A compressor control method and system for body refrigeration equipment

Abstract

This invention relates to the field of equipment control technology and discloses a compressor control method and system for body refrigeration equipment. The method includes collecting real-time temperature, speed, and operating power data of the compressor; extracting operating fluctuations and energy consumption characteristics to calculate a lubrication status score; calculating the low-temperature friction coefficient when the score is below a threshold to complete a mechanical wear risk assessment; calculating the equipment fatigue index based on historical operating data; triggering data backtracking to update the lubrication status score when it exceeds the threshold; optimizing the compressor operation control signal based on the updated score and friction coefficient; dynamically adjusting the speed to balance friction loss and operating energy consumption; and generating start-stop adjustment commands after verification. This method can achieve accurate assessment and energy-saving optimization of the compressor lubrication status, reduce equipment wear risk, extend service life, and meet the stable operation requirements of equipment in funeral refrigeration scenarios.

Description

Technical Field

[0001] This invention relates to the field of equipment control technology, and in particular to a compressor control method and system for a body refrigeration equipment. Background Technology

[0002] Currently, in the field of equipment control technology, the operation status management of compressors in body refrigeration equipment is a key aspect of ensuring stable equipment operation and mitigating the risk of refrigeration service interruptions, and has become a major research focus in the field.

[0003] In one existing technology, a fixed-frequency start-stop method is mainly used to control the operation of the compressor in a body refrigeration equipment. This method relies primarily on fixed temperature threshold triggering or single energy consumption index assessment, such as using preset upper and lower limits of refrigeration temperature to control the compressor's start-stop, and judging the equipment's energy consumption level through simple runtime statistics. However, this approach is clearly insufficient in complex operating environments with low temperatures and low frequencies. Because fixed thresholds cannot adapt to the dynamic changes in the compressor's lubrication status, it is easily affected by low-temperature environments, load fluctuations, and long-term wear. As the equipment's runtime increases, refrigeration conditions become more variable, and low-frequency lubrication deterioration occurs, it can easily lead to increased mechanical wear of the compressor and poor energy consumption control. Furthermore, it lacks the optimization capability of real-time monitoring of multi-dimensional operating data based on supervisory control and data acquisition systems. Especially in complex scenarios where body refrigeration equipment operates continuously for long periods and experiences frequent low-temperature and low-frequency start-stops, it is difficult to avoid equipment failures caused by insufficient lubrication, resulting in a shortened equipment lifespan.

[0004] In summary, existing technologies are insufficient to achieve full-cycle health status management of the compressor in body refrigeration equipment, and cannot meet the core requirement of equipment operational stability when facing long-term low-temperature operation scenarios. Summary of the Invention

[0005] This invention provides a compressor control method and system for body refrigeration equipment, so as to realize the full-cycle health status management of the compressor of body refrigeration equipment and meet the core requirements of equipment operation stability when facing long-term low-temperature operation scenarios.

[0006] In a first aspect, to solve the above-mentioned technical problems, the present invention provides a compressor control method for a body refrigeration device, comprising:

[0007] Obtain the real-time operating parameters of the compressor in the body refrigeration equipment;

[0008] Feature extraction is performed on the real-time operating parameters to obtain operating fluctuation features and energy consumption status features. A score is calculated based on the operating fluctuation features and the energy consumption status features to obtain a lubrication status score.

[0009] If the lubrication status score is lower than the preset lubrication judgment threshold, the low-temperature friction coefficient is calculated based on the real-time operating parameters, and the wear risk assessment is performed based on the low-temperature friction coefficient to obtain the mechanical wear risk result.

[0010] By combining the real-time operating parameters with the pre-acquired historical operating logs, the deviation is calculated to obtain the operating deviation value. Based on the operating deviation value and the mechanical wear risk result, a fatigue assessment is performed to obtain the equipment fatigue index.

[0011] If the fatigue index of the equipment is higher than the preset fatigue judgment threshold, the operation fluctuation characteristics are updated by backtracking based on the historical operation log to obtain the updated operation fluctuation characteristics. The score is recalculated based on the updated operation fluctuation characteristics to obtain the updated lubrication status score.

[0012] Based on the updated lubrication condition score and the low-temperature friction coefficient, parameter optimization calculations are performed to obtain the operation control signal;

[0013] The operating deviation value is recalculated based on the operating control signal to obtain an updated operating deviation value. If the updated operating deviation value is lower than a preset safety judgment threshold, a start / stop adjustment command is generated.

[0014] Secondly, the present invention provides a compressor control system for a body refrigeration device, comprising:

[0015] The data acquisition module is used to obtain the real-time operating parameters of the compressor in the body refrigeration equipment;

[0016] The feature extraction and scoring module is used to extract features from the real-time operating parameters to obtain operating fluctuation features and energy consumption status features, and to calculate the lubrication status score based on the operating fluctuation features and the energy consumption status features.

[0017] The wear risk assessment module is used to calculate the low-temperature friction coefficient based on the real-time operating parameters when the lubrication status score is lower than the preset lubrication judgment threshold, and to perform wear risk assessment based on the low-temperature friction coefficient to obtain the mechanical wear risk result.

[0018] The fatigue assessment module is used to calculate the deviation by combining the real-time operating parameters with the pre-acquired historical operating logs to obtain the operating deviation value, and to perform fatigue assessment calculation based on the operating deviation value and the mechanical wear risk result to obtain the equipment fatigue index.

[0019] The data backtracking module is used to update the operating fluctuation characteristics based on the historical operating logs when the fatigue index of the equipment is higher than the preset fatigue judgment threshold, so as to obtain the updated operating fluctuation characteristics. The scoring is then recalculated based on the updated operating fluctuation characteristics to obtain the updated lubrication status score.

[0020] The parameter optimization module is used to perform parameter optimization calculations based on the updated lubrication status score and the low-temperature friction coefficient to obtain the operation control signal;

[0021] The start / stop control module is used to recalculate the operating deviation value based on the operating control signal to obtain an updated operating deviation value. If the updated operating deviation value is lower than a preset safety judgment threshold, a start / stop adjustment command is generated.

[0022] Compared with the prior art, the present invention has the following beneficial effects:

[0023] (1) This invention obtains real-time operating parameters by collecting and combining real-time temperature, speed and operating power data of the compressor, extracts operating fluctuations and energy consumption status characteristics and performs calculations to obtain an accurate lubrication status score. This method overcomes the limitation of traditional fixed-frequency start-stop that cannot adapt to low-temperature and low-frequency scenarios, eliminates the interference of extreme environments on lubrication performance, effectively improves the monitoring and evaluation accuracy of compressor lubrication status, and solves the problem that traditional methods cannot detect low-temperature lubrication degradation in real time.

[0024] (2) When the lubrication status score is low, this invention calculates the low-temperature friction coefficient based on real-time temperature to assess the risk of mechanical wear, and calculates the operating deviation value and equipment fatigue index based on historical logs. When fatigue exceeds the limit, a data backtracking mechanism is triggered to update the lubrication score. This method accurately quantifies the mechanical wear and fatigue evolution law under long-term low-temperature and low-frequency operation, provides a multi-dimensional judgment basis for equipment health management, and significantly improves the accuracy of identifying faults caused by insufficient early lubrication.

