Intelligent frequency conversion integrated irrigation head flow control method

Abstract

The embodiment of the invention provides an intelligent frequency conversion integrated irrigation head flow control method. The method comprises the steps that the working frequency of a frequency converter is adjusted based on soil humidity data collected by a sensor and a preset threshold value; performing temperature and humidity compensation correction on the sensor data; calculating a difference value between the compensated actual flow and the set flow; adjusting the opening degree of the valve according to the difference value so as to correct the flow deviation; and flow control parameters are updated based on real-time data analysis, and the flow regulation and control precision is optimized. Through the scheme of the embodiment of the invention, the problem of flow measurement error caused by precision reduction of the sensor in a high-humidity environment in an intelligent variable-frequency integrated irrigation system can be solved.

Description

Technical Field

[0001] The present application relates to the technical field of agricultural irrigation, and in particular to an intelligent variable frequency integrated irrigation head flow control method. Background Art

[0002] An intelligent variable frequency integrated irrigation head flow control method refers to a method of accurately controlling farmland irrigation flow by integrating advanced sensor technology and automatic control system. This method uses intelligent variable frequency technology to dynamically adjust the working frequency of the water pump to achieve water supply on demand, improve water resource utilization, and reduce unnecessary energy consumption. However, in this intelligent variable frequency integrated irrigation system, since the field environment usually has a high humidity, the sensor is in a humid environment for a long time, resulting in a gradual decrease in its detection accuracy, which in turn causes the problem of flow measurement error. This error will directly affect the precise regulation effect of the irrigation system, resulting in a mismatch between the actual water supply and demand. Summary of the invention

[0003] In view of this, the embodiments of the present disclosure provide an integrated irrigation head flow control method with intelligent frequency conversion, which at least partially solves the problems existing in the prior art.

[0004] An integrated irrigation head flow control method with intelligent frequency conversion, comprising: Adjust the operating frequency of the inverter based on the soil moisture data collected by the sensor and the preset threshold; Perform temperature and humidity compensation correction on sensor data; Calculate the difference between the actual flow after compensation and the set flow; Adjust the valve opening according to the difference to correct the flow deviation; Update flow control parameters based on real-time data analysis to optimize flow control accuracy.

[0005] In one specific embodiment, performing temperature and humidity compensation correction on sensor data includes: Calculate the relative humidity H of the environment based on the collected soil moisture data; Calculate the air temperature T to evaluate the impact on the humidity sensor; A compensation algorithm is used to correct the humidity value in the sensor data to improve the measurement accuracy under high humidity conditions. The calculation formula is: Corrected humidity data = H × f(T), where f(T) is the humidity correction factor corresponding to temperature T, which is used to reduce the impact of temperature T on humidity reading H; The corrected humidity data is used in subsequent processing to improve the overall measurement accuracy and reliability.

[0006] In a specific embodiment, the step of correcting the humidity value in the sensor data using the compensation algorithm includes: Comparing the actual value T read based on the ambient temperature with the preset standard temperature T0 to obtain the temperature offset; If |T - T0| > ΔT, it means that the temperature T in the current environment deviates from the standard state, and correction needs to be performed according to the f(T) formula; Determine the humidity correction curve y = T / 10 + 1 for the current temperature, where y is the final application coefficient T in the formula, and T is the actual value of the temperature at this moment; Calculate the corrected humidity data using the obtained y value: Hnew = Horig × y.

[0007] In a specific embodiment, the following logical conditions are used to process the difference between the above temperature T and the preset standard temperature T0: Calculate the absolute difference value between the two; If this difference exceeds the set maximum allowable temperature fluctuation range △, that is, when the condition |T - T0| > △ holds; Perform a detailed compensation processing program; Otherwise, directly use the uncorrected original humidity data to continue executing other functions, ensuring that no extra compensation measures are required within the temperature range close to normal operation to reduce the processing load while maintaining sufficient accuracy.

