CN117939421A - Garden plant growth monitoring and management method and system based on Internet of things - Google Patents
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Abstract
The invention relates to the technical field of plant growth monitoring, and discloses a garden plant growth monitoring management method and system based on the Internet of things, wherein the algorithm comprises the following steps: arranging a plurality of sensor nodes in gardens, wherein each node comprises a soil humidity sensor, a temperature sensor and an illumination sensor, and the environmental parameters around plants are monitored in real time through the sensors; the sensor nodes send the acquired data to the cloud platform through wireless communication, and the cloud platform receives and stores the data to ensure the reliability and safety of the data; processing and analyzing the data acquired by the sensor, and extracting the information of the growth state of the garden plants; according to the plant growth state information, a growth state abnormality detection model is constructed for detecting abnormal conditions and performing parameter optimization, and once the abnormal plant growth is found, an alarm is given, so that monitoring and management of garden plant growth are realized.
Description
Technical Field
The invention relates to the technical field of plant growth monitoring, in particular to a garden plant growth monitoring management method and system based on the Internet of things.
Background
With the increasing progress of global urbanization, protection and management of urban landscape plants is becoming increasingly important. The garden plants are used as core components of urban greening, not only beautify urban environment and provide ecological services, but also play an important role in improving climate, purifying air, regulating hydrologic cycle and the like. However, due to the specificity of urban environments, the growth conditions of garden plants are affected by various factors, such as air quality, soil humidity, temperature, etc. Therefore, the garden plant growth monitoring management research based on the Internet of things is developed, and the method has important significance for realizing sustainable development of urban landscaping. The utility model provides a landscape plant growth monitoring management method and system based on thing networking, acquires landscape plant's growth information accurately, improves management efficiency.
Disclosure of Invention
In view of the above, the invention provides a garden plant growth monitoring and management method based on the internet of things, which aims at: 1) The method comprises the steps that environmental parameters around plants are monitored in real time through sensors, collected data are sent to a cloud platform through wireless communication to process and analyze the collected data, and information of growth states of garden plants is extracted; 2) And constructing a growth state abnormality detection model for detecting abnormal conditions of plant growth states, and performing parameter tuning on the constructed SVM model by utilizing a GA algorithm so as to realize rapid and accurate monitoring of abnormal plant growth states.
In order to achieve the above purpose, the invention provides a garden plant growth monitoring and management method based on the Internet of things, which comprises the following steps:
S1: arranging a plurality of sensor nodes in gardens, wherein each node comprises a soil humidity sensor, a temperature sensor and an illumination sensor, and the environmental parameters around plants are monitored in real time through the sensors;
S2: the sensor nodes send the acquired data to the cloud platform through wireless communication, and the cloud platform receives and stores the data to ensure the reliability and safety of the data;
S3: processing and analyzing the data acquired by the sensor, and extracting the information of the growth state of the garden plants;
S4: according to the information of the plant growth state, a growth state abnormality detection model is constructed for detecting abnormal conditions and optimizing parameters, and an alarm is given once the abnormal plant growth is found.
As a further improvement algorithm of the present invention:
further, the step S1 of arranging a plurality of sensor nodes in gardens includes:
s11: according to the garden scale and the plant distribution condition, the number and the layout mode of the required sensors are estimated to obtain more comprehensive data;
S12: determining the position of a sensor node according to the characteristics of gardens and the requirements of plants;
S13: according to the designed sensor position, the sensor nodes are arranged at the corresponding positions, so that the proper distance between the sensor and the plant is ensured, and the environmental parameters can be accurately acquired;
s14: and connecting the sensor node to an Internet of things communication network, and connecting and setting a unique identifier of the sensor node through a configuration network to ensure that data can be correctly transmitted to the cloud platform.
Further, the cloud platform in the step S2 receives and stores the data to ensure the reliability and safety of the data, and the method includes:
S21: dividing the received data into data blocks with fixed sizes, and generating a checksum or hash value for each data block for verifying the integrity of the data;
s22: encrypting the data block by using an encryption algorithm, and protecting confidentiality of data in the transmission process;
S23: redundancy coding is carried out on data to be transmitted so as to support automatic retransmission and repair of the data;
S24: the error detection and correction technology is adopted to detect and repair errors of the received data, whether the data is transmitted correctly can be determined by comparing the received data checksum with the checksum generated by the sender, and if errors are found, an error correction algorithm is used to recover the original data.
