Refrigerated cabinet power supply system based on artificial intelligence

Abstract

The invention belongs to the technical field of refrigerated cabinet power supply management, and particularly relates to a refrigerated cabinet power supply system based on artificial intelligence, which comprises a mains supply input monitoring module, an MCU control module, a change-over switch module, a power supply risk detection module and a power supply management and control end, according to the invention, the MCU control module judges the state of the commercial power according to the input voltage information of the commercial power and generates the corresponding switching control signal, and the switching between the commercial power supply mode and the storage battery power supply mode is carried out based on the switching control signal, thereby achieving the seamless switching between the commercial power supply and the storage battery power supply. The refrigerated cabinet can still supply power continuously when the mains supply is interrupted or the voltage is low, safe, stable and continuous operation of the refrigerated cabinet can be maintained, the power supply risks of the refrigerated cabinet in different power supply modes are reasonably analyzed and early warned in time through the power supply risk detection module, the power supply continuity and the power supply safety of the refrigerated cabinet are guaranteed, and the power supply safety of the refrigerated cabinet is improved. And the management difficulty of the management personnel for the refrigerated cabinet is obviously reduced.

Description

Technical Field

[0001] The invention relates to the technical field of refrigerator power supply management, and in particular to a refrigerator power supply system based on artificial intelligence. Background Art

[0002] Refrigerator is an important equipment for storing fresh food and other items that need to be refrigerated. It is widely used in restaurants, supermarkets, convenience stores, hotels and restaurants and other commercial fields as well as in family life. The stability and reliability of its power supply system is directly related to the quality and safety of food preservation.

[0003] Traditional refrigerator power supply systems usually use the mains electricity directly for power supply, which makes it difficult to ensure the continuity of power supply. Once the mains electricity is interrupted, the food in the refrigerator will be at risk of spoilage. At the same time, key components such as the compressor are also easily damaged by sudden power outages. It is also impossible to reasonably analyze the power supply risk and potential power supply hazards of the refrigerator and provide timely warnings, which is not conducive to ensuring the continuity and safety of power supply for the refrigerator, and it is difficult to supervise the power supply of the refrigerator;

[0004] In view of the above technical defects, a solution is now proposed. Summary of the invention

[0005] The purpose of the present invention is to provide a refrigerator power supply system based on artificial intelligence, which solves the problem that the prior art is difficult to ensure that the refrigerator can continue to be powered when the city power is interrupted or the voltage is low, and is unable to reasonably analyze the power supply risk and the degree of power supply hidden dangers of the refrigerator and provide timely warnings, which is not conducive to ensuring the power supply continuity and power supply safety of the refrigerator, and the problem of difficulty in power supply supervision.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] An artificial intelligence-based refrigerator power supply system includes a mains input monitoring module, an MCU control module, a switching switch module, a power supply risk detection module and a power supply management and control terminal;

[0008] The mains input monitoring module collects the voltage information of the mains input and transmits it to the MCU control module;

[0009] The MCU control module determines the mains power state according to the voltage information of the mains input, generates a corresponding switching control signal, and sends the generated switching control signal to the switching switch module to control the switching switch module to switch between the mains power supply mode and the battery power supply mode based on the switching control signal;

[0010] The power supply risk detection module conducts real-time analysis on the power supply risk of the refrigerator under the corresponding power supply mode, determines whether a high-risk power supply signal is generated through analysis, and sends the high-risk power supply signal to the power supply control end. When the power supply control end receives the high-risk power supply signal, it issues a corresponding warning.

[0011] Furthermore, the analysis and judgment process of the MCU control module is as follows:

[0012] When the AC power voltage is lower than the set voltage threshold, the MCU control module controls the switching switch module to connect with the connection end of the battery pack to start the battery power supply mode; when the AC power voltage is greater than or equal to the set voltage threshold, the MCU control module controls the switching switch module to connect with the AC power input end to restore the AC power supply mode.

[0013] Furthermore, the specific analysis process of the power supply risk detection module is as follows:

[0014] When the refrigerator is in battery power supply mode, the remaining power of the battery pack is collected and marked as the remaining power, and the remaining power is compared with the preset remaining power threshold. If the remaining power does not exceed the preset remaining power threshold, a high-risk power supply signal is generated;

[0015] If the battery remaining amount does not exceed the preset battery remaining amount threshold, the battery pack's battery state coefficient is obtained, and the battery state coefficient is numerically compared with the preset battery state coefficient threshold. If the battery state coefficient exceeds the preset battery state coefficient threshold, a high-risk power supply signal is generated.