[0025] (3) Based on the updated lubrication condition score and low-temperature friction coefficient, the present invention optimizes the operation control signal, dynamically adjusts the rotation speed to balance friction loss and operating energy consumption, and generates start-stop adjustment commands after verifying the operation deviation. This method makes up for the shortcomings of traditional simple adjustment that lacks full-cycle dynamic optimization, effectively avoids the problem of shortened service life caused by frequent start-stop of equipment, takes into account both operation stability and energy saving effect, and meets the core requirements of service continuity in the funeral industry. Attached Figure Description

[0026] Figure 1 This is a schematic flowchart of a compressor control method for a body refrigeration device provided in the first embodiment of the present invention;

[0027] Figure 2 This is a schematic diagram of a compressor control system for a body refrigeration device provided in the second embodiment of the present invention. Detailed Implementation

[0028] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] Reference Figure 1 The first embodiment of the present invention provides a compressor control method for a body refrigeration device, comprising the following steps:

[0030] S101, Obtain the real-time operating parameters of the compressor of the body refrigeration equipment;

[0031] S102 performs feature extraction on the real-time operating parameters to obtain operating fluctuation features and energy consumption status features. Based on the operating fluctuation features and energy consumption status features, a score is calculated to obtain a lubrication status score.

[0032] S103 If the lubrication status score is lower than the preset lubrication judgment threshold, the low-temperature friction coefficient is calculated based on the real-time operating parameters, and the wear risk assessment is performed based on the low-temperature friction coefficient to obtain the mechanical wear risk result.

[0033] S104 combines the real-time operating parameters with the pre-acquired historical operating logs to calculate the deviation, obtain the operating deviation value, and perform fatigue assessment calculation based on the operating deviation value and the mechanical wear risk result to obtain the equipment fatigue index;

[0034] S105 If the fatigue index of the equipment is higher than the preset fatigue judgment threshold, the operation fluctuation characteristics are updated by backtracking based on the historical operation log to obtain the updated operation fluctuation characteristics. The score is recalculated based on the updated operation fluctuation characteristics to obtain the updated lubrication status score.

[0035] S106 performs parameter optimization calculations based on the updated lubrication status score and the low-temperature friction coefficient to obtain the operation control signal;

[0036] S107, recalculate the operating deviation value according to the operating control signal to obtain an updated operating deviation value. If the updated operating deviation value is lower than a preset safety judgment threshold, generate a start / stop adjustment command.

[0037] In step S101, obtaining the real-time operating parameters of the compressor of the body refrigeration equipment includes:

[0038] The temperature values ​​of the core part of the compressor are collected according to the preset sampling frequency to obtain real-time temperature data;

[0039] Collect the rotation frequency value of the compressor shaft to obtain real-time speed data;

[0040] Collect the real-time operating power value of the compressor, combine it with the rated power value to calculate the power ratio, and obtain the operating power data;

[0041] By aligning the acquisition timing of the real-time temperature data, the real-time rotational speed data, and the operating power data, the real-time operating parameters are obtained.

[0042] It should be noted that, firstly, temperature values ​​of key components of the compressor are collected at a preset sampling frequency to obtain real-time temperature data. High-precision platinum resistance temperature sensors are used for data acquisition, installed at three key locations on the compressor: the crankshaft bearing, the cylinder piston, and the lubrication oil chamber. The basic sampling frequency is set to once per minute based on historical operating conditions, increased to once every 10 seconds during low-frequency periods at night, and decreased to once every 5 minutes during stable daytime periods. During the data acquisition process, an outlier is eliminated using a 3x standard deviation criterion. All retained valid temperature values ​​are bound to a unified acquisition timestamp and a unique device identification code, forming a continuous real-time temperature data sequence.

[0043] Next, the rotation frequency of the compressor shaft is collected to obtain real-time speed data. The speed data is acquired non-contactly at the end of the shaft using a photoelectric speed sensor, with its sampling frequency synchronized with the fundamental frequency of the temperature data. The system uses pulse counting to count the rotation pulses per unit time to obtain the real-time speed, and then uses a sliding window averaging method with a window size of 5 sampling points to smooth and denoise the raw speed. The processed speed value is then synchronously bound to the corresponding timestamp and device identification code to generate real-time speed data. For example, the sensor collects pulse signals at a frequency of once per minute and converts them into a real-time speed of 1500 revolutions per minute, which is then smoothed at 5 points and bound to a timestamp to generate the data.

[0044] Secondly, the real-time operating power value of the compressor is collected, and the power ratio is calculated by combining it with the rated power value to obtain the operating power data. The real-time operating power is synchronously acquired from the power supply circuit using a three-phase intelligent power acquisition module, with the sampling frequency kept completely consistent with the temperature data. The system performs low-pass filtering on the real-time operating power value to filter out high-frequency grid interference. Subsequently, the filtered real-time operating power value is divided by the rated power value indicated on the nameplate to calculate the dimensionless power ratio ranging from 0 to 1. Finally, the real-time operating power, rated power, and power ratio are combined and encapsulated with a unified timestamp to obtain the operating power data.

[0045] Subsequently, the acquisition timelines of the real-time temperature data, real-time speed data, and operating power data are aligned to obtain the real-time operating parameters. Timeline alignment is based on a unified device clock and achieved using linear interpolation: first, the timestamp deviations of each acquisition module are calibrated; then, using the acquisition time point of the temperature data as a reference, linear interpolation is performed on the speed data and operating power data to generate a numerical sequence with perfectly matched time nodes. During data combination, the core numerical values ​​of the three types of data are structurally encapsulated, and all numerical fields are mapped to the 0-1 range using min-max normalization to eliminate magnitude differences.

[0046] In step S102, feature extraction is performed on the real-time operating parameters to obtain operating fluctuation features and energy consumption status features. A lubrication status score is then calculated based on the operating fluctuation features and the energy consumption status features, including:

[0047] The speed fluctuation standard deviation is calculated based on the real-time speed data in the real-time operating parameters, and the speed fluctuation standard deviation is used as the speed stability feature.

[0048] The temperature change amplitude is calculated based on the real-time temperature data, and the temperature fluctuation characteristics are obtained by weighted fusion based on the temperature change amplitude.

[0049] The rotational speed stability feature and the temperature fluctuation feature are fused to obtain the operational fluctuation feature;

[0050] Based on the operating power data in the real-time operating parameters, calculate various energy consumption change indicators, and perform fusion calculation on the various energy consumption change indicators to obtain energy consumption status characteristics;

[0051] The operational fluctuation characteristics and the energy consumption status characteristics are assigned corresponding weights, and a weighted fusion calculation is performed to obtain the lubrication status score.

[0052] It should be noted that, firstly, based on the real-time rotational speed data in the real-time operating parameters, the low-temperature vibration frequency of the compressor is extracted, and the standard deviation of the rotational speed fluctuation is calculated to obtain the rotational speed stability characteristics. The low-temperature vibration frequency of the compressor is extracted using a Fast Fourier Transform (FFT) algorithm. First, continuous time-series real-time rotational speed data is extracted from the real-time operating parameters, using a 24-hour analysis window with a sliding step of 1 hour to fully cover the entire operating cycle of the body refrigeration equipment. A FFT is then performed on the rotational speed time-series data within the window, converting the time-domain rotational speed signal into a frequency-domain vibration spectrum. From this, the fundamental frequency component matching the compressor shaft rotation frequency, as well as the harmonic components caused by shaft friction and bearing wear, are extracted, ultimately obtaining the low-temperature vibration frequency of the compressor. The frequency measurement range covers 0 to 60Hz, adapting to the vibration characteristics of the compressor's low-frequency operation. The standard deviation of the rotational speed fluctuation is calculated using a sliding window statistical method, with 10 consecutive sampling points as the calculation window and a sliding step of 1 sampling point. The standard deviation of the rotational speed values ​​within each window is calculated to obtain the dispersion value of the rotational speed fluctuation; a higher value indicates worse rotational speed stability. The final speed stability characteristics are obtained by weighted fusion of the standard deviation of speed fluctuation and the harmonic proportion of low temperature vibration frequency. All characteristic values ​​are mapped to the range of 0 to 1 through the minimum-maximum normalization method. The closer the value is to 1, the worse the speed stability and the greater the negative impact on lubrication.

