CN102762013A - Intelligent control system for greenhouse LED light source - Google Patents

Abstract

The invention relates to an intelligent control system for LED light sources in a greenhouse, including an environmental monitoring module for collecting environmental parameters of the greenhouse, for receiving control plan information of the plant growth process input by users, and for collecting operation plans for plant growth index parameters The processing module is a control center module used to generate control information, a storage module used to store data, a remote control interface module used for remote online monitoring and control, and an output module used to output environmental analysis information and control process information. An intelligent controller for controlling LED light sources according to control information, and a communication bus for providing a communication environment. Compared with the prior art, the present invention can automatically realize the requirement of regulating the light source according to the environmental parameters in the greenhouse and the growth status of the plants, so that the plants can obtain the best light ratio, save the energy consumption of the greenhouse, and can visualize the control process Express.

Description

Intelligent Control System of Greenhouse LED Light Source

technical field

The present invention relates to an intelligent control technology that applies LED light source regulation technology to the production process of greenhouse plants, and in particular to an intelligent control technology that can automatically realize the regulation of the light source according to the environmental parameters in the greenhouse and the growth status of plants, so that the plants can obtain The best light ratio and intelligent control system to save greenhouse energy consumption. the

Background technique

Illumination is very important for the growth and development of plants. As an autotrophic organism, plants use the energy of sunlight for photosynthesis. The 400-700nm wavelength of sunlight is the wavelength range of plant light and activity, also known as light and active light waves. . Among them, red light and blue light in the spectrum are the main reaction ranges of plant chlorophyll, so now the supplementary light in the greenhouse is usually mainly supplemented by red light and blue light. Saebo et al pointed out the effect of red light on starch accumulation in photosynthesis in Plant Cell, Tissue and Organ Culture in 1995. In 1982, Senger emphasized in Plant Physiology that blue light is related to the development of plant chloroplasts, the formation of chloroplasts and the opening and closing of stomata. the

While the quality of light is very important to plant growth and development, the duration of light also plays a role in plant growth and development. In the current off-season cultivation, daylight time and night light supplementation are very important for regulating (delaying or early) flowering. For example, the flowering light condition of long-day plants is to form a short dark night, which can be used for shading treatment or short-term supplementary light in the dark night, so as to break the plant's "perception" of darkness and achieve off-season flowering. That is to say, the lighting conditions of modern greenhouses can be properly supplemented and shaded according to the flowering time of plants. the

Modern greenhouses must not only be able to regulate the growth cycle of plants, fruit maturity, and flowering time of ornamental plants, but also be able to minimize energy input and gradually change from high energy consumption and high output to low energy consumption and high output. Coordinate the light intensity with the surrounding temperature, carbon dioxide concentration, humidity, etc., so as to maximize the light use efficiency (LUE) of the overall plant, thereby reducing the excessive supply of certain growth conditions (such as excessive carbon dioxide, Greenhouse temperature is too high) the waste of energy caused. However, the complementarity and interaction of all these require complex calculations and precise calculations. To sum up, in greenhouse planting, it is necessary to build an intelligent LED light source control system. the

Contents of the invention

The purpose of the present invention is to provide an intelligent control system for LED light sources for greenhouse plant growth lighting in order to overcome the above-mentioned defects in the prior art. The system can realize real-time collection and calculation of greenhouse environmental parameters and plant growth information, and generate Optimized operation plan, automatic control of LED light source based on information fusion technology, and provide scalable remote control interface for remote management. the

The purpose of the present invention can be achieved through the following technical solutions:

An intelligent control system for greenhouse LED light sources, including:

The environmental monitoring module is used to collect the environmental parameters of the greenhouse;

The operation plan processing module is used to receive the control plan information of the plant growth process input by the user, and collect the growth index parameters of the plant;

The control center module is connected to the environmental monitoring module and the operation plan processing module, according to the algorithm parameters, the reference table, the growth index parameters and control plan information received from the operation plan processing module, and the environmental parameters collected from the environmental monitoring module to generate Control information for information fusion;

