Control method of compressor system, compressor system and air conditioner

Abstract

The invention provides a control method of a compressor system, the compressor system and an air conditioner, and according to the control method of the compressor system, after the power demand change is detected, the target compressor is intelligently determined and started by combining the current power demand data and the power range data of each compressor. And then, after the target compressor operates for a period of time, real-time operation data of the target compressor are obtained, the target compressor is adjusted according to a preset control strategy, and load balance between the compressors is achieved. According to the method, the running state of the compressor is dynamically optimized by comprehensively utilizing real-time and historical running data, excessive use and frequent start and stop of some compressors are avoided, mechanical abrasion and energy consumption are remarkably reduced, and therefore the service life of the whole compressor system is effectively prolonged.

Description

Technical Field

[0001] The present invention relates to the technical field of compressors, and in particular to a control method of a compressor system, a compressor system and an air conditioner. Background Art

[0002] A multi-compressor system is a working system consisting of two or more compressors, which can provide the required compression power or gas flow through the coordinated operation of multiple compressors. The multi-pressure compressor system can be used in refrigeration, air conditioning, industrial gas supply and other fields.

[0003] In a multi-compressor system, there is a technical problem that the compressor control is not appropriate, which leads to a reduction in the service life of the compressor system. Summary of the invention

[0004] The purpose of the present invention is to overcome the above-mentioned technical deficiencies and provide a control method for a compressor system, a compressor system and an air conditioner to solve the technical problem in the related art that in a multi-compressor system, the compressor control is not appropriate, which leads to a reduction in the service life of the compressor system.

[0005] In order to achieve the above technical objectives, the present invention adopts the following technical solutions:

[0006] In a first aspect, the present invention provides a method for controlling a compressor system, wherein the compressor system includes at least two compressors; the method includes:

[0007] After detecting a change in power demand, determining current power demand data of the compressor system and power range data of each compressor in the compressor system;

[0008] Determine a target compressor based on the power demand data and the power range data; wherein the target compressor is a compressor that needs to be in a startup state;

[0009] After the target compressor has been running for a certain period of time, at least real-time operation data of the target compressor is obtained;

[0010] At least based on the real-time operating data of the target compressor and a preset control strategy, at least the target compressor is adjusted to balance the loads of the compressors in the compressor system while meeting the current power demand.

[0011] In a second aspect, the present invention provides a compressor system, comprising: at least two compressors and a controller, wherein the controller is used to execute the above method.

[0012] In a third aspect, the present invention provides an air conditioner, comprising: a controller, wherein the controller is used to execute the above method or the above compressor system.

[0013] Beneficial effects:

[0014] The compressor system control method provided by the present invention intelligently determines and starts the target compressor by combining the current power demand data with the power range data of each compressor after detecting the change in power demand. Subsequently, after the target compressor has been running for a period of time, its real-time operation data is obtained, and it is adjusted according to the preset control strategy to achieve load balance between the compressors. This method dynamically optimizes the operating status of the compressor by comprehensively utilizing real-time and historical operating data, avoids excessive use and frequent start and stop of certain compressors, significantly reduces mechanical wear and energy consumption, and effectively extends the service life of the entire compressor system. At the same time, the reasonable distribution of load improves the operating efficiency and stability of the compressor system, and solves the technical problem of reduced compressor life due to improper control in the existing multi-compressor system. This not only improves the overall reliability of the compressor system, but also reduces maintenance costs, ensuring the efficient and long-term operation of the compressor system under different operating environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a flow chart of a control method of a compressor system provided by an embodiment of the present invention;

[0016] Figure 2 is a flow chart of a control method of a compressor system provided by an embodiment of the present invention;

[0017] Figure 3 is a flow chart of a control method of a compressor system provided by an embodiment of the present invention;

[0018] Figure 4 is a block diagram of a compressor system provided by an embodiment of the present invention;

[0019] Figure 5 is a schematic diagram of the operating power range of two compressors provided in an embodiment of the present invention;

[0020] Figure 6 It is a block diagram of an electronic device used in an embodiment of the present invention. DETAILED DESCRIPTION

[0021] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0022] In related technologies, multi-compressor systems are widely used in industrial refrigeration, commercial air conditioning, and other scenarios that require efficient cooling. Such systems are usually composed of multiple compressors that work together in parallel or series, and are equipped with controllers and various sensors to monitor the operating status and environmental conditions of the system. The controller starts and stops the compressors and distributes the load based on the information fed back by the sensors to meet different power requirements.

[0023] However, existing multi-compressor control methods often adopt fixed start-stop strategies or simple load distribution algorithms, which have many shortcomings in practical applications. First, the fixed start-stop strategy cannot flexibly respond to dynamically changing power demands, resulting in some compressors being in high-load operation for a long time, while other compressors are idle for a long time. This unbalanced load distribution not only reduces the overall operating efficiency of the system, but also accelerates the wear of the frequently started and stopped compressors and shortens the service life of the compressors. In addition, the simple load distribution algorithm lacks a comprehensive assessment of the health status of the compressors and cannot dynamically adjust the working mode of each compressor according to its operating status, further increasing the system's failure risk and maintenance costs.

[0024] Therefore, it is necessary to provide an intelligent multi-compressor control method that can comprehensively evaluate the operating health and current power demand of each compressor based on real-time and historical operating data, and optimize the selection of target compressors and load distribution strategies. Through this technical means, the reasonable distribution of compressor loads can be achieved, and overuse of certain compressors can be avoided, thereby extending the service life of the entire compressor system and improving the operating efficiency and reliability of the system.

[0025] This embodiment provides a control method for a compressor system, wherein the compressor system includes at least two compressors; the method may be performed by a controller of the compressor system, and the method may include:

[0026] Step S12: after detecting that the power demand has changed, determining the current power demand data of the compressor system and the power range data of each compressor in the compressor system.

[0027] In this embodiment, the detection of a change in power demand may be that the controller of the compressor system receives an instruction sent by the user side, for example, it may be a power-on instruction or a temperature adjustment instruction, and the temperature adjustment instruction may be to increase or decrease the temperature, etc.

[0028] In this embodiment, the detection of a change in power demand may also be that the controller detects a change in an environmental parameter, thereby triggering the controller to autonomously determine the current power demand data of the compressor system. For example, the controller monitors the ambient temperature in real time through a temperature sensor built into the compressor system. When the ambient temperature exceeds or falls below a preset threshold, it indicates that a change in power demand is detected, thereby triggering the controller to determine the current power demand data of the compressor system. The environmental parameter may also be a change in humidity and air pressure, etc.

[0029] In this embodiment, the detection of the change in power demand may also be performed by the controller based on time and operation mode. For example, the compressor system may automatically adjust the operation state of the compressor according to a predetermined schedule. For example, when the load is low at night, the number of compressor operations is reduced to save energy. For example, according to different operation modes (energy-saving mode, high-efficiency mode), the controller automatically detects and adjusts the power demand.

[0030] In this embodiment, the step of determining the current power demand data of the compressor system may be:

[0031] First, the controller detects the current ambient temperature in response to a temperature adjustment instruction or a power-on instruction issued by a user; wherein the temperature adjustment instruction or the power-on instruction carries a target temperature.