Further, the step S3 of processing and analyzing the data collected by the sensor to extract the information of the growth state of the landscape plant includes:
s31: preprocessing the acquired sensor original data, wherein the preprocessing comprises abnormal value removal and data smoothing;
s32: extracting key data of plant growth from the preprocessed data, wherein the key data comprise average temperature, soil humidity change rate and sunshine hours;
S33: extracting plant growth characteristics from the extracted key data;
s34: normalizing the extracted features, and mapping the value ranges of different features to a unified interval to eliminate the dimension difference among the different features;
S35: constructing a plant growth state information evaluation model according to the extracted characteristics and the tag data of the known plant growth state, and training the model by using the marked samples;
S36: and carrying out parameter optimization on the constructed plant growth state information evaluation model by using the acquired data to obtain an optimal plant growth state information evaluation model example.
Further, the preprocessing the collected sensor raw data in the step S31 includes:
S311: determining the size n of a moving average window;
s312: randomly initializing a window starting position k according to the window length n;
S313: for each window starting position, calculating an average value of data points in the window, wherein the average value is used as a smoothed current data point, and the calculation formula is as follows:
wherein, Representing smoothed data points, x t-(k-1) representing the kth-1 data point located in the window, n representing the window length;
S314: the above steps are repeated until the calculation completes the moving average of all data points.
Further, the step S4 of constructing a growth state abnormality detection model for detecting an abnormality includes:
Taking the pretreated plant growth state information as input, and whether growth state abnormality exists or not as output, constructing U SVM models, wherein each SVM model is a classification model, and U=10;
For the ith SVM model SVM i(τj,τk), classification of the growth abnormality discrimination results τ j and τ k can be achieved, τ j≠τk, where τ1 indicates the presence of a growth abnormality and τ2 indicates the absence of a growth abnormality;
The output result of any SVM model SVW i(τj,τk) is:
v＝WiZ+bi
Wherein:
v represents the output classification result of the plant growth state information Z of the SVM model, v is less than or equal to 1, if v is a positive value, the probability that |v| represents that the abnormal growth judging result corresponding to the plant growth state information Z is tau j, and if v is a negative value, the probability that |v| represents that the abnormal growth judging result corresponding to the plant growth state information Z is tau k;
w i represents the normal vector of any ith SVM model;
b i denotes a constant factor of any ith SVM model.
Further, the step S4 of performing parameter optimization includes:
S41: building a training objective function of any ith SVM model, wherein the calculation formula is as follows:
Wherein:
w i represents the normal vector of any ith SVM model;
b i denotes a constant factor of any ith SVM model.
S42: and performing parameter tuning on the constructed SVM model by utilizing a GA algorithm, wherein the GA algorithm is a genetic algorithm.
And in the step S42, performing dimension reduction operation on the extracted features to obtain dimension reduced feature vectors, wherein the dimension reduced feature vectors comprise:
The genetic algorithm parameter tuning flow of the ith SVM model is as follows:
s421: randomly generating a plurality of chromosomes, wherein each chromosome represents a parameter value of an SVM model, and the parameter value of the SVM comprises a normal vector W i of the SVM model and a constant factor b i of the SVM model; setting the maximum iteration number of the genetic algorithm as Max;
s422: calculating an objective function value of each chromosome ζ, and taking the calculated objective function value as a fitness value gamma (ζ) of the chromosome;
S423: selecting chromosomes by adopting a roulette method, wherein the probability of each chromosome being selected is as follows:
S424: calculating the replacement recombination rate of the selected chromosome, and if the replacement recombination rate is higher than a specified threshold value, carrying out replacement recombination on a part of the structure of the selected chromosome to generate a new chromosome, wherein the calculation formula of the replacement recombination rate is as follows:
Wherein:
Gamma max represents the maximum fitness value;
Gamma avg represents the average fitness value;
Alpha represents a constant between [0,1], which is set to 0.3;
S425: repeating the iteration steps S422 to S424 until the maximum iteration times Max are reached, and taking the SVM model parameters corresponding to the chromosome with the minimum fitness value as the parameters obtained by training; and finally training to obtain U SVM models.
