UPS (Uninterrupted Power Supply) self-diagnosis system based on artificial intelligence

Abstract

The invention relates to the technical field of UPS intelligent diagnosis, in particular to a UPS self-diagnosis system based on artificial intelligence, which is characterized in that a storage battery assembly can be charged through a rectifier to store electric energy, and a monitoring module monitors the output voltage condition of the storage battery assembly and the temperature and humidity change condition of a storage battery pack area; historical data monitored by a monitor are stored through an AI analysis module, influence periods are divided and operation conditions are monitored through a control module based on power supply influence tendency characterization values corresponding to different battery management influence periods, and power supply output characterization parameters are determined to predict whether the power supply effect of a storage battery pack meets a preset standard or not. Whether the electric energy compensation device is started or not is determined, the influence of temperature and humidity resistance differences of all single batteries of the storage battery pack on the power supply efficiency in the operation process of the UPS self-diagnosis system is considered, the electric energy compensation device is adaptively involved, and then the power supply efficiency and the battery stability of the UPS self-diagnosis system are guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of intelligent diagnosis of UPS power supplies, and particularly to a self-diagnosis system for UPS power supplies based on artificial intelligence. Background Art

[0002] UPS power supplies are indispensable power protection devices in the operation of modern electronic devices and are widely used in scenarios with high requirements for power reliability. In the UPS power supplies in the medical industry, the battery pack is a key part for storing electric energy. When the voltage difference between individual cells exceeds 50 mV, it will cause a significant drop in the capacity of the battery pack. This means that after the mains power is interrupted, the time that the UPS power supply can provide power for medical devices will be greatly shortened, and it may not be able to meet the continuous operation requirements of medical devices in emergency situations, thus posing a serious threat to the treatment of patients and the normal development of medical work.

[0003] Chinese Patent Publication No.: CN218040842U discloses a UPS power supply for medical device equipment, including a device body. The centers of the left and right sides of the device body are symmetrically provided with first heat dissipation slots. The opposite sides of the two first heat dissipation slots are symmetrically fixedly connected with dust-proof nets. The inner wall of the device body is fixedly connected with a sound insulation board. The center of the bottom of the inner cavity of the sound insulation board is fixedly connected with a base. The top of the base is fixedly connected with a damping spring. The top of the damping spring is fixedly connected with a support plate. The center of the top of the support plate is provided with a movable slot. A bidirectional lead screw is arranged in the inner cavity of the movable slot. The top of the bidirectional lead screw is provided with a clamping plate. The inner cavity of the clamping plate is provided with a second heat dissipation slot. When the device of the present invention is in use, it can reduce the influence of noise on patients, and at the same time has a good protection structure and will not cause damage to its internal components due to external factors such as handling.

[0004] Chinese Patent Publication No.: CN106230102A discloses a UPS switching power supply, including a static switch controller, a power supply device, an intelligent battery pack, an inverter, a transformer, a positive-filtered AC voltage, and a central controller. The output end of the static switch controller is connected to the power supply device, the battery pack, the inverter, and the transformer. The inverter is connected to the central controller, and the central controller is connected to the static switch controller. The battery pack includes a step-down and voltage-stabilizing unit and a charging unit connected to the output of the step-down and voltage-stabilizing unit. The UPS switching power supply provided by the present invention meets the requirements of communication equipment for AC power, DC power, and power supply in scenarios where it is not suitable to be close by.

[0005] However, the following problems still exist in the prior art:

[0006] In the prior art, the problem that the individual temperature and humidity resistance properties of the battery pack of the standby UPS power supply for medical devices are affected by environmental temperature and humidity factors in different historical periods, and further affect the power supply effect of the standby UPS power supply is not considered. Summary of the Invention

[0007] To solve the above problems, the present invention provides an AI-based self-diagnosis system for UPS power supplies, which overcomes the problem in the prior art that the individual temperature and humidity resistance properties of the battery packs of the standby UPS power supplies for medical devices are affected by environmental temperature and humidity factors in different historical periods, thereby affecting the power supply effect of the standby UPS power supplies.

