An automated intelligent monitoring and processing system and method for cold storage units

Abstract

The present invention relates to the field of data management, and specifically discloses an automated intelligent monitoring and processing system and method for a cold storage unit, the system comprising: an environmental impact assessment module, an efficiency fluctuation monitoring module, a cold storage unit performance feedback module, and a cold storage unit database. The present invention continuously monitors and analyzes the working environment data of the cold storage unit, accurately identifies the impact of environmental changes on the efficiency of the equipment, and dynamically adjusts the allowable fluctuation range of the thermal engine efficiency to ensure that the equipment operates in the best state. At the same time, the efficiency fluctuation monitoring module is used to collect operating condition data in real time, and efficiency anomalies are immediately discovered and quantified, thereby improving the accuracy and timeliness of fault warnings. By quantitatively evaluating the efficiency anomalies of the compressor, actively performing performance evaluation and adjustment, it is possible to effectively avoid energy waste and poor refrigeration effects caused by reduced efficiency, thereby improving the operating stability and economy of the cold storage unit, reducing maintenance costs, and extending the life of the equipment.

Description

Technical Field

[0001] The present invention relates to the field of data management, and in particular to an automatic intelligent monitoring and processing system and method for a cold storage unit. Background Art

[0002] With the rapid development of the cold chain logistics industry, the operation stability and efficiency of cold storage units, as core equipment for maintaining a low-temperature environment and ensuring food quality and safety, have become the focus of industry attention.

[0003] For example, the invention patent with announcement number CN111915255B is an intelligent cold storage management and information sharing platform, which includes: a collection end, which is used to collect environmental data in the cold storage. There are multiple collection ends, which are respectively set in different partitions of the cold storage, and each collection end has its own number; a processing end, which is used to receive data collected by the collection end, and the processing end stores a correspondence table between the collection end number and the cold storage partition; a management end, which is used to view the environmental data in the corresponding partition according to the area code of the cold storage partition; the management end is also used to input or modify the standard temperature of each cold storage partition, and the processing end is also used to store or update the standard temperature of each cold storage partition.

[0004] For example, the invention patent with announcement number CN116629728B is a cold storage operation management system based on photovoltaic and wind power integrated power supply. The system includes a renewable energy information collection module, a renewable energy information analysis module, a cold storage information collection module, a cold storage information analysis module, a renewable energy regulation and judgment module, a database, a cargo status analysis module, a cold storage power regulation value analysis module, a cold storage power regulation analysis module and a power regulation execution module; the present invention regulates and judges the cold storage power and the power regulation value through the estimated power generation analysis of renewable energy, cold storage information analysis and cargo status analysis, thereby adjusting the operating power of the cold storage.

[0005] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, it was found that the above-mentioned technology has at least the following technical problems: the existing cold storage management systems often focus on the collection and processing of environmental data, the integration and sharing of management information, and power regulation based on renewable energy, but have limitations in improving the reliability and safety of the operation of cold storage units. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides an automated intelligent monitoring and processing system and method for a cold storage unit, which can effectively solve the problems involved in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: The first aspect of the present invention provides an automated intelligent monitoring and processing system for a cold storage unit, including: an environmental impact assessment module, which is used to collect the working environment data of the cold storage unit, comprehensively analyze and obtain the cold storage unit environmental anomaly assessment index, and according to the cold storage unit environmental anomaly assessment index, process and obtain the allowable fluctuation range of each thermal engine efficiency.

[0008] The efficiency fluctuation monitoring module is used to collect the real-time operating data of the cold storage unit, and obtain the abnormal degree evaluation value of each heat engine efficiency according to the allowable fluctuation range of each heat engine efficiency after processing, and obtain the abnormal working efficiency index of the cold storage unit through comprehensive analysis.

[0009] The cold storage unit performance feedback module is used to evaluate the cold storage unit according to the abnormal efficiency index of the cold storage unit's compressor, and to perform feedback processing according to the evaluation results.

[0010] As a further method, the comprehensive analysis obtains the cold storage unit environmental anomaly assessment index, and the specific analysis process is: deploying a number of environmental monitoring points and collecting the working environment data of each environmental monitoring point.

[0011] According to the correlation between environmental factors and the working efficiency of the cold storage unit, the working environment data are classified into positively correlated environmental data, negatively correlated environmental data and balanced environmental data.

[0012] The reference values ​​of various working environment data are obtained from the cold storage unit database, and the cold storage unit environmental abnormality assessment index is obtained through comprehensive analysis.

[0013] As a further method, the processing obtains the allowable fluctuation range of each heat engine efficiency. The specific process is: matching the cold storage unit environmental abnormality assessment index with the isothermal efficiency allowable deviation value, adiabatic efficiency allowable deviation value and polytropic efficiency allowable deviation value corresponding to each cold storage unit environmental abnormality assessment index interval stored in the cold storage unit database, and obtaining the compressor isothermal efficiency allowable deviation value, the compressor adiabatic efficiency allowable deviation value and the compressor polytropic efficiency allowable deviation value.

[0014] The preset compressor isothermal efficiency reference value, compressor adiabatic efficiency reference value and compressor variable efficiency reference value are extracted from the cold storage unit database, and the compressor isothermal efficiency reference value, compressor adiabatic efficiency reference value and compressor variable efficiency reference value are respectively subtracted and summed with the compressor isothermal efficiency allowable deviation value, compressor adiabatic efficiency allowable deviation value and compressor variable efficiency allowable deviation value, and the difference result is marked as the corresponding lower limit of the allowable fluctuation range, and the summation result is marked as the corresponding upper limit of the allowable fluctuation range, so as to obtain the compressor isothermal efficiency allowable fluctuation range, the compressor adiabatic efficiency allowable fluctuation range and the compressor variable efficiency allowable fluctuation range.

[0015] The allowable fluctuation range of the compressor isothermal efficiency, the allowable fluctuation range of the compressor adiabatic efficiency and the allowable fluctuation range of the compressor polytropic efficiency are jointly marked as the allowable fluctuation range of each heat engine efficiency.

[0016] As a further method, the processing obtains the abnormality degree evaluation value of each thermal engine efficiency, and the specific process is: the abnormality degree evaluation value of each thermal engine efficiency includes the isothermal efficiency abnormality degree evaluation value of the compressor, the adiabatic efficiency abnormality degree evaluation value of the compressor and the polytropic efficiency abnormality degree evaluation value of the compressor.

