Humidity-based spray optimization method, device, equipment, medium and product

Abstract

The invention discloses a humidity-based spray optimization method, device and equipment, a medium and a product. The method comprises the following steps: calculating average temperature and total humidity of a spray area according to real-time temperature data and real-time humidity data of a plurality of positions of the spray area; when the total humidity is lower than the target relative humidity and the average temperature is higher than the target set temperature, the spraying device is controlled to start spraying; if the current total humidity is higher than a humidity early warning threshold value and the humidity change rate is higher than a humidity change rate threshold value or the current total humidity is continuously higher than a humidity standard exceeding threshold value within the filtering time length, the spraying amount of the spraying device is reduced; when it is monitored that the spraying area meets the preset stopping condition, the spraying device is controlled to stop spraying. Humidity data of different positions of a spraying area can be collected, the overall humidity condition can be reflected, spraying is dynamically adjusted with humidity as a constraint condition, effective and uniform cooling is achieved, water mist is prevented from being condensed into large water drops, resource waste is avoided, and environmental pollution and potential safety hazards are eliminated.

Description

Technical Field

[0001] This invention relates to the field of spray cooling technology, and in particular to a humidity-based spray optimization method, apparatus, equipment, medium, and product. Background Technology

[0002] Currently, mist cooling technology, as an effective means of localized environmental cooling, has been widely applied in outdoor and semi-outdoor scenarios such as outdoor bus stops, building canopies, and outdoor unit heat dissipation. Mist cooling technology uses a high-pressure plunger pump or water supply pipe to pressurize purified water to a specific pressure of 7-10 MPa, which is then transported through high-pressure resistant pipelines to specially designed nozzles. At the nozzles, the water atomizes due to high-speed motion, forming tiny water mist particles with a diameter of micrometers. These water mist particles rapidly evaporate in the air, absorbing a large amount of heat from the surrounding environment, thereby achieving a significant reduction in localized temperature.

[0003] However, spray cooling technology has the following problems in practical applications: First, due to the lack of real-time monitoring and feedback on the spray effect, overspraying is very likely to occur (micron-sized water mist condenses into large water droplets before it is fully evaporated), affecting user experience and environmental safety. For example, it can cause local air humidity to become oversaturated, which not only fails to bring a cooling feeling but also creates a stuffy and sticky feeling. Moreover, the unevaporated water droplets fall and leave watermarks on glass roofs and public facility surfaces, which easily attract dust and form stains that are difficult to clean, affecting aesthetics and significantly increasing the cost of later cleaning and maintenance. At the same time, water accumulation on the ground can make the ground slippery, posing a safety risk of people slipping and falling. Second, it is impossible to make dynamic judgments based on real-time environmental humidity and water mist evaporation efficiency, resulting in ineffective operation. For example, spraying continues even when the humidity is too high, causing waste of water and electricity resources and low overall energy efficiency. Third, it is impossible to carry out refined cooling management for large areas or areas with complex structures. For example, poor spray uniformity can easily lead to cooling dead zones, resulting in uneven cooling effects and poor overall performance. Summary of the Invention

[0004] This invention provides a humidity-based spray optimization method, device, equipment, medium, and product. It collects humidity data at different locations in the spray area to reflect the overall humidity situation. Using humidity as a constraint, it dynamically adjusts the spray to achieve effective and uniform cooling, while avoiding water mist condensation into large droplets and resource waste. It eliminates environmental pollution and safety hazards at the source and improves the user experience.

[0005] To achieve the above objectives, embodiments of the present invention provide a humidity-based spray optimization method, comprising: Acquire real-time temperature and humidity data at several locations within the spray area, and calculate the average temperature and overall humidity of the spray area based on the real-time temperature and humidity data; When the total humidity is lower than the target relative humidity and the average temperature is higher than the target set temperature, the spray device is controlled to start spraying. During the spraying process, if the current total humidity of the spraying area is higher than the humidity warning threshold and the humidity change rate is higher than the humidity change rate threshold, or if the current total humidity remains higher than the humidity exceeding threshold within the filtering time, the spraying volume of the spraying device shall be reduced. When the spray area is detected to meet the preset stop conditions, the spray device is controlled to stop spraying.