[0008] In a specific embodiment, before executing the detailed compensation processing program, it should also be judged first: When the ambient temperature reaches an extreme situation (T > TA or T < TB), where TA is the high temperature limit and TB is the low temperature limit, trigger special compensation; Redefine the humidity correction function f(T) based on this special situation to make it more suitable for the current extreme conditions; Use the new correction function to readjust the humidity measurement; Finally, ensure that relatively accurate measurement results can be obtained even under harsh conditions through the adjusted data.

[0009] In a specific embodiment, the special compensation condition in the extreme situation is triggered when: After the actual air temperature T sensed by the sensor exceeds the upper critical temperature value TA or is lower than the lower critical temperature TB; Establish the f formula in the specific situation according to the new temperature and humidity distribution; If TA - T <= C1 or TB - T >= C2, it means that the temperature change has affected the effectiveness of the ordinary compensation algorithm (C1 and C2 are the high temperature low tolerance difference and the cold low temperature limit respectively); At this point, a more complex and sophisticated sensor signal calibration process will be initiated to ensure that the measurement is not inaccurate due to large temperature changes.

[0010] In a specific embodiment, a specific method is used to adjust the parameters of a specially designed compensation function f(T) during a special weather pattern to ensure the best performance, including but not limited to the following processing rules: When the environmental condition is detected to be in a high / low temperature warning state; Setting adaptive learning thresholds D1 and D2 (corresponding to high and low sensitivity, respectively) to distinguish between daily temperature fluctuations and extreme phenomenon onset identifiers; When T is found to be beyond the normal operating range (D2>T>T1 or T <db>D2), indicating the entry into the emergency response phase; Dynamically adjust sensitive constants until the emergency situation is exited and restore the original default algorithm configuration.

[0011] The disclosed embodiment provides an intelligent variable frequency integrated irrigation head flow control method, including: adjusting the working frequency of the frequency converter based on the soil moisture data collected by the sensor and the preset threshold; performing temperature and humidity compensation correction on the sensor data; calculating the difference between the actual flow after compensation and the set flow; adjusting the valve opening according to the difference to correct the flow deviation; updating the flow control parameters based on real-time data analysis to optimize the flow control accuracy. Through the solution of the disclosed embodiment, the flow measurement error problem caused by the decrease in sensor accuracy in a high humidity environment in the intelligent variable frequency integrated irrigation system can be solved. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions of the exemplary implementation methods of the embodiments of the present disclosure, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the embodiments of the present disclosure and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0013] Figure 1 It is a flow chart of an integrated irrigation head flow control method with intelligent frequency conversion; DETAILED DESCRIPTION

[0014] The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

[0015] The following describes the embodiments of the present disclosure through specific examples, and those skilled in the art can easily understand other advantages and effects of the present disclosure from the contents disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all of the embodiments. The present disclosure can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present disclosure. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present disclosure.

[0016] Next, referring to the accompanying drawings, an intelligent variable frequency integrated irrigation head flow control method of the present invention is described. First, the basic steps are analyzed one by one, and examples are used to more clearly demonstrate the application of these steps in practice.

[0017] The first step of the irrigation control method is to start the data collection process of key information such as environmental factors such as moisture content at the beginning of the system operation. This is usually done by various soil moisture sensing devices deployed at specific locations in the field. These sensors regularly collect the current moisture value in the soil. Then, based on the information obtained, the system will refer to the previously set standard moisture limit to decisively adjust the core component connected to the motor controller as a speed change mechanism, thereby accurately controlling the liquid flow rate in the delivery pipeline to ensure that crop needs are met while avoiding unnecessary waste; for example, the system monitors in real time through sensors installed in the field that the moisture value of the soil at a certain location is lower than a predetermined threshold (assuming that the value is set to 30% volumetric moisture). At this time, the controller will increase the frequency of driving the pump according to the predetermined program, so that the pump works faster, thereby increasing the amount of water flowing to the area.

