Inverter compressor operation control system suitable for commercial refrigerator operation scene

Abstract

The invention belongs to the field of inverter compressor control, relates to a data analysis technology, and aims to solve the problem that dual-mode self-adaptive switching control cannot be performed in combination with a staged operation state of an inverter compressor in the prior art, in particular to an inverter compressor operation control system suitable for an operation scene of a commercial refrigerator. Comprising a mechanism statistics module, a dual-mode control module and a frosting monitoring module, and the mechanism statistics module, the dual-mode control module and the frosting monitoring module are all in communication connection with a database; according to the method, the real-time operation risk of the commercial refrigerator is intelligently compared with the historical health base line, so that self-adaptive selection of the operation control mode of the inverter compressor is realized; the equipment runs stably without obvious fault risk, and at the moment, the system selects an energy efficiency control mode to maximize the energy utilization efficiency of the inverter compressor; on the contrary, if potential fault risks possibly exist in the equipment, the system is immediately switched to the load control mode, and therefore potential faults are effectively avoided or relieved.

Description

Technical Field

[0001] This invention belongs to the field of variable frequency compressor control and involves data analysis technology. Specifically, it is a variable frequency compressor operation control system applicable to commercial freezer operation scenarios. Background Technology

[0002] The variable frequency compressor operation control system is an intelligent variable frequency control solution developed for commercial refrigerated display cases (supermarket display cases, refrigerated display cases, kitchen cold storage, etc.). The system uses a variable frequency compressor as its core and, through advanced control algorithms and IoT technology, achieves precise control, high energy efficiency, and intelligent management of the refrigeration system.

[0003] The invention patent with publication number CN115711505B discloses a refrigerator, a variable frequency compressor, and a control method for the variable frequency compressor. This control method can effectively adjust the frequency of the variable frequency compressor to achieve better working efficiency and provide users with a good user experience. However, this control method cannot combine the phased operating status of the variable frequency compressor to perform dual-mode adaptive switching control, which results in the inability to identify the failure risk of the variable frequency compressor in a timely manner and the failure probability of the variable frequency compressor cannot be controlled. Summary of the Invention

[0004] The purpose of this invention is to provide a variable frequency compressor operation control system suitable for commercial refrigerator operation scenarios, in order to solve the problem that the existing technology cannot combine the phased operation status of the variable frequency compressor to perform dual-mode adaptive switching control. The technical problem to be solved by this invention is: how to provide a variable frequency compressor operation control system suitable for commercial refrigerator operation scenarios that can perform dual-mode adaptive switching control based on the phased operation status of the variable frequency compressor.

[0005] The objective of this invention can be achieved through the following technical solutions: A variable frequency compressor operation control system suitable for commercial freezer operation scenarios includes a mechanism statistics module, a dual-mode control module, and a frost monitoring module. The mechanism statistics module, dual-mode control module, and frost monitoring module are all connected to a database. The mechanism statistics module is used to statistically analyze the activation status of the autonomous protection mechanism of the commercial freezer: the commercial freezer is marked as the detection object, L1 consecutive statistical cycles are generated, the detection object is maintained at the end of each statistical cycle, and the autonomous protection mechanism activation monitoring is performed on the statistical cycle to obtain electrical protection values ​​and system protection values; if the detection object has a mechanical failure within the statistical cycle, the corresponding statistical cycle is marked as a feature extraction cycle; the electrical protection values ​​and system protection values ​​of all feature extraction cycles are numerically processed to obtain electrical balance values ​​and system balance values; The dual-mode control module is used for dual-mode adaptive control of commercial refrigerators: after the end of L1 detection cycles, a continuous control cycle is generated, the duration of which is equal to the duration of the detection cycle. The control cycle is divided into several control periods. At the end of each control period, the number of times electrical protection and system protection are activated by the detected object during the executed periods in the control cycle is obtained and marked as electrical risk value and system risk value, respectively. The variable frequency compressor control mode to be used in the next control period is selected based on the electrical risk value and system risk value. The frost monitoring module is used to monitor and analyze the frost status of the evaporator of the variable frequency compressor.

[0006] Furthermore, the specific process of monitoring the activation of the autonomous protection mechanism during the statistical period includes: counting the number of times electrical protection and system protection are activated on the monitored object within the statistical period and marking them as electrical protection values ​​and system protection values. Electrical protection includes overcurrent protection, overvoltage protection, undervoltage protection, phase loss protection, and reverse phase protection. System protection includes high and low voltage protection, exhaust overheat protection, and motor overheat protection.

