Operation control method and device of air conditioning system, medium and air conditioning system

Abstract

The invention discloses an operation control method and device of an air conditioning system, a medium and the air conditioning system.The method comprises the steps that cluster operation environment parameters are obtained firstly, and then temperature difference abnormal effect optimization indexes used for optimizing the heat island effect and the cold island effect are determined on the basis of the cluster operation environment parameters; and the outdoor unit with the minimum influence of the temperature difference abnormal effect is screened based on the index to cooperate as a target cluster and control the operation of the target cluster, so that additional temperature difference abnormal effect superposition caused by the operation of invalid outdoor units can be avoided, the interference of the temperature difference abnormal effect on the operation performance of the outdoor units is reduced, and the overall energy efficiency of the system is improved.

Description

Technical Field

[0001] This invention relates to the field of air conditioning technology, and in particular to an operation control method, device, medium, and air conditioning system for an air conditioning system. Background Technology

[0002] When multiple outdoor units in an air conditioning system are arranged together, seasonal differences can create specific regional temperature variations, affecting the system's performance. These variations include: a "heat island" effect in summer, where the temperature around the outdoor units is significantly higher than the surrounding air temperature as the system continues to run in cooling mode, increasing the unit's condensing temperature, reducing cooling capacity, and lowering system energy efficiency; and a "cold island" effect in winter, where the temperature around the outdoor units is significantly lower than the surrounding air temperature as the system continues to run in heating mode, decreasing the unit's evaporating temperature, reducing heating capacity, and lowering system energy efficiency. Summary of the Invention

[0003] Therefore, it is necessary to provide methods, devices, media, and air conditioning systems for the operation and control of air conditioning systems to solve the problem that existing air conditioning systems are susceptible to the effects of heat island or cold island, which leads to reduced operating performance and energy consumption.

[0004] In a first aspect, embodiments of this application provide an operation control method for an air conditioning system, the air conditioning system comprising an outdoor unit cluster composed of multiple outdoor units, the method comprising: Obtain the operating environment parameters of the external machine cluster; Based on the operating environment parameters, the optimization index for the temperature difference anomaly effect of the outdoor unit cluster is determined; wherein, the optimization index for the temperature difference anomaly effect characterizes the index used to optimize the temperature difference anomaly effect, and the temperature difference anomaly effect includes the heat island effect and the cold island effect. Based on the temperature difference abnormality effect optimization index, at least one outdoor unit in the outdoor unit cluster is determined as the target cluster, and the operation of the target cluster is controlled; wherein, the target cluster is the combination of outdoor units in the outdoor unit cluster that are least affected by the temperature difference abnormality effect.

[0005] In some embodiments of this application, determining the optimization index for the temperature difference abnormality effect of the outdoor unit cluster based on the operating environment parameters includes: The optimized characteristic parameters of the temperature difference abnormality effect are determined based on the operating environment parameters; wherein, the optimized characteristic parameters characterize the parameters used to optimize the temperature difference abnormality effect from different dimensions; The optimized index for the temperature difference abnormality effect of the outdoor unit cluster is determined based on the optimized feature parameters.

[0006] In some embodiments of this application, the operating environment parameters include at least one of the following: location data, heat exchange data, energy efficiency data, measured temperature data, and operating time data for each outdoor unit; and the optimized feature parameters include at least one of the following: normalized distance parameter, normalized energy efficiency parameter, normalized temperature parameter, and normalized operating time parameter. The optimization characteristic parameters for determining the temperature difference abnormality effect based on the operating environment parameters include: The reference center position is calculated based on the location data and the heat exchange data; wherein, the reference center position is used to indicate the center point of the area affected by the temperature difference abnormality effect; Calculate the deviation between the reference center position and the position data of each outdoor unit to obtain position deviation data; normalize the position deviation data to obtain the normalized distance parameter; and / or, The energy efficiency data is normalized to obtain the normalized energy efficiency parameters; and / or, The measured temperature data is normalized to obtain the normalized temperature parameter; and / or, The runtime data is normalized to obtain the normalized runtime parameters.

[0007] In some embodiments of this application, the reference center position is calculated using a first calculation model based on the position data and the heat exchange data; The first calculation model is represented as follows:

[0008] In the above formula, This indicates the position of the reference center on the X-axis; N represents the total number of outdoor units in the outdoor unit cluster. This indicates the position of the i-th outdoor unit on the X-axis; This represents the heat exchange data of the i-th outdoor unit; Indicates the position of the reference center on the Y-axis; This indicates the position of the i-th outdoor unit on the Y-axis.

[0009] In some embodiments of this application, the optimized characteristic parameters include at least one of normalized distance parameters, normalized energy efficiency parameters, normalized temperature parameters, and normalized operating time parameters; the temperature difference anomaly effect optimization index includes heat island effect optimization index and cold island effect optimization index; and determining the temperature difference anomaly effect optimization index of the outdoor unit cluster based on the optimized characteristic parameters includes: The normalized distance parameter, the normalized energy efficiency parameter, the normalized temperature correction parameter, and the normalized operating time correction parameter are weighted and summed to obtain the urban heat island effect optimization index; wherein, the normalized temperature correction parameter represents a correction parameter determined based on the normalized temperature parameter and negatively correlated with the normalized temperature parameter, and the normalized operating time correction parameter represents a correction parameter determined based on the normalized operating time parameter and negatively correlated with the normalized operating time parameter; or, The normalized distance parameter, the normalized energy efficiency parameter, the normalized temperature parameter, and the normalized running time correction parameter are weighted and summed to obtain the cold island effect optimization index.

