Circuit board dynamic power consumption optimization control system based on AI
The AI-based dynamic power consumption optimization and control system for circuit boards solves the problem of increased power consumption caused by heat accumulation in circuit board components, and realizes dynamic monitoring and optimization of component power consumption, ensuring safe and efficient operation of equipment.
Patent Information
- Application Number
- CN202511455103.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-10-13
- Publication Date
- 2026-01-06
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
In electronic devices, the temperature of electronic components on printed circuit boards rises due to heat accumulation, leading to increased leakage current and power consumption, creating a vicious cycle that may cause component failure and energy waste. Existing technologies struggle to effectively monitor and optimize power consumption.
An AI-based dynamic power consumption optimization and control system for circuit boards is adopted. The system acquires the operating parameters of components through the data acquisition module, builds an AI dynamic model, performs power consumption analysis and optimization, uses the data analysis module to identify anomalies and generate optimization instructions, and the power consumption optimization module makes adjustments accordingly.
It enables effective monitoring and dynamic optimization of power consumption of circuit board components, reducing lifespan loss and energy waste, and ensuring the safe operation of components.
Smart Images
Figure CN121277333A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of circuit board monitoring and control technology, specifically an AI-based dynamic power consumption optimization control system for circuit boards. Background Technology
[0002] With the widespread adoption and rapid development of electronic devices, the processing speed of electronic components, such as chips, is increasing, and manufacturing processes are constantly improving. The heat generated by these electronic components on printed circuit boards cannot be dissipated in time, causing their temperature to rise continuously. This increased temperature leads to increased leakage current and power consumption, further pushing up the component temperature, creating a vicious cycle. Excessive temperature can cause chips or other electronic components to malfunction, posing safety hazards, while high power consumption results in energy waste.
[0003] How to monitor the operating status of each component on a circuit board, so as to optimize and adjust the power consumption of the component when abnormal power consumption occurs, is the problem we need to solve. To this end, we present an AI-based dynamic power consumption optimization and control system for circuit boards. Summary of the Invention
[0004] The purpose of this invention is to provide an AI-based dynamic power consumption optimization control system for circuit boards.
[0005] The objective of this invention can be achieved through the following technical solution: an AI-based dynamic power consumption optimization control system for circuit boards, comprising a control center, wherein the control center is connected to a data acquisition module, an AI simulation module, a data analysis module, and a power consumption optimization module;
[0006] The data acquisition module is used to obtain the operating parameters of each component on the circuit board;
[0007] The AI simulation module is used to construct an AI dynamic model of the circuit board based on the obtained operating parameters of each component.
[0008] The data analysis module is used to perform power consumption analysis on the circuit board based on the constructed AI dynamic model, and determine power consumption optimization points based on the analysis results.
[0009] The power consumption optimization module is used to optimize and adjust the power consumption of the circuit board at the determined power consumption optimization points.
[0010] Furthermore, the process of acquiring the operating parameters of each component on the circuit board in real time through the data acquisition terminal includes:
[0011] The data acquisition module consists of several data acquisition terminals. Each component laid out on the circuit board is assigned a corresponding data acquisition terminal, and the operating parameters of the corresponding component are acquired in real time through the assigned data acquisition terminal.
[0012] The operating parameters of the components include current, voltage, and temperature.
[0013] The data acquisition module also includes data on the ambient temperature of the environment where the circuit board is located.
[0014] Set a data acquisition cycle T, package the operating parameters of each component acquired within each data acquisition cycle T into a corresponding data packet, and upload the obtained data packet to the AI simulation module.
[0015] Furthermore, the process by which the AI simulation module constructs an AI dynamic model of the circuit board based on the acquired operating parameters of each component includes:
[0016] Based on the circuit board, construct the corresponding circuit board AI basic model. Based on the actual distribution of components on the circuit board surface, construct corresponding virtual components on the constructed circuit board AI basic model, and associate the constructed virtual components with the basic parameters of the components, including rated power, rated voltage, rated current and temperature threshold T0.
[0017] The constructed virtual component is associated with the data acquisition terminal of the corresponding component on the circuit board, and the operating parameters of the component obtained by the data acquisition terminal are uploaded to the virtual component.
[0018] Set corresponding hardware performance data for each virtual component, including thermal conductivity and heat dissipation coefficient;
[0019] The corresponding operating state matrix is obtained based on the operating parameters of each component.
