Air conditioner control method and device, electronic equipment and storage medium
By acquiring temperature and passenger flow forecasts at the location of the air conditioner, performing fuzzy processing and adjusting PID parameters, the problem of energy waste and comfort in the air conditioning system under dynamic environments is solved, and the flexible adjustment and energy consumption reduction of the air conditioning system are realized.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- DONGGUAN SUGAR & LIQUOR GRP MEIYIJIA CONVENIENCE STORE CO L
- Filing Date
- 2026-05-19
- Publication Date
- 2026-07-21
AI Technical Summary
Existing air conditioning control methods cannot adapt to dynamically changing environmental factors, resulting in energy waste and decreased comfort, especially in commercial stores where air conditioning equipment frequently starts and stops, and temperature fluctuates greatly.
By acquiring the indoor temperature and passenger flow forecast of the target air conditioner location, calculating the temperature error and error change rate, performing fuzzification processing, obtaining fuzzy data sets, and using the fuzzy rule base to adjust PID parameters, the air conditioner compressor frequency is dynamically controlled.
It enables the air conditioning system to be flexibly adjusted in dynamic environments, reducing energy consumption and improving comfort, avoiding energy waste and temperature fluctuations caused by the inability to adapt to environmental changes.
Smart Images
Figure CN122429451A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of air conditioning technology, and in particular to an air conditioning control method and device, electronic equipment, and storage medium. Background Technology
[0002] In commercial stores (such as convenience stores), the energy cost of air conditioning systems accounts for a significant portion of overall energy consumption. Currently, air conditioning is typically turned on and off according to a fixed schedule or controlled based on other fixed parameters. This method cannot adapt to dynamically changing environmental factors (such as population density, weather, and sunlight intensity), often resulting in wasted energy and frequent start-ups and shutdowns of the air conditioning equipment, leading to large temperature fluctuations and affecting user comfort.
[0003] Therefore, how to reduce the energy consumption of air conditioners and improve comfort has become an urgent technical problem to be solved. Summary of the Invention
[0004] The main objective of this application is to provide an air conditioning control method and device, electronic device, and storage medium, which aim to reduce the energy consumption of air conditioning and improve comfort.
[0005] To achieve the above objectives, a first aspect of this application provides an air conditioning control method, the method comprising: Obtain the indoor temperature at the target location where the target air conditioner is located; Passenger flow is predicted for the target location to obtain the predicted passenger flow trend; Error parameters are obtained by calculating the error based on the preset target temperature threshold and the indoor temperature; wherein, the error parameters include temperature error and error change rate; The temperature error, the rate of change of the error, and the predicted passenger flow trend are fuzzified to obtain a fuzzy data set; wherein the fuzzy data set includes the fuzzy amount of the temperature error, the fuzzy amount of the rate of change of the error, and the fuzzy amount of the predicted passenger flow. Based on the fuzzy values of the temperature error, the error change rate, and the predicted passenger flow, the adjustment amount is analyzed to obtain the parameter adjustment amount; The target air conditioner is controlled by adjusting the parameters.
[0006] In some embodiments, the step of calculating the error based on a preset target temperature threshold and the indoor temperature to obtain error parameters includes: The temperature error at each time point is obtained by calculating the difference between the preset target temperature threshold and the indoor temperature at each time point. The rate of change of the temperature error at each of the aforementioned time points is calculated to obtain the error change rate.
[0007] In some embodiments, the fuzzification process based on the temperature error, the rate of change of the error, and the predicted passenger flow trend to obtain a fuzzy data set includes: The ambiguity of the temperature error is determined by comparing it with a preset error threshold range; The error change rate fuzziness is determined by comparing the preset change rate threshold range with the error change rate. The predicted passenger flow fuzzy quantity is determined by comparing the preset passenger flow change threshold range with the predicted passenger flow change trend.
[0008] In some embodiments, the error threshold range has a first error threshold, a second error threshold, and a third error threshold, wherein the first error threshold is less than the second error threshold, the second error threshold is less than the third error threshold, and the second error threshold is zero; The step of determining the ambiguity of the temperature error by comparing it with a preset error threshold range includes: If the temperature error is less than the first error threshold, the fuzzy value of the temperature error is determined to be negative and large; If the temperature error is greater than or equal to the first error threshold and the temperature error is less than the second error threshold, the temperature error fuzziness is determined to be negative. If the temperature error is equal to the second error threshold, the fuzzy value of the temperature error is determined to be zero; If the temperature error is greater than the second error threshold and the temperature error is less than the third error threshold, the fuzzy value of the temperature error is determined to be positive and small. If the temperature error is greater than or equal to the third error threshold, the fuzzy value of the temperature error is determined to be positive.
[0009] In some embodiments, the step of performing adjustment analysis based on the temperature error fuzziness, the error change rate fuzziness, and the predicted passenger flow fuzziness to obtain the parameter adjustment amount includes: By using a preset fuzzy rule library, the temperature error fuzzy amount, the error change rate fuzzy amount, and the predicted passenger flow fuzzy amount are matched with rules to obtain the target parameter adjustment rules; The combination of the proportional gain adjustment, integral gain adjustment, and derivative gain adjustment of the target parameter adjustment rule is determined as the parameter adjustment amount.