[0010] In some embodiments of this application, determining at least one outdoor unit in the outdoor unit cluster as a target cluster based on the temperature difference abnormality effect optimization index, and controlling the operation of the target cluster, includes: Obtain the current load of the air conditioning system; The optimization indicators for the abnormal temperature difference effect of each outdoor unit are ranked, and the ranking results are obtained. When the operation optimization conditions are triggered, under the premise of satisfying the current load, the target cluster before the update is updated based on the ranking result of the indicators to obtain the updated target cluster, and the operation of the updated target cluster is controlled.

[0011] In some embodiments of this application, if there is a positive correlation between the numerical value of the temperature difference anomaly effect optimization index and the effect of optimizing the temperature difference anomaly effect, then the step of selecting at least one outdoor unit in the outdoor unit cluster as the target cluster based on the index ranking result, under the premise of satisfying the current load, includes: When the current load increases, the outdoor machines with the largest values ​​in the index ranking results that are not running are added to the target cluster before the update, until the current load is met, and the updated target cluster is obtained. When the current load decreases, the outdoor machines with the smallest values ​​in the index ranking results that are already running are removed from the target cluster before the update, until the current load is met, and the updated target cluster is obtained.

[0012] Secondly, embodiments of this application also provide an operation control device for an air conditioning system, the air conditioning system including an outdoor unit cluster composed of multiple outdoor units, the operation control device for the air conditioning system including: The acquisition module is used to acquire the operating environment parameters of the external machine cluster; The index determination module is used to determine the temperature difference anomaly effect optimization index of the outdoor unit cluster based on the operating environment parameters; wherein, the temperature difference anomaly effect optimization index characterizes the index used to optimize the temperature difference anomaly effect, and the temperature difference anomaly effect includes heat island effect and cold island effect. The operation control module is used to determine at least one outdoor unit in the outdoor unit cluster as a target cluster based on the temperature difference abnormality effect optimization index, and control the operation of the target cluster; wherein, the target cluster is the combination of outdoor units in the outdoor unit cluster that are least affected by the temperature difference abnormality effect.

[0013] Thirdly, embodiments of this application also provide an air conditioning system, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, it implements the steps in the above-described air conditioning system operation control method.

[0014] Fourthly, embodiments of this application also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps in the above-described air conditioning system operation control method.

[0015] Fifthly, embodiments of this application also provide a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods provided in the various optional implementations described in embodiments of this application.

[0016] This invention provides an operation control method, device, medium, and air conditioning system. By first acquiring cluster operating environment parameters, then determining temperature difference abnormality effect optimization indexes for optimizing heat island effect and cold island effect based on cluster operating environment parameters, and then selecting the outdoor unit combination least affected by temperature difference abnormality effect as the target cluster and controlling its operation based on the index, the additional temperature difference abnormality effect superposition caused by ineffective outdoor unit operation can be avoided, the interference of temperature difference abnormality effect on outdoor unit operation performance can be reduced, and the overall energy efficiency of the system can be improved. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] in: Figure 1 A flowchart illustrating the operation control method of an air conditioning system; Figure 2 A schematic diagram of the operation control process of an air conditioning system; Figure 3 A schematic diagram of the operation control device for an air conditioning system; Figure 4 This is a structural block diagram of an air conditioning system. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. 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 includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0021] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0022] This invention provides an operation control method, apparatus, medium, and air conditioning system for an air conditioning system. In some embodiments of this application, the provided operation control method for the air conditioning system can be applied to an air conditioning system. Specifically, the air conditioning system can be applied to different scenarios, including but not limited to industrial air conditioning systems or household air conditioning systems.

[0023] Please see Figure 1 , Figure 1This is a flowchart illustrating the operation control method for an air conditioning system provided in the first embodiment of this application. Although the logical sequence is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than that shown in the figures. Specifically, the air conditioning system includes an outdoor unit cluster composed of multiple outdoor units. The specific flow of the operation control method for the air conditioning system provided in the first embodiment of this application is as follows: S101 retrieves the operating environment parameters of the external cluster.

[0024] The operating environment parameters refer to a set of various data related to the operating status of the outdoor units and their surrounding environment during the operation of the outdoor unit cluster. These include, but are not limited to, at least one of the following: location data, heat exchange data, energy efficiency data, measured temperature data, and operating time data for each outdoor unit. Location data refers to the specific spatial coordinates of each outdoor unit in a preset coordinate system; this can be two-dimensional or three-dimensional. Heat exchange data refers to the numerical information of heat exchange completed by a single outdoor unit per unit time; this can be cooling capacity or heating capacity. Energy efficiency data refers to the ratio of heat exchange to input power during the operation of the outdoor unit. Measured temperature data refers to the real-time temperature information of a preset location or area of ​​the outdoor unit collected by temperature sensing elements. Operating time data refers to the cumulative operating time of a single outdoor unit within the operating cycle of the outdoor unit cluster.

[0025] Optionally, the location data of each outdoor unit can be collected through its built-in positioning module, the heat exchange data can be captured in real time using the built-in heat exchange sensor, the energy efficiency data of each outdoor unit can be read through the energy efficiency monitoring module, the temperature data at the compressor can be collected using a temperature probe, and the running time data can be generated by the outdoor unit's running timer. After all data is collected, it is directly transmitted to the control module for storage. Of course, other parameter acquisition methods can also be used, and are not limited here.

[0026] S102, determine the optimization index of temperature difference abnormality effect of outdoor unit cluster based on operating environment parameters.

[0027] Among them, the temperature difference anomaly effect optimization index is a characterization of the index used to optimize the temperature difference anomaly effect, which includes the heat island effect and the cold island effect.