[0032] Then, the controller calculates the power demand data based on the ambient temperature and the target temperature.

[0033] In this implementation, the step of determining the current power demand data of the compressor system may also be:

[0034] First, when it is detected that the ambient temperature exceeds or falls below a preset threshold, the controller determines that the power demand has changed.

[0035] Then, according to the change in ambient temperature, the controller recalculates the current power demand data.

[0036] In this embodiment, the step of determining the current power demand data of the compressor system may be: the controller automatically adjusts the operating state of the compressor according to a preset schedule. For example, when the load is low at night, the number of compressor operations is reduced to save energy. The controller calculates the corresponding power demand data according to the expected load in different time periods.

[0037] It can be understood that in this embodiment, the detection of a change in power demand is a trigger condition. Once the trigger condition is met, it can trigger the controller to determine the current power demand data of the compressor system, and the process of determining the current power demand data of the compressor system is a quantification process.

[0038] Specifically, detecting a change in power demand is the starting point of the entire control method and a prerequisite for the execution of subsequent steps. This detection process can be implemented by a variety of means. For example, it can be triggered by a user command. When the controller receives a command sent by the user side, such as a power-on command or a temperature adjustment command (including raising or lowering the temperature), it is considered that the power demand has changed. For example, the controller monitors environmental parameters in real time through sensors (temperature sensors, humidity sensors, and air pressure sensors) built into the compressor system. When parameters such as ambient temperature, humidity, or air pressure exceed or fall below a preset threshold, it is determined that the power demand has changed. Once the trigger condition for detecting a change in power demand is met, the controller will start the process of determining the current power demand data. This process is a quantification process. For example, the controller first responds to the command from the user side, detects the current ambient temperature, and calculates the required power demand data in combination with the target temperature set by the user. For example, when environmental parameters change, the controller will comprehensively analyze the current ambient temperature, humidity, air pressure, and other data, and quantify the current power demand based on a preset algorithm or formula.

[0039] In this embodiment, the step of determining the power range data of each compressor in the compressor system may be:

[0040] The controller reads the upper power limit and the lower power limit of each compressor in the compressor system from a preset database.

[0041] It can be understood that the upper power limit value and the lower power limit value are also the power range data.

[0042] In this embodiment, the step of determining the power range data of each compressor in the compressor system may be: the controller continuously monitors the operating parameters (pressure, temperature, flow and energy consumption) of the compressor, and determines the upper power limit and the lower power limit based on the operating parameters.

[0043] In this embodiment, the power demand data may be expressed as the cooling or heating power required to meet the environmental conditions set by the user or the load change of the compressor system.

[0044] In this embodiment, the power range data can be used to indicate the working capacity of each compressor, including its upper power limit and lower power limit. The upper power limit value indicates the maximum power output that each compressor can achieve, ensuring that the compressor will not be damaged due to overload. The lower power limit value indicates the minimum power that each compressor can maintain stable operation, avoiding frequent start and stop of the compressor under low load.

[0045] In this embodiment, the compressor system may include two compressors, or three or more compressors, which may be connected in parallel or in series.

[0046] In this embodiment, the compressor system may be a dual-machine parallel compressor system.

[0047] In this embodiment, the compressor system may also be a multi-compressor parallel system (three compressors or more).

[0048] In this embodiment, the compressor system may also be a multi-stage series compressor system.

[0049] In this embodiment, the compressor system may also be a parallel-series hybrid compressor system.

[0050] In this embodiment, the compressor system may also be a zoned compressor system or a multi-stage compressor system.

[0051] Step S14: determining a target compressor based on the power demand data and the power range data; wherein the target compressor is a compressor that needs to be in a startup state.

[0052] In this embodiment, the determining action may be represented by selecting a compressor from the compressor system as a target compressor and starting the compressor.

[0053] It is understandable that, when the selected compressor is already in the startup state, there is no need to perform startup control, and the startup state can be maintained. Correspondingly, when the selected compressor is in the shutdown state, the controller sends a control instruction to it to start it.

[0054] Specifically, in this embodiment, the step of determining the target compressor based on the power demand data and the power range data may be:

[0055] First, the controller generates a plurality of operating power intervals based on the power range data; wherein each operating power interval corresponds to at least one compressor.

[0056] In this embodiment, the operating power interval can be represented by the power range data (upper power limit and lower power limit) of each compressor in the compressor system, which is used to divide the intervals with different power requirements. These intervals reflect the working ability of the compressor system to match and meet the load requirements under different power demand conditions. Each operating power interval can correspond to one or more compressors, depending on the overlap of the power ranges of the compressors. The purpose of this interval division is to provide the controller with a clear load distribution basis, so as to dynamically select the most suitable compressor combination for operation under different power requirements.

[0057] Therefore, it is understandable that after the operating power interval is divided, the controller can clearly determine which compressors should bear a certain power demand, avoid excessive load concentration on a certain compressor, and balance the service life of the equipment. Secondly, based on the operating power interval division, the controller can quickly match the power demand and the compressor combination, reduce complex calculations and logical judgments, and improve the response speed of the compressor system.

[0058] Then, the controller determines a target operating power interval from the plurality of operating power intervals based on the power demand data.

[0059] Finally, the controller selects at least one compressor from the target operating power interval as the target compressor and starts the target compressor.

[0060] In a possible embodiment, the compressor system may include two compressors, wherein the power range data of one compressor M1 may be (a, c), and the power range data of the other compressor M2 may be (b, d), wherein a<b<c<d. Based on the two power range data, the following operating power intervals may be determined: (a, b), (b, c), (c, d). The operating power interval (a, b) may correspond to the M1 compressor, the operating power interval (b, c) may correspond to the M1 compressor and the M2 compressor, and the operating power interval (c, d) may correspond to the M2 compressor.

[0061] In a possible embodiment, the compressor system may include three compressors, M1, M2, and M3, and the power range data of each compressor is as follows:

[0062] The power range data of compressor M1 is (a, c): the power range is from a = 5 kW to c = 15 kW;

[0063] The power range data of compressor M2 is (b, d): the power range is from b = 10 kW to d = 25 kW;

[0064] The power range data of the compressor M3 is (e, f): the power range is from e=20 kW to f=35 kW.

[0065] The controller first lists all the split points based on the above power range data:

[0066] {a=5, b=10, c=15, d=25, e=20, f=35}.

[0067] The controller then sorts these split points from smallest to largest and removes duplicates:

[0068] {5, 10, 15, 20, 25, 35}.

[0069] Next, the controller determines each continuous power interval as an operating power interval based on the above segmentation points:

[0070] 1. Interval (5, 10);

[0071] 2. Interval (10, 15);

[0072] 3. Interval (15, 20);

[0073] 4. Interval (20, 25);

[0074] 5. Interval (25, 35).

[0075] Finally, the controller combines the power range data of each compressor to determine the compressor corresponding to each operating power range:

[0076] 1. Interval (5, 10):

[0077] Only the power range (5, 15) of the compressor M1 covers this interval. Therefore, the interval (5, 10) corresponds to the compressor M1.

[0078] 2. Interval (10, 15):

[0079] The power range (5, 15) of the compressor M1 covers this interval, and the power range (10, 25) of the compressor M2 also covers this interval. Therefore, the interval (10, 15) corresponds to the compressor M1 and the compressor M2.

