A control method of a compressor system, a compressor system, and an air conditioner

Abstract

The application provides a compressor system control method, a compressor system and an air conditioner, and the compressor system control method provided by the application intelligently determines and starts a target compressor by combining current power demand data and power range data of each compressor after detecting a power demand change. Then, after the target compressor operates for a period of time, real-time operation data of the target compressor is acquired, and the target compressor is adjusted according to a preset control strategy, so that load balance among the compressors is achieved. The method dynamically optimizes the operation state of the compressors by comprehensively utilizing real-time and historical operation data, avoids overuse and frequent start-stop of some compressors, significantly reduces mechanical wear and energy consumption, and thus effectively prolongs the service life of the entire compressor system.

Description

TECHNICAL FIELD

[0001] The present application 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

[0002] A multi-compressor system is a working system composed of two or more compressors, which can provide required compression power or gas flow through the collaborative operation of multiple compressors. The multi-compressor system can be applied to the fields of refrigeration, air conditioning, industrial gas supply, etc.

[0003] In the multi-compressor system, there is a technical problem that the compressor control is not appropriate enough, thereby reducing the service life of the compressor system. SUMMARY

[0004] The present application aims to overcome the above technical deficiencies and provide a control method of a compressor system, a compressor system and an air conditioner to solve the technical problem in the related art that in the multi-compressor system, the compressor control is not appropriate enough, thereby reducing the service life of the compressor system.

[0005] To achieve the above technical purpose, the present application adopts the following technical solutions:

[0006] In a first aspect, the present application provides a control method of a compressor system, the compressor system comprising at least two compressors; the method comprising:

[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] Based on the power demand data and the power range data, determining a target compressor; wherein the target compressor is a compressor that needs to be in a start state;

[0009] After the target compressor runs for a certain period of time, obtaining at least real-time running data of the target compressor;

[0010] Based at least on the real-time running data of the target compressor and a preset control strategy, at least performing adjustment on the target compressor to balance the load of each compressor in the compressor system under the condition of meeting the current power demand.

[0011] In a second aspect, the present application provides a compressor system comprising at least two compressors and a controller, the controller being configured to execute the above method.

[0012] In a third aspect, the present application provides an air conditioner comprising a controller, the controller being configured to execute the above method or the above compressor system.

[0013] Advantages:

[0014] The compressor system control method provided by the application 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. Then, after the target compressor has been running for a period of time, real-time running data of the target compressor is obtained, and the target compressor is adjusted according to a preset control strategy, so as to realize load balancing among the compressors. The method dynamically optimizes the running state of the compressors by comprehensively utilizing real-time and historical running data, avoids overuse and frequent start-stop of some compressors, significantly reduces mechanical wear and energy consumption, and thus effectively prolongs the service life of the entire compressor system. Meanwhile, reasonable distribution of the load improves the running efficiency and stability of the compressor system, and solves the technical problem of reduced service life of the compressors due to improper control in the existing multi-compressor system. This not only improves the overall reliability of the compressor system, but also reduces the maintenance cost, and ensures efficient and persistent running of the compressor system under different running environments. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 is a flowchart of a control method of a compressor system provided by an embodiment of the application;

[0016] Figure 2 is a flowchart of a control method of a compressor system provided by an embodiment of the application;

[0017] Figure 3 is a flowchart of a control method of a compressor system provided by an embodiment of the application;

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

[0019] Figure 5 is a schematic diagram of running power ranges of two compressors provided by an embodiment of the application;

[0020] Figure 6 is a block diagram of an electronic device used by an embodiment of the application. DETAILED DESCRIPTION

[0021] In order to enable persons skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by persons skilled in the art without creative labor should fall within the scope of protection of the present application.

[0022] In the related art, multi-compressor systems are widely used in industrial refrigeration, commercial air conditioning, and other scenarios requiring efficient cooling. Such systems are typically composed of multiple compressors working in parallel or series, equipped with controllers and various sensors for monitoring the system's operating state and environmental conditions. The controller executes start-stop and load distribution of the compressors based on sensor feedback to meet different power requirements.

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

[0024] Therefore, it is necessary to provide an intelligent multi-compressor control method that can comprehensively evaluate the operating health of each compressor and the current power demand 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 load can be achieved, avoiding the overuse of some compressors, thereby prolonging the service life of the entire compressor system and improving the operating efficiency and reliability of the system.

[0025] The embodiment provides a control method of a compressor system, the compressor system comprising at least two compressors; the execution subject of the method can be a controller of the compressor system, and the method can comprise:

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

[0027] In the present embodiment, the detection of the change in power demand can be that the controller of the compressor system receives an instruction sent by the user side, such as a start instruction or a temperature adjustment instruction, which can be a temperature increase or a temperature decrease, etc.

[0028] In the embodiment, the detecting of the change of the power requirement can also be detecting of the change of an environmental parameter by the controller, so as to trigger the controller to autonomously determine the current power requirement data of the compressor system. For example, the controller monitors the environmental temperature in real time through a temperature sensor built in the compressor system. When the environmental temperature exceeds or is lower than a preset threshold, it is detected that the power requirement changes, so as to trigger the controller to determine the current power requirement data of the compressor system. The environmental parameter can also be humidity change, air pressure change, etc.

[0029] In the embodiment, the detecting of the change of the power requirement can also be detecting by the controller based on time and operation mode. For example, the compressor system can 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 operations of the compressor 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 requirement.