[0008] To achieve the above object, the present invention provides an AI-based self-diagnosis system for UPS power supplies, including a static switch, a rectifier, an inverter, a control circuit, a charger, and a filter. It is characterized in that it further includes:

[0009] A battery assembly, which includes a battery pack for charging through a rectifier to store electrical energy, several single cells connected to the battery pack for storing electrical energy, and an electrical energy compensation device for changing the electrical energy of each single cell of the battery pack;

[0010] A monitoring module, which includes several voltage sensors for monitoring the output voltage of each single cell of the battery assembly, several temperature and humidity sensors for monitoring the temperature and humidity in the area of the battery pack, and an electrical energy monitoring unit for obtaining the electrical energy parameters output by the battery pack;

[0011] An AI analysis module, which is connected to the monitoring module, for storing the historical data monitored by the monitoring module, for identifying the battery management impact period based on the output voltage difference of each single cell of the battery pack in different historical periods, and for analyzing the power supply impact tendency characterization value of the battery pack in different battery management impact periods based on the change of the electrical energy parameters;

[0012] A control module, which is respectively connected to the battery assembly, the monitoring module, and the AI analysis module, for dividing the impact period based on the power supply impact tendency characterization value corresponding to different battery management impact periods, and for monitoring the operating status of the battery pack based on the division result, including,

[0013] Determining a power supply output characterization parameter based on the change amount of the electrical energy output by the battery pack within a predetermined period and the change ratio of the temperature and humidity in the area of the battery pack, predicting whether the power supply effect of the battery pack meets a predetermined standard based on the power supply output characterization parameter, and determining whether to enable the electrical energy compensation device based on the temperature difference in the current area of the battery pack;

[0014] Or, not enabling the electrical energy compensation device.

[0015] Further, the AI analysis module is used to identify the battery management impact period based on the output voltage difference of each single cell of the battery pack in different historical periods, wherein,

[0016] To calculate the variance of the output voltage difference of each single battery in the battery pack for each historical period;

[0017] If the variance corresponding to a single historical period is greater than a predetermined variance threshold, it is determined that the historical period is a battery management influence period.

[0018] Further, the AI analysis module is used to analyze the power supply influence tendency characterization value corresponding to the battery pack in different battery management influence periods based on the change of the electric energy parameters, including,

[0019] To calculate the ratio of the change in the storage power of the battery pack to a predetermined change rate threshold and determine it as the first data parameter feature;

[0020] To calculate the ratio of the mean value of the output voltage difference of each single battery to a predetermined difference threshold and determine it as the second data parameter feature;

[0021] To calculate the sum of the first data parameter feature and the second data parameter feature and determine it as the power supply influence tendency characterization value.

[0022] Further, the control module is used to divide the influence periods based on the power supply influence tendency characterization value corresponding to different battery management influence periods, including,

[0023] If the power supply influence tendency characterization value is greater than or equal to a preset influence tendency characterization value, it is determined as the first influence period;

[0024] If the power supply influence tendency characterization value is less than the preset influence tendency characterization value, it is determined as the second influence period.

[0025] Further, the control module monitors the operating condition of the battery pack based on the division result, including,

[0026] If the division result is the first influence period, based on the change amount of the electric energy output by the battery pack within a predetermined period and the change ratio of the temperature in the battery pack area, determine the power supply output characterization parameter to predict whether the power supply effect of the battery pack meets the predetermined standard, and based on the temperature difference in the current battery pack area, determine whether to enable the electric energy compensation device;

[0027] If the division result is the second influence period, do not enable the electric energy compensation device.

[0028] Further, it is characterized in that the control module is used to determine the power supply output characterization parameter based on the change amount of the electric energy output by the battery pack within a predetermined period and the change ratio of the temperature and humidity in the battery pack area, including,

[0029] To calculate the ratio of the change amount of the output voltage of the battery pack to a predetermined output voltage change threshold and determine it as the first power supply output parameter feature;

[0030] Calculating the ratio of the change in the temperature of the battery pack area to a predetermined temperature change threshold is determined as the second power supply output parameter feature;

[0031] Calculating the ratio of the change in the humidity of the battery pack area to a predetermined humidity change threshold is determined as the third power supply output parameter feature;

[0032] Calculating the sum of the first power supply output parameter feature, the second power supply output parameter feature, and the third power supply output parameter is determined as the power supply output characterization parameter.

[0033] Further, the control module is used to predict whether the power supply effect of the battery pack meets a predetermined standard based on the power supply output characterization parameter, including,

[0034] If the power supply output characterization parameter is less than or equal to a preset power supply characterization parameter, it is predicted that the power supply effect of the battery pack meets the predetermined standard;

[0035] If the power supply output characterization parameter is greater than the preset power supply characterization parameter, it is predicted that the power supply effect of the battery pack does not meet the predetermined standard.