[0018] Then we proceed to the second step - perform correction processing operations on the information fed back by the detection unit. This link mainly takes into account the meter reading errors that may be caused by factors such as air temperature and excessive soil humidity. In order to effectively overcome these problems, we use built-in algorithms to make necessary improvements to the original collected data to make it closer to the actual situation; in one embodiment, the sensor not only monitors humidity but also the current air temperature. If the soil humidity data is 95% and the air temperature is 28 degrees Celsius, the original humidity signal may fluctuate by about 1% to 3%. At this time, the control center uses a specially written mathematical model to offset the negative effects of such external disturbances, such as combining the original humidity value with the temperature compensation coefficient under standard conditions to obtain a result close to the actual situation, and use the same method to correct other types of errors to ensure that the accuracy of decision-making in subsequent processes is not affected, interfered with or misled, so that the overall system performs more reliable and stable.

[0019] Calculating the specific difference between the actual flow rate after correction and the predetermined target value is the main task to be carried out in the third step. Through such an activity, we can clearly understand how much the actual operating status is different from the ideal status and provide guidance for further appropriate measures. Specifically, when the adjustment is completed, the system will calculate the difference between the actual water flow rate after correction and the preset given amount. In this example, the target flow value is set to ten cubic meters per hour. If the actual calculated result is eight cubic meters per hour, it means that the water flow rate is two units lower every sixty minutes.

[0020] Based on the differential information obtained above, the fourth major item is to timely adjust the terminal output part, that is, to change the switch position of the pipe diameter leading to the crop supply port so as to prompt the system to return to the established track to achieve precise pouring function. For example, according to the aforementioned differential analysis, it is known that the water supply needs to be increased, so the control software automatically instructs the actuator to open more angles to allow sufficient volume to flow into the field.

[0021] The last step, the fifth step, relies on the detailed dynamic conditions accumulated from the previous rounds of repeated calculations to achieve the goal of further improving the overall control performance and reducing the investment required for long-term operation through continuous inspection and summary. In the context of this case, it means that after the entire operation cycle is over, the management end will conduct an in-depth analysis of all important records during this period and extract key knowledge points based on this to update the reference framework for future work, so that the machine can gradually master more effective adjustment skills in the process of learning and accumulation day by day. Based on real-time data analysis, if it is found after a period of observation that the same correction factor can be adjusted to achieve good control effects under similar conditions (for example, similar temperature and humidity in the morning and evening every day), the experience data will be summarized and sorted, and entered into the learning database for direct call next time, saving redundant computing resources and improving the overall efficiency of the irrigation automation system. In this way, the entire control system can not only give immediate response to existing environmental changes, but also continuously self-iterate and improve in the long-term operation accumulation, moving towards higher standards.

[0022] Next, the temperature and humidity compensation correction of the sensor data of the present invention is described. According to the content of this description, the process can be subdivided into multiple steps for implementation. First, the relative humidity H of the environment is calculated based on the collected soil moisture data, where H represents the relative humidity of the air in the environment, which is a percentage value, varying between 0% and 100%. This step mainly infers the saturation of water vapor in the environment by measuring the moisture state in the soil and combining meteorological factors. Subsequently, the air temperature T is calculated to evaluate the impact on the humidity sensor. T is usually expressed in degrees Celsius, and the range of values ​​varies depending on the actual environmental changes. In some agricultural environments, T can be between 0°C and 40°C. After obtaining these data, the compensation algorithm can be further applied. Specifically, the compensation algorithm is used to correct the humidity value in the sensor data to improve the measurement accuracy under high humidity conditions. Corrected humidity data = H × f(T), in this formula, f(T) is used as the humidity correction factor corresponding to temperature T, the purpose of which is to weaken the interference caused by temperature changes on the original humidity readings and ensure that the data reflects the real situation as much as possible. f(T) can be obtained by consulting data or experimental determination. It is usually an empirical coefficient between 0 and 1. For most cases, the best compensation effect is maintained, and the preferred value is close to 1 but less than 1. The last task is to apply the corrected humidity data to the subsequent process to ensure that the irrigation system can more accurately determine when and how much water is needed to maintain the optimal moisture conditions of the soil, thereby enhancing overall performance and efficiency.

