Cold chain system, fault prediction method and device thereof and readable storage medium

Abstract

The invention discloses a cold chain system, a fault prediction method and device thereof and a readable storage medium. The method comprises the following steps: acquiring an operation parameter value of the cold chain system under a current working condition; judging whether the current working condition is running for the first time or not, if yes, judging whether the difference value between the running parameter value and a factory test parameter value of the corresponding working condition is within an allowable range or not, if yes, recording the running parameter value to obtain a user parameter value of the corresponding working condition, and if not, prompting maintenance; and if not, calling the user parameter value of the corresponding working condition, and performing cold chain system fault prediction according to the operation parameter value, the user parameter value of the corresponding working condition and the factory test parameter value. Various fault problems of the cold chain system can be quickly and accurately evaluated, a user is reminded to carry out corresponding maintenance, frequent maintenance is avoided, and meanwhile the shutdown risk caused by faults is reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of cold chain system fault prediction, and relates to a cold chain system and a fault prediction method, device and readable storage medium thereof. Background Art

[0002] Compared with the air-conditioning industry, the stable operation of the refrigeration function is the top priority of the cold chain industry. This is because the objects stored in the cold chain are often of high value or scarce and are easily affected by temperature changes. If the refrigeration fails, the consequences will be unimaginable.

[0003] Current cold chain unit designs typically only offer protective measures to address unit anomalies, such as reducing cooling efficiency or shutting down the unit to prevent damage. However, as units operate longer, problems such as system aging, compressor failure, and refrigerant shortages are inevitable. Without maintenance, users risk significant losses due to the deterioration of stored items. However, excessively frequent maintenance increases the cost of using the cold chain system. Summary of the Invention

[0004] In order to address the deficiencies in the prior art, the present invention provides a cold chain system and its fault prediction method, device and readable storage medium, which predicts unit failures through the operating data received by various sensors on the system, and informs the user in advance whether unit maintenance is required, thereby avoiding frequent maintenance and reducing the risk of downtime due to failures.

[0005] The present invention adopts the following technical solutions.

[0006] A first aspect of the present invention provides a cold chain system fault prediction method, comprising:

[0007] Collect the operating parameter values ​​of the cold chain system under the current working conditions and determine whether the current working conditions are the first operation:

[0008] If so, determine whether the difference between the operating parameter value and the factory test parameter value of the corresponding working condition is within the allowable range. If so, record the operating parameter value to obtain the user parameter value of the corresponding working condition. Otherwise, prompt for maintenance.

[0009] If not, the user parameter value of the corresponding working condition is called, and the cold chain system failure prediction is performed based on the operating parameter value, the user parameter value of the corresponding working condition, and the factory test parameter value.

[0010] Preferably, the types of operating parameters, user parameters and factory test parameters are consistent, including the temperature difference between internal and external environments, bus voltage, bus voltage fluctuation range, compressor temperature, steady-state operating current at a given compressor speed, steady-state operating current fluctuation range, exhaust pressure under stable speed operation, suction pressure, compressor speed, and compressor speed fluctuation rate.

[0011] Preferably, the compressor speed fluctuation rate is the absolute value of the difference between the current sampling speed and the last sampling speed.

[0012] Preferably, the method for determining whether the current operating condition is the first operation includes:

[0013] Determine whether the cold chain system has been operated under the current internal and external temperature difference. If it has been operated, the current operating condition is not the first operation; otherwise, the current operating condition is the first operation.

[0014] Preferably, the cold chain system fault prediction based on the operating parameter values ​​and the user parameter values ​​and factory test parameter values ​​of the corresponding working conditions includes:

[0015] Power supply anomaly prediction based on the operating parameter values ​​of the bus voltage and its fluctuation range and the corresponding user parameter values;

[0016] Compressor failure prediction is performed based on the operating parameter value and user parameter value of the steady-state working current corresponding to the given compressor speed, the operating parameter value of the compressor speed, the operating parameter value of the speed fluctuation rate and the factory test parameter value;

[0017] Other faults are predicted based on the operating parameter values ​​of steady-state operating current, exhaust pressure and suction pressure corresponding to a given compressor speed, as well as the user parameter values. Other faults include too little refrigerant, insufficient compressor lubricating oil or clogged heat sink fins, clogged condenser, clogged evaporator and too much refrigerant.

