embodiments of the present application, the drawings needed for the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings can be obtained by those skilled in the art without inventive effort.
Fig. 1 is a flowchart of a method for predicting environmental information of an internet of things according to an embodiment of the present disclosure;
fig. 2 is a flowchart of another method for predicting environmental information of an internet of things according to an embodiment of the present disclosure;
fig. 3 is a structural diagram of a prediction apparatus for internet of things environmental information according to an embodiment of the present disclosure.
Detailed Description
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 only a part of the embodiments of the present application, and not all the embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present application without any creative effort belong to the protection scope of the present application.
The core of the application is to provide the method and the device for predicting the environmental information of the internet of things, which can realize the real-time performance and accuracy of prediction and avoid the condition that corresponding data is lacked when the failure environmental information of a communication module is not transmitted.
In order that those skilled in the art will better understand the disclosure, the following detailed description will be given with reference to the accompanying drawings.
Fig. 1 is a flowchart of a method for predicting environmental information of an internet of things according to an embodiment of the present disclosure. As shown in fig. 1, the method for predicting the environmental information of the internet of things includes:
s10: and acquiring the environmental information acquired by the sensor.
The environmental information at least comprises one of temperature information, humidity information, harmful gas concentration, smoke concentration, illumination intensity and soil pH value. The form of the environmental information acquired by the sensor is not limited, and the environmental information may be a numerical value representing environmental information with corresponding meaning, for example, when the environmental information acquired by the sensor is temperature information, the numerical value is 36 ℃; or a binary data string of the numerical value representing the environmental information with the corresponding meaning after the numerical value conversion, for example, when the environmental information acquired by the sensor is the smoke concentration, the binary data string is 00101011; more specifically, the environmental information and the specific numerical value corresponding to the environmental information may be directly output, for example, when the environmental information acquired by the sensor is the soil PH value, the output content is "soil PH value: 5". The form of the environmental information collected by the sensor mentioned above is only one of the embodiments, and the form is not limited.
S11: and preprocessing the environmental information.
In order to make the environmental information more accurate, the environmental information is preprocessed.
Wherein, the environment information is preprocessed by adopting Lauda criterion, and the preprocessing comprises the following steps:
acquiring a sample array of the environmental information, wherein the sample array contains a plurality of sample measurement data;
determining an arithmetic mean of all sample measurement data in the sample array and a residual error of all sample measurement data;
determining a standard deviation according to the arithmetic mean and the residual error;
judging whether the residual error is larger than the integral multiple of the standard deviation;
if yes, determining the sample measurement data as an abnormal value and deleting the abnormal value;
if not, retaining the sample measurement data.
According to the above mentioned sample array, it is assumed that a set of sample measurement data is obtained, wherein all the sample measurement data are X respectively1,X2,…,Xn. The average value of the sample measurement data is calculated to obtain the arithmetic average value
According to the obtained arithmetic mean
By the formula
Calculating to obtain a residual error Vi. And obtaining the standard deviation sigma according to a calculation method for calculating the standard deviation. Judging each measurement data X in the sample array1,X2,…,XnWhether the requirement is satisfied, which in this embodiment is
If it satisfies
Determining the sample measurement data as an abnormal value and deleting the abnormal value; if it satisfies
The sample measurement data is retained. It should be noted that, in the step of preprocessing the environment information by using the ralda criterion, the residual error V is determinediWhether greater than an integer multiple of the standard deviation sigma. As a more preferred embodiment, a standard deviation σ of 3 times is taken. However, in this embodiment, the integer multiple of the standard deviation σ is not required, and may be determined according to the specific implementation.
S12: and establishing a data prediction model according to the environmental information obtained after preprocessing.
And establishing a data prediction model for the environmental information obtained after preprocessing by adopting a sliding window algorithm. The establishing of the data prediction model comprises the following steps:
acquiring an environment information array of a node to be predicted, wherein each data in the environment information array is sequentially environment information acquired according to a time sequence;
acquiring an average value of the first n-3 data in the environment information array, wherein n is a serial number of data to be predicted in the environment information array and is greater than 3;
determining a first intermediate value from the average value and the first coefficient;
acquiring the (n-2) th data in the environment information array;
determining a second intermediate value according to the (n-2) th data and the second coefficient;
acquiring the (n-1) th data in the environment information array;
determining a third intermediate value according to the (n-1) th data and the third coefficient;
and superposing the first intermediate value, the second intermediate value and the third intermediate value to obtain a data prediction model.