[0028] Optionally, the optimization index for temperature difference anomaly effect can be determined through functional relationships. First, historical operating environment parameters such as historical temperature, historical humidity, historical wind speed, and historical equipment power of the outdoor unit cluster are collected. Then, the impact of each historical parameter on the heat island and cold island effects is analyzed. Next, a function model to be fitted is constructed using methods such as multiple linear regression. The coefficients in the function model to be fitted are determined by iterative fitting of the data using the least squares method and historical operating environment parameters, thus obtaining the objective function model. Finally, the real-time operating environment parameters are substituted into the objective function model to calculate the optimization index for temperature difference anomaly effect.

[0029] Alternatively, optimization metrics can be determined using a predictive neural network model. First, historical operating environment parameters of the outdoor unit cluster, such as historical temperature, humidity, wind speed, and equipment power, are collected. After cleaning, filling in missing values, and normalization preprocessing, a multilayer perceptron architecture is used to build the model. The model is configured with an input layer of nodes matching the number of historical operating environment parameters, N hidden layers, and one output layer node. Then, a stochastic gradient descent optimizer is used to train the neural network model until the target network model is obtained. Finally, the operating environment parameters are input into the target network model to obtain the optimization metrics for temperature difference abnormal effects.

[0030] S103, based on the temperature difference abnormal effect optimization index, determines at least one outdoor unit in the outdoor unit cluster as the target cluster, and controls the operation of the target cluster.

[0031] The target cluster is the combination of outdoor units that are least affected by abnormal temperature differences in the outdoor unit cluster.

[0032] Optionally, when the larger the optimization index for temperature difference anomaly effect indicates that the corresponding outdoor unit is less affected by the temperature difference anomaly effect, the top N outdoor units are selected to form a target cluster, sorted by optimization index from largest to smallest. Conversely, when the smaller the index indicates that the corresponding outdoor unit is less affected by the temperature difference anomaly effect, the top N outdoor units are selected to form a target cluster, sorted by index from smallest to largest. This achieves precise selection of the combination of outdoor units least affected by the temperature difference anomaly effect. Here, N can be a fixed value set by the user, determined based on actual load demand, or determined in other ways; it is not limited here.

[0033] Optionally, after determining the target cluster, the air conditioning system sends a control command to start only the outdoor units in the target cluster, while keeping the other outdoor units in a stopped state, thereby reducing the impact of abnormal temperature differences on the system's operating performance.

[0034] The above embodiments first obtain the cluster operating environment parameters, then determine the temperature difference abnormality effect optimization index for optimizing the heat island effect and cold island effect based on the cluster operating environment parameters, and then select the outdoor unit combination least affected by the temperature difference abnormality effect as the target cluster and control its operation based on the index. This can avoid the superposition of additional temperature difference abnormality effects caused by the operation of ineffective outdoor units, reduce the interference of temperature difference abnormality effects on the operation performance of outdoor units, and improve the overall energy efficiency of the system.

[0035] Please see Figure 2 , Figure 2 This is a flowchart illustrating the operation control method for an air conditioning system provided in the second embodiment of this application. Although the logical sequence is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than that shown in the figures. Specifically, the air conditioning system includes an outdoor unit cluster composed of multiple outdoor units. The specific flow of the operation control method for the air conditioning system provided in the second embodiment of this application is as follows: S201 retrieves the operating environment parameters of the external cluster.

[0036] In some embodiments of this application, the logic of S201 above is basically the same as that of S101 in the air conditioning system operation control method provided in the first embodiment, so it will not be described again.

[0037] S202, Determine the optimized characteristic parameters of temperature difference abnormality effect based on operating environment parameters.

[0038] Among them, the optimized characteristic parameters characterize the parameters used to optimize the abnormal effects of temperature differences from different dimensions.

[0039] In some embodiments of this application, the operating environment parameters include at least one of the following: location data, heat exchange data, energy efficiency data, measured temperature data, and operating time data for each outdoor unit. The optimized feature parameters include at least one of the following: normalized distance parameter, normalized energy efficiency parameter, normalized temperature parameter, and normalized operating time parameter. S202, determining the optimized feature parameters for temperature difference anomaly effects based on the operating environment parameters, includes the following steps: calculating a reference center position based on the location data and heat exchange data; calculating the deviation between the reference center position and the location data of each outdoor unit to obtain position deviation data; normalizing the position deviation data to obtain a normalized distance parameter; and / or normalizing the energy efficiency data to obtain a normalized energy efficiency parameter; and / or normalizing the measured temperature data to obtain a normalized temperature parameter; and / or normalizing the operating time data to obtain a normalized operating time parameter.

[0040] The normalized distance parameter refers to the standardized parameter obtained after normalizing the actual distance deviation between the outdoor unit and the reference center position of the area affected by the temperature difference anomaly. The normalized energy efficiency parameter refers to the standardized parameter obtained after normalizing the raw energy efficiency data of the outdoor unit. The normalized temperature parameter refers to the standardized parameter obtained after normalizing the raw measured temperature data of the outdoor unit. The normalized operating time parameter refers to the standardized parameter obtained after normalizing the raw operating time data of the outdoor unit. The reference center position is used to indicate the center point location of the area affected by the temperature difference anomaly.

[0041] In some embodiments of this application, the reference center position is calculated by a first calculation model using position data and heat exchange data.

[0042] The first calculation model is represented as follows:

[0043] In the above formula, This indicates the position of the reference center on the X-axis. N represents the total number of outdoor units in the outdoor unit cluster. This indicates the position of the i-th outdoor unit on the X-axis. This represents the heat exchange data of the i-th outdoor unit. This indicates the position of the reference center on the Y-axis. This indicates the position of the i-th outdoor unit on the Y-axis.

