Intelligent ventilation control method, device, equipment and storage medium

Abstract

This application discloses an intelligent ventilation control method, device, equipment, and storage medium, relating to the field of ventilation control technology. It is applied to an intelligent ventilation control system including an environmental controller, at least one electronically commutated (EC) fan, and a gas pressure differential sensor. The method includes: acquiring the target total ventilation volume and real-time air pressure differential required for the livestock shed; updating the target air pressure differential after the air volume change based on the real-time air pressure differential and a preset time threshold; querying a pre-stored fan parameter table according to the target total ventilation volume and the target air pressure differential, and calculating the target speed of each EC fan based on the optimal energy efficiency constraint of the target total ventilation volume; and controlling each EC fan to operate at its corresponding target speed via a communication bus. This application constructs a closed-loop control system based on a gas pressure differential sensor and an optimal energy efficiency algorithm using precisely speed-regulated EC fans, achieving precise and uniform ventilation environment control and significantly reducing system energy consumption.

Description

Technical Field

[0001] This application relates to the field of ventilation control technology, and in particular to an intelligent ventilation control method, device, equipment and storage medium. Background Technology

[0002] In large-scale, intensive farming, maintaining a stable and uniform ventilation environment within the sheds is a key technical requirement for ensuring animal health and production performance.

[0003] Traditional ventilation control systems typically use alternating current (AC) fans as actuators, and regulate airflow through simple start-stop logic or the addition of frequency converters. However, existing solutions have inherent drawbacks: intermittent fan start-stop can cause abrupt changes in ventilation volume, leading to drastic fluctuations in temperature and airflow within the enclosure, which is detrimental to animal welfare; while solutions using frequency converters to drive AC fans cannot obtain the fan's true performance curves and real-time operating status, and can only perform open-loop control based on frequency estimation. This not only limits the accuracy of airflow regulation but also fails to achieve system-level energy efficiency optimization. Furthermore, there is a risk that fan failures may not be detected in a timely manner, affecting the system's reliability and economy.

[0004] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0005] The main purpose of this application is to provide an intelligent ventilation control method, device, equipment and storage medium, which aims to solve the technical problem of how to simultaneously meet the requirements of high-precision air volume control and optimal overall operating energy efficiency in ventilation control.

[0006] To achieve the above objectives, this application proposes an intelligent ventilation control method, applied to an intelligent ventilation control system including an environmental controller, at least one electronically commutated (EC) fan, and a gas differential pressure sensor. The method includes:

[0007] Obtain the target total ventilation volume and real-time air pressure difference required for the breeding sheds;

[0008] The target air pressure difference is updated based on the real-time air pressure difference and the preset time threshold after the air volume change;

[0009] Based on the target total ventilation volume and the target air pressure difference, the pre-stored fan parameter table is queried, and the target speed of each EC fan is calculated based on the optimal energy efficiency constraint of the target total ventilation volume.

[0010] Each EC fan is controlled via a communication bus to operate at its corresponding EC fan target speed.

[0011] In one embodiment, the real-time air pressure difference between the two sides of the ventilation barrier where the multiple EC fans are located is obtained by the gas differential pressure sensor.

[0012] In one embodiment, the step of updating the target air pressure difference based on the real-time air pressure difference and a preset time threshold after the air volume change includes:

[0013] The real-time air pressure difference is used as the initial target air pressure difference for the breeding shed;

[0014] Determine whether the current cumulative running time of the EC fan is less than the preset time threshold;

[0015] If the time difference is less than the preset time threshold, then the initial target pressure difference is set as the target pressure difference;

[0016] Otherwise, the target pressure difference is calculated based on multiple historical average pressure differences and corresponding air volume changes detected periodically within the preset time threshold, according to a preset linear relationship.

[0017] In one embodiment, the fan parameter table includes the correspondence between the speed, ventilation volume, and power of the EC fan under different air pressure differences; the step of querying the pre-stored fan parameter table based on the target total ventilation volume and the target air pressure difference, and calculating the target speed of each EC fan based on the optimal energy efficiency algorithm that satisfies the target total ventilation volume includes:

[0018] Based on the target air pressure difference and the pre-stored fan parameter table, the first functional relationship between the speed, fan ventilation volume and power of the EC fan under the target air pressure difference is determined.

[0019] Based on the first air volume constraint and the first power constraint, the target ventilation volume allocation for each EC fan is calculated; wherein, the first air volume constraint means that the total ventilation volume of all EC fans is equal to the target total ventilation volume, and the first power constraint means that the minimum total power of all EC fans.

[0020] Based on the target ventilation volume allocation and the first functional relationship, the target rotational speed of each EC fan is obtained.

