Power management control system of thermal infrared detection system

Abstract

The invention relates to the technical field of power management control, in particular to a power management control system of a thermal infrared detection system. The power supply conversion and distribution unit is used for converting an external power supply into multiple paths of voltage and distributing the voltage for the first time; the voltage monitoring stability control unit monitors the power supply voltage state of each module in the system in real time; the current protection current limiting unit monitors a power supply current state in real time; the dynamic load regulation unit uses an entropy weight optimization load regulation technology to monitor the entropy change of load fluctuation and the power supply of a priority weight dynamic regulation module in real time; and the redundant power supply switching unit predicts the state change of the main and standby power supplies by using a nonlinear predictive switching optimization technology to optimize a switching path, and performs power supply switching according to the switching path and the switching time. The power supply management control system of the thermal infrared detection system dynamically distributes power supply resources through an entropy weight optimization load adjusting technology, and seamless switching of a main power supply and a standby power supply is realized through a nonlinear prediction switching optimization technology.

Description

Technical Field

[0001] The present invention relates to the technical field of power management control, in particular to a power management control system of a thermal infrared detection system. Background Art

[0002] A power management control system for a thermal infrared detection system aims to improve the power supply distribution efficiency and the stability of the main and standby power supply switching under complex load environments. Through entropy weight optimization load regulation technology and nonlinear predictive switching optimization technology, the impact of load fluctuations on power supply and the main and standby power supply switching delay are controlled to achieve efficient, accurate and stable power management.

[0003] Existing power management and control systems usually find it difficult to prioritize the power supply needs of key modules in dynamic load fluctuation scenarios while taking into account the global power supply energy efficiency optimization. In addition, since the decline of the main power supply status cannot be predicted in advance and the energy loss of the backup power supply response path is high, key modules will be underpowered due to large fluctuations, the overall energy efficiency of the system will be reduced, and there will be switching delays and energy waste during the switching of the main and backup power supplies. Therefore, a power management and control system for a thermal infrared detection system is designed. Summary of the invention

[0004] The purpose of the present invention is to provide a power management and control system for a thermal infrared detection system to solve the problems raised in the above background technology, such as the inability to predict in advance the decline of the main power supply status and the high energy loss of the backup power supply response path, which may lead to insufficient power supply to key modules due to large fluctuations, reduced overall system energy efficiency, and switching delays and energy waste during the switching of the main and backup power supplies.

[0005] To achieve the above object, the present invention provides a power management control system for a thermal infrared detection system, comprising:

[0006] A power conversion and distribution unit, which is used to convert an external 28V DC power supply into multiple voltages through a DC / DC converter and initially distribute the voltage according to module requirements;

[0007] It also includes a voltage monitoring and stabilization control unit, which uses a high-precision sensor to monitor the power supply voltage status of each module in the system in real time;

[0008] It also includes a current protection and current limiting unit, which uses a surge suppression circuit and a fast current limiting technology to monitor the power supply current state in real time and provide multi-level protection for the infrared sensor;

[0009] It also includes a dynamic load adjustment unit, which uses entropy weight optimization load adjustment technology according to real-time load demand, monitors the entropy value change of load fluctuation and the priority weight to dynamically adjust the power supply of the module in real time;

[0010] It also includes a redundant power supply switching unit, which uses nonlinear predictive switching optimization technology to predict the state change of the main and backup power supplies and optimize the switching path, and immediately performs power switching according to the switching path and switching time when the main power supply fails.

[0011] The power conversion and distribution unit includes a secondary power module and a voltage distribution control module;

[0012] The secondary power supply module uses a DC / DC converter to convert an external 28V DC power supply into multiple voltages, including +5V, +12V, +15V and -15V;

[0013] The voltage distribution control module dynamically distributes the multi-channel voltages output by the secondary power supply module according to the module requirements, as follows:

[0014] S1.1, allocating voltage to the image processing module in the thermal infrared detection system;

[0015] S1.2, allocating voltage to the infrared sensor module in the thermal infrared detection system;

[0016] S1.3, providing voltage for auxiliary function modules, analog circuits and filter circuits;

[0017] Wherein, the image processing module and the infrared sensor module are modules in the thermal infrared detection system.

[0018] As a further improvement of the technical solution, the voltage monitoring and stabilization unit includes a voltage acquisition module and a voltage analysis and alarm module;

[0019] Wherein, the voltage acquisition module collects multi-channel voltage data of all power supply paths in the thermal infrared detection system in real time, covering the image processing module and the infrared sensor module;

[0020] The voltage analysis alarm module is used to analyze the collected multi-channel voltage data in real time, determine whether it exceeds the preset range, and send an alarm signal when an abnormality is detected, as follows:

[0021] S2.1. Set different voltage safety ranges according to the power supply requirements of different modules. When the voltage deviates from the safety range, an alarm is triggered;

[0022] S2.2, the voltage analysis alarm module supports LED indication, sound and light alarm and signal output alarm modes.