From the above-mentioned environment information arrays, it is assumed that a series of environment information arrays { Z }are acquired1,Z2,…,ZnAnd the 11 th data in the context information array is to be predicted, then n is 11 at this time. The environment information array contains 10 data at this time. The first (n-3) to 11-3 to 8 data out of 10 data, and the average value of the first 8 data was calculated and based on the average valueThe relation of the mean value and the first coefficient obtains a first intermediate value. In a preferred embodiment, the average value is multiplied by a first coefficient to obtain a first intermediate value. Wherein the first intermediate value can be expressed as
It will be understood that Xn-10、Xn-9、…、Xn-3The 10 th data, the 9 th data, …, and the 3 rd data measured before the predicted data, that is, the 1 st data, the 2 nd data, the 3 rd data, …, and the 8 th data in the environment information array, respectively.
And acquiring the (n-2) th data in the environment information array, and determining a second intermediate value according to the (n-2) th data and the second coefficient. In a preferred embodiment, the (n-2) th data is multiplied by a second coefficient to obtain a second intermediate value. Wherein the second intermediate value can be represented as Y2＝β×Xn-2. It will be understood that Xn-2The 2 nd data measured before the predicted data, that is, the 9 th data in the environment information array, respectively.
And acquiring the (n-1) th data in the environment information array, and determining a third intermediate value according to the (n-1) th data and a third coefficient. In a preferred embodiment, the (n-1) th data is multiplied by a third coefficient to obtain a third intermediate value. Wherein the third intermediate value can be represented as Y3＝γ×Xn-1. It will be understood that Xn-1The 1 st data measured before the predicted data, that is, the 10 th data in the environment information array, respectively.
At this time, the first intermediate value, the second intermediate value and the third intermediate value are superposed to obtain the predicted 11 th data
In addition, α, β, and γ need to satisfy the relationship of α + β + γ ═ 1; alpha is more than 0 and less than 1, beta is more than 0 and less than 1, and gamma is more than 0 and less than 1. If alpha is close to 1, the 11 th predicted data is more dependent on the average value of the data in the previous 8 environment information arrays, and the method is suitable for a gentle data scene; if alpha is close to 0, the 11 th data which represents the prediction is more dependent on the data in the 9 th and 10 th environmental information arrays, and the relationship with the data in the first 8 environmental information arrays is not large, so that the method is suitable for the scene of change-sensitive application; in the actual model, the recommended values of alpha, beta and gamma are 0.5, 0.3 and 0.2 respectively.
Thus, the 11 th data can be predicted according to the above example by regular reasoning, and the data prediction model is
Note that α, β, and γ need to satisfy a relationship of α + β + γ ═ 1; alpha is more than 0 and less than 1, beta is more than 0 and less than 1, and gamma is more than 0 and less than 1.
S13: and predicting the environmental information according to the data prediction model.
Wherein predicting the environmental information according to the data prediction model comprises:
judging whether the predicted value determined according to the data prediction model is larger than the threshold value of the node to be predicted or not;
if so, outputting a decision scheme and regulating and controlling the environmental information according to the decision scheme; the decision scheme can be output by an alarm device or a feedback controller to adjust the value of the environmental information of the monitored environment at the current time in advance so as to realize intelligent feedback and regulation in advance.
If not, returning to the step of acquiring the environmental information acquired by the sensor.
The method for predicting the environmental information of the Internet of things comprises the following steps: acquiring environmental information acquired by a sensor, wherein the environmental information at least comprises one of temperature information, humidity information, harmful gas concentration, smoke concentration, illumination intensity and soil pH value; preprocessing the environment information; establishing a data prediction model according to the environment information obtained after the preprocessing; and predicting the environmental information according to the data prediction model. When the communication module is in power failure or failure, the established data prediction model can predict the unfinished transmitted environmental information through the data prediction model, so that the missing data can be supplemented, and the prediction real-time performance and accuracy are realized. The condition that corresponding data is lacked when the transmission of the communication module fault environment information is not finished is avoided.
Fig. 2 is a flowchart of another method for predicting environmental information of an internet of things according to an embodiment of the present disclosure. As shown in fig. 2, after the environmental information is preprocessed and before the data prediction model is built according to the environmental information obtained after the preprocessing, the method further includes:
s20: and denoising the environmental information obtained after preprocessing.
Considering that various interferences exist in the environment, the noise is particularly strong. In order to make the prediction result more accurate, the existence of noise is not needed, so the environmental information obtained after preprocessing is subjected to denoising processing. The method for denoising the environmental information by adopting EMD comprises the following steps:
decomposing the environment information to obtain a plurality of components;
processing the components according to the noise superposition times;
and reconstructing the processed components to realize denoising processing.