[0010] In some embodiments, the parameter adjustment amount includes a proportional gain adjustment amount, an integral gain adjustment amount, and a derivative gain adjustment amount; The step of controlling the target air conditioner according to the parameter adjustment amount includes: Obtain the base proportional gain, base integral gain, and base differential gain of the target air conditioner; The updated proportional gain is obtained by summing the base proportional gain and the proportional gain adjustment. The updated integral gain is obtained by summing the base integral gain and the integral gain adjustment. The updated differential gain is obtained by summing the basic differential gain and the differential gain adjustment. The compressor frequency is determined by the update proportional gain, update integral gain and update derivative gain of the PID controller, and the compressor frequency control quantity is obtained to adjust the compressor frequency of the target air conditioner according to the compressor frequency control quantity.
[0011] In some embodiments, before calculating the error parameter based on a preset target temperature threshold and the indoor temperature, the method further includes: If the predicted passenger flow trend is upward, the preset first temperature threshold is reduced to obtain the second temperature threshold. The second temperature threshold is determined as the target temperature threshold.
[0012] To achieve the above objectives, a second aspect of this application provides an air conditioning control device, the device comprising: The temperature acquisition module is used to acquire the indoor temperature at the target location where the target air conditioner is located; The passenger flow prediction module is used to predict the passenger flow at the target location and obtain the predicted passenger flow trend. An error calculation module is used to calculate the error based on a preset target temperature threshold and the indoor temperature to obtain error parameters; wherein, the error parameters include temperature error and error change rate; The fuzzification processing module is used to perform fuzzification processing based on the temperature error, the error change rate, and the predicted passenger flow trend to obtain a fuzzy data set; wherein, the fuzzy data set includes the temperature error fuzzy amount, the error change rate fuzzy amount, and the predicted passenger flow fuzzy amount; The adjustment amount analysis module is used to perform adjustment amount analysis based on the temperature error fuzziness, the error change rate fuzziness, and the predicted passenger flow fuzziness to obtain the parameter adjustment amount; An air conditioning control module is used to control the target air conditioner by adjusting the parameters.
[0013] To achieve the above objectives, a third aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described in the first aspect.
[0014] To achieve the above objectives, a fourth aspect of the present application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in the first aspect.
[0015] The air conditioning control method, device, electronic equipment, and storage medium proposed in this application acquire the indoor temperature of the target location where the target air conditioner is located and predict passenger flow trends at the target location. This allows for real-time perception of the current indoor thermal environment and prediction of future changes in passenger load, providing basic information for subsequent control. Error calculations are performed based on a preset target temperature threshold and indoor temperature to obtain temperature error and error change rate. Fuzzy processing is then applied to the temperature error, error change rate, and predicted passenger flow trends to obtain fuzzy quantities for temperature error, error change rate, and predicted passenger flow. This transforms precise numerical values into fuzzy linguistic variables, effectively handling the uncertainty of environmental factors (such as population density, weather, and solar radiation intensity). Adjustment analysis is performed based on the fuzzy quantities for temperature error, error change rate, and predicted passenger flow to obtain parameter adjustment values, which are then used to control the target air conditioner. This allows for flexible and adaptive dynamic adjustment of the air conditioner, rather than using a fixed schedule or fixed parameters. This avoids energy waste, frequent start-ups and shutdowns of air conditioning equipment, and large temperature fluctuations caused by the inability to adapt to dynamically changing environmental factors, thereby reducing energy consumption and improving comfort. Attached Figure Description
[0016] Figure 1 This is a flowchart of the air conditioning control method provided in the embodiments of this application; Figure 2 yes Figure 1 The flowchart for step 103 in the text; Figure 3 yes Figure 1 The flowchart for step 104 in the document; Figure 4 yes Figure 3 The flowchart for step 301 in the document; Figure 5 yes Figure 1 The flowchart for step 105 in the document; Figure 6 yes Figure 1 The flowchart for step 106 in the document; Figure 7This is a flowchart of an air conditioning control method provided in another embodiment of this application; Figure 8 This is a flowchart illustrating an application example provided in an embodiment of this application; Figure 9 This is a schematic diagram illustrating a specific implementation of an air conditioning control method provided in an embodiment of this application; Figure 10 This is a schematic diagram of the structure of the air conditioning control device provided in the embodiments of this application; Figure 11 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation
[0017] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.
[0018] It should be noted that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.
[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.
[0020] First, let's analyze some of the terms used in this application: Artificial Intelligence (AI) is a new technical science that studies and develops theories, methods, technologies, and application systems for simulating, extending, and expanding human intelligence. AI is a branch of computer science that attempts to understand the essence of intelligence and produce intelligent machines that can react in a way similar to human intelligence. Research in this field includes robotics, speech recognition, image recognition, natural language processing, and expert systems. AI can be a simulation of the information processes of human consciousness and thought. AI can also be the theory, methods, technologies, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results. Basic AI technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing technology, operating / interactive systems, and mechatronics. AI software technologies mainly include computer vision, robotics, biometrics, speech processing, natural language processing, and machine learning / deep learning. This application can acquire and process relevant data based on AI technology.
