An air conditioning equipment temperature regulation method, device and readable storage medium

Abstract

The application provides an air conditioner temperature regulation method, device and readable storage medium, obtains operation parameter data of an air conditioner, temperature data of a temperature sensing device and a high temperature alarm threshold of an environment temperature, analyzes the correlation between the temperature data and the operation parameter data, obtains the correlation between the temperature data and the operation parameter data, determines a first cooling amplitude corresponding to the temperature sensing device, and determines a temperature regulation strategy of the air conditioner according to the temperature data, the first cooling amplitude and the correlation between the temperature data and the operation parameter data. The application fully considers the temperature change of the temperature sensing device in the past, the present and the future, reasonably converts the cooling amplitude required by the temperature sensing device into the regulation amplitude of the air conditioner temperature in combination with the linear relationship between the temperature sensing temperature and the return air temperature of the air conditioner, and the calculation result is more accurate and reliable, so that the temperature is effectively controlled and the waste of cold energy is reduced.

Description

Technical Field

[0001] This application relates to the field of air conditioning temperature control technology, and in particular to a method, device and readable storage medium for temperature regulation of air conditioning equipment. Background Technology

[0002] With the rapid popularization of 5G communication, data centers are growing in scale and becoming increasingly important. Data center server rooms need to use air conditioning equipment to maintain the ambient temperature within a suitable range to avoid problems such as server damage and data loss due to excessive temperature.

[0003] Existing technologies for controlling computer room temperature generate air conditioning adjustment commands based on fuzzy PID algorithms and predict indoor temperature using neural network prediction algorithms to achieve advance air conditioning control. However, existing technologies mainly rely on fuzzy PID algorithms to process real-time indoor temperature and humidity and obtain the output temperature as the air conditioning adjustment command. The temperature processing results are directly used as the control of the air conditioning, and advance control is only considered based on temperature prediction, which cannot guarantee the accuracy of the control operation.

[0004] Therefore, improving the accuracy of temperature control for air conditioning equipment in computer rooms has become a problem that needs to be solved. Summary of the Invention

[0005] The technical problem to be solved by this application is to address the above-mentioned deficiencies of the prior art by providing a method, apparatus and readable storage medium for temperature control of air conditioning equipment, so as to solve the problems existing in the prior art.

[0006] Firstly, this application provides a method for temperature control in an air conditioning device, wherein the method...

[0007] The law includes:

[0008] S1. Obtain operating parameter data of air conditioning equipment, temperature data of temperature sensing equipment, and high temperature alarm threshold of ambient temperature;

[0009] S2. Analyze the correlation between the temperature data and the operating parameter data to obtain the correlation relationship between the temperature data and the operating parameter data;

[0010] S3. Based on the temperature data and the high temperature alarm threshold, determine the first cooling range corresponding to the temperature sensing device;

[0011] S4. Determine the temperature control strategy of the air conditioning equipment based on the temperature data, the first cooling range, and the correlation between the temperature data and the operating parameter data.

[0012] In some embodiments, S2 includes:

[0013] S21. Analyze the correlation between the temperature data and the operating parameter data to obtain the correlation between the temperature sensing device and the air conditioning device;

[0014] S22. Determine the target air conditioning device that is most correlated with the temperature sensing device, and associate the temperature sensing device with the target air conditioning device;

[0015] S23. Determine the linear regression slope between the temperature sensing data of the temperature sensing device and the operating parameter data of the target air conditioning device, wherein the correlation between the temperature data and the operating parameter data includes the linear regression slope.

[0016] In some embodiments, S23 includes:

[0017] A linear regression model is established based on the temperature sensing data of the temperature sensing device and the operating parameter data of the target air conditioning device.

[0018] The linear regression model is used to calculate the linear regression equation y = kx + b between the temperature sensing data of the temperature sensing device and the operating parameter data of the target air conditioning device, where y represents the temperature sensing data of the temperature sensing device, x represents the operating parameter data of the target air conditioning device, k represents the linear regression slope between the temperature sensing data of the temperature sensing device and the operating parameter data of the target air conditioning device, and b is a constant.

[0019] In some embodiments, S3 includes:

[0020] When the difference between the temperature data and the high temperature alarm threshold is less than the first preset threshold, the first cooling amplitude corresponding to the temperature sensing device is determined by the PID control algorithm.