[0036] Further, when the control module predicts that the power supply effect of the battery pack does not meet the predetermined standard, it determines whether to enable the power compensation device based on the temperature difference of the current battery pack area.

[0037] Further, the control module is used to determine whether to enable the power compensation device based on the average value of the temperature differences of the individual batteries in the current battery pack area, including,

[0038] If the current average temperature difference value is less than or equal to a predetermined temperature difference threshold, it is determined to enable the power compensation device;

[0039] If the current average temperature difference value is greater than the predetermined temperature difference threshold, it is determined not to enable the power compensation device.

[0040] Further, it also includes a cooling compensation device for reducing the temperature of the battery pack area. When the control module determines not to enable the power compensation device, it enables the cooling compensation device; the cooling amplitude of the cooling compensation device is directly proportional to the average value of the temperature differences of the individual batteries in the current battery pack area.

[0041] Compared with the prior art, the present invention provides an artificial intelligence-based self-diagnosis system for UPS power supplies, which includes a static switch, a rectifier, an inverter, a control circuit, a charger, and a filter. It also includes a battery module, a monitoring module, an AI analysis module, and a control module, so as to enable the UPS power supply to switch to the battery power supply mode within an extremely short time. The battery module can be charged through the rectifier to store electrical energy. The battery pack consists of several single cells. The monitoring module monitors the output voltage of each single cell in the battery module, the change of temperature and humidity in the battery pack area, and the electrical energy parameters output by the battery pack. The AI analysis module stores the historical data monitored by the monitor. Moreover, the control module divides the influence period based on the power supply influence tendency characterization value corresponding to different battery management influence periods, monitors the operating status of the battery pack, determines the power supply output characterization parameters, predicts whether the power supply effect of the battery pack meets the predetermined standard, determines whether to activate the electrical energy compensation device, considers the influence of the temperature and humidity resistance properties of each single cell in the battery pack on the power supply efficiency during the operation of the UPS power supply self-diagnosis system, and adaptively intervenes in the electrical energy compensation device, thereby ensuring the power supply efficiency and battery stability of the UPS power supply self-diagnosis system.

[0042] In particular, the present invention can accurately obtain the law of the influence of temperature and humidity on the battery pack in different time periods by identifying the battery management influence period. By calculating the variance of the output voltage difference of each single cell in the battery pack in each historical period, the battery management influence period of the historical period can be accurately determined. Moreover, by analyzing the change of the electrical energy parameters, the power supply influence tendency characterization value of the battery pack in different battery management influence periods can be accurately analyzed, improving the efficiency and stability of the battery pack.

[0043] In particular, the present invention can determine the power supply output characterization parameters through the change amount of the electrical energy output by the battery pack and the change ratio of the temperature and humidity in the battery pack area, improving the accuracy of the power supply effect of the battery pack. Through the power supply output characterization parameters, the power supply effect of the battery pack can be predicted, and whether the power supply performance of the battery pack will decline can be predicted in advance, reducing the occurrence of the situation where the power supply efficiency of the battery pack decreases due to the influence of temperature and humidity, and improving the stability and power supply efficiency of the battery pack.

[0044] In particular, the present invention can divide the first influence period and the second influence period through the power supply influence tendency characterization value corresponding to different influence periods, improving the utilization efficiency of the battery pack. Moreover, by determining whether to activate the electrical energy compensation device for the battery pack, the energy utilization efficiency is further improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a structural block diagram of the artificial intelligence-based self-diagnosis system for UPS power supplies according to an embodiment of the present invention;

[0046] Figure 2 It is a logical decision diagram for identifying the battery management impact cycle in the embodiments of the present invention;

[0047] Figure 3 It is a logical decision diagram for analyzing the power supply impact tendency characterization values corresponding to different battery management impact cycles of the battery pack in the embodiments of the present invention;

[0048] Figure 4 It is a logical decision diagram for dividing the impact cycle based on the power supply impact tendency characterization value in the embodiments of the present invention. Detailed implementation manners

[0049] In order to make the objectives and advantages of the present invention more clear and understandable, the present invention will be further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0050] The preferred implementation manners of the present invention will be described below with reference to the accompanying drawings. Those skilled in the art should understand that these implementation manners are only used to explain the technical principles of the present invention and do not limit the protection scope of the present invention.