[0044] For example, the reference center position calculated by the above model for outdoor unit 1 (X=2, Y=2, heat exchange = 2), outdoor unit 2 (X=6, Y=2, heat exchange = 2), and outdoor unit 3 (X=4, Y=6, heat exchange = 2) is (4, 4). However, the data after only increasing the heat exchange of outdoor unit 1 can be: outdoor unit 1 (X=2, Y=2, heat exchange = 8), outdoor unit 2 (X=6, Y=2, heat exchange = 2), and outdoor unit 3 (X=4, Y=6, heat exchange = 2). The reference center position calculated by the above model again is (3, 2.67). It can be seen that there is a significant shift towards outdoor unit 1 with increased heat exchange compared to the previous model. Therefore, it can be understood that the above first calculation model gives outdoor units with large heat exchange and strong influence a greater "voice" in the reference center location, ensuring that the calculated center point can truly reflect the core range of the abnormal temperature difference effect.

[0045] Furthermore, the deviation between the position data of each outdoor unit and the reference center position can be expressed as: ,in, This represents the deviation of the i-th outdoor unit. Finally, the position deviation data is normalized to obtain the normalized distance parameter, which can be expressed as: ,in, This represents the normalized distance parameter of the i-th outdoor unit. , Understandably, the larger the value of this normalized distance parameter, the farther the outdoor unit is from the core area of ​​the temperature difference anomaly effect (heat island / cold island), the smaller the direct impact of the temperature difference anomaly effect, and the lower its contribution to the regional temperature anomaly. Conversely, the smaller the value of this normalized distance parameter, the closer the outdoor unit is to the core area of ​​the temperature difference anomaly effect, the greater the direct impact of the temperature difference anomaly effect, and the more obvious the superimposed effect on the regional temperature anomaly.

[0046] Optionally, the energy efficiency data is normalized to obtain normalized energy efficiency parameters, expressed as follows: ,in, This represents the normalized energy efficiency parameter of the i-th outdoor unit; This represents the energy efficiency data of the i-th outdoor unit; , Understandably, a higher value for this normalized energy efficiency parameter indicates higher energy utilization efficiency of the outdoor unit after standardization, resulting in more cooling / heating capacity under the same input power, lower energy consumption during operation, and a more significant mitigation effect on temperature anomalies. Conversely, a lower value indicates lower energy utilization efficiency of the outdoor unit after standardization, relatively higher energy loss, and a greater tendency to exacerbate temperature anomalies during operation (such as generating more extra heat under the heat island effect).

[0047] Optionally, the measured temperature data is normalized to obtain normalized temperature parameters, expressed as follows: , among which, among which, This represents the normalized temperature parameter of the i-th outdoor unit; This represents the measured temperature data of the i-th outdoor unit, which can specifically be the median temperature over a preset time period; , It is understandable that a higher value for this normalized temperature parameter indicates a higher real-time temperature in the corresponding preset location or area of ​​the outdoor unit. Conversely, a lower value indicates a lower real-time temperature in the corresponding preset location or area of ​​the outdoor unit.

[0048] Optionally, the runtime data is normalized to obtain normalized runtime parameters, expressed as follows: ,in, This represents the normalized runtime parameter of the i-th outdoor unit; This represents the measured running time data of the i-th outdoor unit, which can specifically be the cumulative running time within the operating cycle of the air conditioning system; , Understandably, a larger value for this normalized operating time parameter indicates a longer cumulative operating time of the outdoor unit after standardization, resulting in greater heat / cold energy accumulation due to long-term operation, a more significant sustained contribution to the temperature difference anomaly effect, and a higher probability of its own performance being affected. Conversely, a smaller value for this normalized operating time parameter indicates a shorter cumulative operating time of the outdoor unit after standardization, less heat / cold energy accumulation, a weaker sustained impact on the temperature difference anomaly effect, and a relatively more stable operating state.

[0049] S203, Determine the optimization index of the abnormal temperature effect of the outdoor unit cluster based on the optimized characteristic parameters.

[0050] In some embodiments of this application, the optimized characteristic parameters include at least one of normalized distance parameters, normalized energy efficiency parameters, normalized temperature parameters, and normalized operating time parameters. The temperature difference anomaly effect optimization index includes heat island effect optimization index and cold island effect optimization index. S203, determining the temperature difference anomaly effect optimization index of the outdoor unit cluster based on the optimized characteristic parameters, includes the following steps: weighted summation of the normalized distance parameters, normalized energy efficiency parameters, normalized temperature correction parameters, and normalized operating time correction parameters to obtain the heat island effect optimization index. Alternatively, weighted summation of the normalized distance parameters, normalized energy efficiency parameters, normalized temperature parameters, and normalized operating time correction parameters to obtain the cold island effect optimization index.

[0051] Among them, the normalized temperature correction parameter is a correction parameter that is determined based on the normalized temperature parameter and is negatively correlated with the normalized temperature parameter, and the normalized running time correction parameter is a correction parameter that is determined based on the normalized running time parameter and is negatively correlated with the normalized running time parameter.

[0052] Optionally, the normalized temperature correction parameter is expressed as 1- Of course, it can also take other forms, and is not limited here. The normalized runtime correction parameter is expressed as 1- Of course, it can also be in other forms, and is not limited here.

[0053] Correspondingly, the formula for calculating the optimization index of the heat island effect can be expressed as: In the above formula, , , , These are the weighting coefficients corresponding to the normalization parameters, which can be determined according to the project conditions and host characteristics, and are not limited here.

[0054] Understandably, a higher value for the heat island effect optimization index indicates a stronger ability of the outdoor unit to mitigate the heat island effect and a lesser impact from it. Specifically, this manifests as the outdoor unit being far from the heat island core, having high energy efficiency, operating in low ambient temperatures, and running for a moderate duration. Starting such an outdoor unit can effectively alleviate the heat island effect while ensuring efficient operation of the air conditioning system. Conversely, a lower value for the heat island effect optimization index indicates a weaker ability of the outdoor unit to mitigate the heat island effect, and may even exacerbate it. This manifests as the outdoor unit being close to the heat island core, having low energy efficiency, operating in high ambient temperatures, or running continuously for extended periods. Starting such an outdoor unit can easily lead to a cumulative heat island effect, reducing the overall performance of the system.