[0023] As a further improvement of the technical solution, the current protection and current limiting unit includes an EMI filter module, a surge suppression module and a fast current limiting module;

[0024] The device used to suppress electromagnetic interference at the power input terminal adopts a low-pass filter structure to suppress high-frequency electromagnetic noise, allowing direct current and low-frequency signals to pass through, and combines common-mode inductance and differential-mode capacitance to filter common-mode interference and differential-mode interference respectively;

[0025] The surge suppression module is used to protect the system from voltage surges, combining gas discharge tubes and transient suppression diodes to suppress surges;

[0026] The fast current limiting module uses a high-precision current sensor to monitor the power supply current status in real time, and quickly limits the current flowing into the system module when overload, short circuit and abnormal current fluctuation occur.

[0027] As a further improvement of the technical solution, the dynamic load adjustment unit includes a load real-time monitoring module and a load adjustment optimization module;

[0028] The load real-time monitoring module acquires the load demand data of each module in real time, including the load demand and load fluctuation amplitude of each module, and the total load power consumption of the thermal infrared detection system;

[0029] The load regulation optimization module uses entropy weight optimization load regulation technology based on the load demand data of each module, and dynamically adjusts the power supply distribution of the module according to the entropy value change and priority weight of the load fluctuation;

[0030] The entropy weight optimized load regulation technology combines the thermodynamic entropy increase principle with the Lagrangian optimization method to dynamically monitor and calculate the load demand of each module in real time.

[0031] As a further improvement of the technical solution, the load real-time monitoring module obtains the load demand data of each module in real time, including the load demand, load fluctuation amplitude and load demand distribution probability of each module. The specific method steps are as follows:

[0032] S4.1.1、Define the system module set as The monitoring period is ; Collect the voltage of each module in real time through voltage sensors and current sensors and current ;

[0033] Among them, the It is a module for thermal infrared detection system; is the total number of modules of the thermal infrared detection system; The time point for starting monitoring; The monitoring end time point; is the total number of monitoring time points; is the module label, ; To mark the monitoring time points, ; For modules exist Monitor the voltage at a given time point; For modules exist Monitor the current at a given time point;

[0034] S4.1.2. Calculation module At the monitoring time Load demand :

[0035] ;

[0036] S4.1.3. Based on the load requirements of all modules, a load requirement matrix is ​​constructed within the monitoring period. :

[0037] ;

[0038] S4.1.4. Calculation module Load fluctuation range :

[0039] ;

[0040] ;

[0041] in, For modules The load fluctuation range; For modules The average load demand during the monitoring period;

[0042] S4.1.5 Module At the monitoring time Load demand Discretize into 𝑅 intervals:

[0043] ;

[0044] in, is the discrete interval of load demand; is the load demand discrete interval The lower limit of is the load demand discrete interval The upper limit value of is the total number of discrete intervals of load demand;

[0045] Statistical Load Load Demand The number of occurrences in each interval , calculate the load demand distribution probability :

[0046] ;

[0047] in, is the load demand distribution probability; For modules Load demand At the monitoring time Falling into the discrete range of load demand The number of occurrences of For modules At the monitoring time The total number of load demand sampling times.

[0048] As a further improvement of the technical solution, the load regulation optimization module uses entropy weight optimization load regulation technology based on the load demand data of each module, and dynamically adjusts the power supply distribution of the module according to the entropy value change and priority weight of the load fluctuation. The specific method steps are as follows:

[0049] S4.2.1. According to the thermodynamic entropy increase principle, the fluctuation characteristics of module load demand are analogized to the disordered changes of thermodynamic systems, and the information entropy of load demand is defined as the module load entropy value;

[0050] Based on load demand distribution probability Computing Module Module load entropy value :

[0051] ;

[0052] in, For modules At the monitoring time Module load entropy value;

[0053] S4.2.2. Calculate the total entropy of the system based on the priority weight of each module :

[0054] ;

[0055] in, is the total entropy of the system; For modules The priority weight of

[0056] S4.2.3. Based on the total entropy of the system Combined with the power supply error, the optimization objective function is constructed :

[0057] ;

[0058] Constraints:

[0059] ;

[0060] ;

[0061] in, To optimize the objective function; is the weight factor of the total entropy value of the system; is the power supply error weight factor; The maximum power supply capacity of the system; For modules Minimum power supply requirements;

[0062] S4.2.4. Based on the optimization objective function Define the Lagrangian function and use the Lagrangian multiplier method to solve the optimization objective function :

[0063] Lagrangian function :

[0064] ;

[0065] in, is the Lagrange multiplier;

[0066] For the Lagrangian function Find the partial derivative and solve the load demand Optimal power distribution;

[0067] S4.2.5. Dynamically adjust module power supply according to optimal power supply allocation.