[0080] 3. Interval (15, 20):

[0081] The power range (10, 25) of compressor M2 covers this interval and corresponds to compressor M2.

[0082] 4. Interval (20, 25):

[0083] The power range (10, 25) of compressor M2 covers this interval, and the power range (20, 35) of compressor M3 covers this interval, corresponding to compressor M1 and compressor M2.

[0084] 5. Interval (25, 35):

[0085] The power range (20, 35) of the compressor M3 covers this interval. Corresponding to the compressor M3.

[0086] Step S16: After the target compressor has been running for a certain period of time, at least real-time operating data of the target compressor is obtained.

[0087] In this embodiment, the plurality of time periods may be thirty minutes, one hour, two hours, or the like.

[0088] In this embodiment, after the target compressor has been running for a certain period of time, real-time operating data of the target compressor may be acquired.

[0089] In this embodiment, after the target compressor has been running for a certain period of time, real-time operating data and historical operating data of the target compressor may be acquired.

[0090] In this embodiment, after the target compressor has been running for a certain period of time, the real-time operation data and historical operation data of the target compressor may be acquired, and the historical operation data of the non-target compressors may also be acquired.

[0091] It can be understood that the non-target compressor refers to a compressor that is not currently started.

[0092] In this implementation, the real-time operation data may include: current operation time, current operation temperature or current operation frequency.

[0093] Specifically, the current operating time can be expressed as the length of time the compressor has been running continuously since it was started, which is used to determine its load condition and whether adjustment (switching or frequency reduction) is required.

[0094] The current operating temperature may indicate the current operating temperature of the compressor. If the temperature exceeds a threshold, a frequency reduction or switching operation may be performed.

[0095] The current operating frequency can represent the current operating load of the target compressor. Through the operating frequency, it can be determined whether it is in an efficient operating state.

[0096] In this embodiment, the historical operation data may include: historical operation time, historical average operation temperature, historical average operation frequency or fault occurrence record.

[0097] Specifically, the historical operating time can represent the accumulated operating time, which can reflect the service life and wear degree of the compressor, and can provide a basis for health assessment.

[0098] The historical average operating temperature can represent the long-term operating temperature average value, which can be used to evaluate the heat dissipation performance of the compressor and whether it is in a high temperature state for a long time.

[0099] The historical average operating frequency can reflect the average load level of the compressor.

[0100] The fault occurrence record can record the historical fault conditions of the compressor, including the fault type, occurrence frequency, etc.

[0101] More specifically, the real-time operating data can be used to evaluate the immediate health status of the target compressor, while the historical data can be used to comprehensively evaluate whether the target compressor needs to adjust its operating status (frequency reduction, switching). Therefore, by combining the real-time operating data and historical operating data of the target compressor and non-target compressors, the controller can dynamically allocate the load among multiple compressors based on factors such as health, load balancing requirements, and operating life.

[0102] In this embodiment, the controller may send instructions to a monitoring sensor preset in the compressor system to obtain the above-mentioned real-time operating data.

[0103] Specifically, a time recording module or an operating status sensor can be pre-set in the compressor system, and the time recording module or operating status sensor can record the continuous running time of the compressor from startup to the current time. A temperature sensor (thermocouple or thermistor) can be pre-set in the compressor system, and the temperature sensor can monitor the temperature of the key components of the compressor (compressor casing, exhaust port, and intake port) in real time. A frequency detection module can be pre-set in the compressor system, and the frequency detection module can detect the motor operating frequency of the compressor to reflect the current operating load and adjustment status. The frequency detection module can be a frequency sensing module built into the frequency converter, or a Hall effect sensor, or a current sensor, and the frequency can be indirectly inferred by monitoring the motor operating current.

[0104] The controller can trigger the sensor to collect real-time data by sending a request signal to the above-mentioned sensor. The above-mentioned sensor feeds back the collected operation data to the controller in the form of a signal. The controller digitizes the collected analog signal (for example, through an AD converter). These data can be stored in the controller's internal memory or uploaded to a cloud database for subsequent analysis.

[0105] In this embodiment, the controller can read the historical operation data from a preset database.

[0106] Specifically, the database may be a local database. Specifically, the database may be stored in an internal memory of the controller or a local storage device (such as an SD card, an embedded storage chip) directly connected to the controller.

[0107] The database may be a cloud database, in other words, the historical operating data may be stored on a remote server or cloud platform and communicate with the controller via a network interface.

[0108] In this embodiment, the database may be a relational database or a non-relational database.

[0109] In this embodiment, the controller can directly read the data in the memory through an embedded interface (such as SPI, I2C). For example, the controller can query the historical operation data according to the compressor ID and timestamp index.

[0110] In this embodiment, the controller can also communicate with the cloud database through a network protocol (HTTP, MQTT, MODBUS TCP), send query requests and receive returned historical operation data.

[0111] It is understandable that after the target compressor has been running for a period of time, the controller collects the real-time operation data of the target compressor through the built-in sensor of the compressor system, and further obtains the historical operation data of the target compressor and the historical operation data of other compressors that have not been started (non-target compressors) according to specific needs. The acquisition of these data can provide accurate operation status information and health assessment basis for the execution of subsequent control strategies.

[0112] Step S18: Based at least on the real-time operating data of the target compressor and a preset control strategy, at least the target compressor is adjusted to balance the loads of the compressors in the compressor system while meeting the current power demand.

[0113] In this embodiment, the controller may adjust the target compressor based on the real-time operating data of the target compressor and a preset control strategy, so as to balance the loads of the compressors in the compressor system while meeting the current power demand.

[0114] Specifically, the controller can monitor the operating status of the target compressor (for example, current temperature, frequency, duration, etc.) in real time, compare it with the preset control strategy, and determine whether the target compressor needs to be adjusted.

[0115] More specifically, when the operating temperature of a target compressor in the target compressors exceeds a safety threshold, the controller can reduce the operating frequency of the target compressor or even shut it down. Correspondingly, the controller can increase the operating frequency of the target compressor whose operating temperature does not exceed the safety threshold, so as to balance the loads of the compressors in the compressor system while meeting the current power demand.

[0116] More specifically, in this case, the preset control strategy may be a rule base. The rule base may include multiple sets of predefined rules, and the rules set safety thresholds based on the operating parameters of the compressor. For example:

[0117] Temperature safety threshold: If the operating temperature is greater than 80°C, frequency reduction is triggered.

[0118] Frequency safety threshold: If the operating frequency is close to 90% of the maximum frequency, a warning or limit is triggered.

[0119] Safety threshold of running time: If the continuous running time is greater than 2 hours, it triggers the reduction of running frequency or the implementation of cooling measures.

[0120] The rule base can be defined in the form of "condition-action" and can be judged by the rule engine.

[0121] In this embodiment, the controller may also perform adjustments on the target compressor and the non-target compressor based on the real-time operating data of the target compressor and a preset control strategy to balance the loads of the compressors in the compressor system while meeting the current power demand.

[0122] Specifically, the controller can monitor the operating status of the target compressor (for example, current temperature, frequency, duration, etc.) in real time, compare it with the preset control strategy, and perform adjustments on both the target compressor and the non-target compressor.

