Full-life-cycle management system for audio and video equipment

Abstract

The invention discloses an audio and video equipment full life cycle management system, which relates to the technical field of audio and video equipment management and comprises a dynamic load monitoring module, a dynamic operation information acquisition and analysis module, an overload risk assessment module, a risk response and protection strategy module and an operation state monitoring and optimization module. And the overload risk assessment module constructs a power amplifier equipment overload risk assessment model for the generated output power change index and the load accumulation influence index, generates an overload risk assessment coefficient, analyzes the overload risk assessment coefficient after the overload risk assessment coefficient is generated, assesses the risk level of equipment overload in the process of adjusting the output power of the power amplifier equipment, and provides a power amplifier equipment overload risk assessment result. And according to the evaluation result, the equipment state is divided into three grades of low risk, medium risk and high risk. The overload risk problem possibly occurring in the process of adjusting the output power of the power amplifier equipment is solved, safe and stable operation of the equipment is ensured through dynamic monitoring and accurate evaluation, and the reliability and efficiency of the system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of audio and video equipment management, and in particular to a full life cycle management system for audio and video equipment. Background Art

[0002] Audio and video equipment refers to hardware devices used for audio and video capture, processing, transmission, and playback. They are widely used in broadcasting, conferencing systems, education and training, and entertainment and leisure. This type of equipment includes, but is not limited to, cameras, microphones, speakers, monitors, video switchers, and encoders. The full lifecycle of audio and video equipment encompasses all stages of its lifecycle, from procurement, installation, commissioning, operation, maintenance, upgrades, to final retirement and retirement. Lifecycle management involves systematic, digitalized tracking and optimized management of all equipment activities throughout its lifecycle, ensuring efficient and stable operation at every stage, reducing failure rates, extending service life, and improving return on investment. Lifecycle management of audio and video equipment is essential because these devices typically involve high investment costs, and over time, their performance may decline due to failures, aging, or technological upgrades. Lifecycle management allows for real-time monitoring of equipment health, enabling the optimal scheduling of maintenance and upgrades, minimizing losses caused by unexpected failures, and ensuring optimal equipment operation at every stage of use. This improves equipment utilization, reduces operating costs, and ensures overall system stability and efficiency.

[0003] Existing audio and video equipment lifecycle management technologies typically utilize integrated software platforms to manage the entire equipment lifecycle, from procurement to retirement. These systems typically include modules for equipment procurement management, installation and commissioning, operational monitoring, maintenance, upgrades, and retirement management. During the equipment procurement phase, the management system records information such as the equipment model, specifications, manufacturer, and procurement date, and tracks equipment inventory and usage through inventory and asset management modules. During the installation and commissioning phase, the system records the equipment's installation location, configuration, and initial settings, and uses automated testing tools to perform debugging to ensure optimal equipment operation. During the equipment's operation phase, the management system integrates with the device's own monitoring system to collect real-time operational data, such as power consumption, temperature, and fault logs, enabling remote monitoring and fault warning. During the maintenance phase, the system generates maintenance plans based on equipment usage frequency and fault history, reminding personnel to perform scheduled inspections and recording all maintenance operations and parts replacement information. During equipment upgrades, the system records technical updates and software version changes, tracking hardware replacements or upgrades to ensure the equipment maintains the latest performance levels. Finally, when equipment reaches the end of its service life or malfunctions beyond repair, the system uses the scrap management module to handle its retirement and record its disposal, including recycling, disassembly, and reuse. Overall, existing audio and video equipment lifecycle management technology, through integrated data management and intelligent monitoring, helps companies achieve efficient equipment management, preventive maintenance, and cost optimization.

[0004] The existing technology has the following deficiencies:

[0005] In a concert hall's sound system, when the power amplifier equipment operates at high load for a long period of time and needs to dynamically adjust its output power according to the performance content, there may be a risk of equipment overload. This is because the load status of the power amplifier equipment is not only affected by the current output power, but is also closely related to the frequency of load changes and the cumulative operating time of the equipment. However, existing full-life cycle management technologies for audio and video equipment mainly judge the equipment status by monitoring the absolute power output of the power amplifier equipment, but fail to comprehensively consider the combined impact of load changes and operating time on the risk of equipment overload. As a result, the equipment fails to issue a timely warning when the load approaches the limit. If this problem is not identified in time, it may cause audio signal interruption or damage to the power amplifier equipment during the performance, seriously affecting the performance effect. At the same time, the overload of the power amplifier equipment may also have a collateral impact on other audio equipment, increasing the overall maintenance cost of the system, thereby shortening the equipment's service life and increasing operating expenses.

[0006] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0007] The purpose of the present invention is to provide a full life cycle management system for audio and video equipment to solve the problems in the above-mentioned background technology.

[0008] To achieve the above objectives, the present invention provides the following technical solutions: a full life cycle management system for audio and video equipment, comprising a dynamic load monitoring module, a dynamic operation information collection and analysis module, an overload risk assessment module, a risk response and protection strategy module, and an operation status monitoring and optimization module;

[0009] The dynamic load monitoring module performs real-time detection on the power amplifiers in the concert hall and establishes a dynamic load monitoring framework for the power amplifiers when it detects that the output power of the power amplifiers needs to be adjusted.