The storage module, connected to the control center module, is used to store the generated control information, the reference tables required during the system operation, algorithm parameters, collected growth index parameters, environmental parameters and input control plan information;

Remote control interface module for remote online monitoring and control;

The output module is used to output environmental analysis information and control process information;

The intelligent controller controls the LED light source according to the control information generated by the control module;

The communication bus connects the control center module, the remote control interface module, the output module and the intelligent controller. the

The environment monitoring module includes a power supply unit and a central control unit powered by the power supply unit, a temperature detection unit, a humidity detection unit, a carbon dioxide detection unit, an illumination detection unit, and a radio frequency unit. The temperature detection unit, the humidity detection unit, the carbon dioxide The detection unit, illuminance detection unit, and radio frequency unit are all connected to the central control unit. The temperature detection unit, humidity detection unit, carbon dioxide detection unit, and illuminance detection unit respectively collect the parameters of temperature, humidity, carbon dioxide, and illuminance in the greenhouse and send them to the central control unit. Processing is performed, and the processing result is sent to the control center module through the radio frequency unit. the

The operation plan processing module includes an operation plan formulation unit, a greenhouse area planning unit, a planting process information collection unit and an output unit, and the operation plan formulation unit and the greenhouse area planning unit are used to input the control plan information of the user, planting The process information collection unit is used to collect plant growth index parameters, and the output unit sends control plan information and growth index parameters to the control central module. the

The control central unit includes a parameter information input unit, a primary information fusion unit, a secondary information fusion unit, an information fusion evaluation unit and a control information output unit, and the parameter information input unit receives the environmental parameters collected by the environmental monitoring module And the growth index parameters and control plan information collected from the operation plan processing module are sequentially input to the first-level information fusion unit and the second-level information fusion unit for processing, and at the same time, the algorithm parameters and reference table are called to generate control information, which is carried out through the control information output unit. send. the

The remote control interface module includes a remote control client, a wireless gateway unit, a wireless network and an identity authentication unit, and the remote control client, the identity authentication unit and the wireless gateway unit perform data transmission through a wireless network, and the wireless gateway The unit routes the transmitted data and connects to the identity authentication unit, which identifies the intelligent controller, the remote control client and the sensor of the environmental monitoring module. the

The wireless network includes GSM network, GPRS network or Zigbee network. the

The communication bus supports communication protocols including HTTP, MODBUS and Zigbee, and the communication methods adopted include RS485 bus, CAN bus and Zigbee wireless mode. the

Compared with prior art, the present invention has the following advantages:

1) The present invention can collect the environmental parameters of the greenhouse in real time and perform digital preprocessing. the

2) The present invention can perform real-time non-destructive detection on growing plants and feed back to the light source control system. the

3) The present invention can customize the plant growth process control plan according to the user's needs and the plant growth history data preset in the storage unit. the

4) The present invention can coordinate and comprehensively process information from multiple sensors and other boundary condition reference values, and perform control strategy generation based on information fusion technology and automatic adjustment of LED light sources. the

5) The present invention can provide remote comprehensive control of the LED light source by technicians based on an open network. the

6) The present invention can visually express the complete intelligent control process of the greenhouse and the LED light source. the

Description of drawings

Figure 1 is the internal structure diagram of the greenhouse LED light source intelligent control system;

Figure 2(a) is a node structure diagram of the environmental monitoring module;

Fig. 2 (b) is the flow chart of the control program of the environmental monitoring module;

Fig. 3 is the internal structural diagram of operation plan optimization module;

Fig. 4 is the internal structural diagram of control center module;

Fig. 5 is the internal structural diagram of remote control interface module;

Fig. 6 is a division diagram of the greenhouse area according to the embodiment of the present invention. the

Detailed ways

The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments. the

Example

As shown in Figure 1, an intelligent control system for greenhouse LED light sources includes an environmental monitoring module 101, a job plan processing module 102, a control center module 104, a storage module 103, a remote control interface module 106, and an output module 107. N intelligent controllers and a communication bus 105 are set. the