[0016] Furthermore, when the refrigerator is in the mains power supply mode, a voltage curve of the mains voltage per unit time is collected, a rectangular coordinate system is established with time as the X-axis and voltage as the Y-axis, the voltage curve of the mains voltage is placed in the first quadrant of the rectangular coordinate system, and the starting point of the voltage curve is located on the Y-axis; a number of coordinate points are marked on the voltage curve, and the X-direction distances between two adjacent groups of coordinate points are the same;

[0017] The Y-axis coordinate values ​​of all coordinate points are obtained, and the variance of the Y-axis coordinate values ​​of all coordinate points is calculated to obtain the unstable supply voltage value. The unstable supply voltage value is numerically compared with the preset unstable supply voltage threshold value. If the unstable supply voltage value exceeds the preset unstable supply voltage threshold value, a high-risk power supply signal is generated.

[0018] If the supply pressure instability value does not exceed the preset supply pressure instability threshold, two adjacent groups of coordinate points are connected by a line segment and the corresponding line segment is marked as a verification line segment, and the acute angle formed between the verification line segment and the horizontal line is marked as a pressure change test value; the pressure change test value is numerically compared with the preset pressure change test threshold, and if the pressure change test value exceeds the preset pressure change test threshold, the corresponding pressure change test value is marked as a pressure change test value;

[0019] The number of pressure variation verification values ​​is obtained and marked as pressure variation condition values, and the pressure variation excess value is obtained by averaging the excess values ​​of all pressure variation verification values ​​compared with the preset pressure variation inspection threshold value. The supply pressure danger table value is obtained by numerically calculating the supply pressure unstable value, the pressure variation condition value and the pressure variation excess value. The supply pressure danger table value is numerically compared with the preset supply pressure danger table threshold. If the supply pressure danger table value exceeds the preset supply pressure danger table threshold, a high-risk power supply signal is generated.

[0020] Furthermore, the power supply risk detection module is communicatively connected to the battery pack monitoring module, which monitors the battery pack of the refrigerator, obtains the battery pack state coefficient through analysis, and sends the battery pack state coefficient to the power supply risk detection module.

[0021] Furthermore, the analysis and acquisition method of the power storage state coefficient is as follows:

[0022] The real-time temperatures of several positions in the battery pack are collected, the real-time temperatures of all positions are averaged, and the average calculation result is ratioed with the preset temperature threshold to obtain the battery temperature meter value; the deviation values ​​of the smoke concentration and real-time humidity of the environment where the battery pack is located compared with the set appropriate humidity value are collected and marked as the battery smoke meter value and the battery humidity meter value respectively, and the vibration amplitude of the battery pack is collected and marked as the battery vibration meter value;

[0023] The battery dangerous condition value is obtained by numerically calculating the battery temperature meter value, the battery smoke meter value, the battery humidity meter value and the battery vibration meter value, and the battery dangerous condition value is numerically compared with the preset battery dangerous condition threshold value. If the battery dangerous condition value exceeds the preset battery dangerous condition threshold value, it is determined that the battery pack is in an unfavorable power supply state;

[0024] All the battery risk condition values ​​within a unit time are obtained and their average is calculated to obtain the battery risk assessment value, and the total time that the battery group is in an unfavorable power supply state within a unit time is marked as the power supply non-favorable condition value, and the number of occurrences in which the single duration of the battery group in the power supply non-favorable state within a unit time exceeds the corresponding preset single duration threshold is marked as the power supply non-favorable abnormal value; the battery state coefficient is obtained by numerically calculating the battery risk assessment value, the power supply non-favorable condition value and the power supply non-favorable abnormal value.

[0025] Further, the power supply risk detection module is communicatively connected to the power supply hidden danger auxiliary judgment module, the power supply risk detection module sends the power supply high risk signal to the power supply hidden danger auxiliary judgment module, and the power supply hidden danger auxiliary judgment module stores the number of occurrences of the power supply high risk signal;

[0026] The power supply hidden danger auxiliary judgment module will conduct a step-by-step progressive evaluation and analysis of the power supply hidden dangers of the refrigerator during the detection period, and generate a high power supply hidden danger signal or a low power supply hidden danger signal based on this, and send the high power supply hidden danger signal or the low power supply hidden danger signal to the power supply control end. When the power supply control end receives the high power supply hidden danger signal, it will issue a corresponding warning.

