Refrigerator power supply system based on artificial intelligence

Abstract

The application 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 commercial power input monitoring module, an MCU control module, a switching module, a power supply risk detection module and a power supply management end; the MCU control module judges the commercial power state according to the voltage information of the commercial power input and generates a corresponding switching control signal, the switching control signal is used for switching between the commercial power supply mode and the battery power supply mode, seamless switching between the commercial power supply and the battery pack power supply is realized, the refrigerated cabinet can still be continuously powered when the commercial power is interrupted or the voltage is low, which is beneficial to maintaining the safe, stable and continuous operation of the refrigerated cabinet, the power supply risk of the refrigerated cabinet under different power supply modes is reasonably analyzed and timely warned by the power supply risk detection module, the power supply continuity and safety of the refrigerated cabinet are ensured, and the management difficulty of the management personnel for the refrigerated cabinet is significantly reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of refrigerated cabinet power supply management, in particular to a refrigerated cabinet power supply system based on artificial intelligence. BACKGROUND

[0002] The refrigerated cabinet is an important equipment for storing fresh food and other cold storage goods, and is widely used in commercial fields such as restaurants, supermarkets, convenience stores, hotel restaurants and family life, and the stability and reliability of its power supply system are directly related to the quality and safety of food preservation.

[0003] The traditional refrigerated cabinet power supply system usually uses mains power directly, which is difficult to ensure the continuity of power supply. Once the mains power is interrupted, the food in the refrigerated cabinet will face the risk of deterioration, and the compressor and other key components are also prone to damage due to sudden power failure. Moreover, it is difficult to analyze and timely warn the power supply risk and power supply hidden danger of the refrigerated cabinet, which is not conducive to ensuring the continuity and safety of the refrigerated cabinet power supply, and the power supply supervision of the refrigerated cabinet is difficult.

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

[0005] The purpose of the present application is to provide a refrigerated cabinet power supply system based on artificial intelligence, which solves the problem that the prior art cannot ensure that the refrigerated cabinet can still supply power continuously when the mains power is interrupted or the voltage is low, and cannot analyze and timely warn the power supply risk and power supply hidden danger of the refrigerated cabinet, which is not conducive to ensuring the continuity and safety of the refrigerated cabinet power supply, and the power supply supervision is difficult.

[0006] To achieve the above purpose, the present application provides the following technical scheme:

[0007] A refrigerated cabinet power supply system based on artificial intelligence, comprising a mains input monitoring module, an MCU control module, a switching module, a power supply risk detection module and a power supply control end;

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

[0009] The MCU control module determines the mains 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 module to control the switching module to switch between mains power supply mode and battery power supply mode based on the switching control signal;

[0010] The power supply risk detection module analyzes the power supply risk of the refrigeration cabinet in the corresponding power supply mode in real time, determines whether to generate a high power supply risk signal through the analysis, and sends the high power supply risk signal to the power supply management end. When the power supply management end receives the high power supply risk signal, it issues a corresponding early warning.

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

[0012] When the mains voltage is lower than the set voltage threshold, the MCU control module controls the conduction of the connection end of the switching switch module and 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 conduction of the switching switch module and the mains input end, and restores the mains power supply mode.

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

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

[0015] If the battery remaining capacity does not exceed the preset battery remaining capacity threshold value, the battery state coefficient of the battery pack is obtained, and the battery state coefficient is compared with the preset battery state coefficient threshold value. If the battery state coefficient exceeds the preset battery state coefficient threshold value, a high power supply risk signal is generated.

[0016] Further, when the refrigeration cabinet is in the mains power supply mode, the voltage curve of the mains voltage in 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. Mark several coordinate points on the voltage curve, and the X-direction distance between adjacent two groups of coordinate points is the same;

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

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

[0019] The number of pressure variation test values is obtained and marked as a pressure variation test value, and the exceeding values of all pressure variation test values compared with the preset pressure variation test threshold are averaged to obtain a pressure variation exceeding test value. The pressure supply risk table value is obtained by numerically calculating the pressure supply non-stable value, the pressure variation test value and the pressure variation exceeding test value. The pressure supply risk table value is compared with the preset pressure supply risk table threshold. If the pressure supply risk table value exceeds the preset pressure supply risk table threshold, a power supply high risk signal is generated.

[0020] Further, the power supply risk detection module is communicatively connected to the battery pack monitoring module. The battery pack monitoring module monitors the battery pack of the refrigerated cabinet. The state of charge coefficient of the battery pack is obtained by analysis and sent to the power supply risk detection module.