[0006] As an improvement to the above solution, the step of acquiring real-time temperature and humidity data at several locations within the spray area, and calculating the average temperature and overall humidity of the spray area based on the real-time temperature and humidity data, includes: Acquire real-time temperature and humidity data at several locations within the spray area; The average temperature of the spray area is calculated based on the real-time temperature data. The humidity weight of each location is calculated using a comprehensive weighting formula, and the overall humidity of the spray area is calculated based on the real-time humidity data and the humidity weight of the corresponding location.

[0007] As an improvement to the above scheme, the comprehensive weighting formula is based on a combination of urgency weights and impact weights.

[0008] As an improvement to the above solution, during the spraying process, if the current total humidity of the spray area is higher than the humidity warning threshold and the humidity change rate is higher than the humidity change rate threshold, or if the current total humidity remains higher than the humidity exceeding the standard threshold within the filtering time, then the spray volume of the spraying device is reduced, including: During the spraying process, the current temperature and humidity data at several locations in the spraying area are continuously acquired; Update the current total humidity and average temperature of the spray area based on the current temperature and humidity data, and record the total humidity and average temperature each time; The humidity change rate of the spray area is calculated based on the total humidity of the previous few times and the current total humidity; If the current total humidity is higher than the humidity warning threshold and the humidity change rate is higher than the humidity change rate threshold, or if the current total humidity remains higher than the humidity exceeding threshold during the filtering period, then the spray volume of the spray device is reduced.

[0009] As an improvement to the above solution, the method for calculating the spray volume of the spraying device includes: Calculate the target humidity at the current average temperature based on the current average temperature of the spray area and the target relative humidity; The spray volume of the spraying device is calculated based on the current total humidity of the spray area and the target humidity.

[0010] As an improvement to the above solution, the preset stopping condition includes at least one of the following: The current average temperature is lower than the target set temperature; The current overall humidity remains below the target relative humidity; There was no human activity within the spray area.

[0011] To achieve the above objectives, embodiments of the present invention provide a humidity-based spray optimization device, comprising: The real-time data acquisition module is used to acquire real-time temperature data and real-time humidity data at several locations in the spray area, and to calculate the average temperature and overall humidity of the spray area based on the real-time temperature data and real-time humidity data. The spray device activation module is used to control the spray device to activate spraying when the total humidity is lower than the target relative humidity and the average temperature is higher than the target set temperature. The spray device adjustment module is used to reduce the spray volume of the spray device if, during the spraying process, the current total humidity of the spray area is higher than the humidity warning threshold and the humidity change rate is higher than the humidity change rate threshold, or if the current total humidity continues to be higher than the humidity exceeding threshold within the filtering time. The spray device stop module is used to control the spray device to stop spraying when the spray area is detected to meet the preset stop conditions.

[0012] To achieve the above objectives, embodiments of the present invention provide a humidity-based spray optimization device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the above-described humidity-based spray optimization method.

[0013] To achieve the above objectives, embodiments of the present invention also provide a computer-readable storage medium, the computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the above-described humidity-based spray optimization method.

[0014] To achieve the above objectives, embodiments of the present invention also provide a computer program product, which is stored in a storage medium and executed by at least one processor to implement the steps of the humidity-based spray optimization method described above.