[0053] Next, the temperature change amplitude is calculated based on the real-time temperature data, and the temperature fluctuation characteristics are obtained by weighted fusion based on the temperature change amplitude. The real-time temperature data includes the collected values ​​from three parts: the compressor crankshaft bearing, cylinder piston, and lubricating oil chamber. The calculation process prioritizes the temperature data of the lubricating oil chamber as the primary calculation benchmark, with the temperature data from the other two parts serving as auxiliary verification. The temperature change amplitude includes two-dimensional calculation indicators: the absolute difference between the highest and lowest temperatures within a 24-hour sliding window, and the hourly temperature change rate. The values ​​of the two dimensions are mapped to the interval between 0 and 1 using the minimum-maximum normalization method. The temperature fluctuation characteristics are obtained by weighted fusion of the two-dimensional indicators, with the weight of the 24-hour absolute temperature difference set to 0.6 and the weight of the hourly temperature change rate set to 0.4. This weight combination is based on historical data of the low-temperature operation of the mortuary refrigeration equipment compressor. Long-term temperature change amplitude has a greater impact on lubricating oil viscosity and is more indicative of lubrication status, thus it is given a higher weight. The instantaneous temperature change rate is an auxiliary influencing factor and is given a lower weight. The final temperature fluctuation characteristic values ​​are fixed between 0 and 1. The closer the value is to 1, the more severe the temperature fluctuation and the greater the negative impact on the performance of the lubricating oil.

[0054] Secondly, the speed stability feature and the temperature fluctuation feature are fused to obtain the operational fluctuation feature. The integration process first performs minimum-maximum normalization on the harmonic proportion of the low-temperature vibration frequency to ensure its value range is consistent with the speed stability and temperature fluctuation features, uniformly mapping it to the range of 0 to 1. Then, corresponding weights are assigned to the three sub-features: the weight of the speed stability feature is set to 0.5, the weight of the temperature fluctuation feature is set to 0.3, and the weight of the low-temperature vibration frequency harmonic proportion is set to 0.2. This weight combination is based on historical case data of compressor lubrication failures in mortuary refrigeration equipment over the past 5 years. Abnormal speed stability is the most direct manifestation of insufficient lubrication and contributes the most to operational fluctuations, thus receiving the highest weight. Temperature fluctuations and vibration frequency harmonic components are auxiliary judgment indicators, with weights assigned progressively according to their degree of influence. After weighted fusion, the calculation results undergo secondary standardization, ultimately obtaining operational fluctuation features with a fixed value range between 0 and 1. The closer the value is to 1, the more severe the fluctuation in the compressor's operating state and the higher the risk of lubrication deterioration.

[0055] Subsequently, various energy consumption change indicators are calculated based on the operating power data in the real-time operating parameters. These indicators are then fused to obtain the energy consumption status characteristics. The operating power data includes real-time operating power values ​​and power percentage values. The calculation of energy consumption change patterns is achieved using a time series decomposition algorithm. First, a 24-hour sliding window is used to separate the trend and random components of the continuous power percentage time series data, extracting the long-term trend and instantaneous fluctuation characteristics of power changes. Then, three core indicators are calculated: the fluctuation variance of the power percentage, the energy consumption ratio per unit cooling capacity, and the 24-hour cumulative energy consumption change rate. All three indicators are mapped to the range of 0 to 1 using the minimum-maximum normalization method. The energy consumption status characteristics are obtained by weighted fusion of the three core indicators, where the weight of the energy consumption ratio per unit cooling capacity is set to 0.5, the weight of the power percentage fluctuation variance is set to 0.3, and the weight of the 24-hour cumulative energy consumption change rate is set to 0.2. This weighting combination is based on historical test data relating compressor lubrication status and energy consumption. The energy consumption per unit cooling capacity directly reflects the decrease in mechanical efficiency caused by insufficient lubrication and has the strongest indicative power for energy consumption status, thus receiving the highest weight. The other two indicators are auxiliary verification items, with weights allocated progressively according to their degree of influence. The final energy consumption status characteristic values ​​are fixed between 0 and 1. The closer the value is to 1, the higher the degree of abnormal compressor energy consumption and the greater the possibility of lubrication deterioration.

[0056] It is worth noting that corresponding weights are assigned to the operational fluctuation characteristics and the energy consumption status characteristics, and a weighted fusion calculation is performed to obtain a lubrication status score. The lubrication status score adopts a percentage scoring method, with a maximum score of 100 points. The higher the score, the better the lubrication status of the compressor; the lower the score, the higher the risk of lubrication deterioration. In the weight allocation process, the weight of the operational fluctuation characteristics is set to 0.6, and the weight of the energy consumption status characteristics is set to 0.4. This weight combination is based on historical statistical data of compressor lubrication failures in cadaver refrigeration equipment over the past 5 years. The operational fluctuation characteristics directly reflect the changes in mechanical operating status caused by insufficient lubrication, and have a higher accuracy in assessing the lubrication status, thus being assigned a higher weight. The energy consumption status characteristics are an indirect manifestation of lubrication deterioration and are used as an auxiliary assessment indicator, thus being assigned a lower weight. The weighted fusion calculation process first uses 100 points as the base maximum score, subtracts the normalized value of the corresponding feature from 1, and then multiplies it by the ratio of the corresponding weight to the 100-point score. After accumulating the calculation results of the two features, a standardized lubrication status score is finally obtained. The scoring calculation process is simultaneously constrained by upper and lower limits, with the minimum score not lower than 0 points and the maximum score not exceeding 100 points, to avoid numerical out-of-bounds issues.

[0057] In step S103, if the lubrication status score is lower than a preset lubrication judgment threshold, the low-temperature friction coefficient is calculated based on the real-time operating parameters, and a wear risk assessment is performed based on the low-temperature friction coefficient to obtain a mechanical wear risk result, including:

[0058] The lubrication status score is compared with a preset lubrication judgment threshold.

[0059] If the lubrication status score is lower than the preset lubrication judgment threshold, then real-time temperature data is extracted based on the real-time operating parameters, and the low-temperature friction coefficient corresponding to the real-time temperature data is calculated by combining the preset lubricating oil viscosity and temperature correspondence.

[0060] Based on the real-time operating parameters, state calculations are performed to obtain the estimated values ​​of lubricating oil viscosity change rate and oil film thickness.

[0061] Wear risk values ​​are calculated based on the low-temperature friction coefficient, the lubricating oil viscosity change rate, and the estimated oil film thickness. Risk levels are then classified based on these wear risk values ​​to obtain mechanical wear risk results.

[0062] It should be noted that, firstly, the lubrication status score is compared with a preset lubrication judgment threshold. The lubrication judgment threshold is set based on historical statistical data of lubrication failures in mortuary refrigeration equipment compressors over the past 5 years and industry operation and maintenance standards. The lowest lubrication status score corresponding to all confirmed insufficient lubrication failures after disassembly and inspection is statistically analyzed, and the basic lubrication judgment threshold is set to 60 points. For older equipment with more than 5 years of operation, the threshold can be increased to 65 points, and for new equipment with less than 1 year of operation, the threshold can be decreased to 55 points. This threshold has been verified by long-term operational data from multiple funeral service institutions and can reliably distinguish between normal lubrication status and abnormal status with deterioration risks. While ensuring the detection rate of insufficient lubrication potential, it effectively reduces the probability of false alarms caused by fluctuations in normal operation. The comparison process uses a one-to-one numerical comparison method, comparing the real-time calculated lubrication status score with the preset threshold sequentially. The comparison frequency is consistent with the update frequency of the lubrication status score to ensure the real-time nature of the comparison results. The comparison results are divided into two categories: below the threshold and above the threshold, each corresponding to different subsequent processing logic.