[0123] More specifically, for example, when the operating temperatures of all target compressors exceed a safety threshold, the controller may choose to start non-target compressors to balance the loads of the compressors in the compressor system while meeting the current power demand.

[0124] More specifically, in this case, the preset control strategy can be based on the above rule base, adding a priority decision algorithm to determine the order of adjusting the target compressor and enabling the non-target compressor. For example, the priority decision algorithm can be used to assign start and stop weights to the target and non-target compressors. The target compressor is adjusted first, and the non-target compressor is started on demand.

[0125] In this embodiment, the controller may also adjust the target compressor based on the real-time operating data, historical operating data and preset control strategy of the target compressor to balance the loads of the compressors in the compressor system while meeting the current power demand.

[0126] Specifically, the controller can not only monitor the operating status of the target compressor in real time (for example, current temperature, frequency, duration, etc.), but also extract the historical operating data of the target compressor, compare it with the preset control strategy, and perform adjustments on the target compressor.

[0127] More specifically, for example, when the operating temperature of a target compressor in the target compressors exceeds the safety threshold, the controller can reduce the operating frequency of the target compressor or even shut it down. Accordingly, the controller evaluates the health of the target compressors whose operating temperatures do not exceed the safety threshold based on historical operating data, and then increases the operating frequency of the target compressors with the highest health and whose operating temperatures do not exceed the safety threshold, so as to balance the loads of the compressors in the compressor system while meeting the current power demand.

[0128] In this embodiment, the controller can also perform adjustments on the target compressor and the non-target compressor based on the real-time operating data, historical operating data and preset control strategy of the target compressor to balance the load of each compressor in the compressor system while meeting the current power demand.

[0129] Specifically, the controller can not only monitor the operating status of the target compressor in real time (for example, current temperature, frequency, duration, etc.), but also extract the historical operating data of the target compressor, compare it with the preset control strategy, and perform adjustments on both the target compressor and the non-target compressor.

[0130] More specifically, for example, when the operating temperature of a target compressor among the target compressors exceeds the safety threshold, the controller can reduce the operating frequency of the target compressor or even shut it down. Accordingly, the controller evaluates the health of the target compressors whose operating temperatures do not exceed the safety threshold based on historical operating data, and then increases the operating frequency of the target compressors with the highest health (or that which meets the health threshold) and whose operating temperatures do not exceed the safety threshold. When the operating frequency of the corresponding target compressor is increased but still cannot meet the current power demand, the controller can continue to start one or more non-target compressors to balance the loads of the compressors in the compressor system while meeting the current power demand.

[0131] In this embodiment, the controller can also perform adjustments on the target compressor and the non-target compressor based on the real-time operating data, historical operating data of the target compressor, the historical operating data of the non-target compressor and a preset control strategy, so as to balance the load of each compressor in the compressor system while meeting the current power demand.

[0132] Specifically, the controller can not only monitor the operating status of the target compressor in real time (for example, current temperature, frequency, duration, etc.), but also extract the historical operating data of the target compressor, and extract the historical operating data of the non-target compressor, and compare it with the preset control strategy, and perform adjustments on both the target compressor and the non-target compressor.

[0133] More specifically, for example, when the operating temperature of a target compressor among the target compressors exceeds a safety threshold, the controller can reduce the operating frequency of the target compressor or even shut it down. Accordingly, the controller evaluates the health of the target compressors whose operating temperatures do not exceed the safety threshold based on historical operating data, and then increases the operating frequency of the target compressors whose operating temperatures do not exceed the safety threshold with the highest health (or meet the health threshold). If the current power demand still cannot be met after the operating frequency of the corresponding target compressor is increased, the controller can evaluate the health of the non-target compressors based on the historical operating data of the non-target compressors, and continue to start the non-target compressors with the highest health (or meet the health threshold) to balance the loads of the compressors in the compressor system while meeting the current power demand.

[0134] The compressor system control method provided in this embodiment intelligently determines and starts the target compressor by combining the current power demand data with the power range data of each compressor after detecting the change in power demand. Subsequently, after the target compressor has been running for a period of time, its real-time operation data is obtained, and it is adjusted according to the preset control strategy to achieve load balance between the compressors. This method dynamically optimizes the operating status of the compressor by comprehensively utilizing real-time and historical operating data, avoids excessive use and frequent start and stop of certain compressors, significantly reduces mechanical wear and energy consumption, and effectively extends the service life of the entire compressor system. At the same time, the reasonable distribution of load improves the operating efficiency and stability of the system, and solves the technical problem of reduced compressor life due to improper control in existing multi-compressor systems. This not only improves the overall reliability of the compressor system, but also reduces maintenance costs, ensuring the efficient and long-term operation of the compressor system under different operating environments.

[0135] In some embodiments, after detecting a change in power demand, the step of determining current power demand data of the compressor system comprises:

[0136] Step S122: In response to a temperature adjustment instruction or a power-on instruction issued by the user side, detecting the current ambient temperature; wherein the temperature adjustment instruction or the power-on instruction carries a target temperature.

[0137] Step S124: Calculating the power demand data based on the ambient temperature and the target temperature.

[0138] In this embodiment, the controller client calculates the power demand data based on the ambient temperature and the target temperature in the following manner:

[0139] First, the controller calculates the difference between the ambient temperature and the target temperature.

[0140] Then, the controller determines the required power demand data according to the temperature difference through a preset power demand mapping relationship table (or a power calculation formula).

[0141] In this embodiment, by responding to the temperature adjustment instruction or power-on instruction issued by the user side, the current ambient temperature is detected, and the power demand data of the compressor system is calculated in combination with the target temperature carried in the instruction. This method can realize the dynamic adjustment and accurate calculation of power demand, and effectively meet the user's requirements for real-time and accuracy of temperature regulation. At the same time, the calculation method combining ambient temperature and target temperature can ensure that the calculation of power demand data is more in line with actual load requirements, thereby optimizing the operating efficiency of the compressor, avoiding excess or insufficient power, and improving the energy-saving effect and operating stability of the system. In addition, this response mechanism further enhances the user interaction capability of the compressor system and improves the overall user experience.

[0142] In some embodiments, the step of determining power range data for each compressor in the compressor system comprises:

[0143] Step S126: Reading the upper power limit and the lower power limit of each compressor in the compressor system from a preset database.

[0144] In this embodiment, the database may be a local database. Specifically, the database may be stored in an internal memory of the controller or a local storage device directly connected to the controller (such as an SD card, an embedded storage chip).

[0145] In this embodiment, the database may be a cloud database. In other words, the historical operation data may be stored in a remote server or cloud platform and communicate with the controller via a network interface.

[0146] In this embodiment, the database may be a relational database or a non-relational database.

[0147] In this embodiment, by reading the upper power limit and lower power limit of each compressor in the compressor system from a preset database, the power range data of each compressor can be quickly and accurately determined. This method not only simplifies the process of obtaining the power range, but also avoids errors that may be caused by complex real-time calculations or manual input. By pre-storing the upper power limit and the lower power limit in the database, the compressor system can quickly match the corresponding power range data according to different models or operating conditions, thereby improving the real-time and accuracy of the control strategy. In addition, this implementation method provides stable basic data for the dynamic load distribution of multi-compressor systems, ensuring that the system can reasonably distribute the load under different power requirements, further improving operating efficiency and extending the service life of the compressor.