[0068] As a further improvement of the technical solution, the redundant power supply switching unit includes a power supply state prediction module and a path optimization and switching execution module;

[0069] Wherein, the power state prediction module dynamically predicts the change trend of the main and standby power states based on the nonlinear dynamic model in the nonlinear prediction switching optimization technology;

[0070] The path optimization and switching execution module dynamically calculates the switching path with minimum energy loss based on the Fermat path optimization principle in the nonlinear predictive switching optimization technology, and performs the main backup power supply switching when the main power supply fails;

[0071] The nonlinear predictive switching optimization technology uses a nonlinear dynamic model to predict the changing trend of the main and standby power supply states, and combines the Fermat path optimization principle to dynamically calculate the switching path with the minimum energy loss.

[0072] As a further improvement of the technical solution, the power state prediction module dynamically predicts the change trend of the main and standby power states based on the nonlinear dynamic model in the nonlinear prediction switching optimization technology. The specific method steps are as follows:

[0073] S5.1.1. Based on the nonlinear dynamic model and according to the current state of the main backup power supply, define the evolution equation of the main backup power supply state:

[0074] Main power state evolution equation:

[0075] ;

[0076] in, At the monitoring time point The current status of the main power supply; and is the system dynamics parameter; At the monitoring time point External load disturbance;

[0077] Backup power state evolution equation:

[0078] ;

[0079] in, At the monitoring time point The current status of the backup power supply; and is the system dynamics parameter;

[0080] S5.1.2. Use the Runge-Kutta method to numerically solve the state evolution equations of the main power supply and the backup power supply to predict the future state of the main power supply. and the future state of backup power :

[0081] ;

[0082] ;

[0083] in, is the prediction step length;

[0084] S5.1.3, set the main power drop threshold to and backup power availability threshold ;

[0085] when When the main power supply is in failure, there is a risk of failure;

[0086] when When the backup power supply is available;

[0087] When both meet and When the switching request is received, a switching recommendation signal is sent to the path optimization and switching execution module.

[0088] As a further improvement of the present technical solution, the path optimization and switching execution module dynamically calculates the switching path with the minimum energy loss based on the Fermat path optimization principle in the nonlinear predictive switching optimization technology, and performs the main backup power supply switching when the main power supply fails. The specific method steps are as follows:

[0089] S5.2.1. Define the total energy loss of the switching path as follows:

[0090] ;

[0091]

[0092] in, is the total energy loss of the switching path; is the switching start time; is the switching end time; is the dynamic impedance of the switching path;

[0093] The inherent impedance introduced by the switching components; Line impedance during switching;

[0094] S5.2.2. Total energy loss based on switching path , construct the path optimization objective function:

[0095] ;

[0096] Solve the partial derivative of the path optimization objective function to obtain the optimal switching start time and optimal switching end time and sends a power switching instruction to the thermal infrared detection system;

[0097] S5.2.3. The thermal infrared detection system uses a high-speed electronic switch to switch between the main and standby power supplies.

[0098] Compared with the prior art, the present invention has the following beneficial effects:

[0099] 1. In the power management control system of this thermal infrared detection system, the entropy weight optimization load regulation technology is used. The system can dynamically allocate resources according to the changes in the entropy value of the module load demand, giving priority to ensuring the stable operation of key modules.

[0100] 2. In the power management control system of this thermal infrared detection system, nonlinear predictive switching optimization technology is used to utilize a dynamic model to predict the status of the main and standby power supplies and combine it with Fermat path optimization to achieve millisecond-level low-loss seamless switching of the main and standby power supplies, solving the problems of insufficient power supply accuracy and switching timeliness in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0101] Figure 1 It is the overall flow chart of the present invention;

[0102] The meaning of each number in the figure is:

[0103] 1. Power conversion and distribution unit; 2. Voltage monitoring and stabilization unit; 3. Current protection and current limiting unit; 4. Dynamic load adjustment unit; 5. Redundant power switching unit. DETAILED DESCRIPTION

[0104] 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.

[0105] Example

[0106] See also Figure 1 As shown, a power management control system for a thermal infrared detection system is provided, comprising: a power conversion and distribution unit 1, wherein the power conversion and distribution unit 1 is used to convert an external 28V DC power supply into a multi-channel voltage through a DC / DC converter, and initially distribute the voltage according to module requirements;

[0107] In this embodiment, the power conversion and distribution unit 1 includes a secondary power module and a voltage distribution control module;

[0108] The secondary power supply module uses a DC / DC converter to convert an external 28V DC power supply into multiple voltages, including +5V, +12V, +15V and -15V;

[0109] The voltage distribution control module dynamically distributes the multi-channel voltages output by the secondary power supply module according to the module requirements, as follows:

[0110] S1.1, allocating voltage to the image processing module in the thermal infrared detection system;

[0111] S1.2, allocating voltage to the infrared sensor module in the thermal infrared detection system;

[0112] S1.3, providing voltage for auxiliary function modules, analog circuits and filter circuits;

[0113] Wherein, the image processing module and the infrared sensor module are modules in the thermal infrared detection system.