[0051] It should be noted that in the description of the present invention, the term "connection" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0052] Please refer to Figure 1 as shown, which is a structural block diagram of an AI-based UPS power supply self-diagnosis system in the embodiments of the present invention. The present invention provides an AI-based UPS power supply self-diagnosis system, including a static switch, a rectifier, an inverter, a control circuit, a charger, a filter, and further including:

[0053] a battery assembly, which includes a battery pack for storing electrical energy through charging by the rectifier, several single cells connected to the battery pack for storing electrical energy, and an electrical energy compensation device for changing the electrical energy of each single cell of the battery pack;

[0054] a monitoring module, which includes several voltage sensors for monitoring the output voltages of different single cells of the battery assembly, several temperature sensors for monitoring the temperature of the battery pack area, and an electrical energy monitoring unit for obtaining the electrical energy parameters output by the battery pack;

[0055] An AI analysis module, which is connected to the monitoring module, is used to store the historical data monitored by the monitoring module, identify the battery management impact period based on the output voltage difference of each single battery in the battery pack in different historical cycles, and analyze the power supply impact tendency characterization value of the battery pack in different battery management impact periods based on the change of the electrical energy parameters;

[0056] A control module, which is respectively connected to the battery assembly, the monitoring module and the AI analysis module, is used to divide the impact period based on the power supply impact tendency characterization value corresponding to different battery management impact periods, and monitor the operation status of the battery pack based on the division result, including,

[0057] Determine the power supply output characterization parameter based on the change amount of the electrical energy output by the battery pack and the change ratio of the temperature in the battery pack area within a predetermined period, predict whether the power supply effect of the battery pack meets the predetermined standard based on the power supply output characterization parameter, and determine whether to enable the power compensation device based on the average temperature difference value of the single battery in the current battery pack area;

[0058] Or, do not enable the power compensation device.

[0059] Specifically, the UPS power supply self-diagnosis system of the present invention includes a static switch, a rectifier, an inverter, a control circuit, a charger and a filter, and also includes a battery assembly, a monitoring module, an AI analysis module, and a control module, so as to enable the UPS power supply to switch to the battery power supply mode in an extremely short time. The battery assembly can be charged through the rectifier to store electrical energy. The battery pack is composed of several single batteries. The monitoring module monitors the output voltage of each single battery in the battery pack, the change of the temperature and humidity in the battery pack area, and the electrical energy parameter output by the battery pack. The AI analysis module stores the historical data monitored by the monitor. Moreover, the control module divides the impact period based on the power supply impact tendency characterization value corresponding to different battery management impact periods, monitors the operation status of the battery pack, determines the power supply output characterization parameter, predicts whether the power supply effect of the battery pack meets the predetermined standard, and determines whether to enable the power compensation device, considering the influence of the difference in the temperature and humidity resistance properties of each single battery in the battery pack during the operation of the UPS power supply self-diagnosis system on the power supply efficiency, and adaptively intervenes in the power compensation device, thereby ensuring the power supply efficiency and battery stability of the UPS power supply self-diagnosis system.

[0060] Specifically, the present invention is applied to the self-diagnosis system of the standby UPS power supply for medical devices. Commonly, the UPS power supply includes a static switch, a rectifier, an inverter, a control circuit, a charger, a filter, and a battery module containing several single cells. Each single cell is usually installed in series or in parallel. Due to environmental temperature factors, there are differences in the temperature and humidity resistance properties of each single cell. Moreover, in some cases, there may also be certain differences in the electricity storage and discharge of different single cells.

[0061] Specifically, no specific limitations are imposed on the structures of the monitoring module, the AI analysis module, the control module itself, and each unit therein. They can be composed of logic components, and the logic components include a field programmable processor, a computer, or a microprocessor in the computer.

[0062] Specifically, the form of the power compensation device is not limited. A compensation device using a DC / DC conversion circuit can be adopted, or a compensation device using pulse charging can be adopted. For example, for a single cell with a lower voltage or a larger internal resistance, the amplitude or width of the pulse can be increased to increase the charging current and accelerate the power compensation speed. Other forms can also be adopted, which will not be elaborated here.

[0063] Specifically, no specific limitations are imposed on the installation positions of the temperature and humidity sensors. The temperature and humidity sensors can be installed in different areas of each single cell of the battery module, which will not be elaborated here.

[0064] Specifically, the power detection unit can be a logic component for obtaining information. For example, it can access the battery management system to obtain relevant parameters, which will not be elaborated here.