[0015] Compared with existing technologies, the present invention discloses a humidity-based spray optimization method, apparatus, device, medium, and product. This method acquires real-time temperature and humidity data at several locations within a spray area, and calculates the average temperature and overall humidity of the spray area based on this data. When the overall humidity is lower than the target relative humidity and the average temperature is higher than the target set temperature, the spray device is controlled to start spraying. During spraying, if the current overall humidity of the spray area is higher than a humidity warning threshold and the humidity change rate is higher than a humidity change rate threshold, or if the current overall humidity remains higher than a humidity exceeding threshold within the filtering time, the spray volume of the spray device is reduced. When the spray area is detected to meet preset stop conditions, the spray device is controlled to stop spraying. This method can collect humidity data at different locations within a spray area, reflecting the overall humidity situation. Using humidity as a constraint, the spray is dynamically adjusted to achieve effective and uniform cooling, while avoiding water droplet condensation and resource waste. This eliminates environmental pollution and safety hazards at the source and improves the user experience. Attached Figure Description

[0016] Figure 1 This is a schematic flowchart of a humidity-based spray optimization method provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of a spray system structure provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of a humidity-based spray optimization device provided in an embodiment of the present invention; Figure 4 This is a structural block diagram of a humidity-based spray optimization device provided in an embodiment of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] It should be noted that the terms "comprising" and "specific" in this invention, and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such process, method, product, or device.

[0019] Please see Figure 1 , Figure 1This is a schematic flowchart of a humidity-based spray optimization method provided in an embodiment of the present invention. The humidity-based spray optimization method includes: S1, acquire real-time temperature data and real-time humidity data at several locations in the spray area, and calculate the average temperature and overall humidity of the spray area based on the real-time temperature data and real-time humidity data; S2, when the total humidity is lower than the target relative humidity and the average temperature is higher than the target set temperature, control the spray device to start spraying; S3, during the spraying process, if the current total humidity of the spraying area is higher than the humidity warning threshold and the humidity change rate is higher than the humidity change rate threshold, or if the current total humidity continues to be higher than the humidity exceeding threshold within the filtering time, then the spraying volume of the spraying device is reduced. S4, when the spray area is detected to meet the preset stop conditions, control the spray device to stop spraying.

[0020] For example, the humidity-based spray optimization method described in this embodiment of the invention can be implemented by a spray system, which is installed as a software program on a terminal and can interact with the user. Figure 2 As shown, Figure 2 This is a schematic diagram of a spray system structure provided by an embodiment of the present invention. The spray system includes several air temperature and humidity sensors, a data transmission module, a PLC controller, a spray device, and a host computer. The sensors are connected to the PLC controller through the data transmission module, and the PLC controller establishes communication with the spray device and the host computer.

[0021] For example, several (e.g., 5) air temperature and humidity sensors are arranged within the spray area. These sensors are installed at the edge of the spray area, near pedestrian traffic, in the center of the area, and near the equipment. Preset parameters are input to the PLC controller via a host computer. These preset parameters include the target set temperature (26-30℃), humidity exceedance threshold (75-85% RH), humidity warning threshold (70-80% RH), regular data collection interval (15-25 minutes / time), spraying interval (3-7 seconds / time), time window filtering duration (8-12 seconds), and humidity change rate threshold (0.4-0.6%). The PLC controller sends a self-test command to the sensor to confirm that the sensor is working properly and completes initialization. The PLC controller sends data acquisition commands to the sensor at the normal humidity acquisition interval. The sensor acquires real-time temperature and humidity data at its location and feeds them back to the PLC controller. The PLC controller calculates the total humidity of the spray area based on the preset humidity weight for each location and calculates the average temperature of the spray area. If the total humidity is lower than the target relative humidity and the average temperature is higher than the target set temperature, a start command is sent to the spray device. The spray device starts and operates according to the initial parameters. After the spray device starts, the PLC controller adjusts the acquisition interval to the spray interval, continuously receives temperature and humidity data from the sensor, and updates the current total humidity and average temperature in real time. The PLC controller records the total humidity data at the spray humidity acquisition interval, selects the fourth most recent recorded total humidity data to calculate the humidity change rate. If the current total humidity is higher than the humidity warning threshold and the humidity change rate is higher than the humidity change rate, the PLC controller will initiate a system to detect the humidity change rate. If the humidity rises too rapidly, the system will trigger an early, gentle reduction in spray. The PLC controller will call the PID module, input the target relative humidity and the current total humidity, calculate the humidity deviation, and determine the spray volume adjustment amount through PID calculation. It will then send a spray volume adjustment command to the spraying device. If the current total humidity is higher than the humidity threshold, the PLC controller will start a time window filter. If the current total humidity remains higher than the humidity threshold within the filter period, a spray volume reduction command will be triggered. If the current total humidity falls below the humidity threshold within the filter period, the timer will be reset. If the PLC controller detects that the current average temperature is lower than the target set temperature, the current total humidity remains lower than the target relative humidity, or there is no personnel activity in the spraying area, it will send a stop command to the spraying device, and the spraying device will stop operating. After the spraying stops, the PLC controller will upload the data from this spraying process to the host computer. The host computer will analyze the data, calculate the spray energy efficiency ratio and humidity control accuracy. If the humidity control accuracy does not meet the requirements, it will prompt optimization of PID parameters or humidity weights at each location, update the parameters, and enter the next spraying cycle.