[0114] In this embodiment, voltage is allocated to the image processing module in the thermal infrared detection system, and the specific allocation is as follows:

[0115] IO voltage (3.3V): used to support the power requirements of the input and output interfaces. Responsible for the communication between FPGA and external devices and the power supply of peripheral interfaces;

[0116] Core voltage (1.2V): It is used to operate the core logic unit of the FPGA, ensuring the stability of its high-performance computing capability. It has high power supply accuracy requirements to avoid voltage fluctuations affecting the operation of the FPGA.

[0117] The bank voltage (2.5V) supports logic switching between multi-function blocks within the FPGA, providing energy support for high-speed signal transmission and multi-bank operations within the FPGA chip.

[0118] The PROM voltage (1.8V) is used to support the power supply of the FPGA configuration memory (PROM). During the FPGA startup phase, it provides a stable power supply for loading configuration files.

[0119] In this embodiment, voltage is allocated to the infrared sensor module in the thermal infrared detection system, and the specific allocation is as follows:

[0120] VDD20 (2.0V, 200mA): Provides low voltage power supply for the digital circuit part of the infrared sensor to support the operation of signal processing and control circuits;

[0121] VDD30 (3.3V, 100mA) provides power for the high-speed interface part to support high-speed transmission of image signals;

[0122] VDDPIX (3.0V, 20mA) drives the pixel array of the infrared sensor and provides basic energy support for the collection of image data;

[0123] Vres_h (3.3V, 500mA) is used to support the operation of the infrared image sensor core circuit.

[0124] It also includes a voltage monitoring and stabilization control unit 2, which uses a high-precision sensor to monitor the power supply voltage status of each module in the system in real time;

[0125] In this embodiment, the voltage monitoring and stabilization unit 2 includes a voltage acquisition module and a voltage analysis and alarm module;

[0126] Wherein, the voltage acquisition module collects multi-channel voltage data of all power supply paths in the thermal infrared detection system in real time, covering the image processing module and the infrared sensor module;

[0127] The voltage analysis alarm module is used to analyze the collected multi-channel voltage data in real time, determine whether it exceeds the preset range, and send an alarm signal when an abnormality is detected, as follows:

[0128] S2.1. Set different voltage safety ranges according to the power supply requirements of different modules. When the voltage deviates from the safety range, an alarm is triggered;

[0129] S2.2, the voltage analysis alarm module supports LED indication, sound and light alarm and signal output alarm modes.

[0130] In this embodiment, different voltage safety ranges are set according to the power supply requirements of different modules, as follows:

[0131] In the image processing module:

[0132] The safe range of IO voltage (3.3V) is: ;

[0133] The safe range of core voltage (1.2V) is: ;

[0134] The safe range of Bank voltage (2.5V) is: ;

[0135] The safe range of PROM voltage (1.8V) is: ;

[0136] In the infrared sensor module:

[0137] The safe range of VDD20 (2.0V) is: ;

[0138] The safe range of VDD30 (3.3V) is: ;

[0139] The safe range of VDDPIX (3.0V) is: ;

[0140] The safe range of Vres_h (3.3V) is: .

[0141] It also includes a current protection and current limiting unit 3, which uses a surge suppression circuit and a fast current limiting technology to monitor the power supply current state in real time and provide multi-level protection for the infrared sensor;

[0142] The current protection and current limiting unit 3 includes an EMI filter module, a surge suppression module and a fast current limiting module;

[0143] The device used to suppress electromagnetic interference at the power input terminal adopts a low-pass filter structure to suppress high-frequency electromagnetic noise, allowing direct current and low-frequency signals to pass through, and combines common-mode inductance and differential-mode capacitance to filter common-mode interference and differential-mode interference respectively;

[0144] The surge suppression module is used to protect the system from voltage surges, combining gas discharge tubes and transient suppression diodes to suppress surges;

[0145] The fast current limiting module uses a high-precision current sensor to monitor the power supply current status in real time, and quickly limits the current flowing into the system module when overload, short circuit and abnormal current fluctuation occur.