[0007] Furthermore, the specific process of numerical processing of electrical protection values ​​and system protection values ​​for all feature extraction cycles includes: constructing an electrical protection set from the electrical protection values ​​of all feature extraction cycles, constructing a system protection set from the system protection values ​​of all feature extraction cycles, and performing data cleaning on the electrical protection set and system protection set to obtain electrical balance values ​​and system balance values.

[0008] Furthermore, the specific process of data cleaning for the electrical protection set includes: calculating the variance of all elements in the electrical protection set to obtain the electrical distribution coefficient; retrieving the electrical distribution threshold from the database; comparing the electrical distribution coefficient with the electrical distribution threshold; if the electrical distribution coefficient is greater than or equal to the electrical distribution threshold, removing the largest and smallest elements in the electrical protection set, and then recalculating the electrical distribution coefficient, and so on, until the electrical distribution coefficient is less than the electrical distribution threshold; if the electrical distribution coefficient is less than the electrical distribution threshold, marking the smallest element in the electrical protection set as the electrical balance value; the data cleaning process for the system protection set is the same as the data cleaning process for the electrical protection set.

[0009] Furthermore, the specific process for selecting the variable frequency compressor control mode to be used in the next control period includes: comparing the electrical risk value and the system risk value with the electrical balance value and the system balance value, respectively; if the electrical risk value is less than the electrical balance value and the system risk value is less than the system balance value, it is determined that the object under test does not have a fault risk in the current control period, and the energy efficiency control mode is used for variable frequency compressor operation control in the next control period; otherwise, it is determined that the object under test has a fault risk in the current control period, and the load control mode is used for variable frequency compressor operation control in the next control period.

[0010] Furthermore, the specific process of using energy efficiency control mode to control the operation of variable frequency compressors includes: under the premise that the temperature inside the object being detected is accurate, stable and uniform, performing PID control on the variable frequency compressor based on the principle of maximizing the energy efficiency ratio; The specific process of using load control mode to control the operation of variable frequency compressors includes: dynamically limiting the maximum operating frequency based on the condensing pressure of the detected object and the ambient temperature.

[0011] Furthermore, the frosting monitoring module monitors and analyzes the frosting status of the evaporator of the variable frequency compressor: a miniature camera or infrared camera is installed in front of or inside the evaporator, and an image of the fins is captured at the end of the monitoring period and marked as a monitoring image. The monitoring image is enlarged into a pixel grid image and grayscale transformation is performed. The grayscale range of the frost layer is retrieved, and the pixels whose grayscale values ​​are within the grayscale range of the frost layer are marked as frost grids. The ratio of the number of frost grids to the total number of pixels is marked as the frost coefficient. The frost coefficient is used to determine whether the frost status of the evaporator meets the requirements during the current monitoring period.

[0012] Furthermore, the specific process for determining whether the evaporator's frosting status meets the requirements during the current monitoring period includes: retrieving the frosting threshold from the database and comparing the frosting coefficient with the frosting threshold; if the frosting coefficient is less than the frosting threshold, the evaporator's frosting status during the current monitoring period is determined to meet the requirements; if the frosting coefficient is greater than or equal to the frosting threshold, the evaporator's frosting status during the current monitoring period is determined to not meet the requirements, a defrosting signal is generated, and the defrosting signal is sent to the system controller.