[0020] Furthermore, the process of obtaining the corresponding operating state matrix based on the operating parameters of each component includes:
[0021] Construct a multivariate two-dimensional coordinate system, wherein the multivariate two-dimensional coordinate system is a two-dimensional coordinate system of voltage, current and temperature with respect to time;
[0022] The obtained data packets are parsed to obtain the operating parameters of each component. Based on the obtained operating parameters of the components, the current change curve, voltage change curve and temperature change curve for the corresponding time period of the data acquisition cycle are generated.
[0023] The obtained current change curves, voltage change curves, and temperature change curves are mapped into a multivariate two-dimensional coordinate system;
[0024] Within the multivariate two-dimensional coordinate system, a corresponding ambient temperature curve is generated based on the ambient temperature data of the environment where the circuit board is located.
[0025] The maximum operating power of the components is obtained based on the current change curve and voltage change curve.
[0026] Based on the obtained temperature change curve, obtain the maximum and minimum temperature values of the components;
[0027] Obtain the operating power consumption of the components within the data acquisition cycle corresponding to the data packet;
[0028] Based on the obtained maximum temperature T max Minimum temperature T min Including operating power consumption, the operating status matrix of the component is obtained within the data acquisition cycle;
[0029] Extract the portion of the multivariate two-dimensional coordinate system corresponding to the data acquisition cycle, and associate the extracted portion with the operating state matrix.
[0030] Furthermore, the data analysis module performs power consumption analysis on the circuit board based on the constructed AI dynamic model, and the process of determining power consumption optimization points based on the analysis results includes:
[0031] Based on the maximum and minimum temperature values in the component's operating status matrix, obtain the corresponding temperature difference value;
[0032] The time corresponding to the maximum temperature in the operating state matrix is denoted as t1, and the time corresponding to the minimum temperature is denoted as t2.
[0033] When T max When T0 is less than or equal to 0, it indicates that the component is operating normally.
[0034] When T max >T0≥T min When the maximum and minimum temperatures are obtained, the times corresponding to these values are denoted as t1 and t2, respectively.
[0035] When t1 < t2, it indicates that the component is operating abnormally with fluctuations, so no operation is performed.
[0036] When t1 > t2, the ambient temperature at time t1 is denoted as T. 环 ;
[0037] When T max ≤T 环 When this happens, the first power consumption evaluation coefficient of the component is obtained;
[0038] When T max>T 环 When this happens, the second power consumption evaluation coefficient Rg2 for that component is obtained;
[0039] Set the power consumption evaluation coefficient threshold, denoted as R;
[0040] The obtained first power consumption evaluation coefficient or second power consumption evaluation coefficient is compared with the power consumption evaluation coefficient threshold, and the comparison result is used to determine whether there is any abnormality in the power consumption operation of the component.
[0041] When Rg1 > R, it indicates that the power consumption of the component is abnormal within the corresponding data acquisition period T. The component is then marked as a power optimization point, and the first power optimization instruction is generated.
[0042] When Rg1≤R, it means that the power consumption of the component is normal within the corresponding data acquisition period T;
[0043] When Rg2 > R, it indicates that the power consumption of the component is abnormal within the corresponding data acquisition period T. The component is then marked as a power optimization point, and a second power optimization instruction is generated.
[0044] When Rg2≤R, it means that the power consumption of the component is operating normally within the corresponding data acquisition period T;
[0045] When T0 < T min When the time is right, an early warning message is generated for the component, the warning message is mapped to the corresponding virtual component, and an overheat protection command is generated directly.
[0046] Furthermore, the process by which the power optimization module adjusts the circuit board power consumption at the determined power optimization points includes:
[0047] When the first power optimization command is generated, the ambient temperature of the circuit board's environment is reduced so that it is lower than T during that data acquisition cycle. min ;
[0048] Based on the operating parameters of the component in the next data acquisition cycle after the ambient temperature adjustment is completed, determine whether there is any abnormality in the operating status. If there is no abnormality, it means that the power consumption optimization process of the component is completed. If there is an abnormality, it means that the cause of the power consumption abnormality is the component itself, and the first power optimization instruction is changed to the second power optimization instruction.