In order to solve the above problems, the present invention provides a garden plant growth monitoring and management system based on the internet of things, which is characterized in that the system comprises:
The data acquisition module is used for monitoring environmental parameters around plants in real time through the soil humidity sensor, the temperature sensor and the illumination sensor;
The data preprocessing module is used for preprocessing the collected plant environment parameter data;
And the growth state abnormality detection module is used for judging whether the growth state abnormality exists or not by taking the pretreated plant growth state information as input.
In order to solve the above-mentioned problems, the present invention also provides an electronic apparatus including:
A memory storing at least one instruction;
The communication interface is used for realizing the communication of the electronic equipment; and
And the processor executes the instructions stored in the memory to realize the landscape plant growth monitoring and managing method and system based on the Internet of things.
In order to solve the above problems, the present invention further provides a computer readable storage medium, in which at least one instruction is stored, and the at least one instruction is executed by a processor in an electronic device to implement the above garden plant growth monitoring management method based on the internet of things.
Compared with the prior art, the invention provides a garden plant growth monitoring management method based on the Internet of things, which has the following advantages:
Firstly, the present solution proposes a growth state abnormality detection model for detecting abnormal conditions, including:
Taking the pretreated plant growth state information as input, and whether growth state abnormality exists or not as output, constructing U SVM models, wherein each SVM model is a classification model, and U=10;
For the ith SVM model SVM i(τj,τk), classification of the growth abnormality discrimination results τ j and τ k can be achieved, τ j≠τk, where τ1 indicates the presence of a growth abnormality and τ2 indicates the absence of a growth abnormality;
The output result of any SVM model SVW i(τj,τk) is:
v＝WiZ+bi
Wherein:
v represents the output classification result of the plant growth state information Z of the SVM model, v is less than or equal to 1, if v is a positive value, the probability that |v| represents that the abnormal growth judging result corresponding to the plant growth state information Z is tau j, and if v is a negative value, the probability that |v| represents that the abnormal growth judging result corresponding to the plant growth state information Z is tau k;
w i represents the normal vector of any ith SVM model;
b i denotes a constant factor of any ith SVM model.
Meanwhile, the scheme also provides a model parameter tuning method, which comprises the following steps:
The genetic algorithm parameter tuning flow of the ith SVM model is as follows:
s421: randomly generating a plurality of chromosomes, wherein each chromosome represents a parameter value of an SVM model, and the parameter value of the SVM comprises a normal vector W i of the SVM model and a constant factor b i of the SVM model; setting the maximum iteration number of the genetic algorithm as Max;
s422: calculating an objective function value of each chromosome ζ, and taking the calculated objective function value as a fitness value gamma (ζ) of the chromosome;
S423: selecting chromosomes by adopting a roulette method, wherein the probability of each chromosome being selected is as follows:
S424: calculating the replacement recombination rate of the selected chromosome, and if the replacement recombination rate is higher than a specified threshold value, carrying out replacement recombination on a part of the structure of the selected chromosome to generate a new chromosome, wherein the calculation formula of the replacement recombination rate is as follows:
Wherein:
Gamma max represents the maximum fitness value;
Gamma avg represents the average fitness value;
Alpha represents a constant between [0,1], which is set to 0.3;
S425: repeating the iteration steps S422 to S424 until the maximum iteration times Max are reached, and taking the SVM model parameters corresponding to the chromosome with the minimum fitness value as the parameters obtained by training; and finally training to obtain U SVM models.
Drawings
Fig. 1 is a schematic flow chart of a landscape plant growth monitoring and managing method based on the internet of things according to an embodiment of the present invention;
fig. 2 is a functional block diagram of a landscape plant growth monitoring and managing system based on the internet of things according to an embodiment of the present invention;
Fig. 3 is a schematic structural diagram of an electronic device for implementing a landscape plant growth monitoring and managing method based on the internet of things according to an embodiment of the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The embodiment of the application provides a garden plant growth monitoring and management method based on the Internet of things. The execution main body of the landscape plant growth monitoring management method based on the Internet of things comprises at least one of electronic equipment, such as a server side, a terminal and the like, which can be configured to execute the algorithm provided by the embodiment of the application. In other words, the garden plant growth monitoring and managing method based on the internet of things can be executed by software or hardware installed in a terminal device or a server device, and the software can be a blockchain platform. The service end includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like.
Example 1:
s1: a plurality of sensor nodes are arranged in gardens, each node comprises a soil humidity sensor, a temperature sensor and an illumination sensor, and environmental parameters around plants are monitored in real time through the sensors.