[0023] For example, in a specific application example, a variety of sensors are installed in a farmland with automated irrigation management. Among them, the soil moisture sensor constantly monitors the moisture level of the land, but when the ambient temperature is too high or too low, the directly measured data may be inaccurate, especially during the season change period. At this time, if the temperature is high (assuming it is 30°C), then the actual humidity data calculated based on the optimal temperature compensation coefficient f(30°C)=0.9 obtained through historical records or experiments in advance should be slightly lower than the humidity value before compensation, so that the irrigation operation can be guided more accurately, reducing the risk of wasting water resources due to over-irrigation or the possibility of crop yield reduction due to lack of water, thereby making intelligent flow regulation more scientific and reasonable.

[0024] Next, the steps of using the compensation algorithm of the present invention to correct the humidity value in the sensor data are described. First, the actual value T read based on the ambient temperature is compared with the preset standard temperature T0 to obtain the temperature offset, that is, the temperature change in the environment is evaluated by calculating the difference between T and the preset standard temperature T0. If the absolute value of the temperature difference between the two, |T - T0|, is greater than the predefined threshold ΔT, it indicates that the temperature T in the current environment has deviated from the standard state. At this time, the collected data needs to be further processed to improve the accuracy of the measurement; the reason for setting ΔT here is to avoid unnecessary processing caused by small temperature fluctuations. The reasonable range is usually between 1 and 5 degrees Celsius, and the optimal value can be selected as 2°C. Then, the function f(T) is applied to adjust the original humidity value of the sensor according to the temperature difference.

[0025] Specifically, when it is determined that the temperature needs to be adjusted, the formula y=T / 10+1 is used to obtain the corresponding humidity correction coefficient y. This means that the final application coefficient y is a linear gain term obtained by proportional addition based on the actual reading T of the ambient temperature. This setting is intended to reflect that every ten-degree change in ambient temperature will bring an amplification effect to the humidity correction, thereby ensuring that the humidity information obtained at different temperatures is more accurate and reliable.

[0026] After applying the y value, the next step is to apply this proportional gain to correct the data part of the humidity in the original reading, and re-determine the actual available humidity value Hnew according to the formula Hnew=Horig×y. The parameter Horig refers to the initial humidity information measured by the sensor without any modification or calibration; in this way, it is ensured that even if the external conditions change, it will not affect the performance of the overall function of the system, so that the humidity data finally calculated is more accurate and reliable and suitable for the application requirements of the intelligent variable frequency integrated irrigation control strategy. For example, in one embodiment, when the system detects that the ambient temperature of the farmland area is 32°C and the set standard temperature is 30°C and exceeds the preset error limit, the temperature deviation coefficient is used for humidity correction calculation-take T = 32°C at this time y = 32 / 10 + 1 = 4.2, if the original sensed humidity value is 60%, the calculated corrected humidity value is 60*4.2 = 252%. Obviously, this is only an example and not a real number, but the principle is correct-this ensures that the field soil moisture state close to the real state can be obtained under different temperature difference conditions, so as to make reasonable irrigation control decisions.

[0027] Next, the present invention describes how to process the difference between the above temperature T and the preset standard temperature T0 using the following logical conditions: Calculate the absolute difference between the two; This step involves obtaining the specific numerical value of the difference between the current temperature T (the actual measured temperature in the environment or inside the device) and the expected or nominal normal operating temperature T0. This operation is achieved through the mathematical absolute value formula: calculate the result of |T-T0|. The absolute value means that no matter what the actual T is or what T0 is, when comparing, only the size of the distance between the two is considered, regardless of the positive and negative directions of the temperature difference. For example, if the ambient temperature T is recorded as 32 degrees Celsius and the ideal operating temperature of the device is set to T0=25 degrees Celsius, the temperature difference between the two is |32-25| = 7 degrees Celsius.

[0028] If the difference exceeds the maximum temperature fluctuation range △, that is, when the condition |T-T0|> △ is met; This conditional judgment step is used to confirm whether the actual temperature difference calculated above exceeds the allowable change threshold, recorded as △. This is usually to ensure that the equipment can maintain effective working efficiency and accuracy under the condition of acceptable small temperature changes. If |T-T0|=7, and the preset maximum temperature allowable fluctuation range △ is 10 degrees Celsius, then because the condition 7 < 10, the condition has not yet reached the situation of triggering additional processing. However, if △ is reduced to a more stringent value such as 5 degrees Celsius, the result of 7 > 5 will be found, leading to the next more detailed data compensation link.