[0021] In some embodiments, the first cooling amplitude corresponding to the temperature sensing device is determined using the following formula through a PID control algorithm:

[0022]

[0023] Among them, V PID T represents the first temperature drop corresponding to the temperature sensing device. i T represents the temperature data at time i out of the last n times. n T represents the temperature data at time n out of the most recent n times. n-1 This represents the temperature data at time n-1 out of the most recent n times, and target represents the target value for temperature control of the temperature sensing device. The target value is the difference between the high temperature alarm threshold and the first preset threshold.

[0024] In some embodiments, S4 includes:

[0025] S41. Fit the temperature data into a quadratic function curve, and determine the first derivative and the second derivative of the quadratic function, wherein the first derivative represents the current trend of temperature change, and the second derivative represents the overall trend of temperature change.

[0026] S42. Based on the relationship between the current temperature data of the temperature sensing device and the high temperature alarm threshold, the first derivative and the second derivative, the first cooling amplitude, and the correlation between the temperature data and the operating parameter data, determine the temperature control strategy of the air conditioning device.

[0027] In some embodiments, S42 includes:

[0028] If the current temperature data is greater than or equal to the sum of the target value of the temperature control device and the second preset threshold, or if the air conditioning device has performed a cooling operation within the most recent preset time period and the first derivative is greater than 0, then the second cooling amplitude of the air conditioning device is determined to be:

[0029]

[0030] Among them, V cool V represents the second cooling rate of the air conditioning equipment. PID The first cooling amplitude corresponding to the temperature sensing device is represented by d1, the first derivative is represented by k, the linear regression slope of the temperature sensing data of the temperature sensing device and the operating parameter data of the air conditioning device is represented by m, and the second preset threshold is represented by m.

[0031] In some embodiments, S42 includes:

[0032] If the current temperature data is greater than the target value controlled by the temperature sensor, less than the sum of the target value controlled by the temperature sensor and the second preset threshold, and the air conditioning unit has not performed a cooling operation within the most recent preset time period, then the second cooling range of the air conditioning unit is determined to be:

[0033]

[0034] Among them, V cool V represents the second cooling rate of the air conditioning equipment. PID d1 represents the first cooling amplitude corresponding to the temperature sensing device, d1 represents the first derivative, and k represents the linear regression slope of the temperature sensing data of the temperature sensing device and the operating parameter data of the air conditioning device.

[0035] In some embodiments, S42 includes:

[0036] If the air conditioning unit has already performed a cooling operation within the most recent preset time period, and the current temperature data is greater than the target value controlled by the temperature sensor but less than the sum of the target value controlled by the temperature sensor and the second preset threshold, and the first derivative is less than 0 while the second derivative is greater than 0, then the second cooling amplitude of the air conditioning unit is determined to be:

[0037]

[0038] Among them, V cool V represents the second cooling rate of the air conditioning equipment. PID The value represents the first temperature drop corresponding to the temperature sensing device, and k represents the linear regression slope between the temperature sensing data of the temperature sensing device and the operating parameter data of the air conditioning device.

[0039] Secondly, this application provides a temperature control device for an air conditioning system, the device comprising:

[0040] The data acquisition module is configured to acquire operating parameter data of the air conditioning equipment, temperature data of the temperature sensing device, and high temperature alarm threshold of the ambient temperature.

[0041] The correlation analysis module is configured to analyze the correlation between the temperature data and the operating parameter data to obtain the correlation relationship between the temperature data and the operating parameter data;

[0042] The first determining module is configured to determine the first cooling range corresponding to the temperature sensing device based on the temperature data and the high temperature alarm threshold.

[0043] The second determining module is configured to determine the temperature control strategy of the air conditioning equipment based on the temperature data, the first cooling amplitude, and the correlation between the temperature data and the operating parameter data.

[0044] Thirdly, this application provides a temperature control device for an air conditioning unit, including a memory and a processor. The memory stores a computer program, and the processor is configured to run the computer program to implement the temperature control method for the air conditioning unit described in the first aspect.

[0045] Fourthly, this application provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the air conditioning equipment temperature control method described in the first aspect.