In the step S1, a plurality of sensor nodes are arranged in gardens, including:
s11: according to the garden scale and the plant distribution condition, the number and the layout mode of the required sensors are estimated to obtain more comprehensive data;
S12: determining the position of a sensor node according to the characteristics of gardens and the requirements of plants;
S13: according to the designed sensor position, the sensor nodes are arranged at the corresponding positions, so that the proper distance between the sensor and the plant is ensured, and the environmental parameters can be accurately acquired;
s14: and connecting the sensor node to an Internet of things communication network, and connecting and setting a unique identifier of the sensor node through a configuration network to ensure that data can be correctly transmitted to the cloud platform.
S2: the sensor nodes send the collected data to the cloud platform through wireless communication, and the cloud platform receives and stores the data, so that the reliability and safety of the data are ensured.
The step S2 of the cloud platform receiving and storing the data to ensure the reliability and safety of the data comprises the following steps:
S21: dividing the received data into data blocks with fixed sizes, and generating a checksum or hash value for each data block for verifying the integrity of the data;
s22: encrypting the data block by using an encryption algorithm, and protecting confidentiality of data in the transmission process;
S23: redundancy coding is carried out on data to be transmitted so as to support automatic retransmission and repair of the data;
S24: the error detection and correction technology is adopted to detect and repair errors of the received data, whether the data is transmitted correctly can be determined by comparing the received data checksum with the checksum generated by the sender, and if errors are found, an error correction algorithm is used to recover the original data.
S3: and processing and analyzing the data acquired by the sensor, and extracting the information of the growth state of the garden plants.
And S3, processing and analyzing the data acquired by the sensor in the step of extracting the information of the growth state of the landscape plant, wherein the method comprises the following steps:
s31: preprocessing the acquired sensor original data, wherein the preprocessing comprises abnormal value removal and data smoothing;
s32: extracting key data of plant growth from the preprocessed data, wherein the key data comprise average temperature, soil humidity change rate and sunshine hours;
S33: extracting plant growth characteristics from the extracted key data;
s34: normalizing the extracted features, and mapping the value ranges of different features to a unified interval to eliminate the dimension difference among the different features;
S35: constructing a plant growth state information evaluation model according to the extracted characteristics and the tag data of the known plant growth state, and training the model by using the marked samples;
S36: and carrying out parameter optimization on the constructed plant growth state information evaluation model by using the acquired data to obtain an optimal plant growth state information evaluation model example.
The step S31 of preprocessing the collected sensor raw data includes:
S311: determining the size n of a moving average window;
s312: randomly initializing a window starting position k according to the window length n;
S313: for each window starting position, calculating an average value of data points in the window, wherein the average value is used as a smoothed current data point, and the calculation formula is as follows:
wherein, Representing smoothed data points, x t-(k-1) representing the kth-1 data point located in the window, n representing the window length;
S314: the above steps are repeated until the calculation completes the moving average of all data points.
S4: according to the information of the plant growth state, a growth state abnormality detection model is constructed for detecting abnormal conditions and optimizing parameters, and an alarm is given once the abnormal plant growth is found.
And in the step S4, constructing a growth state abnormality detection model for detecting abnormal conditions, wherein the method comprises the following steps:
Taking the pretreated plant growth state information as input, and whether growth state abnormality exists or not as output, constructing U SVM models, wherein each SVM model is a classification model, and U=10;
For the ith SVM model SVM i(τj,τk), classification of the growth abnormality discrimination results τ j and τ k can be achieved, τ j≠τk, where τ1 indicates the presence of a growth abnormality and τ2 indicates the absence of a growth abnormality;
The output result of the arbitrary SVM model SVM i(τj,τk) is:
v＝WiZ+bi
Wherein:
v represents the output classification result of the plant growth state information Z of the SVM model, v is less than or equal to 1, if v is a positive value, the probability that |v| represents that the abnormal growth judging result corresponding to the plant growth state information Z is tau j, and if v is a negative value, the probability that |v| represents that the abnormal growth judging result corresponding to the plant growth state information Z is tau k;
w i represents the normal vector of any ith SVM model;
b i denotes a constant factor of any ith SVM model.
And in the step S4, parameter optimization is carried out, which comprises the following steps:
S41: building a training objective function of any ith SVM model, wherein the calculation formula is as follows:
Wherein:
w i represents the normal vector of any ith SVM model;
b i denotes a constant factor of any ith SVM model.