[0029] Conduct detailed compensation processing procedures; Once the temperature exceeds the limit conditions described above, the collected data needs to be corrected in more detail to prevent inappropriate external factors from affecting the quality of decision-making. Especially in the process of flow control in irrigation systems, it is particularly important to ensure that the measurement results are accurate and reliable.

[0030] Otherwise, the uncorrected raw humidity data is used directly to continue executing other functions, ensuring that no unnecessary compensation measures are required to reduce the processing load while maintaining sufficient accuracy when the temperature is close to the normal operating range.

[0031] On the contrary, when the temperature deviation between the measurement result and the ideal situation is within a negligible error (not exceeding △), in order to optimize the operating efficiency of the system and reduce unnecessary computing overhead, there is no need to implement additional information correction actions, thereby keeping the data processing process during system operation more efficient and fast while still ensuring the required working performance indicators.

[0032] In one embodiment, assume there is a controller device for intelligently adjusting the farmland water replenishment rate, which is monitoring the soil humidity in the field. The sensor readings indicate that the weather is relatively warm at that time and the soil is slightly dry, and the displayed ambient air temperature reaches T = 32°C. However, the target operating parameters based on this intelligent system design show that the optimal air temperature for activating the automatic drip irrigation function is T0 = 24 ± 6°C, where △ = 10 is specified here. So |T - T0| = |32 - 24| = 8. Since the calculated temperature difference of 8°C falls within the maximum allowable deviation △ = 10, no subsequent additional temperature correction steps will be triggered at this time. The system can directly complete the assessment of the soil humidity status based on the existing data, automatically adjust the water supply according to the results, ensure that the farmland is moderately moistened while reducing resource waste. If the future climate becomes unstable and the temperature difference is greater than 10°C, a complex temperature compensation algorithm should be activated to further calibrate the measured humidity value to cope with this adverse effect and ensure that the precise irrigation operation is not disturbed.

[0033] Next, before describing the detailed compensation processing procedure of the present invention, it should first be judged that when the ambient temperature reaches extreme conditions (T > TA or T < TB), where TA is the high temperature limit and TB is the low temperature limit, special compensation is triggered; based on this special situation, the humidity correction function f(T) is redefined to make it more adaptable to the current extreme conditions; the humidity measurement is readjusted using the new correction function; and finally, through the adjusted data, more accurate measurement results can be obtained even under harsh conditions.

[0034] First, before performing the detailed compensation processing, it is necessary to judge whether the ambient temperature exceeds the predetermined threshold values. These threshold values are the ambient high temperature limit TA and the low temperature limit TB. When the temperature value is higher than TA or lower than TB, it is determined that the extreme ambient temperature condition requiring special compensation has been reached. Here, it is assumed that TA is set to 40°C, which is a relatively high temperature point, and TB is set to 0°C as the low temperature limit. Setting these threshold values can avoid false triggering in normal environments. For example, in the operation of a farmland automatic irrigation system, if the ambient temperature is too high or too low, special attention needs to be paid to the change in the water loss rate because this change may exceed the normal conditions.

[0035] Once it is confirmed that the ambient temperature exceeds the normal range, the originally adopted basic humidity correction algorithm is corrected according to the actual situation. In this case, the calculation method of humidity correction is redefined as the humidity correction function f(T), which takes into account the more complex non-linear relationship between ambient temperature and humidity, especially considering the influence of temperature on the evaporation rate, so that the humidity value measured by the sensor in an abnormal temperature environment can be better calibrated.

[0036] For example, in a specific embodiment: If it is detected that the temperature in the field during summer continuously exceeds 40°C (i.e., reaching the extreme heat condition TA = 40°C), then the actual water content in the soil needs to be recalibrated. At this time, the new humidity calibration algorithm may add a coefficient to compensate for the deviation caused by the accelerated drying of the soil at high temperatures. This coefficient is based on the established historical temperature-humidity response model, optimizes the original signal output by the sensor under extreme conditions, and generates a new humidity measurement value with special compensation.