[0021] In one embodiment, the step of determining the first functional relationship between the rotational speed, ventilation volume, and power of the EC fan under the target pressure difference, based on the target pressure difference and a pre-stored fan parameter table, includes:

[0022] According to the fan parameter table, obtain the reference air pressure difference that is equal to or two adjacent to the target air pressure difference, and obtain the discrete data set of the speed, ventilation volume and power of the EC fan under the reference air pressure difference;

[0023] Based on the target air pressure difference and discrete data set, the target discrete data set of ventilation volume and power of the EC fan under the target air pressure difference is calculated by linear interpolation.

[0024] Based on the target discrete data set, a first functional relationship between the speed, ventilation volume and power of the EC fan under the target air pressure difference is constructed by piecewise linear interpolation or curve fitting.

[0025] In one embodiment, calculating the target ventilation volume allocation for each EC fan based on the first air volume constraint and the first power constraint specifically includes:

[0026] Determine whether all the EC fans are of the same model;

[0027] If all the EC fans are of the same model, the target ventilation volume allocation for the minimum total power of all the EC fans is determined based on the first functional relationship and Jensen's inequality; otherwise, the target ventilation volume allocation is determined by a binary search method.

[0028] In one embodiment, the step of obtaining the target rotational speed of each EC fan based on the target ventilation volume allocation and the first functional relationship includes:

[0029] For each EC fan and its corresponding target ventilation volume allocation, two reference ventilation volume data points adjacent to the target ventilation volume and their corresponding reference rotation speeds are obtained based on the fan parameter table.

[0030] Based on the target ventilation volume, the two reference ventilation volume data points, and the corresponding reference rotation speed, the target rotation speed of each EC fan is calculated by linear interpolation.

[0031] Furthermore, to achieve the above objectives, this application also proposes an intelligent ventilation control device, which includes:

[0032] The parameter acquisition module is used to obtain the target total ventilation volume and real-time air pressure difference required for the breeding shed.

[0033] The air pressure difference update module is used to update the target air pressure difference after the change in air volume based on the real-time air pressure difference and the preset time threshold.

[0034] The rotational speed calculation module is used to query a pre-stored fan parameter table based on the target total ventilation volume and the target air pressure difference, and calculate the target rotational speed of each EC fan based on the optimal energy efficiency constraint of the target total ventilation volume.

[0035] The speed control module controls each EC fan to operate at its corresponding EC fan target speed via a communication bus.

[0036] In addition, to achieve the above objectives, this application also proposes an intelligent ventilation control device, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the intelligent ventilation control method described above.

[0037] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the intelligent ventilation control method described above.

[0038] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the intelligent ventilation control method described above.

[0039] One or more technical solutions proposed in this application have at least the following technical effects:

[0040] This application achieves high-precision and uniform control of ventilation volume by acquiring the target total ventilation volume and real-time air pressure difference required for the breeding shed; updating the target air pressure difference after changes in air volume based on the real-time air pressure difference and a preset time threshold; querying a pre-stored fan parameter table according to the target total ventilation volume and the target air pressure difference, and calculating the target speed of each EC fan based on the optimal energy efficiency constraint of the target total ventilation volume; and controlling each EC fan to operate at its corresponding EC fan target speed via a communication bus. This application achieves high-precision and uniform control of ventilation volume by employing a technical means that combines EC fans, gas pressure difference sensors, fan parameter tables, and a closed-loop optimal algorithm; system-level global energy efficiency optimization, ensuring that multiple fans always work collaboratively in the high-efficiency range, significantly reducing overall energy consumption; and real-time monitoring and intelligent fault tolerance of equipment status. Attached Figure Description

[0041] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0042] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 This is a flowchart illustrating an embodiment of the intelligent ventilation control method of this application.

[0044] Figure 2 This is a flowchart illustrating Embodiment 2 of the intelligent ventilation control method of this application;

[0045] Figure 3 Fan parameter diagram provided for the intelligent ventilation control method of this application;

[0046] Figure 4 This is a flowchart illustrating Embodiment 3 of the intelligent ventilation control method of this application;

[0047] Figure 5 This is a schematic diagram of the modular structure of the intelligent ventilation control device according to an embodiment of this application;

[0048] Figure 6 This is a schematic diagram of the equipment structure of the hardware operating environment involved in the intelligent ventilation control method in the embodiments of this application.

[0049] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0050] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0051] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0052] Existing technologies have limitations in simultaneously achieving high-precision airflow control and optimal overall energy efficiency in ventilation control.

[0053] This application provides a solution that obtains the target total ventilation volume and real-time air pressure difference required for the breeding shed; updates the target air pressure difference after the air volume change based on the real-time air pressure difference and a preset time threshold; queries a pre-stored fan parameter table according to the target total ventilation volume and the target air pressure difference, and calculates the target speed of each EC fan based on the optimal energy efficiency constraint of the target total ventilation volume; and controls each EC fan to operate at its corresponding EC fan target speed via a communication bus. This application achieves high-precision and uniform control of ventilation volume by employing a technical means that combines EC fans, gas pressure difference sensors, fan parameter tables, and a closed-loop optimal algorithm; system-level global energy efficiency optimization, ensuring that multiple fans always work collaboratively in the high-efficiency range, significantly reducing overall energy consumption; and real-time monitoring and intelligent fault tolerance of equipment status.