[0022] It's worth noting that monitoring multiple humidity levels within the spray area serves several purposes. Besides preventing excessively high local humidity (e.g., >80%) that prevents moisture evaporation and creates a stuffy, sticky feeling, the multi-sensor array approach effectively corrects for sensor malfunctions. The system's control objective is no longer simply cooling, but ensuring unsaturated humidity while achieving effective cooling. The overall humidity is obtained by weighting the humidity from multiple detection points and combining this with a preset humidity threshold to determine the trigger conditions for starting the spray. Once the spray is triggered, the temperature (real-time monitored value) and relative humidity (target value) are input into the PID controller, and the target humidity (target setpoint) is calculated using an enthalpy-humidity diagram model. The corresponding spray volume is output based on the deviation between the overall humidity and the target humidity. Advance prediction is made based on the rate of change of humidity value per unit time. To achieve precise control, humidity sensors, temperature sensors, and a PLC controller are required to improve system accuracy. Furthermore, humidity is a dynamically changing parameter, and instantaneous fluctuations may not be representative; therefore, the operating mechanism and control method need to eliminate filtering to ensure more stable system operation.

[0023] Specifically, step S1 includes: S11, acquire real-time temperature and humidity data at several locations in the spray area; S12, calculate the average temperature of the spray area based on the real-time temperature data; S13, calculate the humidity weight of each location using a comprehensive weighting formula, and calculate the overall humidity of the spray area based on the real-time humidity data and the humidity weight of the corresponding location.

[0024] For example, in the sensor feedback mechanism, five air temperature and humidity sensors are arranged in each spray zone, installed in representative locations (center, edge, and near pedestrian traffic). Variables Humidity_Sensor1, Humidity_Sensor2, Humidity_Sensor3, Humidity_Sensor4, and Humidity_Sensor5 are created for each sensor in the PLC. The difference from traditional algorithms (taking the maximum value or calculating the gradient) is: to "prevent localized over-humidity," scores are assigned based on the urgency and impact dimensions of the five humidity points. The total humidity is used as the trigger condition for device activation. The comprehensive weight formula is: Comprehensive Weight (Humidity Weight) = Urgency Weight * Global Urgency Weight + Impact Weight * Global Impact Weight, where the global urgency weight in both dimensions is considered as 0.5. The weights of the five sensors are normalized to ensure the sum of all weights is 1. The total humidity = Σ(humidity value at each point × normalized weight at that point), summed to obtain the total humidity value. This adds a reasonable trigger mechanism to the device's sensors in conjunction with the algorithm. The weighting rules are shown in Table 1.