[0013] The present invention has the following beneficial effects: This application, through the autonomous protection mechanism activation monitoring of statistical cycles, can systematically collect and quantify abnormal events of equipment at the electrical and system levels. The mechanism's statistical module continuously monitors the operating status of the monitored object within each consecutive statistical cycle. Once an electrical or system protection event occurs, the mechanism's statistical module accurately counts the number of times these protections are activated. This meticulous statistical approach allows the system to comprehensively understand the potential failure modes and frequencies of commercial refrigerators from multiple dimensions. By clearly defining the specific types of electrical and system protection, the integrity and accuracy of data collection are ensured, avoiding statistical biases caused by ambiguous protection types. These precise protection values ​​serve as the basis for subsequent data processing and risk assessment, enabling the system to more accurately identify feature extraction cycles with mechanical failure risks. This provides reliable raw data for calculating electrical and system balance values, thereby improving the entire variable frequency compressor operation control system's ability to perceive the operating status of commercial refrigerators and the accuracy of fault warnings. This application, when statistically analyzing the activation status of the autonomous protection mechanism of commercial freezers, can effectively process the electrical protection values ​​and system protection values ​​of all feature extraction cycles. By constructing electrical protection sets and system protection sets and performing data cleaning, outliers and noise in the original data can be effectively removed, thereby obtaining more accurate and representative electrical balance values ​​and system balance values. This avoids the distortion of balance values ​​caused by fluctuations or anomalies in the original data, significantly improving the accuracy and reliability of the subsequent dual-mode control module in fault risk assessment and control mode selection. This enables the variable frequency compressor to operate and control based on a more stable benchmark, thereby improving the overall operating efficiency and stability of the commercial freezer. This application achieves adaptive selection of the variable frequency compressor's operating control mode by intelligently comparing the real-time operational risks of the commercial freezer with historical health baselines. When the equipment is operating smoothly and there is no obvious risk of failure, the system will select the energy efficiency control mode to maximize the energy utilization efficiency of the variable frequency compressor. Conversely, if there is a potential risk of failure, the system will immediately switch to the load control mode to reduce the load by limiting the compressor's operating intensity, thereby effectively avoiding or mitigating the occurrence of potential failures and prioritizing the safe and stable operation of the equipment. This risk assessment-based adaptive control strategy enables the system to dynamically optimize energy efficiency while ensuring equipment reliability, avoiding the safety hazards or energy efficiency losses that may be caused by a single control mode. This application enables precise and quantitative monitoring of evaporator frosting status. Compared to traditional defrosting methods based on temperature or time, this solution directly acquires image information of the evaporator fins and performs image processing and analysis to accurately identify the distribution and coverage area of ​​the frost layer, thereby obtaining an objective frosting coefficient. This visual recognition-based quantitative evaluation method avoids the lag or inaccuracy that may exist in traditional methods, ensuring the timeliness and necessity of defrosting operations. When the frosting coefficient reaches a preset threshold, the system can trigger defrosting in a timely manner, effectively preventing problems such as decreased refrigeration efficiency, increased energy consumption, and excessive compressor load caused by excessive frosting, thus ensuring the stable operation and energy efficiency performance of commercial refrigerators. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 This is a system block diagram of Embodiment 1 of the present invention; Figure 2 This is a flowchart of the method in Embodiment 2 of the present invention. Detailed Implementation

[0016] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] In variable frequency compressor operation control systems, existing technologies lack the ability to dynamically monitor the compressor's phased operating status and cannot achieve dual-mode adaptive switching control based on the startup state of the autonomous protection mechanism. Specifically, the control system fails to effectively identify the correlation between the startup modes of electrical and system protection and mechanical faults, resulting in the failure to detect and respond to the accumulation of fault risks in a timely manner, thus affecting system reliability and operational stability. Furthermore, the delayed identification of fault risks allows the compressor to continue operating under potentially abnormal conditions, increasing the likelihood of system failure.

[0018] For example, in a typical operating scenario of a supermarket refrigerated display case, when the ambient temperature and humidity remain at high levels, the frost formation rate on the evaporator surface accelerates significantly, causing abnormal fluctuations in the compressor load. During this process, electrical protection mechanisms (including overcurrent and overvoltage protection) are triggered multiple times, and system protection mechanisms (including high and low pressure protection and exhaust overheat protection) also activate frequently. However, the control system fails to distinguish whether this phenomenon is a temporary overload caused by environmental factors or a potential mechanical failure, and continues to use the energy efficiency control mode for frequency adjustment. As a result, the compressor continues to operate under high stress, and the activation status of the autonomous protection mechanism is not included in the phased evaluation, ultimately leading to an increased probability of mechanical failure.

[0019] If the above issues are not addressed, the inverter compressor may experience sudden malfunctions without warning, causing a disruption in the refrigeration system. This will lead to uncontrolled temperatures of stored goods, increase system maintenance complexity, and potentially trigger a chain reaction of equipment damage, further reducing the overall operating efficiency of the commercial freezer.

[0020] Example 1: As Figure 1 As shown, the variable frequency compressor operation control system suitable for commercial freezer operation scenarios includes a mechanism statistics module, a dual-mode control module, and a frost monitoring module. The mechanism statistics module, dual-mode control module, and frost monitoring module are all connected to the database.

[0021] For ease of understanding, the following explains some key terms in this embodiment: Commercial refrigerated display cases refer to the environment and operating conditions under which refrigeration equipment used in commercial applications, such as supermarket display cases, refrigerated display cases, and kitchen cold storage, actually operates. This scenario typically involves precise control of parameters such as temperature, humidity, and load to ensure the freshness and quality of food or goods.