[0010] The dynamic operation information collection and analysis module acquires the dynamic operation information generated by the power amplifier device during the output power adjustment process in real time, analyzes it after acquisition, and generates the output power variation index and load cumulative impact index of the power amplifier device;

[0011] The overload risk assessment module constructs an overload risk assessment model for the power amplifier equipment based on the generated output power variation index and load cumulative impact index, generates an overload risk assessment coefficient, and analyzes it after generation to assess the risk level of equipment overload during the output power adjustment process of the power amplifier equipment. Based on the assessment results, the equipment status is classified into three levels: low risk, medium risk, and high risk.

[0012] The risk response and protection strategy module takes corresponding measures for power amplifier equipment in three risk categories: low risk, medium risk, and high risk.

[0013] The operation status monitoring and optimization module continuously monitors the operation status of the power amplifier equipment and dynamically adjusts the operating parameters and protection strategies based on the real-time monitoring results to ensure the safety of the power amplifier equipment operation and the stability of the system.

[0014] Preferably, in the dynamic operation information collection and analysis module, the dynamic operation information generated by the power amplifier device during the output power adjustment process is obtained in real time, and preprocessed after acquisition; the output power fluctuation information and load change history information in the preprocessed dynamic operation information are extracted, and analyzed after extraction to generate the output power variation index and load cumulative impact index of the power amplifier device respectively.

[0015] Preferably, the logic for obtaining the output power variation index of the power amplifier device is as follows:

[0016] Extract the output power fluctuation information from the pre-processed dynamic operation information, including the output power change of the power amplifier device at different times during a period of time during the output power adjustment process, the number of power adjustments, the duration of power adjustment, and the average output power of the power amplifier device during the period, the average frequency of power adjustment, and the average power change duration, and calibrate them as ΔP respectively. m 、fA m TA m , PP, fP and TP, ΔP m Indicates the change in output power of the power amplifier device at time m during the process of adjusting the output power, fA m Indicates the number of power adjustments at time m during the process of adjusting the output power of the power amplifier. m represents the duration of power adjustment at time m during a period of time when the power amplifier device is adjusting its output power. PP represents the average output power of the power amplifier device during this period. fP represents the average frequency of power adjustment of the power amplifier device during this period. TP represents the average duration of power change of the power amplifier device during this period. m = 1, 2, 3, ..., g, where g is a positive integer.

[0017] Calculate the output power variation index of the power amplifier equipment. The specific calculation formula is as follows:

[0018]

[0019] Where PVI is the output power variation index of the power amplifier equipment.

[0020] Preferably, the logic for obtaining the load cumulative impact index of the power amplifier device is as follows:

[0021] Extract the load change history information from the pre-processed dynamic operation information, including the amplitude of the equipment load change at different times during a period of time when the power amplifier equipment adjusts the output power, the number of times the equipment load changes, and the standard deviation of the power amplifier equipment load during the period, and calibrate them as FD n 、FZB n and σFZ,FD n Indicates the amplitude of the equipment load change at time n during a period of time when the power amplifier equipment adjusts its output power. n represents the number of times the load of the power amplifier device changes at time n within a period of time during the process of adjusting the output power of the power amplifier device. σFZ represents the standard deviation of the load of the power amplifier device within this period of time. n = 1, 2, 3, ..., u, where u is a positive integer.

[0022] Calculate the load cumulative impact index of the power amplifier equipment. The specific calculation formula is as follows:

[0023]

[0024] Where LAII is the load cumulative impact index of the power amplifier equipment.

[0025] Preferably, in the overload risk assessment module, an overload risk assessment model for the power amplifier device is constructed based on the generated output power variation index and the load cumulative impact index to generate an overload risk assessment coefficient, specifically including the following steps:

[0026] Collect several output power variation indexes, load cumulative impact indexes and corresponding overload risk assessment coefficients generated in the past period of time, and calibrate them as PVI x LAII x and ORAC x , x represents the number of output power variation indexes, load cumulative impact indexes, and corresponding overload risk assessment coefficients generated in the past period, x = 3, 4, 5, ..., d, d is a positive integer, and the data collected in the past period are formed into a historical data set;

[0027] The multivariate regression model is selected as the power amplifier equipment overload risk assessment model, and is trained through historical data sets to determine the value of the regression coefficient according to the formula:

[0028] ORAC x =β0+β1*PVI x +β2*LAII x

[0029] Where β0, β1 and β2 are regression coefficients;

[0030] By minimizing the error between the predicted value and the actual value, the regression coefficient is optimized and the values ​​of the regression coefficients β0, β1 and β2 are finally determined;

[0031] The final regression coefficient is used to input the real-time generated output power variation index PVI and load cumulative impact index LAII into the constructed power amplifier equipment overload risk assessment model to generate the overload risk assessment coefficient ORAC in real time.