[0018] Preferably, the prediction method for predicting power supply anomaly based on the operating parameter values ​​of the bus voltage and its fluctuation range and the corresponding user parameter values ​​includes:

[0019] Determine whether the bus voltage operating parameter value exceeds the set range or the bus voltage fluctuation range operating parameter value exceeds the range limited by the corresponding user parameter value. If so, it is predicted that the power supply is abnormal, otherwise the power supply is normal.

[0020] Preferably, the compressor fault prediction or the other fault prediction is selected according to the compressor temperature condition, specifically:

[0021] If the difference between the operating parameter value of the compressor temperature and the corresponding user parameter value exceeds a limited positive value, the other fault prediction is performed; otherwise, the compressor fault prediction is performed.

[0022] Preferably, the compressor failure prediction method includes:

[0023] Determine whether the operating parameter value of the steady-state operating current corresponding to the given speed of the compressor exceeds the corresponding user parameter value and the operating parameter value of the compressor speed is outside the limited range of the factory-set speed. If so, predict compressor failure. If not, determine whether the operating parameter value of the speed fluctuation rate is outside the limited range of the corresponding factory test parameter value. If so, predict compressor failure, otherwise end.

[0024] Preferably, the other fault prediction methods include:

[0025] Determine whether the difference between the operating parameter value of the steady-state working current at a given speed of the compressor and the corresponding user parameter value is lower than the limited negative value. If so, it is predicted that the refrigerant is too little. Otherwise, determine whether the difference between the operating parameter value of the steady-state working current at a given speed of the compressor and the corresponding user parameter value is higher than the limited positive value. If not, it is predicted that the compressor is short of lubricating oil or the heat dissipation fins are clogged. If so, the exhaust pressure and suction pressure under stable speed operation are determined:

[0026] If the difference between the operating parameter value of the suction pressure and the corresponding user parameter value is lower than a limited negative value and the difference between the operating parameter value of the discharge pressure and the corresponding user parameter value is higher than a limited positive value, it is predicted that the condenser is blocked;

[0027] If the difference between the operating parameter value of the suction pressure and the corresponding user parameter value is higher than a limited positive value and the difference between the operating parameter value of the discharge pressure and the corresponding user parameter value is lower than a limited negative value, it is predicted that the evaporator is blocked;

[0028] If neither is the case, then too much refrigerant is predicted.

[0029] A second aspect of the present invention provides a cold chain system fault prediction device, comprising:

[0030] The collection unit is used to collect the operating parameter values ​​of the cold chain system under the current working conditions;

[0031] The prediction unit is used to determine whether the current working condition is the first operation: if so, determine whether the difference between the operating parameter value and the factory test parameter value of the corresponding working condition is within the allowable range; if so, record the operating parameter value to obtain the user parameter value of the corresponding working condition, otherwise prompt for maintenance; if not, call the user parameter value of the corresponding working condition, and predict the cold chain system failure based on the operating parameter value, the user parameter value of the corresponding working condition, and the factory test parameter value.

[0032] A third aspect of the present invention provides a cold chain system comprising the above-mentioned device.

[0033] A fourth aspect of the present invention provides a readable storage medium having a program stored thereon, which implements the prediction method when executed by a processor.

[0034] Compared with the prior art, the beneficial effects of the present invention include at least:

[0035] The present invention uses factory test parameter values ​​as original reference data. When the unit is operated under specific working conditions for the first time, the operating parameter values ​​are recorded when the errors between the collected operating parameter values ​​and the factory test parameter values ​​are within a limited range to obtain user parameter values ​​for the corresponding working conditions. This allows the unit to be called up for self-intelligent fault prediction when it is operated under the corresponding working conditions again in the future. When the unit is not operated under specific working conditions for the first time, the user parameter values ​​for the corresponding working conditions are directly called up for self-intelligent fault prediction. This can quickly and accurately predict various fault problems in the cold chain system, remind users to perform corresponding maintenance, avoid frequent maintenance, and reduce risks such as downtime due to faults. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 Obtain parameter flow chart for cold chain system;

[0037] Figure 2 Provide a flow chart for cold chain system failure prediction;

[0038] Figure 3 This is the compressor abnormality prediction flow chart. DETAILED DESCRIPTION

[0039] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. The embodiments described in this application are only part of the embodiments of the present invention, not all of them. Based on the spirit of the present invention, other embodiments obtained by ordinary technicians in this field without making creative efforts are all within the scope of protection of the present invention.