[0022] The variable frequency compressor operation control system is an intelligent control solution designed for commercial freezers. The system takes the variable frequency compressor as its core and integrates advanced control algorithms and data processing technologies to achieve precise adjustment, energy efficiency optimization and intelligent management of the refrigeration system.

[0023] A database is a collection of information used to store and manage all data generated during the operation of a system. This data includes historical protection mechanism activation records, balance values, risk values, and frost status, providing data support for decision-making in various modules.

[0024] Autonomous protection mechanisms refer to the functions inherent in commercial freezers that automatically activate when abnormal operating conditions are detected to protect the equipment from damage. These mechanisms typically include electrical protection and system protection.

[0025] The statistical period refers to the time period set by the statistical module when collecting and analyzing data. During this time period, the system continuously monitors the operational status of the monitored objects.

[0026] Mechanical failure refers to mechanical problems that occur during the operation of commercial freezers, leading to a decline in equipment performance or malfunction.

[0027] The feature extraction period refers to a specific statistical period during which a mechanical failure occurs in the detected object, and this period is marked for further analysis.

[0028] The control cycle refers to the time period set by the dual-mode control module when performing mode selection and control. This cycle usually has the same length as the detection cycle.

[0029] Evaporator frosting refers to the formation and accumulation of frost on the surface of the evaporator of the inverter compressor. Excessive frosting can affect heat exchange efficiency and reduce cooling performance.

[0030] The mechanism statistics module is used to statistically analyze the activation status of the autonomous protection mechanism of commercial refrigerators. The commercial refrigerator is marked as the detection object, and L1 consecutive statistical cycles are generated. At the end of each statistical cycle, the detection object is maintained. Within the statistical cycle, the number of times electrical and system protection activations occur on the detection object is counted and marked as electrical protection values ​​and system protection values, respectively. Electrical protections include overcurrent protection, overvoltage protection, undervoltage protection, phase loss protection, and reverse phase protection. System protections include high and low voltage protection, exhaust overheat protection, and motor overheat protection. If a mechanical fault occurs on the detection object within the statistical cycle, the corresponding statistical cycle is marked as a feature extraction cycle. The electrical protection set is composed of the electrical protection values ​​from all feature extraction cycles, and the system protection set is composed of the system protection values ​​from all feature extraction cycles. The data cleaning process for the electrical protection set and the system protection set yields the electrical balance value and the system balance value. The specific steps for cleaning the electrical protection set include: calculating the variance of all elements within the set to obtain the electrical distribution coefficient; retrieving the electrical distribution threshold from the database; comparing the electrical distribution coefficient with the threshold; if the electrical distribution coefficient is greater than or equal to the threshold, removing the largest and smallest elements from the set and recalculating the coefficient, and so on, until the coefficient is less than the threshold; if the coefficient is less than the threshold, marking the smallest element as the electrical balance value. The data cleaning process for the system protection set is the same as that for the electrical protection set.

[0031] Overcurrent protection prevents the motor from burning out due to excessive current; overvoltage and undervoltage protection ensure that the voltage remains within a safe range, preventing equipment damage or unstable operation; phase loss protection prevents damage caused by motor operation in a phase loss state; and reverse phase protection prevents the motor from reversing. Clearly defining these specific electrical protection types ensures comprehensive monitoring of potential electrical faults. These protection functions are typically implemented by the variable frequency compressor's drive or independent electrical protection module hardware. When a corresponding electrical abnormality is detected, this hardware immediately triggers the protection action and sends a protection signal or status code to the mechanism statistics module. Alternatively, the mechanism statistics module can also be implemented by integrating corresponding detection algorithms and logic. For example, by collecting three-phase current and voltage signals, it calculates the RMS current value, RMS voltage value, phase sequence, etc., in real time and compares them with preset thresholds. Once the threshold is exceeded or an abnormal phase sequence is detected, it is determined as a corresponding electrical protection event.