[0030] In the embodiment, the step of determining the current power requirement data of the compressor system can be:

[0031] Firstly, the controller detects the current environmental temperature in response to a temperature adjustment instruction or a start instruction issued by a user, wherein the temperature adjustment instruction or the start instruction carries a target temperature.

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

[0033] In the embodiment, the step of determining the current power requirement data of the compressor system can also be:

[0034] Firstly, when it is detected that the environmental temperature exceeds or is lower than a preset threshold, the controller determines that the power requirement changes.

[0035] Then, according to the change amplitude of the environmental temperature, the controller recalculates the current power requirement data.

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

[0037] It can be understood that in the embodiment, the detection of the change in power requirement is a trigger condition, and once the trigger condition is met, the controller determines the current power requirement data of the compressor system, and the process of determining the current power requirement data of the compressor system is a quantification process.

[0038] Specifically, the detection of the change in power requirement is the starting point of the entire control method and a prerequisite for the execution of subsequent steps. This detection process can be achieved in various ways. For example, it can be triggered by a user instruction. When the controller receives an instruction sent by the user side, such as a start instruction or a temperature adjustment instruction (including temperature increase or temperature decrease), it is considered that the power requirement has changed. For example, the controller monitors environmental parameters in real time through sensors (temperature sensors, humidity sensors, and air pressure sensors) built in the compressor system. When the environmental temperature, humidity, or air pressure exceeds or is lower than the preset threshold, it is determined that the power requirement has changed. Once the trigger condition of the detection of the change in power requirement is met, the controller will start the process of determining the current power requirement data. This process is a quantification process. For example, the controller first detects the current environmental temperature in response to the instruction from the user side, and calculates the required power requirement data in combination with the target temperature set by the user. For example, in the case of changes in environmental parameters, the controller will comprehensively analyze the current environmental temperature, humidity, air pressure, and other data, and quantize the current power requirement based on a preset algorithm or formula.

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

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

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

[0042] In the embodiment, the step of determining the power range data of each compressor in the compressor system can be that the controller continuously monitors the operating parameters (pressure, temperature, flow, and energy consumption) of the compressor, and determines the upper limit value and the lower limit value of the power according to the operating parameters.

[0043] In the embodiment, the power requirement data can represent the required refrigeration or heating power to meet the environmental conditions set by the user or the change in the load of the compressor system.

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

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

[0046] In the embodiment, the compressor system can be a dual-compressor parallel compressor system.

[0047] In the embodiment, the compressor system can also be a multi-compressor parallel compressor system (three compressors and above).

[0048] In the embodiment, the compressor system can also be a multi-stage serial compressor system.

[0049] In the embodiment, the compressor system can also be a parallel-serial hybrid compressor system.

[0050] In the embodiment, the compressor system can 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 start state.

[0052] In the embodiment, the determination action can be represented as selecting a compressor from the compressor system as a target compressor and starting the compressor.

[0053] It can be understood that when the selected compressor is already in a start state, it can not need to be controlled to start again, and can remain in the start state. Accordingly, when the selected compressor is in an off state, the controller sends a control instruction to it to start it.

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

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

[0056] In the present embodiment, the operation power intervals can be represented as intervals generated from the power range data (upper and lower power limits) of each compressor in the compressor system, for dividing different power requirements. These intervals reflect the working capacity of the compressor system to match and satisfy the load requirements under different power requirements. Each operation power interval can correspond to one or more compressors, depending on the power range overlap of the compressors. The purpose of such interval division is to provide a clear load distribution basis for the controller, so as to dynamically select the most suitable compressor combination for operation under different power requirements.

[0057] Therefore, it can be understood that after the operation power intervals are divided, the controller can clearly determine which compressors should bear a certain power requirement, avoid excessive concentration of load on a certain compressor, and balance the service life of the equipment. Secondly, based on the operation power interval division, the controller can quickly match the power requirement and the compressor combination, reduce complex calculation and logical judgment, and improve the response speed of the compressor system.

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

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

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

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

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

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

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

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

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

[0067] Then, the controller sorts these split points from small to large and removes duplicates:

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

[0069] Next, the controller determines each continuous power interval as a running power interval based on the split points above:

[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 determines the corresponding compressor for each running power interval in combination with the power range data of each compressor:

[0076] 1, interval (5, 10):

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

[0078] 2, interval (10, 15):

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

[0080] 3, interval (15, 20):

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

[0082] 4, interval (20, 25):

[0083] The power range (10, 25) of the compressor M2 covers this interval, and the power range (20, 35) of the compressor M3 covers this interval. Corresponding to the compressor M1 and the 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 runs for a certain period of time, at least real-time running data of the target compressor is acquired.

[0087] In the embodiment, the certain period of time can be thirty minutes, one hour, two hours, or the like.

[0088] In the embodiment, after the target compressor runs for a certain period of time, real-time running data of the target compressor can be acquired.

[0089] In the embodiment, after the target compressor runs for a certain period of time, real-time running data and historical running data of the target compressor can be acquired.

[0090] In the embodiment, after the target compressor runs for a certain period of time, real-time running data and historical running data of the target compressor can be acquired, and historical running data of a non-target compressor can also be acquired.

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

[0092] In the embodiment, the real-time running data can include a current running duration, a current running temperature, or a current running frequency.