S42: and performing parameter tuning on the constructed SVM model by utilizing a GA algorithm, wherein the GA algorithm is a genetic algorithm.
Performing dimension reduction operation on the extracted features to obtain dimension-reduced feature vectors, wherein the dimension-reduced feature vectors comprise:
The genetic algorithm parameter tuning flow of the ith SVM model is as follows:
s421: randomly generating a plurality of chromosomes, wherein each chromosome represents a parameter value of an SVM model, and the parameter value of the SVM comprises a normal vector W i of the SVM model and a constant factor b i of the SVM model; setting the maximum iteration number of the genetic algorithm as Max;
s422: calculating an objective function value of each chromosome ζ, and taking the calculated objective function value as a fitness value gamma (ζ) of the chromosome;
S423: selecting chromosomes by adopting a roulette method, wherein the probability of each chromosome being selected is as follows:
S424: calculating the replacement recombination rate of the selected chromosome, and if the replacement recombination rate is higher than a specified threshold value, carrying out replacement recombination on a part of the structure of the selected chromosome to generate a new chromosome, wherein the calculation formula of the replacement recombination rate is as follows:
Wherein:
Gamma max represents the maximum fitness value;
Gamma avg represents the average fitness value;
Alpha represents a constant between [0,1], which is set to 0.3;
S425: repeating the iteration steps S422 to S424 until the maximum iteration times Max are reached, and taking the SVM model parameters corresponding to the chromosome with the minimum fitness value as the parameters obtained by training; and finally training to obtain U SVM models.
The landscape plant growth monitoring and managing system 100 based on the Internet of things can be installed in electronic equipment. According to the realized functions, the landscape plant growth monitoring management system based on the Internet of things can comprise a data acquisition module 101, a data preprocessing module 102 and a growth state abnormality detection module 103. The module according to the invention, which can also be referred to as a unit, refers to a series of computer program segments, which are stored in the memory of the electronic device, capable of being executed by the processor of the electronic device and of performing a fixed function.
The data acquisition module 101 is used for monitoring environmental parameters around plants in real time through a soil humidity sensor, a temperature sensor and an illumination sensor;
The data preprocessing module 102 is used for preprocessing the collected plant environment parameter data;
The growth state abnormality detection module 103 is configured to determine whether or not there is a growth state abnormality by using the pretreated plant growth state information as an input.
In detail, the modules in the landscape plant growth monitoring and managing system 100 based on the internet of things in the embodiment of the present invention use the same technical means as the landscape plant growth monitoring and managing method based on the internet of things described in fig. 1, and can produce the same technical effects, which are not described herein.
Example 3:
fig. 3 is a schematic structural diagram of an electronic device for implementing a landscape plant growth monitoring and managing method based on the internet of things according to an embodiment of the present invention.
The electronic device 1 may comprise a processor 10, a memory 11, a communication interface 13 and a bus, and may further comprise a computer program, such as program 12, stored in the memory 11 and executable on the processor 10.
The memory 11 includes at least one type of readable storage medium, including flash memory, a mobile hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device 1, such as a removable hard disk of the electronic device 1. The memory 11 may in other embodiments also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a smart memory card (SMART MEDIA CARD, SMC), a Secure Digital (SD) card, a flash memory card (FLASH CARD) or the like, which are provided on the electronic device 1. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device 1. The memory 11 may be used not only for storing application software installed in the electronic device 1 and various types of data, such as codes of the program 12, but also for temporarily storing data that has been output or is to be output.
The processor 10 may be comprised of integrated circuits in some embodiments, for example, a single packaged integrated circuit, or may be comprised of multiple integrated circuits packaged with the same or different functions, including one or more central processing units (Central Processing unit, CPU), microprocessors, digital processing chips, graphics processors, combinations of various control chips, and the like. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects respective parts of the entire electronic device using various interfaces and lines, executes or executes programs or modules (a program 12 for implementing landscape plant growth monitoring management based on the internet of things, etc.) stored in the memory 11, and invokes data stored in the memory 11 to perform various functions of the electronic device 1 and process the data.
The communication interface 13 may comprise a wired interface and/or a wireless interface (e.g. WI-FI interface, bluetooth interface, etc.), typically used to establish a communication connection between the electronic device 1 and other electronic devices and to enable connection communication between internal components of the electronic device.