[0017] The gas inlet pressure, gas outlet pressure, gas inlet temperature and gas outlet temperature of the compressor are collected when it is working, and the preset polytropic index and the mass flow rate, constant-pressure specific heat capacity and specific heat ratio of the target compressed gas are obtained from the cold storage unit database. After processing, the isothermal compression work of the compressor, the adiabatic compression work of the compressor and the polytropic compression work of the compressor are obtained.

[0018] A compression cycle of the compressor is obtained, the working current and working voltage of the compressor during the compression cycle are monitored, and the actual compression work of the compressor is obtained after processing.

[0019] Based on the isothermal compression work of the compressor, the adiabatic compression work of the compressor, the variable compression work of the compressor, the actual compression work of the compressor and the allowable fluctuation range of each heat engine efficiency, a comprehensive analysis is conducted to obtain the evaluation value of the abnormality degree of each heat engine efficiency.

[0020] As a further method, the comprehensive analysis obtains the abnormal working efficiency index of the cold storage unit. The specific analysis process is: according to the evaluation value of the abnormal degree of efficiency of each thermal engine, the abnormal working efficiency index of the cold storage unit is obtained by comprehensive analysis. The abnormal working efficiency index of the cold storage unit is used to quantitatively evaluate the abnormal degree of the working efficiency of the cold storage unit.

[0021] As a further method, the abnormality evaluation of the cold storage unit is carried out, and the specific process is: comparing the work efficiency abnormality index of the cold storage unit with the work efficiency abnormality index threshold stored in the cold storage unit database; if the work efficiency abnormality index is greater than or equal to the work efficiency abnormality index threshold, the work efficiency of the cold storage unit is evaluated as abnormal; if the work efficiency abnormality index is less than the work efficiency abnormality index threshold, the work efficiency of the cold storage unit is evaluated as normal.

[0022] The second aspect of the present invention provides an automated intelligent monitoring and processing method for a cold storage unit, comprising: collecting the working environment data of the cold storage unit, comprehensively analyzing to obtain an environmental anomaly assessment index for the cold storage unit, and processing to obtain an allowable fluctuation range of each thermal engine efficiency based on the environmental anomaly assessment index for the cold storage unit.

[0023] The real-time operating data of the cold storage unit is collected, and according to the allowable fluctuation range of each thermal engine efficiency, the abnormal degree evaluation value of each thermal engine efficiency is obtained after processing, and the abnormal working efficiency index of the cold storage unit is obtained through comprehensive analysis.

[0024] According to the abnormal efficiency index of the compressor of the cold storage unit, the cold storage unit is evaluated abnormally, and feedback processing is performed based on the evaluation results.

[0025] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0026] (1) The present invention provides an automated intelligent monitoring and processing system and method for cold storage units, continuously monitors and analyzes the working environment data of cold storage units, accurately identifies the impact of environmental changes on equipment efficiency, and dynamically adjusts the allowable fluctuation range of thermal engine efficiency to ensure that the equipment operates in the best state. At the same time, by quantitatively evaluating the efficiency anomalies of the compressor, actively performing performance evaluation and adjustment, it effectively avoids energy waste and poor refrigeration effect caused by reduced efficiency, thereby improving the operating stability and economy of the cold storage unit, reducing maintenance costs, and extending the life of the equipment.

[0027] (2) The present invention can provide early warning of environmental factors that may affect the efficiency of the cold storage unit by monitoring the working environment of the cold storage unit, so as to take preventive measures and avoid sudden failures. Quantitative evaluation of the degree of environmental abnormality helps to adjust the unit operating parameters to ensure efficient and stable operation in any environment, reduce energy waste and equipment wear. At the same time, by timely identifying and correcting adverse environmental factors, the risk of damage to the cold storage unit caused by harsh environment can be effectively reduced, and its service life can be extended.

[0028] (3) The present invention can help identify the cause of compressor performance degradation by comprehensively analyzing the isothermal efficiency, adiabatic efficiency and polytropic efficiency of the cold storage unit compressor, and timely maintenance can avoid greater system failures and downtime. At the same time, regular analysis of the isothermal efficiency, adiabatic efficiency and polytropic efficiency of the compressor can reveal the trend of system aging or efficiency reduction, prompting the optimization or upgrading of the system to maintain or restore high performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0030] Figure 1 It is a schematic diagram of system module connection of the present invention.

[0031] Figure 2 It is a schematic diagram of the method flow of the present invention.

[0032] Figure 3 It is a schematic diagram of the functional relationship between the compressor isothermal efficiency abnormality degree evaluation value and the work efficiency abnormality index involved in the embodiment of the present invention. DETAILED DESCRIPTION

[0033] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0034] Reference Figure 1 As shown, the first aspect of the present invention provides an automated intelligent monitoring and processing system for a cold storage unit, including: an environmental impact assessment module, for collecting the working environment data of the cold storage unit, comprehensively analyzing to obtain an environmental anomaly assessment index of the cold storage unit, and according to the environmental anomaly assessment index of the cold storage unit, processing to obtain the allowable fluctuation range of each thermal engine efficiency.

[0035] The efficiency fluctuation monitoring module is used to collect the real-time operating data of the cold storage unit, and obtain the abnormal degree evaluation value of each heat engine efficiency according to the allowable fluctuation range of each heat engine efficiency after processing, and obtain the abnormal working efficiency index of the cold storage unit through comprehensive analysis.

[0036] The cold storage unit performance feedback module is used to evaluate the cold storage unit according to the abnormal efficiency index of the cold storage unit's compressor, and to perform feedback processing according to the evaluation results.

[0037] Specifically, the cold storage unit environmental anomaly assessment index is obtained through comprehensive analysis. The specific analysis process is: deploying several environmental monitoring points and collecting the working environment data of each environmental monitoring point.

[0038] It should be understood that the various working environment data in this embodiment can be collected by different environmental sensors, such as a temperature sensor, a humidity sensor, and an atmospheric pressure sensor.

[0039] According to the correlation between environmental factors and the working efficiency of the cold storage unit, the working environment data are classified into positively correlated environmental data, negatively correlated environmental data and balanced environmental data.

[0040] It should be understood that the correlation between the environmental factors and the working efficiency of the cold storage unit in this embodiment includes three correlation relationships, namely, a positive correlation relationship, a negative correlation relationship and a variable relationship with a reference value.

[0041] It should be understood that the positively correlated environmental data in this embodiment refers to environmental factors that are positively correlated with the working efficiency of the cold storage unit, that is, when the values ​​of these environmental factors increase, the working efficiency of the cold storage unit will also increase accordingly. For example, an increase in air flow rate can promote heat exchange between cold air and goods, helping to quickly reduce the temperature. Faster air flow can accelerate heat transfer, making the cold air more evenly distributed throughout the cold storage space, reducing temperature gradients, and avoiding the phenomenon of high temperatures in local areas, thereby improving refrigeration efficiency. At the same time, accelerated air flow helps to remove moisture from the air and reduce the formation of frost, thereby maintaining the high efficiency of the cold storage unit.