[0093] Specifically, the current running duration can represent a duration that the compressor has been continuously running since the compressor is started, for judging a load condition and whether adjustment (switching or frequency reduction) is needed.

[0094] The current running temperature can represent a current working temperature of the compressor. If the temperature exceeds a threshold value, frequency reduction or switching operation can be performed.

[0095] The current running frequency can represent a current running load of the target compressor, and through the running frequency, it can be judged whether the target compressor is in an efficient running state.

[0096] In the embodiment, the historical running data can include a historical running duration, a historical average running temperature, a historical average running frequency, or a fault occurrence record.

[0097] Specifically, historical runtime can represent cumulative runtime, reflect the compressor's lifespan and wear level, and provide a basis for health assessment.

[0098] Historical average operating temperature represents the long-term average operating temperature and can be used to evaluate the compressor's heat dissipation performance and whether it has been operating at high temperatures for an extended period.

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

[0100] The fault log can record the compressor's historical faults, including fault type, frequency of occurrence, etc.

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

[0102] In this embodiment, the controller can send commands to the preset monitoring sensors in the compressor system to obtain the aforementioned real-time operating data.

[0103] Specifically, a time recording module or operating status sensor can be pre-installed in the compressor system to record the compressor's continuous operating time from startup to the present. A temperature sensor (thermocouple or thermistor) can be pre-installed in the compressor system to monitor the temperature of key compressor components (compressor casing, discharge port, suction port) in real time. A frequency detection module can be pre-installed in the compressor system to detect the compressor motor's operating frequency, reflecting the current operating load and regulation status. This frequency detection module can be a frequency sensing module built into the frequency converter, a Hall effect sensor, or a current sensor, and the frequency can be indirectly calculated by monitoring the motor's operating current.

[0104] The controller can trigger the sensors to acquire data in real time by sending request signals to them. The sensors then feed back the acquired operational data to the controller in signal form. The controller digitizes the acquired analog signals (e.g., using an analog-to-digital converter). This 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 historical operation data from a preset database.

[0106] Specifically, the database can be a local database. Specifically, the database can be stored in the 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 can be a cloud database, in other words, the historical running data can be stored on a remote server or a cloud platform, and communicated with the controller through a network interface.

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

[0109] In the embodiment, the controller can directly read the data in the memory through an embedded interface (such as SPI, I 2C). For example, the controller can query the historical running data according to the compressor ID and the time stamp index.

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

[0111] It can be understood that after the target compressor runs for a period of time, the controller collects the real-time running data of the target compressor through the sensors built in the compressor system, and further acquires the historical running data of the target compressor and the historical running data of other non-started (non-target compressor) compressors according to specific requirements. The acquisition of these data can provide accurate running state information and health evaluation basis for the subsequent execution of the control strategy.

[0112] Step S18: at least based on the real-time running data of the target compressor and the preset control strategy, at least adjusting the target compressor to balance the load of each compressor in the compressor system under the condition of meeting the current power demand.

[0113] In the embodiment, the controller can adjust the target compressor based on the real-time running data of the target compressor and the preset control strategy to balance the load of each compressor in the compressor system under the condition of meeting the current power demand.

[0114] Specifically, the controller can compare the running state of the target compressor (for example, the current temperature, frequency, time length, etc.) with the preset control strategy to determine whether the target compressor needs to be adjusted.

[0115] More specifically, when the operating temperature of a certain target compressor exceeds the 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, to balance the load of each compressor in the compressor system while meeting the current power demand.

[0116] More specifically, in this case, the preset control strategy can be a rule base. The rule base can include a plurality of pre-defined rules, and the rules set safety thresholds based on operating parameters of the compressor. For example:

[0117] Safety threshold of temperature: if the operating temperature > 80℃, trigger frequency reduction.

[0118] Safety threshold of frequency: if the operating frequency is close to the maximum frequency 90%, trigger a warning or limit.

[0119] Safety threshold of operating time: if the continuous operating time > 2 hours, trigger frequency reduction or perform cooling measures.

[0120] The rule base can be defined in the form of "condition-action", and a rule engine can be used for judgment.

[0121] In the embodiment, the controller can also perform adjustment on both the target compressor and the non-target compressor based on real-time operating data of the target compressor and the preset control strategy, to balance the load of each compressor in the compressor system while meeting the current power demand.

[0122] Specifically, the controller can compare the operating state of the target compressor (e.g., current temperature, frequency, time length, etc.) with the preset control strategy, and perform adjustment on both the target compressor and the non-target compressor.

[0123] More specifically, for example, when the operating temperature of all target compressors exceeds the safety threshold, the controller can select to start the non-target compressor, to balance the load of each compressor in the compressor system while meeting the current power demand.

[0124] More specifically, in this case, the preset control strategy can be to add a priority decision algorithm to the rule base described above, to determine the order of target compressor adjustment and non-target compressor activation. For example, the priority decision algorithm can assign start-stop weights to the target and non-target compressors. The target compressor is adjusted first, and the non-target compressor is activated as needed.

[0125] In the embodiment, the controller can also perform adjustment on the target compressors based on real-time operation data, historical operation data and preset control strategy of the target compressors, so as to balance the load of each compressor in the compressor system while meeting the current power demand.

[0126] Specifically, the controller can not only monitor the operation state (e.g., current temperature, frequency, time length, etc.) of the target compressors in real time, but also extract historical operation data of the target compressors and compare the historical operation data with the preset control strategy to perform adjustment on the target compressors.