[0148] In some embodiments, the step of determining a target compressor based on the power demand data and the power range data comprises:

[0149] Step S142: generating a plurality of operating power intervals based on the power range data; wherein each operating power interval corresponds to at least one compressor.

[0150] In this embodiment, the operating power interval can be represented by the power range data (upper power limit and lower power limit) of each compressor in the compressor system, which is used to divide the intervals with different power requirements. These intervals reflect the working ability of the compressor system to match and meet the load requirements under different power demand conditions. Each operating power interval can correspond to one or more compressors, depending on the overlap of the power ranges of the compressors. The purpose of this interval division is to provide the controller with a clear load distribution basis, so as to dynamically select the most suitable compressor combination for operation under different power requirements.

[0151] Therefore, it is understandable that after the operating power interval is divided, the controller can clearly determine which compressors should bear a certain power demand, avoid excessive load concentration on a certain compressor, and balance the service life of the equipment. Secondly, based on the operating power interval division, the controller can quickly match the power demand and the compressor combination, reduce complex calculations and logical judgments, and improve the response speed of the compressor system.

[0152] In this implementation, the controller may generate a plurality of operating power intervals based on the power range data in the following manner:

[0153] First, the controller collects the upper and lower power limits of all compressors and aggregates the upper and lower power limits of all compressors into a set.

[0154] Then, the set of upper and lower limits of the controller power is deduplicated and sorted in order from small to large to generate an ordered set of partition points.

[0155] Next, the controller constructs a continuous power interval based on the above-mentioned ordered segmentation point set, and then traverses each operating power interval in turn to determine which compressors have power ranges overlapping with the interval.

[0156] Finally, the controller generates a final mapping table for all operating power intervals and their corresponding compressors.

[0157] Step S144: determining a target operating power interval from the plurality of operating power intervals based on the power demand data.

[0158] In this embodiment, the controller may traverse all operating power intervals one by one according to the current power demand data, and determine whether the current power demand data falls into the corresponding operating power interval, thereby determining the target operating power interval.

[0159] Step S146: selecting at least one compressor from the target operating power range as the target compressor and starting the target compressor.

[0160] In this embodiment, a compressor may be randomly selected from the target operating power interval as the target compressor and the target compressor may be started.

[0161] In this embodiment, the historical operation data of each compressor in the target operating power range can be further obtained, and then the health of the corresponding compressor is evaluated based on the historical operation data. Finally, the compressor with the highest health or meeting the health threshold is selected as the target compressor, which can be one compressor or multiple compressors.

[0162] In this embodiment, by generating several operating power intervals based on the power range data, and determining the target operating power interval in combination with the power demand data, the most suitable compressor is further selected and started from the target operating power interval as the target compressor, thereby effectively realizing the precision and intelligence of the load distribution of the compressor system. This method can ensure that the operation of the compressor is more in line with the actual power demand, and avoid the situation where a single compressor is overloaded or wastes resources. In addition, the division of the operating power interval enables the system to quickly match the power demand with the compressor capacity. By optimizing the compressor, the redundancy of the operating power is reduced, the energy utilization efficiency is improved, the overall energy consumption is reduced, and the load of each compressor is balanced, extending the service life of the equipment.

[0163] In some implementations, the step of selecting at least one compressor from the target operating power range as the target compressor includes:

[0164] Step S1462: Obtain historical operating data of each compressor in the target operating power range; wherein the historical operating data includes historical operating time, historical average operating temperature, historical average operating frequency or fault occurrence record.

[0165] Step S1464: Evaluate the health of each compressor in the target operating power range based on the historical operating data.

[0166] In this embodiment, the controller may input the historical operating data into a preset health evaluation model to evaluate the health of each compressor in the target operating power range.

[0167] In a specific embodiment, the health assessment model may be a weighted scoring model for quantifying the impact of different historical data on health, for example, it may be:

[0168] H=100-(w1·S 运行 +w2·S 温度 +w3·S 频率 +w4·S 故障 )

[0169] In the formula, H represents the health score, which ranges from 0 to 100. The higher the value, the healthier the compressor is. If the health score H<H 阈值 (For example, H 阈值 is 50), the compressor is considered unfit for operation. w1-w4 are weight parameters, each weight reflects the influence of the corresponding indicator on the health, and the weight value satisfies:

[0170]

[0171] S 运行 Expressed as a quantitative score of historical running time, S 温度 Expressed as a quantitative score of the historical average operating temperature, S 频率 Expressed as a quantitative score of the historical average running frequency, S 故障 Expressed as a quantitative score of the fault occurrence record.

[0172] In a specific embodiment, the quantitative score of the historical operating time can be the impact of the operating time on health. It is understandable that the operating time reflects the cumulative use of the compressor. The longer the operating time, the higher the degree of wear of its internal components, and the lower the health. Each compressor has a design life value, such as 10,000 hours, which means that the operation of the equipment is relatively reliable within this time range. The controller records the cumulative operating time of each compressor. The impact of health can be reflected by the proportion of the cumulative operating time to the design life: when the cumulative operating time approaches the upper limit of the design life, the negative impact on health will gradually increase. For example, if the operating time is 50% of the design life, the impact on health is moderate; if the operating time is 80% of the design life, the impact on health is large. The impact of operating time is a gradually decreasing process, that is, as the cumulative operating time increases, the health gradually decreases.

[0173] In a specific embodiment, the quantitative score of the historical average operating temperature can be the impact of the average temperature on health. It is understandable that the impact of temperature on health is related to the operating heat load of the equipment. When the equipment is operated at high temperature for a long time, the performance of its internal components (electrical insulation materials, lubricating oil, etc.) will be significantly affected, thereby accelerating the aging of the equipment and reducing the health. Each compressor has a safe normal operating temperature range, which can be defined by the manufacturer. For example, 60°C may be the upper limit. The controller calculates the long-term average operating temperature of the equipment and compares it with the safe temperature range. If the average temperature exceeds the upper limit of the normal operating temperature, the health will be negatively affected, and the higher the temperature, the greater the impact. The controller will convert this excess into a decrease in health according to the degree of temperature excess (that is, the difference between the average temperature and the normal operating temperature) using a proportional relationship: if the temperature only slightly exceeds the normal range (for example, 65°C exceeds 60°C), the impact on health is small. If the temperature exceeds the normal range significantly (for example, 75°C exceeds 60°C), the impact on health is significant. The impact of temperature is gradually increasing, and the controller will accumulate each degree of temperature difference that exceeds the temperature range as a decrease in health.

[0174] In a specific embodiment, the quantitative score of the historical average operating frequency can be the impact of the average frequency on health. It is understandable that the frequency is a direct reflection of the workload of the equipment. The higher the operating frequency and the heavier the load, the greater the impact on the health of the equipment. Long-term high-frequency operation will accelerate the wear of internal components, such as bearings, motor speed and vibrating parts. Each compressor has a rated operating frequency, such as 50Hz, which is the recommended operating frequency of the equipment under the design load conditions. The controller calculates the long-term average operating frequency of the equipment. By comparing the average operating frequency of the equipment with the rated operating frequency, the impact on health is evaluated: when the average frequency is close to the rated operating frequency, the impact on health is small. When the average frequency is higher than the rated operating frequency, the negative impact on health gradually increases. The higher the frequency exceeds, the more the health decreases. The controller can use a proportional relationship to convert the proportion of the excess frequency into a decrease in health: for example, a 10% excess of the rated frequency has a slight impact on health. Exceeding the rated frequency by 20% or more will have a significant impact on health.

