Compressor performance monitoring method and device, compressor and medium

Abstract

The invention discloses a compressor performance monitoring method and device, a compressor and a medium. The method comprises the steps that current operation data of a compressor are obtained; judging whether the compressor currently meets a first preset condition or not based on the current operation data; if yes, performance monitoring is conducted on the compressor according to the current operation data and a preset model, and a performance monitoring result corresponding to the compressor is obtained; the preset model is obtained by updating the historical effective operation data of the compressor in the normal state, so that compressor performance monitoring can be efficiently and accurately realized through the preset model and the current operation data of the compressor without external storage equipment or operation resources; and the historical effective operation data of the compressor in the normal state can be fully utilized to update the preset model, so that the parameters of the preset model can better fit the actual operation condition of the compressor to be monitored, and the model precision and the accuracy of compressor performance monitoring are improved.

Description

Technical Field

[0001] The present application relates to the field of HVAC technology, and in particular to a compressor performance monitoring method, device, compressor, and medium. Background Art

[0002] A centrifugal compressor is a speed-dependent compressor and a core component of a chiller. Typically, the unit adjusts the refrigerant flow rate in the system by adjusting the compressor speed and guide vane opening, thereby regulating cooling capacity. The compressor's operating performance (flow rate, efficiency, energy consumption, etc.) is crucial to the unit's operational stability and energy efficiency. Compressor failure is common in centrifugal chillers, and in severe cases, it can easily cause shutdowns, severely impacting the user experience and increasing repair costs.

[0003] In the related art, either an external computer, server or other equipment or computing resources are required to monitor the compressor performance, or a general model or data pre-stored in the server is directly used to monitor the compressor performance. Summary of the Invention

[0004] The embodiments of the present application provide a compressor performance monitoring method, device, compressor, and medium, which can not only efficiently and accurately monitor compressor performance through a preset model and the current operating data of the compressor, but also fully utilize the historical effective operating data of the compressor under normal conditions to update the preset model, so that the parameters of the preset model can better fit the actual operating conditions of the compressor to be monitored, thereby improving the model precision and the accuracy of compressor performance monitoring. The above technical solutions are as follows:

[0005] In a first aspect, an embodiment of the present application provides a compressor performance monitoring method, the method comprising:

[0006] Get the current operating data of the compressor;

[0007] Determining whether the compressor currently meets a first preset condition based on the current operating data;

[0008] If so, the performance of the compressor is monitored according to the current operating data and the preset model to obtain the corresponding performance monitoring result of the compressor; the preset model is updated based on the historical effective operating data of the compressor in a normal state.

[0009] In a possible implementation, the performance monitoring of the compressor is performed based on the current operating data and the preset model to obtain a performance monitoring result corresponding to the compressor, including:

[0010] Input the current operating data into a preset model to output the current ideal power of the compressor;

[0011] If the absolute deviation between the current ideal power and the current actual power of the compressor is greater than a threshold, it is determined that the performance monitoring result corresponding to the compressor is that the compressor is in an abnormal state.

[0012] In a possible implementation, the determining whether the compressor currently meets the first preset condition based on the current operating data includes:

[0013] Determine whether there is valid data in the current data grid area corresponding to the current operating data; the valid data is the average value of each historical operating parameter in the historical valid operating data corresponding to the current data grid area;

[0014] Determining whether the current cumulative operating time of the compressor is greater than a preset operating time, and / or determining whether the number of target data grid areas currently corresponding to the compressor is greater than a first preset number; the target data grid area is used to represent a data grid area storing the valid data;

[0015] Among them, the above-mentioned compressor corresponds to multiple data grid areas, and the above-mentioned multiple data grid areas are divided according to corresponding preset intervals based on the preset value ranges corresponding to the various target operating parameters of the above-mentioned compressor; the above-mentioned target operating parameters include speed, guide vane opening and pressure ratio; the current speed in the above-mentioned current operating data is within the speed range corresponding to the above-mentioned current data grid area, the current guide vane opening in the above-mentioned current operating data is within the guide vane opening range corresponding to the above-mentioned current data grid area, and the current pressure ratio in the above-mentioned current operating data is within the pressure ratio range corresponding to the above-mentioned current data grid area.

[0016] In a possible implementation, after determining whether the compressor currently meets the first preset condition based on the current operating data, the method further includes:

[0017] If not, then, if the current operating data is valid operating data, calibrating or updating the preset model based on the current operating data; the valid operating data is used to represent operating data of the compressor in a normal state and a stable state that meets the second preset condition;

[0018] Among them, the above-mentioned operating data include the following operating parameters: intake pressure, exhaust pressure, intake temperature, exhaust temperature, speed, guide vane opening, and actual power; the above-mentioned second preset condition includes that each operating parameter in the above-mentioned operating data does not exceed the corresponding preset parameter limit and / or the parameter dispersion coefficient corresponding to the above-mentioned operating data is less than the discrete threshold.

[0019] In a possible implementation, the calibrating or updating the preset model based on the current operating data includes:

[0020] If the preset model is the first model, the first parameter of the first model is calibrated and tested online based on the current operating data; the first model is trained based on the test data of the compressor;

[0021] If the preset model is the second model, the second parameter of the second model is updated, trained and tested based on the current operating data; the second model is trained based on the historical effective operating data of the compressor.

[0022] In a possible implementation, after calibrating or updating the preset model based on the current operating data, the method further includes:

[0023] The average value of each current operating parameter in the current operating data is saved as valid data in the current data grid area corresponding to the current operating data.

[0024] In a possible implementation, when the amount of the test data is greater than or equal to a second preset amount, the preset model is the first model; when the amount of the test data is less than the second preset amount, the preset model is the second model.

[0025] In one possible implementation, the test data includes suction pressure, discharge pressure, suction temperature, discharge temperature, speed, guide vane opening, test power, and cooling capacity of the compressor in a normal state; the first model includes a performance monitoring sub-model and a cooling sub-model;

[0026] The performance monitoring of the compressor is performed based on the current operating data and the preset model to obtain the corresponding performance monitoring results of the compressor, including:

[0027] When the preset model is the first model, the current operating data is input into the first model, and the current ideal power corresponding to the compressor is output based on the performance monitoring sub-model, and the current cooling capacity and / or current refrigerant exhaust flow rate corresponding to the compressor is output based on the cooling sub-model;

[0028] The performance monitoring result corresponding to the compressor is determined based on the current ideal power and / or the current cooling capacity and / or the current refrigerant exhaust flow rate.

[0029] In a possible implementation, the current operating data includes the following current operating parameters corresponding to the compressor: current suction pressure, current discharge pressure, current suction temperature, current discharge temperature, current speed, current guide vane opening, and current actual power;

[0030] The above-mentioned inputting the above-mentioned current operating data into the preset model and outputting the current ideal power of the above-mentioned compressor includes:

[0031] If the preset model is the first model, the current operating data is input into the first model, a current head pressure is determined based on the current suction pressure, the current discharge pressure, the current suction temperature, and the current discharge temperature, and a current ideal power of the compressor is output based on the current head pressure, the current speed, the current guide vane opening, and the first parameter of the first model, and a current cooling capacity and / or a current refrigerant discharge flow rate is output based on the current head pressure, the current suction temperature, the current discharge temperature, the current speed, the current guide vane opening, and the first parameter;

[0032] If the preset model is the second model, the current operating data is input into the second model, the current pressure head is determined based on the current intake pressure, the current exhaust pressure, the current intake temperature and the current exhaust temperature, and the current ideal power of the compressor is output based on the current pressure head, the current speed, the current guide vane opening and the second parameter of the second model.