[0025] Table 1 Weighting Allocation Rules

[0026] Part of the code is as follows: import numpy as np import pandas as pd class HumidityAggregatorMethodB: def __init__(self): self.point_weights = None self.normalized_weights = None self.point_names = None self.global_urgency_weight = 0.5 # Default global urgency weight self.global_impact_weight = 0.5 # Default global impact weight def set_global_dimension_weights(self,urgency_weight, impact_weight): """ Set global weights for two dimensions. urgency_weight: Global weight of urgency (0-1) impact_weight: Global weight of impact (0-1) """ # Verify if the sum of weights is 1 if abs(urgency_weight + impact_weight - 1.0)>0.001: Raise ValueError("The sum of the weights of urgency and impact must be 1") self.global_urgency_weight = urgency_weight self.global_impact_weight = impact_weight print(f"Global dimension weights have been set - Urgency: {urgency_weight:.2f}, Impact: {impact_weight:.2f}") def setup_weights(self, point_data): """ Set the weights for each temperature and humidity point and calculate the normalized weights. point_data: A dictionary where the keys are point names and the values ​​are lists of [urgency, impact]. """ self.point_weights = point_data # Calculate the overall weight of each point, considering the global dimension weights. self.point_names = list(point_data.keys()) # Overall weight = (urgency weight × global urgency weight) + (impact weight × global impact weight) combined_weights = [] for name in self.point_names: urgency, impact = point_data[name] combined_weight = (urgency * self.global_urgency_weight) + (impact *self.global_impact_weight) combined_weights.append(combined_weight) # Normalize the weights so that the sum is 1 self.normalized_weights=np.array(combined_weights) / np.sum(combined_weights) return self.normalized_weights def calculate_overall_humidity(self, humidity_values): """ Calculate the overall humidity value humidity_values: A list of humidity values ​​for each point, ordered in the same way as point_names. """ if len(humidity_values) != len(self.point_names): raise ValueError(f"Required {len(self.point_names)} humidity values, but provided {len(humidity_values)}") # Calculate the weighted average overall_humidity = np.dot(humidity_values, self.normalized_weights) return overall_humidity def calculate_detailed(self, humidity_values): """ # Usage Example def main(): # Your data: Urgency and impact weights of 5 temperature and humidity points point_data = { 'Humidity_Sensor1': [0.25, 0.75], 'Humidity_Sensor2': [0.25, 0.75], 'Humidity_Sensor3': [0.75, 0.25], 'Humidity_Sensor4': [0.75, 0.25], 'Humidity_Sensor5': [0.25, 0.75] } # Assume the measured humidity value (which can be a relative humidity of 0-100%, or a standardized value of 0-1). humidity_values ​​= [65.2, 72.8, 58.3, 61.7, 69.5] # Example data # Create an aggregator instance aggregator = HumidityAggregatorMethodB() # Set global dimension weights (this is a crucial step for adding new ones) # For example, if you consider urgency more important than impact, you can set it to 0.7 and 0.3. aggregator.set_global_dimension_weights(urgency_weight=0.6, impact_weight=0.4) # Set the weights of each point aggregator.setup_weights(point_data) # Calculate total humidity overall_humidity = aggregator.calculate_overall_humidity(humidity_values) print(f"Overall humidity value: {overall_humidity:.4f}").

[0027] Specifically, the comprehensive weighting formula is a combination of urgency weight and impact weight.

[0028] For example, the humidity weight is calculated using a comprehensive weight formula, which is: Comprehensive Weight = Urgency Weight × 0.5 + Influence Weight × 0.5. The urgency weight of sensors at the edge and near pedestrian traffic is set to 0.2-0.3, and the influence weight is set to 0.7-0.8. The urgency weight of sensors in the area center and near equipment is set to 0.7-0.8, and the influence weight is set to 0.2-0.3. After normalizing the comprehensive weight of each sensor, the overall humidity is calculated as: Overall Humidity = Σ(Humidity value of each sensor × corresponding normalized weight).

[0029] Specifically, step S3 includes: S31, During the spraying process, continuously acquire the current temperature and humidity data at several locations in the spraying area; S32, update the current total humidity and average temperature of the spray area based on the current temperature and humidity data, and record the total humidity and average temperature each time; S33, calculate the humidity change rate of the spray area based on the total humidity of the previous several times and the current total humidity; S34, if the current total humidity is higher than the humidity warning threshold and the humidity change rate is higher than the humidity change rate threshold, or if the current total humidity continues to be higher than the humidity exceeding threshold within the filtering time, then reduce the spray volume of the spray device.