[0063] If the lubrication status score is lower than the preset lubrication judgment threshold, the low-temperature friction coefficient corresponding to the real-time temperature data is calculated based on the real-time temperature data in the real-time operating parameters and the preset correspondence between lubricating oil viscosity and temperature. The calculation process for the low-temperature friction coefficient will only be initiated when the lubrication status score is lower than the lubrication judgment threshold. If the score is not lower than the threshold, the lubrication status is directly determined to be normal, and subsequent wear risk assessment operations are not performed. The preset correspondence between lubricating oil viscosity and temperature is derived from the factory test data of the refrigeration lubricating oil used in the compressor, combined with long-term test results from the low-temperature operation scenario of the body refrigeration equipment. It is implemented using a viscosity-temperature mathematical model, which can accurately reflect the change in lubricating oil viscosity with temperature within the range of -40 degrees Celsius to 20 degrees Celsius. The calculation of the low-temperature friction coefficient uses the lubricating oil cavity temperature in the real-time temperature data as the main input. First, the dynamic viscosity of the lubricating oil under the current low-temperature environment is calculated using the viscosity-temperature model. Then, combined with the real-time speed data and the structural parameters of the compressor bearing, the dynamic friction coefficient under the low-temperature environment, i.e., the low-temperature friction coefficient, is calculated using the boundary lubrication friction model.

[0064] During the calculation process, the accuracy of the temperature value directly determines the accuracy of the friction coefficient calculation. Therefore, the real-time temperature data of the lubricating oil chamber is used as the only temperature input to ensure that the calculation results are consistent with the actual working environment of the lubricating oil inside the compressor.

[0065] Secondly, based on the real-time operating parameters, state calculations are performed to obtain the lubricating oil viscosity change rate and oil film thickness estimates. The lubricating oil viscosity change rate is calculated using the rated viscosity of the lubricating oil under a standard environment of 20 degrees Celsius as the benchmark value, and the actual viscosity calculated under the current low-temperature environment as the comparison value. The change in actual viscosity relative to the benchmark value is calculated to obtain the viscosity change rate, which is presented as a percentage, intuitively reflecting the impact of the low-temperature environment on the fluidity of the lubricating oil. The oil film thickness estimate is calculated using a fluid lubrication model based on the Reynolds equation. The model's input parameters include the current lubricating oil viscosity, real-time speed data, the structural dimensions of the compressor bearings and pistons, and the operating load. By numerically solving the Reynolds equation, the lubricating oil film thickness on the friction pair surfaces of the compressor shaft bearings and cylinders is estimated, with the calculation results in micrometers. During the calculation process, all input parameters are derived from real-time operating parameters and the fixed structural parameters of the equipment from the factory, ensuring that the calculation results closely match the actual operating state of the compressor and eliminating calculation deviations caused by empirical parameters.

[0066] For example, by combining the lubricating oil chamber temperature of -18.5 degrees Celsius and the real-time speed of 1500 rpm in the real-time operating parameters, the change rate of the lubricating oil viscosity relative to the 20-degree Celsius reference value is first calculated to be 12.3%. Then, by numerically solving the Reynolds equation, the estimated value of the oil film thickness of the compressor bearing friction pair is 3.2 micrometers.

[0067] Subsequently, the low-temperature friction coefficient, the lubricating oil viscosity change rate, and the estimated oil film thickness are subjected to minimum-maximum normalization, uniformly mapped to the 0-1 range, and then weighted and fused according to preset weights to obtain a wear risk value between 0 and 1. The closer the value is to 1, the higher the risk. In terms of the parameter setting and execution logic of the weighted fusion, the default basic weight configuration is: low-temperature friction coefficient 0.4, estimated oil film thickness 0.35, and lubricating oil viscosity change rate 0.25. In actual execution, the weights need to be dynamically adjusted according to the type of refrigeration lubricating oil used in the equipment: if the system identifies it as mineral-based refrigeration lubricating oil, the viscosity change rate weight is increased to 0.35, and the oil film thickness estimate weight is decreased to 0.25; if it is identified as synthetic refrigeration lubricating oil, the low-temperature friction coefficient weight is increased to 0.45, and the viscosity change rate weight is decreased to 0.2. After calculating the wear risk value, the system directly classifies the risk level based on the following value ranges: values ​​between 0 and 0.25 are considered low risk, 0.25 to 0.5 are considered medium risk, 0.5 to 0.75 are considered high risk, and 0.75 to 1 are considered extremely high risk. Finally, the system integrates the wear risk value, risk level, values ​​of each sub-indicator, corresponding timestamps, and the equipment's unique identifier to generate a complete mechanical wear risk result, which can be directly used for subsequent equipment fatigue assessment and operating parameter optimization.

[0068] In step S104, the deviation calculation is performed by combining the real-time operating parameters with the pre-acquired historical operating logs to obtain the operating deviation value. Then, based on the operating deviation value and the mechanical wear risk result, a fatigue assessment calculation is performed to obtain the equipment fatigue index, including:

[0069] Retrieve the pre-acquired historical operation logs and extract the standard operating parameter range under normal operating conditions from the historical operation logs;

[0070] The real-time operating parameters are compared with the standard operating parameter range, and the cumulative value of speed deviation and the duration of temperature anomaly within a preset period are calculated. The cumulative value of speed deviation and the duration of temperature anomaly are weighted and calculated to obtain the operating deviation value.

[0071] Based on the operational deviation value and the mechanical wear risk result, fatigue assessment calculation is performed using a preset fatigue assessment model to obtain the equipment fatigue index.

[0072] It should be noted that, firstly, the pre-acquired historical operation logs are retrieved, and the standard operating parameter range under normal operating conditions is extracted from these logs. The pre-acquired historical operation logs originate from a full-volume operation data repository since the target equipment was put into operation. This includes all steady-state operation data after the equipment was installed and commissioned, with no fault alarms and confirmed to be in normal condition through regular maintenance. The data covers all scenarios of operation under different seasons and load conditions. All data has undergone validity verification, and non-steady-state data from the equipment start-up, shutdown, and maintenance / commissioning phases has been removed. The standard operating parameter range is extracted using a statistical normal distribution method. For the three core indicators in the real-time operating parameters—real-time speed, real-time temperature, and operating power—the arithmetic mean and standard deviation of the corresponding indicators under normal operating conditions are calculated. The upper and lower limits of the standard operating parameters for the corresponding indicators are then determined using the mean plus or minus twice the standard deviation, forming a complete standard operating parameter range.

[0073] The interval extraction process is segmented and adapted according to the equipment's operating time. For new equipment that has been in operation for less than one year, the stable operating data of the first three months after commissioning is used as the extraction benchmark. For older equipment that has been in operation for more than five years, the stable operating data of the most recent 12 months is used as the extraction benchmark to ensure that the interval fits the current actual operating characteristics of the equipment.

[0074] Next, the real-time operating parameters are compared with the standard operating parameter range. The cumulative value of speed deviation and the duration of temperature anomalies within a preset period are calculated. The cumulative value of speed deviation and the duration of temperature anomalies are weighted and calculated to obtain the operating deviation value. The preset period is set based on the equipment's operating conditions and historical fault patterns. The basic preset period is set to 7 days. In scenarios where the equipment operates at low frequency at night, the period can be shortened to 3 days. In scenarios where the equipment operates stably for a long period, the period can be extended to 15 days. The method for calculating the cumulative value of speed deviation is as follows: within the preset period, the deviation of the real-time speed value from the standard operating parameter range is counted one by one. All deviations exceeding the range are summed to obtain the cumulative value of speed deviation. The deviation is calculated as the absolute value exceeding the upper and lower limits of the range. The deviation of values ​​within the range is counted as 0. The method for calculating the duration of temperature anomalies is as follows: within the preset period, the continuous duration of real-time temperature data exceeding the standard operating parameter range is counted. The duration of all abnormal periods is summed to obtain the duration of temperature anomalies. The unit of time is minutes.