[0065] Please refer to Figure 2 As shown in the figure, it is the logic decision diagram for identifying the battery management influence period in the embodiment of the present invention. The AI analysis module of the present invention is used to identify the battery management influence period based on the output voltage difference of each single cell in the battery module in different historical periods. Among them,

[0066] It is used to calculate the variance of the output voltage difference of each single cell in the battery module in each historical period;

[0067] If the variance corresponding to a single historical period is less than or equal to a predetermined variance threshold, it is determined that the historical period is a non-battery management influence period;

[0068] If the variance corresponding to a single historical period is greater than the predetermined variance threshold, it is determined that the historical period is a battery management influence period.

[0069] Specifically, the variance threshold is determined based on the average variance of the difference between the output voltage of each single cell and the output voltage mean value obtained in several historical periods, and is set to be between 1.15 times and 1.25 times of the average variance.

[0070] Specifically, to reflect the monitorability of the output voltage change of each single battery and its impact on the battery pack, the historical period is selected within the range of [3 min, 6 min].

[0071] Please refer to Figure 3 As shown, it is a logical decision diagram for analyzing the power supply impact tendency characterization value of the battery pack corresponding to different battery management impact periods in the embodiment of the present invention. The AI analysis module of the present invention is used to analyze the power supply impact tendency characterization value of the battery pack corresponding to different battery management impact periods based on the change of the electric energy parameters, including,

[0072] Calculating the ratio of the change in the storage power of the battery pack to a predetermined change rate threshold to determine the first data parameter feature;

[0073] Calculating the ratio of the average value of the voltage difference between each single battery to a predetermined difference threshold to determine the second data parameter feature;

[0074] Calculating the sum of the first data parameter feature and the second data parameter feature to determine the power supply impact tendency characterization value.

[0075] Specifically, the change rate of the storage power of the battery pack reflects the change in the efficiency of storing electric energy by the battery pack under different temperature impact periods. The change rate threshold is a reference value used to measure whether the degree of this change is significant. In implementation, it is set as the average change rate of electric energy within several historical periods. By calculating the ratio, the change rate can be standardized, which is convenient for comparison and comprehensive analysis with other features.

[0076] Specifically, the ratio of the voltage difference between the outputs of each single battery to the predetermined difference threshold is the same. The voltage difference between the outputs of each single battery is the difference between the output voltage of each single battery and the average value of the predetermined output voltages of each single battery. Then, the differences are added together. The change of each single battery is also affected by the ambient temperature and humidity. This ratio can reflect the influence degree of temperature and humidity on the change of the output voltage of each battery.

[0077] Specifically, by adding the first data parameter feature and the second data parameter feature, the power supply impact tendency characterization value is obtained. This value comprehensively reflects the affected situations of multiple parameters such as storage power change and change of each single battery under different impact periods, and can relatively comprehensively reflect the battery management impact tendency of the battery pack under specific temperature and humidity impact periods.

[0078] Specifically, by analyzing the output parameters of the battery pack and each individual battery, the influence degree of environmental temperature and humidity on the battery pack can be evaluated more accurately. Different parameters reflect the performance changes of the battery pack from different perspectives. Considering these parameters comprehensively can reduce the limitations of single-parameter evaluation and provide a more comprehensive and accurate evaluation result of temperature influence.

[0079] Please refer to Figure 4 as shown, which is the logical decision diagram for dividing the influence period based on the power supply influence tendency characterization value in the embodiment of the present invention. The control module of the present invention is used to divide the influence period based on the power supply influence tendency characterization value corresponding to different battery management influence periods, including,

[0080] If the power supply influence tendency characterization value is greater than or equal to the preset influence tendency characterization value, it is determined as the first influence period;

[0081] If the power supply influence tendency characterization value is less than the preset influence tendency characterization value, it is determined as the second influence period.

[0082] Specifically, the preset influence tendency characterization value, as a key judgment criterion, plays a role in dividing the first influence period and the second influence period. In implementation, the preset influence tendency characterization value is selected within the range [2.25, 2.35].

[0083] Specifically, when the power supply influence tendency characterization value is greater than or equal to the preset influence tendency characterization value, it is determined as the first influence period. This indicates that within this period, the influence of each individual battery on the battery pack is relatively significant, which may lead to relatively large changes in the performance of the battery pack. Preferably, there are differences in the electrolyte properties of each individual battery, and the electrolyte capacity of each individual battery results in a reduction in charge and discharge efficiency, or rapid changes in environmental temperature and humidity may cause stress changes inside the battery, affecting the life and safety of the battery pack.