[0021] PID parameters: These are the three core control parameters in a proportional-integral-derivative (PID) controller. The proportional gain (Kp) determines the system's response strength to errors. The integral gain (Ki) is used to eliminate steady-state errors. The derivative gain (Kd) is used for predictive adjustment based on error trends.
[0022] Energy costs are a significant portion of operating expenses for commercial stores (such as convenience stores), with air conditioning systems accounting for the largest share. Current energy-saving control methods include timed control, threshold control, and fixed-parameter PID control. Timed control starts and stops the air conditioning according to a fixed schedule, which cannot adapt to dynamically changing customer traffic and weather conditions, often leading to energy waste or decreased comfort. Threshold control operates the air conditioning at full power when the temperature exceeds a set threshold, shutting off only when the temperature falls below the threshold. This control mode results in frequent equipment start-ups and shutdowns, large temperature fluctuations, low energy efficiency, and severe equipment wear. Fixed-parameter PID control uses fixed parameters (proportional parameter P, integral parameter I, and derivative parameter D), making it difficult to adapt to the real-time changing thermodynamic environment of the store (such as personnel density, solar radiation intensity, seasonal changes, etc.). It cannot maintain optimal performance under changing operating conditions, easily leading to slow air conditioning response, overshoot, or continuous oscillation. For example, it cannot cope with instantaneous and drastic heat load disturbances caused by sudden changes in customer traffic, resulting in large fluctuations in indoor temperature, compromised comfort, and low dynamic energy efficiency. Furthermore, the current system may not respond adequately when the air conditioning energy load is high, causing the system to operate at a suboptimal energy efficiency point for extended periods. In addition, the current solution lacks foresight, only responding with a lag after the temperature has already changed.
[0023] Based on this, embodiments of this application provide an air conditioning control method and apparatus, electronic device, and storage medium, aiming to reduce the energy consumption of air conditioning and improve comfort.
[0024] The air conditioning control method, device, electronic equipment, and storage medium provided in the embodiments of this application are specifically described through the following embodiments. First, the air conditioning control method in the embodiments of this application is described.
[0025] The air conditioning control method provided in this application can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, etc.; the server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms; the software can be an application that implements the air conditioning control method, but is not limited to the above forms.
[0026] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.
[0027] It should be noted that in all specific embodiments of this application, when processing data related to user identity or characteristics, such as user information, user behavior data, user historical data, and user location information, user permission or consent is obtained first. Furthermore, the collection, use, and processing of this data comply with relevant laws, regulations, and standards. In addition, when embodiments of this application require access to sensitive personal information of users, separate permission or consent from the user is obtained through pop-ups or redirection to confirmation pages. Only after obtaining the user's separate permission or consent is the necessary user-related data required for the proper functioning of these embodiments acquired.
[0028] Figure 1 This is an optional flowchart of the air conditioning control method provided in the embodiments of this application. Figure 1 The method may include, but is not limited to, steps 101 to 106.
[0029] Step 101: Obtain the indoor temperature at the target location where the target air conditioner is located; Step 102: Forecast passenger flow at the target location to obtain the predicted passenger flow trend; Step 103: Calculate the error based on the preset target temperature threshold and indoor temperature to obtain error parameters; wherein, the error parameters include temperature error and error change rate; Step 104: Perform fuzzification processing based on temperature error, error change rate, and predicted passenger flow trend to obtain a fuzzy data set; wherein, the fuzzy data set includes the fuzzy amount of temperature error, the fuzzy amount of error change rate, and the fuzzy amount of predicted passenger flow. Step 105: Analyze the adjustment amount based on the fuzzy amount of temperature error, the fuzzy amount of error change rate, and the fuzzy amount of predicted passenger flow to obtain the parameter adjustment amount; Step 106: Adjust the target air conditioner according to the parameters.
[0030] The beneficial effects of this application's embodiments include, but are not limited to: obtaining the indoor temperature of the target location where the target air conditioner is located, and predicting passenger flow trends at the target location to obtain predicted passenger flow trends, allows for real-time perception of the current indoor thermal environment and prediction of future changes in passenger load, providing basic information for subsequent control. Error calculation is performed based on a preset target temperature threshold and indoor temperature to obtain temperature error and error change rate. Fuzzy processing is then performed on the temperature error, error change rate, and predicted passenger flow trends to obtain fuzzy quantities for temperature error, error change rate, and predicted passenger flow. This converts precise numerical values into fuzzy linguistic variables, effectively handling the uncertainty of environmental factors (such as population density, weather, and solar radiation intensity). Adjustment quantity analysis is performed based on the fuzzy quantities for temperature error, error change rate, and predicted passenger flow to obtain parameter adjustment quantities, which are then used to control the target air conditioner. This allows for flexible and adaptive dynamic adjustment of the air conditioner, rather than using a fixed schedule or fixed parameters, thus avoiding energy waste and problems such as frequent start-ups and shutdowns of air conditioning equipment and large temperature fluctuations caused by the inability to adapt to dynamically changing environmental factors. This reduces air conditioning energy consumption and improves comfort.