[0046] The air conditioning equipment temperature control method, device, and readable storage medium provided in this application specifically involve: acquiring operating parameter data of the air conditioning equipment, temperature data of a temperature sensing device, and a high-temperature alarm threshold of the ambient temperature; analyzing the correlation between the temperature data and the operating parameter data to obtain the correlation relationship between the temperature data and the operating parameter data; determining a first cooling amplitude corresponding to the temperature sensing device based on the temperature data and the high-temperature alarm threshold; and determining the temperature control strategy of the air conditioning equipment based on the temperature data, the first cooling amplitude, and the correlation relationship between the temperature data and the operating parameter data. This application proposes a method and device for precise air conditioning temperature control based on PID and regression algorithms. Correlation analysis is used to identify the air conditioner with the highest correlation for each temperature sensor as its associated air conditioner, and the linear regression slope between the temperature sensor temperature and the return air temperature of the associated air conditioner is calculated. Simultaneously, the PID control algorithm is used to calculate the required cooling range for the high-temperature temperature sensor, and the temperature data is fitted into a quadratic function curve to determine the current and overall temperature change trend. Based on the above analysis results, the required cooling range for the associated air conditioner of the high-temperature temperature sensor is calculated comprehensively. In analyzing the cooling operation of the high-temperature temperature sensor, this application fully considers the past, present, and future temperature changes of the temperature sensor; and combines the linear relationship between the temperature sensor temperature and the return air temperature of the associated air conditioner to reasonably convert the required cooling range of the temperature sensor into the air conditioning temperature control range. The calculation results are more accurate and reliable, achieving effective temperature control while reducing the waste of cooling capacity. Attached Figure Description

[0047] 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.

[0048] Figure 1 A flowchart of a temperature control method for an air conditioning device provided in this application embodiment;

[0049] Figure 2 A schematic diagram illustrating the determination of the temperature control strategy for the air conditioning equipment provided in an embodiment of this application;

[0050] Figure 3 This is a schematic diagram of the structure of an air conditioning equipment temperature control device provided in an embodiment of this application;

[0051] Figure 4 This is a schematic diagram of another air conditioning equipment temperature control device provided in an embodiment of this application.

[0052] The accompanying drawings have illustrated specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to specific embodiments. Detailed Implementation

[0053] To enable those skilled in the art to better understand the technical solution of this application, the embodiments of this application will be further described in detail below with reference to the accompanying drawings.

[0054] It is understood that the specific embodiments and accompanying drawings described herein are merely for explaining this application and are not intended to limit this application.

[0055] It is understood that, without conflict, the various embodiments and features in the embodiments of this application can be combined with each other.

[0056] It is understood that, for ease of description, only the parts relevant to this application are shown in the accompanying drawings, while parts unrelated to this application are not shown in the drawings.

[0057] It is understood that each unit or module involved in the embodiments of this application may correspond to only one entity structure, or may be composed of multiple entity structures, or multiple units or modules may be integrated into one entity structure.

[0058] It is understood that the terms "first," "second," etc., used in the embodiments of this application are used to distinguish different objects or to distinguish different treatments of the same object, rather than to describe a specific order of objects.

[0059] It is understood that, without conflict, the functions and steps marked in the flowcharts and block diagrams of this application may occur in a different order than those marked in the accompanying drawings.

[0060] It is understood that the flowcharts and block diagrams of this application illustrate the possible architecture, functions, and operations of systems, apparatuses, devices, and methods according to various embodiments of this application. Each block in a flowchart or block diagram may represent a unit, module, program segment, or code, containing executable instructions for implementing the specified function. Furthermore, each block or combination of blocks in the block diagrams and flowcharts may be implemented using a hardware-based system to implement the specified function, or using a combination of hardware and computer instructions.

[0061] It is understood that the units and modules involved in the embodiments of this application can be implemented by software or by hardware. For example, the units and modules can be located in the processor.

[0062] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0063] This application provides a method for temperature control of an air conditioning device. The working process of this method can be implemented by electronic devices, such as computers, handheld smart terminals, etc. For ease of explanation, the embodiments of this application are described with the computer as the subject of the method execution.

[0064] Figure 1 This is a schematic diagram of the air conditioning equipment temperature control method provided in the embodiments of this application, as shown below. Figure 1 As shown, this application provides a method for temperature control of an air conditioning device, the method comprising S1-S4, as follows:

[0065] S1. Obtain operating parameter data of air conditioning equipment, temperature data of temperature sensing equipment, and high temperature alarm threshold of ambient temperature;

[0066] Among them, air conditioning equipment and temperature sensing equipment refer to equipment installed in the data center computer room. Temperature sensing equipment includes thermometers, and ambient temperature refers to the temperature of the data center computer room.

[0067] The operating parameter data of the air conditioning equipment includes, but is not limited to: air conditioning switch status, return air temperature setpoint, return air temperature, compressor 1 / 2 switch status, fan speed, etc.