Among them, the environmental monitoring module 101 is used to collect the environmental parameters of the greenhouse, such as: light, temperature, relative humidity, carbon dioxide and air. The greenhouse environmental parameters collected here need to follow the threshold reference table and conversion calculation preset in the storage module 103. Reference table for digitization preprocessing. the

The operation plan processing module 102 is used to receive the control plan information of the plant growth process input by the user, or provide the optimal strategy for large-scale continuous production according to the production schedule plan and plant growth history data preset in the storage unit 103, And collect the growth index parameters of the plants. the

The control center module 104 is used to coordinate and comprehensively process the information from multiple sensors and other boundary condition reference values, by transforming it on the domain of discourse to make it fuzzy, and then on the basis of fuzzy set theory, based on The expansion principle performs decision-level information fusion and gives the optimal control strategy. the

The storage module 103 is connected to the control center module 104, and is used to store generated control information, reference tables required during system operation, algorithm parameters, collected growth index parameters, environmental parameters, and input control plan information. the

The remote control interface module 106 is used for remote online monitoring and control, provides interface functions based on TCP/IP network protocol, adopts external data access and control commands, and finally provides users with remote comprehensive control based on open networks. As an element of the upper application system, this module improves system compatibility. the

The output unit 107 is used to output the analysis information of the greenhouse environment and the control process information, and perform visual representation. The intelligent controller controls the LED light source according to the control information generated by the control module. the

The communication bus 105 includes a plurality of communication management units. Each communication management unit includes a communication port type (for example: RS485 port, CAN port, ZigBee port, etc.), communication port parameters, communication mode (for example: point-to-point mode, one-to-many mode), etc., and the communication bus 105 is connected to the control center module 104 , a remote control interface module 106, an output module 107 and an intelligent controller to ensure information communication between these devices. the

Fig. 2 (a) shows the node structure of the environmental monitoring module 101 of the present invention, adopts wireless sensor network, specifically includes: central control unit, temperature detection unit, humidity detection unit, carbon dioxide detection unit, illuminance detection unit, radio frequency unit and power supply unit. The temperature detection unit, humidity detection unit, carbon dioxide detection unit and illuminance detection unit respectively collect the parameters of temperature, humidity, carbon dioxide and illuminance of the greenhouse, and send them to the central control unit for processing, and the processing results are sent to the control center module through the radio frequency unit. the

Fig. 2(b) shows the control program flow of the environment monitoring module 101 of the present invention. As shown in Figure 2 (b), in the present invention, the node first completes the initialization task of the hardware, including setting each parameter, detecting whether the hardware works normally, initializing memory, RAM, communication rate, serial port working mode, etc.; Start to initialize the intermediate communication protocol layer, then define the registration endpoint, define the input and output clusters, then actively scan the channel, select the appropriate PAN, send a connection request after completing these initialization tasks, and wait for the response of the network protocol. If the connection response information is sent out, the network access request of the node is completed, and the collection node enters the task cycle. the

The present invention fully considers the problem of energy consumption in the design. In addition to reducing energy consumption as much as possible during hardware design, the most important thing is that each part of the node cannot be allowed to process the working state for a long time during software control. The general situation of the acquisition node is periodic After being woken up, different processing will be done according to different types of events. Events in the present invention are divided into two types: control command and collection command, so after receiving the command, first judge the function code of the command, if it is a control command, then execute the control task, if it is a collection command, then start the collection The sensor completes the collection of environmental data, and after the collected data is processed, the API of the protocol layer is called to complete the data transmission. After the command is executed, the acquisition node will quickly enter the sleep state to save power consumption. the

Fig. 3 shows the internal structure of the operation plan processing module 102 of the present invention, including the operation plan formulation unit 201, the greenhouse area planning unit 202, the planting process information collection unit 203, and the output unit 204, wherein the operation plan formulation unit 201 is used to receive The plant planting operation plan input by the user includes detailed growth plans such as supplementary light of LED lights, day and night temperature during the growth period, etc., and the operation plan is stored in the storage module 103 after input. Taking the multi-headed chrysanthemums (9 varieties in total) planted in the modern greenhouse of the Pujiang base of Shanghai Jiaotong University as an example, in the initial stage of planting, the chrysanthemums were supplemented with light at night according to the growth plan. At this time, the lights at night will be turned on on time from 9:00 p.m. to 2:00 a.m. the next day according to the instructions issued by the system, so as to meet the production plan of plants with delayed flowering and flowering at the end of April. the