[0031] In step 101 of some embodiments, the target air conditioner is an air conditioning system that requires energy-saving control. Specifically, the target air conditioner can be an inverter air conditioner, whose compressor frequency can be adjusted. It should be noted that the target location refers to the indoor location where the target air conditioner is located, such as the room where the target air conditioner is installed. For example, in a store scenario, the target location may include a convenience store. In another embodiment, the target location may also include other locations, and is not limited thereto. It should be noted that the indoor temperature is the indoor temperature of the target location. Specifically, the indoor temperature can be collected through the air conditioning system, or through a temperature acquisition module or other devices, and is not limited thereto.
[0032] In step 102 of some embodiments, predicting the trend of passenger flow changes refers to the predicted future changes in passenger flow at the target location. For example, the predicted trend of passenger flow changes can be any of the following: upward, stable (e.g., unchanged), or downward. In some embodiments, the predicted trend of passenger flow changes can be analyzed and predicted using artificial intelligence models such as a passenger flow prediction module. For example, the passenger flow prediction module can predict the predicted trend of passenger flow changes for the next 15 minutes. In another embodiment, in addition to predicting the trend of passenger flow changes, short-term weather forecasts (such as sunshine intensity) and store schedules (such as the operating time of baking equipment) can be used as additional input variables.
[0033] In step 103 of some embodiments, the target temperature threshold is a preset temperature. Specifically, the target temperature threshold can be a temperature that makes the human body feel comfortable, such as any value between 24 and 26 degrees Celsius. Temperature error refers to the difference between the measured indoor temperature and the target temperature threshold. Error change rate refers to the rate of change of the temperature error over time.
[0034] In step 104 of some embodiments, the fuzzy data set includes a temperature error fuzzy quantity, an error change rate fuzzy quantity, and a predicted passenger flow fuzzy quantity. Specifically, the temperature error fuzzy quantity is a fuzzy quantity of the temperature error. Specifically, the fuzzy subset of the temperature error is {negative large, negative small, zero, positive small, positive large}, and the temperature error fuzzy quantity can be any element in this fuzzy subset. Similarly, the error change rate fuzzy quantity or the predicted passenger flow fuzzy quantity can also be any element in this fuzzy subset.
[0035] In some embodiments, three progressively increasing thresholds (one less than 0, one 0, and one greater than 0) can be used as the basis for defining the fuzzy quantity, so that the fuzzy quantity is determined as any element in {negative large, negative small, zero, positive small, positive large}. For example, suppose that when fuzzifying temperature error, the first threshold used is -10 degrees, the second threshold is 0 degrees, and the third threshold is +10 degrees. In some embodiments, suppose the temperature error e is -20 degrees. Since the temperature error e is less than -10 degrees, the temperature error fuzzy quantity is negative large. In another embodiment, suppose the temperature error e is 5 degrees. Since the temperature error e is greater than zero and less than +10 degrees, the temperature error fuzzy quantity is positive small. In another embodiment, suppose the temperature error e is 0, then the temperature error fuzzy quantity is zero.
[0036] In step 105 of some embodiments, the parameter adjustment amount can specifically be an adjustment amount to the PID parameters. For example, the adjustment amount to the PID parameters can be derived from the input fuzzy values of temperature error, error change rate, and predicted passenger flow using a preset fuzzy rule base. The parameter adjustment amount may include the proportional gain adjustment amount. Integral gain adjustment and differential gain adjustment In some embodiments, a portion of the fuzzy rule base is shown in the table below:
[0037] Table 1 For example, when the temperature is much lower than the preset temperature threshold (i.e., the temperature error fuzziness is positive), but the temperature is rising rapidly (i.e., the error change rate fuzziness is negative), and a large number of customers are predicted to enter (i.e., the predicted customer flow fuzziness is positive), the air conditioning system will significantly increase its proportional gain adjustment. To quickly suppress the temperature rise, the integral action (i.e., the integral gain adjustment) should be appropriately reduced. (To be negative) to prevent overshoot and enhance the differential action (i.e., differential gain adjustment). (To be in the center) in order to cope with the expected rapid changes.
[0038] In another embodiment, algorithms such as neural networks or model predictive control (MPC) can also be used to map the fuzzy values of temperature error, error rate of change, and predicted passenger flow into parameter adjustment values.
[0039] In step 106 of some embodiments, the compressor frequency of the air conditioner compressor can be calculated based on the parameter adjustment amount, and the compressor frequency of the target air conditioner can be adjusted to the calculated compressor frequency to reduce the energy consumption of the air conditioner.
[0040] In some embodiments, the proportional gain adjustment, integral gain adjustment, and derivative gain adjustment output from fuzzy inference can be converted into precise values for real-time correction of the PID controller parameters. For example, the proportional gain adjustment... The updated proportional gain Kp_new is obtained by adding the base proportional gain Kp_base. Then, the updated PID parameters can be used to calculate the control quantity (such as compressor frequency) through the PID controller, which is then sent to the target air conditioner to adjust its compressor frequency.