[0075] The operational deviation value is calculated through a weighted fusion of two indicators, with the cumulative value of speed deviation weighted at 0.6 and the duration of abnormal temperature at 0.4. This weighting combination is based on historical statistical data on the precursors of compressor failures in the mortuary refrigeration equipment. Sustained speed deviation is the most direct precursor to mechanical fatigue and contributes more to operational deviation, thus receiving a higher weight. The duration of abnormal temperature is a secondary indicator and is given a lower weight. Before calculation, both indicators are mapped to a range of 0 to 1 using the minimum-maximum normalization method to eliminate differences in magnitude between the indicators. The resulting weighted fusion operational deviation value is fixed between 0 and 1; the closer the value is to 1, the more severe the deviation of the equipment's operating state from the normal range.

[0076] Subsequently, based on the operational deviation value and the mechanical wear risk result, a fatigue assessment calculation is performed using a preset fatigue assessment model to obtain the equipment fatigue index. Specifically, the preset fatigue assessment model is a quantitative evaluation model constructed based on a multi-index weighted fusion algorithm. This model not only receives the aforementioned operational deviation value and mechanical wear risk result as input items reflecting the immediate mechanical state, but also simultaneously introduces the fault precursor signal strength and historical data abnormal frequency extracted from the equipment lifecycle management system as input items reflecting the long-term evolution trend, thus forming a feature matrix with four assessment dimensions. The equipment fatigue index adopts a percentage scoring method, with a maximum score of 100 points. The higher the score, the more severe the mechanical fatigue of the equipment and the higher the risk of failure. In the weighted fusion calculation process of the fatigue assessment model, the weights of the four input indicators are as follows: operational deviation value weight 0.3, mechanical wear risk result weight 0.3, fault precursor signal strength weight 0.2, and historical data abnormal frequency weight 0.2. This weighting combination is based on the machine learning feature importance ranking results of compressor failure cases over the past 10 years. Operating deviation and mechanical wear risk directly reflect the current mechanical operating state and wear degree of the equipment, contributing the most to equipment fatigue and thus receiving the highest weights. Failure precursor signal strength and historical anomaly frequency reflect the long-term evolution trend of equipment failure and are used as auxiliary evaluation indicators, receiving the same lower weights. Before calculation, the wear risk value from the mechanical wear risk results is directly used as input. The other three indicators are normalized to the range of 0 to 1, then weighted and fused according to their corresponding weights. Finally, the fused result is linearly converted into an equipment fatigue index ranging from 0 to 100 points. The calculation process sets upper and lower limits for the values, with the lowest score not lower than 0 and the highest score not exceeding 100 to avoid numerical out-of-bounds issues.

[0077] It is worth noting that the weighting of the equipment fatigue index calculation can be dynamically adjusted based on the equipment's cumulative operating time. For older equipment with over 8 years of operating time, the weight of mechanical wear risk can be increased to 0.4, while the weight of historical data anomaly frequency can be decreased to 0.1, to better reflect the more significant impact of accumulated wear on fatigue levels in older equipment. For newer equipment with less than 2 years of operating time, the weight of operating deviation can be increased to 0.4, while the weight of mechanical wear risk can be decreased to 0.2, to better reflect the more pronounced impact of operating deviations on fatigue levels during the break-in period of new equipment. After the weighting adjustment, the match between the equipment fatigue index and the actual probability of failure can be significantly improved.

[0078] In step S105, if the equipment fatigue index is higher than a preset fatigue judgment threshold, the operating fluctuation characteristics are updated based on the historical operating logs to obtain updated operating fluctuation characteristics. The scoring is then recalculated based on the updated operating fluctuation characteristics to obtain an updated lubrication status score, including:

[0079] The fatigue index of the equipment is compared with a preset fatigue judgment threshold.

[0080] If the fatigue index of the equipment is higher than the preset fatigue judgment threshold, the data backtracking mechanism is triggered to retrieve the historical operating data within the set historical period. Based on the historical operating data, the temperature fluctuation amplitude and speed stability characteristics are recalculated, the operating fluctuation characteristics are updated, and the updated operating fluctuation characteristics are obtained.

[0081] By combining the updated operational fluctuation characteristics with the energy consumption status characteristics, a scoring calculation is performed to obtain the updated lubrication status score.

[0082] It should be noted that, firstly, the equipment fatigue index is compared with a preset fatigue judgment threshold. The fatigue judgment threshold is set based on historical statistical data of compressor fatigue failures in mortuary refrigeration equipment over the past 10 years and industry operation and maintenance standards. The lowest equipment fatigue index corresponding to all confirmed mechanical fatigue failures after disassembly is statistically analyzed, and the basic fatigue judgment threshold is set to 50 points. For older equipment with a running time exceeding 5 years, the threshold can be lowered to 45 points, and for newer equipment with a running time of less than 1 year, the threshold can be raised to 55 points. This threshold has been verified by long-term operational data from multiple funeral service institutions and can reliably distinguish between normal equipment fatigue accumulation and abnormal fatigue states with potential failure risks. While ensuring the detection rate of potential equipment fatigue hazards, it effectively reduces the probability of false triggers caused by fluctuations in normal operation. The comparison process uses a one-to-one numerical comparison method, comparing the real-time calculated equipment fatigue index with the preset threshold sequentially. The comparison frequency is consistent with the update frequency of the equipment fatigue index to ensure the real-time nature of the comparison results. The comparison results are divided into two categories: above the threshold and below the threshold, each corresponding to different subsequent processing logic.

[0083] Next, if the equipment fatigue index exceeds a preset fatigue judgment threshold, a data backtracking mechanism is triggered. Historical operating data within a set historical period is retrieved, and based on this data, the temperature fluctuation amplitude and speed stability characteristics are recalculated, updating the operating fluctuation characteristics to obtain the updated operating fluctuation characteristics. The data backtracking mechanism is only triggered when the equipment fatigue index exceeds the fatigue judgment threshold. If the index is not higher than the threshold, the original operating fluctuation characteristics and lubrication status score are directly used, and no further update operations are performed. The historical period is set according to the equipment fatigue index level. The basic historical period is 15 days, which can be extended to 30 days for severe fatigue and shortened to 7 days for moderate fatigue, ensuring that the backtracked historical data fully reflects the long-term trend of the equipment's operating status. The data backtracking process retrieves all real-time operating parameters within the corresponding historical period from the equipment's historical operating logs, including complete real-time speed and temperature data. The data covers all operating conditions day and night, eliminating invalid data from maintenance, debugging, and power outage periods. The temperature fluctuation amplitude was recalculated using the historical period as the statistical window. The temperature change amplitudes of three parts—the lubricating oil cavity, crankshaft bearing, cylinder, and piston—were calculated separately. The weighted average of these three parts was taken as the updated temperature fluctuation characteristic. The weight of the lubricating oil cavity temperature was set to 0.6, and the combined weight of the other two parts was set to 0.4. The speed stability characteristic was recalculated based on continuous speed data within the historical period. The compressor's low-temperature vibration frequency was re-extracted, and the standard deviation of speed fluctuations over a longer period was calculated. Simultaneously, the long-term cumulative value of the speed deviation was calculated to obtain the updated speed stability characteristic. The updated operational fluctuation characteristic was obtained by weighted fusion of the updated speed stability characteristic, low-temperature vibration frequency, and temperature fluctuation characteristic. The weighting rules were consistent with the initial operational fluctuation characteristic calculation. The final updated operational fluctuation characteristic value ranged from 0 to 1, with values ​​closer to 1 indicating more severe operational fluctuations.