The greenhouse area planning unit 202 is used to receive the greenhouse area division data input by the user. In this embodiment, according to the arrangement of light switches and the different types of lights, the greenhouse of 2300 square meters is divided into 10 areas, as shown in Figure 6 , A1, A2, B1, B2, B3, and B4 correspond to different lighting types and lighting time respectively. the

The above-mentioned greenhouse area division data will also be stored in the storage module 103, and a template will be automatically generated for the user to refer to when repeating similar operation plans. the

The planting process information collection unit 203 is used to collect various index parameters during the growth process of the plant, after structured processing, and output the above data to the output unit 204 as an input source of the control center module 104 . the

Fig. 4 shows the internal structure of the control center module 104 of the present invention, which module includes: a parameter information input unit 301, a primary information fusion unit 302, a secondary information fusion unit 303, an information fusion evaluation unit 304 and a control signal output unit 305 . the

Wherein, the parameter information input unit 301 is used to receive the data collected from the sensor of the environmental monitoring module, and the data should be A/D converted and preprocessed. Because in general, multi-sensors often describe the same feature in the environment from different coordinate frames, and the time, space and expression they represent may be different, so they must be unified into a common space-time reference system . The preprocessing of data completes the overall coordinated management of time factors, space factors and work factors, and selects sensors and puts in the most suitable and reliable sensor groups to adapt to different conditions. Secondly, the parameter information input unit 301 should also receive the output from the job plan processing module 102 . the

Since there is always noise in the data measured by the sensor, there is an estimation error in the estimated value obtained according to the detection data, and the estimation error is also a random quantity, so the evaluation of an estimation algorithm is generally based on the mean square error. . For the detection information of multiple sensors, due to the different measurement accuracy and measurement environment of each sensor, the measurement accuracy must be different. If the multi-sensors are treated equally and the detection data are processed and utilized without distinction, it will inevitably bring about different detection results. Accuracy leads to errors in the processing results of the system, and sometimes this error can be large. Therefore, it is necessary to selectively distinguish the importance of sensors according to the position of each sensor in the detection system and the accuracy of detection. This is the basis of the adaptive weighted data fusion algorithm. the

The primary information fusion unit 302 adopts the self-adaptive weighted data fusion method, which is mainly used for the primary fusion of the detection data of the underlying sensors of the monitoring system, so as to provide more accurate on-site detection information and system status information for the secondary fusion. The comprehensive evaluation result of the weighted summation method can be expressed as:

$\mathrm{<span\; class="notranslate">\; S</span>}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; no</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}{\mathrm{<span\; class="notranslate">\; g</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>$

Among them, i=1, 2,...n, g _i is a single factor evaluation index, h _i is the corresponding weight. The change of the evaluation index provided in this method is linear, and it is a simple and easy method for rough evaluation. The key is to determine the weight of each factor. The determination of weight in the present invention can be based on following 3 kinds of methods:

(1) Determined based on experience

If the measurement data does not contain any basis for determining the weight. The weights of such unequal precision measurement data are determined based on experience. This method of determination requires extensive measurement experience and knowledge of errors to be competent. Usually, there is no experience to determine the weight by dividing the weight into four grades: the weight of the measured data judged to be negligence and error is 0; the weight of the less reliable measured data is 1; the weight of the good measured data is the best measurement The weight of the data is 3. the

(2) Determined according to the number of measurements

For equal-precision measurement, due to the different measurement times, the measurement results are not equal in precision. It is relatively simple to determine the weight of different precision data, and the measurement times can be directly used as the weight. The greater the number of measurements, the higher the accuracy of the obtained data and the greater the weight. In fact, this is consistent with the role played by weight in unequal precision measurement, which specifically characterizes the reliability of each data. the