[0054] Based on this, this application provides an intelligent ventilation control method, applied to an intelligent ventilation control system including an environmental controller, at least one electronically commutated (EC) fan, and a gas differential pressure sensor, as described above. Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the intelligent ventilation control method of this application.

[0055] In this embodiment, the intelligent ventilation control method includes steps S10 to S40:

[0056] Step S10: Obtain the target total ventilation volume and real-time air pressure difference required for the breeding shed;

[0057] It should be noted that in this embodiment, the target total ventilation volume is the total volume of air that needs to be introduced into or expelled from the shed per unit time to meet the environmental requirements of animals in a specific breeding shed (such as meeting the standards for temperature, humidity, and harmful gas concentration). The unit is typically cubic meters per hour (m³ / h). Real-time air pressure difference refers to the gas pressure difference between the two sides of the ventilation barrier (commonly known as the "fan wall") formed by the multiple EC fans during operation, measured in real time by a gas pressure difference sensor. This value directly and quickly reflects the actual ventilation intensity of the current system. The target total ventilation volume can be obtained manually by the operator or automatically calculated by the environmental controller based on its built-in breeding environment model and sensor readings of temperature, humidity, carbon dioxide concentration, etc.

[0058] Step S20: Update the target air pressure difference after the change in air volume based on the real-time air pressure difference and the preset time threshold;

[0059] It should be noted that in this embodiment, the preset time threshold is a pre-set time boundary value used to divide the system operation phases. The purpose of this step is to dynamically determine an accurate and reasonable "target pressure difference," rather than simply using instantaneous measurements.

[0060] Step S30: Based on the target total ventilation volume and the target air pressure difference, query the pre-stored fan parameter table, and calculate the target speed of each EC fan based on the optimal energy efficiency constraint of the target total ventilation volume;

[0061] It should be noted that in this embodiment, the aim is to decompose the macroscopic environmental control requirements (target total ventilation volume) and real-time operating conditions (target air pressure difference) into specific, unique, and optimal action commands (target speed) for each actuator (EC fan). The fan parameter table refers to a data structure pre-stored in the environmental controller's memory. It records the stable performance output of a specific model of EC fan under different speeds (RPM) and different gas pressure differences (Pa) in tabular form. It mainly includes key parameters such as ventilation volume (m³ / h), energy efficiency ratio, and input power (W). This table is obtained through experimental calibration and reflects the fan's true external characteristics. The optimal energy efficiency constraint refers to, under the primary constraint of meeting the total ventilation volume requirement, adding an optimization objective that minimizes the total input power of the entire fan system.

[0062] Step S40: Control each EC fan to run at the corresponding EC fan target speed via the communication bus.

[0063] It should be noted that in this embodiment, the communication bus refers to the physical medium and protocol specification used to establish a digital communication connection between the environmental controller and multiple EC wind turbines to realize bidirectional data transmission, such as the RS-485 bus. It adopts a master-slave structure and has the characteristics of strong anti-interference ability and support for long-distance multi-device networking.

[0064] In one possible implementation, the environmental controller periodically polls each EC fan to read its actual speed, current, fault codes, and other status information. If it detects that the actual speed of a fan deviates from the target value for an extended period, or if a fault alarm is received, or if a communication timeout occurs, the environmental controller determines that the fan is abnormal and may trigger audible, visual, and SMS alarms. In the next control cycle, the faulty fan will be removed from the recalculation, and the remaining normal fans will be reassigned airflow tasks, thereby achieving fault-tolerant operation of the system.

[0065] This embodiment acquires the target total ventilation volume and real-time air pressure difference, and calculates the optimal speed of each EC fan based on dynamic prediction of air pressure difference and fan parameter table lookup using an optimization algorithm aimed at minimizing the total system power. Finally, it issues speed commands through the communication bus and monitors their execution, thus constructing a complete closed-loop intelligent control system. This effectively solves the problems of low precision, poor energy efficiency, and unknown status in traditional aquaculture ventilation control, achieving high-precision uniform control of shed ventilation, system-level energy-saving optimization, and real-time monitoring and intelligent fault tolerance of equipment status, significantly improving environmental stability, operational economy, and system reliability.

[0066] In one possible implementation, the real-time air pressure difference across the ventilation barrier where the multiple EC fans are located is obtained through the gas differential pressure sensor.