[0032] High and low pressure protection prevents excessively high or low pressure in the refrigeration system, avoiding system damage or inefficiency; discharge overheat protection prevents excessively high compressor discharge temperatures, avoiding lubricant carbonization and compressor damage; motor overheat protection prevents the compressor motor from overheating due to overload or poor heat dissipation. Clearly defining these specific system protection types ensures comprehensive monitoring of potential faults in the refrigeration system and compressor itself. These protection functions are typically implemented by sensors (such as pressure sensors and temperature sensors) installed on the refrigeration system piping and compressor itself, along with corresponding controllers (such as high and low pressure protectors and thermistors). When an anomaly is detected, these sensors or protectors trigger protection actions and send signals to the mechanism statistics module. Alternatively, the mechanism statistics module can also be implemented by reading digital signals from sensors inside the refrigeration system controller or compressor. For example, it can periodically acquire data such as high pressure, low pressure, discharge temperature, and motor winding temperature via Modbus, CAN, or other communication protocols and compare them with preset safety thresholds. Once a threshold is exceeded, it is determined as a corresponding system protection event.

[0033] For example, monitoring the activation of the autonomous protection mechanism for a statistical cycle can be implemented as follows: The variable frequency compressor controller of a commercial refrigerator integrates multiple protection loops and corresponding status registers. For instance, when overcurrent protection occurs, the overcurrent protection loop is triggered, setting a specific bit in the status register. The mechanism statistics module can periodically read the status register of the variable frequency compressor controller via the Modbus communication protocol. At the beginning of each statistical cycle, the mechanism statistics module clears the internally maintained electrical protection counters and system protection counters. During the statistical cycle, whenever a bit indicating overcurrent protection, overvoltage protection, undervoltage protection, phase loss protection, or reverse phase protection is set in the status register, the corresponding electrical protection counter increments by 1. Similarly, when a bit indicating high / low voltage protection, exhaust overheat protection, or motor overheat protection is set, the corresponding system protection counter also increments by 1. At the end of the statistical cycle, the mechanism statistics module marks the value of the electrical protection counter as the electrical protection value and the value of the system protection counter as the system protection value, stores these values ​​in the database, and simultaneously clears the counters to prepare for data acquisition in the next statistical cycle.

[0034] The dual-mode control module is used for dual-mode adaptive control of commercial refrigerators: After the end of L1 detection cycles, a continuous control cycle is generated, with the duration of the control cycle equal to that of the detection cycle. The control cycle is divided into several control periods. At the end of each control period, the number of times electrical protection and system protection were activated during the executed periods of the control cycle is obtained and marked as electrical risk value and system risk value, respectively. The electrical risk value and system risk value are compared with the electrical balance value and system balance value, respectively. If the electrical risk value is less than the electrical balance value and the system risk value is less than the system balance value, it is determined that the detected object does not have a fault risk in the current control period, and the variable frequency compressor is operated in the energy efficiency control mode in the next control period. Under the premise that the temperature inside the detected object is accurate, stable and uniform, the variable frequency compressor is controlled by PID based on maximizing the energy efficiency ratio. Otherwise, it is determined that the detected object has a fault risk in the current control period, and the variable frequency compressor is operated in the load control mode in the next control period. The maximum operating frequency is dynamically limited according to the condensing pressure of the detected object and the ambient temperature.

[0035] The determination of whether a monitored object poses a risk of failure or not during the current control period is based on classifying the risk level of the object's current operating status according to the comparison results mentioned above. When the real-time risk value is lower than or equal to the historical health baseline, it indicates that the current operating environment is relatively stable and the possibility of equipment failure is low; therefore, it is determined as "no risk of failure." Conversely, when the real-time risk value is higher than the historical health baseline, the equipment is considered to have potential failure hazards, requiring preventive measures; therefore, it is determined as "risk of failure." This determination serves as the basis for subsequent selection of the variable frequency compressor control mode, and its accuracy directly affects the system's safety and energy efficiency. This determination can be a simple Boolean judgment, i.e., no risk if certain conditions are met, otherwise risky; or it can be a multi-level risk assessment based on fuzzy logic or machine learning models, ultimately leading to the selection of two control modes.

[0036] The variable frequency compressor is controlled using an energy efficiency control mode. This mode aims to minimize energy consumption by optimizing the compressor's operating parameters while ensuring the freezer meets normal cooling requirements. When the system determines that the monitored object poses no risk of failure, this mode can be confidently used to pursue optimal energy efficiency. This mode typically involves fine-tuning parameters such as compressor speed, evaporation temperature, and condensation temperature. For example, a proportional-integral-derivative (PID) control algorithm can be used to dynamically adjust the compressor frequency based on parameters such as the freezer's internal temperature, set temperature, and ambient temperature, keeping it near its highest energy efficiency ratio while meeting cooling demands. Alternatively, a model predictive control (MPC) approach can be employed to predict future load changes and optimize the compressor's operating strategy in advance.