(3) Determined according to the precision parameters of the data

For the unequal precision data to be processed, when the precision parameters of each data are known, in order to determine the weight of each data, these unequal precision data can be regarded as equivalent to the measurement times under the same precision measurement conditions. For the unequal precision formed by different, the precision parameter given by each data is regarded as the precision parameter equivalent to the measurement result obtained by different measurement times. the

In the same way, the data fusion values of other environmental parameters can be obtained. According to the respective weighting factors obtained from the variance of each sensor, the sensor with higher measurement accuracy has a higher weighting factor, and as the number of times increases, its weighting factor is calculated according to each measurement data in each measurement, which is in The importance of detection data processing is reflected by the weighting factor, so that based on the advantages of multi-sensors, the interference of environmental and other factors can be fully considered, the impact of large deviation data on measurement accuracy can be reduced, and the accuracy of the measurement system can be improved. sex. the

Taking the temperature sensor as an example, in this embodiment, 8 temperature sensors are installed in the greenhouse, and the data detected from the sensors are combined with the first-level fusion based on the adaptive weighting algorithm. The corresponding weights and variances calculated are shown in the following table :

The calculated fusion value is: 0.704

The secondary information fusion unit 303 is a decision-level fusion, and its purpose is to make a system output decision based on the result of the primary fusion. For the second-level fusion, because the amount of input information is different from each other, it cannot be directly fused at the data level, and it can be blurred by transforming it on the domain of discourse. Then, on the basis of fuzzy set theory, decision-level information fusion is carried out based on the expansion principle. the

In this embodiment, let X be the domain of discourse, m: σ→[0, 1] is the fuzzy measure, A∈σ, h is σ measurable, then the fuzzy integral of h on A is defined as:

Wherein, H _λ ={x|h(x)≥λ}. The above formula is the Sugeno fuzzy integral with respect to the fuzzy measure m.

When the universe of discourse X={x ₁ , x ₂ ,…x _n } is a finite set, and h(x ₁ )≥h(x ₂ )≥…≥h(x _n ), from the formula (2), we can get h in The Sugeno integral on X is defined as:

Wherein, X _i ={x ₁ , x ₂ , . . . x _i }. Due to the complexity of the calculation of the fuzzy measure m(X _i ), according to the method of constructing the g _λ measure, the calculation formula of the Sugeno integral is further obtained as:

Wherein, H(x ₁ )=g ₁ , H( _xi )=g _i +H(x _i1 )+λg _i H(x _i1 ).

Furthermore, the Choquet integral for the F measure:

$\mathrm{<span\; class="notranslate">\; C</span>}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; A</span>}}{<span\; class="notranslate">\; \&Integral;</span>}\mathrm{<span\; class="notranslate">\; hdg</span>}<span\; class="notranslate">\; =</span>{<span\; class="notranslate">\; \&Integral;</span>}_{\mathrm{<span\; class="notranslate">\; 0</span>}}^{<span\; class="notranslate">\; +</span><span\; class="notranslate">\; \&infin;</span>}\mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}<span\; class="notranslate">\; \&cap;</span>\mathrm{<span\; class="notranslate">\; A</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 5</span>}<span\; class="notranslate">\; )</span>$

Wherein, H _λ ={x|h(x)≥λ}.

When the domain of discourse X={x ₁ , x ₂ ,…x _n } is a finite set, and h(x ₁ )≤h(x ₂ )≤…≤h(x _n ), from the formula (5), we can get h in The calculation formula of the Choquet integral with respect to g on X is:

$\mathrm{<span\; class="notranslate">\; C</span>}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; x</span>}}{<span\; class="notranslate">\; \&Integral;</span>}\mathrm{<span\; class="notranslate">\; hdg</span>}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; no</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}\mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; h</span>}}_{{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; h</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&Center\; Dot;</span>\mathrm{<span\; class="notranslate">\; h</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 6</span>}<span\; class="notranslate">\; )</span>$