[0146] It also includes a dynamic load adjustment unit 4, which uses entropy weight optimization load adjustment technology according to real-time load demand, monitors the entropy value change of load fluctuation and the priority weight to dynamically adjust the power supply of the module in real time;

[0147] In this embodiment, the dynamic load adjustment unit 4 includes a load real-time monitoring module and a load adjustment optimization module;

[0148] The load real-time monitoring module acquires the load demand data of each module in real time, including the load demand and load fluctuation amplitude of each module, and the total load power consumption of the thermal infrared detection system;

[0149] The load regulation optimization module uses entropy weight optimization load regulation technology based on the load demand data of each module, and dynamically adjusts the power supply distribution of the module according to the entropy value change and priority weight of the load fluctuation;

[0150] The entropy weight optimized load regulation technology combines the thermodynamic entropy increase principle with the Lagrangian optimization method to dynamically monitor and calculate the load demand of each module in real time.

[0151] In this embodiment, the entropy weight optimization load regulation technology builds a multi-objective optimization model based on the entropy value of load fluctuations and the task priority weights, and achieves precise control of power distribution by minimizing the system entropy value and maximizing the priority task performance; in high-volatility load scenarios, this technology prioritizes resources to key modules, while dynamically adjusting the global power supply constraints to ensure the efficiency and flexibility of energy distribution. This method effectively adapts to complex load environments and improves the operating efficiency and responsiveness of the system.

[0152] In this embodiment, the load real-time monitoring module obtains the load demand data of each module in real time, including the load demand, load fluctuation range and load demand distribution probability of each module. The specific method steps are as follows:

[0153] S4.1.1、Define the system module set as The monitoring period is ; Collect the voltage of each module in real time through voltage sensors and current sensors and current ;

[0154] Among them, the It is a module for thermal infrared detection system; is the total number of modules of the thermal infrared detection system; The time point for starting monitoring; The monitoring end time point; is the total number of monitoring time points; is the module label, ; To mark the monitoring time points, ; For modules exist Monitor the voltage at a given time point; For modules exist Monitor the current at a given time point;

[0155] S4.1.2. Calculation module At the monitoring time Load demand :

[0156] ;

[0157] S4.1.3. Based on the load requirements of all modules, a load requirement matrix is ​​constructed within the monitoring period. :

[0158] ;

[0159] S4.1.4. Calculation module Load fluctuation range :

[0160] ;

[0161] ;

[0162] in, For modules The load fluctuation range; For modules The average load demand during the monitoring period;

[0163] S4.1.5 Module At the monitoring time Load demand Discretize into 𝑅 intervals:

[0164] ;

[0165] in, is the discrete interval of load demand; is the load demand discrete interval The lower limit of is the load demand discrete interval The upper limit value of is the total number of discrete intervals of load demand;

[0166] Statistical Load Load Demand The number of occurrences in each interval , calculate the load demand distribution probability :

[0167] ;

[0168] in, is the load demand distribution probability; For modules Load demand At the monitoring time Falling into the discrete range of load demand The number of occurrences of For modules At the monitoring time The total number of load demand sampling times.

[0169] In this embodiment, the load regulation optimization module uses entropy weight optimization load regulation technology based on the load demand data of each module, and dynamically adjusts the power supply distribution of the module according to the entropy value change and priority weight of the load fluctuation. The specific method steps are as follows:

[0170] S4.2.1. According to the thermodynamic entropy increase principle, the fluctuation characteristics of module load demand are analogized to the disordered changes of thermodynamic systems, and the information entropy of load demand is defined as the module load entropy value;

[0171] Based on load demand distribution probability Computing Module Module load entropy value :

[0172] ;

[0173] in, For modules At the monitoring time Module load entropy value;

[0174] In this embodiment, a lower module load entropy value indicates that the module load is relatively stable; a higher module load entropy value indicates that the module load fluctuates greatly;

[0175] S4.2.2. Calculate the total entropy of the system based on the priority weight of each module :

[0176] ;

[0177] in, is the total entropy of the system; For modules The priority weight of

[0178] In this embodiment, the module Priority weight It is a specific value set manually based on the importance of each module;

[0179] S4.2.3. Based on the total entropy of the system Combined with the power supply error, the optimization objective function is constructed :

[0180] ;

[0181] Constraints:

[0182] ;

[0183] ;

[0184] in, To optimize the objective function; is the weight factor of the total entropy value of the system; is the power supply error weight factor; The maximum power supply capacity of the system; For modules Minimum power supply requirements;

[0185] In this embodiment, the system total entropy weight factor and power supply error weight factor The selection needs to be made according to the needs of the actual application scenario. If the goal is to improve the stability of the system and reduce the impact of load fluctuations on the system, a weight factor can be given to the total entropy value of the system. Higher weight; if the goal is to more accurately match the power supply requirements of the module and reduce power waste or over-powering, a power supply error weight factor can be assigned Higher weight; initial setting ; ;

[0186] After the actual operation, the following strategies can be used for assignment:

[0187] Prioritize stability: The system runs in a high-fluctuation load environment, and it is hoped that the impact of load fluctuations on the system will be minimized: ; ;

[0188] Prioritize power supply accuracy: High power supply accuracy is required, especially for image processing modules, infrared sensor modules or other sensitive equipment power supply modules: ; ;

[0189] Balanced stability and power supply accuracy: The system operates under relatively balanced requirements, which requires both load fluctuation control and power supply accuracy: ; ;

[0190] S4.2.4. Based on the optimization objective function Define the Lagrangian function and use the Lagrangian multiplier method to solve the optimization objective function :

[0191] Lagrangian function :

[0192] ;

[0193] in, is the Lagrange multiplier;

[0194] For the Lagrangian function Find the partial derivative and solve the load demand Optimal power distribution;

[0195] S4.2.5. Dynamically adjust module power supply according to optimal power supply allocation.