[0042] It should be understood that the negatively correlated environmental data in this embodiment refers to environmental factors that are inversely proportional to the working efficiency of the cold storage unit, that is, the increase of these factors will lead to a decrease in the efficiency of the unit. For example, when the outside temperature rises, in order to maintain the low temperature state inside the cold storage, the unit needs to consume more energy to offset the temperature difference, which will reduce the overall energy utilization efficiency. Therefore, the ambient temperature is a negatively correlated environmental data in this context.

[0043] It should be understood that the balanced environmental data in this embodiment refers to environmental factors with one or more optimal values ​​or ranges. Exceeding or falling below this range will have an adverse effect on the working efficiency of the cold storage unit. Taking humidity as an example, for most commercial refrigeration systems, the recommended relative humidity control target is to reduce the humidity as much as possible without affecting the storage quality, generally controlled between 40% and 60%, so as to maintain the high efficiency of the compressor and avoid damage to the stored items. Too high humidity will cause frost on the evaporator surface and reduce the heat exchange efficiency; while too low humidity may cause product drying loss.

[0044] The reference values ​​of various working environment data are obtained from the cold storage unit database, and the cold storage unit environmental abnormality assessment index is obtained through comprehensive analysis.

[0045] It should be understood that the working environment data reference values ​​in this embodiment are standards or benchmarks for evaluating and comparing the performance of cold storage units under different environmental conditions, including positive correlation environmental data reference values, negative correlation environmental data reference values ​​and balanced environmental data reference values.

[0046] In a specific embodiment, the environmental anomaly assessment influencing factors corresponding to the preset positive correlation environment, negative correlation environment and balanced environment are extracted from the cold storage unit database, and the preset allowable deviation values ​​of each balanced environment data are obtained from the cold storage unit database, and the cold storage unit environmental anomaly assessment index is obtained by comprehensive analysis. The specific numerical expression is:

[0047]

[0048] In the formula, εH represents the abnormal environment assessment index of the cold storage unit, HZ i It represents the abnormal index of positively correlated environmental data at the i-th environmental monitoring point, and is used to comprehensively evaluate the abnormal degree of positively correlated environmental data. represents the q1th positively correlated environmental data of the ith environmental monitoring point, represents the reference value of the q1th positively correlated environmental data, q1 represents the number of each positively correlated environmental data, q1=1,2,3,...,k1, k1 represents the total number of positively correlated environmental data, HF i It represents the negative correlation environmental data anomaly index of the ith environmental monitoring point, and is used to comprehensively evaluate the degree of anomaly of negative correlation environmental data. represents the q2th negatively correlated environmental data of the ith environmental monitoring point, represents the reference value of the q2th negatively correlated environmental data, q2 represents the number of each negatively correlated environmental data, q2=1,2,3,...,k2, k2 represents the total number of negatively correlated environmental data, HJ i It represents the abnormal index of the balanced environmental data of the i-th environmental monitoring point, and is used to comprehensively evaluate the abnormal degree of the balanced environmental data. represents the q3th equilibrium environmental data of the ith environmental monitoring point, represents the q3th equilibrium environment data reference value, represents the allowable deviation value of the q3th balanced environmental data, q3 represents the number of each balanced environmental data, q3=1,2,3,...,k3, k3 represents the total number of balanced environmental data, i represents the number of each environmental monitoring point, i=1,2,3,...n, n represents the total number of environmental monitoring points, ω1 represents the environmental anomaly assessment impact factor corresponding to the preset positive correlation environment, ω2 represents the environmental anomaly assessment impact factor corresponding to the preset negative correlation environment, ω3 represents the environmental anomaly assessment impact factor corresponding to the preset balanced environment.

[0049] It should be understood that in this embodiment, the value of the positively correlated environmental data can be adjusted to avoid being equal to zero. Taking temperature as an example, the unit of temperature can be converted from Celsius to Kelvin temperature. In reality, Kelvin temperature cannot be zero. The preset reference value of each negatively correlated environmental data and the allowable deviation value of each balanced environmental data are not zero.

[0050] It should be understood that the value range of the environmental anomaly assessment impact factor corresponding to the positive correlation environment, negative correlation environment and balanced environment in this embodiment is between 0 and 1. Through the relationship between the positive correlation environment data anomaly index, negative correlation environment data anomaly index and balanced environment data anomaly index and the cold storage unit environmental anomaly assessment index in the historical data, a mapping set of positive correlation environment data anomaly index, negative correlation environment data anomaly index and balanced environment data anomaly index and their corresponding environmental anomaly assessment impact factors is constructed, and the positive correlation environment data anomaly index, negative correlation environment data anomaly index and balanced environment data anomaly index are input, and the environmental anomaly assessment impact factor corresponding to the positive correlation environment, the environmental anomaly assessment impact factor corresponding to the negative correlation environment and the environmental anomaly assessment impact factor corresponding to the balanced environment are obtained from the mapping set.

[0051] It should be understood that the cold storage unit environmental anomaly assessment index in this embodiment is a quantitative indicator obtained by analyzing each positively correlated environmental data, each negatively correlated environmental data and each balanced environmental data, and is used to quantitatively assess the degree of interference of the working environment of the cold storage unit on the working efficiency of the cold storage unit.

[0052] The cold storage unit environmental anomaly assessment index is jointly determined by the positively correlated environmental data, the negatively correlated environmental data and the balanced environmental data. The smaller the positively correlated environmental data, the larger the negatively correlated environmental data, the more the balanced environmental data deviate from the corresponding reference value, and the larger the corresponding cold storage unit environmental anomaly assessment index, indicating that the working environment of the cold storage unit interferes more with the working efficiency of the cold storage unit.

[0053] In a specific embodiment, by monitoring the working environment of the cold storage unit, environmental factors that may affect the efficiency of the cold storage unit can be warned in advance, so that preventive measures can be taken to avoid sudden failures. Quantitative assessment of the degree of environmental abnormality helps to adjust the unit's operating parameters to ensure efficient and stable operation in any environment, reduce energy waste and equipment wear. At the same time, by timely identifying and correcting adverse environmental factors, the risk of damage to the cold storage unit caused by harsh environment can be effectively reduced, and its service life can be extended.