[0037] Finally, the revised calibration function is used to readjust the humidity. In this way, the measurement results obtained can still be close to the actual humidity level even in extreme climate environments. This means that whether in high temperature or freezing conditions, the system can rely on more accurate real-time data to adjust the irrigation process, thereby achieving the effect of saving resources and optimizing crop growth. For example, when the ambient temperature suddenly drops below the freezing point (T < TB) (such as setting TB equal to zero degree Celsius), the standard humidity measurement error caused by ice crystal formation is corrected through the newly developed low-temperature adaptation calibration strategy, and then accurate data collection and analysis are maintained to support the normal operation of the intelligent automatic irrigation system.

[0038] Next, the special compensation conditions for triggering extreme situations of the present invention are described as follows: When the actual air temperature T sensed by the sensor exceeds the upper critical temperature value TA or is lower than the lower critical temperature TB; a specific situation f formula is established according to the new temperature and humidity distribution; if TA - T ≤ C1 or TB - T ≥ C2, it means that the temperature change has affected the effectiveness of the ordinary compensation algorithm (C1 and C2 are the high-temperature low-tolerance difference and the cold low-temperature limit respectively); at this time, a more complex and refined sensor signal calibration process will be started to ensure that the measurement is not affected by large temperature fluctuations and becomes inaccurate.

[0039] The steps include: Enter the special compensation mode when the temperature T exceeds TA or T is lower than TB, where TA is the highest allowable working temperature and TB is the lowest working temperature, and these two values are determined based on the performance of the sensor material, such as TA being 70°C and TB being -20°C. Subsequently, the compensation formula is adjusted according to the new environmental conditions to ensure its applicability to the current conditions, and the f formula is calculated by collecting multiple variable information such as temperature and humidity. C1 is the value obtained by subtracting the measured T from TA and being less than or equal to a certain threshold, and the optimal value of this numerical range is from 2 to 10; C2 represents that TB minus the measured temperature is greater than or equal to a threshold within a certain range, and the preferred range is from 1 to 5. These parameter settings can effectively determine when a complex process should be adopted to deal with the situation where abnormal temperatures affect the measurement accuracy.

[0040] For example, in summer, when the temperature sensor detects an environment above 70°C, it indicates that the critical point has been reached. At this time, the above formula will be automatically applied to analyze the temperature and humidity data, and the signal output will be adjusted according to the results. Even if the temperature is as high as 80°C or even higher, the irrigation system can still obtain accurate data to make accurate control responses due to the activation of a complex correction scheme.

[0041] In one embodiment, if the temperature drops suddenly to -30°C, the system evaluates TB-T. Assuming that the current threshold selection is close to the limit, i.e., -18 to -25°C, since the absolute zero difference exceeds 5 degrees, this means that the ordinary algorithm is no longer effective and the high-precision correction program is immediately started to re-evaluate the working parameters of the entire system. By handling it in this way, problems such as improper irrigation water allocation caused by errors caused by extreme conditions can be avoided, thereby effectively improving the overall performance and reliability of the irrigation system. Specifically, when a temperature drop event occurs, causing the temperature sensor that was originally working within the normal range to exceed the established tolerance limit, it will automatically switch to a preset more accurate and complex signal compensation technology to maintain the original accuracy level and keep the effective management and control of the irrigation flow without interference.

[0042] Next, the invention describes the use of a specific method to adjust parameters of a specially designed compensation function f(T) during special weather patterns to ensure optimal performance, including but not limited to the following processing rules: when the environment is monitored to be in a high / low temperature warning state; setting adaptive learning thresholds D1 and D2 (corresponding to high and low sensitivities, respectively) to distinguish between daily temperature fluctuations and extreme phenomenon onset identifiers; when it is found that T exceeds the normal operating range (D2>T>T1 or T <db>D2) indicates that the emergency response phase has been entered; the sensitive constants are dynamically adjusted until the emergency situation is exited and the original default algorithm configuration is restored.