[0084] Secondly, a scoring calculation is performed by combining the updated operational fluctuation characteristics and the energy consumption status characteristics to obtain an updated lubrication status score. The updated lubrication status score calculation follows the initial scoring rule of a percentage system, with a maximum score of 100. A higher score indicates better compressor lubrication status, while a lower score indicates a higher risk of lubrication deterioration. During weight allocation, the weight of the updated operational fluctuation characteristics is set to 0.6, and the weight of the energy consumption status characteristics is set to 0.4. This weight combination is consistent with the initial scoring rule, ensuring the comparability and continuity of the scoring results before and after the update. The weighted fusion calculation process first uses 100 as the base maximum score. The normalized value of the updated operational fluctuation characteristics and the value of the energy consumption status characteristics are subtracted from 1, and then multiplied by the corresponding weights and the percentage of the 100-point score. The results of the two features are then summed to obtain the final standardized updated lubrication status score. Upper and lower limits are simultaneously set during the scoring calculation process: the minimum score is not lower than 0, and the maximum score does not exceed 100, to avoid numerical out-of-bounds issues. At the same time, after the score calculation is completed, the updated lubrication status score will be compared with the initial score to analyze the magnitude and direction of the score change, providing a quantitative basis for subsequent optimization of operating parameters.

[0085] It's worth noting that the historical data backtracking period can be dynamically adjusted based on the extent to which the equipment fatigue index exceeds the threshold. The greater the fatigue index exceeds the threshold, the longer the corresponding historical backtracking period. When the fatigue index exceeds the threshold by less than 10 points, the backtracking period is fixed at 7 days; when it exceeds the threshold by 10 to 30 points, the backtracking period is fixed at 15 days; and when it exceeds the threshold by more than 30 points, the backtracking period is fixed at 30 days. This period adjustment rule is based on the evolutionary pattern of equipment fatigue failure. The greater the extent to which the fatigue index exceeds the threshold, the longer the period of equipment condition deterioration, requiring longer historical data to accurately reflect the changing trends of operating characteristics. Simultaneously, the weighting of the operational fluctuation characteristic update calculation can be fine-tuned based on the equipment's operating conditions within the backtracking period. If the equipment's nighttime low-frequency operation time accounts for more than 60% of the backtracking period, the weight of the speed stability characteristic can be increased to 0.55, while the weight of the temperature fluctuation characteristic can be decreased to 0.25, adapting to the characteristic that speed fluctuations have a more significant impact on lubrication status under low-frequency operating scenarios.

[0086] In step S106, the step of performing parameter optimization calculations based on the updated lubrication condition score and the low-temperature friction coefficient to obtain the operation control signal includes:

[0087] Based on the updated lubrication status score, the compressor speed adjustment range is calculated;

[0088] Based on the speed adjustment range, a speed control command is generated to obtain the initial operation control signal;

[0089] Based on the initial operation control signal, parameter simulation is performed to calculate the corresponding speed deviation and temperature change amplitude.

[0090] The initial operating control signal is corrected based on the corresponding speed deviation and temperature change amplitude to obtain the operating control signal.

[0091] It should be noted that, firstly, the compressor speed adjustment range is calculated based on the updated lubrication condition score. The compressor speed adjustment range uses the steady-state operating speed within the equipment's rated speed range as the baseline value, and the updated lubrication condition score and low-temperature friction coefficient as the main calculation bases, employing a linear piecewise fitting method. The lower the lubrication condition score and the higher the low-temperature friction coefficient, the greater the corresponding upward adjustment range of the speed. Increasing the compressor's operating speed increases the circulation flow of lubricating oil, improving the lubrication effect in low-temperature environments and reducing friction and wear. Strict upper and lower limits are set for the speed adjustment process: the minimum speed is no less than 80% of the compressor's rated minimum speed, and the maximum speed does not exceed 90% of the compressor's rated maximum speed. Simultaneously, considering the set temperature requirements of the refrigerated compartment, the maximum range of a single speed adjustment is limited to no more than 15% of the baseline speed to avoid significant speed adjustments affecting the temperature stability of the refrigerated remains. In the calculation of the speed adjustment range, the weight of the updated lubrication condition score is set to 0.6, and the weight of the low-temperature friction coefficient is set to 0.4. This weighting combination is based on historical test data of low-temperature lubrication optimization of the compressor in the body refrigeration equipment. The lubrication status score directly reflects the current degree of lubrication deterioration of the equipment and is more indicative of the need for speed adjustment, so it is given a higher weight. The low-temperature friction coefficient reflects the actual friction state of the friction pair and serves as an auxiliary calibration basis for speed adjustment, so it is given a lower weight.

[0092] Next, a speed control command is generated based on the speed adjustment range, resulting in an initial operating control signal. The speed control command is generated using the compressor driver's communication protocol and control logic. The command content includes four key pieces of information: target operating speed, speed adjustment gradient, command execution cycle, and execution priority. The speed adjustment gradient is set to no more than 2% of the target speed per second to avoid sudden speed changes impacting the compressor's mechanical components and to reduce operating noise and energy consumption fluctuations during speed adjustment. The command execution cycle is consistent with the equipment operating parameter acquisition cycle, with a basic execution cycle set to 1 minute, which can be adjusted synchronously according to temperature fluctuations in the refrigeration compartment. The command generation process is synchronously linked to the real-time temperature data of the refrigeration compartment. If the refrigeration compartment temperature deviates from the set value by more than 2 degrees Celsius, priority is given to ensuring refrigeration temperature control requirements, and a secondary constraint is applied to the speed adjustment range to ensure that the equipment's basic refrigeration function is not affected. The generated initial operating control signal is bound to the corresponding equipment's unique identification code and the command generation timestamp to ensure the uniqueness and traceability of the command.

[0093] Secondly, parameter simulation is performed based on the initial operating control signal to calculate the corresponding speed deviation and temperature change amplitude. The parameter simulation is implemented using a multiple linear regression prediction model built based on historical compressor operating data. The model's input parameters include the target speed and adjustment rate of the initial operating control signal, as well as four types of boundary conditions: current refrigeration compartment load, ambient temperature, initial temperature of the lubricating oil chamber, and real-time operating power of the compressor. The model's output parameters include the deviation between the actual speed and the target speed during steady-state compressor operation (i.e., speed deviation), and the temperature change amplitude of two key components: the compressor crankshaft bearing and the lubricating oil chamber. The simulation process uses an iterative calculation method, with an upper limit of 50 iterations. The convergence condition is that the fluctuation of the calculated speed and temperature values ​​in a single iteration is less than 0.5%, ensuring that the simulation results accurately reflect the equipment's operating state after the control command is executed. The simulation process simultaneously verifies the temperature change trend of the refrigeration compartment after speed adjustment to ensure that speed adjustment does not negatively affect the refrigeration effect.

[0094] Subsequently, the initial operating control signal is calibrated based on the corresponding speed deviation and temperature change amplitude to obtain the operating control signal. The parameter calibration employs a closed-loop feedback calibration method. First, based on the simulated speed deviation, the target speed of the initial operating control signal is compensated in reverse. When the speed deviation is positive, the target speed is synchronously lowered; when the speed deviation is negative, the target speed is synchronously raised to eliminate speed execution deviation. Next, based on the simulated temperature change amplitude, the speed adjustment gradient and execution cycle are calibrated. When the temperature rise exceeds 2 degrees Celsius, the speed adjustment gradient is reduced, and the command execution cycle is extended to reduce the temperature rise of mechanical components caused by speed adjustment. During the calibration process, the weight of speed deviation compensation is set to 0.6, and the weight of temperature change amplitude calibration is set to 0.4. This weight combination is based on historical execution accuracy data of compressor speed control. Speed ​​deviation directly affects the execution accuracy of control commands and contributes more to the calibration effect, thus receiving a higher weight. Temperature change amplitude reflects the impact of control commands on equipment lubrication and operational safety; as an auxiliary calibration item, it is given a lower weight. After calibration, the validity of the operation control signal is verified to ensure that the calibrated target speed is still within the rated speed constraint range. At the same time, the execution effect of the calibrated command is simulated again to confirm the effect of the calibrated command and avoid overcalibration.