[0041] Please see Figure 2 In some embodiments, step 103 may include, but is not limited to, steps 201 to 202: Step 201: Calculate the difference between the preset target temperature threshold and the indoor temperature at each time point to obtain the temperature error at each time point. Step 202: Calculate the rate of change of temperature error at each time point to obtain the error rate of change.
[0042] The advantage of this embodiment lies in that it calculates the temperature error at each time point by comparing the preset target temperature threshold with the indoor temperature at each time point, and calculates the rate of change of the temperature error at each time point to obtain the error change rate. This allows it to capture the trend of temperature error over time, i.e., whether the error is increasing or decreasing, and at what rate, thereby predicting the future trend of the air conditioning system. Suppression or compensation can be applied before the temperature deviates significantly from the set value. Subsequent air conditioning control can take into account both the current deviation and the future trend, enabling the air conditioning system to accurately adjust its output even under the influence of dynamic factors such as occupancy density and temperature, thereby reducing energy consumption and improving comfort.
[0043] In some embodiments, indoor temperatures can be collected at N (e.g., 5) time points to calculate the temperature error at N time points, and then the error change rate can be calculated based on the temperature error at all time points. Here, N is a positive integer.
[0044] Please see Figure 3 In some embodiments, step 104 may include, but is not limited to, steps 301 to 303: Step 301: Based on the preset error threshold range and the temperature error, determine the fuzzy amount of the temperature error; Step 302: Based on the preset change rate threshold range and the error change rate, determine the fuzzy amount of the error change rate; Step 303: Based on the comparison between the preset passenger flow change threshold range and the predicted passenger flow change trend, determine the fuzzy amount of predicted passenger flow.
[0045] The advantage of this embodiment lies in that it determines the fuzzy quantity of temperature error by comparing it with a preset error threshold range, determines the fuzzy quantity of error change rate by comparing it with a preset change rate threshold range, and determines the fuzzy quantity of predicted passenger flow by comparing it with a preset passenger flow change threshold range. This maps continuous precise values (including temperature error, error change rate, and predicted passenger flow change trend) to corresponding fuzzy quantities, thereby converting actual physical quantities into fuzzy quantities that can be processed by fuzzy inference. This provides input for subsequent fuzzy decision-making, enabling comprehensive decision-making based on multiple factors such as temperature deviation, change trend, and passenger flow disturbance. This allows for more precise adjustment of the air conditioning system, reducing energy consumption and improving comfort.
[0046] In some embodiments, the specific process of determining the ambiguity amount by the threshold range can be referred to the description of steps 401 to 405 below, and will not be repeated here.
[0047] Please see Figure 4 In some embodiments, the error threshold range has a first error threshold, a second error threshold, and a third error threshold, wherein the first error threshold is less than the second error threshold, the second error threshold is less than the third error threshold, and the second error threshold is zero; Step 301 may include, but is not limited to, steps 401 through 405: Step 401: If the temperature error is less than the first error threshold, the temperature error fuzzy value is determined to be negative. Step 402: If the temperature error is greater than or equal to the first error threshold and the temperature error is less than the second error threshold, the temperature error fuzzy value is determined to be negative. Step 403: If the temperature error is equal to the second error threshold, the fuzzy value of the temperature error is set to zero. Step 404: If the temperature error is greater than the second error threshold and less than the third error threshold, the temperature error fuzzy amount is determined to be positive and small. Step 405: If the temperature error is greater than or equal to the third error threshold, the fuzzy amount of the temperature error is determined to be positive.
[0048] The advantage of this embodiment lies in comparing the temperature error with a preset error threshold range (containing a first error threshold, a second error threshold, and a third error threshold). By determining whether the temperature error is less than the first error threshold, greater than or equal to the first error threshold but less than the second error threshold, equal to the second error threshold, greater than the second error threshold but less than the third error threshold, or greater than or equal to the third error threshold, the fuzzy value of the temperature error is respectively defined as negative large, negative small, zero, positive small, and positive large. This allows continuous precise temperature error values to be divided into fuzzy levels with clear physical meaning, enabling the fuzzy controller to provide graded responses based on different degrees of temperature deviation from the target (e.g., significantly lower, slightly lower, zero deviation, slightly higher, significantly higher), thereby applying corresponding intensity of adjustment actions for different deviation amplitudes. For example, when the deviation is small, a gentle adjustment is used to reduce unnecessary energy consumption and temperature fluctuations; when the deviation is large, a strong adjustment is used to quickly restore comfort. This allows for flexible adaptation to dynamically changing environmental factors, thereby reducing air conditioning energy consumption and improving comfort.
[0049] In some embodiments, for example, the first error threshold may be -15, the second error threshold may be zero, and the third error threshold may be +15. In another embodiment, the first, second, and third error thresholds may also be other values, which are not limited in this application.
[0050] Please see Figure 5 In some embodiments, step 105 may include, but is not limited to, steps 501 to 502: Step 501: Using a preset fuzzy rule library, perform rule matching on the fuzzy amount of temperature error, the fuzzy amount of error change rate, and the fuzzy amount of predicted passenger flow to obtain the target parameter adjustment rules; Step 502: Determine the combination of the proportional gain adjustment, integral gain adjustment, and derivative gain adjustment of the target parameter adjustment rule as the parameter adjustment amount.