[0043] The process includes several core steps such as monitoring, judgment, response and recovery. Under certain circumstances, the system needs to detect whether it is operating in a high / low temperature warning state. At this time, temperature T is the focus of monitoring. Once the T value in the detection environment has exceeded the normal operating range and triggered the set sensitivity limit, the compensation mechanism will be activated to optimize the operating performance. The key parameters involved here include two automatic learning thresholds D1 and D2, which represent high or low temperature warning levels, respectively, to assist in identifying whether the system is facing a regular weather change or is about to encounter an extreme temperature difference event.

[0044] For example, in one embodiment, the control device of the integrated irrigation system with intelligent variable frequency predicts and adjusts the operation in advance according to the weather forecast information. If the temperature is expected to rise to a higher critical point of 33 degrees Celsius (D2=33) in the next few hours, and the normal operating range is 15 to 28 degrees Celsius (T1=15, DB=28), the control system begins to monitor the actual environmental value T; as the temperature rises and approaches or even exceeds the set safety upper limit (D2>T), it means that abnormal temperature rise is about to or has already occurred, which requires emergency response measures to intervene. At this time, through the pre-programmed design, the device automatically lowers the operating frequency to reduce power consumption and reduce the possibility of overheating of the equipment.

[0045] Dynamically adjusting sensitive constants is intended to enable the machine to respond quickly to external changes while avoiding overly aggressive regulation. For example, if the temperature continues to exceed the warning standard for a period of time (such as half an hour later) and does not drop on its own, the system will take more stringent cooling measures; assuming that through practical analysis, it is known that under such climatic conditions, lowering the frequency to 75% can effectively suppress internal heat accumulation without affecting normal irrigation. This is the best solution specifically customized for the current environment. On the contrary, if the ambient temperature then falls back to the safe value range and maintains a stable state for a period of time, it can be considered that the crisis has been resolved, and the system can then re-activate the original basic operation strategy. This method can flexibly adapt to different situations, ensuring efficient and long-term service of the equipment while protecting natural resources from unnecessary loss. This process shows how to customize the response behavior of the irrigation system based on weather changes to maintain a balance between optimal performance and resource efficiency.

[0046] An intelligent variable frequency integrated irrigation head flow control method of the present invention comprises: In the first step, the system first collects soil moisture data through sensors, which is directly related to the accuracy and timely response of subsequent processing; in the second step, after obtaining this basic information, the system automatically corrects the data using algorithms combined with set thresholds. This critical temperature and humidity compensation operation aims to improve the true reflection of the original readings; in the third step, through precise calculations, we compare the data obtained from the sensor and the ideal irrigation standard preset in advance to determine whether there is a deviation or difference between the two, and further determine the precise amount of flowing water; then in the fourth step, when the difference between the above two values ​​is identified, the error signal will be immediately fed back to the execution structure such as the valve device to adjust and correct the action to ensure that the final water flow can reach the expected target value range, that is, to achieve the effect of precise irrigation. In the last step of the entire control process, in order to continuously improve the working efficiency of the equipment and adapt to external changing conditions, we will quickly iterate the model learning experience based on the new information continuously received to better optimize the control variable parameters to reduce the possibility of errors. The above-mentioned processes are closely linked and work together to ensure that water control is still accurate enough in high-humidity working environments, effectively avoiding measurement errors caused by the environment and thus improving the risk of excessive or insufficient irrigation in traditional irrigation processes that may bring adverse risks to agricultural production benefits.

[0047] The specific implementation methods described above further illustrate the purpose, technical solutions and beneficial effects of the embodiments of the present disclosure. It should be understood that the above description is only the specific implementation method of the embodiments of the present disclosure and is not intended to limit the protection scope of the embodiments of the present disclosure. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present disclosure should be included in the protection scope of the embodiments of the present disclosure.< / db> < / db>

Claims

1. An integrated irrigation head flow control method with intelligent frequency conversion, characterized in that: Including: Adjust the operating frequency of the frequency converter based on the soil moisture data collected by the sensor and the preset threshold; Perform temperature and humidity compensation and correction on the sensor data; Calculate the difference between the actual flow rate after compensation and the set flow rate; Adjust the valve opening according to the size of the difference to correct the flow deviation; Update the flow control parameters based on real-time data analysis to optimize the flow control accuracy.