[0054] Specifically, the allowable fluctuation range of each heat engine efficiency is obtained by processing. The specific process is: matching the cold storage unit environmental abnormality assessment index with the isothermal efficiency allowable deviation value, adiabatic efficiency allowable deviation value and polytropic efficiency allowable deviation value corresponding to each cold storage unit environmental abnormality assessment index interval stored in the cold storage unit database, and obtaining the compressor isothermal efficiency allowable deviation value, compressor adiabatic efficiency allowable deviation value and compressor polytropic efficiency allowable deviation value.

[0055] It should be understood that the efficiencies of the various heat engines in this embodiment include the compressor isothermal efficiency, the compressor adiabatic efficiency, and the compressor polytropic efficiency. The compressor isothermal efficiency is the ratio of the shaft work in the actual compression process to the shaft work in the reversible isothermal compression process, that is, the ratio of the actual compression work to the theoretical work calculated according to the isothermal process. In the isothermal process, the compression process is accompanied by heat exchange, so that the gas temperature remains constant. The compressor isothermal efficiency takes into account the idealized isothermal compression process, that is, the gas temperature remains unchanged during the compression process, all input work is used to overcome the volume work of the gas, and no additional heat is generated.

[0056] The compressor adiabatic efficiency is the ratio of the shaft work in the actual compression process to the shaft work in the reversible adiabatic compression process, that is, the ratio of the actual compression work to the theoretical work calculated according to the adiabatic process. In the adiabatic process, there is no heat exchange with the outside world, and all input work is converted into an increase in internal energy. The compressor adiabatic efficiency focuses on the ideal situation without heat exchange, and evaluates the efficiency of the compressor under adiabatic conditions, that is, the completeness of energy conversion.

[0057] The compressor polytropic efficiency reflects the ratio of the work absorbed from the outside world during the actual working process of the compressor to the theoretical work calculated according to the polytropic process. The polytropic process is an idealized compression process between isothermal compression and adiabatic compression. The compressor polytropic efficiency considers the energy conversion efficiency during the compression process from a more general perspective, covering the changes in thermodynamic state during the actual compression process and the energy loss.

[0058] The compressor isothermal efficiency, compressor adiabatic efficiency and compressor polytropic efficiency represent the performance of the compressor in different idealized thermodynamic processes. In practical applications, compressors often cannot fully conform to any idealized process, but these efficiencies can help engineers analyze and optimize the design and operation of the compressor to improve its overall performance and efficiency. Under different compressor types and operating conditions, these efficiency indicators will have different reference values ​​and target ranges.

[0059] The preset compressor isothermal efficiency reference value, compressor adiabatic efficiency reference value and compressor variable efficiency reference value are extracted from the cold storage unit database, and the compressor isothermal efficiency reference value, compressor adiabatic efficiency reference value and compressor variable efficiency reference value are respectively subtracted and summed with the compressor isothermal efficiency allowable deviation value, compressor adiabatic efficiency allowable deviation value and compressor variable efficiency allowable deviation value, and the difference result is marked as the corresponding lower limit of the allowable fluctuation range, and the summation result is marked as the corresponding upper limit of the allowable fluctuation range, so as to obtain the compressor isothermal efficiency allowable fluctuation range, the compressor adiabatic efficiency allowable fluctuation range and the compressor variable efficiency allowable fluctuation range.

[0060] It should be understood that in this embodiment, the allowable fluctuation range of the compressor working efficiency under different ideal conditions is determined according to the working environment of the cold storage unit. By understanding the expected efficiency range of the compressor under specific environmental conditions, it can help to reasonably set performance goals. When the efficiency of the compressor deviates from the preset range, it may indicate that maintenance or replacement of parts is required to restore performance. Abnormal fluctuations in compressor efficiency may be a sign of early failure. Timely detection and correction can prevent major failures from occurring and reduce downtime and maintenance costs.

[0061] The allowable fluctuation range of the compressor isothermal efficiency, the allowable fluctuation range of the compressor adiabatic efficiency and the allowable fluctuation range of the compressor polytropic efficiency are jointly marked as the allowable fluctuation range of each heat engine efficiency.

[0062] Specifically, the abnormality evaluation value of each heat engine efficiency is obtained by processing, and the specific process is: the abnormality evaluation value of each heat engine efficiency includes the abnormality evaluation value of the isothermal efficiency of the compressor, the abnormality evaluation value of the adiabatic efficiency of the compressor and the abnormality evaluation value of the polytropic efficiency of the compressor.

[0063] The gas inlet pressure, gas outlet pressure, gas inlet temperature and gas outlet temperature of the compressor are collected when it is working, and the preset polytropic index and the mass flow rate, constant-pressure specific heat capacity and specific heat ratio of the target compressed gas are obtained from the cold storage unit database. After processing, the isothermal compression work of the compressor, the adiabatic compression work of the compressor and the polytropic compression work of the compressor are obtained.

[0064] It should be understood that in this embodiment, the inlet and outlet temperatures of the gas may be collected by a temperature sensor, and the inlet and outlet pressures of the gas may be collected by a pressure sensor.

[0065] In a specific embodiment, the isothermal compression work of the compressor, the adiabatic compression work of the compressor and the polytropic compression work of the compressor can be calculated respectively according to existing calculation formulas.

[0066] Among them, the calculation formula of the isothermal compression work of the compressor is:

[0067] The calculation formula of the adiabatic compression work of the compressor is:

[0068] The calculation formula of the variable compression work of the compressor is:

[0069] Where W iso isothermal compression work of the compressor, W adi is the adiabatic compression work of the compressor, W poly represents the variable compression work of the compressor, m represents the mass flow rate of the preset target compressed gas, C prepresents the constant-pressure specific heat capacity of the preset target compressed gas, γ represents the preset specific heat ratio of the target compressed gas, a represents the preset polytropic index, P1 represents the gas inlet pressure, P2 represents the gas outlet pressure, T1 represents the gas inlet temperature, and T2 represents the gas outlet temperature.

[0070] A compression cycle of the compressor is obtained, the working current and working voltage of the compressor during the compression cycle are monitored, and the actual compression work of the compressor is obtained after processing.

[0071] In a specific embodiment, the numerical expression of the actual compression work of the compressor is:

[0072]

[0073] Where W act represents the actual compression work of the compressor, PO(t)=U(t)*I(t), PO(t) represents the compression power at time t, U(t) represents the working voltage at time t, I(t) represents the working current at time t, t represents the time variable, t∈[t1,t2], t1 represents the starting time point of the compression cycle, and t2 represents the end time point of the compression cycle.

[0074] Based on the isothermal compression work of the compressor, the adiabatic compression work of the compressor, the variable compression work of the compressor, the actual compression work of the compressor and the allowable fluctuation range of each heat engine efficiency, a comprehensive analysis is conducted to obtain the evaluation value of the abnormality degree of each heat engine efficiency.