[0033] In a second aspect, an embodiment of the present application provides a compressor performance monitoring device, the compressor performance monitoring device comprising:

[0034] An acquisition module is used to obtain the current operating data of the compressor;

[0035] a judgment module, configured to judge whether the compressor currently meets a first preset condition based on the current operating data;

[0036] The performance monitoring module is used to monitor the performance of the above-mentioned compressor according to the above-mentioned current operating data and the preset model to obtain the performance monitoring results corresponding to the above-mentioned compressor; the above-mentioned preset model is updated based on the historical effective operating data of the above-mentioned compressor in a normal state.

[0037] In a third aspect, an embodiment of the present application provides a compressor, comprising: a processor and a memory;

[0038] The processor is connected to the memory;

[0039] The aforementioned memory is used to store executable program code;

[0040] The processor runs the program corresponding to the executable program code by reading the executable program code stored in the memory, so as to execute the method provided in the first aspect of the embodiment of this specification or any possible implementation of the first aspect.

[0041] In a fourth aspect, an embodiment of the present application provides a computer storage medium, which stores multiple instructions, and the instructions are suitable for being loaded by a processor and executing the method steps provided in the first aspect of the embodiment of the present application or any possible implementation of the first aspect.

[0042] In one or more embodiments of the present application, current operating data of the compressor is obtained; based on the above current operating data, it is determined whether the above compressor currently meets the first preset condition; if so, the performance of the above compressor is monitored according to the above current operating data and the preset model to obtain the performance monitoring result corresponding to the above compressor; the above preset model is updated based on the historical effective operating data of the above compressor in a normal state, which can not only make full use of the historical effective operating data of the compressor in a normal state to update the preset model, but also make the parameters of the preset model more in line with the actual operating conditions of the compressor to be monitored, so that the preset model can be effectively and accurately applied to various scenarios or environments, expand the applicable area of ​​the preset model, and improve the model accuracy and the accuracy of compressor performance monitoring, without the need for external storage devices or computing resources. When the compressor currently meets the first preset condition, it can be considered that the preset model has been updated under the normal operating state corresponding to the current operating data, and the preset model can be used to monitor the current performance of the compressor, further ensuring that the current performance monitoring result of the compressor can be effectively and accurately obtained according to the current operating data of the compressor through the preset model.

[0043] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0045] Figure 1 A schematic structural diagram of a compressor provided by an exemplary embodiment of the present application;

[0046] Figure 2 A flow chart of a compressor performance monitoring method provided by an exemplary embodiment of the present application;

[0047] Figure 3 A schematic diagram of a data grid area provided by an exemplary embodiment of the present application;

[0048] Figure 4 A schematic diagram of a flow chart for implementing a compressor performance monitoring method provided by an exemplary embodiment of the present application;

[0049] Figure 5 A schematic diagram of an implementation process of another compressor performance monitoring method provided by an exemplary embodiment of the present application;

[0050] Figure 6 A schematic diagram of a process for determining effective operation data provided by an exemplary embodiment of the present application;

[0051] Figure 7 A schematic diagram of an implementation process of another compressor performance monitoring method provided by an exemplary embodiment of the present application;

[0052] Figure 8 A schematic structural diagram of a compressor performance monitoring device provided by an exemplary embodiment of the present application;

[0053] Figure 9 A schematic structural diagram of a compressor provided as an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0054] To make the features and advantages of this application more obvious and easy to understand, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts shall fall within the scope of protection of this application.

[0055] The terms "first," "second," "third," and the like in the specification and claims of this application and the accompanying drawings are used to distinguish between different objects, not to describe a particular order. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or elements is not limited to the listed steps or elements, but may optionally include steps or elements not listed, or may optionally include other steps or elements inherent to the process, method, product, or apparatus.

[0056] In the related art, either an external computer, server or other equipment or computing resources are required to monitor the performance of the compressor, or a general model or data pre-stored in the server is directly used to monitor the performance of the compressor. However, for chillers, on the one hand, the method of using an external computer, server or other equipment or computing resources to monitor the performance of the compressor will not only lead to excessively high communication and installation costs, but also inevitably generate large delays due to the transmission of data and models to external equipment, complex model calculations, etc., resulting in information loss or misjudgment of status, affecting the efficiency and accuracy of compressor performance monitoring; on the other hand, the operating conditions and performance status of the compressor in the test phase and the actual operation phase vary greatly. The method of using a general model or data pre-stored in the server to directly monitor the performance of the compressor is often not in line with the actual operating conditions of the compressor, which is likely to lead to misjudgment problems or excessively high initial testing costs.

[0057] Based on this, an embodiment of the present application provides a compressor performance monitoring method. On the one hand, it can make full use of the historical effective operating data of the compressor under normal conditions to update the preset model, which not only reduces the early (pre-factory) testing cost of the compressor, but also makes the parameters of the preset model more in line with the actual operating conditions of the compressor to be monitored, so that the preset model can be effectively and accurately applied to various scenarios or environments, expand the applicable area of ​​the preset model, and improve the accuracy of the preset model and the accuracy of the compressor performance monitoring; on the other hand, not only does it not require external storage devices or computing resources, it reduces the cost required for compressor performance monitoring, saves the time required for transmitting the corresponding data and model of the compressor to the external device, and improves the efficiency of compressor performance monitoring. When the compressor currently meets the first preset condition, it can be considered that the preset model has been updated under the normal operating state corresponding to the current operating data. The compressor can then directly use the preset model to monitor the current performance of the compressor, further ensuring that the current performance monitoring results of the compressor can be effectively and accurately obtained according to the current operating data of the compressor through the preset model.

[0058] Please refer to the following Figure 1 , which is a schematic diagram of the structure of a compressor provided by an exemplary embodiment of the present application. Figure 1 As shown, the compressor 100 includes: a controller 110, a motor 120, a pressure sensor 130, a temperature sensor 140, a guide vane opening sensor 150, a speed sensor 160, an air inlet 171 and an air outlet 172. Among them:

[0059] The air inlet 171 is an inlet of the compressor 100 and is used to inhale air.

[0060] The air outlet 172 is the outlet of the compressor 100 and is used to discharge compressed air or gas.

[0061] The pressure sensor 130 is used to detect the suction pressure of the air inlet 171 and the exhaust pressure of the air outlet 172 of the compressor 100 .

[0062] The temperature sensor 140 is used to detect the intake temperature of the air inlet 171 and the exhaust temperature of the air outlet 172 of the compressor 100 .

[0063] The guide vane opening sensor 150 is used to detect the opening degree of the guide vanes between the air inlet 171 and the air outlet 172 of the compressor 100 .

[0064] The rotation speed sensor 160 is connected to the motor 120 and is used to detect the rotation speed of the motor 120 in the compressor 100 , that is, to detect the rotation speed of the compressor 100 .

[0065] The motor 120 is used to provide required power for the compressor 100 .

[0066] The controller 110 is connected to the motor 120, the pressure sensor 130, the temperature sensor 140, the guide vane opening sensor 150, and the speed sensor 160, and is used to obtain the current operating data of the compressor 100. The above-mentioned current operating data includes, for example but not limited to, the following current operating parameters: obtaining the current suction pressure of the air inlet 171 of the compressor 100 and the current exhaust pressure of the air outlet 172 detected by the pressure sensor 130, obtaining the suction temperature of the air inlet 171 of the compressor 100 and the exhaust temperature of the air outlet 172 detected by the temperature sensor 140, obtaining the current guide vane opening degree between the air inlet 171 and the air outlet 172 of the compressor 100 detected by the guide vane opening sensor 150, obtaining the current speed of the compressor 100 detected by the speed sensor 160, etc.