[0175] In a specific embodiment, the quantitative score of the fault occurrence record can be the impact of the fault occurrence record on health. It is understandable that the fault record is an important evaluation dimension of the equipment health, because the frequency and severity of the fault directly reflect the current state of the equipment and potential reliability problems. The fault record of each compressor may include the number of faults and the type of fault (e.g., minor faults, major faults). The increase in the number of faults will reduce the health of the equipment, and the controller can evaluate based on the accumulation of the number of faults: the more faults, the more obvious the decline in health. The severity of the fault type will further aggravate the impact of health. Minor faults (e.g., short-term overload alarms) have little impact on health, but frequent occurrences will have cumulative effects. Severe faults (e.g., shutdowns caused by excessive temperature) have a very significant impact on health. The controller can assign impact weights to different types of faults. For example, each time a minor fault occurs, the impact on health is a smaller value. Each time a serious fault occurs, the impact on health is a larger value. Finally, the controller can calculate the overall impact of the fault on health based on the combined impact of the number and type of faults.

[0176] In this embodiment, the health assessment model can be a machine learning model. It is understandable that the health assessment model can be constructed using machine learning technology, and the health assessment model is trained according to historical operation data to predict the health of the compressor. Specifically, the health assessment model can be a regression model (for example, a linear regression model or a random forest regression model), and the health assessment model can also be a classification model (for example, a logistic regression model, a support vector machine model).

[0177] In this embodiment, the health evaluation model may also be a neural network model or a fuzzy logic model, etc.

[0178] In this embodiment, the health evaluation model may also be a physical model based on state estimation. For example, the health model may be established using the physical characteristics of the compressor to estimate the health state of the equipment based on the operating data.

[0179] Step S1466: Select a compressor that meets the health threshold in the target operating power range as the target compressor.

[0180] In this embodiment, the health threshold may be a fixed value for uniformly standardizing the determination of the operating status of the compressor. For example, the health threshold is set to 50 (out of a total of 100). Only compressors with a health score greater than or equal to 50 can be selected as target compressors.

[0181] In this embodiment, the health threshold may also be a threshold that can be adjusted dynamically. In other words, the health threshold may be dynamically adjusted according to the operating requirements or environmental conditions of the compressor system. For example, during peak load periods (when the compressor demand is high), the threshold may be appropriately lowered, for example, from 50 to 40, to increase the number of available compressors to meet power requirements. During low load periods (when the compressor demand is low), the threshold may be increased, for example, from 50 to 60, to prioritize the protection of compressors in better health.

[0182] In this embodiment, by obtaining the historical operating data of each compressor in the target operating power range (historical operating time, average operating temperature, average operating frequency and fault occurrence records), the controller can comprehensively evaluate the health of each compressor, and select the compressor that meets the health threshold as the target compressor for startup operation based on the health. This method makes full use of the historical operating data of the compressor, ensures accurate judgment of the compressor status, and gives priority to the operation of compressors with higher health, thereby effectively balancing the service life of the compressor and avoiding compressors with lower health or failure risks from participating in high-load operation. This method not only improves the reliability and safety of the compressor system operation, but also realizes the rational scheduling and optimal utilization of compressor resources, further extending the service life of the entire compressor system and reducing maintenance costs.

[0183] In some embodiments, the step of at least adjusting the target compressor based on the real-time operating data of the target compressor and a preset control strategy includes:

[0184] Step S182: Based on the real-time operating data of the target compressor, the health level and the preset control strategy, adjustments are performed on the target compressor and the non-target compressors in the target operating power range.

[0185] In this embodiment, the controller may first determine whether the real-time operating data of the target compressor (eg, current operating time, current operating temperature or current operating frequency) exceeds a corresponding safety threshold.

[0186] If the corresponding safety threshold is not exceeded, the judgment will be made again after a certain period of time.

[0187] If at least one target compressor exceeds the corresponding safety threshold, it can be understood that exceeding the corresponding safety threshold may mean that at least one type of real-time operating data among the above multiple types of real-time operating data exceeds the corresponding safety threshold, then the first control strategy and the second control strategy are executed.

[0188] Specifically, in a specific embodiment, the first control strategy may be:

[0189] Shut down the target compressor whose real-time operating data exceeds the safety threshold;

[0190] Reduce the target compressor operating frequency when the real-time operating data exceeds the safety threshold.

[0191] The second control strategy may be:

[0192] The non-target compressors meeting the health threshold in the target operating power range are started.

[0193] Furthermore, in a specific embodiment, the first control strategy may further include:

[0194] The output power of the target compressor whose real-time operating data exceeds the safety threshold is reduced, for example, the operating power of the target compressor is reduced to a preset minimum power value.

[0195] The target compressor whose real-time operating data exceeds the safety threshold is switched to "rest mode", temporarily shut down for a period of time, and restarted after the equipment cools down or the load is restored.

[0196] The cooling system (for example, a fan or a water cooling device) of the target compressor whose real-time operating data exceeds the safety threshold is activated to assist in cooling down to delay shutdown.

[0197] Furthermore, in a specific embodiment, the second control strategy may further include:

[0198] After starting a non-target compressor that meets the health threshold in the target operating power range, dynamically increase its operating frequency to quickly compensate for the power demand;

[0199] If the health of the non-target compressor meets the minimum threshold, but the state is close to the health boundary (for example, slightly higher than the health threshold), the non-target compressor can be gradually started, that is, it is first operated in low power mode to observe whether its real-time operating data is stable. If it is stable, the power is gradually increased;

[0200] If there are multiple non-target compressors with relatively high health levels within the target operating power range, multiple compressors can be started simultaneously and the load can be distributed among them to avoid a single compressor from operating at high load;

[0201] When selecting non-target compressors, the compressor with the highest health can be prioritized to bear high load operation, while the compressor with slightly lower health bears lower load.

[0202] After the non-target compressor is started, its health status can be monitored in real time. If its health status is significantly reduced due to frequent operation, it will be switched to standby mode and other compressors will be started to share the load.

[0203] In this embodiment, by adjusting the target compressor and the non-target compressors in the target operating power range based on the real-time operating data, health and preset control strategy of the target compressor, efficient load distribution and dynamic optimization of the compressor system can be achieved. This method not only performs fine-grained regulation on the target compressor (for example, frequency reduction, shutdown or frequency increase), but also comprehensively considers the health status and real-time needs of the non-target compressors, and starts and stops or adjusts the frequency of the non-target compressors when necessary. In this way, the system can further balance the load of each compressor while meeting the current power demand, avoid overload operation of a single compressor, and extend the overall life of the equipment. In addition, by comprehensively considering real-time operating data and historical health, the method can intelligently adjust the operating strategy, reduce the risk of failure, improve the reliability and stability of the compressor system operation, and maximize energy efficiency.