[0067] It should be noted that in this embodiment, the ventilation barrier in the field of aquaculture environmental control specifically refers to a physical structure composed of multiple fans installed side by side, commonly known as a "fan wall," which creates a stable pressure difference between the inside and outside of the shed when it is working. The purpose of the environmental controller in acquiring the real-time air pressure difference is to directly and quickly quantify the actual working intensity and effect of the current ventilation system. It can reflect the change in the instantaneous total ventilation volume more instantly and linearly, fundamentally avoiding the inherent errors and lags of traditional solutions that indirectly estimate air volume through frequency converters, thus ensuring the reliability of the control accuracy from the source.

[0068] Optionally, the gas differential pressure sensor can employ a high-precision micro differential pressure sensing chip and be connected to the environmental controller via a shielded cable to resist complex electromagnetic interference in the farm.

[0069] In one possible implementation, the step of updating the target air pressure difference based on the real-time air pressure difference and a preset time threshold after the air volume change includes:

[0070] The real-time air pressure difference is used as the initial target air pressure difference for the breeding shed;

[0071] Determine whether the current cumulative running time of the EC fan is less than the preset time threshold;

[0072] If the time difference is less than the preset time threshold, then the initial target pressure difference is set as the target pressure difference;

[0073] Otherwise, the target pressure difference is calculated based on multiple historical average pressure differences and corresponding air volume changes detected periodically within the preset time threshold, according to a preset linear relationship.

[0074] It should be noted that this embodiment aims to move the environmental controller from static feedback control to dynamic predictive control. The preset time threshold is a key time parameter pre-set by the system to divide the control strategy phases. The historical average air pressure difference is the average air pressure difference value collected and calculated by the environmental controller from the gas pressure difference sensor over one or more past complete control cycles. It is used to smooth instantaneous fluctuations and extract effective pressure change trends. The preset linear relationship is used to describe the approximate proportional relationship between changes in ventilation volume and the resulting changes in air pressure difference. Specifically, in the initial stage of system startup, due to the lack of historical data, measured values ​​are used as the control benchmark to ensure a smooth start-up. After the system has run for a sufficiently long time and accumulated reliable historical trend data, it switches to predictive mode. When a new target total ventilation volume command is received, it can estimate in advance the new stable air pressure difference that the system will reach after the air volume adjustment based on past change patterns. This predicted value is used as the control target, significantly reducing fluctuations in environmental parameters (such as temperature) inside the shed caused by step adjustments in ventilation volume, thus improving control quality.

[0075] Optionally, the environmental controller determines whether to employ an initialization strategy or a prediction strategy by comparing the total operating time of the fan group with a preset time threshold. This prediction strategy is based on the pressure difference change caused by a unit change in air volume. Specifically, the prediction pressure formula is:

[0076]

[0077] in, , , and These represent the previous and current target total ventilation volumes and their corresponding historical average air pressure differences. For the new target total ventilation volume, The calculated predictive target pressure difference.

[0078] This embodiment divides the control phases by introducing a preset time threshold. In the initial stage of system startup, the measured air pressure difference is used as the control target to ensure stability. After the system is running stably, the future stable air pressure difference is predicted based on historical air pressure difference and air volume change data through a preset linear relationship, thereby achieving a smooth transition from feedforward prediction to feedback correction. This effectively overcomes the response lag problem of traditional pure feedback control. Through the dynamic prediction mechanism, it achieves advanced and smooth control of ventilation volume adjustment, significantly reducing the drastic fluctuations in the shed environment (such as temperature) caused by abrupt changes in air volume, thereby improving the dynamic quality and stability of the entire control system.

[0079] Furthermore, referring to Figure 2 The second embodiment of the intelligent ventilation control method of this application provides a flowchart, based on the above. Figure 2 The embodiment shown includes a fan parameter table that shows the correspondence between the speed, ventilation volume, and power of the EC fan under different pressure differences. The step S30, "based on the target total ventilation volume and the target pressure difference, querying the pre-stored fan parameter table and calculating the target speed of each EC fan based on the optimal energy efficiency algorithm that satisfies the target total ventilation volume," is further refined into steps A201-A203:

[0080] Step A201: Based on the target air pressure difference and the pre-stored fan parameter table, determine the first functional relationship between the speed of the EC fan, the fan ventilation volume and the power under the target air pressure difference;

[0081] It should be noted that in this embodiment, the fan parameter table is a structured data table pre-stored in the non-volatile memory of the environmental controller. It records the actual ventilation volume (m³ / h), energy efficiency ratio ((m³ / h) / W), and input power consumption (W) that a specific model of EC fan can provide under stable operating conditions at different speeds (RPM) and pressure differences (Pa). This data is typically obtained through standard wind tunnel testing and calibration before the fan leaves the factory, reflecting the fan's true external characteristic curve. The first functional relationship refers to transforming the discrete, finite data points in the fan parameter table into a mathematical model that continuously describes the mapping relationship between the three key parameters—fan speed, ventilation volume, and power—under a specific "target pressure difference" condition. For example, Where Q is power, A is ventilation volume, and E is energy efficiency ratio.

