In order to speed up the intelligent and standardized management of rice seed soaking and germination promotion in Heilongjiang reclamation area, and improve the germination rate of rice seed soaking and germination promotion, a design method of fuzzy control was put forward based on Mitsubishi FX2N series PLC as the controller, aiming at the temperature control requirements in the process of rice seed soaking and germination promotion. In order to meet the requirements of seed soaking and germination process, intelligent control of temperature rise, cooling, temperature control and heat preservation of rice buds was realized in the process of seed soaking and germination promotion. When the temperature of seed soaking and germination is controlled by fuzzy control, the control precision of the system is enhanced, the time of temperature regulation is shortened, and the energy consumption of the system is reduced, which provides reliable guarantee for the quality of subsequent seedling raising. Rice production mainly includes three stages: seed soaking and germination, seedling raising and field planting. By studying the temperature control method in the requirement of rice seed soaking and germination promotion technology, it has practical significance to improve the germination rate and seedling quality of bud seeds and ensure high yield of rice.
The existing research mainly studies the temperature control requirements and process characteristics of seed soaking and germination promotion from the mechanism of water soaking and the structure of seed soaking and germination promotion equipment and process control process.
At the same time, the traditional PID control method based on MCU is adopted to adjust the temperature by electric heater. Although the cost is low, the reliability and stability are poor, and it is not conducive to the expansion of the system. Therefore, this study proposes a fuzzy control system based on PLC, which can effectively improve the accuracy of temperature control, overcome the non-linear defects of the control system, and do not need to establish an accurate mathematical model, so as to enhance the degree of intelligence of the system. According to the temperature control requirements in seed soaking and germination process, the fuzzy control rules were established. The power of electric heater was regulated by PLC to achieve the germination temperature of rice in each working period of seed soaking and germination process, and to ensure the safety and reliability of seed soaking and germination. In the process of seed soaking and germination promotion, the core task is to control the temperature and time in the three stages of seed soaking, breast breaking and germination promotion [4,5]. Temperature control is accomplished by adjusting the heating power of electric heater. The fuzzy control system of seed soaking and germination is an intelligent and automatic control system based on PLC. The upper computer uses Huayan industrial computer to exchange data with the lower computer by wireless communication. The data of temperature sensor is collected and analyzed. As the feedback value of the fuzzy control, the fuzzy output is realized by the controller, and the temperature control function is realized by adjusting the electric heater in real time, so as to meet the temperature control process requirements of seed soaking and germination.
The system block diagram is shown in Figure 1. As one of the main methods of intelligent control theory, fuzzy control is mainly used to solve the control functions of complex systems which are difficult to be solved by traditional methods. The research object has the following characteristics. Model uncertainty. Common control systems are based on fixed model control system, similar to the requirements of Soaking Seed Germination on temperature, in the past, the temperature requirements were judged by human experience, which makes the control object have strong uncertainty. Mathematical model can not be found by traditional way, which can be solved by fuzzy control. Nonlinearity. In the traditional automatic control system, the system is required to be linear. For non-linear control objects, such as temperature control, it has large time delay and non-linearity, and the mathematical model is difficult to establish, which makes the traditional PID closed-loop temperature control effect worse [8]. Fuzzy control does not need to consider the mathematical model and complex situation of the control object. It can complete the formulation of control rules only according to the experience of operators, and the control rules are not accurate. Requirements of Intelligent System. The corresponding intelligent control system is mainly faced with complex control objects, which requires the control system to have its own decision rules and decision-making ability. At the same time, the controlled quantity can not only be adjusted at a fixed value, but also the whole control system can realize the functions of automatic fault diagnosis and emergency treatment. For the temperature control system of seed soaking and germination, it just has the characteristics of the above-mentioned fuzzy control. For this system, in the past, the temperature control of seed soaking and germination mainly relies on manual experience to adjust, the labor intensity is greater, and the process requirements are more complex, the coordination between various links is more, the operation is cumbersome, and the conventional PID regulator can not achieve the control. Effect. Therefore, the stability and reliability of the whole system can be improved by using the fuzzy control method to control the temperature in each operation period.