[0068] Among them, the operating parameter data of the air conditioning equipment and the temperature data of the temperature sensing equipment can be acquired in real time, and the high temperature alarm threshold of the ambient temperature can be preset according to the actual situation for each temperature sensing device.

[0069] S2. Analyze the correlation between the temperature data and the operating parameter data to obtain the correlation relationship between the temperature data and the operating parameter data;

[0070] In this step, a correlation analysis algorithm can be used to analyze the correlation between the temperature data and the operating parameter data.

[0071] In some embodiments, S2 includes:

[0072] S21. Analyze the correlation between the temperature data and the operating parameter data to obtain the correlation between the temperature sensing device and the air conditioning device;

[0073] For example, based on the air conditioner operating parameter data and temperature sensing data within the most recent time period (e.g., within the last day), the correlation coefficient between each temperature sensing data and each air conditioner operating parameter data is calculated using the Pearson correlation analysis method.

[0074] S22. Determine the target air conditioning device that is most correlated with the temperature sensing device, and associate the temperature sensing device with the target air conditioning device;

[0075] Specifically, the average correlation coefficient between a single temperature sensor and various operating parameters of a single air conditioner is taken as the comprehensive correlation coefficient between the temperature sensor and the air conditioner. The air conditioner with the highest comprehensive correlation coefficient for each temperature sensor is identified as the associated air conditioner for that temperature sensor. This step analyzes the correlation between the temperature sensor and various operating parameters of the air conditioner to determine the relationship between the temperature sensor and the air conditioner, objectively identifying the air conditioner that has the greatest impact on the temperature sensor, thus improving the accuracy of high-temperature control operations.

[0076] S23. Determine the linear regression slope between the temperature sensing data of the temperature sensing device and the operating parameter data of the target air conditioning device, wherein the correlation between the temperature data and the operating parameter data includes the linear regression slope.

[0077] In some embodiments, S23 includes:

[0078] A linear regression model is established based on the temperature sensing data of the temperature sensing device and the operating parameter data of the target air conditioning device.

[0079] The linear regression model is used to calculate the linear regression equation y = kx + b between the temperature sensing data of the temperature sensing device and the operating parameter data of the target air conditioning device, where y represents the temperature sensing data of the temperature sensing device, x represents the operating parameter data of the target air conditioning device, k represents the linear regression slope between the temperature sensing data of the temperature sensing device and the operating parameter data of the target air conditioning device, and b is a constant.

[0080] Specifically, the operating parameter data of the air conditioning equipment is taken from the return air temperature data. A linear regression model is established using the temperature sensing data within the most recent time period (e.g., within the last hour) and the return air temperature data of the associated air conditioner. The linear regression equation y = kx + b between the temperature sensing temperature and the return air temperature of the associated air conditioner is calculated, where y represents the temperature sensing temperature, x represents the return air temperature of the associated air conditioner, k represents the linear regression slope between the temperature sensing temperature and the return air temperature of the associated air conditioner, and b is a constant.

[0081] S3. Based on the temperature data and the high temperature alarm threshold, determine the first cooling range corresponding to the temperature sensing device;

[0082] Specifically, if the temperature data is close to the high temperature alarm threshold, it indicates that the temperature sensing device is a high temperature sensing device with alarm risk. In this case, the first temperature drop corresponding to the temperature sensing device can be determined first. Since the temperature of the temperature sensing device cannot be controlled and only the temperature of the air conditioning device can be controlled, the first temperature drop corresponding to the temperature sensing device is calculated first, and then the temperature control strategy of the air conditioning device is determined based on the first temperature drop.

[0083] In some embodiments, S3 includes: when the difference between the temperature data and the high-temperature alarm threshold is less than a first preset threshold, determining the first cooling amplitude corresponding to the temperature sensing device through a PID control algorithm. The PID algorithm refers to the process of obtaining the system output and then performing proportional, integral, and derivative operations on the output, which are then superimposed onto the input to control the system's algorithmic behavior. In this step, during the analysis of the high-temperature temperature sensing cooling operation, the required cooling amplitude is initially calculated based on the PID control algorithm, reducing the risk of control failure.

[0084] In some embodiments, the first cooling amplitude corresponding to the temperature sensing device is determined using the following formula through a PID control algorithm:

[0085]

[0086] Among them, V PID T represents the first temperature drop corresponding to the temperature sensing device. i T represents the temperature data at time i out of the last n times. n T represents the temperature data at time n out of the most recent n times. n-1 This represents the temperature data at time n-1 out of the most recent n times, and target represents the target value for temperature control of the temperature sensing device. The target value is the difference between the high temperature alarm threshold and the first preset threshold.