[0084] Specifically, if the power supply influence tendency characterization value is less than the preset influence tendency characterization value, it is determined as the second influence period. Within this period, the influence of each individual battery on the battery pack is relatively small, and the performance changes of the battery pack are relatively weak. At this time, the battery pack can work in a relatively stable state, and performance indicators such as charge and discharge efficiency and life are relatively stable.

[0085] Specifically, the control module monitors the operating conditions of the battery pack based on the division result, including,

[0086] If the division result is the first influence period, then based on the change amount of the electric energy output by the battery pack within a predetermined period and the change ratio of the temperature and humidity in the battery pack area, determine the power supply output characterization parameter to predict whether the power supply effect of the battery pack meets the predetermined standard, so as to determine whether to enable the electric energy compensation device based on the temperature difference in the current battery pack area;

[0087] If the division result is the second influence period, the power compensation device is not activated.

[0088] Specifically, within the first influence period, each single battery has a greater impact on the battery pack. By monitoring the change in temperature and humidity in the battery area, the degree of influence of external environmental temperature and humidity changes on the self-diagnosis system of the UPS power supply can be understood. At the same time, the change ratio of the output efficiency of the battery pack reflects the performance change of the battery pack under different temperature and humidity conditions. Combining these three parameters to determine the power supply output characterization parameter can more comprehensively evaluate the influence of temperature and humidity on the entire self-diagnosis system of the UPS power supply.

[0089] Specifically, when the temperature in the battery pack area rises sharply, it may cause a change in the output voltage of the battery pack, thereby affecting the power supply efficiency of the battery pack. By analyzing the change in temperature, the change in humidity, and the change ratio of the output electric energy, it can be predicted whether the power supply effect of the battery pack under such drastic temperature and humidity changes meets the predetermined standard.

[0090] Specifically, within the first influence period, the usage cost and benefits of the power compensation device need to be considered. The power consumption of the power compensation device is an important consideration. If the power consumption caused by activating the power compensation device is too large, it may lead to a reduction in the efficiency of the entire self-diagnosis system of the UPS power supply. At the same time, the current temperature in the battery pack area needs to be considered. If the difference in temperature change in the battery area is small, activating the power compensation device may be necessary to reduce the power loss caused by temperature effects.

[0091] Specifically, if the difference in temperature change in the battery area is large, considering that the charging of the power compensation device causes its ambient temperature to rise faster, the power compensation device may not be activated, but other measures may be taken, such as activating a cooling compensation device, to reduce the influence of temperature on the system.

[0092] Specifically, the control module is used to determine the power supply output characterization parameter based on the change in the output electric energy of the battery pack and the change ratio of the temperature in the battery pack area within a predetermined period, including,

[0093] Calculating the ratio of the change in the output voltage of the battery pack to the predetermined output voltage change threshold to determine the first power supply output parameter characteristic;

[0094] Calculating the ratio of the change in the temperature in the battery pack area to the predetermined temperature change threshold to determine the second power supply output parameter characteristic;

[0095] Calculating the ratio of the change in the humidity in the battery pack area to the predetermined humidity change threshold to determine the third power supply output parameter characteristic;

[0096] The sum of the first power supply output parameter characteristics, the second power supply output parameter characteristics, and the third power supply output parameter is calculated and determined as the power supply output characterization parameter.

[0097] For a predetermined output voltage change threshold, the output voltage change threshold in this embodiment is set based on the average voltage change of the battery pack within a historical period, and is set to be between 0.45 times and 0.55 times the average voltage change.

[0098] The change amount of the temperature of the battery pack area is set based on the average temperature of the battery pack within a historical period, and is set to be between 0.55 times and 0.65 times the average temperature.

[0099] The change amount of the humidity in the battery area is set based on the average humidity of the battery within a historical period, and is set to be between 0.5 times and 0.8 times the average humidity.

[0100] Specifically, the change amount of the output voltage of the battery pack reflects the performance change of the energy storage battery pack under different temperature and humidity conditions.

[0101] Specifically, by adding the first power supply output parameter characteristics, the second power supply output parameter characteristics, and the third power supply output parameter characteristics, the power supply output characterization parameter is obtained. This parameter comprehensively considers the influence of the temperature and humidity changes of the battery pack and the change of the output voltage of the battery pack on the entire UPS power self-diagnosis system.