[0021] Further, the analysis and acquisition method of the state of charge 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 compared with the preset temperature threshold to obtain a battery storage temperature table value. The deviation of the smoke concentration and the real-time humidity of the environment in which the battery pack is located from the set suitable humidity value is collected and marked as a battery storage smoke table value and a battery storage humidity table value, respectively. The vibration amplitude of the battery pack is collected and marked as a battery storage vibration table value.

[0023] The battery storage risk condition value is obtained by numerically calculating the battery storage temperature table value, the battery storage smoke table value, the battery storage humidity table value and the battery storage vibration table value. The battery storage risk condition value is compared with the preset battery storage risk condition threshold. If the battery storage risk condition value exceeds the preset battery storage risk condition threshold, it is determined that the battery pack is in a power supply disadvantageous state.

[0024] All battery storage risk condition values in a unit time are obtained and averaged to obtain a battery storage risk evaluation value. The total duration of the battery pack in a power supply disadvantageous state in a unit time is marked as a power supply non-beneficial time condition value. The number of occurrences of a single continuous duration of the battery pack in a power supply non-beneficial state in a unit time exceeding the corresponding preset single continuous duration threshold is marked as a power supply non-beneficial abnormal holding value. The state of charge coefficient is obtained by numerically calculating the battery storage risk evaluation value, the power supply non-beneficial time condition value and the power supply non-beneficial abnormal holding 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. 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 performs progressive evaluation and analysis on the power supply hidden danger of the refrigerated cabinet in the detection period, thereby generating a power supply high hidden danger signal or a power supply low hidden danger signal, and sending the power supply high hidden danger signal or the power supply low hidden danger signal to the power supply management and control end. When the power supply management and control end receives the power supply high hidden danger signal, a corresponding early warning is issued.

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

[0028] The number of times of generating the power supply high risk signal in the detection period is collected and marked as a power supply risk alarm value. The power supply risk alarm value is 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, a power supply high hidden danger signal is generated.

[0029] If the power supply risk alarm value exceeds the preset power supply risk alarm threshold value, the time when the MCU control module generates the corresponding switching control signal is collected and marked as a signal start time, and the time when the switching switch module completes the corresponding power supply mode switching is collected and marked as a switching end time. The interval between the switching end time and the signal start time is marked as a switching efficiency detection value.

[0030] The switching efficiency detection value is compared with a preset switching efficiency detection threshold value. If the switching efficiency detection value exceeds the preset switching efficiency detection threshold value, the corresponding switching efficiency detection value is marked as a switching abnormality detection value. The ratio of the number of switching abnormality detection values to the number of switching efficiency detection values in the detection period is calculated to obtain a switching abnormality occupancy value. The average of all switching efficiency detection values in the detection period is calculated to obtain a switching efficiency analysis value.

[0031] The power supply risk alarm value, the switching abnormality occupancy value, and the switching efficiency analysis value are numerically calculated to obtain a power supply hidden danger coefficient. The power supply hidden danger coefficient is compared with a preset power supply hidden danger coefficient threshold value. If the power supply hidden danger coefficient exceeds the preset power supply hidden danger coefficient threshold value, a power supply high hidden danger signal is generated.

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

[0033] The production interval value, the profile coincidence value, and the charge and discharge detection frequency value are numerically calculated to obtain a decay prediction coefficient. The decay prediction coefficient is compared with a preset decay prediction coefficient threshold value. If the decay prediction coefficient exceeds the preset decay prediction coefficient threshold value, a power supply high hidden danger signal is generated. If the decay prediction coefficient does not exceed the preset decay prediction coefficient threshold value, a power supply low 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 status based on the voltage information of the mains input and generates a corresponding switching control signal. Based on the switching control signal, the mains power supply mode and the battery power supply mode are switched, ensuring that the refrigerator can still be powered when the mains power is interrupted or the voltage is low. This is conducive to maintaining the safe, stable and continuous operation of the refrigerator. In addition, the power supply risk detection module reasonably analyzes the power supply risk of the refrigerator under different power supply modes and issues timely warnings, ensuring the power supply continuity and safety of the refrigerator, and significantly reducing the management difficulty of the refrigerator for managers.

[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 dangers 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 will be further described below with reference to 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 Example 2 of the present invention. DETAILED DESCRIPTION

[0040] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

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

[0042] The mains input monitoring module collects voltage information when the mains is input, and transmits the voltage information to the MCU control module, wherein the mains input monitoring module comprises a voltage reduction 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 mains is mainly connected to provide the main power supply for the refrigerator cabinet.