[0049] When the second power optimization instruction is generated, it is determined whether the current and voltage values exceed the rated current and rated voltage within the current data acquisition cycle. If so, the rated current and rated voltage are set as the voltage and current limits for the next data acquisition cycle, so that the voltage and current values of the component are not allowed to exceed the set voltage and current limits in the next data acquisition cycle. Based on the operating parameters of the component in the next data acquisition cycle, it is determined whether there is any abnormality in the operating status. If there is no abnormality, it means that the power consumption optimization process of the component is completed. If there is an abnormality, the component is marked as a fault point, and a fault warning information is generated and mapped to the corresponding virtual component for visualization.
[0050] If no current or voltage value exceeds the rated current and rated voltage within the data acquisition period, the component is marked as a fault point, and a fault warning message is generated and mapped to the corresponding virtual component for visualization.
[0051] Compared with the prior art, the beneficial effects of the present invention are:
[0052] By periodically acquiring the operating parameters of each component, a corresponding AI dynamic model is constructed. Then, based on the constructed AI dynamic model, the power consumption of each component is analyzed to determine whether there are any abnormalities in the power consumption of the component. When an abnormality occurs, the cause of the abnormal power consumption of the circuit board is analyzed based on the temperature of the component itself and the ambient temperature of the circuit board. Based on the analysis results, corresponding power consumption optimization strategies or early warning information are executed. This achieves effective monitoring of the power consumption abnormalities of each component on the circuit board, while performing corresponding dynamic power consumption optimization at the abnormal power consumption points, reducing the lifespan loss of components due to power consumption abnormalities and unnecessary power waste. Attached Figure Description
[0053] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.
[0054] Figure 1 This is a schematic diagram of the present invention;
[0055] Figure 2 This is a schematic block diagram showing the connection relationship between the data acquisition module and the circuit board in this invention. Detailed Implementation
[0056] like Figure 1As shown, an AI-based dynamic power consumption optimization control system for circuit boards includes a control center, which is connected to a data acquisition module, an AI simulation module, a data analysis module, and a power consumption optimization module.
[0057] The data acquisition module is used to obtain the operating parameters of each component on the circuit board;
[0058] The AI simulation module is used to construct an AI dynamic model of the circuit board based on the obtained operating parameters of each component.
[0059] The data analysis module is used to perform power consumption analysis on the circuit board based on the constructed AI dynamic model, and determine power consumption optimization points based on the analysis results.
[0060] The power consumption optimization module is used to optimize and adjust the power consumption of the circuit board at the determined power consumption optimization points.
[0061] It needs to be further explained that, in the specific implementation process, such as Figure 2 As shown, the data acquisition module consists of several data acquisition terminals. These terminals acquire the operating parameters of various components on the circuit board in real time. The specific process includes:
[0062] Each component on the circuit board is assigned a corresponding data acquisition terminal, and the operating parameters of the corresponding component are acquired in real time through the assigned data acquisition terminal.
[0063] Each component is labeled as i, where i = 1, 2, ..., n;
[0064] Components are connected by laying corresponding lines on the circuit board, and corresponding component transfer relationships are set according to the connections between components; the component transfer relationships include unidirectional transfer relationships, bidirectional transfer relationships, and no transfer relationships;
[0065] In the specific implementation process, it should be noted that if there are component A and component B, the one-way transmission relationship means that component A and component B are connected, and only component A can send instructions to component B. Therefore, taking component A as the reference, component A and component B have a one-way transmission relationship.
[0066] The bidirectional transmission relationship is that component A is connected to component B, and component A and component B can send instructions to each other.
[0067] The absence of a transmission relationship means that component A and component B are not connected, or component A and component B are connected but only component A can send instructions to component B. In this case, component B is the reference and component B and component A have no transmission relationship.
[0068] The operating parameters of the components include current, voltage, and temperature.
[0069] The operating parameters corresponding to the component with the number i are denoted as Ay. i Uy i And Ty i ;
[0070] The data acquisition module also includes data on the ambient temperature of the environment where the circuit board is located.
[0071] Set a data acquisition cycle T, package the operating parameters of each component acquired within each data acquisition cycle T into a corresponding data packet, and upload the obtained data packet to the AI simulation module.
[0072] The process by which the AI simulation module constructs an AI dynamic model of the circuit board based on the acquired operating parameters of each component includes:
[0073] Based on the circuit board, a corresponding circuit board AI basic model is constructed. Based on the actual distribution of components on the surface of the circuit board, corresponding virtual components are constructed on the constructed circuit board AI basic model. The constructed virtual components are then associated with the basic parameters of the components, including rated power, rated voltage, rated current, and temperature threshold.