[0087] Specifically, taking the most recent n moments as the most recent 10 minutes and the first preset threshold as 1 as an example, when the difference between the temperature data from the temperature sensor and the ambient high temperature alarm threshold is less than 1, it indicates that the temperature sensor is a high-temperature sensor with an alarm risk. Based on the temperature data from the temperature sensor over the past 10 minutes, the PID control algorithm calculates the magnitude of the temperature sensor's cooling rate as follows:

[0088]

[0089] Among them, T i This represents the temperature felt in the i-th minute within the last 10 minutes; similarly, T... 10 T9 and T9 represent the current and previous temperature readings, respectively; target represents the target value for temperature control, which is the ambient high temperature alarm threshold -1.

[0090] S4. Determine the temperature control strategy of the air conditioning equipment based on the temperature data, the first cooling range, and the correlation between the temperature data and the operating parameter data.

[0091] In some embodiments, S4 includes:

[0092] S41. Fit the temperature data into a quadratic function curve, and determine the first derivative and the second derivative of the quadratic function, wherein the first derivative represents the current trend of temperature change, and the second derivative represents the overall trend of temperature change.

[0093] Specifically, the temperature data of the high-temperature sensor in the most recent time period (e.g., the last 20 minutes) is fitted into a quadratic function curve, and the first derivative d1 and the second derivative d2 of the function at the current moment are calculated.

[0094] Among them, d1 reflects the current temperature change trend of the temperature sensor. Specifically, d1>0 indicates that the temperature sensor is currently in a rising state, d1=0 indicates that the temperature sensor is currently in a stable state, and d1>0 indicates that the temperature sensor is currently in a cooling state.

[0095] Among them, d2 reflects the overall trend of temperature change. Specifically, d2>0 indicates that the temperature sensation shows a trend of first decreasing and then increasing, while d2<0 indicates that the temperature sensation shows a trend of first increasing and then decreasing.

[0096] S42. Based on the relationship between the current temperature data of the temperature sensing device and the high temperature alarm threshold, the first derivative and the second derivative, the first cooling amplitude, and the correlation between the temperature data and the operating parameter data, determine the temperature control strategy of the air conditioning device.

[0097] This embodiment combines the current and future temperature change trend judgment results of the temperature sensor to reasonably adjust the high-temperature temperature drop range calculated by the PID control algorithm, making the calculation results more accurate and reliable, and reducing the waste of cooling capacity while achieving effective temperature control.

[0098] Figure 2 This is a schematic diagram illustrating the determination of the temperature control strategy for the air conditioning equipment provided in an embodiment of this application, as shown below. Figure 2 As shown, in some embodiments, S42 includes:

[0099] If the current temperature data is greater than or equal to the sum of the target value of the temperature control device and the second preset threshold, or if the air conditioning device has performed a cooling operation within the most recent preset time period and the first derivative is greater than 0, then the second cooling amplitude of the air conditioning device is determined to be:

[0100]

[0101] Among them, V cool V represents the second cooling rate of the air conditioning equipment. PID The first cooling amplitude corresponding to the temperature sensing device is represented by d1, the first derivative is represented by k, the linear regression slope of the temperature sensing data of the temperature sensing device and the operating parameter data of the air conditioning device is represented by m, and the second preset threshold is represented by m.

[0102] Specifically, taking T 10 to represent the temperature data at the current moment, and taking the second preset threshold as 0.5 for example, if the current temperature T of the temperature sensor 10 ≥ target + 0.5, or the air conditioner has performed a cooling operation within the last 5 minutes and d1 > 0, it means that the temperature of the temperature sensor is too high or the air conditioner has performed a cooling operation but the temperature of the temperature sensor is still rising, and it is necessary to immediately perform a large-scale cooling on this temperature sensor. Therefore, the cooling amplitude of the air conditioner associated with this temperature sensor is:

[0103]

[0104] In this step, during the process of judging whether it is necessary to regulate the air conditioner to perform a cooling operation, considering whether the air conditioner has recently performed a cooling operation effectively reduces the phenomena of repeated cooling operations and frequent regulation of the air conditioner.