[0040] Example 1 of the present invention provides a cold chain system fault prediction method, such as Figure 1 Shown, including:

[0041] Collect the operating parameter values ​​of the cold chain system under the current working conditions and determine whether the current working conditions are the first operation:

[0042] If so, determine whether the difference between the operating parameter value and the factory test parameter value of the corresponding working condition is within the allowable range. If so, record the operating parameter value to obtain the user parameter value of the corresponding working condition. Otherwise, prompt for maintenance.

[0043] If not, the user parameter value of the corresponding working condition is called, and the cold chain system failure prediction is performed based on the operating parameter value, the user parameter value of the corresponding working condition, and the factory test parameter value.

[0044] The specific implementation plan is as follows:

[0045] It's necessary to record sensor data from the unit's operation under various operating conditions. For the cold chain, the most significant factor affecting operating conditions is the temperature difference between the internal and external units. A larger temperature difference indicates a greater cooling capacity requirement. For the compressor alone, an increase in cooling capacity requires an increase in compressor speed and current, and vice versa. Therefore, the specific operating data for each operating condition will vary.

[0046] To this end, the entire cold chain equipment undergoes a series of tests before leaving the factory to ensure that the unit can operate stably under various operating conditions. During this stage, testers are required to record the following data under different operating conditions to obtain first-hand operating data as an original reference data:

[0047] The steady-state operating current of the compressor at the set speed; the fluctuation range of the steady-state current; the temperature of the compressor; the compressor speed and speed fluctuation rate, where the speed fluctuation rate is the absolute value of the difference between the current speed and the last sampled speed; the bus voltage; the bus voltage fluctuation range; the exhaust pressure and suction pressure under stable speed operation; the internal and external ambient temperature difference calculated from the internal and external ambient temperatures under different working conditions, etc.

[0048] All recorded parameter values ​​are stored in the driver chip and are collectively referred to as factory test parameter values.

[0049] Accordingly, the types of set operating parameters, user parameters and factory test parameters are consistent, including the internal and external ambient temperature difference, bus voltage, bus voltage fluctuation range, compressor temperature, steady-state operating current at a given compressor speed, steady-state operating current fluctuation range, exhaust pressure under stable speed operation, suction pressure, compressor speed, compressor speed fluctuation rate, etc.

[0050] In terms of users, because user usage conditions may vary slightly, by monitoring and recording various sensor data of the stable operation of the unit under different working conditions, in future use, when there is an abnormality in the unit data, it can immediately and intelligently predict faults and remind users to perform corresponding maintenance. Specifically, when a customer uses the cold chain equipment, the unit will first read the current internal and external ambient temperatures to calculate the temperature difference, and then determine whether the current working conditions have been experienced during the user's use:

[0051] If the unit has never been used before, meaning it is the first time operating under a specific operating condition, the factory test parameter values ​​for the corresponding operating condition will be retrieved and compared. If the difference between the current sampled operating data and the original reference value is within an acceptable error range, the operating data will be recorded and saved, referred to as the user parameter value. When the current operating condition reappears in the future, the user's first run data will be retrieved for self-intelligent fault prediction. If the difference is large, the user should be prompted to notify a technician for analysis. If the operating condition has been experienced before, the corresponding user parameter value will be directly retrieved for comparison with the current operating parameter value, and if necessary, combined with the factory test parameter value for fault prediction.