[0127] More specifically, for example, when the operation temperature of a certain target compressor exceeds a safety threshold, the controller can reduce the operation frequency of the target compressor or even shut down the target compressor. Accordingly, the controller can evaluate the health degree of the target compressors whose operation temperature does not exceed the safety threshold based on the historical operation data, and then increase the operation frequency of the target compressor whose operation temperature does not exceed the safety threshold and whose health degree is the highest (or meets a health degree threshold), so as to balance the load of each compressor in the compressor system while meeting the current power demand.

[0128] In the embodiment, the controller can also perform adjustment on the target compressors and non-target compressors based on real-time operation data, historical operation data and preset control strategy of the target compressors, so as 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 operation state (e.g., current temperature, frequency, time length, etc.) of the target compressors in real time, but also extract historical operation data of the target compressors and compare the historical operation data with the preset control strategy to perform adjustment on the target compressors and non-target compressors.

[0130] More specifically, for example, when the operation temperature of a certain target compressor exceeds a safety threshold, the controller can reduce the operation frequency of the target compressor or even shut down the target compressor. Accordingly, the controller can evaluate the health degree of the target compressors whose operation temperature does not exceed the safety threshold based on the historical operation data, and then increase the operation frequency of the target compressor whose operation temperature does not exceed the safety threshold and whose health degree is the highest (or meets a health degree threshold), and when the operation frequency of the corresponding target compressor is increased, if the current power demand still cannot be met, the controller can continue to start one or more non-target compressors, so as to balance the load of each compressor in the compressor system while meeting the current power demand.

[0131] In the embodiment, the controller can also perform adjustment on the target compressor and the non-target compressor based on real-time operation data, historical operation data of the target compressor, historical operation 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 operation state of the target compressor in real time (for example, the current temperature, frequency, time length and the like), but also extract historical operation data of the target compressor and historical operation data of the non-target compressor, and compare them with the preset control strategy to perform adjustment on the target compressor and the non-target compressor.

[0133] More specifically, for example, when the operation temperature of a certain target compressor exceeds the safety threshold, the controller can reduce the operation frequency of the target compressor or even stop it. Accordingly, the controller evaluates the health degree of the target compressor whose operation temperature does not exceed the safety threshold based on the historical operation data, and then increases the operation frequency of the target compressor whose operation temperature does not exceed the safety threshold and whose health degree is the highest (or meets a health degree threshold). When the operation frequency of the corresponding target compressor is increased, if the current power demand cannot still be met, the controller can evaluate the health degree of the non-target compressor based on the historical operation data of the non-target compressor, and then start the non-target compressor whose health degree is the highest (or meets the health degree threshold) to balance the load of each compressor in the compressor system while meeting the current power demand.

[0134] The compressor system control method provided by the embodiment intelligently determines and starts the target compressor by combining the current power demand data and the power range data of each compressor after detecting the change in power demand. Then, after the target compressor operates for a period of time, real-time operation data of the target compressor is obtained, and adjustment is made on the target compressor according to the preset control strategy to balance the load among the compressors. This method dynamically optimizes the operation state of the compressors by comprehensively utilizing real-time and historical operation data, avoids overuse and frequent start-stop of some compressors, significantly reduces mechanical wear and energy consumption, and thus effectively prolongs the service life of the entire compressor system. At the same time, reasonable distribution of the load improves the operation efficiency and stability of the system, and solves the technical problem of reduced service life of the compressors caused by improper control in the existing multi-compressor system. This not only improves the overall reliability of the compressor system, but also reduces the maintenance cost, and ensures efficient and persistent operation of the compressor system under different operating environments.

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

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

[0137] Step S124: Based on the ambient temperature and the target temperature, calculating the power requirement data.

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

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

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

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

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

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

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

[0145] In the present embodiment, the database can be a cloud database, in other words, the historical operation data can be stored on a remote server or a cloud platform, and communicates with the controller through a network interface.

[0146] In the present embodiment, the database can be a relational database or a non-relational database.

[0147] In this embodiment, by reading the upper and lower power limit values of each compressor in the compressor system from the pre-set 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 caused by complex real-time calculations or manual input. By pre-storing the upper and lower power limit values in the database, the compressor system can quickly match the corresponding power range data according to different models or operating states, thereby improving the real-time and accuracy of the control strategy. In addition, this implementation provides stable basic data for dynamic load distribution of multi-compressor systems, ensuring that the system can reasonably distribute the load under different power demands, further improving the operating efficiency and prolonging the service life of the compressor.

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

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

[0150] In this embodiment, the operating power interval can be represented as an interval generated by the power range data (upper and lower power limit values) of each compressor in the compressor system, used to divide different power demands. These intervals reflect the working capacity of the compressor system that can 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 power range overlap of the compressors. The purpose of this interval division is to provide a clear load distribution basis for the controller, so that the most suitable compressor combination can be dynamically selected for operation under different power demands.

[0151] Therefore, it can be understood that after the operating power interval is divided, the controller can clearly determine which compressors should bear a certain power demand, avoiding excessive load concentration on a single compressor and balancing 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, reducing complex calculations and logical judgments, and improving the response speed of the compressor system.

[0152] In this embodiment, the controller can generate a plurality of operating power intervals based on the power range data in the following ways:

[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 controller de-duplicates the set of controller power upper and lower bounds and sorts them in ascending order to generate an ordered set of split points.