Among them, H(λ _i )={ _xi ,xi ₊₁ ,...,x _n }, h(x ₀ )=0

When fuzzy integral is used for comprehensive evaluation, fuzzy measure can represent the importance of each single factor evaluation index, and the determination of fuzzy measure is the key of this evaluation method. the

The information fusion evaluation unit 304 evaluates and compares the decision information obtained by the secondary fusion unit 303. In this embodiment, the root mean square error RMSE between the estimated value and the original value is used as the evaluation metric. the

$\mathrm{<span\; class="notranslate">\; RMSE</span>}<span\; class="notranslate">\; =</span>\sqrt{\mathrm{<span\; class="notranslate">\; mean</span>}<span\; class="notranslate">\; [</span>{<span\; class="notranslate">\; (</span>{\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; the\; y</span>}}}_{\mathrm{<span\; class="notranslate">\; it</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; it</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; ]</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 7</span>}<span\; class="notranslate">\; )</span>$

The control signal output unit 305 takes the output of the information fusion evaluation unit 304 as an important basis for the control signal, and the signal that satisfies the optimization model preset in the system is transmitted to the control signal output unit for output and used to control the distributed intelligent controller, so as to achieve The purpose of intelligent regulation of LED light source. the

The communication bus 105 is mainly used to ensure smooth communication between various devices, and supports various protocols such as HTTP, MODBUS, ZigBee, etc., and the communication mode can be selected from CAN bus, RS485 bus and ZigBee wireless mode. In order to improve the anti-interference performance of the system, photoelectric isolation is adopted between the transmission medium and the intelligent receiver of the lamp. the

If the intelligent receiver interface of the controlled LED lighting is an RS485 bus, the RS485 communication method and corresponding modules can be used. CAN bus and RS485 bus are connected in a similar way. The CAN bus has a complete communication protocol, and the speed is better than RS485, and the CAN node has the function of automatically closing the output in case of serious errors, so that the operation of other nodes on the bus will not be affected. the

The use of CAN and RS485 bus communication technologies for data transmission requires special investment in the laying of communication lines. Because the data transmission bus often has high-voltage pulse intrusion or long-term contact with high-voltage lines, the communication quality is easily affected. ZigBee is an emerging short-distance wireless communication technology based on the IEEE802.15.4 protocol. It can effectively solve the above problems and is very suitable for applications in the control field where there are many and scattered measurement nodes and do not require high data transmission rates. The LED lighting controller in the present invention is equipped with a ZigBee module, and can implement ZigBee networking simply and quickly. the

Figure 5 shows the internal structure of the remote control interface module 106 of the present invention, including: remote control client 401, GSM/GPRS network 402, wireless gateway unit 403, ZigBee wireless communication network 404 and identity authentication unit 405, for environmental detection The sensors are distributed in the monitoring area, and the collected data are sent to the nearest wireless routing node. The routing node selects the best route according to the routing algorithm, and establishes a corresponding routing list, which includes its own information and neighbor gateway information. The data is transmitted to the client of the remote monitoring center through the gateway, which is convenient for users to monitor and manage remotely. The identity authentication unit 405 identifies the smart controller, the remote control client, and the sensors of the environmental monitoring module. the

The remote control interface module has the following functions:

1) Group control: LED light sources can be divided into several groups according to different environments and operation plans, and different timing control schemes can be adopted for different groups. the

2) Point-by-point control: According to the lighting effect of the LED light source, the lighting band is controlled, and multiple controllers are accurately timed and controlled point-by-point to achieve the effect of saving electric energy. the

3) Remote PDA control: According to temporary control requirements or functional manual inspection requirements, each circuit or the entire system can be remotely controlled by logging in with a PDA mobile phone or sending a short message via GSM. the

4) PDA alarm function: Through the system settings, the relevant failure, early warning, and alarm information can be sent to the mobile phone of the responsible person through SMS. the

5) Electricity measurement and collection function: the data collected by the electric meter can be transmitted to the control center remotely, so as to make statistics on the electricity consumption of each power supply. the