[0055] Correspondingly, the formula for calculating the optimization index of the cold island effect can be expressed as: In the above formula, , , , These are the weighting coefficients corresponding to the normalization parameters, which can be determined according to the project conditions and host characteristics, and are not limited here.

[0056] Understandably, a higher value for the cold island effect optimization index indicates a stronger ability of the outdoor unit to optimize for the cold island effect, less susceptibility to its influence, and more stable heating performance. Specifically, outdoor units located far from the core of the cold island, with high heating efficiency, in relatively high ambient temperatures, and with moderate cumulative operating time, effectively mitigate the cold island effect while ensuring the heating output efficiency of the air conditioning system and preventing heating capacity degradation due to low temperatures. Conversely, a lower value for the cold island effect optimization index indicates a weaker ability of the outdoor unit to optimize for the cold island effect, greater susceptibility to its influence, and a greater likelihood of heating performance degradation. Specifically, outdoor units located near the core of the cold island, with low heating efficiency, in excessively low ambient temperatures, or operating continuously for extended periods, not only fail to meet heating demands but may also exacerbate the cold island effect, leading to a decrease in the overall heating efficiency of the entire cluster.

[0057] S204, based on the optimization index of abnormal temperature difference, determines at least one outdoor unit in the outdoor unit cluster as the target cluster, and controls the operation of the target cluster.

[0058] In some embodiments of this application, step S204, which determines at least one outdoor unit in the outdoor unit cluster as the target cluster based on the temperature difference anomaly effect optimization index and controls the operation of the target cluster, includes the following steps: obtaining the current load of the air conditioning system; sorting the temperature difference anomaly effect optimization index of each outdoor unit to obtain the index sorting result; and when the operation optimization condition is triggered, updating the target cluster before the update based on the index sorting result, under the premise of satisfying the current load, to obtain the updated target cluster, and controlling the operation of the updated target cluster.

[0059] The current load refers to the total cooling or heating output required by the air conditioning system at the current moment to meet the cooling / heating needs of users or scenarios. The index ranking result is an ordered list obtained by arranging the temperature difference anomaly effect optimization indices (heat island effect optimization indices or cold island effect optimization indices) of each outdoor unit in the outdoor unit cluster according to preset rules (such as from largest to smallest or smallest to largest). The operation optimization conditions refer to preset triggering conditions that cause the air conditioning system to update the target cluster, including but not limited to fixed time intervals (such as every hour), load fluctuation amplitude (such as the difference between the current load and the current output load of the target cluster exceeding 10%), and changes in the intensity of temperature difference anomaly effects (such as the difference between the outdoor unit area temperature and the surrounding ambient temperature exceeding 5℃), etc., which are not limited here.

[0060] In some embodiments of this application, if there is a positive correlation between the value of the optimization index for temperature difference anomaly effects and the effectiveness of optimizing temperature difference anomaly effects, then, under the premise of satisfying the current load, at least one outdoor unit in the outdoor unit cluster is selected as the target cluster based on the index ranking results. This includes the following steps: When the current load increases, the outdoor unit with the largest value in the index ranking results that is not running is added to the target cluster before the update, until the current load is satisfied, resulting in the updated target cluster. When the current load decreases, the outdoor unit with the smallest value in the index ranking results that is running is removed from the target cluster before the update, until the current load is satisfied, resulting in the updated target cluster.

[0061] For example, taking the central air conditioning system of a commercial complex as an example, the system contains 8 outdoor units (each with a rated cooling / heating capacity of 80kW). The heat island / cold island effect needs to be optimized according to the season, and the optimization index value of the temperature difference abnormal effect is positively correlated with the optimization effect. Assume that the index ranking result is: outdoor unit 1 (9.5) > outdoor unit 2 (9.1) > outdoor unit 3 (8.7) > outdoor unit 4 (8.2) > outdoor unit 5 (7.8) > outdoor unit 6 (7.3) > outdoor unit 7 (6.9) > outdoor unit 8 (6.5).

[0062] Summer load increase scenario: The target cluster before the update consisted of outdoor units 2, 3, and 4, with a total cooling capacity of 240kW. The current load increases from 240kW to 320kW (requiring an additional 80kW of cooling capacity), triggering the cluster update logic after the load increase. Based on the ranking of indicators, units are selected sequentially from the non-operating outdoor units with the highest values, meaning outdoor unit 1, ranked first, is added first. The updated target cluster consists of outdoor units 1, 2, 3, and 4, with a total cooling capacity of 320kW, meeting the current load demand. Furthermore, the selected outdoor units represent the combination with the strongest ability to optimize the heat island effect, minimizing it to the greatest extent possible.

[0063] Winter load reduction scenario: The target cluster before the update consisted of outdoor units 1, 2, 3, 4, and 5, with a total heating capacity of 400kW. The current load has decreased from 400kW to 240kW (requiring a reduction of 160kW of heating capacity), triggering the cluster update logic after the load reduction. Based on the index ranking results, outdoor units with the smallest values ​​are removed sequentially, i.e., outdoor unit 5 is removed first. At this point, the total heating capacity of 320kW still exceeds the demand, so the next outdoor unit with the smallest value, outdoor unit 4, is removed. The target cluster after the update consists of outdoor units 1, 2, and 3, with a total heating capacity of 240kW, meeting the current load demand. Furthermore, the retained outdoor units are combinations with strong optimization capabilities for the cooling island effect, effectively mitigating the cooling island effect.