[0027] Furthermore, the specific analysis process of the power supply hidden danger auxiliary judgment module is as follows:

[0028] The number of times a high-risk power supply signal is generated during the detection period is collected and marked as a power supply risk alarm value, and the power supply risk alarm value is numerically compared with a preset power supply risk alarm threshold. If the power supply risk alarm value exceeds the preset power supply risk alarm threshold, a high-risk power supply signal is generated;

[0029] If the power supply risk alarm value exceeds the preset power supply risk alarm threshold, the moment when the MCU control module generates the corresponding switching control signal is collected and marked as the signal start moment, and the moment when the switching switch module completes the corresponding power supply mode switching is collected and marked as the switching end moment, and the interval between the switching end moment and the signal start moment is marked as the switching effectiveness inspection value;

[0030] The switching effectiveness check value is numerically compared with the preset switching effectiveness check threshold. If the switching effectiveness check value exceeds the preset switching effectiveness check threshold, the corresponding switching effectiveness check value is marked as the switching abnormality check value, and the number of switching abnormality check values ​​in the detection period is calculated by ratio to the number of switching effectiveness check values ​​to obtain the switching abnormality value, and all switching effectiveness check values ​​in the detection period are averaged to obtain the switching effectiveness value;

[0031] The power supply hidden danger coefficient is obtained by numerically calculating the power supply risk alarm value, the switching abnormal occupancy value and the switching efficiency analysis value, and the power supply hidden danger coefficient is numerically compared with the preset power supply hidden danger coefficient threshold. If the power supply hidden danger coefficient exceeds the preset power supply hidden danger coefficient threshold, a high power supply hidden danger signal is generated.

[0032] Furthermore, if the power supply hidden danger coefficient does not exceed the preset power supply hidden danger coefficient threshold, the interval between the production date of the battery pack and the current date is collected and marked as the production interval value, and the actual surface size contour of the battery pack is compared with the initial size contour to obtain the contour coincidence value, and the number of charge and discharge times of the battery pack in the historical stage is marked as the charge and discharge frequency detection value;

[0033] The attenuation prediction coefficient is obtained by numerically calculating the production interval value, contour overlap value and charge and discharge frequency detection value, and the attenuation prediction coefficient is numerically compared with the preset attenuation prediction coefficient threshold. If the attenuation prediction coefficient exceeds the preset attenuation prediction coefficient threshold, a high power supply hidden danger signal is generated; if the attenuation prediction coefficient does not exceed the preset attenuation prediction coefficient threshold, a low power supply hidden danger signal is generated.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] 1. In the present invention, the MCU control module determines the mains power state according to the voltage information of the mains input and generates a corresponding switching control signal, and switches the mains power supply mode and the battery power supply mode based on the switching control signal, ensuring that the refrigerator can continue to be powered when the mains power is interrupted or the voltage is low, which is conducive to maintaining the safe, stable and continuous operation of the refrigerator. The power supply risk detection module reasonably analyzes the power supply risk of the refrigerator under different power supply modes and issues a timely warning, ensuring the power supply continuity and power supply safety of the refrigerator, and significantly reducing the management difficulty of the refrigerator for the management personnel;

[0036] 2. In the present invention, the power supply hidden danger auxiliary judgment module conducts a step-by-step progressive evaluation and analysis of the power supply hidden danger of the refrigerator during the detection period, and generates a high power supply hidden danger signal or a low power supply hidden danger signal accordingly. When the high power supply hidden danger signal is generated, the management personnel are reminded to strengthen the power supply supervision of the refrigerator in the future, further ensuring the safe and stable power supply of the refrigerator to avoid damage to the refrigerated items, and the degree of intelligence is high. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings;

[0038] Figure 1 This is a system block diagram of Embodiment 1 of the present invention;

[0039] Figure 2 This is a system block diagram of Embodiment 2 of the present invention. DETAILED DESCRIPTION

[0040] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0041] Embodiment 1: Figure 1 As shown, the present invention proposes an artificial intelligence-based refrigerator power supply system, including a mains input monitoring module, an MCU control module, a switching switch module, a power supply risk detection module and a power supply management and control terminal;

[0042] The AC input monitoring module collects the voltage information during AC input and transmits it to the MCU control module. The AC input monitoring module includes a step-down circuit, a rectifier circuit and a voltage stabilizing circuit to ensure that the collected voltage information is accurate and stable. It should be noted that the main power supply for the refrigerator is mainly provided by connecting to the external AC power.