[0075] In a specific embodiment, the lower limit value and the upper limit value of the interval of the allowable fluctuation range of the compressor isothermal efficiency are extracted, and they are marked as the isothermal interval lower limit value and the isothermal interval upper limit value, respectively. The lower limit abnormal impact factor, the upper limit abnormal impact factor and the abnormal impact factor within the interval corresponding to the preset isothermal efficiency are obtained from the cold storage unit database, and the compressor isothermal efficiency abnormality degree evaluation value is obtained by comprehensive analysis. The specific numerical expression is:

[0076]

[0077] Where, δW represents the abnormal degree evaluation value of the compressor isothermal efficiency, μW0 represents the compressor isothermal efficiency, μW1 represents the lower limit of the range of allowable fluctuation of the compressor isothermal efficiency, μW2 represents the upper limit of the range of allowable fluctuation of the compressor isothermal efficiency, μWC represents the reference value of the compressor isothermal efficiency, ΔW represents the allowable deviation value of the compressor isothermal efficiency, Indicates the abnormal impact factor of the lower limit of the interval corresponding to the preset isothermal efficiency, Indicates the abnormal impact factor in the interval corresponding to the preset isothermal efficiency, It represents the abnormal influence factor of the upper limit of the interval corresponding to the preset isothermal efficiency, a1 represents the preset formula model baseline value when the isothermal efficiency is lower than the lower limit of the interval, a2 represents the preset formula model baseline value when the isothermal efficiency is within the interval, and a3 represents the preset formula model baseline value when the isothermal efficiency is higher than the upper limit of the interval.

[0078] It should be understood that in this embodiment, the value range of the lower limit abnormal impact factor of the interval, the upper limit abnormal impact factor of the interval and the abnormal impact factor within the interval corresponding to the isothermal efficiency is between 0 and 1. Through the relationship between the compressor isothermal efficiency and the compressor isothermal efficiency abnormality degree evaluation value in the historical data, a mapping set between the compressor isothermal efficiency and the lower limit abnormal impact factor of the interval, the upper limit abnormal impact factor of the interval and the abnormal impact factor within the interval is constructed, and the actual value of the compressor isothermal efficiency is input, and the lower limit abnormal impact factor of the interval, the upper limit abnormal impact factor of the interval and the abnormal impact factor within the interval corresponding to the isothermal efficiency are obtained from the mapping set.

[0079] Table 1 Example of data for evaluation value of abnormal degree of compressor isothermal efficiency

[0080] Serial number Compressor isothermal efficiency Evaluation value of abnormal degree of compressor isothermal efficiency 1 0.45 1.267 2 0.55 0.967 3 0.65 0.9 4 0.75 0.967 5 0.85 1.1

[0081] As shown in Table 1, in a specific embodiment, μW1=0.5, μW2=0.8, μWC=0.65, ΔW=0.15, a1=a3=1, a2=0.9, It should be understood that the compressor isothermal efficiency abnormality evaluation value in this embodiment is used to quantitatively evaluate the abnormality of the compressor isothermal efficiency. When μW0<μWC, the compressor isothermal efficiency abnormality evaluation value is negatively correlated with the compressor isothermal efficiency. The larger the compressor isothermal efficiency, the smaller the corresponding compressor isothermal efficiency abnormality evaluation value is, indicating that the compressor isothermal efficiency is lower abnormal; when μW0≥μWC, the compressor isothermal efficiency abnormality evaluation value is positively correlated with the compressor isothermal efficiency. The larger the compressor isothermal efficiency, the larger the corresponding compressor isothermal efficiency abnormality evaluation value is, indicating that the compressor isothermal efficiency is higher abnormal.

[0082] Similarly, the evaluation value of the abnormal degree of the compressor adiabatic efficiency and the evaluation value of the abnormal degree of the compressor polytropic efficiency are obtained by comprehensive analysis. In a specific embodiment, the lower limit value and the upper limit value of the allowable fluctuation range of the compressor adiabatic efficiency and the allowable fluctuation range of the compressor polytropic efficiency are extracted, and the lower limit abnormal impact factor, the upper limit abnormal impact factor and the abnormal impact factor within the interval corresponding to the preset adiabatic efficiency and polytropic efficiency are obtained from the cold storage unit database.

[0083] The evaluation value of the abnormal degree of compressor adiabatic efficiency is expressed as follows:

[0084]

[0085] In the formula, δR represents the abnormality evaluation value of the compressor adiabatic efficiency, μR0 represents the compressor adiabatic efficiency, μR1 represents the lower limit of the allowable fluctuation range of the compressor adiabatic efficiency, μR2 represents the upper limit of the allowable fluctuation range of the compressor adiabatic efficiency, μRC represents the reference value of the compressor adiabatic efficiency, ΔR represents the allowable deviation value of the compressor adiabatic efficiency, τ1 represents the preset lower limit abnormal impact factor of the interval corresponding to the adiabatic efficiency, τ2 represents the preset in-range abnormal impact factor of the adiabatic efficiency, τ3 represents the preset upper limit abnormal impact factor of the interval corresponding to the adiabatic efficiency, b1 represents the preset formula model baseline value when the adiabatic efficiency is lower than the lower limit of the interval, b2 represents the preset formula model baseline value when the adiabatic efficiency is within the interval, and b3 represents the preset formula model baseline value when the adiabatic efficiency is higher than the upper limit of the interval.

[0086] The evaluation value of abnormal degree of compressor polynomial efficiency is expressed as follows:

[0087]

[0088] In the formula, δB represents the abnormal degree evaluation value of the compressor variable efficiency, μB0 represents the compressor variable efficiency, μB1 represents the lower limit of the allowable fluctuation range of the compressor variable efficiency, μB2 represents the upper limit of the allowable fluctuation range of the compressor variable efficiency, μBC represents the reference value of the compressor variable efficiency, ΔB represents the allowable deviation value of the compressor variable efficiency, σ1 represents the preset lower limit abnormal influence factor of the interval corresponding to the variable efficiency, σ2 represents the preset in-range abnormal influence factor of the variable efficiency, σ3 represents the preset upper limit abnormal influence factor of the interval corresponding to the variable efficiency, c1 represents the preset formula model baseline value when the variable efficiency is lower than the lower limit of the interval, c2 represents the preset formula model baseline value when the variable efficiency is within the interval, and c3 represents the preset formula model baseline value when the variable efficiency is higher than the upper limit of the interval.