[0043] The MCU control module determines the mains state according to the voltage information of the mains input, and 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 determination 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 connection end of the switching switch module and the battery pack to be conductive, 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 switch module and the mains input end to be conductive, and restores the mains power supply mode, realizing seamless switching between mains power supply and battery pack power supply, ensuring that the refrigerator cabinet can still be powered continuously when the mains is interrupted or the voltage is low, which is conducive to maintaining the safe, stable and continuous operation of the refrigerator cabinet.

[0045] The power supply risk detection module analyzes the power supply risk of the refrigerator cabinet in the corresponding power supply mode in real time, determines whether to generate a high power supply risk signal through analysis, and sends the high power supply risk signal to the power supply control end. When the power supply control end receives the high power supply risk signal, it issues a corresponding early warning, which can reasonably analyze the power supply risk of the refrigerator cabinet in different power supply modes and timely warn to remind the management personnel to take appropriate improvement measures quickly, to ensure the power supply continuity and power supply safety of the refrigerator cabinet, significantly reduce the management difficulty of the management personnel, and have high intelligent degree; the specific analysis process of the power supply risk detection module is as follows:

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

[0047] If the battery remaining capacity does not exceed the preset battery remaining capacity threshold value, the battery state coefficient ZY of the battery pack is obtained, and the battery state coefficient ZY is compared with the preset battery state coefficient threshold value, if the battery state coefficient ZY exceeds the preset battery state coefficient threshold value, a high power supply risk signal is generated.

[0048] Further, when the refrigeration cabinet is in the mains power supply mode, a voltage curve of the mains voltage in a 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 plurality of coordinate points are marked on the voltage curve, and the X-direction distance between adjacent two groups of coordinate points is the same;

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

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

[0051] The number of pressure variation abnormal test values is obtained and marked as a pressure variation abnormality value, and the exceeding values of all the pressure variation abnormal test values compared with the preset pressure variation test threshold value are subjected to mean value calculation to obtain a pressure variation abnormality value;

[0052] The supply voltage instability value YR, the pressure variation abnormality value YS and the pressure variation abnormality value YF are subjected to numerical calculation by the formula YL=rw1*YR+rw2*YS+rw3*YF to obtain a supply voltage risk table value YL, wherein rw1, rw2 and rw3 are preset proportion coefficients with values greater than zero, and the larger the value of the supply voltage risk table value YL, the worse the comprehensive performance of the mains voltage; the supply voltage risk table value YL is compared with a preset supply voltage risk table threshold value, if the supply voltage risk table value YL exceeds the preset supply voltage risk table threshold value, it indicates that the comprehensive performance of the mains voltage is poor, and a power supply high risk signal is generated.

[0053] It should be noted that the power supply risk detection module is in communication connection with the battery pack monitoring module, the battery pack monitoring module monitors the battery pack of the refrigeration cabinet, obtains the battery state coefficient ZY of the battery pack through analysis, and sends the battery state coefficient ZY of the battery pack to the power supply risk detection module, which not only can accurately feedback the real-time running risk condition of the battery pack, but also can provide data support for the analysis process of the power supply risk detection module, and ensure the accuracy of the analysis result; the analysis and acquisition method of the battery state coefficient is as follows:

[0054] The real-time temperatures of several locations within the battery pack are collected, the real-time temperatures of all locations are averaged, and the average calculation result is compared with the preset temperature threshold to obtain the battery temperature meter value; the deviation of the smoke concentration and real-time humidity of the battery pack environment compared to the set appropriate humidity value is collected and marked as the battery smoke meter value and 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 hygrometer value XN, and the battery vibration meter value XW using the formula XR = (hy1*XF+hy2*XP+hy3*XN+hy4*XW) / 4. Here, hy1, hy2, hy3, and hy4 are preset proportional coefficients with values ​​greater than zero. A larger value of the battery dangerous condition value XR indicates a worse real-time safety condition of the battery pack.

[0056] Compare the battery danger value XR with a 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 storage risk condition values ​​within a unit time are obtained and their average is calculated to obtain the storage risk assessment value, and the total time that the battery pack is 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 in which the single duration of the battery pack in the power supply unfavorable state exceeds 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 abnormality value ZM are numerically calculated to obtain the battery state coefficient ZY; among them, ny1, ny2, and ny3 are preset proportional coefficients with values ​​greater than zero, and the larger the value of the battery state coefficient ZY, the worse the operating state of the battery pack and the greater the operating risk.

[0059] Example 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 a high power supply 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 high power supply risk signal.