[0043] The MCU control module determines the mains power state according to the voltage information of the mains input, generates a corresponding switching control signal, and sends the generated switching control signal to the switching switch module to control the switching switch module to switch between the mains power supply mode and the battery power supply mode based on the switching control signal; specifically, the analysis and judgment process of the MCU control module is as follows:

[0044] When the mains voltage is lower than the set voltage threshold, the MCU control module controls the switching module to connect with the connection end of the battery pack, and starts the battery power supply mode; when the mains voltage is greater than or equal to the set voltage threshold, the MCU control module controls the switching module to connect with the mains input end, and restores the mains power supply mode, realizing seamless switching between mains power supply and battery pack power supply, ensuring that the refrigerator can continue to be powered when the mains power is interrupted or the voltage is low, which is conducive to maintaining the safe, stable and continuous operation of the refrigerator.

[0045] The power supply risk detection module analyzes the power supply risk of the refrigerator in the corresponding power supply mode in real time, determines whether a high-risk power supply signal is generated through analysis, and sends the high-risk power supply signal to the power supply control end. When the power supply control end receives the high-risk power supply signal, it issues a corresponding warning. It can reasonably analyze the power supply risk of the refrigerator in different power supply modes and issue a timely warning to remind the management personnel to quickly take corresponding improvement measures to ensure the power supply continuity and power supply safety of the refrigerator, significantly reduce the management personnel's management difficulty for the refrigerator, and has a high degree of intelligence; the specific analysis process of the power supply risk detection module is as follows:

[0046] When the refrigerator is in battery power supply mode, the remaining power of the battery pack is collected and marked as the remaining power, and the remaining power is compared with the preset remaining power threshold. If the remaining power does not exceed the preset remaining power threshold, a high-risk power supply signal is generated;

[0047] If the battery remaining amount does not exceed the preset battery remaining amount threshold, the battery pack's battery state coefficient ZY is obtained, and the battery state coefficient ZY is numerically compared with the preset battery state coefficient threshold. If the battery state coefficient ZY exceeds the preset battery state coefficient threshold, a high-risk power supply signal is generated.

[0048] Further, when the refrigerator is in the mains power supply mode, the voltage curve of the mains voltage per unit time is collected, and a rectangular coordinate system is established with time as the X-axis and voltage as the Y-axis. The voltage curve of the mains voltage is placed in the first quadrant of the rectangular coordinate system, and the starting point of the voltage curve is located on the Y-axis; a number of coordinate points are marked on the voltage curve, and the X-direction distances between two adjacent groups of coordinate points are the same;

[0049] The Y-axis coordinate values ​​of all coordinate points are obtained, and the variance of the Y-axis coordinate values ​​of all coordinate points is calculated to obtain the unstable supply voltage value. The unstable supply voltage value is numerically compared with the preset unstable supply voltage threshold. If the unstable supply voltage value exceeds the preset unstable supply voltage threshold, it indicates that the stability of the mains voltage is poor and there is a large fluctuation, and a high-risk power supply signal is generated;

[0050] If the supply pressure instability value does not exceed the preset supply pressure instability threshold, two adjacent groups of coordinate points are connected by a line segment and the corresponding line segment is marked as a verification line segment, and the acute angle formed between the verification line segment and the horizontal line is marked as a pressure change test value; the pressure change test value is numerically compared with the preset pressure change test threshold, and if the pressure change test value exceeds the preset pressure change test threshold, the corresponding pressure change test value is marked as a pressure change test value;

[0051] The number of pressure variation verification values ​​is obtained and marked as pressure variation condition values, and the pressure variation excess value is obtained by averaging all pressure variation verification values ​​compared with the excess values ​​of the preset pressure variation test threshold value;

[0052] The voltage supply unstable value YR, the voltage variation condition value YS and the voltage variation over-condition value YF are numerically calculated by the formula YL=rw1*YR+rw2*YS+rw3*YF to obtain the voltage supply danger table value YL, wherein rw1, rw2, and rw3 are preset proportional coefficients whose values ​​are greater than zero, and the larger the value of the voltage supply danger table value YL, the worse the overall performance of the mains voltage is; the voltage supply danger table value YL is numerically compared with the preset voltage supply danger table threshold value. If the voltage supply danger table value YL exceeds the preset voltage supply danger table threshold value, indicating that the overall performance of the mains voltage is poor, a high-risk power supply signal is generated.