[0032] Preferably, the generated overload risk assessment coefficient ORAC is compared with a preset overload risk assessment coefficient threshold interval [ORAC min , ORAC max ] to compare, and based on the comparison results, evaluate the risk level of equipment overload during the output power adjustment process of the power amplifier equipment. Based on the evaluation results, the equipment status is divided into three levels: low risk, medium risk and high risk. The specific comparison analysis is as follows:

[0033] If ORAC <ORAC min The risk level of equipment overload during output power adjustment of the power amplifier equipment is low;

[0034] If ORAC min ≤ORAC≤ORAC max The risk level of equipment overload during output power adjustment of the power amplifier equipment is medium risk level;

[0035] If ORAC>ORAC max The risk level of equipment overload during the output power adjustment process of the power amplifier equipment is high.

[0036] Preferably, in the risk response and protection strategy module, corresponding measures are taken for power amplifier devices in the three risk levels of low risk, medium risk and high risk, respectively, according to the classification results of the risk levels of the power amplifier devices. Specifically,

[0037] For amplifiers classified as low risk, the following measures should be taken: continue to operate the amplifiers normally, monitor load changes in real time, and ensure that the equipment is adjusted within the normal operating range. Regularly assess the health status and working environment of the equipment to ensure that risks in long-term operation are controllable.

[0038] For power amplifiers classified as medium risk, the following measures are implemented: While operating normally, adjust the output power of the equipment to reduce load fluctuations and increase the safety protection threshold of the equipment; strengthen the tracking of the operating parameters of the power amplifier equipment through a real-time monitoring system, and set up an alarm mechanism. When the risk level continues to rise, quickly initiate emergency response procedures, such as increasing the operation of the cooling system or suspending high-load tasks of the equipment to ensure that the equipment is not overloaded;

[0039] For power amplifier equipment classified as high-risk, the specific measures taken are: immediately start the overload protection program, limit the output power of the equipment, reduce its workload, and conduct emergency inspections and shutdowns of the equipment to prevent damage due to overload; report the risk status of the equipment to the system management platform, and automatically adjust system resources to optimize the load distribution of the power amplifier equipment, promptly repair faults and restore normal operation.

[0040] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0041] 1. The present invention introduces a dynamic load monitoring module and a dynamic operation information collection and analysis module, which can comprehensively capture key parameters such as load changes, load change frequency, and load change amplitude of the power amplifier equipment during the process of adjusting the output power. These factors play a vital role in the overload risk assessment of the equipment. Compared with the existing technology that only focuses on the absolute value of power output, the present invention can more accurately determine whether the equipment is overloaded through multi-dimensional monitoring and analysis of the load, thereby effectively avoiding equipment failure or damage caused by overload. Therefore, the stability and safety of the power amplifier equipment have been significantly improved. Especially in high-load or complex performance environments, the system can take timely measures to ensure the long-term stable operation of the equipment.

[0042] 2. The overload risk assessment module proposed in the present invention can accurately assess the overload risk that the power amplifier equipment may face during the output power adjustment process by calculating and analyzing the output power variation index and the load cumulative impact index. Through the multivariate regression model, the equipment overload risk coefficient is calculated in combination with historical data, providing a dynamic and accurate assessment system for the overload risk of the equipment. According to the assessment results, the system can divide the risk level of the equipment into three categories: low risk, medium risk and high risk, and take differentiated response measures according to different levels. This risk assessment method based on real-time data and precise calculations enables the operating status of the equipment to be responded to in a timely and reasonable manner, and minimizes system crashes or abnormal operations caused by equipment overload.

[0043] 3. The present invention can dynamically adjust the operating parameters and protection strategies of the power amplifier equipment to ensure the overall stability of the system. For example, for low-risk equipment, normal operation continues and regular health assessments are conducted; for medium-risk equipment, prevention is carried out by adjusting the output power, strengthening monitoring and pre-setting alarm mechanisms; and for high-risk equipment, failures are prevented by immediately starting the overload protection program and limiting the output power. Through this flexible adjustment mechanism, the system can respond to environmental changes and equipment status in real time to ensure the stability and safety of the system. In addition, continuous operating status monitoring and optimization further improve the reliability of the power amplifier equipment, especially in complex environments such as performances or long-term operations. The system can make necessary adjustments based on real-time feedback, thereby improving the system's resilience and overall performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction to the drawings required for use in the embodiments will be given below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0045] Figure 1The present invention is a module diagram of a full life cycle management system for audio and video equipment. DETAILED DESCRIPTION

[0046] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of this disclosure will be thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art.

[0047] The present invention provides Figure 1 The full life cycle management system of audio and video equipment shown in the figure includes a dynamic load monitoring module, a dynamic operation information collection and analysis module, an overload risk assessment module, a risk response and protection strategy module, and an operation status monitoring and optimization module;

[0048] The dynamic load monitoring module performs real-time detection on the power amplifiers in the concert hall and establishes a dynamic load monitoring framework for the power amplifiers when it detects that the output power of the power amplifiers needs to be adjusted.

[0049] By deploying a high-precision sensor network and data acquisition modules, real-time monitoring of the power amplifiers in a concert hall can be achieved. These sensors can be installed at key locations on the amplifiers, such as output ports, heat sinks, and circuit load nodes, collecting real-time operating parameters, including output power, load impedance, operating temperature, and load fluctuation frequency. The data acquisition module transmits this real-time data in digitized form to a backend data processing system. The backend system uses software algorithms to analyze the collected data in real time, extracting the current operating status and load trends of the amplifiers. Furthermore, the system utilizes dynamic data update technology to instantly calculate key indicators each time new data is collected, ensuring real-time and accurate monitoring. This approach ensures continuous monitoring of the amplifier's status, providing a data foundation for subsequent risk analysis.