[0067] Specifically, after obtaining the above-mentioned current operating data of the compressor 100, the controller 110 can determine whether the above-mentioned compressor 100 currently meets the first preset condition based on the above-mentioned current operating data; if so, the performance of the above-mentioned compressor 100 is monitored according to the above-mentioned current operating data and the preset model to obtain the corresponding performance monitoring result of the above-mentioned compressor 100; the above-mentioned preset model is updated based on the historical effective operating data of the above-mentioned compressor in a normal state.

[0068] Optionally, the controller 110 can also control the motor 120 to perform corresponding actions based on the above performance monitoring results or the above current operating data, for example but not limited to when the above performance monitoring results show that the compressor 100 is in an abnormal state, controlling the motor 120 to stop running, etc.

[0069] It can be understood that the above-mentioned compressor 100 can be but is not limited to a centrifugal compressor. For example, but not limited to, it can be used as a core component of a chiller, so that the chiller can adjust the refrigerant flow rate in the corresponding refrigeration system by adjusting the speed and guide vane opening of the compressor 100, thereby achieving the purpose of adjusting the cooling capacity, etc. The embodiments of the present application are not limited to this.

[0070] Next, combine Figure 1 , introduces a compressor performance monitoring method provided by an exemplary embodiment of this application. For details, please refer to Figure 2 , which is a flow chart of a compressor performance monitoring method provided by an exemplary embodiment of the present application. Figure 2 As shown, the compressor performance monitoring method includes the following steps:

[0071] S201, obtaining the current operating data of the compressor.

[0072] Specifically, during the operation of the compressor, its controller can obtain the current operating data of the compressor through multiple sensors. The above-mentioned current operating data may include but is not limited to the following current operating parameters corresponding to the above-mentioned compressor: current suction pressure, current exhaust pressure, current suction temperature, current exhaust temperature, current speed, current guide vane opening, and current actual power.

[0073] S202: Determine whether the compressor currently meets a first preset condition based on current operating data.

[0074] Specifically, after obtaining the current operating data of the compressor, in order to ensure that subsequent compressor performance monitoring can be more in line with the actual operating conditions of the compressor, it is possible to first determine whether the compressor currently meets the first preset condition based on the current operating data to determine whether the preset model used for performance monitoring of the compressor has been updated under the normal operating state corresponding to the current operating data, thereby ensuring the accuracy of subsequent performance monitoring of the compressor through the preset model.

[0075] The operating state of the compressor is mainly determined by the three target operating parameters: the compressor speed (SPD), the guide vane opening (IGV) and the pressure ratio (PR). The above pressure ratio (PR) is equal to the exhaust pressure (P dis ) / inspiratory pressure (P suc ), that is, PR=P dis / P suc In order to use a very small storage space and retain the preset model for training or updating the compressor performance data under normal conditions to the maximum extent, before the compressor starts running for the first time, Figure 3As shown, on the coordinate axis corresponding to (SPD, IGV, PR), the preset value range corresponding to each target operating parameter of the compressor can be first divided according to the corresponding preset interval to obtain multiple data grid areas, and then the valid data corresponding to the training data and update data (i.e., historical effective operating data) of the preset model are stored in the corresponding data grid area, so that the subsequent compressor can efficiently judge whether the preset model has overfitting based on the number of target data grid areas with valid data, and efficiently determine whether the preset model can be used for compressor performance monitoring based on the current operating data based on whether there is valid data in the current data grid area corresponding to the current operating data of the compressor. The data grid area to which the training data and update data (i.e., historical effective operating data) of the above preset model belong can be defined by the numbers (idx_01, idx_02, idx_03). Among them, the above idx_01=int((SPD-SPD min +5) / D1), the above idx_02=int((IGV-IGV min ) / D2), the above idx_03=int((PR-PR min ) / D3), the above SPD min is the minimum speed value within the preset value range corresponding to the speed of the compressor, D1 is the preset frequency interval (preset interval) of the grid divided by the speed of the compressor, and the above IGV min is the minimum guide vane opening value within the preset value range corresponding to the guide vane opening of the compressor, D2 is the preset guide vane opening interval (preset interval) of the grid division corresponding to the guide vane opening of the compressor, and the above PR min D1 is the minimum pressure ratio value within the preset value range corresponding to the compressor's pressure ratio, and D3 is the preset pressure ratio interval (preset interval) for the grid division of the compressor's pressure ratio. The preset value range is the range between the preset minimum value and the preset maximum value that can be achieved by the corresponding target operating parameter when the compressor is operating. The above-mentioned D1, D2, and D3 can be determined, but are not limited to, based on the local storage resources of the compressor and / or the number of parameters to be trained or updated in the preset model. For example, the smaller the local storage resources of the compressor, the less valid data it can store, the fewer the total number of data grid areas required for division, and to a certain extent, the larger the values ​​of D1, D2, and D3.

[0076] It is understandable that due to the limited local storage resources of the compressor, the number of the above-mentioned data grid areas may be greater than 200 and less than 500, and this embodiment of the present application does not limit this.

[0077] Optionally, the above-mentioned S202, judging whether the compressor currently meets the first preset condition based on the current operating data, may include: judging whether there is valid data in the current data grid area corresponding to the current operating data, the above-mentioned valid data being the average value of each historical operating parameter in the historical valid operating data corresponding to the current data grid area, and the above-mentioned historical valid operating data being the historical operating data involved in the training or updating of the preset model. The above-mentioned historical valid operating data may include, but is not limited to, the following historical operating parameters corresponding to the above-mentioned compressor: historical suction pressure, historical exhaust pressure, historical suction temperature, historical exhaust temperature, historical speed, historical guide vane opening, and historical actual power. The above-mentioned valid operating data is used to represent historical operating data that meets the second preset condition when the compressor is in a normal state and a stable state. The above-mentioned second preset condition includes that each historical operating parameter in the historical operating data does not exceed the corresponding preset parameter limit and / or the parameter dispersion coefficient corresponding to the historical operating data is less than the discrete threshold. When the current speed in the current operating data is within the speed range corresponding to the current data grid area, the current guide vane opening in the current operating data is within the guide vane opening range corresponding to the current data grid area, and the current pressure ratio in the current operating data is within the pressure ratio range corresponding to the current data grid area, it can be considered that the current operating data corresponds to the current data grid area, that is, the current operating data belongs to operating data that may be generated by a compressor in the current data grid area.

[0078] Optionally, the above S202, the implementation process of judging whether the compressor currently meets the first preset condition based on the current operating data, in addition to judging whether there is valid data in the current data grid area corresponding to the current operating data, may also include, but is not limited to: judging whether the current cumulative operating time of the compressor is greater than the preset time, and / or judging whether the number of target data grid areas currently corresponding to the compressor is greater than a first preset number. The above current cumulative operating time is used to characterize the cumulative total operating time in the time period from the time the compressor first starts running to the current time. The above preset time can be pre-set according to actual conditions, for example, but not limited to 24 hours, 48 ​​hours, etc., and this application does not limit this. In order to avoid the problem of overfitting of parameters in the preset model as much as possible, the above first preset number can be, but is not limited to, equal to two or three times the total number of parameters to be trained, updated, or fitted in the preset model. The above target data grid area is used to characterize the data grid area where valid data is stored, that is, the data grid area corresponding to the historical valid operating data used for training or updating by the preset model.