[0196] It also includes a redundant power supply switching unit 5, which uses a nonlinear predictive switching optimization technology to predict the state change of the main and standby power supplies to optimize the switching path, and immediately switches the power supply according to the switching path and switching time when the main power supply fails;

[0197] In this embodiment, the redundant power supply switching unit 5 includes a power supply state prediction module and a path optimization and switching execution module;

[0198] Wherein, the power state prediction module dynamically predicts the change trend of the main and standby power states based on the nonlinear dynamic model in the nonlinear prediction switching optimization technology;

[0199] The path optimization and switching execution module dynamically calculates the switching path with minimum energy loss based on the Fermat path optimization principle in the nonlinear predictive switching optimization technology, and performs the main backup power supply switching when the main power supply fails;

[0200] The nonlinear predictive switching optimization technology uses a nonlinear dynamic model to predict the changing trend of the main and standby power supply states, and combines the Fermat path optimization principle to dynamically calculate the switching path with the minimum energy loss.

[0201] In this embodiment, the power state prediction module dynamically predicts the change trend of the main and standby power states based on the nonlinear dynamic model in the nonlinear prediction switching optimization technology. The specific method steps are as follows:

[0202] S5.1.1. Based on the nonlinear dynamic model and according to the current state of the main backup power supply, define the evolution equation of the main backup power supply state:

[0203] Main power state evolution equation:

[0204] ;

[0205] in, At the monitoring time point The current status of the main power supply; and is the system dynamics parameter; At the monitoring time point External load disturbance;

[0206] Backup power state evolution equation:

[0207] ;

[0208] in, At the monitoring time point The current status of the backup power supply; and is the system dynamics parameter;

[0209] In this embodiment, the system dynamics parameters , , and , use the least squares method to determine the specific value:

[0210] : Natural attenuation rate of main power supply;

[0211] : The influence coefficient of main power supply affected by external load disturbance;

[0212] : The sensitivity of the backup power supply in response to the main power supply status;

[0213] : Natural attenuation rate of backup power supply;

[0214] S5.1.2. Use the Runge-Kutta method to numerically solve the state evolution equations of the main power supply and the backup power supply to predict the future state of the main power supply. and the future state of backup power :

[0215] ;

[0216] ;

[0217] in, is the prediction step length;

[0218] S5.1.3, set the main power drop threshold to and backup power availability threshold ;

[0219] when When the main power supply is in failure, there is a risk of failure;

[0220] when When the backup power supply is available;

[0221] When both meet and When the switching request is received, a switching recommendation signal is sent to the path optimization and switching execution module.

[0222] In this embodiment, the Runge-Kutta method is a general method for numerically calculating the initial value problem of ordinary differential equations; in solving the nonlinear dynamic equations of the main power supply and the backup power supply states, the state evolution equations of the main power supply and the backup power supply contain nonlinear and coupling relationships, which are difficult to solve directly by analytical methods; the Runge-Kutta method can perform efficient numerical solutions to these equations.

[0223] In this embodiment, the path optimization and switching execution module dynamically calculates the switching path with minimum energy loss based on the Fermat path optimization principle in the nonlinear predictive switching optimization technology, and performs the main backup power supply switching when the main power supply fails. The specific method steps are as follows:

[0224] S5.2.1. Define the total energy loss of the switching path as follows:

[0225] ;

[0226]

[0227] in, is the total energy loss of the switching path; is the switching start time; is the switching end time; is the dynamic impedance of the switching path;

[0228] The inherent impedance introduced by the switching components; Line impedance during switching;

[0229] S5.2.2. Total energy loss based on switching path , construct the path optimization objective function:

[0230] ;

[0231] Solve the partial derivative of the path optimization objective function to obtain the optimal switching start time and optimal switching end time and sends a power switching instruction to the thermal infrared detection system;

[0232] S5.2.3. The thermal infrared detection system uses a high-speed electronic switch to switch between the main and standby power supplies.