[0102] Specifically, the control module is used to predict whether the power supply effect of the battery pack meets the predetermined standard based on the power supply output characterization parameter, including,

[0103] If the power supply output characterization parameter is less than or equal to the preset power supply characterization parameter, it is predicted that the power supply effect of the battery pack meets the predetermined standard;

[0104] If the power supply output characterization parameter is greater than the preset power supply characterization parameter, it is predicted that the power supply effect of the battery pack does not meet the predetermined standard.

[0105] Specifically, the preset power supply characterization parameter, as a key reference value for judging whether the power supply effect of the battery pack meets the predetermined standard, is comprehensively determined according to factors such as the design performance of the battery pack, historical operation data, and the system's requirements for the power supply effect. Preferably, by analyzing a large number of power supply effect situations of the same type of battery pack in different temperature and humidity environments, a value that can divide whether the power supply effect meets the standard is determined.

[0106] Specifically, when the control module predicts that the power supply effect of the battery pack does not meet the predetermined standard, it determines whether to enable the power compensation device based on the average temperature difference of the single cells in the current battery pack area.

[0107] Specifically, in the UPS power supply self-diagnosis system, when it is predicted that the power supply effect of the battery pack does not meet the predetermined standard, measures are considered to improve this situation. The power compensation device is a possible solution, but it also generates heat itself. Therefore, it is necessary to comprehensively evaluate the power compensation device.

[0108] Specifically, the control module is used to determine whether to enable the power compensation device based on the average temperature difference value of the single cells in the current battery pack area, including,

[0109] If the current average temperature difference value is less than or equal to the predetermined temperature difference threshold, it is determined to enable the power compensation device;

[0110] If the current average temperature difference value is greater than the predetermined temperature difference threshold, it is determined not to enable the power compensation device.

[0111] Specifically, it further includes a cooling compensation device for reducing the temperature of the battery pack area. When the control module determines not to enable the power compensation device, it enables the cooling compensation device; the cooling amplitude of the cooling compensation device is in a proportional relationship with the average temperature difference value of the single cells in the current battery pack area.

[0112] The form of the cooling compensation device is not limited, it can be fan cooling or other forms of cooling, which will not be elaborated here.

[0113] Specifically, it further includes a display unit. The display unit is connected to the monitoring module and can display the data monitored by the monitoring module in real time. This is crucial for the operation and management of the UPS power supply self-diagnosis system. Preferably, users can understand the output voltage, storage power, temperature, humidity and other key parameters of the battery pack at any time through the display unit. In practical applications, operators can intuitively see the current state of the system, discover abnormal situations in time and take corresponding measures.

[0114] So far, the technical solution of the present invention has been described in combination with the preferred embodiments shown in the drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or replacements to the relevant technical features, and the technical solutions after these changes or replacements will all fall within the protection scope of the present invention.

Claims

1. An artificial intelligence-based self-diagnosis system for UPS power supplies, comprising a static switch, a rectifier, an inverter, a control circuit, a charger, and a filter, characterized in that, Further comprising: A battery assembly, which includes a battery pack for charging through a rectifier to store electrical energy, several single cells connected to the battery pack for storing electrical energy, and an electrical energy compensation device for changing the electrical energy of each single cell of the battery pack; A monitoring module, which includes several voltage sensors for monitoring the output voltage of each single cell of the battery assembly, several temperature and humidity sensors for monitoring the temperature and humidity in the area of the battery pack, and an electrical energy monitoring unit for obtaining the electrical energy parameters output by the battery pack; An AI analysis module, which is connected to the monitoring module, for storing the historical data monitored by the monitoring module, for identifying the battery management impact period based on the output voltage difference of each single cell of the battery pack in different historical periods, and for analyzing the power supply impact tendency characterization value of the battery pack in different battery management impact periods based on the change of the electrical energy parameters; A control module, which is respectively connected to the battery assembly, the monitoring module and the AI analysis module, for dividing the impact period based on the power supply impact tendency characterization value corresponding to different battery management impact periods, and for monitoring the operating status of the battery pack based on the division result, including, Determining the power supply output characterization parameter based on the change amount of the electrical energy output by the battery pack within a predetermined period and the change ratio of the temperature and humidity in the area of the battery pack, predicting whether the power supply effect of the battery pack meets the predetermined standard based on the power supply output characterization parameter, and determining whether to enable the electrical energy compensation device based on the temperature difference in the current area of the battery pack; Or, not enabling the electrical energy compensation device.