[0079] S203: If yes, the performance of the compressor is monitored according to the current operating data and the preset model to obtain the corresponding performance monitoring result of the compressor.

[0080] Specifically, if there is valid data in the current data grid area corresponding to the current operating data of the compressor, it can be considered that the preset model has been updated under the normal operating state corresponding to the current operating data, or there is valid data in the current data grid area and the current cumulative operating time of the compressor is greater than the preset time, or there is valid data in the current data grid area and the number of target data grid areas currently corresponding to the compressor is greater than the first preset number, or there is valid data in the current data grid area and the current cumulative operating time of the compressor is greater than the preset time and the number of target data grid areas currently corresponding to the compressor is greater than the first preset number, it can be considered that the preset model has been updated under the normal operating state corresponding to the current operating data and the preset model does not currently have an overfitting situation, and can be accurately applied to performance monitoring of the compressor under the current operating conditions, that is, the compressor currently meets the first preset condition, then the performance of the compressor can be monitored according to the current operating data of the compressor and the preset model, and the performance monitoring results corresponding to the compressor can be accurately obtained.

[0081] In order to avoid the situation where the operating conditions corresponding to the compressor data involved in the preset model training are different from the actual operating conditions involved in the actual operation of the compressor, resulting in the preset model being unable to be accurately applied to the performance monitoring of the compressor in the current actual operating conditions, the above-mentioned preset model can be updated based on the historical effective operating data of the compressor in a normal state, so that the preset model is updated and adjusted according to the normal operating data (historical effective operating data) of the compressor in a normal and stable operation process before performance monitoring, so that it can learn and update the correlation between the operating parameters such as suction pressure, exhaust pressure, suction temperature, exhaust temperature, speed and guide vane opening and power (ideal power) in the historical effective operating data when the compressor is in a normal state under actual operating conditions, thereby improving the accuracy and effectiveness of the preset model under the actual operating conditions of the compressor, thereby improving the accuracy of compressor performance monitoring.

[0082] In the embodiment of the present application, on the one hand, the historical effective operating data of the compressor under normal conditions can be fully utilized to update the preset model, which not only reduces the early (pre-factory) testing cost of the compressor, but also makes the parameters of the preset model more in line with the actual operating conditions of the compressor to be monitored, so that the preset model can be effectively and accurately applied to various scenarios or environments, expand the applicable area of ​​the preset model, and improve the accuracy of the preset model and the accuracy of the compressor performance monitoring; on the other hand, not only does it not require external storage devices or computing resources, it reduces the cost required for compressor performance monitoring, saves the time required for transmitting the corresponding data and model of the compressor to the external device, and improves the efficiency of compressor performance monitoring. When the compressor currently meets the first preset condition, it can be considered that the preset model has been updated under the normal operating state corresponding to the current operating data. The compressor can then directly use the preset model to monitor the current performance of the compressor, further ensuring that the current performance monitoring results of the compressor can be effectively and accurately obtained according to the current operating data of the compressor through the preset model.

[0083] Further, if Figure 4 As shown, the above-mentioned S203 performs performance monitoring on the compressor according to the current operating data and the preset model to obtain the corresponding performance monitoring result of the compressor, which may include: inputting the current operating data into the preset model and outputting the current ideal power of the compressor; if the absolute deviation value between the current ideal power of the compressor and the current actual power in the current operating data of the compressor is greater than a threshold value, it indicates that the current actual power of the compressor deviates significantly from the ideal power corresponding to the compressor when the compressor is operating under normal conditions with the current suction pressure, current discharge pressure, current suction temperature, current discharge temperature, current speed, and current guide vane opening, and the corresponding performance monitoring result of the compressor is determined to be in an abnormal state. If the absolute deviation value between the current ideal power of the compressor and the current actual power in the current operating data of the compressor is less than or equal to the threshold value, it indicates that the current actual power of the compressor deviates significantly from the ideal power corresponding to the compressor when the compressor is operating under normal conditions with the current suction pressure, current discharge pressure, current suction temperature, current discharge temperature, current speed, and current guide vane opening, and the corresponding performance monitoring result of the compressor is determined to be in a normal state.

[0084] Optionally, the above-mentioned process of inputting the current operating data into the preset model and outputting the current ideal power of the compressor may include: if the preset model is the first model, the current operating data may be input into the first model, and the first model first determines the current pressure head based on the current suction pressure, current exhaust pressure, current suction temperature and current exhaust temperature in the current operating data, and then outputs the current ideal power of the compressor based on the current pressure head, current speed, current guide vane opening and the first parameter of the first model, and outputs the current cooling capacity and / or current refrigerant exhaust flow based on the current pressure head, current suction temperature, current exhaust temperature, current speed, current guide vane opening and the first parameter.

[0085] For example, the current head can be calculated according to, but not limited to, the following formula:

[0086] Among them, suc is the inspiratory specific volume, which can be calculated based on the inspiratory pressure P suc and suction temperature T suc Confirm, dis is the exhaust volume, which can be calculated based on the exhaust pressure P dis and exhaust temperature T dis The current ideal power of the compressor can be obtained by, but is not limited to, calculating the current ideal power of the compressor based on the current pressure head, the current speed, the current guide vane opening, and the first parameter of the first model according to a corresponding preset formula or by the neural network layer of the first model. The first model can include, but is not limited to, one or more first parameters, such as, but not limited to, a first parameter θ1, ..., θ k , k≤3. The above preset formula can be in the form of, but not limited to, a polynomial or exponential equation, for example, but not limited to, the current ideal power

[0087] Among them, c ij , d1, d2, d3, e1, and e2 are all coefficients set before the compressor leaves the factory. θ1, θ2, and θ3 are all first parameters of the first model. SPD is the current speed of the compressor, and IGV is the current guide vane opening of the compressor. The above-mentioned current cooling capacity and / or current refrigerant exhaust flow rate can be obtained by, but is not limited to, calculating the current pressure head, current suction temperature, current exhaust temperature, current speed, current guide vane opening, and the first parameter of the first model according to the corresponding preset formula or by the neural network layer of the first model, such as, but not limited to, the current refrigerant exhaust flow rate. Current cooling capacity Q=θ3·Vr·ρ·(H suc -H dis,satliq ), ρ is the exhaust density, H suc、H dis,satliq They are the suction enthalpy value and the refrigerant enthalpy value at the condenser outlet connected to the compressor, both of which can be based on the suction pressure P suc , suction temperature T suc , exhaust pressure P dis , exhaust temperature T dis Calculated.