[0082] Optionally, the environmental controller locates the two reference pressure difference data columns closest to the current target pressure difference in the fan parameter table. Then, it calculates a series of "speed-ventilation volume-power" data points under the current target pressure difference using linear interpolation. Finally, it uses these interpolation points to construct a continuous functional relationship through piecewise linear interpolation or curve fitting methods. Specifically, refer to... Figure 3 The fan parameter diagram shows that if the current target air pressure difference for the environmental controller is 35 Pa, the controller queries the pre-stored EC630 fan parameter table and finds two columns of complete data at 25 Pa and 50 Pa. The controller first performs linear interpolation on the ventilation volume and power values ​​corresponding to the same rotational speed at 25 Pa and 50 Pa, calculating a discrete performance dataset at 35 Pa. Then, with ventilation volume as the horizontal axis and power as the vertical axis, these discrete points are connected sequentially with line segments to form a piecewise linear function describing the relationship between the fan's ventilation volume and power at the target air pressure difference—the first functional relationship.

[0083] Step A202: Based on the first air volume constraint and the first power constraint, calculate the target ventilation volume allocation for each EC fan; wherein, the first air volume constraint means that the total ventilation volume of all EC fans is equal to the target total ventilation volume, and the first power constraint means that the minimum total power of all EC fans;

[0084] It should be noted that in this embodiment, the first air volume constraint means that the sum of the individual ventilation outputs of all operating EC fans must be exactly equal to the target total ventilation volume determined by the environmental control requirements; the first power constraint is to determine a specific ventilation volume distribution scheme among the fans under the first air volume constraint condition, so that the total input power consumed by all fans reaches the minimum value.

[0085] Step A203: Based on the target ventilation volume allocation and the first functional relationship, obtain the target rotational speed of each EC fan.

[0086] It should be noted that in this embodiment, the aim is to convert the target ventilation volume into a specific control command (target speed) for the EC fan. The target speed refers to the motor rotor rotation speed that the environmental controller expects a particular EC fan to reach and maintain.

[0087] Optionally, for each EC fan, the environmental controller finds the two known ventilation volume data points that are closest to the target ventilation volume based on the performance data set of the target ventilation volume that has been allocated to it and the current target air pressure difference, obtains the speed values ​​corresponding to these two data points, and finally calculates the required target speed through linear interpolation.

[0088] This application establishes a fan performance function model based on the fan parameter table and target air pressure difference. Under the optimization algorithm with total air volume as constraint and minimum total system power as objective, it solves the optimal ventilation volume allocation for each fan. Finally, based on this allocation, it obtains the precise target speed of each fan through interpolation calculation. This changes the traditional fan control mode that relies on experience or simple rules. Under the premise of absolutely meeting the total ventilation demand, it ensures that the entire fan group always operates at the theoretically highest energy efficiency state. At the same time, it transforms the optimal decision into precise speed commands that the equipment can execute without distortion, thereby fundamentally achieving the unity of precise control and maximum energy saving.

[0089] Furthermore, referring to Figure 4 The third embodiment of the intelligent ventilation control method of this application provides a flowchart, based on the above. Figure 4 The illustrated embodiment further refines the step of "determining the first functional relationship between the speed, ventilation volume, and power of the EC fan under the target pressure difference based on the target pressure difference and a pre-stored fan parameter table," including steps A301 to A303:

[0090] Step A301: Obtain a reference air pressure difference that is equal to or two adjacent to the target air pressure difference according to the fan parameter table, and obtain a discrete data set of the speed, ventilation volume and power of the EC fan under the reference air pressure difference;

[0091] It should be noted that in this embodiment, "two adjacent reference pressure differences that are equal or equal" specifically refers to the two closest pressure difference entries in the fan parameter table that are equal to the target pressure difference, or, if not equal, are exactly less than and greater than the target pressure difference. For example, when the target pressure difference is 35 Pa, the entries for 25 Pa and 50 Pa in the table are its adjacent references. "Discrete data set" refers to a series of discrete data rows extracted from the fan parameter table, corresponding to a specific reference pressure difference. Each row completely records a specific speed point and its corresponding stable output ventilation volume and input power under that pressure difference condition.

[0092] Step A302: Based on the target air pressure difference and the discrete data set, calculate the target discrete data set of ventilation volume and power of the EC fan under the target air pressure difference by linear interpolation;

[0093] It should be noted that this embodiment aims to calculate fan performance data based on a performance curve that perfectly matches the current operating conditions, thereby enhancing the control system's adaptability to complex and ever-changing real-world environments. Specifically, for each identical speed point, the ventilation volume values ​​A_low and A_high and the power values ​​Q_low and Q_high at the reference pressure differences P_low and P_high are taken respectively. Then, based on the target pressure difference P_target and the predicted pressure formula, independent linear interpolation calculations are performed on the ventilation volume and power, traversing all speed points to obtain a complete target discrete data set.