2. The AI-based UPS power supply self-diagnosis system according to claim 1, wherein The AI analysis module is used to identify the battery management impact period based on the output voltage difference of each single cell of the battery pack in different historical periods, wherein, Calculating the variance of the output voltage difference of each single cell of the battery pack in each historical period; If the variance corresponding to a single historical period is greater than the predetermined variance threshold, it is determined that the historical period is a battery management impact period.

3. The AI-based UPS power supply self-diagnosis system according to claim 1, wherein The AI analysis module is used to analyze the power supply impact tendency characterization value of the battery pack corresponding to different battery management impact periods based on the change of the electrical energy parameters, including, Calculating the ratio of the change of the storage power of the battery pack to the predetermined change rate threshold to determine the first data parameter feature; Calculating the ratio of the mean value of the output voltage difference of each single cell to the predetermined difference threshold to determine the second data parameter feature; Calculating the sum of the first data parameter feature and the second data parameter feature to determine the power supply impact tendency characterization value.

4. The AI-based UPS power supply self-diagnosis system according to claim 3, characterized in that The control module is used to divide the impact period based on the power supply impact tendency characterization value corresponding to different battery management impact periods, including, If the power supply impact tendency characterization value is greater than or equal to the preset power supply impact tendency characterization value, it is determined as the first impact period; If the power supply impact tendency characterization value is less than the preset power supply impact tendency characterization value, it is determined as the second impact period.

5. The AI-based UPS power supply self-diagnosis system according to claim 1, characterized in that The control module monitors the operating status of the battery pack based on the division result, including, If the division result is the first influence period, determine whether the power supply effect of the battery pack meets the predetermined standard based on the change in the electrical energy output by the battery pack within a predetermined period and the change ratio of the temperature in the battery pack area, so as to determine whether to enable the electrical energy compensation device based on the temperature difference in the current battery pack area; If the division result is the second influence period, the electrical energy compensation device is not enabled.

6. The AI-based UPS power supply self-diagnosis system according to claim 1, characterized in that, The control module is used to determine the power supply output characterization parameters based on the change in the electrical energy output by the battery pack within a predetermined period and the change ratio of the temperature and humidity in the battery pack area, including, Calculating the ratio of the change in the output voltage of the battery pack to the predetermined output voltage change threshold to determine the first power supply output parameter characteristic; Calculating the ratio of the change in the temperature in the battery pack area to the predetermined temperature change threshold to determine the second power supply output parameter characteristic; Calculating the ratio of the change in the humidity in the battery pack area to the predetermined humidity change threshold to determine the third power supply output parameter characteristic; Calculating the sum of the first power supply output parameter characteristic, the second power supply output parameter characteristic, and the third power supply output parameter to determine the power supply output characterization parameter.

7. The AI-based UPS power supply self-diagnosis system according to claim 6, characterized in that, The control module is used to predict whether the power supply effect of the battery pack meets the predetermined standard based on the power supply output characterization parameter, including, If the power supply output characterization parameter is less than or equal to the preset power supply characterization parameter, it is predicted that the power supply effect of the battery pack meets the predetermined standard; If the power supply output characterization parameter is greater than the preset power supply characterization parameter, it is predicted that the power supply effect of the battery pack does not meet the predetermined standard.

8. The AI-based UPS power supply self-diagnosis system according to claim 7, characterized in that The control module is used to determine whether to enable the electrical energy compensation device based on the temperature difference in the current battery pack area when predicting that the power supply effect of the battery pack does not meet the predetermined standard.

9. The AI-based UPS power supply self-diagnosis system according to claim 1, characterized in that The control module is used to determine whether to enable the electrical energy compensation device based on the average value of the temperature differences of the single cells in the current battery pack area, including, If the current average temperature difference is less than or equal to the predetermined temperature difference threshold, it is determined to enable the electrical energy compensation device; If the current average temperature difference is greater than the predetermined temperature difference threshold, it is determined not to enable the electrical energy compensation device.

10. The AI-based UPS power supply self-diagnosis system according to claim 1, wherein It also includes a cooling compensation device for reducing the temperature in the battery pack area. When the control module determines not to enable the electrical energy compensation device, the cooling compensation device is enabled; the cooling amplitude of the cooling compensation device is directly proportional to the average value of the temperature differences of the single cells in the current battery pack area.

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