[0089] It should be understood that the compressor adiabatic efficiency abnormality evaluation value in this embodiment is used to quantitatively evaluate the abnormality of the compressor adiabatic efficiency. When μR0<μRC, the compressor adiabatic efficiency abnormality evaluation value is negatively correlated with the compressor adiabatic efficiency. The greater the compressor adiabatic efficiency, the smaller the corresponding compressor adiabatic efficiency abnormality evaluation value, indicating that the compressor adiabatic efficiency abnormality is lower; when μR0≥μRC, the compressor adiabatic efficiency abnormality evaluation value is positively correlated with the compressor adiabatic efficiency. The greater the compressor adiabatic efficiency, the greater the corresponding compressor adiabatic efficiency abnormality evaluation value, indicating that the compressor adiabatic efficiency abnormality is higher.

[0090] It should be understood that the compressor variable efficiency abnormality evaluation value in this embodiment is used to quantitatively evaluate the abnormality of the compressor variable efficiency. When μB0<μBC, the compressor variable efficiency abnormality evaluation value is negatively correlated with the compressor variable efficiency. The larger the compressor variable efficiency, the smaller the corresponding compressor variable efficiency abnormality evaluation value, indicating that the compressor variable efficiency is lower abnormal; when μB0≥μBC, the compressor variable efficiency abnormality evaluation value is positively correlated with the compressor variable efficiency. The larger the compressor variable efficiency, the larger the corresponding compressor variable efficiency abnormality evaluation value, indicating that the compressor variable efficiency is higher abnormal.

[0091] It should be understood that in this embodiment, the value range of the lower limit abnormal impact factor of the interval, the upper limit abnormal impact factor of the interval and the abnormal impact factor within the interval corresponding to the isothermal efficiency is between 0 and 1. Through the relationship between the compressor isothermal efficiency and the compressor isothermal efficiency abnormality degree evaluation value in the historical data, a mapping set between the compressor isothermal efficiency and the lower limit abnormal impact factor of the interval, the upper limit abnormal impact factor of the interval and the abnormal impact factor within the interval is constructed, and the actual value of the compressor isothermal efficiency is input, and the lower limit abnormal impact factor of the interval, the upper limit abnormal impact factor of the interval and the abnormal impact factor within the interval corresponding to the isothermal efficiency are obtained from the mapping set.

[0092] It should be understood that in this embodiment, the value range of the interval lower limit abnormal impact factor, interval upper limit abnormal impact factor and interval intra-abnormal impact factor corresponding to the variable efficiency is between 0 and 1. Through the relationship between the compressor variable efficiency and the compressor variable efficiency abnormality degree evaluation value in the historical data, a mapping set between the compressor variable efficiency and the interval lower limit abnormal impact factor, interval upper limit abnormal impact factor and interval intra-abnormal impact factor is constructed, and the actual value of the compressor variable efficiency is input to obtain the interval lower limit abnormal impact factor, interval upper limit abnormal impact factor and interval intra-abnormal impact factor corresponding to the variable efficiency from the mapping set.

[0093] It should be understood that the formula model baseline values ​​such as a1, b1 and c1 in this embodiment are used to introduce a basic value into the formula model to ensure that the model can fit the data points, making the model more flexible and better adapted to the real distribution of the data. The formula model baseline value is directly obtained from the cold storage unit database and can be obtained by estimating the gap between the formula model prediction value and the real value by the mean square error method.

[0094] Specifically, the abnormal working efficiency index of the cold storage unit is obtained by comprehensive analysis. The specific analysis process is: according to the abnormal degree evaluation value of each thermal engine efficiency, the abnormal working efficiency index of the cold storage unit is obtained by comprehensive analysis. The abnormal working efficiency index of the cold storage unit is used to quantitatively evaluate the abnormal degree of the working efficiency of the cold storage unit.

[0095] In a specific embodiment, the work efficiency abnormality evaluation influencing factors corresponding to the preset isothermal efficiency, adiabatic efficiency and polytropic efficiency are obtained from the cold storage unit database, and the work efficiency abnormality index of the cold storage unit is obtained by comprehensive analysis. The specific numerical expression is:

[0096] β=tanh(δW*θ1+δR*θ2+δB*θ3)

[0097] In the formula, β represents the abnormal efficiency index of the cold storage unit, δW represents the abnormal degree evaluation value of the compressor isothermal efficiency, δR represents the abnormal degree evaluation value of the compressor adiabatic efficiency, δB represents the abnormal degree evaluation value of the compressor polytropic efficiency, θ1 represents the abnormal efficiency evaluation influencing factor corresponding to the preset isothermal efficiency, θ2 represents the abnormal efficiency evaluation influencing factor corresponding to the preset adiabatic efficiency, and θ3 represents the abnormal efficiency evaluation influencing factor corresponding to the preset polytropic efficiency.

[0098] It should be understood that in this embodiment, the value range of the work efficiency abnormality assessment influencing factors corresponding to the isothermal efficiency, adiabatic efficiency and polytropic efficiency is between 0 and 1. Through the relationship between the compressor isothermal efficiency abnormality degree assessment value, the compressor adiabatic efficiency abnormality degree assessment value and the compressor polytropic efficiency abnormality degree assessment value and the work efficiency abnormality index in the historical data, a mapping set between the compressor isothermal efficiency abnormality degree assessment value, the compressor adiabatic efficiency abnormality degree assessment value and the compressor polytropic efficiency abnormality degree assessment value and their corresponding work efficiency abnormality assessment influencing factors is constructed, and the compressor isothermal efficiency abnormality degree assessment value, the compressor adiabatic efficiency abnormality degree assessment value and the compressor polytropic efficiency abnormality degree assessment value are input to obtain the work efficiency abnormality assessment influencing factors corresponding to the isothermal efficiency, the adiabatic efficiency and the polytropic efficiency from the mapping set.

[0099] like Figure 3 As shown, in a specific embodiment, δB=1.6, θ1=0.4, θ2=0.5, θ3=0.4, when δR=2, the functional relationship between the compressor isothermal efficiency abnormality degree evaluation value and the work efficiency abnormality index is shown as curve a; when δR=2.5, the functional relationship between the compressor isothermal efficiency abnormality degree evaluation value and the work efficiency abnormality index is shown as curve b; when δR=3, the functional relationship between the compressor isothermal efficiency abnormality degree evaluation value and the work efficiency abnormality index is shown as curve c.