[0204] In some embodiments, the real-time operation data includes current operation duration, current operation temperature, or current operation frequency;

[0205] The step of adjusting the target compressor and the non-target compressors in the target operating power range based on the real-time operating data of the target compressor, the health level and the preset control strategy includes:

[0206] Step S1822: determining whether there is a target compressor among the target compressors whose real-time operation data exceeds the safety threshold; wherein, the real-time operation data exceeding the safety threshold means that at least one type of real-time operation data exceeds the corresponding safety threshold;

[0207] Step S1824: In the event that there is a target compressor whose real-time operating data exceeds the safety threshold, execute a preset first control strategy and a preset second control strategy; wherein the first control strategy at least includes a control strategy for shutting down the target compressor whose real-time operating data exceeds the safety threshold or reducing the operating frequency of the target compressor whose real-time operating data exceeds the safety threshold; and the second control strategy at least includes starting a non-target compressor that meets the health threshold in the target operating power range.

[0208] In this embodiment, by real-time monitoring of the operating data of the target compressor (including operating time, operating temperature, operating frequency, etc.) and determining whether there is a situation that exceeds the safety threshold, the controller can take active adjustment measures before the equipment reaches a dangerous operating state. If the real-time operating data of the target compressor exceeds the safety threshold, the preset first control strategy (such as shutting down the target compressor or reducing its operating frequency) is executed to effectively prevent the target compressor from failing or being damaged due to overload or overheating. At the same time, by executing the preset second control strategy (such as starting a non-target compressor that meets the health threshold in the target operating power range), the load can be quickly distributed while maintaining the current power demand, ensuring the continuity and stability of the operation of the compressor system. This method comprehensively considers real-time operating data, safety thresholds, health and control strategies, which not only effectively avoids excessive loss of a single compressor, but also improves the reliability and service life of the system, while realizing dynamic optimization distribution of loads, significantly enhancing the operating safety and efficiency of the compressor system.

[0209] In some embodiments, the step of at least acquiring real-time operating data of the target compressor includes:

[0210] Real-time operating data of a target compressor is obtained and historical operating data of at least part of the compressors in the compressor system is obtained.

[0211] In this embodiment, historical operating data of some compressors in the compressor system may be obtained, and the historical operating data of some compressors may include the historical operating data of the target compressor, or may not include the historical operating data of the target compressor.

[0212] In this embodiment, the historical operation data of all compressors in the compressor system may be obtained.

[0213] After the step of acquiring the real-time operating data of the target compressor and acquiring the historical operating data of at least part of the compressors in the compressor system, the method further includes:

[0214] Step S110: inputting the real-time operation data of the target compressor and / or the historical operation data of at least part of the compressor into a preset fault risk identification model for identification, so as to determine whether there is a fault risk for the compressor in the compressor system.

[0215] In this embodiment, the real-time operation data of the target compressor may be input into a preset fault risk identification model for identification to determine whether the compressor in the compressor system has a fault risk. For example, it is determined whether the current target compressor has a fault risk.

[0216] Specifically, in this case, the preset fault risk identification model can be a rule-based monitoring model or a statistical analysis model. It can be a preset rule base, for example, if the temperature exceeds 70°C and lasts for 5 minutes, a fault warning is triggered, and if the frequency exceeds 120% of the rated frequency, it is judged that there is an overload risk.

[0217] In this embodiment, the real-time operation data of the target compressor and the historical operation data of the target compressor can be input into a preset fault risk identification model for identification to determine whether the compressor in the compressor system has a fault risk. For example, it can be determined whether the current target compressor has a fault risk.

[0218] Specifically, in this case, the preset fault risk identification model can be a time series analysis model or a hybrid model based on rules and machine learning. For example, a dynamic safety threshold can be constructed by combining real-time operation data and historical operation data. A supervised learning model (random forest or logistic regression) can be trained to predict the failure risk of the target compressor. Historical operation data can be used as features (e.g., cumulative duration, historical average temperature, historical load frequency) and real-time operation data can be used as input.

[0219] In this embodiment, the real-time operation data of the target compressor, the historical operation data of the target compressor, and the historical operation data of some non-target compressors can be input into a preset fault risk identification model for identification to determine whether the compressors in the compressor system have a fault risk. For example, it is determined whether the current target compressor and some non-target compressors have a fault risk.

[0220] Specifically, in this case, the preset fault risk identification model can be a health assessment model or a cluster analysis model. More specifically, the historical data of non-target compressors can be used to build a compressor system benchmark (for example, the average operating temperature and frequency of healthy compressors in the compressor system). If the real-time data of the target compressor deviates greatly from the compressor system benchmark, it is judged that its fault risk has increased.

[0221] More specifically, an unsupervised learning method (e.g., K-means clustering) can also be used to cluster the historical operation data of the compressor. The real-time operation data of the target compressor can be compared with the cluster to which it belongs in history. If it deviates from the characteristics of the cluster, a fault warning is triggered.

[0222] In this embodiment, the real-time operating data of the target compressor, the historical operating data of the target compressor, and the historical operating data of all non-target compressors can be input into a preset fault risk identification model for identification to determine whether there is a fault risk for the compressor in the compressor system.

[0223] Specifically, in this case, the preset fault risk identification model can be a prediction model based on deep learning. A recurrent neural network (RNN) or a long short-term memory network (LSTM) can be used to analyze the multi-dimensional time series data of the target compressor and the non-target compressor to capture potential fault risk trends. For example, large-scale training of historical operation data and real-time input of real-time operation data can be used to identify complex nonlinear fault modes.

[0224] In this embodiment, by obtaining the real-time operating data of the target compressor and the historical operating data of at least part of the compressors in the compressor system, and inputting these data into the preset fault risk identification model for identification, it is possible to achieve intelligent prediction and rapid response to the potential fault risks of the compressor system. Combining real-time operating data (such as current temperature, frequency, duration, etc.) and historical operating data (such as cumulative operating duration, historical average temperature, fault records, etc.), the fault risk identification model can comprehensively analyze the operating status of the compressor and capture the early characteristics or abnormal trends of the fault. By timely identifying and warning compressors that may have failure risks, this method can effectively reduce the impact of sudden failures on system operation, reduce the incidence of unplanned equipment downtime, and at the same time extend the overall service life of the compressor, significantly improving the reliability and safety of system operation. In addition, this intelligent risk assessment process realizes the transition from passive maintenance to active prevention, providing strong technical support for the efficient operation of the compressor system.

[0225] In a specific and possible embodiment, a compressor system is provided, the compressor system as Figure 4 As shown, it may include:

[0226] Health detection module 1 and health detection module 2: respectively used to monitor the operating status data of the two compressors (M1 and M2) in real time, which may include historical operating time, temperature, load, current, voltage, vibration, etc.

[0227] Sampling circuit: can receive monitoring data from health detection module 1 and health detection module 2, and perform signal sampling and digital processing.

[0228] Main control chip: core control unit, receives data transmitted by the sampling circuit, performs data analysis, power demand calculation and compressor system control methods, etc.

[0229] Control circuit: According to the control instructions generated by the main control chip, it controls the start and stop, frequency increase and decrease and other operating parameters of the two compressors (M1 and M2).