[0233] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments, and the above embodiments and descriptions are only preferred examples of the present invention, and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

Claims

1. A power management control system for a thermal infrared detection system, characterized in that: include: A power conversion and distribution unit (1), the power conversion and distribution unit (1) is used to convert an external 28V direct current power supply into multiple voltages through a DC / DC converter, and to initially distribute the voltages according to module requirements; A voltage monitoring and stabilization control unit (2), wherein the voltage monitoring and stabilization control unit (2) uses a high-precision sensor to monitor the power supply voltage status of each module in the system in real time; A current protection and current limiting unit (3), wherein the current protection and current limiting unit (3) uses a surge suppression circuit and a fast current limiting technology to monitor the power supply current state in real time and provide multi-level protection for the infrared sensor; A dynamic load adjustment unit (4), wherein the dynamic load adjustment unit (4) uses entropy weight optimization load adjustment technology according to real-time load demand, monitors entropy value changes of load fluctuations and priority weights in real time to dynamically adjust module power supply; A redundant power supply switching unit (5) uses a nonlinear predictive switching optimization technology to predict the state change of the main and standby power supplies to optimize the switching path, and immediately performs power supply switching according to the switching path and switching time when the main power supply fails.

2. The power management control system of the thermal infrared detection system according to claim 1, characterized in that: The power conversion and distribution unit (1) comprises a secondary power supply module and a voltage distribution control module; The secondary power supply module uses a DC / DC converter to convert an external 28V DC power supply into multiple voltages, including +5V, +12V, +15V and -15V; The voltage distribution control module dynamically distributes the multi-channel voltages output by the secondary power supply module according to the module requirements, as follows: S1.1, allocating voltage to the image processing module in the thermal infrared detection system; S1.2, allocating voltage to the infrared sensor module in the thermal infrared detection system; S1.3, providing voltage for auxiliary function modules, analog circuits and filter circuits; Wherein, the image processing module and the infrared sensor module are modules in the thermal infrared detection system.

3. The power management control system of the thermal infrared detection system according to claim 1, characterized in that: The voltage monitoring and stabilization unit (2) comprises a voltage acquisition module and a voltage analysis and alarm module; Wherein, the voltage acquisition module collects multi-channel voltage data of all power supply paths in the thermal infrared detection system in real time, covering the image processing module and the infrared sensor module; The voltage analysis alarm module is used to analyze the collected multi-channel voltage data in real time, determine whether it exceeds the preset range, and send an alarm signal when an abnormality is detected, as follows: S2.

1. Set different voltage safety ranges according to the power supply requirements of different modules. When the voltage deviates from the safety range, an alarm is triggered; S2.2, the voltage analysis alarm module supports LED indication, sound and light alarm and signal output alarm modes.

4. The power management control system of the thermal infrared detection system according to claim 1, characterized in that: The current protection and current limiting unit (3) comprises an EMI filter module, a surge suppression module and a fast current limiting module; The device used to suppress electromagnetic interference at the power input terminal adopts a low-pass filter structure to suppress high-frequency electromagnetic noise, allowing direct current and low-frequency signals to pass through, and combines common-mode inductance and differential-mode capacitance to filter common-mode interference and differential-mode interference respectively; The surge suppression module is used to protect the system from voltage surges, combining gas discharge tubes and transient suppression diodes to suppress surges; The fast current limiting module uses a high-precision current sensor to monitor the power supply current status in real time, and quickly limits the current flowing into the system module when overload, short circuit and abnormal current fluctuation occur.

5. The power management control system of the thermal infrared detection system according to claim 1, characterized in that: The dynamic load adjustment unit (4) comprises a load real-time monitoring module and a load adjustment optimization module; The load real-time monitoring module acquires the load demand data of each module in real time, including the load demand and load fluctuation amplitude of each module, and the total load power consumption of the thermal infrared detection system; The load regulation optimization module uses entropy weight optimization load regulation technology based on the load demand data of each module, and dynamically adjusts the power supply distribution of the module according to the entropy value change and priority weight of the load fluctuation; The entropy weight optimized load regulation technology combines the thermodynamic entropy increase principle with the Lagrangian optimization method to dynamically monitor and calculate the load demand of each module in real time.

6. The power management control system of the thermal infrared detection system according to claim 5, characterized in that: The load real-time monitoring module obtains the load demand data of each module in real time, including the load demand, load fluctuation amplitude and load demand distribution probability of each module. The specific method steps are as follows: S4.1.1、Define the system module set as The monitoring period is ; Collect the voltage of each module in real time through voltage sensors and current sensors and current ; Among them, the It is a module for thermal infrared detection system; is the total number of modules of the thermal infrared detection system; The time point for starting monitoring; The monitoring end time point; is the total number of monitoring time points; is the module label, ; To mark the monitoring time points, ; For modules exist Monitor the voltage at a given time point; For modules exist Monitor the current at a given time point; S4.1.

2. Calculation module At the monitoring time Load demand : ； S4.1.

3. Based on the load requirements of all modules, a load requirement matrix is ​​constructed within the monitoring period. : ； S4.1.