[0074] The constructed virtual component is associated with the data acquisition terminal of the corresponding component on the circuit board, and the operating parameters of the component obtained by the data acquisition terminal are uploaded to the virtual component.
[0075] For each virtual component, corresponding hardware performance data is set, including thermal conductivity and heat dissipation coefficient. The thermal conductivity and heat dissipation coefficient of the component labeled i are denoted as Dg, respectively. i and Sg i ;
[0076] The corresponding operating state matrix is obtained based on the operating parameters of each component. The specific process includes:
[0077] Construct a multivariate two-dimensional coordinate system, wherein the multivariate two-dimensional coordinate system is a two-dimensional coordinate system of voltage, current and temperature with respect to time;
[0078] The obtained data packets are parsed to obtain the operating parameters of each component. Based on the obtained operating parameters of the components, the current change curve, voltage change curve and temperature change curve for the corresponding time period of the data acquisition cycle are generated.
[0079] The obtained current change curves, voltage change curves, and temperature change curves are mapped into a multivariate two-dimensional coordinate system;
[0080] Within the multivariate two-dimensional coordinate system, a corresponding ambient temperature curve is generated based on the ambient temperature data of the environment where the circuit board is located.
[0081] The maximum operating power of the component is obtained from the current and voltage change curves, denoted as P. max ;
[0082] Based on the obtained temperature change curve, the maximum and minimum temperatures of the component are obtained and denoted as T. max and T min ;
[0083] The power consumption of the components during the data acquisition period corresponding to the data packet is obtained and denoted as Wh. i ;
[0084] in, ;
[0085] Based on the obtained maximum and minimum temperature values and operating power consumption, the operating state matrix of the component during this data acquisition cycle is obtained, denoted as K. i = (T max T min Wh i ) T ;
[0086] Extract the portion of the multivariate two-dimensional coordinate system corresponding to the data acquisition cycle, and associate the extracted portion with the operating state matrix.
[0087] The data analysis module performs power consumption analysis on the circuit board based on the constructed AI dynamic model, and the process of determining power consumption optimization points based on the analysis results includes:
[0088] Based on the maximum and minimum temperature values in the component's operating status matrix, the corresponding temperature difference value is obtained and denoted as Wc.
[0089] The time corresponding to the maximum temperature in the operating state matrix is denoted as t1, and the time corresponding to the minimum temperature is denoted as t2.
[0090] When T max When T0 is less than or equal to 0, it indicates that the component is operating normally.
[0091] When T max >T0≥T min When the maximum and minimum temperatures are obtained, the times corresponding to these values are denoted as t1 and t2, respectively.
[0092] When t1 < t2, it indicates that the component is operating abnormally with fluctuations, so no operation is performed.
[0093] When t1 > t2, the ambient temperature at time t1 is denoted as T. 环 ;
[0094] When T max ≤T 环 When the power consumption evaluation coefficient of the component is obtained, it is denoted as Rg1.
[0095] in, ;
[0096] When T max >T 环 When the time is right, the second power consumption evaluation coefficient of the component is obtained, denoted as Rg2;
[0097] in, ;
[0098] Where P 额 This is the rated power of the component;
[0099] Set the power consumption evaluation coefficient threshold, denoted as R;
[0100] The obtained first power consumption evaluation coefficient or second power consumption evaluation coefficient is compared with the power consumption evaluation coefficient threshold, and the comparison result is used to determine whether there is any abnormality in the power consumption operation of the component.
[0101] When Rg1 > R, it indicates that the power consumption of the component is abnormal within the corresponding data acquisition period T. The component is then marked as a power optimization point, and the first power optimization instruction is generated.
[0102] When Rg1≤R, it means that the power consumption of the component is normal within the corresponding data acquisition period T;
[0103] When Rg2 > R, it indicates that the power consumption of the component is abnormal within the corresponding data acquisition period T. The component is then marked as a power optimization point, and a second power optimization instruction is generated.
[0104] When Rg2≤R, it means that the power consumption of the component is operating normally within the corresponding data acquisition period T;
[0105] When T0 < T min When the time is right, an early warning message is generated for the component, the warning message is mapped to the corresponding virtual component, and an overheat protection command is generated directly.