[0030] For example, using a Timer function block, a timer (e.g., for 10 seconds) is started when Humidity_Max (the current total humidity) > 80%. If the current total humidity remains above 80% within these 10 seconds, the timer ends, triggering an adjustment action. If the humidity drops during this period, the timer is reset. Alternatively, the Humidity_Max value can be recorded every 5 seconds and stored in an array or queue (e.g., recording the last 4 values, totaling 20 seconds of data). Using a simple mathematical operation function block, the humidity change rate Humidity_Rate is obtained by dividing (latest value - value 20 seconds ago) by 20 seconds. A change rate threshold is set, for example, Rate_Set = 0.5% / second. If the current total humidity is higher than the humidity warning threshold and the humidity change rate is higher than the humidity change rate threshold, the spray volume is reduced in advance.

[0031] It's worth noting that the goal of the control method is to make the feedback calculation signal more meaningful. This requires fully considering that humidity is a dynamically changing parameter, and instantaneous fluctuations may not be representative. Directly using a single instantaneous value for control would lead to frequent system starts and stops, resulting in instability. Therefore, two judgment mechanisms are introduced through the PLC: (if the current total humidity is higher than the humidity warning threshold and the humidity change rate is higher than the humidity change rate threshold, or if the current total humidity remains higher than the humidity exceeding the threshold within the filtering period); instead of immediately acting when the humidity momentarily exceeds 80%, it determines whether it has remained above the threshold for a period of time, effectively filtering out brief disturbances and making the control smoother. To improve user comfort, not only is the humidity level considered, but also its rate of increase. This is achieved by calculating the rate of change (slope) of humidity per unit time. If the humidity rises rapidly, even if the current value has not yet reached 80%, the spray volume should be reduced in advance as a preventative measure.

[0032] Specifically, the method for calculating the spray volume of the spraying device includes: Calculate the target humidity at the current average temperature based on the current average temperature of the spray area and the target relative humidity; The spray volume of the spraying device is calculated based on the current total humidity of the spray area and the target humidity.

[0033] For example, firstly, the real-time temperature at the site is detected. relative humidity of the target These two parameters are input into the enthalpy-humidity diagram model to calculate the target humidity value at the current temperature. Finally, the target humidity is compared with the current total humidity, and the difference can be used to calculate the required spray volume. For example, by substituting the real-time temperature and target relative humidity into an environmental model (such as an enthalpy-humidity diagram or its fitting formula), the corresponding target humidity can be solved. Calculate the humidity deviation between the current total humidity and the target humidity. Based on the preset control algorithm (such as the fitting curve of the table method), and according to the humidity deviation... Calculate the final spray volume Among them, target humidity The functional relationship is as follows: , The functional relationship of humidity deviation is: , spray volume The functional relationship is as follows: , In the formula, This represents the control law of the system. For example, proportional-integral (PI) control not only considers the magnitude of the current deviation (proportional term) but also accumulates the sum of historical deviations (integral term), thereby forcing the system to eventually reach the target value. The spray volume can be controlled by installing a PI valve on the water pipe.

[0034] Specifically, the preset stop condition includes at least one of the following: The current average temperature is lower than the target set temperature; The current overall humidity remains below the target relative humidity; There was no human activity within the spray area.

[0035] This invention discloses a humidity-based spray optimization method. It acquires real-time temperature and humidity data at several locations within a spray area, and calculates the average temperature and overall humidity of the spray area based on this data. When the overall humidity is lower than the target relative humidity and the average temperature is higher than the target set temperature, the spray device is activated. During spraying, if the current overall humidity of the spray area exceeds a humidity warning threshold and the humidity change rate exceeds a humidity change rate threshold, or if the current overall humidity remains above a humidity exceedance threshold within a filtering period, the spray volume of the spray device is reduced. When the spray area meets a preset stop condition, the spray device is stopped. This method can collect humidity data at different locations within a spray area, reflecting the overall humidity situation. By using humidity as a constraint, the spray can be dynamically adjusted to achieve effective and uniform cooling, while avoiding water droplet condensation and resource waste. This eliminates environmental pollution and safety hazards at the source, and improves the user experience.