[0230] like Figure 5 As shown, the power range of M1 is (0, power 1). The power range of M2 is (power 2, maximum power). There is an overlapping area between the operating power ranges of M1 and M2 (power 1 to power 2). Therefore, when the power demand of the compressor system is small (less than power 1), the low-power compressor M1 can be started first to avoid the idling waste of the high-power compressor. When the power demand is in the overlapping range (power 1 to power 2), the main control chip will select a compressor with a lighter load based on the historical operating data of the two compressors (such as operating time). When the power demand is large (exceeding power 2), the two compressors will run at the same time to meet the load demand.

[0231] The control method of the compressor system may include:

[0232] When the unit is powered on, select which compressor to start based on the unit's required power. If the unit's required power is less than power 1, M1 is turned on first; if the unit's required power is greater than power 1 and less than power 2, the historical operating time of the two compressors is compared, and the compressor with the shorter operating time is turned on first; when the power is greater than power 2, both compressors are turned on at the same time to avoid only one compressor running at high load, causing wear on the compressor and reducing the service life of the compressor. During the operation of the compressor, the health detection module also detects the compressor temperature, current, voltage and other data in real time, predicts the risk of failure, and promptly warns the compressor with the risk of failure to shut down for maintenance. When the temperature of the compressor exceeds D1 during operation, in order to prevent damage to the compressor, the operating temperature of the compressor itself needs to be lowered. When only one compressor is running, turn off the running compressor and turn on the other compressor; when two compressors are running at the same time, the compressor with the higher temperature is frequency-reduced, and the compressor with the lower temperature is frequency-increased to alleviate the damage to the compressor caused by the high temperature of the compressor itself.

[0233] The health detection module collects data such as the compressor's operating time, temperature, load, current, voltage, vibration, etc. in real time, and then processes the data to remove outliers and missing values ​​and extract features such as average temperature, maximum load, current fluctuation, etc. The fault prediction algorithm is then used to evaluate the compressor's health status and determine whether the compressor has a potential fault. If a potential fault is predicted, an alarm is issued to notify maintenance personnel and the compressor's operating status is automatically adjusted, such as reducing the load or shutting down for maintenance. If there is no potential fault, data collection continues. Maintenance information is recorded during maintenance, and the compressor is restored to normal operation after maintenance is completed.

[0234] According to an embodiment of the present invention, a compressor system is provided, including: at least two compressors and a controller, wherein the controller is used to execute the above-mentioned control method of the compressor system.

[0235] According to an embodiment of the present invention, there is provided an air conditioner, characterized in that it comprises: a controller, wherein the controller is used to execute the above-mentioned control method of the compressor system or the above-mentioned compressor system.

[0236] According to an embodiment of the present invention, an electronic device is provided. Figure 6 The electronic device in this embodiment may include one or more of the following components: a processor, a network interface, a memory, a non-volatile memory, and one or more applications, wherein the one or more applications may be stored in the non-volatile memory and configured to be executed by one or more processors, and the one or more programs are configured to execute the method described in the aforementioned method embodiment.

[0237] According to an embodiment of the present invention, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a computer, the computer executes the method described in any of the above embodiments.

[0238] According to an embodiment of the present invention, a computer program product including instructions is also provided. When the instructions are executed by a computer, the computer executes a method in any one of the above embodiments.

[0239] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0240] Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments, and this embodiment will not be described in detail here.

[0241] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0242] In the above embodiments of the present application, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0243] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for controlling a compressor system, characterized in that: The compressor system comprises at least two compressors; the method comprises: After detecting a change in power demand, determining current power demand data of the compressor system and power range data of each compressor in the compressor system; Determine a target compressor based on the power demand data and the power range data; wherein the target compressor is a compressor that needs to be in a startup state; After the target compressor has been running for a certain period of time, at least real-time operation data of the target compressor is obtained; At least based on the real-time operating data of the target compressor and a preset control strategy, at least the target compressor is adjusted to balance the loads of the compressors in the compressor system while meeting the current power demand.

2. The method according to claim 1, characterized in that: The step of determining the current power demand data of the compressor system after detecting a change in the power demand comprises: In response to a temperature adjustment instruction or a power-on instruction issued by a user side, detecting the current ambient temperature; wherein the temperature adjustment instruction or the power-on instruction carries a target temperature; The power demand data is calculated based on the ambient temperature and the target temperature.

3. The method according to claim 1, characterized in that: The step of determining the power range data of each compressor in the compressor system comprises: The upper power limit value and the lower power limit value of each compressor in the compressor system are read from a preset database.

4. The method according to claim 1, characterized in that: The step of determining a target compressor based on the power demand data and the power range data comprises: Based on the power range data, a plurality of operating power intervals are generated; wherein the operating power intervals correspond to at least one compressor; determining a target operating power interval from the plurality of operating power intervals based on the power demand data; At least one compressor is selected from the target operating power interval as the target compressor and the target compressor is started.

5. The method according to claim 4, characterized in that The step of selecting at least one compressor from the target operating power range as the target compressor comprises: Obtaining historical operating data of each compressor in the target operating power range; wherein the historical operating data includes historical operating time, historical average operating temperature, historical average operating frequency or fault occurrence record; evaluating the health of each compressor in the target operating power range according to the historical operating data; A compressor that meets a health threshold in the target operating power range is selected as the target compressor.

6. The method according to claim 5, characterized in that The step of at least adjusting the target compressor based on the real-time operating data of the target compressor and a preset control strategy comprises: Based on the real-time operating data of the target compressor, the health level and the preset control strategy, adjustments are performed on the target compressor and non-target compressors in the target operating power range.

7. The method according to claim 6, characterized in that The real-time operation data includes current operation time, current operation temperature or current operation frequency; The step of adjusting the target compressor and the non-target compressors in the target operating power range based on the real-time operating data of the target compressor, the health level and the preset control strategy includes: Determine whether there is a target compressor among the target compressors whose real-time operation data exceeds a safety threshold; wherein the real-time operation data exceeding the safety threshold means that at least one type of real-time operation data exceeds a corresponding safety threshold; In the event that there is a target compressor whose real-time operating data exceeds the safety threshold, a preset first control strategy and a preset second control strategy are executed; wherein the first control strategy at least includes a control strategy for shutting down the target compressor whose real-time operating data exceeds the safety threshold or reducing the operating frequency of the target compressor whose real-time operating data exceeds the safety threshold; and the second control strategy at least includes starting a non-target compressor that meets the health threshold in the target operating power range.

8. The method according to claim 1, characterized in that The step of at least acquiring the real-time operating data of the target compressor comprises: Acquire real-time operating data of a target compressor and acquire historical operating data of at least some compressors in the compressor system; After the step of acquiring the real-time operating data of the target compressor and acquiring the historical operating data of at least part of the compressors in the compressor system, the method further includes: The real-time operating data of the target compressor and / or the historical operating data of at least part of the compressor are input into a preset fault risk identification model for identification to determine whether there is a fault risk for the compressor in the compressor system.

9. A compressor system, characterized in that: include: At least two compressors and a controller, wherein the controller is used to execute the method according to any one of claims 1-8.

10. An air conditioner, characterized in that: include: A controller, wherein the controller is used to execute the method according to any one of claims 1 to 8 or the compressor system according to claim 9.

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