[0037] The variable frequency compressor is controlled using a load control mode. This mode aims to reduce the system load by limiting the compressor's operating intensity when a potential fault is detected, thereby reducing the likelihood or severity of a fault and prioritizing the safe and stable operation of the equipment. This mode typically sacrifices some energy efficiency for higher reliability. For example, the maximum operating frequency of the compressor can be limited to prevent it from reaching full load, thus reducing electrical and mechanical stress; or the operating pressure and temperature of the compressor can be reduced by adjusting the refrigerant flow rate, setting a higher evaporation temperature, or a lower condensation temperature, allowing it to operate under more conservative conditions.

[0038] The frosting monitoring module is used to monitor and analyze the frosting status of the evaporator of the variable frequency compressor. A miniature camera or infrared camera is installed in front of or inside the evaporator. At the end of the monitoring period, an image of the fins is captured and marked as the monitoring image. The monitoring image is magnified into a pixel grid image and grayscale transformation is performed. The grayscale range of the frost layer is retrieved, and the pixels whose grayscale values ​​are within the grayscale range of the frost layer are marked as frost grids. The ratio of the number of frost grids to the total number of pixels is marked as the frost coefficient. The frost threshold is retrieved from the database, and the frost coefficient is compared with the frost threshold. If the frost coefficient is less than the frost threshold, the frost status of the evaporator in the current monitoring period is determined to meet the requirements. If the frost coefficient is greater than or equal to the frost threshold, the frost status of the evaporator in the current monitoring period is determined to not meet the requirements. A defrosting signal is generated and sent to the system controller.

[0039] Specifically, installing miniature or infrared cameras in front of or inside the evaporator is a key hardware component for acquiring image data of the evaporator surface. Miniature cameras capture visible light images, providing intuitive visual information; infrared cameras capture infrared radiation, using temperature differences to help determine frost conditions, especially in low light or when penetration of certain media is required. For example, a miniature camera can be fixed to the windward or exhaust side of the evaporator fin assembly, ensuring its field of view covers most of the fin area for comprehensive frost monitoring. Alternatively, an infrared camera can be installed in a suitable location inside the evaporator, such as in an air duct, to identify frost areas by monitoring the temperature distribution on the fin surface. Taking images of the fins at the end of the monitoring period and marking them as monitoring images is a method for periodically acquiring evaporator status data. Taking images at the end of a preset monitoring period ensures that the latest frost information is obtained at a specific time, avoiding data redundancy and processing burden caused by continuous shooting, while ensuring timely monitoring. For example, the system controller can have a built-in timer that triggers the camera to capture images when the timer reaches the preset monitoring period duration, storing the captured image data in memory as the monitoring image. Alternatively, the start and end of the monitoring period can be triggered by an external sensor, with the image processing unit sending a command to the camera to take a picture at the end of the monitoring period. Enlarging the monitoring image to a pixel-level image and performing grayscale conversion is a preprocessing step to make the original image suitable for subsequent frosting analysis. Enlarging to a pixel-level image standardizes the image size and resolution for unified processing; grayscale conversion converts the color image to a grayscale image, simplifying the image data and highlighting brightness information, as frost typically appears as areas with a significant brightness difference from the background. For example, the image processing unit can receive the monitoring image data, enlarge it to a preset pixel-level size using an image processing algorithm, and then apply a standard grayscale conversion formula to convert the color image to a grayscale image. Alternatively, grayscale image output can be supported at the image acquisition hardware level, or an image processing function can be called through a software library to directly convert the original image to a grayscale pixel-level image of a specified resolution. Retrieving the grayscale range of the frost layer is the basis for identifying frost areas in the image. A pre-determined grayscale range for frost is a crucial threshold for distinguishing frost from other background elements, ensuring the accuracy of subsequent analysis. For example, the grayscale range of frost can be pre-determined experimentally or empirically and stored in a database or system configuration parameters. During analysis, the image processing module reads these pre-defined grayscale value ranges from the database. Alternatively, machine learning algorithms can be used to train on a large number of evaporator images with and without frost, automatically learning and dynamically adjusting the grayscale range of frost. Marking pixels whose grayscale values ​​fall within the frost grayscale range as frost grids is a key step in image segmentation, used to accurately identify frost regions in the image.By identifying pixels that fall within a specific grayscale range, the distribution and area of ​​frost can be quantified. For example, the image processing unit iterates through each pixel in the pixel grid image, obtains its grayscale value, and compares it with a preset frost grayscale range. If the pixel's grayscale value falls within this range, it is marked as a frost grid. Alternatively, the threshold segmentation function in the image processing software library can be used to directly binarize the grayscale image based on the retrieved frost grayscale range. The ratio of the number of frost grids to the total number of pixel grids is marked as the frost coefficient, which is an indicator of the degree of frost on the evaporator. The frost coefficient intuitively reflects the proportion of the evaporator surface covered by frost, providing a quantitative basis for subsequent frost status determination. For example, after marking the frost grids, the image processing unit counts the number of all pixels marked as frost grids and divides it by the total number of pixels in the pixel grid image to obtain the frost coefficient. Alternatively, the proportion of the area of ​​white pixels in the binarized image to the total area can be directly calculated using an image processing algorithm. Determining whether the evaporator's frost condition meets requirements during the current monitoring period using a frost coefficient is a crucial step in making decisions based on quantitative indicators. By comparing the calculated frost coefficient with preset standards, it can be determined whether the evaporator needs defrosting, thereby optimizing the freezer's operation. For example, the frost monitoring module receives the calculated frost coefficient and compares it with preset frost thresholds in a database. Based on the comparison result, it outputs a Boolean value or status signal indicating whether the frost condition meets the requirements.