[0105] Reference Figure 2 , in some embodiments, S42 includes:

[0106] If the temperature data at the current moment is greater than the target value of the temperature control of the temperature sensor device, less than the sum of the target value of the temperature control of the temperature sensor device and the second preset threshold, and the air conditioner device has not performed a cooling operation within the recent preset time period, then determine the second cooling amplitude of the air conditioner device as:

[0107]

[0108] Among them, V cool represents the second cooling amplitude of the air conditioner device, V PID represents the first cooling amplitude corresponding to the temperature sensor device, d1 represents the first derivative, and k represents the linear regression slope of the temperature data of the temperature sensor device and the operation parameter data of the air conditioner device.

[0109] Specifically, taking T 10 to represent the temperature data at the current moment, and taking the second preset threshold as 0.5 for example, if target < T 10 < target + 0.5, and the air conditioner has not performed a cooling operation within the last 5 minutes, it means that the temperature of the temperature sensor is on the high side and needs to be appropriately cooled. Therefore, the cooling amplitude of the air conditioner associated with this temperature sensor is:

[0110]

[0111] Reference Figure 2 , in some embodiments, S42 includes:

[0112] If the air conditioning equipment has performed a cooling operation within the most recent preset time period, the temperature data at the current moment is greater than the target value of the temperature control of the temperature sensing device, less than the sum of the target value of the temperature control of the temperature sensing device and the second preset threshold, the first derivative is less than 0, and the second derivative is greater than 0, then it is determined that the second cooling amplitude of the air conditioning equipment is:

[0113]

[0114] where V cool represents the second cooling amplitude of the air conditioning equipment, V PID represents the first cooling amplitude corresponding to the temperature sensing device, and k represents the linear regression slope of the temperature sensing data of the temperature sensing device and the operating parameter data of the air conditioning equipment.

[0115] Specifically, taking T 10 to represent the temperature data at the current moment and taking the second preset threshold as 0.5 for example, if the air conditioner has performed a cooling operation within the past 5 minutes, target < T 10 < target + 0.5, d1 < 0 and d2 > 0, which indicates that the temperature sensed by the temperature sensor is on the high side. Although the current temperature is in a downward process, it may rise in the future and a small amount of cooling is required to prevent high temperature. Therefore, the cooling amplitude of the air conditioner associated with this temperature sensor is:

[0116]

[0117] It can be understood that if the above conditions are not all met, it indicates that the possibility of a high-temperature alarm for the temperature sensor is relatively low. It is only necessary to continue to monitor the temperature change situation and no cooling operation is required temporarily. Therefore, the cooling amplitude V cool = 0.

[0118] This application proposes an air conditioner temperature precise control method and device based on PID and regression algorithms. The correlation analysis method is used to find the air conditioner with the greatest correlation corresponding to each temperature sensor as its associated air conditioner, and the linear regression slope of the temperature sensed by the temperature sensor and the return air temperature of the associated air conditioner is calculated. At the same time, the PID control algorithm is used to calculate the cooling amplitude required for the high-temperature temperature sensor, and the temperature data is fitted into a quadratic function curve to judge the current and overall temperature change trends. The cooling amplitude required for the air conditioner associated with the high-temperature temperature sensor is comprehensively calculated according to the above analysis results. In the process of analyzing the cooling operation of the high-temperature temperature sensor, this application fully considers the past, current and future temperature change situations of the temperature sensor. And in combination with the linear relationship between the temperature sensed by the temperature sensor and the return air temperature of the associated air conditioner, the cooling amplitude required for the temperature sensor is reasonably converted into the regulation amplitude of the air conditioner temperature, and the calculation result is more accurate and reliable, reducing the waste of cooling capacity while effectively controlling the temperature.

[0119] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some of the steps in the figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0120] Figure 3 This is a schematic diagram of the air conditioning equipment temperature control device provided in the embodiments of this application, as shown below. Figure 3 As shown, this application provides a temperature control device for an air conditioning unit, the device comprising:

[0121] The data acquisition module 11 is configured to acquire the operating parameter data of the air conditioning equipment, the temperature data of the temperature sensing device, and the high temperature alarm threshold of the ambient temperature.

[0122] The correlation analysis module 12 is configured to analyze the correlation between the temperature data and the operating parameter data to obtain the correlation relationship between the temperature data and the operating parameter data;

[0123] The first determining module 13 is configured to determine the first cooling range corresponding to the temperature sensing device based on the temperature data and the high temperature alarm threshold.

[0124] The second determining module 14 is configured to determine the temperature control strategy of the air conditioning equipment based on the temperature data, the first cooling amplitude, and the correlation between the temperature data and the operating parameter data.

[0125] Regarding the limitations on the temperature control device for air conditioning equipment, please refer to the limitations on the temperature control method for air conditioning equipment in the above embodiments of this application, which will not be repeated here.