[0106] The process by which the power optimization module adjusts the circuit board power consumption at the determined power optimization points includes:
[0107] When the first power optimization command is generated, the ambient temperature of the circuit board's environment is reduced so that it is lower than T during that data acquisition cycle. min It should be further noted that, in the specific implementation process, the ambient temperature of the environment where the circuit board is located is adjusted by using an air conditioning system or a ventilation system.
[0108] Based on the operating parameters of the component in the next data acquisition cycle after the ambient temperature adjustment is completed, determine whether there is any abnormality in the operating status. If there is no abnormality, it means that the power consumption optimization process of the component is completed. If there is an abnormality, it means that the cause of the power consumption abnormality is the component itself, and the first power optimization instruction is changed to the second power optimization instruction.
[0109] When the second power optimization instruction is generated, it is determined whether the current and voltage values exceed the rated current and rated voltage within the current data acquisition cycle. If so, the rated current and rated voltage are set as the voltage and current limits for the next data acquisition cycle, so that the voltage and current values of the component are not allowed to exceed the set voltage and current limits in the next data acquisition cycle. Based on the operating parameters of the component in the next data acquisition cycle, it is determined whether there is any abnormality in the operating status. If there is no abnormality, it means that the power consumption optimization process of the component is completed. If there is an abnormality, the component is marked as a fault point, and a fault warning information is generated and mapped to the corresponding virtual component for visualization.
[0110] If no current or voltage value exceeds the rated current and rated voltage within the data acquisition period, the component is marked as a fault point, and a fault warning message is generated and mapped to the corresponding virtual component for visualization.
[0111] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any modifications or equivalent substitutions made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.
Claims
1. An AI-based circuit board dynamic power consumption optimization control system comprising a regulation center, characterized in that, The regulation center is connected with a data acquisition module, an AI simulation module, a data analysis module and a power consumption optimization module; The data acquisition module is used for acquiring the operating parameters of each component on the circuit board; The AI simulation module is used for constructing an AI dynamic model of the circuit board according to the acquired operating parameters of each component; The data analysis module is used for performing power consumption operation analysis on the circuit board according to the constructed AI dynamic model, and determining a power consumption optimization point according to the analysis result; The power consumption optimization module is used for performing circuit board power consumption optimization adjustment on the determined power consumption optimization point.
2. The AI-based circuit board dynamic power consumption optimization control system of claim 1, wherein, The process of acquiring the operating parameters of each component on the circuit board in real time through the data acquisition terminal includes: The data acquisition module is composed of a plurality of data acquisition terminals, each component on the circuit board is provided with a corresponding data acquisition terminal, and the operating parameters of the corresponding component are acquired in real time through the provided data acquisition terminal; The operating parameters of the component include current value, voltage value and temperature value; The data acquisition module further includes an environmental temperature data acquisition terminal for acquiring the environmental temperature data of the environment where the circuit board is located; A data acquisition cycle T is set, the operating parameters of each component acquired in each data acquisition cycle T are packaged to obtain a corresponding data packet, and the obtained data packet is uploaded to the AI simulation module.
3. The AI-based circuit board dynamic power consumption optimization control system of claim 2, wherein, The process of constructing an AI dynamic model of the circuit board by the AI simulation module according to the acquired operating parameters of each component includes: A corresponding circuit board AI basic model is constructed according to the circuit board, a corresponding virtual component is constructed on the constructed circuit board AI basic model according to the actual distribution of the components on the surface of the circuit board, and the constructed virtual component is associated with the basic parameters of the component, including rated power, rated voltage, rated current and temperature threshold T0; The constructed virtual component is associated with the data acquisition terminal of the corresponding component on the circuit board, and the operating parameters of the component obtained by the data acquisition terminal are uploaded to the virtual component; A corresponding hardware index data is set for each virtual component, including thermal conductivity and heat dissipation coefficient; A corresponding operating state matrix is obtained according to the operating parameters of each component.