[0100] It should be understood that, in the present embodiment, the abnormal working efficiency index of the cold storage unit is jointly determined by the compressor isothermal efficiency abnormality evaluation value, the compressor adiabatic efficiency abnormality evaluation value and the compressor variable efficiency abnormality evaluation value. The larger the compressor isothermal efficiency abnormality evaluation value, the compressor adiabatic efficiency abnormality evaluation value and the compressor variable efficiency abnormality evaluation value, the larger the corresponding abnormal working efficiency index of the cold storage unit, indicating that the working efficiency of the cold storage unit is more abnormal.

[0101] In a specific embodiment, the isothermal efficiency, adiabatic efficiency and polytropic efficiency of the compressor are interrelated, and they will change with the change of the operating conditions of the compressor. The change of these efficiencies is determined by the nature of the compression process, that is, the change of the gas state inside the compressor, and the influence of the compressor design and operating parameters. The isothermal efficiency, adiabatic efficiency and polytropic efficiency all depend on the characteristics of the compression process, including factors such as the compression ratio, the nature of the working medium, the type of compressor (piston, centrifugal, screw, etc.), the operating speed, the cooling efficiency and the mechanical loss of the compressor. When the operating conditions of the compressor change, such as the compression ratio increases, the compression process will become more adiabatic, which may cause the adiabatic efficiency to increase, while the isothermal efficiency will decrease relatively, because the actual process deviates more from the isothermal conditions. Similarly, the polytropic efficiency will also be affected by this change. If the cooling capacity of the compressor is improved, the compression process will be closer to the isothermal process. At this time, the isothermal efficiency will increase, while the adiabatic efficiency may be relatively low, and the polytropic efficiency will also increase, because the actual process is closer to the ideal isothermal compression.

[0102] In a specific embodiment, by comprehensively analyzing the isothermal efficiency, adiabatic efficiency, and polytropic efficiency of the cold storage unit compressor, the operating efficiency of the compressor under different ideal environmental conditions is reflected. Analyzing and improving these efficiencies can help reduce energy consumption, thereby reducing operating costs. Monitoring these efficiencies can help identify the causes of compressor performance degradation, such as wear, leakage, or improper operating conditions, and timely maintenance can avoid greater system failures and downtime. Regular analysis of these efficiencies can reveal trends in system aging or reduced efficiency, prompting optimization or upgrades to the system to maintain or restore high performance.

[0103] Specifically, the cold storage unit is evaluated for abnormalities, and the specific process is: compare the work efficiency abnormality index of the cold storage unit with the work efficiency abnormality index threshold stored in the cold storage unit database; if the work efficiency abnormality index is greater than or equal to the work efficiency abnormality index threshold, the work efficiency of the cold storage unit is evaluated as abnormal; if the work efficiency abnormality index is less than the work efficiency abnormality index threshold, the work efficiency of the cold storage unit is evaluated as normal.

[0104] It should be understood that in this embodiment, feedback processing is performed based on the evaluation results. When the working efficiency of the cold storage unit is normal, the system does not need to perform additional feedback and continues to monitor the data of the cold storage unit. When the working efficiency of the cold storage unit is abnormal, the system immediately sends an alarm to the maintenance personnel or the control center to inform that there is a potential problem with the cold storage unit, which can be achieved through SMS, email, phone or a dedicated monitoring software interface.

[0105] The cold storage unit database is used to store relevant data for cold storage unit efficiency evaluation, including indicators such as reference values ​​for each working environment data and allowable deviation values ​​for each equilibrium environment data. The data in the cold storage unit database is usually set based on historical data, industry standards or specifications provided by equipment manufacturers to help managers understand the expected performance of equipment under specific environmental conditions.

[0106] Reference Figure 2 As shown, the second aspect of the present invention provides an automated intelligent monitoring and processing method for a cold storage unit, comprising: collecting the working environment data of the cold storage unit, comprehensively analyzing to obtain an environmental anomaly assessment index for the cold storage unit, and processing to obtain an allowable fluctuation range of each thermal engine efficiency based on the environmental anomaly assessment index for the cold storage unit.

[0107] The real-time operating data of the cold storage unit is collected, and according to the allowable fluctuation range of each thermal engine efficiency, the abnormal degree evaluation value of each thermal engine efficiency is obtained after processing, and the abnormal working efficiency index of the cold storage unit is obtained through comprehensive analysis.

[0108] According to the abnormal efficiency index of the compressor of the cold storage unit, the cold storage unit is evaluated abnormally, and feedback processing is performed based on the evaluation results.

[0109] In a specific embodiment, the present invention provides an automated intelligent monitoring and processing system and method for a cold storage unit, continuously monitors and analyzes the working environment data of the cold storage unit, accurately identifies the impact of environmental changes on equipment efficiency, and dynamically adjusts the allowable fluctuation range of thermal engine efficiency to ensure that the equipment operates in the best state. At the same time, by quantitatively evaluating the efficiency abnormality of the compressor, actively performing performance evaluation and adjustment, it effectively avoids energy waste and poor refrigeration effect caused by reduced efficiency, thereby improving the operating stability and economy of the cold storage unit, reducing maintenance costs, and extending the life of the equipment.

[0110] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should all fall within the protection scope of the present invention.