[0155] Next, the controller constructs continuous power intervals based on the ordered set of split points and iterates through each of the operational power intervals to determine which compressors have power ranges that overlap with the interval.

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

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

[0158] In this embodiment, the controller can iterate through all operational power intervals according to the current power demand data to determine whether the current power demand data falls within the corresponding operational power interval, thereby determining the target operational power interval.

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

[0160] In this embodiment, one compressor can be randomly selected from the target operational power interval as the target compressor and started.

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

[0162] In this embodiment, by generating a plurality of operational power intervals based on the power range data and determining a target operational power interval in combination with the power demand data, the most suitable compressor is selected and started as the target compressor from the target operational power interval, effectively achieving the precision and intelligence of the compressor system load distribution. This method can ensure that the operation of the compressor is more in line with the actual power demand, avoiding the situation of single compressor overload operation or resource waste. In addition, the division of operational power intervals enables the system to quickly match the power demand and compressor capacity, optimizes the compressor, reduces the redundancy of operational power, improves the energy utilization efficiency, reduces the overall energy consumption, balances the load of each compressor, and prolongs the service life of the equipment.

[0163] In some embodiments, the step of selecting at least one compressor from the target operational power interval as the target compressor comprises:

[0164] Step S1462: obtaining historical operation data of each compressor in the target operation power range; wherein, the historical operation data comprises historical operation duration, historical average operation temperature, historical average operation frequency or failure occurrence record.

[0165] Step S1464: evaluating the health degree of each compressor in the target operation power range according to the historical operation data.

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

[0167] In one specific embodiment, the health degree evaluation model can be a weighted scoring model for quantifying the influence of different historical data on the health degree, such as:

[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 degree score, the health degree score ranges from 0 to 100, and the higher the value, the healthier the state of the compressor. If the health degree score H < H 阈值 ( e.g., H 阈值 = 50), the compressor is considered unsuitable for operation. w1-w4 are weight parameters, each weight reflects the influence of the corresponding index on the health degree, and the weight value satisfies:

[0170]

[0171] S 运行 represents the quantified score of the historical operation duration, S 温度 represents the quantified score of the historical average operation temperature, S 频率 represents the quantified score of the historical average operation frequency, S 故障 represents the quantified score of the failure occurrence record.

[0172] In one specific embodiment, the quantified score of the historical runtime length can be the impact of the runtime length on the health. It can be appreciated that the runtime length reflects the cumulative usage of the compressor, and the longer the runtime, the higher the wear and tear of the internal components, and the lower the health. Each compressor has a designed lifetime value, for example, 10,000 hours, which indicates that the equipment is relatively reliable within this time frame. The controller will record the cumulative runtime of each compressor. The impact on the health can be reflected by the proportion of the cumulative runtime to the designed lifetime: when the cumulative runtime approaches the upper limit of the designed lifetime, the negative impact on the health will gradually increase. For example, if the runtime length is 50% of the designed lifetime, the impact on the health is moderate; if the runtime length is 80% of the designed lifetime, the impact on the health is significant. The impact of the runtime length is a gradual process, i.e., the health gradually decreases as the cumulative runtime increases.

[0173] In one specific embodiment, the quantified score of the historical average operating temperature can be the impact of the average temperature on the health. It can be appreciated that the impact of the temperature on the health is related to the operating thermal load of the equipment. The performance of the internal components (electrical insulation materials, lubricating oil, etc.) of the equipment will be significantly affected under long-term high-temperature operation, 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 can be the upper limit value. The controller will calculate the long-term average operating temperature of the equipment and compare 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 a proportional relationship based on the extent of the temperature exceeding (i.e., the difference between the average temperature and the normal operating temperature): if the temperature only slightly exceeds the normal range (e.g., 65°C exceeds 60°C), the impact on the health is small. If the temperature significantly exceeds the normal range (e.g., 75°C exceeds 60°C), the impact on the health is significant. The impact of the temperature is gradually increasing, and the controller will accumulate each degree of temperature exceeding the temperature range as a decrease in health.

[0174] In one specific embodiment, the quantified score of the historical average operating frequency can be the impact of average frequency on healthiness. It can be appreciated that frequency is a direct reflection of the workload of the equipment, the higher the operating frequency, the heavier the load, and the greater the impact on the healthiness of the equipment. Long-term high-frequency operation can accelerate the wear and tear of internal components, such as bearings, motor speed, and vibrating parts. Each compressor has a rated operating frequency, for example, 50 Hz, which is the recommended operating frequency of the equipment under 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 healthiness is evaluated: when the average frequency is close to the rated operating frequency, the impact on healthiness is small. When the average frequency is higher than the rated operating frequency, the negative impact on healthiness increases gradually. The higher the frequency exceeds, the more the healthiness decreases. The controller can use a proportional relationship to convert the proportion of the frequency that exceeds to the decrease in healthiness: for example, 10% of the rated frequency is slightly affected. 20% or more of the rated frequency has a significant impact on healthiness.