[0040] Example 2: Figure 2 As shown, the variable frequency compressor operation control method applicable to commercial freezer operation scenarios includes the following steps: Step 1: Statistical analysis of the activation status of the autonomous protection mechanism of the commercial freezer: Mark the commercial freezer as the detection object and generate L1 consecutive statistical cycles. If the detection object has a mechanical failure within the statistical cycle, mark the corresponding statistical cycle as the feature extraction cycle. Numerical processing is performed on the electrical protection value and system protection value of all feature extraction sets to obtain the electrical balance value and system balance value. Step 2: Implement dual-mode adaptive control for commercial freezers: After the L1 detection cycle ends, generate a continuous control cycle with the same duration as the detection cycle. Divide the control cycle into several control periods and determine whether the detected object has a fault risk at the end of each control period. Step 3: Monitor and analyze the frost status of the evaporator of the variable frequency compressor: At the end of the monitoring period, take a picture of the fins and mark it as a monitoring image. Perform image processing on the monitoring image to obtain the frost coefficient. Use the frost coefficient to determine whether the frost status of the evaporator meets the requirements.

[0041] This variable frequency compressor operation control system, applicable to commercial freezer operation scenarios, marks the commercial freezer as the detection object during operation, generating L1 consecutive statistical cycles. If the detection object experiences a mechanical failure within a statistical cycle, the corresponding statistical cycle is marked as a feature extraction cycle. Electrical protection values ​​and system protection values ​​from all feature extraction sets are numerically processed to obtain electrical balance values ​​and system balance values. After the L1 detection cycles end, a continuous control cycle is generated, with the duration equal to that of the detection cycle. The control cycle is divided into several control periods. At the end of each control period, it is determined whether the detection object has a failure risk. At the end of each monitoring period, a fin image is captured and marked as a monitoring image. Image processing is performed on the monitoring image to obtain a frosting coefficient, which is used to determine whether the evaporator's frosting state meets the requirements.

[0042] The above description is merely an example and illustration of the structure of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the structure of the invention or exceed the scope defined in the claims, all of which should fall within the protection scope of the present invention.

[0043] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0044] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A variable frequency compressor operation control system suitable for commercial freezer operation scenarios, characterized in that, It includes a mechanism statistics module, a dual-mode control module, and a frosting monitoring module, all of which are connected to a database. The mechanism statistics module is used to statistically analyze the activation status of the autonomous protection mechanism of the commercial freezer: the commercial freezer is marked as the detection object, L1 consecutive statistical cycles are generated, the detection object is maintained at the end of each statistical cycle, and the autonomous protection mechanism activation monitoring is performed on the statistical cycle to obtain the electrical protection value and the system protection value. If the object being detected experiences a mechanical failure within the statistical period, the corresponding statistical period will be marked as the feature extraction period. Numerical processing is performed on the electrical protection values ​​and system protection values ​​for all feature extraction cycles to obtain the electrical balance value and system balance value; The dual-mode control module is used for dual-mode adaptive control of commercial refrigerators: after the end of L1 detection cycles, a continuous control cycle is generated, the duration of which is equal to the duration of the detection cycle. The control cycle is divided into several control periods. At the end of each control period, the number of times electrical protection and system protection are activated by the detected object during the executed periods in the control cycle is obtained and marked as electrical risk value and system risk value, respectively. The variable frequency compressor control mode to be used in the next control period is selected based on the electrical risk value and system risk value. The frost monitoring module is used to monitor and analyze the frost status of the evaporator of the variable frequency compressor.