[0051] The advantage of this embodiment lies in its ability to match fuzzy parameters such as temperature error, rate of change of error, and predicted passenger flow using a pre-defined fuzzy rule base. This yields target parameter adjustment rules, allowing for the logical association of multiple dimensions of fuzzy inputs (temperature deviation, trend, and passenger flow disturbance) with corresponding control strategies. The combination of proportional gain, integral gain, and derivative gain adjustments from the target parameter adjustment rules is used to determine the parameter adjustment values. This enables simultaneous adjustment of the three core parameters of the PID controller, resulting in fine-tuning of the air conditioning output. This flexible and adaptive dynamic adjustment of the air conditioning, rather than relying on fixed schedules or parameters, reduces energy consumption and improves comfort.
[0052] In some embodiments, the fuzzy rule base includes multiple candidate parameter adjustment rules, which are rules used to map the combination of temperature error fuzziness, error rate of change fuzziness, and predicted passenger flow fuzziness to parameter adjustment amounts. The target parameter adjustment rule is a rule that conforms to the input temperature error fuzziness, error rate of change fuzziness, and predicted passenger flow fuzziness. For example, assuming the input temperature error fuzziness, error rate of change fuzziness, and predicted passenger flow fuzziness are "positive large," "negative small," and "positive large," respectively, then the target parameter adjustment rule is a rule used to indicate the corresponding parameter adjustment amount when the temperature error fuzziness, error rate of change fuzziness, and predicted passenger flow fuzziness are "positive large," "negative small," and "positive large," respectively.
[0053] Please see Figure 6 In some embodiments, the parameter adjustment amounts include proportional gain adjustment amounts, integral gain adjustment amounts, and derivative gain adjustment amounts; Step 106 may include, but is not limited to, steps 601 to 605: Step 601: Obtain the basic proportional gain, basic integral gain, and basic differential gain of the target air conditioner; Step 602: Summate the base proportional gain and the proportional gain adjustment to obtain the updated proportional gain; Step 603: Summate the base integral gain and the integral gain adjustment to obtain the updated integral gain; Step 604: Summate the basic differential gain and the differential gain adjustment to obtain the updated differential gain; Step 605: The compressor frequency is determined by updating the proportional gain, updating the integral gain and updating the derivative gain through the PID controller to calculate the control quantity, so as to obtain the compressor frequency control quantity, and adjust the compressor frequency of the target air conditioner according to the compressor frequency control quantity.
[0054] The advantage of this embodiment lies in obtaining the basic proportional gain, basic integral gain, and basic derivative gain of the target air conditioner, and then calculating the updated proportional gain by summing the basic proportional gain and the proportional gain adjustment, the updated integral gain by summing the basic integral gain and the integral gain adjustment, and the updated derivative gain by summing the basic derivative gain and the derivative gain adjustment. This allows for dynamic correction of the PID controller parameters based on the parameter adjustment values obtained through fuzzy inference, enabling adaptive adjustment of the PID parameters according to temperature error, error change rate, and predicted passenger flow trends. By updating the proportional gain, integral gain, and derivative gain using the PID controller, the compressor frequency control value is determined, and the compressor frequency of the target air conditioner is adjusted accordingly. This allows the dynamically adjusted PID parameters to be applied to compressor frequency control, ensuring that the air conditioner's output capacity matches the current indoor heat load (including changes in occupancy density, weather effects, etc.) in real time, thereby reducing energy consumption and improving comfort.
[0055] In some embodiments, it should be noted that the PID controller (Proportional-Integral-Derivative Controller) is a classic feedback-based automatic control algorithm, widely used in industrial automation, home appliances, and other fields. The compressor frequency control quantity is a parameter used to adjust the compressor frequency. In another embodiment, the controlled object of this method is not limited to variable frequency air conditioners, but can also be applied to the control of equipment such as fresh air handling units and electric sunshades.
[0056] Please see Figure 7 In some embodiments, prior to step 103, the air conditioning control method may include, but is not limited to, steps 701 to 702: Step 701: If the predicted passenger flow trend is upward, the preset first temperature threshold is reduced to obtain the second temperature threshold. Step 702: Determine the second temperature threshold as the target temperature threshold.
[0057] The advantage of this embodiment is that if the predicted passenger flow trend is upward, the preset first temperature threshold is reduced to obtain a second temperature threshold, which is then determined as the target temperature threshold. This allows the target temperature to be proactively lowered when passenger flow is about to increase, enabling the air conditioning system to enter a pre-cooling state in advance. This adjusts the indoor temperature to a lower level before the increased indoor heat load due to rising passenger density, avoiding sudden temperature spikes and high-frequency full-load compressor operation caused by a sudden increase in passenger flow. This reduces large temperature fluctuations caused by delayed adjustment, thereby reducing air conditioning energy consumption and improving comfort.
[0058] In some embodiments, the temperature threshold can be dynamically fine-tuned within a comfortable temperature range based on predicted peak or off-peak passenger flow. For example, before the midday peak passenger flow, the setpoint can be lowered from 26 degrees to 25.5 degrees in advance, utilizing the building's thermal inertia to store cold air in advance, so as to more smoothly pass through the peak and further optimize energy consumption.