The noise signal of the sensor node is assumed to be Gaussian white noise Wi(t) the raw signal of the sensor node is denoted as Xi(t) of (d). Can be represented by formula Xi(t)＝X(t)+Wi(t) i ═ 1,2, …, n, yields the raw signals of the sensor nodes. Wherein Xi(t) node signals of the sensor after the ith noise superposition; wiAnd (t) is a noise signal after the ith superposition on the sensor node, and d is the superposition frequency of the noise.
To Xi(t) decomposing the environmental information into m eigenmode functions (IMFs) by Empirical Mode Decomposition (EMD), wherein each IMF component comprises a ringLocal feature signals of different time scales of the environmental information. From this, the IMF component C is obtainedij(t) j ═ 1,2, …, m, and the remainder ri(t) of (d). Superimposing d times noise signal WiAfter (t), obtaining
Wherein C isij(t) is the ith superimposed Gaussian white noise Wi(t) post-decomposing the resulting jth IMF component. Computing each IMF component Cij(t) the average value of the (j) th IMF component obtained after EMD decomposition is
And finally, reconstructing the processed M IMF components to realize the denoising processing of the sensor nodes.
In all of the above mentioned embodiments, the number of deployed sensors in the monitoring environment is plural, and as a preferred implementation, the time interval required for the sensors to collect the environmental information is 2 min. The prediction method of the environmental information of the Internet of things can be flexibly adjusted according to various obtained scenes of different environmental information, so that feedback processing of the condition exceeding the threshold value of the sensor node is started in advance, and decision real-time performance of the system is improved.
In the above embodiment, a prediction method of environmental information of the internet of things is described in detail, and the application further provides an embodiment corresponding to the prediction device of the environmental information of the internet of things. Fig. 3 is a structural diagram of a prediction apparatus for internet of things environment information according to an embodiment of the present disclosure. As shown in fig. 3, the present application further provides a prediction apparatus for environmental information of the internet of things, including:
the acquisition module 30 is configured to acquire environmental information acquired by the sensor, where the environmental information at least includes one of temperature information, humidity information, harmful gas concentration, smoke concentration, illumination intensity, and soil PH value;
a preprocessing module 31, configured to preprocess the environment information;
the establishing module 32 is used for establishing a data prediction model according to the environmental information obtained after preprocessing;
and the prediction module 33 is configured to predict the environmental information according to the data prediction model. Since the embodiments of the apparatus portion and the method portion correspond to each other, please refer to the description of the embodiments of the method portion for the embodiments of the apparatus portion, which is not repeated here.
In addition, the embodiment of the application further provides a prediction system of the environmental information of the internet of things, which comprises:
a memory for storing a computer program;
a processor, configured to implement the steps of the method for predicting the environment information of the internet of things as mentioned in the above embodiments when executing the computer program.
The prediction system for the environmental information of the internet of things provided by the embodiment may include, but is not limited to, a smart phone, a tablet computer, a notebook computer, a desktop computer, or the like.
The processor may include one or more processing cores, such as a 4-core processor, an 8-core processor, and so on. The processor may be implemented in at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), and Programmable Logic Array (PLA). The processor may also include a main processor and a coprocessor, where the main processor is a processor for Processing data in a wake state, and is also called a Central Processing Unit (CPU); a coprocessor is a low power processor for processing data in a standby state. In some embodiments, the processor may be integrated with a Graphics Processing Unit (GPU) that is responsible for rendering and drawing the content that the display screen needs to display. In some embodiments, the processor may further include an Artificial Intelligence (AI) processor for processing computational operations related to machine learning.
The memory may include one or more computer-readable storage media, which may be non-transitory. The memory may also include high speed random access memory, as well as non-volatile memory, such as one or more magnetic disk storage devices, flash memory storage devices. In this embodiment, the memory is at least used for storing a computer program, where after the computer program is loaded and executed by the processor, the relevant steps of the prediction method for environmental information of the internet of things disclosed in any one of the foregoing embodiments can be implemented. In addition, the resources stored by the memory may also include an operating system, data and the like, and the storage mode may be a transient storage mode or a permanent storage mode. The operating system may include Windows, Unix, Linux, and the like.
In some embodiments, the system for predicting the environmental information of the internet of things may further include a display screen, an input/output interface, a communication interface, a power supply, and a communication bus.
Those skilled in the art will appreciate that the above-mentioned architecture does not constitute a limitation of a prediction system for internet of things environment information and may include more or less components than the above-mentioned architecture.