[0053] It should be noted that the power supply risk detection module is connected to the battery pack monitoring module in communication. The battery pack monitoring module monitors the battery pack of the refrigerator, obtains the battery pack state coefficient ZY through analysis, and sends the battery pack state coefficient ZY to the power supply risk detection module, which can not only accurately feedback the real-time operation risk status of the battery pack, but also provide data support for the analysis process of the power supply risk detection module to ensure the accuracy of its analysis results; the analysis and acquisition method of the battery state coefficient is as follows:

[0054] The real-time temperatures of several positions in the battery pack are collected, the real-time temperatures of all positions are averaged, and the average calculation result is ratioed with the preset temperature threshold to obtain the battery temperature meter value; the deviation values ​​of the smoke concentration and real-time humidity of the environment where the battery pack is located compared with the set appropriate humidity value are collected and marked as the battery smoke meter value and the battery humidity meter value respectively, and the vibration amplitude of the battery pack is collected and marked as the battery vibration meter value;

[0055] The battery dangerous condition value XR is obtained by numerically calculating the battery temperature meter value XF, the battery smoke meter value XP, the battery humidity meter value XN and the battery vibration meter value XW through the formula XR=(hy1*XF+hy2*XP+hy3*XN+hy4*XW) / 4; wherein hy1, hy2, hy3 and hy4 are preset proportional coefficients with values ​​greater than zero, and the larger the value of the battery dangerous condition value XR is, the worse the real-time safety condition of the battery pack is;

[0056] Compare the battery danger value XR with the preset battery danger threshold. If the battery danger value XR exceeds the preset battery danger threshold, it indicates that the real-time safety status of the battery pack is poor, and the battery pack is judged to be in an unfavorable power supply state.

[0057] All the power storage risk condition values ​​within a unit time are obtained and their average is calculated to obtain the power storage risk assessment value, and the total time duration of the battery group in an unfavorable power supply state within a unit time is marked as the power supply unfavorable time condition value, and the number of occurrences of the single duration of the battery group in the power supply unfavorable state exceeding the corresponding preset single duration threshold within a unit time is marked as the power supply unfavorable abnormal value;

[0058] By formula The battery risk assessment value ZX, the power supply unfavorable condition value ZP and the power supply unfavorable abnormal value ZM are numerically calculated to obtain the battery state coefficient ZY; wherein ny1, ny2, ny3 are preset proportional coefficients whose values ​​are greater than zero, and the larger the value of the battery state coefficient ZY, the worse the operating state of the battery pack is, and the greater the operating risk is.

[0059] Embodiment 2: Figure 2 As shown, the difference between this embodiment and the first embodiment is that the power supply risk detection module is communicatively connected to the power supply hidden danger auxiliary judgment module, the power supply risk detection module sends the power supply high risk signal to the power supply hidden danger auxiliary judgment module, and the power supply hidden danger auxiliary judgment module stores the number of occurrences of the power supply high risk signal;

[0060] The power supply hidden danger auxiliary judgment module will conduct a step-by-step progressive evaluation and analysis of the power supply hidden dangers of the refrigerator during the detection period, and generate a high power supply hidden danger signal or a low power supply hidden danger signal based on this, and send the high power supply hidden danger signal or the low power supply hidden danger signal to the power supply control end. When the power supply control end receives the high power supply hidden danger signal, it will issue a corresponding warning to remind the management personnel to strengthen the power supply supervision of the refrigerator in the future, ensure the safe and stable power supply of the refrigerator, and further avoid damage to the refrigerated items. The specific analysis process of the power supply hidden danger auxiliary judgment module is as follows:

[0061] A detection period is set, preferably, the detection period is thirty days; the number of times a high-risk power supply signal is generated during the detection period is collected and marked as a power supply risk alarm value, and the power supply risk alarm value is numerically compared with a preset power supply risk alarm threshold value. If the power supply risk alarm value exceeds the preset power supply risk alarm threshold value, indicating that the power supply safety for the refrigerator during the detection period is not good, a high-risk power supply signal is generated;