[0064] In the above embodiments, under the premise that the optimization index and optimization effect of temperature difference anomaly effect are positively correlated, when the load increases, the non-operating outdoor unit with the largest index is added first, and when the load decreases, the operating outdoor unit with the smallest index is removed first. This can ensure that the target cluster is always the combination with the strongest ability to optimize temperature difference anomaly effect (heat island / cold island effect) among the outdoor unit clusters while meeting the current cooling / heating load demand, thus minimizing the interference of temperature difference anomaly effect on the operating performance of the air conditioning system, and avoiding energy waste caused by ineffective outdoor unit operation.

[0065] To facilitate better implementation of the air conditioning system operation control method of this application, this application also provides an air conditioning system operation control device based on the above-described air conditioning system operation control method. The meanings of the terms used are the same as in the above-described air conditioning system operation control method, and specific implementation details can be found in the descriptions of the method embodiments.

[0066] Please see Figure 3 , Figure 3 This is a schematic diagram of the structure of the air conditioning system operation control device provided in the embodiments of this application, which may specifically include: The acquisition module 301 is used to acquire the operating environment parameters of the external machine cluster; The index determination module 302 is used to determine the optimization index of the temperature difference abnormality effect of the outdoor unit cluster based on the operating environment parameters; wherein, the temperature difference abnormality effect optimization index characterizes the index used to optimize the temperature difference abnormality effect, which includes the heat island effect and the cold island effect. The operation control module 303 is used to determine at least one outdoor unit in the outdoor unit cluster as the target cluster based on the temperature difference abnormal effect optimization index, and control the operation of the target cluster; wherein, the target cluster is the combination of outdoor units in the outdoor unit cluster that are least affected by the temperature difference abnormal effect.

[0067] The operation control device of the aforementioned air conditioning system includes an acquisition module 301, which first acquires the cluster operating environment parameters; an index determination module 302, which then determines the temperature difference abnormality effect optimization index for optimizing the heat island effect and cold island effect based on the cluster operating environment parameters; and an operation control module 303, which then selects the outdoor unit combination least affected by the temperature difference abnormality effect as the target cluster based on the index and controls its operation. This avoids the superposition of additional temperature difference abnormality effects caused by ineffective outdoor unit operation, reduces the interference of temperature difference abnormality effects on the operating performance of outdoor units, and improves the overall energy efficiency of the system.

[0068] In some embodiments of this application, the index determination module 302 determines the optimization index of the temperature difference abnormality effect of the outdoor unit cluster based on the operating environment parameters, including: The optimized characteristic parameters of the temperature difference anomaly effect are determined based on the operating environment parameters; wherein, the optimized characteristic parameters characterize the parameters used to optimize the temperature difference anomaly effect from different dimensions. The optimization index for the abnormal temperature effect of the outdoor unit cluster is determined based on the optimized characteristic parameters.

[0069] In some embodiments of this application, the operating environment parameters include at least one of the following: location data, heat exchange data, energy efficiency data, measured temperature data, and operating time data for each outdoor unit; and the optimized feature parameters include at least one of the following: normalized distance parameter, normalized energy efficiency parameter, normalized temperature parameter, and normalized operating time parameter. The indicator determination module 302 determines the optimized characteristic parameters of the temperature difference abnormality effect based on the operating environment parameters, including: The reference center position is calculated based on location data and heat exchange data; the reference center position is used to indicate the center point of the area affected by the temperature difference anomalous effect. Calculate the deviation between the reference center position and the position data of each outdoor unit to obtain position deviation data; normalize the position deviation data to obtain normalized distance parameters; and / or, Normalize the energy efficiency data to obtain normalized energy efficiency parameters; and / or, The measured temperature data are normalized to obtain normalized temperature parameters; and / or, The runtime data is normalized to obtain normalized runtime parameters.

[0070] In some embodiments of this application, the reference center position is calculated by the index determination module 302 using a first calculation model based on the position data and heat exchange data; The first calculation model is represented as follows:

[0071] In the above formula, This indicates the position of the reference center on the X-axis; N represents the total number of outdoor units in the outdoor unit cluster. This indicates the position of the i-th outdoor unit on the X-axis; This represents the heat exchange data of the i-th outdoor unit; Indicates the position of the reference center on the Y-axis; This indicates the position of the i-th outdoor unit on the Y-axis.

[0072] In some embodiments of this application, the optimized characteristic parameters include at least one of normalized distance parameters, normalized energy efficiency parameters, normalized temperature parameters, and normalized operating time parameters. The temperature difference anomaly effect optimization index includes heat island effect optimization index and cold island effect optimization index. The index determination module 302 determines the temperature difference anomaly effect optimization index of the outdoor unit cluster based on the optimized characteristic parameters, including: The normalized distance parameter, normalized energy efficiency parameter, normalized temperature correction parameter, and normalized operating time correction parameter are weighted and summed to obtain the heat island effect optimization index. The normalized temperature correction parameter represents a correction parameter determined based on the normalized temperature parameter and negatively correlated with it; the normalized operating time correction parameter represents a correction parameter determined based on the normalized operating time parameter and negatively correlated with it. Alternatively, The normalized distance parameter, normalized energy efficiency parameter, normalized temperature parameter, and normalized operating time correction parameter are weighted and summed to obtain the optimization index of the cold island effect.

[0073] In some embodiments of this application, the operation control module 303 determines at least one outdoor unit in the outdoor unit cluster as the target cluster based on the temperature difference abnormal effect optimization index, and controls the operation of the target cluster, including: Obtain the current load of the air conditioning system; The optimization indicators for the abnormal temperature difference effect of each outdoor unit are ranked, and the ranking results are obtained. When the operation optimization conditions are triggered, the target cluster before the update is updated based on the index ranking results, provided that the current load is met, so as to obtain the updated target cluster and control the operation of the updated target cluster.