[0050] The establishment of a dynamic load monitoring framework can be achieved through intelligent judgment algorithms and dynamic load modeling technology. Specifically, the system analyzes the data collected in real time. When it detects that the output power variation index of the power amplifier device (such as the output power exceeds the set safety threshold, the load fluctuation frequency is abnormal, or the device temperature is too high) exceeds the set threshold, it triggers the start of the dynamic load monitoring framework. The monitoring framework automatically generates a load dynamic curve through software, records the load change trend, and compares and analyzes it with historical operating data to establish a load characteristic model. At the same time, the framework has a built-in response mechanism to dynamically adjust the monitoring strategy based on real-time data, such as increasing the data sampling frequency and increasing the monitoring weight of key indicators. The monitoring framework also displays the load status of the device in real time through a visual interface, providing administrators with a clear basis for decision-making. This method can not only capture the dynamic load status of the power amplifier device in a timely manner, but also provide a real-time updated load model for subsequent risk assessment.

[0051] This design aims to comprehensively address a core issue with existing full-lifecycle management technologies for audio and video equipment: the inability to comprehensively account for the combined impact of amplifier load frequency and cumulative operating time on overload risk. In high-load concert hall scenarios, the load status of amplifiers is dynamically influenced by multiple factors. Traditional methods that rely on absolute power output to determine device status struggle to capture these complex dynamic characteristics. By monitoring the operating status of amplifiers in real time and establishing a dynamic load monitoring framework, we can accurately monitor both instantaneous changes and long-term trends in device load, providing critical data support for risk assessment. Furthermore, this dynamic load monitoring framework can issue timely warnings when loads approach limits and, through modeling, provide dynamic control strategies to effectively avoid failures caused by equipment overload, thereby ensuring performance quality and reducing system maintenance costs. This design, based on real-time detection and dynamic monitoring, not only resolves technical issues but also provides a more efficient and secure solution for equipment management.

[0052] The dynamic operation information collection and analysis module acquires the dynamic operation information generated by the power amplifier device during the output power adjustment process in real time, analyzes it after acquisition, and generates the output power variation index and load cumulative impact index of the power amplifier device;

[0053] In this embodiment, in the dynamic operation information collection and analysis module, the dynamic operation information generated by the power amplifier device during the output power adjustment process is obtained in real time and preprocessed after acquisition; the output power fluctuation information and load change history information in the preprocessed dynamic operation information are extracted, and analyzed after extraction to generate the output power variation index and load cumulative impact index of the power amplifier device, respectively.

[0054] To obtain real-time dynamic operating information generated by power amplifiers during output power adjustment, the following methods can be used. First, sensors and monitoring interfaces can be installed on key components of the power amplifier (such as the power amplifier, temperature control system, and power supply module) to collect data in real time. These sensors can detect and transmit important indicators such as the power output, temperature changes, and voltage and current fluctuations of the power amplifier. Using embedded software or a real-time operating system (RTOS), a data acquisition module can be deployed on the power amplifier device, transmitting this information in real time to a central data acquisition system via wireless or wired networks (such as Wi-Fi, Ethernet, or Bluetooth). To ensure the real-time and accurate data, device status can be regularly polled or event-triggered to automatically collect data when the device status changes. These methods ensure timely acquisition of dynamic operating information from the power amplifier device and support subsequent analysis.

[0055] Data preprocessing is performed to improve the accuracy and efficiency of subsequent analysis and ensure that valuable signals are extracted from the collected dynamic operating information. Key objectives of data preprocessing are noise reduction, data format standardization, missing value filling, and outlier removal. For example, the power output fluctuations and current variations of power amplifiers may be affected by environmental factors or device noise. Therefore, filtering algorithms (such as low-pass filtering, high-pass filtering, or Kalman filtering) are needed to remove high-frequency noise. Indicators such as temperature and current may exhibit periodic fluctuations or sudden abnormal data, necessitating data smoothing using statistical methods (such as mean removal and standard deviation normalization). Furthermore, data collected at different frequencies and units requires standardization, such as converting all values ​​into a unified time window format or units. This allows subsequent analytical models to process and integrate dynamic information from different sources. These preprocessing steps ensure data quality and consistency, thereby improving the reliability of subsequent analytical results and laying a solid foundation for generating load indices and overload risk factors for power amplifiers.

[0056] To extract output power fluctuation information and load change history from preprocessed dynamic operating information, a time series analysis approach can be employed. First, output power fluctuation information can be extracted by calculating the power change rate of the PA device within a specific time window. This can be achieved by performing sliding window processing on the power output data (for example, selecting a time window of one second or one minute) and calculating the power fluctuation amplitude or rate of change within each window to obtain the dynamic characteristics of the power fluctuation. Large fluctuations in the PA device's power output within the time window indicate strong fluctuations; small fluctuations indicate weak fluctuations. Load change history information can be extracted by analyzing load change trends during device operation. Specifically, the cumulative load change or frequency of fluctuations can be extracted based on the historical time series of changes in the device's load data (such as current, voltage, and power). The cumulative load impact can be determined by summing or weighted averaging the historical data. By extracting these fluctuations and change trends, the PA device's load history can be clearly identified, providing the necessary input data for subsequent overload risk assessment. This process can be automated at the software level through data analysis and statistical methods, and the required dynamic indicators can be output in real time.