The core of the fuzzy control of PLC is to use the theory of fuzzy sets to detect the feedback value of the controlled object in real time and get the input deviation by comparing it with the given value. The precision of the input deviation is transformed into the fuzzy quantity, that is, the process of fuzzification. The fuzzy inference between the two is used to get a fuzzy control knot. Finally, it is converted into precise quantity, that is, the process of solving ambiguity, thus completing the fuzzy control of the whole PLC. In the temperature control system of seed soaking and germination, the temperature needed in each working period is regarded as the critical point of temperature control. The temperature of seed soaking and germination is collected in real time by FX2N-4AD. The deviation E and deviation rate EC are selected as the input variables of the controller, and the control variable U is used as the output variable of the control period. The output is controlled by FX2N-4AD analog output module. The power regulation circuit completes the power regulation of the heater. Among them, the judgment basis of fuzzy control of deviation e is divided into PB (positive large), PS (positive small), PZ (positive zero), NZ (negative zero), NS (negative small), NB (negative big), and the quantization domain of E is selected as (-3,3); while the deviation change rate EC and output u all adopt five fuzzy states, namely PB, PS, Z (zero), NS and NB, whose quantization domains are (-3,3) and (-4,4), respectively. According to the industrial requirements of seed soaking and germination control, when the water temperature deviation is large, the error should be slowly eliminated; when the error is small, the control accuracy should be improved to avoid overshoot, and the actual experience and process requirements of operators should be taken into account to formulate fuzzy rules, and the fuzzy rules in the controller should adopt “IF… AND… THEN… “Structural model. Therefore, the fuzzy inference rules are obtained as shown in Table 1. Let the actual variation range of deviation be (-2,2), the actual variation range of deviation rate be (-1,1). When the deviation is beyond the range, full power heating and stopping heating are adopted. The quantization factor of deviation e is Ke=1.5, and the quantization factor of deviation rate EC is Kec=3. The fuzzy deviation of u can be obtained by multiplying the quantization factor with the temperature value of sampling. The scattered values are shown in Table 2. For the output signal of u is analog signal 4-20 mA, the quantization factor Ku=16/9 of the control quantity u is obtained.
The weighted average method is used to solve the ambiguity, and the precise value of the output quantity U of the fuzzy control is obtained. Finally, the output of the fuzzy control quantity u is output to the power regulating circuit through FX2N-4AD, which completes the power regulation of the heater and achieves the technological essentials of the temperature control of seed soaking and germination. Ask. Fuzzy control of PLC is realized according to the fuzzy algorithm of the controller. The program flow of FX2N series PLC is shown in Figure 2. Through FX2N-4A D analog acquisition module, the four channels of soaking seed germination temperature were A/D converted and stored in a designated register. After comparing the set value with the sampling value, the deviation and the deviation change rate were obtained. The response speed of the system is greatly improved by deciding whether the sampling value exceeds the limit or not. If the input is fuzzified within the scope of the fuzzy control, the discrete value of the fuzzy output is obtained according to the fuzzy rules. Finally, the fuzzy output is converted to the actual output. The output quantity completes the process of deblurring, and the analog signal is output by FX2N-4AD.
The power regulation circuit is controlled to complete the power regulation of the heater, so as to realize the fuzzy control function of temperature in the process of seed soaking and germination. The experimental results show that the temperature regulation function of the controller using the fuzzy control algorithm in the process of seed soaking and germination is better than that of the PID control and the temperature control of the switching quantity used in the past. The temperature control effect is shown in Fig. 3. From Fig. 3, it can be seen that the static error of the fuzzy control system decreases greatly, about 0.5 C, and there is no negative deviation. When the temperature of seed soaking and germination changes slightly, the controller can control and adjust.
The dynamic control performance of the system is good and the precision is high, so that the temperature fluctuation range in the whole process of temperature regulation meets the requirements of the control technology. Ask. At the same time, the power consumption of the heater is about 30% lower than that of the traditional on-off control. Therefore, the use of fuzzy temperature control can improve the temperature in the process of immersion seed soaking and germination, which has the defects of large heat capacity, high volume coefficient and long time lag of system control. It has obvious effect on improving the control accuracy of the system and meets the requirement of seed soaking and germination. Process requirements for operation. Mitsubishi FX2N series PLC is used as the core of the temperature fuzzy controller for rice seed soaking and germination. The temperature control in each stage of rice seed soaking and germination is realized. The output of the controller is used to control the on-off of thyristor in the power regulating circuit, which effectively improves the working efficiency of the heater and the stability of the system. Through the actual operation of farms in reclamation area for more than one year, the system has the advantages of simple operation, friendly man-machine interface, convenient operation and maintenance, and easy expansion of the system.
It can effectively complete seed soaking and germination, thermostatic element improve the germination rate of rice, provide reliable guarantee for improving rice yield and quality, and is more conducive to the production of rice in reclamation area farms. Standardized operation management can satisfy the control precision required by production and has broad application prospects.