[0059] Please see Figure 8 In one application example, the air conditioning control method includes the following steps: obtaining the temperature error e, the error change rate ec, and predicting passenger flow. It is then input into the fuzzy inference system, and the output is the proportional gain adjustment amount. Integral gain adjustment and differential gain adjustment Then, adjust the proportional gain amount respectively. Add the base proportional gain Kp_base to obtain the updated proportional gain Kp_new; adjust the integral gain. Add the base integral gain Ki_base to obtain the updated integral gain Ki_new; adjust the differential gain. The updated differential gain Kd_new is obtained by adding the base differential gain Kd_base. Then, the control quantity u(t) is calculated by the PID controller based on the updated proportional gain Kp_new, integral gain Ki_new, and differential gain Kd_new, where the control quantity u(t) can be a cooling load command or compressor frequency. The control quantity u(t) is input to the compressor or fan of the variable frequency air conditioning system to regulate the indoor temperature T. In some embodiments, the above process can be repeated.
[0060] Please see Figure 9 In one embodiment, the air conditioning control method comprises a data sensing layer, a data processing and prediction layer, an adaptive PID controller, and an execution layer. The data sensing layer includes a temperature / humidity sensor, a passenger flow statistics camera, a meteorological data interface, and a smart meter. The temperature / humidity sensor is used to collect indoor temperature data. The data processing and prediction layer is used to fuse and clean the data acquired from the temperature / humidity sensor, passenger flow statistics camera, and meteorological data interface, and to perform short-term passenger flow prediction based on the electricity data acquired from the smart meter. The adaptive PID controller includes a fuzzy inference engine and a PID controller. The fuzzy inference engine can infer parameter adjustment amounts from the fused and cleaned data and passenger flow prediction data using a parameter tuning rule base, and the PID controller uses the parameter adjustment amounts to obtain the control quantity. At the execution layer, the compressor or fan of the variable frequency air conditioner can be controlled according to the control quantity.
[0061] Please see Figure 10 This application also provides an air conditioning control device that can implement the above-described air conditioning control method. The device includes: Temperature acquisition module 801 is used to acquire the indoor temperature at the target location where the target air conditioner is located; The passenger flow prediction module 802 is used to predict the passenger flow at the target location and obtain the predicted passenger flow trend. The error calculation module 803 is used to calculate the error based on the preset target temperature threshold and the indoor temperature to obtain error parameters; wherein, the error parameters include temperature error and error change rate; The fuzzification processing module 804 is used to perform fuzzification processing based on the temperature error, the error change rate, and the predicted passenger flow trend to obtain a fuzzy data set; wherein, the fuzzy data set includes a fuzzy amount of temperature error, a fuzzy amount of error change rate, and a fuzzy amount of predicted passenger flow. The adjustment amount analysis module 805 is used to perform adjustment amount analysis based on the temperature error fuzziness, the error change rate fuzziness, and the predicted passenger flow fuzziness to obtain the parameter adjustment amount; The air conditioning control module 806 is used to control the target air conditioner according to the parameter adjustment amount.
[0062] The specific implementation method of the air conditioning control device is basically the same as the specific implementation method of the air conditioning control method described above, and will not be repeated here.
[0063] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described air conditioning control method. This electronic device can include any smart terminal such as a tablet computer or an in-vehicle computer.
[0064] Please see Figure 11 , Figure 11 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes: The processor 901 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application. The memory 902 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 902 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 902 and is called and executed by the processor 901 using the air conditioning control method of the embodiments of this application. The input / output interface 903 is used to implement information input and output; The communication interface 904 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 905 transmits information between various components of the device (e.g., processor 901, memory 902, input / output interface 903, and communication interface 904); The processor 901, memory 902, input / output interface 903, and communication interface 904 are connected to each other within the device via bus 905.
[0065] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described air conditioning control method.
[0066] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.
[0067] It should be noted that the models, software tools, or components appearing in the embodiments of this application are merely illustrative examples and do not represent actual use. All user personal information involved in the embodiments of this application has been authorized (with knowledge and consent) by the relevant parties or has been fully authorized by all parties, and the executing entity can obtain it through various legal and compliant means. The collection, storage, use, processing, transmission, provision, and disclosure of the information, data, and signals involved all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.
[0068] The embodiments described in this application are intended to more clearly illustrate the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. Those skilled in the art will know that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.
[0069] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.
[0070] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; 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.
[0071] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.
[0072] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.
[0073] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.
[0074] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. The coupling or direct coupling or communication connection between the shown or discussed units may be through some interfaces, or indirect coupling or communication connection between the apparatus or units, and may be electrical, mechanical, or other forms.
[0075] The units described above as separate components may or may not be physically separate. 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 units can be selected to achieve the purpose of this embodiment according to actual needs.
[0076] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.
[0077] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0078] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.