[0060] The power supply hidden danger auxiliary judgment module performs progressive evaluation and analysis on the power supply hidden danger of the refrigerated cabinet in the detection period, thereby generating a power supply high hidden danger signal or a power supply low hidden danger signal, and sending the power supply high hidden danger signal or the power supply low hidden danger signal to the power supply management and control end. When the power supply management and control end receives the power supply high hidden danger signal, a corresponding early warning is issued to remind the management personnel to strengthen the power supply management of the refrigerated cabinet in the subsequent period, so as to ensure the safe and stable power supply of the refrigerated cabinet and further avoid causing damage to the refrigerated goods. The degree of intelligence is high. The specific analysis process of the power supply hidden danger auxiliary judgment module is as follows:

[0061] The detection period is set, preferably thirty days. The number of times of generating the power supply high risk signal in the detection period is collected and marked as a power supply risk alarm value. The power supply risk alarm value is 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, it indicates that the power supply safety of the refrigerated cabinet in the detection period is not good, and a power supply high hidden danger signal is generated.

[0062] If the power supply risk alarm value exceeds the preset power supply risk alarm threshold value, the time when the MCU control module generates the corresponding switching control signal is collected and marked as the signal start time, and the time when the switching switch module completes the corresponding power supply mode switching is collected and marked as the switching end time. The interval time between the switching end time and the signal start time is marked as a switching efficiency detection value. The greater the value of the switching efficiency detection value, the worse the switching efficiency of the corresponding automatic switching process.

[0063] The switching efficiency detection value is compared with a preset switching efficiency detection threshold value. If the switching efficiency detection value exceeds the preset switching efficiency detection threshold value, it indicates that the switching efficiency of the corresponding automatic switching process is poor, the corresponding switching efficiency detection value is marked as a switching abnormality detection value, and the number of switching abnormality detection values in the detection period is compared with the number of switching efficiency detection values to obtain a switching abnormality occupancy value. The average value of all switching efficiency detection values in the detection period is obtained as a switching efficiency analysis value.

[0064] The formula is The power supply risk alarm value GL, the switching abnormality occupancy value GP and the switching efficiency analysis value GS are numerically calculated to obtain a power supply hidden danger coefficient GY. The values of tu1, tu2 and tu3 are greater than zero. The greater the value of the power supply hidden danger coefficient GY, the more serious the comprehensive power supply hidden danger of the refrigerated cabinet in the detection period.

[0065] The power supply hidden danger coefficient GY is compared with a preset power supply hidden danger coefficient threshold value. If the power supply hidden danger coefficient GY exceeds the preset power supply hidden danger coefficient threshold value, it indicates that the comprehensive power supply hidden danger of the refrigerated cabinet in the detection period is serious, and a power supply high hidden danger signal is generated.

[0066] Further, if the power supply hidden danger coefficient does not exceed the preset power supply hidden danger coefficient threshold, the interval time length of the production date of the battery pack from the current date is collected and marked as a production interval value, and the actual surface size profile of the battery pack and the initial size profile are compared to obtain a profile coincidence value (the smaller the value of the profile coincidence value, the more serious the deformation condition of the battery pack), and the number of charge-discharge times of the historical stage battery pack is marked as a charge-discharge inspection frequency value;

[0067] The production interval value QM, the profile coincidence value QW and the charge-discharge inspection frequency value QF are calculated to obtain a decay prediction coefficient QL by the formula The production interval value QM, the profile coincidence value QW and the charge-discharge inspection frequency value QF are calculated to obtain a decay prediction coefficient QL by the formula

[0068] The decay prediction coefficient is compared with a preset decay prediction coefficient threshold value, if the decay prediction coefficient exceeds the preset decay prediction coefficient threshold value, it indicates that the possibility of serious decay of the performance condition of the battery pack is larger, which is not conducive to ensuring the safe and stable power supply of the refrigerator cabinet, and a power supply high hidden danger signal is generated, if the decay prediction coefficient does not exceed the preset decay prediction coefficient threshold value, it indicates that the possibility of serious decay of the performance condition of the battery pack is smaller, and a power supply low hidden danger signal is generated.

[0069] The working principle of the application is: when in use, the voltage information of the input of commercial power is collected by the commercial power input monitoring module and transmitted to the MCU control module, the MCU control module judges the commercial power state according to the voltage information of the input of commercial power and generates a corresponding switching control signal, and controls the switching switch module to switch between commercial power supply mode and battery power supply mode based on the switching control signal, realizing seamless switching between commercial power supply and battery pack power supply, ensuring that the refrigerator cabinet can still be powered continuously when the commercial power is interrupted or the voltage is low, which is conducive to maintaining the safe and stable and continuous operation of the refrigerator cabinet, and the power supply risk of the refrigerator cabinet under the corresponding power supply mode is analyzed in real time by the power supply risk detection module to determine whether to generate a power supply high risk signal, which can reasonably analyze the power supply risk of the refrigerator cabinet under different power supply modes and timely warning to remind the management personnel to take corresponding improvement measures quickly, ensuring the power supply continuity and power supply safety of the refrigerator cabinet, significantly reducing the management difficulty of the management personnel, and the intelligent degree is high.