The bus may be a peripheral component interconnect standard (PERIPHERAL COMPONENT INTERCONNECT, PCI) bus, or an extended industry standard architecture (extended industry standard architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. The bus is arranged to enable a connection communication between the memory 11 and at least one processor 10 etc.
Fig. 3 shows only an electronic device with components, it being understood by a person skilled in the art that the structure shown in fig. 3 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than shown, or may combine certain components, or may be arranged in different components.
For example, although not shown, the electronic device 1 may further include a power source (such as a battery) for supplying power to each component, and preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management, and the like are implemented through the power management device. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure detection circuit, power converter or inverter, power status indicator, etc. The electronic device 1 may further include various sensors, bluetooth modules, wi-Fi modules, etc., which will not be described herein.
Further, the electronic device 1 may further comprise a user interface, which may be a Display, an input unit, such as a Keyboard (Keyboard), and further, a standard wired interface, a wireless interface. Further, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the electronic device 1 and for displaying a visual user interface.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.
Claims (10)
1. The garden plant growth monitoring and management method based on the Internet of things is characterized by comprising the following steps of:
S1: arranging a plurality of sensor nodes in gardens, wherein each node comprises a soil humidity sensor, a temperature sensor and an illumination sensor, and the environmental parameters around plants are monitored in real time through the sensors;
S2: the sensor nodes send the acquired data to the cloud platform through wireless communication, and the cloud platform receives and stores the data to ensure the reliability and safety of the data;
S3: processing and analyzing the data acquired by the sensor, and extracting the information of the growth state of the garden plants;
S4: according to the information of the plant growth state, a growth state abnormality detection model is constructed for detecting abnormal conditions and optimizing parameters, and an alarm is given once the abnormal plant growth is found.
2. The method for monitoring and managing the growth of garden plants based on the internet of things according to claim 1, wherein the step S1 is to arrange a plurality of sensor nodes in the garden, and comprises the steps of:
s11: according to the garden scale and the plant distribution condition, the number and the layout mode of the required sensors are estimated to obtain more comprehensive data;
S12: determining the position of a sensor node according to the characteristics of gardens and the requirements of plants;
S13: according to the designed sensor position, the sensor nodes are arranged at the corresponding positions, so that the proper distance between the sensor and the plant is ensured, and the environmental parameters can be accurately acquired;
S14: and connecting the sensor node to an Internet of things communication network, and connecting and setting a unique identifier of the sensor node through a configuration network to ensure that data is correctly transmitted to the cloud platform.
3. The method for monitoring and managing the growth of garden plants based on the internet of things according to claim 1, wherein the cloud platform receives and stores the data in the step S2 to ensure the reliability and the safety of the data, comprising:
S21: dividing the received data into data blocks with fixed sizes, and generating a checksum or hash value for each data block for verifying the integrity of the data;
s22: encrypting the data block by using an encryption algorithm, and protecting confidentiality of data in the transmission process;
S23: redundancy coding is carried out on data to be transmitted so as to support automatic retransmission and repair of the data;
S24: and carrying out error detection and repair on the received data by adopting an error detection and correction technology, determining whether the data is transmitted correctly or not by comparing the received data checksum with the checksum generated by the sender, and if errors are found, recovering the original data by using an error correction algorithm.
4. The method for monitoring and managing the growth of garden plants based on the internet of things according to claim 1, wherein the step S3 of processing and analyzing the data collected by the sensor to extract the information of the growth state of the garden plants comprises the following steps:
s31: preprocessing the acquired sensor original data, wherein the preprocessing comprises abnormal value removal and data smoothing;
s32: extracting key data of plant growth from the preprocessed data, wherein the key data comprise average temperature, soil humidity change rate and sunshine hours;
S33: extracting plant growth characteristics from the extracted key data;
s34: normalizing the extracted features, and mapping the value ranges of different features to a unified interval to eliminate the dimension difference among the different features;
S35: constructing a plant growth state information evaluation model according to the extracted characteristics and the tag data of the known plant growth state, and training the model by using the marked samples;
S36: and carrying out parameter optimization on the constructed plant growth state information evaluation model by using the acquired data to obtain an optimal plant growth state information evaluation model example.