6) Redundant function: because the present invention relies on CDMA/GPRS wireless network and Internet, the stability of network will play an important role to the stable operation of this system remote control, just in case, the present invention has the automatic operation function of leaving network, when network When a fault occurs, it will automatically switch to automatic control, and the system will operate independently according to the single control mode. the

The above is only a preferred embodiment of the present invention, it should be pointed out that, for those of ordinary skill in the art, without departing from the principle of the present invention, some improvements and modifications can also be made, and these improvements and modifications can also be made. It should be regarded as the protection scope of the present invention. the

Claims (7)

1. An intelligent control system for a greenhouse LED light source, characterized in that it comprises: The environmental monitoring module is used to collect the environmental parameters of the greenhouse; The operation plan processing module is used to receive the control plan information of the plant growth process input by the user, and collect the growth index parameters of the plant; The control center module is connected to the environmental monitoring module and the operation plan processing module, according to the algorithm parameters, the reference table, the growth index parameters and control plan information received from the operation plan processing module, and the environmental parameters collected from the environmental monitoring module to generate control information for information fusion; The storage module, connected to the control center module, is used to store the generated control information, the reference tables required during the system operation, algorithm parameters, collected growth index parameters, environmental parameters and input control plan information; Remote control interface module for remote online monitoring and control; An output module for outputting environmental analysis information and control process information; The intelligent controller controls the LED light source according to the control information generated by the control module; The communication bus connects the control center module, the remote control interface module, the output module and the intelligent controller. 2. The intelligent control system of greenhouse LED light sources according to claim 1, wherein the environmental monitoring module includes a power supply unit and a central control unit powered by the power supply unit, a temperature detection unit, a humidity detection unit, a carbon dioxide detection Unit, illuminance detection unit, radio frequency unit, the temperature detection unit, humidity detection unit, carbon dioxide detection unit, illuminance detection unit, radio frequency unit are all connected to the central control unit, temperature detection unit, humidity detection unit, carbon dioxide detection unit and illuminance The detection unit collects the temperature, humidity, carbon dioxide and illuminance parameters of the greenhouse respectively, and sends them to the central control unit for processing, and the processing results are sent to the control center module through the radio frequency unit. 3. The intelligent control system of the greenhouse LED light source according to claim 1, wherein the operation plan processing module includes an operation plan formulation unit, a greenhouse area planning unit, a planting process information collection unit and an output unit, the The operation plan formulation unit and the greenhouse area planning unit are used to input the user's control plan information, the planting process information collection unit is used to collect the growth index parameters of the plants, and the output unit sends the control plan information and growth index parameters to the control central module. 4. The intelligent control system of the greenhouse LED light source according to claim 1, wherein the control central unit includes a parameter information input unit, a primary information fusion unit, a secondary information fusion unit, an information fusion evaluation unit and The control information output unit, the parameter information input unit receives the environmental parameters collected from the environmental monitoring module and the growth index parameters and control plan information collected from the operation plan processing module, and sequentially input them to the first-level information fusion unit and the second-level information fusion The unit performs processing, and at the same time calls algorithm parameters and reference tables to generate control information, which is sent through the control information output unit. 5. The intelligent control system of greenhouse LED light sources according to claim 1, wherein the remote control interface module includes a remote control client, a wireless gateway unit, a wireless network and an identity authentication unit, and the remote control The client, the identity authentication unit and the wireless gateway unit perform data transmission through the wireless network. The wireless gateway unit routes the transmitted data and connects to the identity authentication unit. The identity authentication unit controls the intelligent controller, the remote control client and the environment. The sensors of the monitoring module perform identification. 6. The intelligent control system of greenhouse LED light sources according to claim 5, wherein said wireless network comprises a GSM network, a GPRS network or a Zigbee network. 7. The intelligent control system of greenhouse LED light sources according to claim 1, wherein the communication protocols supported by the communication bus include HTTP, MODBUS and Zigbee, and the communication methods adopted include RS485 bus, CAN bus and Zigbee wireless Way.

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