[0126] Figure 4 Another schematic diagram of the air conditioning equipment temperature control device provided in the embodiments of this application is shown below. Figure 4 As shown, in some embodiments, this application provides an air conditioning equipment temperature control device, including a memory 22 and a processor 21. The memory stores a computer program, and the processor is configured to run the computer program to execute the air conditioning equipment temperature control method in the above embodiments of this application.

[0127] The memory is connected to the processor. The memory can be flash memory, read-only memory or other types of memory. The processor can be a central processing unit or a microcontroller.

[0128] In some embodiments, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the air conditioning equipment temperature control method in the above embodiments of this application.

[0129] The computer-readable storage medium includes volatile or non-volatile, removable or non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, computer program modules, or other data). Computer-readable storage media include, but are not limited to, RAM (Random Access Memory), ROM (Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), flash memory or other memory technologies, CD-ROM (Compact Disc Read-Only Memory), DVD or other optical disc storage, cartridges, magnetic tapes, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer.

[0130] It is understood that the above embodiments are merely exemplary implementations used to illustrate the principles of this application, and this application is not limited thereto. For those skilled in the art, various modifications and improvements can be made without departing from the spirit and substance of this application, and these modifications and improvements are also considered to be within the scope of protection of this application.

Claims

1. A method for temperature control in an air conditioning system, characterized in that, The method includes: S1. Obtain operating parameter data of air conditioning equipment, temperature data of temperature sensing equipment, and high temperature alarm threshold of ambient temperature; S2. Analyze the correlation between the temperature data and the operating parameter data to obtain the correlation relationship between the temperature data and the operating parameter data, specifically including: S21. Analyze the correlation between the temperature data and the operating parameter data to obtain the correlation between the temperature sensing device and the air conditioning device; S22. Determine the target air conditioning device that is most correlated with the temperature sensing device, and associate the temperature sensing device with the target air conditioning device; S23. Determine the linear regression slope between the temperature sensing data of the temperature sensing device and the operating parameter data of the target air conditioning device, wherein the correlation between the temperature data and the operating parameter data includes the linear regression slope; S3. Based on the temperature data and the high temperature alarm threshold, determine the first cooling range corresponding to the temperature sensing device, specifically including: when the difference between the temperature data and the high temperature alarm threshold is less than a first preset threshold, determine the first cooling range corresponding to the temperature sensing device through a PID control algorithm. S4. Based on the temperature data, the first cooling rate, and the correlation between the temperature data and the operating parameter data, determine the temperature control strategy of the air conditioning equipment, specifically including: S41. Fit the temperature data into a quadratic function curve, and determine the first and second derivatives of the quadratic function, wherein the first derivative represents the current trend of the temperature sensing, and the second derivative represents the overall trend of the temperature sensing. S42. Determine the temperature control strategy of the air conditioning equipment based on the relationship between the current temperature data of the temperature sensing device and the high temperature alarm threshold, the first derivative and the second derivative, the first cooling amplitude, and the correlation between the temperature data and the operating parameter data. S42 includes at least: If the current temperature data is greater than or equal to the sum of the target value of the temperature control device and the second preset threshold, or if the air conditioning device has performed a cooling operation within the most recent preset time period and the first derivative is greater than 0, then the second cooling amplitude of the air conditioning device is determined to be: ； in, This indicates the second cooling rate of the air conditioning equipment. The first cooling amplitude corresponding to the temperature sensing device is represented by d1, the first derivative is represented by k, the linear regression slope of the temperature sensing data of the temperature sensing device and the operating parameter data of the air conditioning device is represented by m, and the second preset threshold is represented by m.

2. The air conditioning equipment temperature control method according to claim 1, characterized in that, S23 includes: A linear regression model is established based on the temperature sensing data of the temperature sensing device and the operating parameter data of the target air conditioning device. The linear regression model is used to calculate the linear regression equation y=kx+b between the temperature sensing data of the temperature sensing device and the operating parameter data of the target air conditioning device, where y represents the temperature sensing data of the temperature sensing device, x represents the operating parameter data of the target air conditioning device, k represents the linear regression slope between the temperature sensing data of the temperature sensing device and the operating parameter data of the target air conditioning device, and b is a constant.