2. The variable frequency compressor operation control system for commercial freezer operation scenarios according to claim 1, characterized in that, The specific process of monitoring the activation of the autonomous protection mechanism for the statistical period includes: counting the number of times electrical protection and system protection are activated on the detected object within the statistical period and marking them as electrical protection values ​​and system protection values. Electrical protection includes overcurrent protection, overvoltage protection, undervoltage protection, phase loss protection, and reverse phase protection. System protection includes high and low voltage protection, exhaust overheat protection, and motor overheat protection.

3. The variable frequency compressor operation control system for commercial freezer operation scenarios according to claim 2, characterized in that, The specific process of numerical processing of electrical protection values ​​and system protection values ​​for all feature extraction cycles includes: constructing an electrical protection set from the electrical protection values ​​of all feature extraction cycles, constructing a system protection set from the system protection values ​​of all feature extraction cycles, and performing data cleaning on the electrical protection set and system protection set to obtain electrical balance value and system balance value.

4. The variable frequency compressor operation control system for commercial freezer operation scenarios according to claim 3, characterized in that, The specific process of data cleaning for the electrical protection set includes: calculating the variance of all elements in the electrical protection set to obtain the electrical distribution coefficient; retrieving the electrical distribution threshold from the database; comparing the electrical distribution coefficient with the electrical distribution threshold; if the electrical distribution coefficient is greater than or equal to the electrical distribution threshold, removing the largest and smallest elements in the electrical protection set, and then recalculating the electrical distribution coefficient, and so on, until the electrical distribution coefficient is less than the electrical distribution threshold; if the electrical distribution coefficient is less than the electrical distribution threshold, marking the smallest element in the electrical protection set as the electrical balance value; the data cleaning process for the system protection set is the same as the data cleaning process for the electrical protection set.

5. The variable frequency compressor operation control system for commercial freezer operation scenarios according to claim 4, characterized in that, The specific process for selecting the variable frequency compressor control mode for the next control period includes: comparing the electrical risk value and the system risk value with the electrical balance value and the system balance value, respectively; if the electrical risk value is less than the electrical balance value and the system risk value is less than the system balance value, it is determined that the object under test does not have a fault risk in the current control period, and the energy efficiency control mode is used for variable frequency compressor operation control in the next control period; otherwise, it is determined that the object under test has a fault risk in the current control period, and the load control mode is used for variable frequency compressor operation control in the next control period.

6. The variable frequency compressor operation control system for commercial freezer operation scenarios according to claim 5, characterized in that, The specific process of using energy efficiency control mode to control the operation of variable frequency compressors includes: under the premise that the temperature inside the object being detected is accurate, stable and uniform, performing PID control on the variable frequency compressor based on the principle of maximizing the energy efficiency ratio; The specific process of using load control mode to control the operation of variable frequency compressors includes: dynamically limiting the maximum operating frequency based on the condensing pressure of the detected object and the ambient temperature.

7. The variable frequency compressor operation control system for commercial freezer operation scenarios according to claim 6, characterized in that, The frosting monitoring module monitors and analyzes the frosting status of the evaporator of the variable frequency compressor: a miniature camera or infrared camera is installed in front of or inside the evaporator. At the end of the monitoring period, the fin image is captured and marked as the monitoring image. The monitoring image is magnified into a pixel grid image and grayscale transformation is performed. The grayscale range of the frost layer is retrieved. The pixels whose grayscale values ​​are within the grayscale range of the frost layer are marked as frost grids. The ratio of the number of frost grids to the total number of pixels is marked as the frost coefficient. The frost coefficient is used to determine whether the frost status of the evaporator meets the requirements during the current monitoring period.

8. The variable frequency compressor operation control system for commercial freezer operation scenarios according to claim 7, characterized in that, The specific process for determining whether the evaporator's frosting status meets the requirements during the current monitoring period includes: retrieving the frosting threshold from the database and comparing the frosting coefficient with the frosting threshold; if the frosting coefficient is less than the frosting threshold, the evaporator's frosting status during the current monitoring period is determined to meet the requirements; if the frosting coefficient is greater than or equal to the frosting threshold, the evaporator's frosting status during the current monitoring period is determined to not meet the requirements, a defrosting signal is generated, and the defrosting signal is sent to the system controller.

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