[0175] In one specific embodiment, the quantified score of the fault occurrence record can be the impact of fault occurrence record on healthiness. It can be appreciated that fault record is an important evaluation dimension of equipment healthiness, because the frequency and severity of faults directly reflect the current state and potential reliability problems of the equipment. The fault record of each compressor can include the number of faults and the type of faults (e.g., minor faults, major faults). The increase in the number of faults will reduce the healthiness of the equipment, and the controller can evaluate it according to the accumulation of the number of faults: the more the number of faults, the more obvious the decrease in healthiness. The severity of the fault type will further aggravate the impact on healthiness, and the impact of minor faults (e.g., short-time overload alarm) on healthiness is small, but frequent occurrence will have a cumulative impact. The impact of major faults (e.g., shutdown due to high temperature) on healthiness is very significant. The controller can assign impact weights to different types of faults, for example, each occurrence of a minor fault has a small value of impact on healthiness. Each occurrence of a major fault has a large value of impact on healthiness. Finally, the controller can calculate the overall impact of faults on healthiness according to the combined impact of the number and type of faults.

[0176] In the present embodiment, the healthiness evaluation model can be a machine learning model, and it can be appreciated that the healthiness evaluation model can be constructed using machine learning techniques, and the healthiness evaluation model is trained according to historical operation data to predict the healthiness of the compressor. Specifically, the healthiness evaluation model can be a regression model (e.g., a linear regression model or a random forest regression model), and the healthiness evaluation model can also be a classification model (e.g., a logistic regression model, a support vector machine model).

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

[0178] In the embodiment, the health degree evaluation model can also be a physical model based on state estimation. For example, a health degree model can be established by using the physical characteristics of the compressor, and the health state of the device is estimated according to the operation data.

[0179] Step S1466: Selecting the compressor in the target operation power range that meets the health degree threshold as the target compressor.

[0180] In the embodiment, the health degree threshold can be a fixed value, which is used to uniformly standardize the judgment of the operation state of the compressor. For example, the health degree threshold is set to 50 (full score is 100). Only the compressor with a health degree score higher than or equal to 50 can be selected as the target compressor.

[0181] In the embodiment, the health degree threshold can also be a threshold that can be dynamically adjusted. In other words, the health degree threshold can be dynamically adjusted according to the operation demand of the compressor system or the environmental state. For example, during the peak load period (when the demand for the compressor is high), the threshold can be appropriately reduced, for example, from 50 to 40, to increase the number of available compressors and meet the power demand. During the trough period of the load (when the demand for the compressor is low), the threshold can be increased, for example, from 50 to 60, to preferentially protect the compressors with better health states.

[0182] In the embodiment, by obtaining the historical operation data (historical operation time, average operation temperature, average operation frequency and fault occurrence record) of each compressor in the target operation power range, the controller can comprehensively evaluate the health degree of each compressor, and select the compressor that meets the health degree threshold as the target compressor for starting operation based on the health degree. This method fully utilizes the historical operation data of the compressor, ensures accurate judgment of the state of the compressor, and preferentially selects the compressor with a higher health degree to operate, thereby effectively balancing the service life of the compressor and avoiding the participation of the compressor with a lower health degree or a risk of failure in high-load operation. The method not only improves the reliability and safety of the operation of the compressor system, but also realizes the reasonable scheduling and optimized utilization of the compressor resources, further prolongs the service life of the entire compressor system, and reduces the maintenance cost.

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

[0184] Step S182: based on the real-time operation data of the target compressor, the health degree, and the preset control strategy, performing adjustment on the target compressor and the non-target compressor in the target operation power range.

[0185] In the present embodiment, the controller can first determine whether the real-time operation data (e.g., current operation duration, current operation temperature, or current operation frequency) of the target compressor exceeds the corresponding safety threshold.

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

[0187] If there is at least one target compressor that exceeds the corresponding safety threshold. It can be understood that the exceeding of the corresponding safety threshold can mean that at least one of the above-mentioned multiple types of real-time operation 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 can be:

[0189] turning off the target compressor whose real-time operation data exceeds the safety threshold;

[0190] reducing the operation frequency of the target compressor whose real-time operation data exceeds the safety threshold.

[0191] The second control strategy can be:

[0192] starting the non-target compressor in the target operation power range that meets the health degree threshold.

[0193] Further, in a specific embodiment, the first control strategy can further include:

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

[0195] switching the target compressor whose real-time operation data exceeds the safety threshold to a "rest mode" and temporarily stopping for a period of time, and restarting after the equipment is cooled or the load is recovered.

[0196] starting the cooling system (e.g., fan or water cooling device) of the target compressor whose real-time operation data exceeds the safety threshold to assist in cooling to delay shutdown.

[0197] Further, in a specific embodiment, the second control strategy can further include:

[0198] After starting the non-target compressor satisfying the health threshold in the target running power interval, the running frequency of the non-target compressor is dynamically increased to quickly compensate for the power demand;

[0199] If the health of the non-target compressor satisfies 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 started gradually, that is, it is first run in a low power mode, and whether the real-time running data is stable is observed, and if it is stable, the power is gradually increased;

[0200] If there are multiple non-target compressors with high health in the target running power interval, multiple compressors can be started at the same time and the load can be distributed among them to avoid high load operation of a single compressor;

[0201] When selecting a non-target compressor, the compressor with the highest health can be selected first to bear high load operation, and the compressor with slightly lower health can bear lower load.