[0074] In some embodiments of this application, if there is a positive correlation between the numerical value of the temperature difference anomaly effect optimization index and the effect of optimizing the temperature difference anomaly effect, then the operation control module 303, under the premise of meeting the current load, selects at least one outdoor unit in the outdoor unit cluster as the target cluster based on the index ranking result, including: When the current load increases, the outdoor machines with the largest values ​​in the index ranking results that are not running are added to the target cluster before the update until the current load is met, and the updated target cluster is obtained. When the current load decreases, the outdoor machines with the smallest values ​​in the index ranking results that are already running are removed from the target cluster before the update, until the current load is met, and the updated target cluster is obtained.

[0075] In addition, this application also provides an air conditioning system, such as Figure 4 As shown, it illustrates the structural diagram of the air conditioning system involved in this application, specifically: The air conditioning system may include components such as a processor 401 with one or more processing cores, a memory 402 with one or more computer-readable storage media, a power supply 403, and an input unit 404. Those skilled in the art will understand that... Figure 4 The air conditioning system structure shown does not constitute a limitation on the air conditioning system and may include more or fewer components than shown, or combine certain components, or have different component arrangements. Wherein: The processor 401 is the control center of the air conditioning system. It connects to various parts of the system via various interfaces and lines. By running or executing software programs and / or modules stored in the memory 402, and by calling data stored in the memory 402, it performs various functions and processes data of the air conditioning system, thereby providing overall monitoring of the system. Optionally, the processor 401 may include one or more processing cores; preferably, the processor 401 may integrate an application processor and a modem processor. The application processor mainly handles the operating system, user interface, and applications, while the modem processor mainly handles wireless communication. It is understood that the modem processor may not be integrated into the processor 401.

[0076] The memory 402 can be used to store software programs and modules. The processor 401 executes various functional applications and data processing by running the software programs and modules stored in the memory 402. The memory 402 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function, etc.; the data storage area may store data created based on the use of the air conditioning system, etc. In addition, the memory 402 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, the memory 402 may also include a memory controller to provide the processor 401 with access to the memory 402.

[0077] The air conditioning system also includes a power supply 403 that supplies power to various components. Preferably, the power supply 403 can be logically connected to the processor 401 through a power management system, thereby enabling functions such as charging, discharging, and power consumption management through the power management system. The power supply 403 may also include one or more DC or AC power supplies, recharging systems, power equipment debugging circuits, power converters or inverters, power status indicators, and other arbitrary components.

[0078] The air conditioning system may also include an input unit 404, which can be used to receive input digital or character information, and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.

[0079] Although not shown, the air conditioning system may also include a display unit, etc., which will not be described in detail here. Specifically, in this embodiment, the processor 401 in the air conditioning system will load the executable files corresponding to the processes of one or more application programs into the memory 402 according to the following instructions, and the processor 401 will run the application programs stored in the memory 402, thereby realizing the steps in the operation control method of any air conditioning system provided in this application embodiment: obtaining the operating environment parameters of the outdoor unit cluster; determining the temperature difference abnormality effect optimization index of the outdoor unit cluster based on the operating environment parameters; wherein, the temperature difference abnormality effect optimization index characterizes the index used to optimize the temperature difference abnormality effect, and the temperature difference abnormality effect includes the heat island effect and the cold island effect; determining at least one outdoor unit in the outdoor unit cluster as the target cluster based on the temperature difference abnormality effect optimization index, and controlling the operation of the target cluster; wherein, the target cluster is the combination of outdoor units in the outdoor unit cluster that are least affected by the temperature difference abnormality effect.

[0080] The above embodiments first obtain the cluster operating environment parameters, then determine the temperature difference abnormality effect optimization index for optimizing the heat island effect and cold island effect based on the cluster operating environment parameters, and then select the outdoor unit combination least affected by the temperature difference abnormality effect as the target cluster and control its operation based on the index. This can avoid the superposition of additional temperature difference abnormality effects caused by the operation of ineffective outdoor units, reduce the interference of temperature difference abnormality effects on the operation performance of outdoor units, and improve the overall energy efficiency of the system.

[0081] For details on the implementation of each of the above operations, please refer to the previous examples, which will not be repeated here.

[0082] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be performed by instructions, or by instructions controlling related hardware. These instructions can be stored in a computer-readable storage medium and loaded and executed by a processor.

[0083] Therefore, this application provides a computer-readable storage medium storing a computer program that can be loaded by a processor to execute the steps in any of the air conditioning system operation control methods provided in this application.

[0084] For details on the implementation of each of the above operations, please refer to the previous examples, which will not be repeated here.

[0085] The computer-readable storage medium may include: read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0086] Since the instructions stored in the computer-readable storage medium can execute the steps in any of the air conditioning system operation control methods provided in this application, the beneficial effects that any of the air conditioning system operation control methods provided in this application can achieve can be realized, as detailed in the preceding embodiments, and will not be repeated here.

[0087] The above provides a detailed description of the operation control method, apparatus, air conditioning system, and computer-readable storage medium for an air conditioning system provided in this application. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for controlling the operation of an air conditioning system, characterized in that, The air conditioning system includes an outdoor unit cluster composed of multiple outdoor units, and the method includes: Obtain the operating environment parameters of the external machine cluster; Based on the operating environment parameters, the optimization index for the temperature difference anomaly effect of the outdoor unit cluster is determined; wherein, the optimization index for the temperature difference anomaly effect characterizes the index used to optimize the temperature difference anomaly effect, and the temperature difference anomaly effect includes the heat island effect and the cold island effect. Based on the temperature difference abnormality effect optimization index, at least one outdoor unit in the outdoor unit cluster is determined as the target cluster, and the operation of the target cluster is controlled; wherein, the target cluster is the combination of outdoor units in the outdoor unit cluster that are least affected by the temperature difference abnormality effect.