Claims

1. An automatic intelligent monitoring and processing system for cold storage units, characterized in that: include: The environmental impact assessment module is used to collect the working environment data of the cold storage unit, conduct a comprehensive analysis to obtain the cold storage unit environmental anomaly assessment index, and process the cold storage unit environmental anomaly assessment index to obtain the allowable fluctuation range of each heat engine efficiency; The efficiency fluctuation monitoring module is used to collect the real-time working condition data of the cold storage unit, and obtain the abnormal degree evaluation value of each heat engine efficiency according to the allowable fluctuation range of each heat engine efficiency through processing, and obtain the abnormal working efficiency index of the cold storage unit through comprehensive analysis; The cold storage unit performance feedback module is used to evaluate the cold storage unit according to the abnormal efficiency index of the cold storage unit compressor, and to perform feedback processing according to the evaluation results; The comprehensive analysis obtains the cold storage unit environmental abnormality assessment index, and the specific analysis process is as follows: Deploy several environmental monitoring points and collect working environment data at each environmental monitoring point; According to the correlation between environmental factors and the working efficiency of cold storage units, various working environment data are classified, including positively correlated environmental data, negatively correlated environmental data and balanced environmental data; Obtain reference values ​​of various working environment data from the cold storage unit database, and obtain the cold storage unit environment abnormality assessment index through comprehensive analysis; The processing obtains the allowable fluctuation range of each heat engine efficiency, and the specific process is: The cold storage unit environmental anomaly assessment index is matched with the isothermal efficiency allowable deviation value, adiabatic efficiency allowable deviation value and polytropic efficiency allowable deviation value corresponding to each cold storage unit environmental anomaly assessment index interval stored in the cold storage unit database to obtain the compressor isothermal efficiency allowable deviation value, the compressor adiabatic efficiency allowable deviation value and the compressor polytropic efficiency allowable deviation value; Extract the preset compressor isothermal efficiency reference value, compressor adiabatic efficiency reference value and compressor variable efficiency reference value from the cold storage unit database, perform subtraction and sum operations on the compressor isothermal efficiency reference value, compressor adiabatic efficiency reference value and compressor variable efficiency reference value with the compressor isothermal efficiency allowable deviation value, compressor adiabatic efficiency allowable deviation value and compressor variable efficiency allowable deviation value, respectively, and mark the subtraction result as the corresponding lower limit of the allowable fluctuation range, and mark the summation result as the corresponding upper limit of the allowable fluctuation range, so as to obtain the compressor isothermal efficiency allowable fluctuation range, the compressor adiabatic efficiency allowable fluctuation range and the compressor variable efficiency allowable fluctuation range; The allowable fluctuation range of the isothermal efficiency of the compressor, the allowable fluctuation range of the adiabatic efficiency of the compressor and the allowable fluctuation range of the polytropic efficiency of the compressor are jointly marked as the allowable fluctuation range of the efficiency of each heat engine; The processing obtains the abnormal degree evaluation value of each heat engine efficiency, and the specific process is: The abnormality evaluation values ​​of the efficiency of each heat engine include an abnormality evaluation value of the isothermal efficiency of the compressor, an abnormality evaluation value of the adiabatic efficiency of the compressor, and an abnormality evaluation value of the polytropic efficiency of the compressor; The gas inlet pressure, gas outlet pressure, gas inlet temperature and gas outlet temperature of the compressor are collected when the compressor is working, and the preset polytropic index and the mass flow rate, constant pressure specific heat capacity and specific heat ratio of the target compressed gas are obtained from the cold storage unit database. After processing, the isothermal compression work of the compressor, the adiabatic compression work of the compressor and the polytropic compression work of the compressor are obtained; Obtain a compression cycle of the compressor, monitor the working current and working voltage of the compressor during the compression cycle, and obtain the actual compression work of the compressor after processing; According to the isothermal compression work of the compressor, the adiabatic compression work of the compressor, the variable compression work of the compressor, the actual compression work of the compressor and the allowable fluctuation range of the efficiency of each heat engine, the abnormal degree evaluation value of each heat engine efficiency is obtained through comprehensive analysis; The specific numerical expression of the compressor isothermal efficiency abnormality evaluation value is: Where, δW represents the abnormal degree evaluation value of the compressor isothermal efficiency, μW0 represents the compressor isothermal efficiency, W iso represents the isothermal compression work of the compressor, W act represents the actual compression work of the compressor, μW1 represents the lower limit of the range of fluctuation allowed by the isothermal efficiency of the compressor, μW2 represents the upper limit of the range of fluctuation allowed by the isothermal efficiency of the compressor, μWC represents the reference value of the isothermal efficiency of the compressor, ΔW represents the allowable deviation value of the isothermal efficiency of the compressor, Indicates the abnormal impact factor of the lower limit of the interval corresponding to the preset isothermal efficiency, Indicates the abnormal impact factor in the interval corresponding to the preset isothermal efficiency, It represents the abnormal influence factor of the upper limit of the interval corresponding to the preset isothermal efficiency, a1 represents the preset formula model baseline value when the isothermal efficiency is lower than the lower limit of the interval, a2 represents the preset formula model baseline value when the isothermal efficiency is within the interval, and a3 represents the preset formula model baseline value when the isothermal efficiency is higher than the upper limit of the interval.

2. The automatic intelligent monitoring and processing system for cold storage units according to claim 1 is characterized in that: The comprehensive analysis obtains the abnormal working efficiency index of the cold storage unit, and the specific analysis process is as follows: According to the evaluation values ​​of the abnormal degree of efficiency of each heat engine, a comprehensive analysis is performed to obtain the abnormal working efficiency index of the cold storage unit, and the abnormal working efficiency index of the cold storage unit is used to quantitatively evaluate the abnormal degree of the working efficiency of the cold storage unit.

3. The automatic intelligent monitoring and processing system for cold storage units according to claim 1 is characterized in that: The specific process of abnormal evaluation of the cold storage unit is as follows: The abnormal working efficiency index of the cold storage unit is compared with the abnormal working efficiency index threshold stored in the cold storage unit database. If the abnormal working efficiency index is greater than or equal to the abnormal working efficiency index threshold, the working efficiency of the cold storage unit is evaluated as abnormal. If the abnormal working efficiency index is less than the abnormal working efficiency index threshold, the working efficiency of the cold storage unit is evaluated as normal.

4. The automatic intelligent monitoring and processing system for cold storage units according to claim 1 is characterized in that: The cold storage unit environmental anomaly assessment index is a quantitative indicator obtained by analyzing each positively correlated environmental data, each negatively correlated environmental data and each balanced environmental data, and is used to quantitatively assess the degree of interference of the working environment of the cold storage unit on the working efficiency of the cold storage unit.

5. The automatic intelligent monitoring and processing system for cold storage units according to claim 2 is characterized in that: The specific numerical expression of the abnormal working efficiency index of the cold storage unit is: In the formula, β represents the abnormal efficiency index of the cold storage unit, δW represents the abnormal degree evaluation value of the compressor isothermal efficiency, δR represents the abnormal degree evaluation value of the compressor adiabatic efficiency, δB represents the abnormal degree evaluation value of the compressor polytropic efficiency, Indicates the impact factor of abnormal evaluation of work efficiency corresponding to the preset isothermal efficiency. Indicates the impact factor of abnormal work efficiency assessment corresponding to the preset insulation efficiency. Indicates the work efficiency abnormality assessment impact factor corresponding to the preset variable efficiency.

6. A method for the automatic intelligent monitoring and processing system of the cold storage unit according to any one of claims 1 to 5, characterized in that: include: Collect the working environment data of the cold storage unit, and obtain the cold storage unit environment abnormality assessment index through comprehensive analysis. Then, according to the cold storage unit environment abnormality assessment index, obtain the allowable fluctuation range of each heat engine efficiency; The real-time operating data of the cold storage unit is collected, and according to the allowable fluctuation range of each heat engine efficiency, the abnormal degree evaluation value of each heat engine efficiency is obtained through processing, and the abnormal efficiency index of the cold storage unit is obtained through comprehensive analysis; According to the abnormal efficiency index of the compressor of the cold storage unit, the cold storage unit is evaluated abnormally, and feedback processing is performed based on the evaluation results.

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