2. According to claim 1, an integrated irrigation head flow control method with intelligent frequency conversion is characterized in that: Performing temperature and humidity compensation and correction on the sensor data includes: Calculate the relative humidity H of the environment based on the collected soil moisture data; Calculate the air temperature T to evaluate the impact on the humidity sensor; Use a compensation algorithm to correct the humidity value in the sensor data to improve the measurement accuracy under high humidity conditions. The calculation formula is: corrected humidity data = H × f(T), where f(T) is the humidity correction factor corresponding to the temperature T, used to reduce the impact of the temperature T on the humidity reading H; Use the corrected humidity data for subsequent processing to improve the overall measurement accuracy and reliability.

3. The intelligent variable frequency integrated irrigation head flow control method according to claim 2 is characterized in that: The step of using the compensation algorithm to correct the humidity value in the sensor data includes: Compare the actual value T read based on the ambient temperature with the preset standard temperature T0 to obtain the temperature offset; If |T - T0| > ΔT, it means that the temperature T in the current environment deviates from the standard state and needs to be corrected according to the f(T) formula; Determine the humidity correction curve y = T / 10 + 1 for the current temperature. In the formula, y is the final application coefficient T, and T is the actual value of the temperature at this moment; Calculate the corrected humidity data using the obtained y value: Hnew = Horig × y.

4. The intelligent variable frequency integrated irrigation head flow control method according to claim 3 is characterized in that: The difference between the above temperature T and the preset standard temperature T0 is processed using the following logical conditions: Calculate the absolute difference value between the two; If this difference exceeds the set maximum allowable temperature fluctuation range △, that is, when the condition |T - T0| > △ holds; Perform a detailed compensation processing program; Otherwise, directly use the uncorrected original humidity data to continue executing other functions to ensure that no extra compensation measures are required within the temperature range close to normal operation to reduce the processing load while maintaining sufficient accuracy.

5. The intelligent variable frequency integrated irrigation head flow control method according to claim 4 is characterized in that: Before executing the detailed compensation processing program, it should also be judged first: When the ambient temperature reaches extreme conditions (T > TA or T < TB), where TA is the high temperature limit and TB is the low temperature limit, trigger special compensation; Redefine the humidity correction function f(T) based on this special situation to make it more adaptable to the current extreme conditions; Use the new correction function to readjust the humidity measurement; Finally, ensure that relatively accurate measurement results can be obtained even under harsh conditions through the adjusted data.

6. The intelligent variable frequency integrated irrigation head flow control method according to claim 5, characterized in that: The special compensation condition triggered in extreme situations is: After the actual air temperature T sensed by the sensor exceeds the upper critical temperature value TA or is lower than the lower critical temperature TB; Establish the f formula in the specific situation according to the new temperature and humidity distribution; If TA - T <= C1 or TB - T >= C2, it means that the temperature change has affected the effectiveness of the ordinary compensation algorithm (C1 and C2 are the high temperature low tolerance difference and the cold low temperature limit respectively); At this time, a more complex and refined sensor signal calibration process will be started to ensure that the measurement is not affected by large temperature fluctuations and becomes inaccurate.

7. The intelligent variable frequency integrated irrigation head flow control method according to claim 6, characterized in that: For the specially designed compensation function f(T), a specific method is used to adjust the parameters during special weather patterns to ensure the best performance, including but not limited to the following processing rules: When the environmental condition is detected to be in a high / low temperature warning state; Setting adaptive learning thresholds D1 and D2 (corresponding to high and low sensitivity, respectively) to distinguish between daily temperature fluctuations and extreme phenomenon onset identifiers; When T is found to be beyond the normal operating range (D2>T>T1 or T <db> D2), indicating the entry into the emergency response phase;< / db> Dynamically adjust sensitive constants until the emergency situation is exited and restore the original default algorithm configuration.

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