[0057] In this embodiment, the logic for obtaining the output power variation index of the power amplifier device is as follows:

[0058] Extract the output power fluctuation information from the pre-processed dynamic operation information, including the output power change of the power amplifier device at different times during a period of time during the output power adjustment process, the number of power adjustments, the duration of power adjustment, and the average output power of the power amplifier device during the period, the average frequency of power adjustment, and the average power change duration, and calibrate them as ΔP respectively. m 、fA m TA m , PP, fP and TP, ΔP m Indicates the change in output power of the power amplifier device at time m during the process of adjusting the output power, fA m Indicates the number of power adjustments at time m during the process of adjusting the output power of the power amplifier. m represents the duration of power adjustment at time m during a period of time when the power amplifier device is adjusting its output power. PP represents the average output power of the power amplifier device during this period. fP represents the average frequency of power adjustment of the power amplifier device during this period. TP represents the average duration of power change of the power amplifier device during this period. m = 1, 2, 3, ..., g, where g is a positive integer.

[0059] A real-time monitoring system integrated into the power amplifier can be used to obtain quantitative data related to the output power adjustment process of a power amplifier (PA), such as the output power change, the number of power adjustments, the duration of power adjustments, the average output power, the average frequency of power adjustments, and the average duration of power changes. First, by deploying sensors and data acquisition systems (such as power meters, load sensors, and time recording modules) within the PA, fluctuations in the PA's output power can be monitored in real time. The sensors record not only the power value at each moment, but also the frequency and duration of the adjustment process. For example, a power sensor can periodically record the output power at each moment, thereby determining the change in each power adjustment. By detecting the intervals between power changes, the number and duration of power adjustments can be calculated. By recording the start and end times of each adjustment, the average frequency and duration of each adjustment can be further analyzed. Furthermore, the collected power data can be uploaded to a central monitoring system in real time via the data acquisition system for preprocessing and analysis. The analysis process includes calculating the average output power during the entire adjustment process (i.e., the arithmetic mean of all power values ​​within the time period), the average frequency of power adjustments (i.e., the average number of adjustments per unit time), and the duration of power changes (i.e., the average time interval between each adjustment). All of this data extraction and calculation can be automated through the built-in software analysis module, enabling real-time monitoring and evaluation of the power amplifier's operating status.

[0060] Calculate the output power variation index of the power amplifier equipment. The specific calculation formula is as follows:

[0061]

[0062] Where PVI is the output power variation index of the power amplifier equipment.

[0063] The formula for calculating the output power variation index (PVI) of a power amplifier device aims to quantify the load fluctuation risk faced by the power amplifier device during the output power adjustment process by comprehensively considering multiple factors such as power change amount, adjustment frequency and duration. Specifically, It measures the relative amplitude of each power change relative to the average power, reflecting the intensity of load fluctuation; The number of power adjustments is compared with the average frequency, which indicates the frequency of equipment response. Frequent adjustments may indicate higher load fluctuations. The PVI represents the ratio of the duration of each adjustment to the average duration, indicating the degree to which the adjustment duration affects the stability of the device. By comprehensively calculating these three factors, PVI can comprehensively assess the overload risk of power amplifier equipment during power adjustment, providing a scientific basis for subsequent risk assessment and protection strategies.

[0064] The magnitude of the power amplifier's output power variation index (PVI) is closely related to the level of overload risk that occurs when adjusting the output power of the amplifier. A larger PVI value indicates that the amplifier experienced larger power fluctuations, more frequent power adjustments, and longer periods of continuous adjustment when adjusting its output power. These factors may increase the load and overload risk of the equipment. Conversely, a smaller PVI value indicates that the amplifier's power fluctuations are relatively stable, with lower adjustment frequency and duration, and smaller load changes, thereby reducing the possibility of overload. Therefore, by analyzing the PVI value, the overload risk level of the amplifier can be effectively determined. Generally, equipment with higher PVI values ​​may be classified as high-risk, indicating the need for enhanced monitoring and protective measures; while equipment with lower PVI values ​​is at a low-risk level, indicating stable equipment operation.

[0065] In this embodiment, the logic for obtaining the load cumulative impact index of the power amplifier device is as follows:

[0066] Extract the load change history information from the pre-processed dynamic operation information, including the amplitude of the equipment load change at different times during a period of time when the power amplifier equipment adjusts the output power, the number of times the equipment load changes, and the standard deviation of the power amplifier equipment load during the period, and calibrate them as FD n 、FZB n and σFZ,FD n Indicates the amplitude of the equipment load change at time n during a period of time when the power amplifier equipment adjusts its output power. n represents the number of times the load of the power amplifier device changes at time n within a period of time during the process of adjusting the output power of the power amplifier device. σFZ represents the standard deviation of the load of the power amplifier device within this period of time. n = 1, 2, 3, ..., u, where u is a positive integer.