4. The AI-based circuit board dynamic power consumption optimization control system of claim 3, wherein, The process of obtaining a corresponding operating state matrix according to the operating parameters of each component includes: A multi-element two-dimensional coordinate system is constructed, which is a two-dimensional coordinate system of voltage, current and temperature with respect to time; The obtained data packet is analyzed to obtain the operating parameters of each component, and the current change curve, voltage change curve and temperature change curve of the time period corresponding to each data acquisition cycle are generated according to the obtained operating parameters of each component; The obtained current change curve, voltage change curve and temperature change curve are mapped into the multi-element two-dimensional coordinate system; A corresponding environmental temperature curve is generated in the multi-element two-dimensional coordinate system according to the environmental temperature data of the environment where the circuit board is located; The maximum operating power of the component is obtained according to the obtained current change curve and voltage change curve; According to the obtained temperature change curve, the maximum temperature and the minimum temperature of the component are obtained; Obtain the running power consumption of the component in the data acquisition period corresponding to the data packet; According to the obtained temperature maximum T max , temperature minimum T min and operating power consumption, an operating state matrix of the component within the data acquisition cycle is obtained; Part of the multi-element two-dimensional coordinate system corresponding to the data acquisition period is intercepted, and the intercepted part is associated with the running state matrix.
5. The AI-based circuit board dynamic power consumption optimization control system of claim 4, wherein, The process of determining the power consumption optimization point by the data analysis module according to the AI dynamic model constructed includes: According to the maximum temperature and the minimum temperature in the running state matrix of the component, the corresponding temperature difference value is obtained; The time corresponding to the maximum temperature in the running state matrix is recorded as t1, and the time corresponding to the minimum temperature is recorded as t2; When T max ≤ To, it indicates that the operating state of the component is normal; When T max ≥ T min 0, then the time points corresponding to the maximum and minimum temperatures are obtained, denoted as t1 and t2, respectively. When t1 < t2, it indicates that the component is in fluctuating operation abnormality, and no operation is performed; When t1 > t2, the environment temperature corresponding to t1 is recorded as T 环 ; When T max ≤ T 环 , a first power consumption evaluation coefficient of the component is obtained. When T max > T 环 , a second power consumption evaluation coefficient Rg2 of the component is obtained. A power consumption evaluation coefficient threshold is set, denoted as R; The obtained first power consumption evaluation coefficient or second power consumption evaluation coefficient is compared with the power consumption evaluation coefficient threshold, and whether the component power consumption operation is abnormal is judged according to the comparison result; When Rg1 > R, it indicates that the power consumption operation of the component in the corresponding data acquisition period T is abnormal, and the component is marked as a power optimization point, and a first power optimization instruction is generated; When Rg1 ≤ R, it indicates that the power consumption operation of the component in the corresponding data acquisition period T is normal; When Rg2 > R, it indicates that the power consumption operation of the component in the corresponding data acquisition period T is abnormal, and the component is marked as a power optimization point, and a second power optimization instruction is generated; When Rg2 ≤ R, it indicates that the power consumption operation of the component in the corresponding data acquisition period T is normal; When T0 < T min When T0 < T min then a pre-warning information is generated for the component, the pre-warning information is mapped into the corresponding virtual component, and an overheat protection instruction is directly generated.
6. The AI-based circuit board dynamic power consumption optimization control system of claim 5, wherein, The process of the power consumption optimization module for adjusting the power consumption of the circuit board at the determined power consumption optimization point includes: When the first power optimization instruction is generated, the ambient temperature of the environment where the circuit board is located is lowered, so that the ambient temperature of the environment where the circuit board is located is lower than T of the data acquisition period min ; According to the running parameters of the component in the next data acquisition period after the adjustment of the environmental temperature is completed, it is judged whether the running state is abnormal, if not, it indicates that the power consumption optimization process of the component is completed, if there is an abnormality, it indicates that the reason for the abnormal power consumption is the component itself, and the first power optimization instruction is changed to the second power optimization instruction; When the second power optimization instruction is generated, it is judged whether the current value and the voltage value exceed the rated current and the rated voltage in the data acquisition period, if yes, the rated current and the rated voltage are set as the voltage limit value and the current limit value of the next data acquisition period, so that the voltage value and the current value of the component in the next data acquisition period are not allowed to exceed the set voltage limit value and current limit value, and according to the running parameters of the component in the next data acquisition period, it is judged whether the running state is abnormal, if not, it indicates that the power consumption optimization process of the component is completed, if there is an abnormality, the component is marked as a fault point, and fault warning information is generated and mapped to the corresponding virtual component for visual display; If the current value and the voltage value do not exceed the rated current and the rated voltage in the data acquisition period, the component is marked as a fault point, and fault warning information is generated and mapped to the corresponding virtual component for visual display.