[0094] In one specific implementation, the environmental controller processes data at the 700 RPM speed. From the discrete set at 25 Pa, it finds: ventilation volume A_low = 9242 m³ / h, power Q_low = 1200 W; from the discrete set at 50 Pa, it finds: ventilation volume A_high = 8500 m³ / h, power Q_high = 1500 W. Let P_low = 25 Pa, P_high = 50 Pa, P_target = 35 Pa. Substituting into the predicted air pressure formula, it calculates: A_target ≈ 8990 m³ / h, Q_target ≈ 1380 W. Therefore, the data point (700 RPM, 8990 m³ / h, 1380 W) is stored in the target discrete data set. The controller performs this operation for all speed points, ultimately generating a complete performance dataset for the 35 Pa operating condition.

[0095] Step A303: Based on the target discrete data set, construct the first functional relationship between the speed, ventilation volume and power of the EC fan under the target air pressure difference by piecewise linear interpolation or curve fitting.

[0096] It should be noted that, in this embodiment, piecewise linear interpolation refers to connecting adjacent data points with straight line segments, and using the resulting broken line function to approximate the functional relationship between the data points. Curve fitting refers to selecting an appropriate continuous function form (such as a polynomial or exponential function) and using mathematical methods such as the least squares method to make the function curve pass through or approximate all data points as closely as possible.

[0097] This application uses the benchmark data adjacent to the target operating condition in the wind turbine parameter table and generates an accurate performance dataset under the target operating condition based on linear interpolation. Finally, it uses this dataset to construct a continuous function model, thereby dynamically and accurately adapting discrete equipment performance data to continuously changing actual operating conditions, solving the problem that fixed parameter tables cannot directly match arbitrary real-time operating conditions.

[0098] In one possible implementation, calculating the target ventilation volume allocation for each EC fan based on the first air volume constraint and the first power constraint specifically includes:

[0099] Determine whether all the EC fans are of the same model;

[0100] If all the EC fans are of the same model, the target ventilation volume allocation for the minimum total power of all the EC fans is determined based on the first functional relationship and Jensen's inequality; otherwise, the target ventilation volume allocation is determined by a binary search method.

[0101] It should be noted that, in this embodiment, the Jensen inequality is applied to achieve the following: for a convex function, given a fixed sum of independent variables, the sum of its function values ​​is minimized when all independent variables have equal values. The environmental controller utilizes this mathematical principle to directly derive the optimal allocation strategy that minimizes total power after confirming that all wind turbine models are identical. The binary search algorithm is an efficient numerical algorithm that approximates the target value by continuously halving the range within an ordered interval.

[0102] In one possible implementation, for all EC fans of the same model, based on the characteristic that their power-airflow function is a convex function, Jensen's inequality can be directly applied to prove that the optimal allocation that minimizes the total power is the average allocation of ventilation volume to each fan.

[0103] In another possible implementation, for fans of different models, a numerical optimization algorithm, such as a binary search method, is required to iteratively find an allocation scheme that makes the marginal power of each fan equal. Specifically, a common marginal efficiency value is set; for each EC fan, the corresponding ventilation volume at that marginal efficiency value is calculated based on the inverse function of its marginal efficiency value and ventilation volume; if the inverse function does not exist or exceeds the fan's capacity range, its upper or lower limit of ventilation volume is taken; the total ventilation volume corresponding to all fans at that marginal efficiency value is calculated; this total ventilation volume is compared with the target total ventilation volume, and the marginal efficiency value is dynamically adjusted using the binary search method; the above steps are repeated until the error between the total ventilation volume and the target total ventilation volume is within a preset operating range, at which point the obtained ventilation volume is the optimal allocation.

[0104] This application adopts an adaptive optimization solution strategy: the system first determines whether the fan models are consistent. If they are the same model, the average distribution is directly adopted as the optimal solution based on Jensen's inequality. If they are different models, the binary search method is used for iterative solution. Thus, under the premise of satisfying the total air volume constraint, the target ventilation volume distribution scheme that minimizes the total power of the system is accurately calculated, realizing the unity of optimization efficiency and global optimality.

[0105] In one possible implementation, the step of obtaining the target rotational speed of each of the EC fans based on the target ventilation volume allocation and the first functional relationship includes:

[0106] For each EC fan and its corresponding target ventilation volume allocation, two reference ventilation volume data points adjacent to the target ventilation volume and their corresponding reference rotation speeds are obtained based on the fan parameter table.

[0107] Based on the target ventilation volume, the two reference ventilation volume data points, and the corresponding reference rotation speed, the target rotation speed of each EC fan is calculated by linear interpolation.