[0052] Specifically, cold chain system fault prediction is performed based on the operating parameter values, the user parameter values ​​of the corresponding working conditions, and the factory test parameter values, including:

[0053] Power supply anomaly prediction is performed based on the operating parameter values ​​of the bus voltage and its fluctuation range and the corresponding user parameter values. The prediction methods include:

[0054] Determine whether the bus voltage operating parameter value exceeds the set range or the bus voltage fluctuation range operating parameter value exceeds the range defined by the corresponding user parameter value. If so, it is predicted that the power supply is abnormal; otherwise, the power supply is normal;

[0055] Furthermore, compressor fault prediction or other fault prediction is selected based on the compressor temperature situation. If the difference between the operating parameter value of the compressor temperature and the corresponding user parameter value exceeds the limit value, other fault prediction is performed based on the operating parameter values ​​and user parameter values ​​of the steady-state working current, exhaust pressure and suction pressure corresponding to the given compressor speed. Other faults include too little refrigerant, insufficient compressor lubricating oil or fin blockage, condenser blockage, evaporator blockage and too much refrigerant. Otherwise, compressor fault prediction is performed based on the operating parameter value and user parameter value of the steady-state working current corresponding to the given compressor speed, the operating parameter value of the compressor speed, the operating parameter value of the speed fluctuation rate and the factory test parameter value.

[0056] The prediction method of the compressor failure prediction includes:

[0057] Determine whether the operating parameter value of the steady-state operating current corresponding to the given speed of the compressor exceeds the corresponding user parameter value and whether the operating parameter value of the compressor speed is outside the limited range of the factory-set speed. If so, predict compressor failure. If not, determine whether the operating parameter value of the speed fluctuation rate is outside the limited range of the corresponding factory test parameter value. If so, predict compressor failure, otherwise end.

[0058] The other fault prediction methods include:

[0059] Determine whether the difference between the operating parameter value of the steady-state working current at a given speed of the compressor and the corresponding user parameter value is lower than the limited negative value. If so, it is predicted that the refrigerant is too little. Otherwise, determine whether the difference between the operating parameter value of the steady-state working current at a given speed of the compressor and the corresponding user parameter value is higher than the limited positive value. If not, it is predicted that the compressor is short of lubricating oil or the heat dissipation fins are clogged. If so, the exhaust pressure and suction pressure under stable speed operation are determined:

[0060] If the difference between the operating parameter value of the suction pressure and the corresponding user parameter value is lower than a limited negative value and the difference between the operating parameter value of the discharge pressure and the corresponding user parameter value is higher than a limited positive value, it is predicted that the condenser is blocked;

[0061] If the difference between the operating parameter value of the suction pressure and the corresponding user parameter value is higher than a limited positive value and the difference between the operating parameter value of the discharge pressure and the corresponding user parameter value is lower than a limited negative value, it is predicted that the evaporator is blocked;

[0062] If neither is the case, then too much refrigerant is predicted.

[0063] In summary, if Figure 2 As shown, the principle and complete process of self-fault prediction through comparison results are as follows:

[0064] (1) By judging whether the operating parameter value of the bus voltage exceeds the set range or whether the operating parameter value of the bus voltage fluctuation range exceeds the range defined by the corresponding user parameter value, it is detected whether the bus voltage exceeds the set range or whether the bus voltage fluctuation rate is too large. If one of the conditions is met, it is predicted that the power supply is abnormal, and the user is reminded that there is a problem with the current power supply, and the user is advised to check the power supply equipment or power supply;

[0065] In a specific embodiment, each judgment in the judgment process requires an operating parameter value and a user parameter value or a factory test parameter value of a corresponding working condition.

[0066] For example, if the normal value of the AC voltage of the input mains is 220V, then the bus voltage after rectification should be 2 times the square root of 220V, which is about 311V. However, since the input voltage may not be 220V for various reasons, the current voltage of the user is rectified. Figure 1 Determine and record the bus voltage. For example, if the current input voltage is higher than 220V, at 230V, the bus voltage after rectification is approximately 325V. Generally, the unit needs to operate normally within the range of 85% to 115% of the normal input voltage (220V) (187V-253V), that is, the bus voltage is within the corresponding square root of 2 (264V-358V). The factory test parameter value requirements should be consistent with this range. The voltage of 325V meets this range, so the user parameter value of 325V is recorded as the bus voltage. Other user parameter values ​​are obtained in the same way.