[0088] Optionally, the first model is trained based on the test data of the compressor before it leaves the factory. The test data includes the suction pressure, discharge pressure, suction temperature, discharge temperature, speed, guide vane opening, test power and cooling capacity of the compressor in a normal state. Figure 5 As shown, the first model may include a performance monitoring sub-model and a cooling sub-model. The above-mentioned performance monitoring of the compressor according to the current operating data and the preset model to obtain the corresponding performance monitoring result of the compressor may include: when the preset model is the first model, the current operating data may be first input into the first model, and the current ideal power corresponding to the compressor is output based on the performance monitoring sub-model in the first model, and the current cooling capacity and / or current refrigerant exhaust flow rate corresponding to the compressor is output based on the cooling sub-model in the first model, thereby obtaining the cooling capacity data of the chiller where the compressor is located without having the adjustment function of a flow meter (such as but not limited to a water flow meter or a refrigerant flow meter), thereby reducing the cost of obtaining the cooling capacity data of the chiller. Then, the performance monitoring result corresponding to the compressor is determined based on the current ideal power and / or current cooling capacity and / or current refrigerant exhaust flow rate of the compressor. The above performance monitoring results may include, but are not limited to, the performance status of the compressor and / or the health status of the corresponding refrigeration components of the chiller where the compressor is located. The above compressor performance status may be, but is not limited to, determined based on the gap between the current ideal power and the current actual power of the compressor. The above health status may be, but is not limited to, determined based on the gap between the current cooling capacity and / or the current refrigerant exhaust flow rate and the current cooling target. The above performance monitoring sub-model may be, but is not limited to, trained based on the suction pressure, exhaust pressure, suction temperature, exhaust temperature, speed, and guide vane opening corresponding to the known test power in the test data; the above sub-model of cold storage may be, but is not limited to, trained based on the suction pressure, exhaust pressure, suction temperature, exhaust temperature, speed, and guide vane opening corresponding to the known cooling capacity in the test data.

[0089] Optionally, the process of inputting the current operating data into a preset model and outputting the current ideal power of the compressor may include: if the preset model is a second model, the current operating data may be input into the second model; the second model first determines the current head pressure based on the current suction pressure, the current discharge pressure, the current suction temperature, and the current discharge temperature; and then outputs the current ideal power of the compressor based on the current head pressure, the current speed, the current guide vane opening, and a second parameter of the second model. The method for determining the current head pressure using the second model is similar to the method for determining the current head pressure using the first model, and will not be further described here.

[0090] For example, the current ideal power of the compressor can be calculated based on, but not limited to, the current pressure head, the current speed, the current guide vane opening, and the second parameter of the second model according to a preset formula in the form of a polynomial or exponential equation. The second model can include, but is not limited to, one or more second parameters. For example, but not limited to, the current ideal power Among them, the above c ij , d1, d2, d3, e1, e2, and e3 are all coefficients (i.e., second parameters) obtained by training the second model preset after the compressor leaves the factory through actual operating data.

[0091] Optionally, please continue to refer to Figure 2 ,like Figure 2 As shown, after determining whether the compressor currently meets the first preset condition based on the current operating data in S202, the compressor performance monitoring method may also include, but is not limited to:

[0092] S204: If not, if the current operating data is valid operating data, calibrate or update the preset model based on the current operating data.

[0093] Specifically, if the compressor currently does not meet the first preset condition, it can be considered that the preset model used for performance monitoring of the compressor has not been updated under the normal operating state corresponding to the current operating data, that is, the preset model is currently unable to accurately evaluate the current ideal power under the normal operating state corresponding to the current operating data, or it is considered that the preset model training is not sufficient and there is a high possibility of overfitting problems. In this case, when the current operating data is valid operating data, the preset model can be calibrated or updated based on the current operating data, thereby improving the accuracy and evaluation ability of the preset model and ensuring the accuracy of the subsequent performance monitoring of the compressor through the preset model.

[0094] Furthermore, the valid operating data is used to represent operating data that satisfies a second preset condition when the compressor is in a normal and stable state. The operating data includes the following operating parameters: suction pressure, discharge pressure, suction temperature, discharge temperature, speed, guide vane opening, and actual power. The second preset condition includes that each operating parameter in the operating data does not exceed the corresponding preset parameter limit and / or the parameter dispersion coefficient corresponding to the operating data is less than a dispersion threshold.

[0095] That is, if Figure 6 As shown, when the compressor obtains the current operating data and determines based on the current operating data that the compressor currently does not meet the first preset condition for performance monitoring, it can first, but is not limited to, perform status screening based on the current cumulative operating time of the compressor and the compressor status quantity corresponding to the current operating data, that is, determine whether the current operating data is the operating data of the compressor in a normal state and a stable state; for example, but not limited to, when the current cumulative operating time is too short (for example, less than 30 minutes), the compressor can be assumed to be in a normal state, and when the compressor status quantity indicates that the compressor is currently in a stable state (that is, maintains a stable operating state), it can be considered that the current operating data meets the status screening conditions, and the next step of abnormal data elimination judgment can be continued. That is, it is possible to further determine whether the current operating data is abnormal data and whether abnormal data elimination processing is required based on whether each current operating parameter in the current operating data exceeds the corresponding preset parameter limit. If there are current operating parameters in the current operating data that exceed the corresponding preset parameter limit, the current operating data can be considered abnormal data and cannot meet the training or update requirements corresponding to the preset model, and thus need to be eliminated. If each current operating parameter in the current operating data does not exceed the corresponding preset parameter limit, the current operating data can be considered normal operating data, and data smoothing filtering can be performed on it. That is, it is possible to further perform data smoothing filtering on each current operating parameter in the current operating data that is not abnormal data, that is, the parameter dispersion coefficient corresponding to the current operating data, and when the corresponding parameter dispersion coefficient is less than the discrete threshold, it is determined that the current operating data is valid data and can be used for training or updating the preset model.

[0096] Specifically, the implementation process of calibrating or updating the preset model based on the current operating data in S204 may include: if the preset model is a first model, online calibration and testing of first parameters of the first model may be performed based on the current operating data; the first model is trained based on pre-factory test data of the compressor. If the preset model is a second model, updating, training, and testing of second parameters of the second model may be performed based on the current operating data; the second model is trained based on historical effective operating data of the compressor.

[0097] It is understandable that in order to further ensure the accuracy of the preset model and avoid invalid updates of the preset model, if the test accuracy and / or output trend analysis of the preset model after the update meet the model requirements, the parameters of the preset model before the update in the compressor can be updated to the parameters after the preset model is updated and saved; if the test accuracy and / or output trend analysis of the preset model after the update do not meet the requirements, the parameters of the preset model before the update in the compressor can be controlled to remain unchanged. Among them, the update solution method of the parameters in the preset model can include but is not limited to nonlinear solution methods such as gradient descent and Newton's method, linear solution methods such as Gaussian elimination, etc., and these methods can all be implemented through the computing power of the compressor controller.

[0098] Optionally, after the preset model is calibrated or updated based on the current operating data, the average value of each current operating parameter in the current operating data can also be saved as valid data in the current data grid area corresponding to the current operating data, thereby using extremely small storage space to maximize the retention of the performance data of the compressor under normal conditions and the update status of the preset model, so that it can be determined efficiently and accurately in the future whether the preset model can be used directly to monitor the performance of the compressor.

[0099] Further, if Figure 7 As shown, when the amount of test data is greater than or equal to the second preset amount, it can be assumed that the compressor had sufficient measurement data for model training before leaving the factory. In this case, the pre-installed model in the compressor can be a first model trained based on the test data. To avoid poor model training results or low training efficiency due to information loss or transmission delay during data transmission, the first model can be trained directly within the compressor. To reduce the training computing power required for the compressor and lower the cost of compressor performance monitoring, the first model can also be trained by other equipment and transferred to the compressor for storage. When the amount of test data is less than the second preset amount, it can be assumed that the compressor did not have sufficient measurement data for model training before leaving the factory. The model can only be trained online after leaving the factory based on actual effective operating data (historical effective operating data) under normal conditions at the beginning of the compressor's operation. In this case, the pre-installed model in the compressor can be a second model trained based on the compressor's historical effective operating data. The second preset amount can be, but is not limited to, determined based on the number of first parameters in the first model. To avoid overfitting due to insufficient test data during the training of the first model, the first preset amount should be greater than or equal to the number of first parameters in the first model.