[0036] See Figure 3 , Figure 3 This is a schematic diagram of a humidity-based spray optimization device 10 provided in an embodiment of the present invention. The humidity-based spray optimization device 10 includes: The real-time data acquisition module 11 is used to acquire real-time temperature data and real-time humidity data at several locations in the spray area, and calculate the average temperature and overall humidity of the spray area based on the real-time temperature data and real-time humidity data. The spray device activation module 12 is used to control the spray device to activate spraying when the total humidity is lower than the target relative humidity and the average temperature is higher than the target set temperature. The spray device adjustment module 13 is used to reduce the spray volume of the spray device if the current total humidity of the spray area is higher than the humidity warning threshold and the humidity change rate is higher than the humidity change rate threshold, or if the current total humidity is continuously higher than the humidity exceeding threshold within the filtering time. The spray device stop module 14 is used to control the spray device to stop spraying when the spray area is detected to meet the preset stop conditions.

[0037] The humidity-based spray optimization device 10 provided in this embodiment of the invention can realize all the processes of the humidity-based spray optimization method of the above embodiments. The functions and technical effects of each module in the device are the same as those of the humidity-based spray optimization method of the above embodiments, and will not be repeated here.

[0038] See Figure 4 , Figure 4 This is a schematic diagram of a humidity-based spray optimization device 20 provided in an embodiment of the present invention. The humidity-based spray optimization device 20 of this embodiment includes: a processor 21, a memory 22, and a computer program stored in the memory 22 and executable on the processor 21. When the processor 21 executes the computer program, it implements the steps in the above-described humidity-based spray optimization method embodiment. Alternatively, when the processor 21 executes the computer program, it implements the functions of each module in the above-described humidity-based spray optimization device embodiment.

[0039] For example, the computer program may be divided into one or more modules, which are stored in the memory 22 and executed by the processor 21 to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program in the humidity-based spray optimization device 20.

[0040] The humidity-based spray optimization device 20 can be a computing device such as a desktop computer, laptop, handheld computer, or cloud server. The humidity-based spray optimization device 20 may include, but is not limited to, a processor 21 and a memory 22. Those skilled in the art will understand that the schematic diagram is merely an example of the humidity-based spray optimization device 20 and does not constitute a limitation on the humidity-based spray optimization device 20. It may include more or fewer components than shown, or combine certain components, or use different components. For example, the humidity-based spray optimization device 20 may also include input / output devices, network access devices, buses, etc.

[0041] The processor 21 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor. The processor 21 is the control center of the humidity-based spray optimization device 20, connecting all parts of the humidity-based spray optimization device 20 via various interfaces and lines.

[0042] The memory 22 can be used to store the computer programs and / or modules. The processor 21 implements various functions of the humidity-based spray optimization device 20 by running or executing the computer programs and / or modules stored in the memory 22 and calling the data stored in the memory 22. The memory 22 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, phonebook, etc.). In addition, the memory 22 may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0043] The humidity-based spray optimization device 20, if integrated into a module and implemented as a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. This computer program can be stored in a computer-readable storage medium, and when executed by the processor 21, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc. It should be noted that the content contained in the computer-readable medium may be appropriately added to or subtracted from the content as required by the legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium may not include electrical carrier signals and telecommunication signals.

[0044] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0045] This invention also provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the humidity-based spray optimization method as described in the above embodiments.

[0046] Furthermore, embodiments of the present invention also provide a computer program product, which is stored in a storage medium and executed by at least one processor to implement the steps of the humidity-based spray optimization method of the above embodiments.