[0062] If the power supply risk alarm value exceeds the preset power supply risk alarm threshold, the moment when the MCU control module generates the corresponding switching control signal is collected and marked as the signal start moment, and the moment when the switching switch module completes the corresponding power supply mode switching is collected and marked as the cut-off moment, and the interval between the cut-off moment and the signal start moment is marked as the switching efficiency check value; wherein, the larger the value of the switching efficiency check value, the worse the switching efficiency of the corresponding automatic switching process;

[0063] The switching efficiency check value is numerically compared with the preset switching efficiency check threshold. If the switching efficiency check value exceeds the preset switching efficiency check threshold, it indicates that the switching efficiency of the corresponding automatic switching process is poor. The corresponding switching efficiency check value is marked as a switching abnormality check value, and the number of switching abnormality check values ​​in the detection period is calculated by ratio to the number of switching efficiency check values ​​to obtain a switching abnormality value, and all switching efficiency check values ​​in the detection period are averaged to obtain a switching efficiency value;

[0064] By formula The power supply risk alarm value GL, the switching abnormal occupancy value GP and the switching efficiency analysis value GS are numerically calculated to obtain the power supply hidden danger coefficient GY, wherein tu1, tu2 and tu3 are preset proportional coefficients with values ​​greater than zero, and the larger the value of the power supply hidden danger coefficient GY is, the more serious the power supply hidden danger degree for the refrigerator during the detection period is in general;

[0065] The power supply hidden danger coefficient GY is numerically compared with the preset power supply hidden danger coefficient threshold. If the power supply hidden danger coefficient GY exceeds the preset power supply hidden danger coefficient threshold, it indicates that the power supply hidden danger degree for the refrigerator during the detection period is generally serious, and a high power supply hidden danger signal is generated.

[0066] Furthermore, if the power supply hidden danger coefficient does not exceed the preset power supply hidden danger coefficient threshold, the interval between the production date of the battery pack and the current date is collected and marked as the production interval value, and the actual surface size contour of the battery pack is compared with the initial size contour to obtain the contour coincidence value (the smaller the value of the contour coincidence value, the more serious the deformation of the battery pack), and the number of charge and discharge times of the battery pack in the historical stage is marked as the charge and discharge frequency detection value;

[0067] By formula The attenuation prediction coefficient QL is obtained by numerically calculating the production interval value QM, the profile overlap value QW and the charge and discharge detection frequency value QF; wherein up1, up2 and up3 are preset proportional coefficients, and up2>up3>up1>0; and when the value of the attenuation prediction coefficient QL is too large, it indicates that the performance of the battery pack is likely to be seriously attenuated;

[0068] The attenuation prediction coefficient is numerically compared with the preset attenuation prediction coefficient threshold. If the attenuation prediction coefficient exceeds the preset attenuation prediction coefficient threshold, it indicates that the battery pack performance is likely to be severely attenuated, which is not conducive to ensuring the subsequent safe and stable power supply to the refrigerator, and a high power supply hidden danger signal is generated; if the attenuation prediction coefficient does not exceed the preset attenuation prediction coefficient threshold, it indicates that the battery pack performance is likely to be severely attenuated, and a low power supply hidden danger signal is generated.

[0069] The working principle of the present invention is as follows: when in use, the voltage information of the mains input is collected through the mains input monitoring module and transmitted to the MCU control module. The MCU control module determines the mains status according to the voltage information of the mains input and generates a corresponding switching control signal, and controls the switching switch module to switch between the mains power supply mode and the battery power supply mode based on the switching control signal, thereby realizing seamless switching between the mains power supply and the battery pack power supply, ensuring that the refrigerator can continue to be powered when the mains power is interrupted or the voltage is low, which is conducive to maintaining the safe, stable and continuous operation of the refrigerator, and the power supply risk of the refrigerator in the corresponding power supply mode is analyzed in real time through the power supply risk detection module to determine whether a high-risk power supply signal is generated, which can reasonably analyze the power supply risk of the refrigerator in different power supply modes and issue a timely warning to remind the management personnel to quickly take corresponding improvement measures to ensure the power supply continuity and power supply safety of the refrigerator, significantly reduce the management difficulty of the refrigerator for the management personnel, and have a high degree of intelligence.