4. Calculation module Load fluctuation range : ； ； in, For modules The load fluctuation range; For modules The average load demand during the monitoring period; S4.1.5 Module At the monitoring time Load demand Discretize into 𝑅 intervals: ； in, is the discrete interval of load demand; is the load demand discrete interval The lower limit of is the load demand discrete interval The upper limit value of is the total number of discrete intervals of load demand; Statistical Load Load Demand The number of occurrences in each interval , calculate the load demand distribution probability : ； in, is the load demand distribution probability; For modules Load demand At the monitoring time Falling into the discrete range of load demand The number of occurrences of For modules At the monitoring time The total number of load demand sampling times.

7. The power management control system of the thermal infrared detection system according to claim 5, characterized in that: The load regulation optimization module uses entropy weight optimization load regulation technology based on the load demand data of each module, and dynamically adjusts the power supply distribution of the module according to the entropy value change and priority weight of the load fluctuation. The specific method steps are as follows: S4.2.

1. According to the thermodynamic entropy increase principle, the fluctuation characteristics of module load demand are analogized to the disordered changes of thermodynamic systems, and the information entropy of load demand is defined as the module load entropy value; Based on load demand distribution probability Computing Module Module load entropy value : ； in, For modules At the monitoring time Module load entropy value; S4.2.

2. Calculate the total entropy of the system based on the priority weight of each module : ； in, is the total entropy of the system; For modules The priority weight of S4.2.

3. Based on the total entropy of the system Combined with the power supply error, the optimization objective function is constructed : ； Constraints: ； ； in, To optimize the objective function; is the weight factor of the total entropy value of the system; is the power supply error weight factor; The maximum power supply capacity of the system; For modules Minimum power supply requirements; S4.2.

4. Based on the optimization objective function Define the Lagrangian function and use the Lagrangian multiplier method to solve the optimization objective function : Lagrangian function : ； in, is the Lagrange multiplier; For the Lagrangian function Find the partial derivative and solve the load demand Optimal power distribution; S4.2.

5. Dynamically adjust module power supply according to optimal power supply allocation.

8. The power management control system of the thermal infrared detection system according to claim 1, characterized in that: The redundant power supply switching unit (5) comprises a power supply state prediction module and a path optimization and switching execution module; Wherein, the power state prediction module dynamically predicts the change trend of the main and standby power states based on the nonlinear dynamic model in the nonlinear prediction switching optimization technology; The path optimization and switching execution module dynamically calculates the switching path with minimum energy loss based on the Fermat path optimization principle in the nonlinear predictive switching optimization technology, and performs the main backup power supply switching when the main power supply fails; The nonlinear predictive switching optimization technology uses a nonlinear dynamic model to predict the changing trend of the main and standby power supply states, and combines the Fermat path optimization principle to dynamically calculate the switching path with the minimum energy loss.

9. The power management control system of the thermal infrared detection system according to claim 8, characterized in that: The power state prediction module dynamically predicts the change trend of the main and standby power states based on the nonlinear dynamic model in the nonlinear prediction switching optimization technology. The specific method steps are as follows: S5.1.

1. Based on the nonlinear dynamic model and according to the current state of the main backup power supply, define the evolution equation of the main backup power supply state: Main power state evolution equation: ； in, At the monitoring time point The current status of the main power supply; and is the system dynamics parameter; At the monitoring time point External load disturbance; Backup power state evolution equation: ； in, At the monitoring time point The current status of the backup power supply; and is the system dynamics parameter; S5.1.

2. Use the Runge-Kutta method to numerically solve the state evolution equations of the main power supply and the backup power supply to predict the future state of the main power supply. and the future state of backup power : ； ； in, is the prediction step length; S5.1.3, set the main power drop threshold to and backup power availability threshold ; when When the main power supply is in failure, there is a risk of failure; when When the backup power supply is available; When both satisfy and When the switching request is received, a switching recommendation signal is sent to the path optimization and switching execution module.

10. The power management control system of the thermal infrared detection system according to claim 8, characterized in that: The path optimization and switching execution module dynamically calculates the switching path with minimum energy loss based on the Fermat path optimization principle in the nonlinear predictive switching optimization technology, and switches the main power supply to the backup power supply when the main power supply fails. The specific method steps are as follows: S5.2.

1. Define the total energy loss of the switching path as follows: ； in, is the total energy loss of the switching path; is the switching start time; is the switching end time; is the dynamic impedance of the switching path; The inherent impedance introduced by the switching components; Line impedance during switching; S5.2.

2. Total energy loss based on switching path , construct the path optimization objective function: ； Solve the partial derivative of the path optimization objective function to obtain the optimal switching start time and optimal switching end time and sends a power switching instruction to the thermal infrared detection system; S5.2.

3. The thermal infrared detection system uses a high-speed electronic switch to switch between the main and standby power supplies.

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