[0047] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method of spray optimization based on humidity, characterized in that, The method comprises the following steps: acquiring real-time temperature data and real-time humidity data of a plurality of positions in a spraying area, and calculating average temperature and overall humidity of the spraying area according to the real-time temperature data and the real-time humidity data; controlling a spraying device to start spraying when the overall humidity is lower than a target relative humidity and the average temperature is higher than a target set temperature; during spraying, reducing the spraying amount of the spraying device when the current overall humidity of the spraying area is higher than a humidity warning threshold and a humidity change rate is higher than a humidity change rate threshold or the current overall humidity continuously exceeds a humidity over-standard threshold within a filtering duration; controlling the spraying device to stop spraying when it is monitored that the spraying area meets a preset stop condition.

2. The humidity-based spray optimization method of claim 1, wherein, The method of acquiring real-time temperature data and real-time humidity data of a plurality of positions in a spraying area and calculating average temperature and overall humidity of the spraying area according to the real-time temperature data and the real-time humidity data comprises the following steps: acquiring real-time temperature data and real-time humidity data of a plurality of positions in a spraying area; calculating average temperature of the spraying area according to the real-time temperature data; calculating a humidity weight of each position by using a comprehensive weight formula, and calculating overall humidity of the spraying area according to the real-time humidity data and the humidity weight of the corresponding position.

3. The humidity-based spray optimization method of claim 2, wherein, The comprehensive weight formula is combined according to an urgency weight and an influence weight.

4. The humidity-based spray optimization method of claim 1, wherein, The method of reducing the spraying amount of the spraying device when the current overall humidity of the spraying area is higher than a humidity warning threshold and a humidity change rate is higher than a humidity change rate threshold or the current overall humidity continuously exceeds a humidity over-standard threshold within a filtering duration during spraying comprises the following steps: continuously acquiring current temperature data and humidity data of a plurality of positions in the spraying area during spraying; updating the current overall humidity and the average temperature of the spraying area according to the current temperature data and the humidity data, and recording the overall humidity and the average temperature each time; calculating a humidity change rate of the spraying area according to the overall humidity of the previous plurality of times and the current overall humidity; reducing the spraying amount of the spraying device when the current overall humidity is higher than a humidity warning threshold and the humidity change rate is higher than a humidity change rate threshold or the current overall humidity continuously exceeds a humidity over-standard threshold within a filtering duration.

5. The humidity-based spray optimization method of claim 1, wherein, The method for calculating the spraying amount of the spraying device comprises the following steps: calculating a target humidity at the current average temperature according to the current average temperature of the spraying area and the target relative humidity; calculating the spraying amount of the spraying device according to the current overall humidity of the spraying area and the target humidity.

6. The humidity-based spray optimization method of claim 1, wherein, The preset stop condition comprises at least one of the following: the current average temperature is lower than the target set temperature; the current overall humidity continuously is lower than the target relative humidity; there is no personnel activity in the spraying area.

7. A humidity-based spray optimization device, characterized by, The method comprises the following steps: a real-time data acquisition module is configured to acquire real-time temperature data and real-time humidity data of a plurality of positions in a spraying area, and calculate average temperature and overall humidity of the spraying area according to the real-time temperature data and the real-time humidity data; a spraying device starting module is configured to control a spraying device to start spraying when the overall humidity is lower than a target relative humidity and the average temperature is higher than a target set temperature; The spray device adjustment module is configured to, during the spraying process, if the current overall humidity of the spraying area is higher than a humidity warning threshold and a humidity change rate is higher than a humidity change rate threshold or the current overall humidity continuously exceeds a humidity overproof threshold within a filtering duration, reduce a spraying amount of the spray device. The spray device stop module is configured to control the spray device to stop spraying when it is monitored that the spraying area meets a preset stop condition.

8. A humidity-based spray optimization apparatus, characterized by, A computer program product is stored in a storage medium, and the program product is executed by at least one processor to implement the steps of the humidity-based spray optimization method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium comprises a stored computer program, wherein the computer readable storage medium controls a device where the computer readable storage medium is located to execute the humidity-based spray optimization method according to any one of claims 1-6 when the computer program runs.

10. A computer program product, characterised in that, The computer program product is stored in a storage medium, and the program product is executed by at least one processor to implement the steps of the humidity-based spray optimization method according to any one of claims 1-6.

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