[0070] The above formulas are all dimensionless and numerical calculations. The formula is a formula obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formula are set by technicians in this field according to actual conditions. The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the contents of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that technicians in the relevant technical field can understand and use the present invention well. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. A refrigerator power supply system based on artificial intelligence, characterized in that: It includes mains input monitoring module, MCU control module, switching switch module, power supply risk detection module and power supply management and control terminal; The mains input monitoring module collects the voltage information of the mains input and transmits it to the MCU control module; The MCU control module determines the mains power state according to the voltage information of the mains input, generates a corresponding switching control signal, and sends the generated switching control signal to the switching switch module to control the switching switch module to switch between the mains power supply mode and the battery power supply mode based on the switching control signal; The power supply risk detection module conducts real-time analysis on the power supply risk of the refrigerator under the corresponding power supply mode, determines whether a high-risk power supply signal is generated through analysis, and sends the high-risk power supply signal to the power supply control end. When the power supply control end receives the high-risk power supply signal, it issues a corresponding warning.

2. The artificial intelligence-based refrigerator power supply system according to claim 1, characterized in that: The analysis and judgment process of the MCU control module is as follows: When the AC power voltage is lower than the set voltage threshold, the MCU control module controls the switching switch module to connect with the connection end of the battery pack to start the battery power supply mode; when the AC power voltage is greater than or equal to the set voltage threshold, the MCU control module controls the switching switch module to connect with the AC power input end to restore the AC power supply mode.

3. The artificial intelligence-based refrigerator power supply system according to claim 1, characterized in that: The specific analysis process of the power supply risk detection module is as follows: When the refrigerator is in battery power supply mode, the remaining power of the battery pack is collected and marked as the remaining power. If the remaining power does not exceed the preset remaining power threshold, a high-risk power supply signal is generated; If the remaining power does not exceed the preset remaining power threshold, the power state coefficient of the battery pack is obtained; if the power state coefficient exceeds the preset power state coefficient threshold, a power supply high risk signal is generated.

4. The artificial intelligence-based refrigerator power supply system according to claim 3, characterized in that: When the refrigerator is in the mains power supply mode, the voltage curve of the mains voltage per unit time is collected, and a rectangular coordinate system is established with time as the X-axis and voltage as the Y-axis. The voltage curve of the mains voltage is placed in the first quadrant of the rectangular coordinate system, and the starting point of the voltage curve is located on the Y-axis; a number of coordinate points are marked on the voltage curve, and the X-direction distances between two adjacent groups of coordinate points are the same; The Y-axis coordinate values ​​of all coordinate points are obtained, and the variance of the Y-axis coordinate values ​​of all coordinate points is calculated to obtain the unstable supply voltage value. If the unstable supply voltage value exceeds the preset unstable supply voltage threshold, a high-risk power supply signal is generated; If the supply pressure instability value does not exceed the preset supply pressure instability threshold, two adjacent groups of coordinate points are connected by a line segment and the corresponding line segment is marked as a verification line segment, and the acute angle formed between the verification line segment and the horizontal line is marked as a pressure change test value; the pressure change test value is numerically compared with the preset pressure change test threshold, and if the pressure change test value exceeds the preset pressure change test threshold, the corresponding pressure change test value is marked as a pressure change test value; The number of pressure variation verification values ​​is obtained and marked as pressure variation condition values, and the pressure variation excess value is obtained by averaging the excess values ​​of all pressure variation verification values ​​compared with the preset pressure variation test threshold. The pressure supply danger table value is obtained by numerically calculating the supply pressure unstable value, the pressure variation condition value and the pressure variation excess value. If the supply pressure danger table value exceeds the preset supply pressure danger table threshold, a high-risk power supply signal is generated.

5. The artificial intelligence-based refrigerator power supply system according to claim 3, characterized in that: The power supply risk detection module is communicatively connected to the battery pack monitoring module, which monitors the battery pack of the refrigerator, obtains the battery pack state coefficient through analysis, and sends the battery pack state coefficient to the power supply risk detection module.