[0067] The capacitance at the bus end cannot be infinite. When the unit is running, the capacitance of the rectifier circuit is constantly charging and discharging, so the bus voltage will fluctuate. This fluctuation is related to whether the input mains power is stable. For example, when the bus voltage is 230V, the user parameter value is 325V. The bus voltage is allowed to fluctuate by 10%. Therefore, the minimum bus voltage operating parameter value of the unit should not be lower than 293V during operation.

[0068] When making a specific judgment, determine whether the operating parameter value of the bus voltage exceeds the set range (264V-358V) or whether the fluctuation range of the bus voltage operating parameter value exceeds the range limited by the corresponding user parameter value (the minimum should not be lower than 293V).

[0069] (2) By judging whether the difference between the operating parameter value of the compressor temperature and the corresponding user parameter value exceeds the limit value, detect whether the compressor temperature is too high. If the compressor temperature is too high, enter (3) to check for faults such as compressor lubricating oil, otherwise enter (6);

[0070] (3) By judging whether the difference between the operating parameter value of the corresponding steady-state working current at a given speed of the compressor and the corresponding user parameter value is lower than a limited negative value, it is detected whether the corresponding steady-state working current at a given speed of the compressor is too small. If it is too small, it means that the electromagnetic torque of the compressor compressing the refrigerant at the same speed becomes smaller, which means that the amount of compressed refrigerant is insufficient, that is, it is predicted that the refrigerant is too little. Otherwise, it enters (4);

[0071] (4) By judging whether the difference between the operating parameter value of the steady-state working current corresponding to the given speed of the compressor and the corresponding user parameter value is higher than the limited positive value, it is judged whether the steady-state working current corresponding to the given speed of the compressor is too large. If it is too large, it is necessary to enter (5) to further predict where the fault is by checking the data of the exhaust and suction pressure sensors. If it is not too large, it is predicted that the compressor is short of lubricating oil or the heat dissipation fins are blocked;

[0072] (5) If the suction pressure is too low (the difference between the operating parameter value of the suction pressure and the corresponding user parameter value is lower than the limited negative value) and the exhaust pressure is too high (whether the difference between the operating parameter value of the exhaust pressure and the corresponding user parameter value is higher than the limited positive value), it can be predicted that the problem is caused by condenser blockage;

[0073] If the suction pressure is too high and the discharge pressure is too low, the problem is predicted to be a clogged evaporator;

[0074] If neither of them is the case, it can be predicted that the cause is too much refrigerant.

[0075] (6) Analyze and predict the compressor speed and steady-state operating current:

[0076] like Figure 3 As shown, if the operating parameter value of the steady-state operating current corresponding to the given speed of the compressor exceeds the corresponding user parameter value and the operating parameter value of the compressor speed is outside the limited range of the set speed, a compressor failure is predicted. If not, it is determined whether the operating parameter value of the speed fluctuation rate is outside the limited range of the corresponding factory test parameter value. If so, a compressor failure is predicted. Otherwise, there is no abnormality in the entire self-fault prediction process, and the unit is operating well.

[0077] In this embodiment, the current user parameter value is recorded as follows: After a given speed is reached, a period of time is reserved for speed change. The maximum and minimum current values ​​are recorded and the average is calculated. If this value is within the appropriate range compared to the factory settings, it is recorded as the user parameter value. Because the speed is program-defined, the speed and speed fluctuation parameters mentioned above are analyzed using the factory settings and do not require user parameter values.

[0078] In the future, during the operation of this working condition, the current and speed are sampled periodically to determine that the data sampled each time (operating parameter value) should not exceed the range of 95% and 105% of the user parameter value and factory parameter mentioned above.

[0079] The speed fluctuation rate is obtained as follows: the speed is sampled at a sampling frequency of 500 Hz, and the absolute value of the difference between the current sampling value and the last sampling value is taken. If the absolute value of the difference is within the factory range, the compressor is judged to be normal.