The prediction system for the environmental information of the internet of things comprises a memory and a processor, wherein the processor can realize the prediction method for the environmental information of the internet of things when executing the program stored in the memory.
Finally, the application also provides a corresponding embodiment of the computer readable storage medium. The computer-readable storage medium has stored thereon a computer program which, when being executed by a processor, carries out the steps as set forth in the above-mentioned method embodiments.
It is to be understood that if the method in the above embodiments is implemented in the form of software functional units and sold or used as a stand-alone product, it can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium and executes all or part of the steps of the methods described in the embodiments of the present application, or all or part of the technical solutions. And the aforementioned storage medium includes: 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 method and the device for predicting the environmental information of the internet of things provided by the application are described in detail above. The embodiments are described in a progressive manner in the specification, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description. It should be noted that, for those skilled in the art, it is possible to make several improvements and modifications to the present application without departing from the principle of the present application, and such improvements and modifications also fall within the scope of the claims of the present application.
It is further noted that, in the present specification, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, 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. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
Claims (10)
1. A method for predicting environmental information of the Internet of things is characterized by comprising the following steps:
acquiring environmental information acquired by a sensor, wherein the environmental information at least comprises one of temperature information, humidity information, harmful gas concentration, smoke concentration, illumination intensity and soil pH value;
preprocessing the environment information;
establishing a data prediction model according to the environment information obtained after the preprocessing;
and predicting the environmental information according to the data prediction model.
2. The method for predicting the environmental information of the internet of things according to claim 1, wherein the step of establishing a data prediction model according to the environmental information obtained after the preprocessing comprises the steps of:
acquiring an environment information array of a node to be predicted, wherein each data in the environment information array is the environment information acquired according to a time sequence;
acquiring the average value of the first n-3 data in the environment information array, wherein n is the serial number of the data to be predicted in the environment information array and is greater than 3;
determining a first intermediate value from the average value and a first coefficient;
acquiring the (n-2) th data in the environment information array;
determining a second intermediate value based on the (n-2) th said data and a second coefficient;
acquiring the (n-1) th data in the environment information array;
determining a third intermediate value according to the (n-1) th data and a third coefficient;
and superposing the first intermediate value, the second intermediate value and the third intermediate value to obtain the data prediction model.
3. The method for predicting the environmental information of the internet of things according to claim 1, wherein after the preprocessing the environmental information and before the establishing a data prediction model according to the environmental information obtained after the preprocessing, the method further comprises:
and denoising the environmental information obtained after the preprocessing.
4. The method for predicting the environmental information of the internet of things according to claim 1, wherein the preprocessing the environmental information comprises:
acquiring a sample array of the environmental information, wherein the sample array contains a plurality of sample measurement data;
determining an arithmetic mean of all of the sample measurement data in the sample array and a residual error of all of the sample measurement data;
determining a standard deviation from the arithmetic mean and the residual error;
judging whether the residual error is larger than the integral multiple of the standard deviation;
if yes, determining the sample measurement data as an abnormal value and deleting the abnormal value;
and if not, retaining the sample measurement data.
5. The method for predicting the environmental information of the internet of things according to claim 3, wherein the denoising processing of the environmental information obtained after the preprocessing comprises:
decomposing the environment information to obtain a plurality of components;
processing the components according to the noise superposition times;
and reconstructing the processed components to realize denoising processing.
6. The method for predicting the environmental information of the internet of things according to claim 2, wherein the predicting the environmental information according to the data prediction model comprises:
judging whether a predicted value determined according to the data prediction model is larger than a threshold value of the node to be predicted or not;
if so, outputting a decision scheme and regulating and controlling the environmental information according to the decision scheme;
if not, returning to the step of acquiring the environmental information acquired by the sensor.
7. The method for predicting the environmental information of the internet of things according to claim 1, wherein a sliding window algorithm is adopted to predict the environmental information.
8. The method for predicting the environmental information of the internet of things according to claim 1, wherein the environmental information is preprocessed by using a Lauda criterion.
9. The method for predicting the environmental information of the internet of things as claimed in claim 3, wherein the environmental information is denoised by EMD.
10. A prediction device of environmental information of the Internet of things is characterized by comprising:
the acquisition module is used for acquiring environmental information acquired by the sensor, wherein the environmental information at least comprises one of temperature information, humidity information, harmful gas concentration, smoke concentration, illumination intensity and soil pH value;
the preprocessing module is used for preprocessing the environment information;
the establishing module is used for establishing a data prediction model according to the environment information obtained after the preprocessing;
and the prediction module is used for predicting the environmental information according to the data prediction model.
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