6. The artificial intelligence-based refrigerator power supply system according to claim 5, characterized in that: The analysis and acquisition method of the storage state coefficient is as follows: The real-time temperatures of several positions in the battery pack are collected, the real-time temperatures of all positions are averaged, and the average calculation result is ratioed with the preset temperature threshold to obtain the battery temperature meter value; the deviation values ​​of the smoke concentration and real-time humidity of the environment where the battery pack is located compared with the set appropriate humidity value are collected and marked as the battery smoke meter value and the battery humidity meter value respectively, and the vibration amplitude of the battery pack is collected and marked as the battery vibration meter value; The battery dangerous condition value is obtained by numerically calculating the battery temperature meter value, the battery smoke meter value, the battery humidity meter value and the battery vibration meter value. If the battery dangerous condition value exceeds the preset battery dangerous condition threshold value, it is determined that the battery pack is in an unfavorable power supply state. All the battery risk condition values ​​within a unit time are obtained and their average is calculated to obtain the battery risk assessment value, and the total time that the battery group is in an unfavorable power supply state within a unit time is marked as the power supply non-favorable condition value, and the number of occurrences in which the single duration of the battery group in the power supply non-favorable state within a unit time exceeds the corresponding preset single duration threshold is marked as the power supply non-favorable abnormal value; the battery state coefficient is obtained by numerically calculating the battery risk assessment value, the power supply non-favorable condition value and the power supply non-favorable abnormal value.

7. The artificial intelligence-based refrigerator power supply system according to claim 1, characterized in that: The power supply risk detection module is communicatively connected to the power supply hidden danger auxiliary judgment module. The power supply risk detection module sends a power supply high risk signal to the power supply hidden danger auxiliary judgment module. The power supply hidden danger auxiliary judgment module stores the number of occurrences of the power supply high risk signal. The power supply hidden danger auxiliary judgment module will conduct a step-by-step progressive evaluation and analysis of the power supply hidden dangers of the refrigerator during the detection period, and generate a high power supply hidden danger signal or a low power supply hidden danger signal based on this, and send the high power supply hidden danger signal or the low power supply hidden danger signal to the power supply control end. When the power supply control end receives the high power supply hidden danger signal, it will issue a corresponding warning.

8. The artificial intelligence-based refrigerator power supply system according to claim 7, characterized in that: The specific analysis process of the power supply hidden danger auxiliary judgment module is as follows: The number of times a high-risk power supply signal is generated during the detection period is collected and marked as the power supply risk alarm value. If the power supply risk alarm value exceeds the preset power supply risk alarm threshold, a high-risk power supply signal is generated; if the power supply risk alarm value exceeds the preset power supply risk alarm threshold, the moment when the MCU control module generates the corresponding switching control signal is collected and marked as the signal start moment, and the moment when the switching switch module completes the switching of the corresponding power supply mode is collected and marked as the cutting end moment, and the interval between the cutting end moment and the signal start moment is marked as the switching effectiveness inspection value; The switching efficiency inspection value is numerically compared with the preset switching efficiency inspection threshold. If the switching efficiency inspection value exceeds the preset switching efficiency inspection threshold, the corresponding switching efficiency inspection value is marked as the switching abnormal inspection value, and the number of switching abnormal inspection values ​​in the detection period is ratio-calculated to the number of switching efficiency inspection values ​​to obtain the switching abnormality value, and the average of all switching efficiency inspection values ​​in the detection period is calculated to obtain the switching efficiency analysis value; the power supply hidden danger coefficient is obtained by numerically calculating the power supply risk alarm value, the switching abnormality value and the switching efficiency analysis value. If the power supply hidden danger coefficient exceeds the preset power supply hidden danger coefficient threshold, a high power supply hidden danger signal is generated.

9. The artificial intelligence-based refrigerator power supply system according to claim 8, characterized in that: If the power supply hidden danger coefficient does not exceed the preset power supply hidden danger coefficient threshold, the interval between the production date of the battery pack and the current date is collected and marked as the production interval value, and the actual surface size contour of the battery pack is compared with the initial size contour to obtain the contour coincidence value, and the number of charge and discharge times of the battery pack in the historical stage is marked as the charge and discharge frequency detection value; The attenuation prediction coefficient is obtained by numerically calculating the production interval value, contour overlap value and charge and discharge frequency detection value. If the attenuation prediction coefficient exceeds the preset attenuation prediction coefficient threshold, a high power supply hidden danger signal is generated; if the attenuation prediction coefficient does not exceed the preset attenuation prediction coefficient threshold, a low power supply hidden danger signal is generated.

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