[0067] To obtain the magnitude of load changes, the number of load changes, and the standard deviation of the load at different moments during the power amplifier's output power adjustment process, continuous monitoring and analysis of the amplifier's real-time operating data can be performed, using sensors and data acquisition systems for automated data collection. Specifically, power sensors and current sensors can be installed at the amplifier's input and output terminals to record load data at different points in time in real time. These sensors monitor key parameters such as the amplifier's power output, input current, and voltage, thereby capturing dynamic load changes. The data acquisition system can transmit this real-time data to monitoring software for high-frequency acquisition and storage, ensuring data accuracy and timeliness.

[0068] After acquiring this real-time data, data preprocessing can be used to clean and remove noise to ensure data accuracy. Next, based on this processed data, the magnitude of load variation can be calculated—that is, the magnitude of the load change on the amplifier at different times, typically measured by the change in power output. The number of load changes refers to the number of significant changes in the amplifier's load during the monitoring period. A threshold can be set to determine when a load change is considered. Finally, the standard deviation of the load can be calculated using statistical methods to analyze the fluctuations in load variation over time, thereby measuring the operational stability of the amplifier. The collection and analysis of this data can be fully automated using a software system. The data acquisition system continuously monitors the amplifier and uploads the relevant data in real time to a backend processing platform. The algorithms and mathematical models within the platform automatically calculate and analyze the corresponding load variation indicators, providing a basis for subsequent risk assessment.

[0069] Calculate the load cumulative impact index of the power amplifier equipment. The specific calculation formula is as follows:

[0070]

[0071] Where LAII is the load cumulative impact index of the power amplifier equipment.

[0072] The reason for using this formula when calculating the load cumulative impact index LAII of power amplifier equipment is that it comprehensively considers the impact of the amplitude, frequency and stability of load changes on the risk of equipment overload. Specifically, the formula accurately reflects the dynamic load behavior of the power amplifier equipment during the output power adjustment process and its impact on the long-term operation of the equipment through the combination of three key factors. First, the amplitude of the load change FD n It represents the power fluctuation of the power amplifier device at each moment, reflecting the intensity of the instantaneous load pressure, so its square (FD n ) 2 It will have a greater weighted impact on load fluctuations, thereby increasing the negative impact of large load changes on the health of the equipment. nRepresents the frequency of changes in the equipment load. Frequent load fluctuations often cause the equipment to be under greater pressure in a shorter period of time, thus posing a potential threat to the stability of the equipment. This parameter is directly multiplied by the amplitude of the load change in the formula, maximizing the contribution of frequent and drastic load changes to the load cumulative impact index. Finally, the standard deviation of the load fluctuation, σFZ, reflects the stability of the equipment load. It compares the stability of the load change with the amplitude of the fluctuation. The higher the standard deviation of the load fluctuation, the more stable the operation of the power amplifier equipment, and vice versa, it may lead to a higher risk of overload. Therefore, by using the standard deviation of the load fluctuation as the denominator, the index value under stable operation can be reduced, highlighting the harm of unstable load to the equipment. Combining these three data, the formula Some systems incorporate comparative adjustments, indicating that the risk of equipment load increases nonlinearly with increasing load variation and frequency ratio. This calculation method ensures that the risk of overload is sufficiently overestimated when the load changes significantly, thus facilitating early warning. Through these complex calculation steps, LAII can more accurately assess the potential overload risk of power amplifiers during dynamic power adjustments, providing a reliable basis for subsequent risk assessment and management.

[0073] The cumulative load impact index (LAII) of a power amplifier device is directly and closely linked to the risk level of overload during output power adjustment. The LAII reflects the intensity and frequency of load fluctuations experienced by the amplifier over a period of time, as well as its operational stability. Specifically, a higher LAII value indicates that the device has experienced significant load fluctuations and frequent load changes during output power adjustment, significantly increasing the risk of overload. Therefore, a high LAII value indicates that the device has been subjected to significant load fluctuations for an extended period, resulting in poor load stability and a higher likelihood of overload, indicating a medium or high risk level. Conversely, a lower LAII value indicates that the load fluctuations are minimal, the operation is relatively stable, and the magnitude and frequency of load changes are low, resulting in a lower risk of overload and a low risk level. By using the LAII as an assessment criterion and combining it with pre-set thresholds, a device's overload risk can be effectively categorized. When the LAII value exceeds a certain threshold, a risk warning is triggered, prompting timely implementation of appropriate protective measures to prevent overload failure or damage to the amplifier. Therefore, the higher the LAII value, the higher the overload risk level; and the lower the LAII value, the lower the overload risk level. This provides a scientific basis and management basis for the safe operation of power amplifier equipment.

[0074] The overload risk assessment module constructs an overload risk assessment model for the power amplifier equipment based on the generated output power variation index and load cumulative impact index, generates an overload risk assessment coefficient, and analyzes it after generation to assess the risk level of equipment overload during the output power adjustment process of the power amplifier equipment. Based on the assessment results, the equipment status is classified into three levels: low risk, medium risk, and high risk.