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

[0203] In the embodiment, by adjusting the target compressor and the non-target compressor in the target running power interval based on the real-time running data of the target compressor, the health, and the preset control strategy, efficient load distribution and dynamic optimization of the compressor system can be achieved. This method not only finely regulates the target compressor (for example, reduces the frequency, stops, or increases the frequency), but also considers the health state and real-time demand of the non-target compressor, and starts or adjusts the frequency of the non-target compressor when necessary. In this way, the system can meet the current power demand while further balancing the load of each compressor, avoiding overloading of a single compressor, and prolonging the overall life of the equipment. In addition, this method can intelligently adjust the running strategy by considering real-time running data and historical health, reduce the risk of failure, improve the reliability and stability of the compressor system, and maximize energy utilization efficiency.

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

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

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

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

[0208] In the embodiment, by monitoring the running data (including the running time, the running temperature, the running frequency, etc.) of the target compressor in real time and determining whether there is a case of exceeding the safety threshold, the controller can take adjustment measures proactively before the device reaches a dangerous running state. If the real-time running data of the target compressor exceeds the safety threshold, the preset first control strategy (such as shutting down the target compressor or reducing the running frequency thereof) is executed, which effectively prevents the target compressor from malfunctioning or being damaged due to overload or overheat running. At the same time, by executing the preset second control strategy (such as starting a non-target compressor in the target running power interval that meets the health threshold), the load can be quickly distributed on the premise of maintaining the current power demand, thereby guaranteeing the running continuity and stability of the compressor system. This method comprehensively considers the real-time running data, the safety threshold, the health degree, and the control strategy, which not only effectively avoids the excessive wear of a single compressor, but also improves the reliability and service life of the system, while realizing the dynamic optimization distribution of the load and significantly enhancing the running safety and efficiency of the compressor system.

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

[0210] Acquiring the real-time running data of the target compressor and acquiring the historical running data of at least part of the compressors in the compressor system.

[0211] In the embodiment, the historical running data of part of the compressors in the compressor system can be acquired, which can include or not include the historical running data of the target compressor.

[0212] In the embodiment, the historical running data of all the compressors in the compressor system can be acquired.

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

[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 compressors into a preset fault risk identification model for identification, to determine whether the compressors in the compressor system have a fault risk.

[0215] In this embodiment, the real-time operation data of the target compressor 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 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 library, 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 determined 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 compressors in the compressor system have a fault risk. For example, it is 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 in combination with real-time operation data and historical operation data. A supervised learning model (random forest or logistic regression) can be trained to predict the fault 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 as input.

[0219] In this embodiment, the real-time operation data of the target compressor and the historical operation data of the target compressor and part of the historical operation data of 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 part of the 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 clustering analysis model. More specifically, a compressor system benchmark (e.g., the average operating temperature and frequency of healthy compressors in the compressor system) can be constructed using historical data of non-target compressors. If the real-time data of the target compressor deviates significantly from the compressor system benchmark value, it is determined that the fault risk of the target compressor is increased.

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

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

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

[0224] In the present 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, intelligent prediction and rapid response to potential fault risks of the compressor system can be achieved. Combined with 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 state of the compressors, capture early features or abnormal trends of faults. By timely identifying and warning the compressors that may have a fault risk, this method can effectively reduce the impact of sudden failures on system operation, reduce the incidence of unplanned equipment downtime, extend the overall service life of the compressors, and significantly improve 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 efficient operation of the compressor system.

[0225] In one specific and possible embodiment, a compressor system is provided, which can include, as shown in Figure 4 ​

[0226] Health detection module 1 and health detection module 2: used to monitor the running state data of two compressors (M1 and M2) in real time, which can include historical running 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 digitization processing.

[0228] Master chip: core control unit, receiving data transmitted by sampling circuit, performing data analysis, power demand calculation and compressor system control method, etc.

[0229] Control circuit: according to the control instruction generated by the master chip, control the start-stop, frequency rise and fall and other operating parameters of the two compressors (M1 and M2).

[0230] As shown in Figure 5 , the power range of M1 is (0, power 1). The power range of M2 is (power 2, maximum power). There is an overlapping area (power 1 to power 2) between the running power interval of M1 and M2. Therefore, when the power demand of the compressor system is small (less than power 1), the small-power compressor M1 can be started preferentially to avoid the waste of idling of the large-power compressor. When the power demand is in the overlapping interval (power 1 to power 2), the master chip will select a compressor with lighter load according to the historical running data (such as running time) of the two compressors. When the power demand is large (more than power 2), the two compressors will run simultaneously to meet the load demand.

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

[0232] When the unit is powered on, select which compressor to start according to the unit demand power. If the unit demand power is less than power 1, start M1 preferentially; if the unit demand power is greater than power 1 and less than power 2, compare the historical running time of the two compressors, and start the compressor with shorter time preferentially; when the power is greater than power 2, start both compressors to avoid high-load operation of only one compressor, which will cause wear and tear to the compressor and reduce the service life of the compressor. In the process of compressor operation, the health detection module also detects data such as compressor temperature, current and voltage in real time, predicts fault risk, and timely warns the compressor with fault risk to stop and maintain. When the temperature of the compressor exceeds D1 during operation, in order to prevent the compressor from being damaged, the working temperature of the compressor needs to be reduced. When only one compressor is running, the running compressor is turned off and the other compressor is started; when both compressors are running, the compressor with higher temperature is frequency-reduced and the compressor with slightly lower temperature is frequency-increased to relieve the damage to the compressor caused by the excessively high temperature of the compressor itself.