[0100] In some possible embodiments, such as Figure 7As shown, after the preset model is set in the compressor and the operation is started, the current operation data of the compressor can be collected according to the preset frequency, and the implementation process corresponding to S202 is followed to determine whether the compressor currently meets the first preset condition based on the current operation data, and if the compressor currently does not meet the first preset condition, the compressor is judged according to the current operation data. Figure 6 The corresponding implementation process filters the current running data, and updates the preset model accordingly according to the implementation process corresponding to S204. After the preset model is updated and saved, it can also be used according to, but not limited to, Figure 3 The corresponding implementation process described performs corresponding data grid positioning and storage on the current operating data participating in the preset model update. When the compressor currently meets the first preset condition, the current ideal power, current cooling capacity and / or current refrigerant exhaust flow rate can be output according to the current operating data when the preset model is the first model according to the implementation process corresponding to S203 (which can be but is not limited to being used for health monitoring or optimization control of other components such as heat exchangers, etc.; when the preset model is the second model, the current ideal power is output according to the current operating data. After the compressor obtains the current ideal power corresponding to the current operating data, it can directly determine whether the compressor is currently in an abnormal state based on whether the absolute deviation value between the current ideal power and the current actual power of the compressor is greater than a threshold, thereby obtaining the current performance monitoring result of the compressor.

[0101] Please refer to the following Figure 8 , which is a structural diagram of a compressor performance monitoring device provided in an embodiment of the present application. Figure 8 As shown, the compressor performance monitoring device 800 includes:

[0102] An acquisition module 810 is used to acquire current operating data of the compressor;

[0103] A determination module 820 is configured to determine whether the compressor currently meets a first preset condition based on the current operating data;

[0104] The performance monitoring module 830 is used to monitor the performance of the compressor according to the current operating data and the preset model to obtain the performance monitoring result corresponding to the compressor; the preset model is updated based on the historical effective operating data of the compressor in a normal state.

[0105] In one possible implementation, the performance monitoring module 830 includes:

[0106] a first ideal parameter prediction unit, configured to input the current operating data into a preset model and output the current ideal power of the compressor;

[0107] The first determining unit is configured to determine that the performance monitoring result corresponding to the compressor is that the compressor is in an abnormal state if an absolute deviation value between the current ideal power and the current actual power of the compressor is greater than a threshold value.

[0108] In a possible implementation, the determination module 820 includes:

[0109] The first judgment unit is used to judge whether there is valid data in the current data grid area corresponding to the current operation data; the valid data is the average value of each historical operation parameter in the historical valid operation data corresponding to the current data grid area;

[0110] a second determination unit configured to determine whether a current cumulative operating time of the compressor is greater than a preset operating time, and / or determine whether a number of target data grid areas currently corresponding to the compressor is greater than a first preset number; the target data grid areas being used to represent data grid areas storing the valid data;

[0111] Among them, the above-mentioned compressor corresponds to multiple data grid areas, and the above-mentioned multiple data grid areas are divided according to corresponding preset intervals based on the preset value ranges corresponding to the various target operating parameters of the above-mentioned compressor; the above-mentioned target operating parameters include speed, guide vane opening and pressure ratio; the current speed in the above-mentioned current operating data is within the speed range corresponding to the above-mentioned current data grid area, the current guide vane opening in the above-mentioned current operating data is within the guide vane opening range corresponding to the above-mentioned current data grid area, and the current pressure ratio in the above-mentioned current operating data is within the pressure ratio range corresponding to the above-mentioned current data grid area.

[0112] In a possible implementation, the compressor performance monitoring device 800 further includes:

[0113] an updating module, configured to, if not, calibrate or update the preset model based on the current operating data if the current operating data is valid operating data; the valid operating data is used to represent operating data of the compressor in a normal state and a stable state that meets a second preset condition;

[0114] Among them, the above-mentioned operating data include the following operating parameters: intake pressure, exhaust pressure, intake temperature, exhaust temperature, speed, guide vane opening, and actual power; the above-mentioned second preset condition includes that each operating parameter in the above-mentioned operating data does not exceed the corresponding preset parameter limit and / or the parameter dispersion coefficient corresponding to the above-mentioned operating data is less than the discrete threshold.

[0115] In a possible implementation, the update module includes:

[0116] a first updating unit configured to, if the preset model is a first model, perform online calibration and testing on a first parameter of the first model based on the current operating data; the first model is trained based on the test data of the compressor;

[0117] The second updating unit is used to update, train and test the second parameters of the second model based on the current operating data if the preset model is the second model; the second model is trained based on the historical effective operating data of the compressor.

[0118] In a possible implementation, the compressor performance monitoring device 800 further includes:

[0119] The storage module is used to save the average value of each current operation parameter in the above-mentioned current operation data as the valid data in the current data grid area corresponding to the above-mentioned current operation data.

[0120] In a possible implementation, when the amount of the test data is greater than or equal to a second preset amount, the preset model is the first model; when the amount of the test data is less than the second preset amount, the preset model is the second model.

[0121] In one possible implementation, the test data includes suction pressure, discharge pressure, suction temperature, discharge temperature, speed, guide vane opening, test power, and cooling capacity of the compressor in a normal state; the first model includes a performance monitoring sub-model and a cooling sub-model;

[0122] The performance monitoring module 830 includes:

[0123] a second ideal parameter prediction unit, configured to, when the preset model is the first model, input the current operating data into the first model, output the current ideal power corresponding to the compressor based on the performance monitoring sub-model, and output the current cooling capacity and / or current refrigerant exhaust flow rate corresponding to the compressor based on the cooling sub-model;

[0124] The second determining unit is configured to determine a performance monitoring result corresponding to the compressor based on the current ideal power and / or the current cooling capacity and / or the current refrigerant exhaust flow rate.

[0125] In a possible implementation, the current operating data includes the following current operating parameters corresponding to the compressor: current suction pressure, current discharge pressure, current suction temperature, current discharge temperature, current speed, current guide vane opening, and current actual power;

[0126] The first ideal parameter prediction unit is specifically used for:

[0127] If the preset model is the first model, the current operating data is input into the first model, a current head pressure is determined based on the current suction pressure, the current discharge pressure, the current suction temperature, and the current discharge temperature, and a current ideal power of the compressor is output based on the current head pressure, the current speed, the current guide vane opening, and the first parameter of the first model, and a current cooling capacity and / or a current refrigerant discharge flow rate is output based on the current head pressure, the current suction temperature, the current discharge temperature, the current speed, the current guide vane opening, and the first parameter;

[0128] If the preset model is the second model, the current operating data is input into the second model, the current pressure head is determined based on the current intake pressure, the current exhaust pressure, the current intake temperature and the current exhaust temperature, and the current ideal power of the compressor is output based on the current pressure head, the current speed, the current guide vane opening and the second parameter of the second model.

[0129] The division of the modules in the above-mentioned compressor performance monitoring device is for illustration only. In other embodiments, the compressor performance monitoring device can be divided into different modules as needed to complete all or part of the functions of the above-mentioned compressor performance monitoring device. The implementation of each module in the compressor performance monitoring device provided in the embodiments of this specification can be in the form of a computer program. The computer program can be run on a compressor, terminal, or server. The program modules constituted by the computer program can be stored in the memory of the compressor, terminal, or server. When the computer program is executed by the processor, all or part of the steps of the compressor performance monitoring method described in the embodiments of this specification are implemented.