[0075] In this embodiment, in the overload risk assessment module, an overload risk assessment model for the power amplifier device is constructed based on the generated output power variation index and load cumulative impact index to generate an overload risk assessment coefficient, specifically including the following steps:

[0076] Collect several output power variation indexes, load cumulative impact indexes and corresponding overload risk assessment coefficients generated in the past period of time, and calibrate them as PVI x LAII x and ORAC x , x represents the number of output power variation indexes, load cumulative impact indexes, and corresponding overload risk assessment coefficients generated in the past period, x = 3, 4, 5, ..., d, d is a positive integer, and the data collected in the past period are formed into a historical data set;

[0077] To collect several output power variation indices, load cumulative impact indices, and corresponding overload risk assessment coefficients generated over a period of time, the following approach can be used: First, the system monitors the operating data of the power amplifier equipment in real time, regularly collecting power output and load data. This data is uploaded to the monitoring platform via the equipment's sensors or control system. The output power variation index is obtained by recording power output changes and calculating the amplitude and frequency of the changes, while the load cumulative impact index is generated by monitoring the amplitude, frequency, and standard deviation of load changes. All collected data is stored in a database and sorted by timestamp to form a complete historical data set. The system then uses this historical data to generate corresponding overload risk assessment coefficients through a regression model, ensuring that the real-time PVI, LAII, and ORAC values ​​reflect the current risk status of the equipment.

[0078] The reason for limiting the value range of x to positive integers greater than or equal to 3 is based on the basic principles of regression analysis. In a multiple regression model, there is only one target equation, namely the overload risk assessment coefficient, and there are three regression coefficients that need to be calculated: β0, β1, and β2. In order to accurately calculate these regression coefficients, a set of equations must be formed using at least three different data points. Specifically, at least three different output power variation indices, load cumulative impact indices, and corresponding overload risk assessment coefficients are required to solve the values ​​of these three regression coefficients using methods such as the least squares method. If x is less than 3, the number of equations is insufficient to effectively estimate the regression coefficients, which affects the accuracy of the evaluation model.

[0079] The multivariate regression model is selected as the power amplifier equipment overload risk assessment model, and is trained through historical data sets to determine the value of the regression coefficient according to the formula:

[0080] ORAC x =β0+β1*PVI x +β2*LAII x

[0081] Where β0, β1 and β2 are regression coefficients;

[0082] The multiple regression model is a statistical method used to study the relationship between a dependent variable (i.e., target variable) and multiple independent variables (i.e., input variables). By constructing a regression equation, the value of the dependent variable can be predicted, and the best-fitting model can be found by adjusting the coefficients of the independent variables. In the overload risk assessment of power amplifier equipment, the reason for choosing a multiple regression model is that the overload risk of power amplifier equipment is affected by multiple factors, such as the output power variation index and the load cumulative impact index. Through the multiple regression model, the influence of these two factors can be combined to accurately assess the overload risk of power amplifier equipment. In this regression model, β0, β1, and β2 are regression coefficients, representing the constant term in the model and the weight of each independent variable, respectively. Specifically, β0 is the intercept term of the model, which means that when PVI x and LAII x When both are zero, the value of the overload risk assessment coefficient; β1 is the output power variation index PVI x The regression coefficient of PVI x The intensity of the impact on overload risk; β2 is the load cumulative impact index LAII x The regression coefficient reflects LAII x Contribution to overload risk. By training a regression model using historical data sets, the values ​​of these regression coefficients can be accurately determined, thereby generating a model that can accurately predict the overload risk of power amplifier equipment.

[0083] By minimizing the error between the predicted value and the actual value, the regression coefficient is optimized and the values ​​of the regression coefficients β0, β1 and β2 are finally determined;

[0084] Optimizing the regression coefficient by minimizing the error between the predicted value and the actual value is a key step in regression analysis, aiming to make the prediction results of the regression model as close as possible to the actual observed data. This process is usually implemented using the least squares method, which calculates the residual (i.e., error) between the model prediction value and the actual value and minimizes the sum of the squares of all residuals to find the optimal regression coefficient. Specifically, by training the historical data set, we can derive each regression coefficient β0,

[0085] The initial values ​​of β1 and β2 are determined, and these coefficients are repeatedly adjusted to minimize the error between the overload risk assessment coefficients predicted by the model and the actual observed values. The goal of optimization is to ensure that the regression model has high accuracy and reliability in practical applications, so that the overload risk level of power amplifier equipment can be correctly assessed in different situations to avoid equipment failure or system failure.

[0086] The final regression coefficient is used to input the real-time generated output power variation index PVI and load cumulative impact index LAII into the constructed power amplifier equipment overload risk assessment model to generate the overload risk assessment coefficient ORAC in real time.

[0087] In this embodiment, the generated overload risk assessment coefficient ORAC is compared with the preset overload risk assessment coefficient threshold interval [ORAC min , ORAC max ] to compare, and based on the comparison results, evaluate the risk level of equipment overload during the output power adjustment process of the power amplifier equipment. Based on the evaluation results, the equipment status is divided into three levels: low risk, medium risk and high risk. The specific comparison analysis is as follows:

[0088] If ORAC <ORAC min The risk level of equipment overload during output power adjustment of the power amplifier equipment is low;

[0089] This condition indicates that the amplifier's current load is well below its load limit while adjusting its output power, and the device is operating within a safe range. In this case, the amplifier is unlikely to be overloaded, and the system is stable and reliable. Current operating parameters can be maintained without requiring additional protective measures or adjustments. This condition is crucial to the overall stability of the performance or system, helping to extend the equipment's lifespan and reduce maintenance costs. Maintaining a low-risk state over a long period of time reduces the likelihood of unexpected failures, ensuring a smooth performance.