3. The air conditioning equipment temperature control method according to claim 1, characterized in that, The first temperature drop amplitude corresponding to the temperature sensing device is determined using the PID control algorithm and the following formula: in, This indicates the first temperature drop corresponding to the temperature sensing device. This represents the temperature data at time i out of the last n times. This represents the temperature data at time n out of the most recent n times. This represents the temperature data at time n-1 out of the most recent n times, and target represents the target value for temperature control of the temperature sensing device. The target value is the difference between the high temperature alarm threshold and the first preset threshold.

4. The air conditioning equipment temperature control method according to claim 1, characterized in that, S42 also includes: If the current temperature data is greater than the target value controlled by the temperature sensor, less than the sum of the target value controlled by the temperature sensor and the second preset threshold, and the air conditioning unit has not performed a cooling operation within the most recent preset time period, then the second cooling range of the air conditioning unit is determined to be: ； in, This indicates the second cooling rate of the air conditioning equipment. d1 represents the first cooling amplitude corresponding to the temperature sensing device, k represents the first derivative, and k represents the linear regression slope between the temperature sensing data of the temperature sensing device and the operating parameter data of the air conditioning device.

5. The air conditioning equipment temperature control method according to claim 1, characterized in that, S42 also includes: If the air conditioning unit has already performed a cooling operation within the most recent preset time period, and the current temperature data is greater than the target value controlled by the temperature sensor but less than the sum of the target value controlled by the temperature sensor and the second preset threshold, and the first derivative is less than 0 while the second derivative is greater than 0, then the second cooling amplitude of the air conditioning unit is determined to be: ； in, This indicates the second cooling rate of the air conditioning equipment. The value represents the first temperature drop corresponding to the temperature sensing device, and k represents the linear regression slope between the temperature sensing data of the temperature sensing device and the operating parameter data of the air conditioning device.

6. A temperature control device for air conditioning equipment, characterized in that, The device includes: The data acquisition module is configured to acquire operating parameter data of the air conditioning equipment, temperature data of the temperature sensing device, and high temperature alarm threshold of the ambient temperature. The correlation analysis module is configured to analyze the correlation between the temperature data and the operating parameter data to obtain the correlation relationship between the temperature data and the operating parameter data. Specifically, it includes: analyzing the correlation between the temperature data and the operating parameter data to obtain the correlation relationship between the temperature sensing device and the air conditioning device; identifying the target air conditioning device with the strongest correlation to the temperature sensing device and associating the temperature sensing device with the target air conditioning device; and determining the linear regression slope between the temperature sensing data of the temperature sensing device and the operating parameter data of the target air conditioning device, wherein the correlation relationship between the temperature data and the operating parameter data includes the linear regression slope. The first determining module is configured to determine the first cooling range corresponding to the temperature sensing device based on the temperature data and the high temperature alarm threshold. Specifically, it includes: when the difference between the temperature data and the high temperature alarm threshold is less than a first preset threshold, determining the first cooling range corresponding to the temperature sensing device through a PID control algorithm. The second determining module is configured to determine the temperature control strategy of the air conditioning equipment based on the temperature data, the first cooling amplitude, and the correlation between the temperature data and the operating parameter data. Specifically, it includes: fitting the temperature data into a quadratic function curve, determining the first and second derivatives of the quadratic function, wherein the first derivative represents the current temperature sensing trend, and the second derivative represents the overall temperature sensing trend; and determining the temperature control strategy of the air conditioning equipment based on the relationship between the current temperature data of the temperature sensing device and the high temperature alarm threshold, the first and second derivatives, the first cooling amplitude, and the correlation between the temperature data and the operating parameter data. The temperature control strategy for the air conditioning equipment is determined based on the relationship between the current temperature data of the temperature sensing device and the high-temperature alarm threshold, the first derivative and the second derivative, the first cooling amplitude, and the correlation between the temperature data and the operating parameter data. This determination includes at least the following: If the current temperature data is greater than or equal to the sum of the target value of the temperature control device and the second preset threshold, or if the air conditioning device has performed a cooling operation within the most recent preset time period and the first derivative is greater than 0, then the second cooling amplitude of the air conditioning device is determined to be: ； in, This indicates the second cooling rate of the air conditioning equipment. The first cooling amplitude corresponding to the temperature sensing device is represented by d1, the first derivative is represented by k, the linear regression slope of the temperature sensing data of the temperature sensing device and the operating parameter data of the air conditioning device is represented by m, and the second preset threshold is represented by m.

7. A temperature control device for an air conditioning system, characterized in that, The device includes a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to implement the air conditioning equipment temperature control method as described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the air conditioning equipment temperature control method as described in any one of claims 1-5.

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