Claims
1. An air conditioning control method, characterized in that, The method includes: Obtain the indoor temperature at the target location where the target air conditioner is located; Passenger flow is predicted for the target location to obtain the predicted passenger flow trend; Error parameters are obtained by calculating the error based on the preset target temperature threshold and the indoor temperature; wherein, the error parameters include temperature error and error change rate; The temperature error, the rate of change of the error, and the predicted passenger flow trend are fuzzified to obtain a fuzzy data set; wherein the fuzzy data set includes the fuzzy amount of the temperature error, the fuzzy amount of the rate of change of the error, and the fuzzy amount of the predicted passenger flow. Based on the fuzzy values of the temperature error, the error change rate, and the predicted passenger flow, the adjustment amount is analyzed to obtain the parameter adjustment amount; The target air conditioner is controlled by adjusting the parameters.
2. The method according to claim 1, characterized in that, The error calculation based on the preset target temperature threshold and the indoor temperature yields error parameters, including: The temperature error at each time point is obtained by calculating the difference between the preset target temperature threshold and the indoor temperature at each time point. The rate of change of the temperature error at each of the aforementioned time points is calculated to obtain the error change rate.
3. The method according to claim 1, characterized in that, The process of fuzzifying the data based on the temperature error, the rate of change of the error, and the predicted passenger flow trend yields a fuzzy data set, including: The ambiguity of the temperature error is determined by comparing it with a preset error threshold range; The error change rate fuzziness is determined by comparing the preset change rate threshold range with the error change rate. The predicted passenger flow fuzzy quantity is determined by comparing the preset passenger flow change threshold range with the predicted passenger flow change trend.
4. The method according to claim 3, characterized in that, The error threshold range has a first error threshold, a second error threshold, and a third error threshold, wherein the first error threshold is less than the second error threshold, the second error threshold is less than the third error threshold, and the second error threshold is zero. The step of determining the ambiguity of the temperature error by comparing it with a preset error threshold range includes: If the temperature error is less than the first error threshold, the fuzzy value of the temperature error is determined to be negative and large; If the temperature error is greater than or equal to the first error threshold and the temperature error is less than the second error threshold, the temperature error fuzziness is determined to be negative. If the temperature error is equal to the second error threshold, the fuzzy value of the temperature error is determined to be zero; If the temperature error is greater than the second error threshold and the temperature error is less than the third error threshold, the fuzzy value of the temperature error is determined to be positive and small. If the temperature error is greater than or equal to the third error threshold, the fuzzy value of the temperature error is determined to be positive.
5. The method according to any one of claims 1 to 4, characterized in that, The step of analyzing the adjustment amount based on the temperature error fuzziness, the error change rate fuzziness, and the predicted passenger flow fuzziness to obtain the parameter adjustment amount includes: By using a preset fuzzy rule library, the temperature error fuzzy amount, the error change rate fuzzy amount, and the predicted passenger flow fuzzy amount are matched with rules to obtain the target parameter adjustment rules; The combination of the proportional gain adjustment, integral gain adjustment, and derivative gain adjustment of the target parameter adjustment rule is determined as the parameter adjustment amount.
6. The method according to any one of claims 1 to 4, characterized in that, The parameter adjustment amounts include proportional gain adjustment, integral gain adjustment, and derivative gain adjustment. The step of controlling the target air conditioner according to the parameter adjustment amount includes: Obtain the base proportional gain, base integral gain, and base differential gain of the target air conditioner; The updated proportional gain is obtained by summing the base proportional gain and the proportional gain adjustment. The updated integral gain is obtained by summing the base integral gain and the integral gain adjustment. The updated differential gain is obtained by summing the basic differential gain and the differential gain adjustment. The compressor frequency is determined by the update proportional gain, update integral gain and update derivative gain of the PID controller, and the compressor frequency control quantity is obtained to adjust the compressor frequency of the target air conditioner according to the compressor frequency control quantity.
7. The method according to any one of claims 1 to 4, characterized in that, Before calculating the error parameters based on the preset target temperature threshold and the indoor temperature, the method further includes: If the predicted passenger flow trend is upward, the preset first temperature threshold is reduced to obtain the second temperature threshold. The second temperature threshold is determined as the target temperature threshold.
8. An air conditioning control device, characterized in that, The device includes: The temperature acquisition module is used to acquire the indoor temperature at the target location where the target air conditioner is located; The passenger flow prediction module is used to predict the passenger flow at the target location and obtain the predicted passenger flow trend. An error calculation module is used to calculate the error based on a preset target temperature threshold and the indoor temperature to obtain error parameters; wherein, the error parameters include temperature error and error change rate; The fuzzification processing module is used to perform fuzzification processing based on the temperature error, the error change rate, and the predicted passenger flow trend to obtain a fuzzy data set; wherein, the fuzzy data set includes the temperature error fuzzy amount, the error change rate fuzzy amount, and the predicted passenger flow fuzzy amount; The adjustment amount analysis module is used to perform adjustment amount analysis based on the temperature error fuzziness, the error change rate fuzziness, and the predicted passenger flow fuzziness to obtain the parameter adjustment amount; An air conditioning control module is used to control the target air conditioner by adjusting the parameters.
9. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the air conditioning control method according to any one of claims 1 to 7.
10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the air conditioning control method according to any one of claims 1 to 7.