5. The method for monitoring and managing the growth of garden plants based on the internet of things according to claim 4, wherein the preprocessing of the collected sensor raw data in the step S31 comprises the following steps:
S311: determining the size n of a moving average window;
s312: randomly initializing a window starting position k according to the window length n;
S313: for each window starting position, calculating an average value of data points in the window, wherein the average value is used as a smoothed current data point, and the calculation formula is as follows:
wherein, Representing smoothed data points, x t-(k-1) representing the kth-1 data point located in the window, n representing the window length;
S314: the above steps are repeated until the calculation completes the moving average of all data points.
6. The method for monitoring and managing the growth of garden plants based on the internet of things according to claim 1, wherein the constructing a growth state abnormality detection model in the step S4 is used for detecting abnormal conditions, and comprises the following steps:
Taking the pretreated plant growth state information as input, and whether growth state abnormality exists or not as output, constructing U SVM models, wherein each SVM model is a classification model, and U=10;
For the ith SVM model SVM i(τj,τk), classification of the growth abnormality discrimination results τ j and τ k is realized, τ j≠τk, wherein τ is 1 to indicate the presence of growth abnormality, and τ is 2 to indicate the absence of growth abnormality;
the output result of any i-th SVM model SVM i(τj,τk) is:
v＝WiZ+bi
Wherein:
v represents the output classification result of the plant growth state information Z of the SVM model, v is less than or equal to 1, if v is a positive value, the probability that |v| represents that the abnormal growth judging result corresponding to the plant growth state information Z is tau j, and if v is a negative value, the probability that |v| represents that the abnormal growth judging result corresponding to the plant growth state information Z is tau k;
w i represents the normal vector of any ith SVM model;
b i denotes a constant factor of any ith SVM model.
7. The method for monitoring and managing the growth of garden plants based on the internet of things according to claim 1, wherein the step S4 of optimizing parameters comprises the steps of:
S41: building a training objective function of any ith SVM model, wherein the calculation formula is as follows:
Wherein:
w i represents the normal vector of any ith SVM model;
b i denotes a constant factor of any ith SVM model;
S42: and performing parameter tuning on the constructed SVM model by utilizing a GA algorithm, wherein the GA algorithm is a genetic algorithm.
8. The method for monitoring and managing the growth of garden plants based on the internet of things as set forth in claim 7, wherein the step S42 of performing parameter tuning on the constructed SVM model by using the GA algorithm comprises:
The genetic algorithm parameter tuning flow of the ith SVM model is as follows:
s421: randomly generating a plurality of chromosomes, wherein each chromosome represents a parameter value of an SVM model, and the parameter value of the SVM comprises a normal vector W i of the SVM model and a constant factor b i of the SVM model; setting the maximum iteration number of the genetic algorithm as Max;
s422: calculating an objective function value of each chromosome ζ, and taking the calculated objective function value as a fitness value gamma (ζ) of the chromosome;
s423: calculating chromosome selection probability and replacement recombination rate, and if the replacement recombination rate is higher than a specified threshold value, replacing and recombining part of structures of the selected chromosomes to generate new chromosomes;
S424: repeating the iteration steps S422 to S423 until the maximum iteration times Max are reached, and taking the SVM model parameters corresponding to the chromosome with the minimum fitness value as the parameters obtained by training; and finally training to obtain U SVM models.
9. The method for monitoring and managing the growth of garden plants based on the internet of things as set forth in claim 8, wherein the step of calculating the chromosome selection probability and the substitution recombination rate in the step S423 includes:
s91: selecting chromosomes by adopting a roulette method, wherein the probability of each chromosome being selected is as follows:
S92: calculating the replacement recombination rate of the selected chromosome, wherein the calculation formula of the replacement recombination rate is as follows:
Wherein:
Gamma max represents the maximum fitness value;
Gamma avg represents the average fitness value;
Alpha represents a constant between [0,1], which is set to 0.3.
10. Landscape plant growth monitoring management system based on thing networking, its characterized in that, the system includes:
The data acquisition module is used for monitoring environmental parameters around plants in real time through the soil humidity sensor, the temperature sensor and the illumination sensor;
The data preprocessing module is used for preprocessing the collected plant environment parameter data;
The abnormal growth state detection module is used for judging whether abnormal growth state exists or not by taking the pretreated plant growth state information as input so as to realize the garden plant growth monitoring and management method based on the Internet of things according to any one of claims 1-9.
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