[0080] Through the above process, the prediction of common cold chain system faults such as abnormal refrigerant quantity, compressor failure, power supply problems, heat exchanger, and insufficient lubricating oil is achieved.

[0081] It is understandable that Figure 1 、 Figure 2 The judgments are all run periodically, and Figure 2 The judgment frequency is much greater than Figure 1 .

[0082] Embodiment 2 of the present invention provides a cold chain system fault prediction device, the device comprising:

[0083] The collection unit is used to collect the operating parameter values ​​of the cold chain system under the current working conditions;

[0084] The prediction unit is used to determine whether the current working condition is the first operation: if so, determine whether the difference between the operating parameter value and the factory test parameter value of the corresponding working condition is within the allowable range; if so, record the operating parameter value to obtain the user parameter value of the corresponding working condition, otherwise prompt for maintenance; if not, call the user parameter value of the corresponding working condition, and predict the cold chain system failure based on the operating parameter value, the user parameter value of the corresponding working condition, and the factory test parameter value.

[0085] Embodiment 3 of the present invention provides a cold chain system, which includes the device described in claim 5.

[0086] Embodiment 4 of the present invention provides a readable storage medium having a program stored thereon, and the program implements the prediction method when executed by a processor.

[0087] Compared with the prior art, the beneficial effects of the present invention include at least:

[0088] The present invention uses the factory test parameter value as the original reference value. When the unit is operated under a specific working condition for the first time, the operating parameter value will be recorded as the user parameter value of the corresponding working condition when the error between the collected operating parameter value and the factory test parameter value is within a limited range. This will enable the unit to call the data of the first operation for self-intelligent fault prediction when it is operated under the corresponding working condition again in the future. When the unit is not operating under the specific working condition for the first time, the user parameter value of the corresponding working condition will be directly called for self-intelligent fault prediction. This can quickly and accurately evaluate various fault problems in the cold chain system and remind users to perform corresponding maintenance.

[0089] The present disclosure may be a system, method and / or computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0090] A computer-readable storage medium can be a tangible device that can hold and store instructions for use by an instruction execution device. A computer-readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination thereof. As used herein, a computer-readable storage medium is not to be construed as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through an electrical wire.

[0091] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions to be stored in the computer-readable storage medium in each computing / processing device.

[0092] The computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, and conventional procedural programming languages ​​such as "C" language or similar programming languages. Computer-readable program instructions may be executed entirely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., utilizing an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be personalized by utilizing the state information of the computer-readable program instructions. The electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0093] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A cold chain system fault prediction method, characterized in that: The method comprises: Collect the operating parameter values ​​of the cold chain system under the current working conditions and determine whether the current working conditions are the first operation: If so, determine whether the difference between the operating parameter value and the factory test parameter value of the corresponding working condition is within the allowable range. If so, record the operating parameter value to obtain the user parameter value of the corresponding working condition. Otherwise, prompt for maintenance. If not, the user parameter value of the corresponding working condition is called, and the cold chain system failure prediction is performed based on the operating parameter value, the user parameter value of the corresponding working condition, and the factory test parameter value.

2. A cold chain system fault prediction method according to claim 1, characterized in that: The types of operating parameters, user parameters and factory test parameters are consistent, including the internal and external ambient temperature difference, bus voltage, bus voltage fluctuation range, compressor temperature, steady-state operating current at a given compressor speed, steady-state operating current fluctuation range, exhaust pressure under stable speed operation, suction pressure, compressor speed, and compressor speed fluctuation rate; the compressor speed fluctuation rate is the absolute value of the difference between the current sampling speed and the last sampling speed.

3. A cold chain system fault prediction method according to claim 2, characterized in that: Methods for determining whether the current operating condition is the first operation include: Determine whether the cold chain system has been operated under the current internal and external temperature difference. If it has been operated, the current operating condition is not the first operation; otherwise, the current operating condition is the first operation.