[0095] It's worth noting that the weighting of the speed adjustment calculation can be dynamically adjusted based on the equipment's cumulative operating time. For older equipment with over 8 years of operating time, the weighting of the low-temperature friction coefficient can be increased to 0.5, while the weighting of the lubrication condition score can be decreased to 0.5, to better suit the characteristic that accumulated friction and wear have a more significant impact on the operating condition of older equipment. For newer equipment with less than 2 years of operating time, the weighting of the lubrication condition score can be increased to 0.7, while the weighting of the low-temperature friction coefficient can be decreased to 0.3, to better suit the characteristic that lubrication condition changes more gradually during the break-in period of new equipment. After the weighting adjustment, the effect of speed adjustment on improving the equipment's lubrication condition can be significantly enhanced, while avoiding speed adjustment exacerbating equipment wear.

[0096] In step S107, the operating deviation value is recalculated based on the operating control signal to obtain an updated operating deviation value. If the updated operating deviation value is lower than a preset safety judgment threshold, a start / stop adjustment command is generated, including:

[0097] After adjusting the compressor operating parameters according to the operating control signal, real-time operating data of the compressor is collected.

[0098] Calculate the updated operating deviation value based on the real-time operating data and the standard operating parameter range;

[0099] The updated operational deviation value is compared with the preset safety judgment threshold;

[0100] If the updated operating deviation value is lower than the preset safety judgment threshold, a start / stop adjustment command to maintain operation is generated;

[0101] If the updated operating deviation value is higher than the preset safety judgment threshold, a temporary shutdown start / stop adjustment command is generated.

[0102] It should be noted that, firstly, after adjusting the compressor operating parameters according to the aforementioned operating control signal, real-time operating data of the compressor is collected. After the operating control signal is issued, the driver smoothly adjusts the compressor parameters (the transition time does not exceed 30 seconds). Subsequently, the temperature, speed, and operating power of key components of the compressor are collected by sensors, and the arithmetic mean of 60 consecutive seconds is used as the final real-time operating data. At the same time, outliers are eliminated using a 3x standard deviation criterion to accurately reflect the steady-state operating status of the equipment.

[0103] Next, based on the real-time operating data and the standard operating parameter range, the updated operating deviation value is calculated. Using a continuous 7-day statistical period, the cumulative value of speed deviation exceeding the standard range and the duration of temperature anomalies are recalculated. After performing minimum-maximum normalization on these two indicators, they are weighted and fused with weights of 0.6 and 0.4 respectively, resulting in an updated operating deviation value between 0 and 1. The closer the value is to 1, the more severe the deviation from the normal range.

[0104] Next, the updated operating deviation value is compared with a preset safety judgment threshold. The base value of the safety judgment threshold is set to 0.3 (which can be dynamically adjusted between 0.25 and 0.35 based on the equipment's operating time). The updated value is compared with this threshold successively, and a secondary verification is performed simultaneously in conjunction with the mechanical wear risk results and the equipment fatigue index to comprehensively reflect the actual operating safety status of the equipment.

[0105] Subsequently, if the updated operating deviation value is lower than the preset safety threshold, a start / stop adjustment command to maintain operation is generated. This command has a secondary priority, requiring the compressor to maintain stable operation at the current target speed and keep the refrigerated compartment temperature stable. The system continuously monitors data at a 1-minute interval. If the operating deviation value exceeds the safety threshold three times consecutively, the maintenance is immediately terminated and a shutdown protection is triggered.

[0106] If the updated operational deviation value exceeds the preset safety threshold, a temporary shutdown start / stop adjustment command is generated. This command has the highest priority and employs a slow-stop control logic that reduces speed at a rate of 2% per second to avoid mechanical shock, while also activating the standby refrigeration system of the refrigerated compartment. Immediately after shutdown, the compressor start circuit is locked, and a fault warning containing shutdown event and deviation data is pushed to the operation and maintenance management platform. The lockout can only be released after a comprehensive inspection and trial operation verification are completed.

[0107] In summary, this invention discloses a compressor control method for refrigerated body storage equipment. The method includes collecting real-time temperature, speed, and operating power data of the compressor; extracting operating fluctuations and energy consumption characteristics to calculate a lubrication status score; calculating the low-temperature friction coefficient when the score is below a threshold to complete a mechanical wear risk assessment; calculating the equipment fatigue index based on historical operating data; triggering data backtracking to update the lubrication status score when it exceeds the threshold; optimizing the compressor operation control signal based on the updated score and friction coefficient; dynamically adjusting the speed to balance friction loss and operating energy consumption; and generating start / stop adjustment commands after verification. This invention solves the problems of traditional fixed-frequency control being unable to adapt to low-temperature, low-frequency operating scenarios, delayed lubrication status perception, increased mechanical wear, and poor energy consumption control, achieving real-time and accurate assessment and energy-saving optimization control of the compressor's lubrication status.

[0108] Reference Figure 2The second embodiment of the present invention provides a compressor control system for a body refrigeration device, comprising:

[0109] The data acquisition module is used to obtain the real-time operating parameters of the compressor in the body refrigeration equipment;

[0110] The feature extraction and scoring module is used to extract features from the real-time operating parameters to obtain operating fluctuation features and energy consumption status features, and to calculate the lubrication status score based on the operating fluctuation features and the energy consumption status features.

[0111] The wear risk assessment module is used to calculate the low-temperature friction coefficient based on the real-time operating parameters when the lubrication status score is lower than the preset lubrication judgment threshold, and to perform wear risk assessment based on the low-temperature friction coefficient to obtain the mechanical wear risk result.

[0112] The fatigue assessment module is used to calculate the deviation by combining the real-time operating parameters with the pre-acquired historical operating logs to obtain the operating deviation value, and to perform fatigue assessment calculation based on the operating deviation value and the mechanical wear risk result to obtain the equipment fatigue index.

[0113] The data backtracking module is used to update the operating fluctuation characteristics based on the historical operating logs when the fatigue index of the equipment is higher than the preset fatigue judgment threshold, so as to obtain the updated operating fluctuation characteristics. The scoring is then recalculated based on the updated operating fluctuation characteristics to obtain the updated lubrication status score.

[0114] The parameter optimization module is used to perform parameter optimization calculations based on the updated lubrication status score and the low-temperature friction coefficient to obtain the operation control signal;

[0115] The start / stop control module is used to recalculate the operating deviation value based on the operating control signal to obtain an updated operating deviation value. If the updated operating deviation value is lower than a preset safety judgment threshold, a start / stop adjustment command is generated.

[0116] It should be noted that the compressor control system for body refrigeration equipment provided in this embodiment of the invention is used to execute all the process steps of the compressor control method for body refrigeration equipment in the above embodiment. The working principle and beneficial effects of the two are one-to-one, so they will not be described again.

[0117] It should be noted that the system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the system embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0118] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A compressor control method for a body chiller apparatus, characterized by, include: Obtain the real-time operating parameters of the compressor in the body refrigeration equipment; Feature extraction is performed on the real-time operating parameters to obtain operating fluctuation features and energy consumption status features. A score is calculated based on the operating fluctuation features and the energy consumption status features to obtain a lubrication status score. If the lubrication status score is lower than the preset lubrication judgment threshold, the low-temperature friction coefficient is calculated based on the real-time operating parameters, and the wear risk assessment is performed based on the low-temperature friction coefficient to obtain the mechanical wear risk result. By combining the real-time operating parameters with the pre-acquired historical operating logs, the deviation is calculated to obtain the operating deviation value. Based on the operating deviation value and the mechanical wear risk result, a fatigue assessment is performed to obtain the equipment fatigue index. If the fatigue index of the equipment is higher than the preset fatigue judgment threshold, the operation fluctuation characteristics are updated by backtracking based on the historical operation log to obtain the updated operation fluctuation characteristics. The score is recalculated based on the updated operation fluctuation characteristics to obtain the updated lubrication status score. Based on the updated lubrication condition score and the low-temperature friction coefficient, parameter optimization calculations are performed to obtain the operation control signal; The operating deviation value is recalculated based on the operating control signal to obtain an updated operating deviation value. If the updated operating deviation value is lower than a preset safety judgment threshold, a start / stop adjustment command is generated.