[0233] The health detection module collects real-time data of the compressor, such as running time, temperature, load, current, voltage, vibration, etc., and then processes the data, removes abnormal values and missing values, and extracts features such as average temperature, maximum load, current fluctuation, etc. Then use the fault prediction algorithm to evaluate the health status of the compressor, and judge whether the compressor has potential failure. If potential failure is predicted, an alarm is sent to the maintenance personnel, and the working state of the compressor is automatically adjusted, such as reducing the load, stopping for maintenance. If there is no potential failure, continue to collect data. Record maintenance information during maintenance, and after maintenance is completed, the compressor returns to normal operation.

[0234] According to the embodiment of the present application, a compressor system is provided, comprising at least two compressors and a controller, wherein the controller is configured to execute the control method of the compressor system.

[0235] According to the embodiment of the present application, an air conditioner is provided, comprising a controller, wherein the controller is configured to execute the control method of the compressor system or the compressor system.

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

[0237] According to the embodiment of the present application, a computer readable storage medium is provided, which stores a computer program, and the computer program is executed by a computer to make the computer execute the method described in any of the foregoing embodiments.

[0238] According to the embodiment of the present application, a computer program product containing instructions is also provided, and the instructions are executed by a computer to make the computer execute a method in any of the foregoing embodiments.

[0239] It should be noted that the terms "first", "second" and the like in the description and in the claims of the present application are used for distinguishing between similar elements and not necessarily for describing a sequential or chronological order. It is to be understood that the use of these terms is interchangeable under appropriate circumstances such that the descriptive

[0240] Optionally, the specific examples in the embodiments can refer to the examples described in the above embodiments, and the embodiments will not be described here again.

[0241] The serial numbers of the above embodiments of the present application are only for description, not representing the advantages and disadvantages of the embodiments.

[0242] In the above embodiments of the present application, the description of each embodiment has its own focus, and the parts not described in detail in a certain embodiment can be referred to the relevant description of other embodiments.

[0243] The above only describes the preferred embodiments of the present application, and it should be pointed out that for ordinary skilled in the art, without departing from the principles of the present application, a number of improvements and refinements can be made, which should be considered as the protection scope of the present application.

Claims

1. A control method of a compressor system, characterized by, 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; Based on the power demand data and the power range data, determining a target compressor; wherein the target compressor is a compressor that needs to be in a start state; After the target compressor runs for a certain period of time, obtaining at least real-time running data of the target compressor; Based on at least the real-time running data of the target compressor and a preset control strategy, at least performing adjustment on the target compressor to balance the load of each compressor in the compressor system while meeting the current power demand.

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

3. The method of claim 1, wherein, The step of determining the power range data of each compressor in the compressor system comprises: Reading the power upper limit value and the power lower limit value of each compressor in the compressor system from a preset database.

4. The method of claim 1, wherein, The step of determining the target compressor based on the power demand data and the power range data comprises: Based on the power range data, generating a plurality of running power intervals; wherein the running power interval corresponds to at least one compressor; Based on the power demand data, determining a target running power interval from the plurality of running power intervals; Selecting at least one compressor from the target running power interval as the target compressor and starting the target compressor.

5. The method of claim 4, wherein, The step of selecting at least one compressor from the target running power interval as the target compressor comprises: Obtaining historical running data of each compressor in the target running power interval; wherein the historical running data includes historical running time, historical average running temperature, historical average running frequency, or fault occurrence record; According to the historical running data, evaluating the health degree of each compressor in the target running power interval; Selecting a compressor in the target running power interval that meets a health degree threshold as the target compressor.

6. The method of claim 5, wherein, The step of performing adjustment on at least the target compressor based on at least the real-time running data of the target compressor and a preset control strategy comprises: Based on the real-time running data of the target compressor, the health degree, and the preset control strategy, performing adjustment on both the target compressor and non-target compressors in the target running power interval.

7. The method of claim 6, wherein, The real-time running data includes current running time, current running temperature, or current running frequency. The step of performing adjustment on both the target compressor and non-target compressors in the target running power interval based on the real-time running data of the target compressor, the health degree, and the preset control strategy comprises: determining whether there is a target compressor whose real-time running data exceeds a safety threshold in the target compressor; wherein the real-time running data exceeding the safety threshold means that at least one type of real-time running data exceeds a corresponding safety threshold; in the case that there is a target compressor whose real-time running data exceeds a safety threshold, executing a preset first control strategy and a preset second control strategy; wherein the first control strategy at least includes a control strategy of shutting down or reducing the running frequency of the target compressor whose real-time running data exceeds the safety threshold; and the second control strategy at least includes a control strategy of starting a non-target compressor in the target running power interval which satisfies a health degree threshold.

8. The method of claim 1, wherein, The step of acquiring the real-time running data of the target compressor at least includes: acquiring the real-time running data of the target compressor and acquiring the historical running data of at least part of the compressors in the compressor system; after the step of acquiring the real-time running data of the target compressor and acquiring the historical running data of at least part of the compressors in the compressor system, the method further includes: inputting the real-time running data of the target compressor and / or the historical running data of at least part of the compressors into a preset fault risk identification model for identification, and determining whether there is a fault risk in the compressors in the compressor system.

9. A compressor system characterized by, comprise: at least two compressors and a controller, wherein the controller is configured to execute the method according to any one of claims 1-8.

10. An air conditioner characterized by comprising: comprise: a controller configured to execute the method according to any one of claims 1-8 or the compressor system according to claim 9.

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