4. A cold chain system fault prediction method according to claim 2, characterized in that: The cold chain system fault prediction based on the operating parameter values ​​and the user parameter values ​​and factory test parameter values ​​of the corresponding working conditions includes: Power supply anomaly prediction based on the operating parameter values ​​of the bus voltage and its fluctuation range and the corresponding user parameter values; Compressor failure prediction is performed based on the operating parameter value and user parameter value of the steady-state working current corresponding to the given compressor speed, the operating parameter value of the compressor speed, the operating parameter value of the speed fluctuation rate and the factory test parameter value; Other faults are predicted based on the operating parameter values ​​of steady-state operating current, exhaust pressure and suction pressure corresponding to a given compressor speed, as well as the user parameter values. Other faults include too little refrigerant, insufficient compressor lubricating oil or clogged heat sink fins, clogged condenser, clogged evaporator and too much refrigerant.

5. A cold chain system fault prediction method according to claim 4, characterized in that: The prediction method for predicting power supply anomaly based on the operating parameter values ​​of the bus voltage and its fluctuation range and the corresponding user parameter values ​​includes: Determine whether the bus voltage operating parameter value exceeds the set range or the bus voltage fluctuation range operating parameter value exceeds the range limited by the corresponding user parameter value. If so, it is predicted that the power supply is abnormal, otherwise the power supply is normal.

6. A cold chain system fault prediction method according to claim 4, characterized in that: The compressor fault prediction or other fault prediction is selected based on the compressor temperature condition, specifically: If the difference between the operating parameter value of the compressor temperature and the corresponding user parameter value exceeds a limited positive value, the other fault prediction is performed; otherwise, the compressor fault prediction is performed.

7. A cold chain system fault prediction method according to claim 4, characterized in that: The prediction method of the compressor failure prediction includes: Determine whether the operating parameter value of the steady-state operating current at a given compressor speed exceeds the corresponding user parameter value and whether the operating parameter value of the compressor speed is outside the factory-set speed limit: If so, compressor failure is predicted; If not, determine whether the operating parameter value of the speed fluctuation rate is outside the range limited by the corresponding factory test parameter value: if so, predict the compressor failure, otherwise end.

8. A cold chain system fault prediction method according to claim 4, characterized in that: The other fault prediction methods include: Determine whether the difference between the operating parameter value of the steady-state working current at a given speed of the compressor and the corresponding user parameter value is lower than the limited negative value. If so, it is predicted that the refrigerant is too little. Otherwise, determine whether the difference between the operating parameter value of the steady-state working current at a given speed of the compressor and the corresponding user parameter value is higher than the limited positive value. If not, it is predicted that the compressor is short of lubricating oil or the heat dissipation fins are clogged. If so, the exhaust pressure and suction pressure under stable speed operation are determined: If the difference between the operating parameter value of the suction pressure and the corresponding user parameter value is lower than a limited negative value and the difference between the operating parameter value of the discharge pressure and the corresponding user parameter value is higher than a limited positive value, it is predicted that the condenser is blocked; If the difference between the operating parameter value of the suction pressure and the corresponding user parameter value is higher than a limited positive value and the difference between the operating parameter value of the discharge pressure and the corresponding user parameter value is lower than a limited negative value, it is predicted that the evaporator is blocked; If neither is the case, then too much refrigerant is predicted.

9. A cold chain system fault prediction device, used to run the method according to any one of claims 1 to 8, characterized in that: The device comprises: The collection unit is used to collect the operating parameter values ​​of the cold chain system under the current working conditions; The prediction unit is used to determine whether the current working condition is the first operation: if so, determine whether the difference between the operating parameter value and the factory test parameter value of the corresponding working condition is within the allowable range; if so, record the operating parameter value to obtain the user parameter value of the corresponding working condition, otherwise prompt for maintenance; if not, call the user parameter value of the corresponding working condition, and predict the cold chain system failure based on the operating parameter value, the user parameter value of the corresponding working condition, and the factory test parameter value.

10. A cold chain system, characterized in that: The cold chain system includes the device according to claim 9.

11. A readable storage medium having a program stored thereon, characterized in that: When the program is executed by a processor, the prediction method according to any one of claims 1 to 8 is implemented.

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