[0070] The above formula is a dimensionless value calculation, the formula is obtained by collecting a large amount of data to simulate the recent real situation, and the preset parameters in the formula are set by the person skilled in the art according to the actual situation. The preferred embodiments of the application disclosed above are only used to help explain the application. The preferred embodiments do not describe all the details and limit the application to the specific implementation. Obviously, according to the content of the specification, many modifications and changes can be made. The embodiments are selected and specifically described in the specification in order to better explain the principles and practical applications of the application, so that the person skilled in the art can well understand and utilize the application. The application is limited by the claims and the entire scope and equivalents thereof.

Claims

1. A refrigerator power supply system based on artificial intelligence, characterized in that: It includes mains input monitoring module, MCU control module, 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 status 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 analyzes the power supply risk of the refrigerator in the corresponding power supply mode in real time, determines whether a high power supply risk signal is generated through analysis, and sends the high power supply risk signal to the power supply control terminal. Upon receiving the high power supply risk signal, the power supply control terminal issues a corresponding warning; The specific analysis process of the power supply risk detection module is as follows: When the refrigerator is in battery-powered 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 state of charge coefficient of the battery pack is obtained; if the state of charge coefficient exceeds the preset state of charge coefficient threshold, a high-risk power supply signal is generated; When the refrigerator is in mains power supply mode, collect the mains voltage curve per unit time, establish a rectangular coordinate system with time as the X-axis and voltage as the Y-axis, and place the mains voltage curve in the first quadrant of the rectangular coordinate system, with the starting point of the voltage curve located on the Y-axis; mark several coordinate points on the voltage curve, and ensure that the X-direction distance between two adjacent groups of coordinate points is the same; Obtain the Y-axis coordinate values ​​of all coordinate points, calculate the variance of the Y-axis coordinate values ​​of all coordinate points to obtain the supply voltage instability value, and generate a high-risk power supply signal if the supply voltage instability value exceeds the preset supply voltage instability threshold; If the supply pressure instability value does not exceed the preset supply pressure instability threshold, the 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 the pressure change test value; The number of pressure variation verification values ​​is obtained and marked as pressure variation condition values, and the average of all pressure variation verification values ​​exceeding the preset pressure variation test threshold is calculated to obtain the pressure variation excess value. The supply pressure danger meter 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 meter value exceeds the preset supply pressure danger meter threshold, a high-risk power supply signal is generated.

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 mains 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, starting 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 switch module to connect with the mains input end, restoring the mains power supply mode.

3. 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 battery pack monitoring module. The battery pack monitoring module 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.

4. The artificial intelligence-based refrigerator power supply system according to claim 3, characterized in that: The analysis and acquisition method of the storage state coefficient is as follows: The real-time temperatures of several locations within the battery pack are collected, the real-time temperatures of all locations are averaged, and the average calculation result is compared with the preset temperature threshold to obtain the battery temperature meter value; the deviation of the smoke concentration and real-time humidity of the battery pack environment compared to the set appropriate humidity value is collected and marked as the battery smoke meter value and 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 pack is judged to be in an unfavorable power supply state 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 danger value exceeds a preset battery danger threshold, the battery pack is judged to be in an unfavorable power supply state. All storage risk condition values ​​within a unit time are obtained and their average is calculated to obtain a storage 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 an unfavorable power supply condition value, and the number of occurrences in which the single duration of the battery group in the unfavorable power supply state within a unit time exceeds the corresponding preset single duration threshold is marked as an unfavorable power supply abnormality value; the storage state coefficient is obtained by numerically calculating the storage risk assessment value, the unfavorable power supply condition value and the unfavorable power supply abnormality value.

5. 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 high power supply 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 high power supply risk signal. The power supply hidden danger auxiliary judgment module will conduct a step-by-step progressive assessment 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.

6. The artificial intelligence-based refrigerator power supply system according to claim 5, 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 switching end moment, and the interval between the switching 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.

7. The artificial intelligence-based refrigerator power supply system according to claim 6, characterized in that: If the power supply hidden danger coefficient does not exceed the preset power supply hidden danger coefficient threshold, the time interval between the production date of the battery pack and the current date is collected and marked as the production interval value, the actual surface dimension profile of the battery pack is compared with the initial dimension profile to obtain the profile overlap 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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