[0108] It should be noted that in this embodiment, "reference ventilation volume data points and corresponding reference speeds" specifically refer to two ventilation volume values ​​found in the data column corresponding to the current target air pressure difference condition from the fan parameter table. One of these values ​​is slightly less than the target ventilation volume allocation value, and the other is slightly greater than the target ventilation volume allocation value. The speeds corresponding to these two ventilation volume values ​​are the reference speeds. Specifically, for each EC fan, the environmental controller uses its assigned target ventilation volume as the query key to quickly locate the two known data points adjacent to the target value in the performance data set (derived from the fan parameter table) that is already matched with the fan under the current operating condition. Then, it extracts the ventilation volume and its corresponding speed for these two data points. Finally, based on a linear proportional relationship, it calculates the precise speed command that makes the output air volume exactly reach the target value.

[0109] Alternatively, the linear interpolation formula is:

[0110]

[0111] Where A is the target ventilation volume. and Two reference ventilation volumes, and Where is the corresponding reference speed, and N is the target speed.

[0112] In one specific implementation, refer to Figure 3The target ventilation volume allocation for EC fan #1 is determined to be 8990 m³ / h, with a current target pressure difference of 35 Pa. The environmental controller locates the data column under the 35 Pa condition (generated from previous interpolation steps) in the pre-stored fan parameter table and finds two data points adjacent to 8990 m³ / h: 9242 m³ / h at a speed of 700 RPM and 6920 m³ / h at a speed of 550 RPM. The environmental controller substitutes these data into the linear interpolation formula to obtain a target speed of 684 RPM; subsequently, it sends the 684 RPM command value to EC fan #1 via the communication bus. The controller executes this process sequentially for all fans, thereby generating a complete and executable optimal speed command set for the entire fan group.

[0113] This application achieves a high-fidelity conversion from the target total ventilation volume to the target speed by using the optimized allocation target ventilation volume as the query key to locate adjacent reference data points in the fan parameter table and calculating the accurate target speed based on linear interpolation.

[0114] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the intelligent ventilation control method of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0115] This application also provides an intelligent ventilation control device, please refer to... Figure 5 The intelligent ventilation control device includes:

[0116] Parameter acquisition module 10 is used to acquire the target total ventilation volume and real-time air pressure difference required for the breeding shed;

[0117] The air pressure difference update module 20 is used to update the target air pressure difference after the change in air volume based on the real-time air pressure difference and the preset time threshold.

[0118] The rotational speed calculation module 30 is used to query a pre-stored fan parameter table based on the target total ventilation volume and the target air pressure difference, and calculate the target rotational speed of each EC fan based on the optimal energy efficiency constraint of the target total ventilation volume.

[0119] The speed control module 40 controls each EC fan to operate at the corresponding EC fan target speed via a communication bus.

[0120] The intelligent ventilation control device provided in this application, employing the intelligent ventilation control method described in the above embodiments, can solve the technical problem of how to simultaneously achieve high-precision airflow control and optimal overall operational energy efficiency in ventilation control. Compared with the prior art, the beneficial effects of the intelligent ventilation control device provided in this application are the same as those of the intelligent ventilation control method provided in the above embodiments, and other technical features in the intelligent ventilation control device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0121] This application provides an intelligent ventilation control device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the intelligent ventilation control method in the above embodiment 1.

[0122] The following is for reference. Figure 6 The diagram illustrates a structural schematic of an intelligent ventilation control device suitable for implementing embodiments of this application. The intelligent ventilation control device in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 6 The intelligent ventilation control device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0123] like Figure 6As shown, the intelligent ventilation control device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory 1002 or a program loaded from a storage device 1003 into a random access memory 1004. The random access memory 1004 also stores various programs and data required for the operation of the intelligent ventilation control device. The processing unit 1001, the read-only memory 1002, and the random access memory 1004 are interconnected via a bus 1005. An input / output interface 1006 is also connected to the bus. Typically, the following systems can be connected to the input / output interface 1006: input devices 1007 including, for example, a touch screen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. The communication device 1009 allows the intelligent ventilation control equipment to communicate wirelessly or wiredly with other devices to exchange data. Although the figure shows intelligent ventilation control equipment with various systems, it should be understood that implementation or possession of all the systems shown is not required. More or fewer systems may be implemented alternatively.

[0124] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from read-only memory 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0125] The intelligent ventilation control device provided in this application, employing the intelligent ventilation control method described in the above embodiments, can solve the technical problem of how to simultaneously achieve high-precision airflow control and optimal overall operational energy efficiency in ventilation control. Compared with the prior art, the beneficial effects of the intelligent ventilation control device provided in this application are the same as those of the intelligent ventilation control method provided in the above embodiments, and other technical features of this intelligent ventilation control device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0126] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0127] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0128] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the intelligent ventilation control method in the above embodiments.