2. The operation control method for an air conditioning system according to claim 1, characterized in that, The process of determining the optimization index for the abnormal temperature effect of the outdoor unit cluster based on the operating environment parameters includes: The optimized characteristic parameters of the temperature difference abnormality effect are determined based on the operating environment parameters; wherein, the optimized characteristic parameters characterize the parameters used to optimize the temperature difference abnormality effect from different dimensions; The optimized index for the temperature difference abnormality effect of the outdoor unit cluster is determined based on the optimized feature parameters.

3. The operation control method for an air conditioning system according to claim 2, characterized in that, The operating environment parameters include at least one of the following: location data, heat exchange data, energy efficiency data, measured temperature data, and operating time data for each outdoor unit. The optimized feature parameters include at least one of the following: normalized distance parameter, normalized energy efficiency parameter, normalized temperature parameter, and normalized operating time parameter. The optimization characteristic parameters for determining the temperature difference abnormality effect based on the operating environment parameters include: The reference center position is calculated based on the location data and the heat exchange data; wherein, the reference center position is used to indicate the center point of the area affected by the temperature difference abnormality effect; Calculate the deviation between the reference center position and the position data of each outdoor unit to obtain position deviation data; normalize the position deviation data to obtain the normalized distance parameter; and / or, The energy efficiency data is normalized to obtain the normalized energy efficiency parameters; and / or, The measured temperature data is normalized to obtain the normalized temperature parameter; and / or, The runtime data is normalized to obtain the normalized runtime parameters.

4. The operation control method for an air conditioning system according to claim 3, characterized in that, The reference center position is obtained by calculating the position data and the heat exchange data using a first calculation model; The first calculation model is represented as follows: In the above formula, This indicates the position of the reference center on the X-axis; N represents the total number of outdoor units in the outdoor unit cluster. This indicates the position of the i-th outdoor unit on the X-axis; This represents the heat exchange data of the i-th outdoor unit; Indicates the position of the reference center on the Y-axis; This indicates the position of the i-th outdoor unit on the Y-axis.

5. The operation control method for an air conditioning system according to claim 2, characterized in that, The optimized characteristic parameters include at least one of normalized distance parameters, normalized energy efficiency parameters, normalized temperature parameters, and normalized operating time parameters. The optimized index for temperature difference anomalies includes optimized indices for heat island effects and cold island effects. Determining the optimized index for temperature difference anomalies of the outdoor unit cluster based on the optimized characteristic parameters includes: The normalized distance parameter, the normalized energy efficiency parameter, the normalized temperature correction parameter, and the normalized operating time correction parameter are weighted and summed to obtain the urban heat island effect optimization index; wherein, the normalized temperature correction parameter represents a correction parameter determined based on the normalized temperature parameter and negatively correlated with the normalized temperature parameter, and the normalized operating time correction parameter represents a correction parameter determined based on the normalized operating time parameter and negatively correlated with the normalized operating time parameter; or, The normalized distance parameter, the normalized energy efficiency parameter, the normalized temperature parameter, and the normalized running time correction parameter are weighted and summed to obtain the cold island effect optimization index.

6. The operation control method for an air conditioning system according to claim 1, characterized in that, The step of determining at least one outdoor unit in the outdoor unit cluster as the target cluster based on the temperature difference abnormality effect optimization index, and controlling the operation of the target cluster, includes: Obtain the current load of the air conditioning system; The optimization indicators for the abnormal temperature difference effect of each outdoor unit are ranked, and the ranking results are obtained. When the operation optimization conditions are triggered, under the premise of satisfying the current load, the target cluster before the update is updated based on the ranking results of the indicators to obtain the updated target cluster, and the operation of the updated target cluster is controlled.

7. The operation control method for an air conditioning system according to claim 6, characterized in that, If there is a positive correlation between the value of the temperature difference anomaly effect optimization index and the effect of optimizing the temperature difference anomaly effect, then the step of selecting at least one outdoor unit in the outdoor unit cluster as the target cluster based on the index ranking result, under the premise of satisfying the current load, includes: When the current load increases, the outdoor machines with the largest values ​​in the index ranking results that are not running are added to the target cluster before the update, until the current load is met, and the updated target cluster is obtained. When the current load decreases, the outdoor machines with the smallest values ​​in the ranking results that are already running are removed from the target cluster before the update, until the current load is met, and the updated target cluster is obtained.

8. An operation control device for an air conditioning system, characterized in that, The air conditioning system includes an outdoor unit cluster composed of multiple outdoor units, and the operation control device of the air conditioning system includes: The acquisition module is used to acquire the operating environment parameters of the external machine cluster; The index determination module is used to determine the optimization index of the temperature difference abnormality effect of the outdoor unit cluster based on the operating environment parameters; wherein, the temperature difference abnormality effect optimization index characterizes the index used to optimize the temperature difference abnormality effect, and the temperature difference abnormality effect includes heat island effect and cold island effect. The operation control module is used to determine at least one outdoor unit in the outdoor unit cluster as a target cluster based on the temperature difference abnormality effect optimization index, and control the operation of the target cluster; wherein, the target cluster is the combination of outdoor units in the outdoor unit cluster that are least affected by the temperature difference abnormality effect.

9. A computer-readable storage medium, characterized in that, The device stores a computer program that, when executed by a processor, causes the processor to perform the steps of the method as described in any one of claims 1 to 7.

10. An air conditioning system, characterized in that, It includes a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the steps of the method as described in any one of claims 1 to 7.

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