[0130] See next Figure 9 , provides a schematic diagram of the structure of a compressor according to an embodiment of the present application. Figure 9 As shown, the compressor 900 may include at least one processor 910 , at least one network interface 920 , a user interface 930 , a memory 940 , and at least one communication bus 950 .

[0131] The communication bus 950 is used to implement connection and communication between these components.

[0132] The network interface 920 may include but is not limited to a low-power Bluetooth module, a near field communication (NFC) module, a wireless fidelity (Wi-Fi) module, and the like.

[0133] The user interface 930 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 930 may also include a standard wired interface and a wireless interface.

[0134] The processor 910 may include one or more processing cores. The processor 910 utilizes various interfaces and circuits to connect various components within the compressor 900. It executes instructions, programs, code sets, or instruction sets stored in the memory 940, as well as accesses data stored in the memory 940, to perform various functions of the compressor 900 and process data. Optionally, the processor 910 may be implemented using at least one of the following hardware forms: a digital signal processing (DSP), a field-programmable gate array (FPGA), or a programmable logic array (PLA). The processor 910 may integrate one or a combination of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. The CPU primarily processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing content displayed on the display; and the modem handles wireless communications. It is understood that the modem may not be integrated into the processor 910 but implemented as a separate chip.

[0135] Among them, the memory 940 may include a random access memory (RAM) or a read-only memory (ROM). Optionally, the memory 940 includes a non-transitory computer-readable storage medium. The memory 940 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 940 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a determination function, a sending function, a receiving function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 940 may also be optionally at least one storage device located away from the aforementioned processor 910. As Figure 9 As shown, the memory 940 as a computer storage medium may include an operating system, a network communication module, a user interface module, and program instructions.

[0136] In some possible embodiments, Figure 9 In the compressor 900 shown, the user interface 930 is mainly used to provide an input interface for the user and obtain the data input by the user; the above compressor 900 can be Figure 8 The compressor performance monitoring device 800 shown in FIG. 1 is configured to execute the following operations:

[0137] Obtain the current operating data of the compressor; determine whether the compressor currently meets the first preset condition based on the current operating data; if so, monitor the performance of the compressor according to the current operating data and a preset model to obtain a performance monitoring result corresponding to the compressor; the preset model is updated based on the historical effective operating data of the compressor in a normal state.

[0138] In some possible embodiments, when the processor 910 executes the performance monitoring of the compressor based on the current operating data and the preset model and obtains the performance monitoring result corresponding to the compressor, it is specifically used to execute: inputting the current operating data into the preset model and outputting the current ideal power of the compressor; if the absolute deviation value between the current ideal power and the current actual power of the compressor is greater than a threshold value, determining that the performance monitoring result corresponding to the compressor is that the compressor is in an abnormal state.

[0139] In some possible embodiments, when the processor 910 determines whether the compressor currently meets the first preset condition based on the current operating data, it is specifically configured to execute:

[0140] Determine whether there is valid data in the current data grid area corresponding to the above-mentioned current operating data; the above-mentioned valid data is the average value of each historical operating parameter in the historical valid operating data corresponding to the above-mentioned current data grid area; determine whether the current cumulative operating time of the above-mentioned compressor is greater than the preset time, and / or determine whether the number of target data grid areas currently corresponding to the above-mentioned compressor is greater than a first preset number; the above-mentioned target data grid area is used to characterize the data grid area where the above-mentioned valid data is stored; wherein the above-mentioned compressor corresponds to multiple data grid areas, and the above-mentioned multiple data grid areas are divided according to corresponding preset intervals based on the preset value ranges corresponding to each target operating parameter of the above-mentioned compressor; the above-mentioned target operating parameters include speed, guide vane opening and pressure ratio; the current speed in the above-mentioned current operating data is within the speed range corresponding to the above-mentioned current data grid area, the current guide vane opening in the above-mentioned current operating data is within the guide vane opening range corresponding to the above-mentioned current data grid area, and the current pressure ratio in the above-mentioned current operating data is within the pressure ratio range corresponding to the above-mentioned current data grid area.

[0141] In some possible embodiments, after executing the above-mentioned determination based on the above-mentioned current operating data whether the above-mentioned compressor currently meets the first preset condition, the above-mentioned processor 910 is further configured to execute:

[0142] If not, then when the above-mentioned current operating data is valid operating data, the above-mentioned preset model is calibrated or updated based on the above-mentioned current operating data; the above-mentioned valid operating data is used to characterize the operating data of the above-mentioned compressor in a normal state and a stable state that meets the second preset condition; wherein, the above-mentioned operating data includes the following operating parameters: suction pressure, exhaust pressure, suction temperature, exhaust temperature, speed, guide vane opening, actual power; the above-mentioned second preset condition includes that each operating parameter in the above-mentioned operating data does not exceed the corresponding preset parameter limit and / or the parameter dispersion coefficient corresponding to the above-mentioned operating data is less than the discrete threshold.

[0143] In some possible embodiments, when the processor 910 performs the calibration or update of the preset model based on the current operating data, it is specifically configured to perform:

[0144] If the preset model is the first model, the first parameter of the first model is calibrated and tested online based on the current operating data; the first model is trained based on the test data of the compressor; if the preset model is the second model, the second parameter of the second model is updated, trained and tested based on the current operating data; the second model is trained based on the historical effective operating data of the compressor.

[0145] In some possible embodiments, after executing the calibration or updating of the preset model based on the current operating data, the processor 910 is further configured to execute:

[0146] The average value of each current operating parameter in the current operating data is saved as valid data in the current data grid area corresponding to the current operating data.

[0147] In some possible embodiments, when the amount of the test data is greater than or equal to the second preset amount, the preset model is the first model; when the amount of the test data is less than the second preset amount, the preset model is the second model.

[0148] In some possible embodiments, the test data includes suction pressure, discharge pressure, suction temperature, discharge temperature, speed, guide vane opening, test power, and cooling capacity of the compressor in a normal state; the first model includes a performance monitoring sub-model and a cooling sub-model;

[0149] When the processor 910 performs the performance monitoring of the compressor according to the current operating data and the preset model and obtains the performance monitoring result corresponding to the compressor, it is specifically configured to execute:

[0150] When the above-mentioned preset model is the above-mentioned first model, the above-mentioned current operating data is input into the above-mentioned first model, and the current ideal power corresponding to the above-mentioned compressor is output based on the above-mentioned performance monitoring sub-model, and the current cooling capacity and / or current refrigerant exhaust flow corresponding to the above-mentioned compressor are output based on the above-mentioned cold quantum model; the performance monitoring result corresponding to the above-mentioned compressor is determined based on the above-mentioned current ideal power and / or current cooling capacity and / or current refrigerant exhaust flow.

[0151] In some possible embodiments, the current operating data includes the following current operating parameters corresponding to the compressor: current suction pressure, current discharge pressure, current suction temperature, current discharge temperature, current speed, current guide vane opening, and current actual power;

[0152] When the processor 910 inputs the current operating data into the preset model and outputs the current ideal power of the compressor, it is specifically configured to execute:

[0153] If the preset model is the first model, the current operating data is input into the first model, the current pressure head is determined based on the current suction pressure, the current exhaust pressure, the current suction temperature and the current exhaust temperature, and the current ideal power of the compressor is output based on the current pressure head, the current speed, the current guide vane opening and the first parameter of the first model, and the current cooling capacity and / or current refrigerant exhaust flow rate are output based on the current pressure head, the current suction temperature, the current exhaust temperature, the current speed, the current guide vane opening and the first parameter; if the preset model is the second model, the current operating data is input into the second model, the current pressure head is determined based on the current suction pressure, the current exhaust pressure, the current suction temperature and the current exhaust temperature, and the current ideal power of the compressor is output based on the current pressure head, the current speed, the current guide vane opening and the second parameter of the second model.