[0129] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0130] The aforementioned computer-readable storage medium may be included in the intelligent ventilation control equipment; or it may exist independently and not be assembled into the intelligent ventilation control equipment.

[0131] The aforementioned computer-readable storage medium carries one or more programs. When the aforementioned one or more programs are executed by the intelligent ventilation control device, the intelligent ventilation control device: acquires the target total ventilation volume and real-time air pressure difference required for the breeding shed; updates the target air pressure difference after the change in air volume based on the real-time air pressure difference and a preset time threshold; queries a pre-stored fan parameter table according to the target total ventilation volume and the target air pressure difference, calculates the target speed of each EC fan based on the optimal energy efficiency constraint of the target total ventilation volume; and controls each EC fan to operate at the corresponding EC fan target speed through a communication bus.

[0132] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0133] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0134] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0135] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described intelligent ventilation control method. This solves the technical problem of how to simultaneously achieve high-precision airflow control and optimal overall operational energy efficiency in ventilation control. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the intelligent ventilation control method provided in the above embodiments, and will not be repeated here.

[0136] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the intelligent ventilation control method described above.

[0137] The computer program product provided in this application can solve the technical problem of how to simultaneously achieve high-precision airflow control and optimal overall operational energy efficiency in ventilation control. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the intelligent ventilation control method provided in the above embodiments, and will not be repeated here.

[0138] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. An intelligent ventilation control method, characterized in that, An intelligent ventilation control system, applied to an environmental controller, multiple electronically commutated (EC) fans, and a gas differential pressure sensor, includes the following intelligent ventilation control method: The target total ventilation volume and real-time air pressure difference required for the breeding shed are obtained; wherein, the real-time air pressure difference on both sides of the ventilation barrier where the multiple EC fans are located is obtained through the gas pressure difference sensor; The target air pressure difference is updated based on the real-time air pressure difference and a preset time threshold after the air volume change. Specifically, this includes: using the real-time air pressure difference as the initial target air pressure difference for the breeding shed; determining whether the current cumulative running time of the EC fan is less than the preset time threshold; if it is less than the preset time threshold, then setting the initial target air pressure difference as the target air pressure difference; otherwise, based on multiple historical average air pressure differences and corresponding air volume changes periodically detected within the preset time threshold, a predictive air pressure difference is calculated according to a preset linear relationship and used as the target air pressure difference. Based on the target total ventilation volume and the target air pressure difference, the pre-stored fan parameter table is queried, and the target speed of each EC fan is calculated based on the optimal energy efficiency constraint of the target total ventilation volume, including: the fan parameter table includes the correspondence between the speed, ventilation volume and power of the EC fan under different air pressure differences; Based on the target air pressure difference and a pre-stored fan parameter table, a first functional relationship is determined for the rotational speed, fan ventilation volume, and power of the EC fan under the target air pressure difference. Specifically, this includes: obtaining two reference air pressure differences that are equal to or adjacent to the target air pressure difference according to the fan parameter table, and obtaining a discrete data set of the EC fan's rotational speed, ventilation volume, and power under the reference air pressure differences; based on the target air pressure difference and the discrete data set, calculating a target discrete data set of the EC fan's ventilation volume and power under the target air pressure difference using a first linear interpolation; and based on the target discrete data set, constructing a first functional relationship for the EC fan's rotational speed, fan ventilation volume, and power under the target air pressure difference using piecewise linear interpolation. Based on the first air volume constraint and the first power constraint, the target ventilation volume allocation for each EC fan is calculated, specifically including: determining whether all EC fans are of the same model; if all EC fans are of the same model, determining the target ventilation volume allocation for the minimum total power of all EC fans based on the first functional relationship and Jensen's inequality; otherwise, determining the target ventilation volume allocation using a binary search method; wherein, the first air volume constraint means that the total ventilation volume of all EC fans is equal to the target total ventilation volume, and the first power constraint means that the minimum total power of all EC fans; Based on the target ventilation volume allocation and the first functional relationship, the target rotational speed of each EC fan is obtained, specifically including: for each EC fan and its corresponding target ventilation volume allocation, obtaining two reference ventilation volume data points adjacent to the target ventilation volume and their corresponding reference rotational speeds based on the fan parameter table; and calculating the target rotational speed of each EC fan using a second linear interpolation based on the target ventilation volume, the two reference ventilation volume data points, and their corresponding reference rotational speeds; wherein the second linear interpolation formula is: A represents the target ventilation volume. and Two reference ventilation volumes, and Where N is the corresponding reference speed, and N is the target speed; Each EC fan is controlled via a communication bus to operate at its corresponding EC fan target speed.

2. An intelligent ventilation control device, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the intelligent ventilation control method as described in claim 1.

3. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the intelligent ventilation control method as described in claim 1.

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