2. The compressor control method for a cadaver refrigeration apparatus according to claim 1, characterized by, The acquisition of real-time operating parameters of the compressor in the body refrigeration equipment includes: The temperature values ​​of the core part of the compressor are collected according to the preset sampling frequency to obtain real-time temperature data; Collect the rotation frequency value of the compressor shaft to obtain real-time speed data; Collect the real-time operating power value of the compressor, combine it with the rated power value to calculate the power ratio, and obtain the operating power data; By aligning the acquisition timing of the real-time temperature data, the real-time rotational speed data, and the operating power data, the real-time operating parameters are obtained.

3. The compressor control method for a cadaver refrigeration apparatus according to claim 2, characterized by, The process involves extracting features from the real-time operating parameters to obtain operating fluctuation features and energy consumption status features. A lubrication status score is then calculated based on these features, including: The standard deviation of speed fluctuation is calculated based on the real-time speed data in the real-time operating parameters, and the standard deviation of speed fluctuation is used as the speed stability feature. The temperature change amplitude is calculated based on the real-time temperature data, and the temperature fluctuation characteristics are obtained by weighted fusion based on the temperature change amplitude. The rotational speed stability feature and the temperature fluctuation feature are fused to obtain the operational fluctuation feature; Based on the operating power data in the real-time operating parameters, calculate various energy consumption change indicators, and perform fusion calculation on the various energy consumption change indicators to obtain energy consumption status characteristics; The operational fluctuation characteristics and the energy consumption status characteristics are assigned corresponding weights, and a weighted fusion calculation is performed to obtain the lubrication status score.

4. The compressor control method for a cadaver refrigeration apparatus according to claim 1, characterized by, If the lubrication status score is lower than a preset lubrication judgment threshold, then the low-temperature friction coefficient is calculated based on the real-time operating parameters, and a wear risk assessment is performed based on the low-temperature friction coefficient to obtain a mechanical wear risk result, including: The lubrication status score is compared with a preset lubrication judgment threshold. If the lubrication status score is lower than the preset lubrication judgment threshold, then real-time temperature data is extracted based on the real-time operating parameters, and the low-temperature friction coefficient corresponding to the real-time temperature data is calculated by combining the preset lubricating oil viscosity and temperature correspondence. Based on the real-time operating parameters, state calculations are performed to obtain the estimated values ​​of lubricating oil viscosity change rate and oil film thickness. Wear risk values ​​are calculated based on the low-temperature friction coefficient, the lubricating oil viscosity change rate, and the estimated oil film thickness. Risk levels are then classified based on these wear risk values ​​to obtain mechanical wear risk results.

5. The compressor control method for a cadaver refrigeration apparatus according to claim 1, characterized by, The deviation is calculated by combining the real-time operating parameters with the pre-acquired historical operating logs to obtain an operating deviation value. Based on the operating deviation value and the mechanical wear risk results, a fatigue assessment is performed to obtain the equipment fatigue index, including: Retrieve the pre-acquired historical operation logs and extract the standard operating parameter range under normal operating conditions from the historical operation logs; The real-time operating parameters are compared with the standard operating parameter range, and the cumulative value of speed deviation and the duration of temperature anomaly within a preset period are calculated. The cumulative value of speed deviation and the duration of temperature anomaly are weighted and calculated to obtain the operating deviation value. Based on the operational deviation value and the mechanical wear risk result, fatigue assessment calculation is performed using a preset fatigue assessment model to obtain the equipment fatigue index.

6. The compressor control method for a cadaver refrigeration apparatus according to claim 1, characterized by, If the equipment fatigue index is higher than a preset fatigue judgment threshold, the operating fluctuation characteristics are updated based on the historical operating logs to obtain updated operating fluctuation characteristics. The scoring is then recalculated based on the updated operating fluctuation characteristics to obtain an updated lubrication status score, including: The fatigue index of the equipment is compared with a preset fatigue judgment threshold. If the fatigue index of the equipment is higher than the preset fatigue judgment threshold, the data backtracking mechanism is triggered to retrieve the historical operating data within the set historical period. Based on the historical operating data, the temperature fluctuation amplitude and speed stability characteristics are recalculated, the operating fluctuation characteristics are updated, and the updated operating fluctuation characteristics are obtained. By combining the updated operational fluctuation characteristics with the energy consumption status characteristics, a scoring calculation is performed to obtain the updated lubrication status score.

7. The compressor control method for a cadaver refrigeration apparatus according to claim 1, characterized by, The step of performing parameter optimization calculations based on the updated lubrication condition score and the low-temperature friction coefficient to obtain the operation control signal includes: Based on the updated lubrication status score, the compressor speed adjustment range is calculated; Based on the speed adjustment range, a speed control command is generated to obtain the initial operation control signal; Based on the initial operation control signal, parameter simulation is performed to calculate the corresponding speed deviation and temperature change amplitude. The initial operation control signal is corrected based on the corresponding speed deviation and temperature change amplitude to obtain the operation control signal.

8. The compressor control method for a cadaver refrigeration apparatus according to claim 5, characterized by, The step involves recalculating the operating deviation value based on the operating control signal to obtain an updated operating deviation value. If the updated operating deviation value is lower than a preset safety threshold, a start / stop adjustment command is generated, including: After adjusting the compressor operating parameters according to the operating control signal, real-time operating data of the compressor is collected. Calculate the updated operating deviation value based on the real-time operating data and the standard operating parameter range; The updated operational deviation value is compared with the preset safety judgment threshold; If the updated operating deviation value is lower than the preset safety judgment threshold, a start / stop adjustment command to maintain operation is generated; If the updated operating deviation value is higher than the preset safety judgment threshold, a temporary shutdown start / stop adjustment command is generated.

9. A compressor control system for a body refrigeration apparatus, characterized by, include: The data acquisition module is used to obtain the real-time operating parameters of the compressor in the body refrigeration equipment; The feature extraction and scoring module is used to extract features from the real-time operating parameters to obtain operating fluctuation features and energy consumption status features, and to calculate the lubrication status score based on the operating fluctuation features and the energy consumption status features. The wear risk assessment module is used to calculate the low-temperature friction coefficient based on the real-time operating parameters when the lubrication status score is lower than the preset lubrication judgment threshold, and to perform wear risk assessment based on the low-temperature friction coefficient to obtain the mechanical wear risk result. The fatigue assessment module is used to calculate the deviation by combining the real-time operating parameters with the pre-acquired historical operating logs to obtain the operating deviation value, and to perform fatigue assessment calculation based on the operating deviation value and the mechanical wear risk result to obtain the equipment fatigue index. The data backtracking module is used to update the operating fluctuation characteristics based on the historical operating logs when the fatigue index of the equipment is higher than the preset fatigue judgment threshold, so as to obtain the updated operating fluctuation characteristics. The scoring is then recalculated based on the updated operating fluctuation characteristics to obtain the updated lubrication status score. The parameter optimization module is used to perform parameter optimization calculations based on the updated lubrication status score and the low-temperature friction coefficient to obtain the operation control signal; The start / stop control module is used to recalculate the operating deviation value based on the operating control signal to obtain an updated operating deviation value. If the updated operating deviation value is lower than a preset safety judgment threshold, a start / stop adjustment command is generated.

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