[0154] The present application also provides a computer storage medium storing instructions that, when executed on a computer or processor, cause the computer or processor to perform one or more steps of any of the aforementioned methods. If the various components of the compressor performance monitoring device are implemented as software functional units and sold or used as independent products, they may be stored in the storage medium.

[0155] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When software is used for implementation, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted via the computer-readable storage medium. The computer instructions can be transmitted from a website, computer, server or data center to another website, computer, server or data center via a wired, such as coaxial cable, optical fiber, digital subscriber line (DSL), or wireless (such as infrared, wireless, microwave, etc.) method. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrations. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a digital versatile disc (DVD)), or a semiconductor medium (eg, a solid state disk (SSD)).

[0156] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When executed, the program can include the processes of the above-described method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks. The technical features of this embodiment and the implementation scheme can be combined in any manner unless they conflict.

[0157] The embodiments described above are merely preferred embodiments of the present application and are not intended to limit the scope of the present application. Without departing from the design spirit of the present application, various modifications and improvements made to the technical solutions of the present application by ordinary technicians in this field should fall within the scope of protection determined by the claims of the present application.

Claims

1. A compressor performance monitoring method, characterized in that: The method comprises: Get the current operating data of the compressor; determining whether the compressor currently meets a first preset condition based on the current operating data; If so, the performance of the compressor is monitored according to the current operating data and the preset model to obtain the corresponding performance monitoring result of the compressor; the preset model is updated based on the historical effective operating data of the compressor in a normal state.

2. The method according to claim 1, wherein The performing performance monitoring on the compressor according to the current operating data and the preset model to obtain a performance monitoring result corresponding to the compressor includes: Inputting the current operating data into a preset model and outputting the current ideal power of the compressor; If the absolute deviation value between the current ideal power and the current actual power of the compressor is greater than a threshold, it is determined that the performance monitoring result corresponding to the compressor is that the compressor is in an abnormal state.

3. The method according to claim 1, wherein The determining, based on the current operating data, whether the compressor currently meets a first preset condition includes: Determine whether there is valid data in the current data grid area corresponding to the current operation data; the valid data is the average value of each historical operation parameter in the historical valid operation data corresponding to the current data grid area; Determining whether the current cumulative operating time of the compressor is greater than a preset operating time, and / or determining whether the number of target data grid areas currently corresponding to the compressor is greater than a first preset number; the target data grid area is used to represent a data grid area storing the valid data; Among them, the compressor corresponds to multiple data grid areas, and the multiple data grid areas are divided according to corresponding preset intervals based on the preset value ranges corresponding to each target operating parameter of the compressor; the target operating parameters include speed, guide vane opening and pressure ratio; the current speed in the current operating data is within the speed range corresponding to the current data grid area, the current guide vane opening in the current operating data is within the guide vane opening range corresponding to the current data grid area, and the current pressure ratio in the current operating data is within the pressure ratio range corresponding to the current data grid area.

4. The method according to any one of claims 1 to 3, wherein After determining whether the compressor currently meets a first preset condition based on the current operating data, the method further includes: If not, calibrating or updating the preset model based on the current operating data when the current operating data is valid operating data; the valid operating data is used to represent operating data of the compressor in a normal state and a stable state that meets the second preset condition; Among them, the operating data includes the following operating parameters: intake pressure, exhaust pressure, intake temperature, exhaust temperature, speed, guide vane opening, and actual power; the second preset condition includes that each operating parameter in the operating data does not exceed the corresponding preset parameter limit and / or the parameter dispersion coefficient corresponding to the operating data is less than the discrete threshold.

5. The method according to claim 4, wherein The calibrating or updating the preset model based on the current operating data includes: If the preset model is a first model, performing online calibration and testing on a first parameter of the first model based on the current operating data; the first model is trained based on the test data of the compressor; If the preset model is the second model, the second parameter of the second model is updated, trained and tested based on the current operating data; the second model is trained based on the historical effective operating data of the compressor.

6. The method according to claim 5, wherein After calibrating or updating the preset model based on the current operating data, the method further includes: The average value of each current operating parameter in the current operating data is saved as valid data in the current data grid area corresponding to the current operating data.

7. The method according to claim 5, wherein When the amount of the test data is greater than or equal to a second preset amount, the preset model is the first model; when the amount of the test data is less than the second preset amount, the preset model is the second model.

8. The method according to claim 5, wherein The test data includes the suction pressure, discharge pressure, suction temperature, discharge temperature, speed, guide vane opening, test power and cooling capacity of the compressor in a normal state; the first model includes a performance monitoring sub-model and a cooling sub-model; The performing performance monitoring on the compressor according to the current operating data and the preset model to obtain a performance monitoring result corresponding to the compressor includes: When the preset model is the first model, the current operating data is input into the first model, and the current ideal power corresponding to the compressor is output based on the performance monitoring sub-model, and the current cooling capacity and / or current refrigerant exhaust flow rate corresponding to the compressor is output based on the cooling sub-model; The performance monitoring result corresponding to the compressor is determined based on the current ideal power and / or the current cooling capacity and / or the current refrigerant exhaust flow rate.

9. The method according to claim 2, wherein The current operating data includes the following current operating parameters corresponding to the compressor: current suction pressure, current discharge pressure, current suction temperature, current discharge temperature, current speed, current guide vane opening, and current actual power; Inputting the current operating data into a preset model and outputting the current ideal power of the compressor includes: If the preset model is a first model, the current operating data is input into the first model, a current head pressure is determined based on the current suction pressure, the current discharge pressure, the current suction temperature, and the current discharge temperature, and a current ideal power of the compressor is output based on the current head pressure, the current speed, the current guide vane opening, and a first parameter of the first model, and a current cooling capacity and / or a current refrigerant discharge flow rate is output based on the current head pressure, the current suction temperature, the current discharge temperature, the current speed, the current guide vane opening, and the first parameter; If the preset model is the second model, the current operating data is input into the second model, the current head pressure is determined based on the current intake pressure, the current exhaust pressure, the current intake temperature, and the current exhaust temperature, and the current ideal power of the compressor is output based on the current head pressure, the current speed, the current guide vane opening, and the second parameter of the second model.

10. A compressor performance monitoring device, characterized in that: The compressor performance monitoring device comprises: An acquisition module is used to obtain the current operating data of the compressor; a judgment module, configured to judge whether the compressor currently meets a first preset condition based on the current operating data; A performance monitoring module is used to monitor the performance of the compressor according to the current operating data and the preset model to obtain the performance monitoring result corresponding to the compressor; the preset model is updated based on the historical effective operating data of the compressor in a normal state.

11. A compressor, characterized in that: include: processor and memory; The processor is connected to the memory; The memory is used to store executable program code; The processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to execute the method according to any one of claims 1 to 9.

12. A computer storage medium, characterized in that The computer storage medium stores a plurality of instructions, which are suitable for being loaded by a processor and executing the method steps according to any one of claims 1 to 9.

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