[0090] If ORAC min ≤ORAC≤ORAC max The risk level of equipment overload during output power adjustment of the power amplifier equipment is medium risk level;

[0091] This situation means that while adjusting the output power of the amplifier, the load has approached its safe operating range, posing a certain risk of overload. While the equipment has not yet exceeded its design limits, its stability begins to decline as the load fluctuations become more frequent and larger. A medium-risk status indicates that operators need to monitor the equipment's operating status and may need to implement protective measures, such as adjusting the output power fluctuation range and optimizing load distribution, to prevent the risk from escalating to a high-risk level. For system maintenance and performance effectiveness, this stage requires enhanced monitoring and adjustments to prevent damage or performance degradation caused by equipment overload.

[0092] If ORAC>ORAC max The risk level of equipment overload during the output power adjustment process of the power amplifier equipment is high.

[0093] This condition indicates that while adjusting the output power of the amplifier, the load has approached or exceeded its maximum capacity, creating a high risk of overload. This condition could lead to device failure, overheating, or protective mechanisms being activated, potentially resulting in audio signal interruption or system crash. This high-risk condition requires immediate emergency action, such as reducing output power, activating backup equipment, or initiating other protective measures to prevent damage and compromised performance. This condition poses a serious threat to the long-term lifespan of the equipment and overall system stability, and must be addressed as a priority to avoid catastrophic consequences.

[0094] Determining the pre-set overload risk assessment coefficient threshold range typically requires combining historical operating data and the equipment's technical specifications with data analysis and empirical verification. First, by monitoring the equipment's actual operating data over a long period of time, particularly key indicators such as the output power variation index and the load cumulative impact index generated during output power adjustments, data clustering or statistical analysis can be performed to identify typical performance under different operating conditions. Second, the manufacturer's technical manual and recommended values ​​can be referenced, with parameters such as the equipment's rated output power, load capacity, and maximum load serving as the basis for setting the threshold range. Based on high-risk scenarios identified in historical data, corresponding threshold ranges can be set, such as low-risk thresholds, warning thresholds, and high-risk thresholds. This approach allows the system to dynamically adjust these threshold ranges through an algorithmic model to ensure timely response to changes and risk prediction during actual equipment operation. Furthermore, the software system can continuously optimize and adjust these thresholds based on the equipment's operating environment, load variation patterns, and performance degradation trends to improve prediction accuracy and emergency response capabilities.

[0095] The risk response and protection strategy module takes corresponding measures for power amplifier equipment in three risk categories: low risk, medium risk, and high risk.

[0096] In this embodiment, in the risk response and protection strategy module, corresponding measures are taken for power amplifier devices in the three risk levels of low risk, medium risk, and high risk, respectively, based on the classification results of the risk levels of the power amplifier devices. Specifically,

[0097] For amplifiers classified as low risk, the following measures should be taken: continue to operate the amplifiers normally, monitor load changes in real time, and ensure that the equipment is adjusted within the normal operating range. Regularly assess the health status and working environment of the equipment to ensure that risks in long-term operation are controllable.

[0098] For power amplifiers with low risk ratings, measures include "continuing normal operation of the equipment, monitoring load changes in real time, ensuring adjustments are made within the normal operating range, and regularly assessing the equipment's health and operating environment to ensure that risks are manageable during long-term operation." These measures can be implemented through the real-time monitoring capabilities of the software system. By integrating load sensor data with the operating status of the power amplifier, the software system can track the equipment's power output, load changes, and other key operating parameters in real time, ensuring that the equipment remains within pre-set safety limits. Through the data acquisition and analysis module, the software analyzes historical trends in the equipment's operating status and generates equipment health reports, regularly assessing potential risks. For real-time load monitoring, the system can set thresholds to ensure that the equipment operates within a safe load range. Any abnormal load fluctuations will trigger an alarm, ensuring long-term operational safety. This approach aims to mitigate the potential accumulation of equipment risks through precise real-time data acquisition and trend analysis, enabling continuous monitoring and timely assessment in low-risk situations.

[0099] For power amplifiers classified as medium risk, the following measures are implemented: While operating normally, adjust the output power of the equipment to reduce load fluctuations and increase the safety protection threshold of the equipment; strengthen the tracking of the operating parameters of the power amplifier equipment through a real-time monitoring system, and set up an alarm mechanism. When the risk level continues to rise, quickly initiate emergency response procedures, such as increasing the operation of the cooling system or suspending high-load tasks of the equipment to ensure that the equipment is not overloaded;

[0100] For power amplifiers with a medium-risk rating, measures include "adjusting device output power while maintaining normal operation to reduce load fluctuations and increase safety protection thresholds; strengthening tracking of the amplifier's operating parameters through a real-time monitoring system, and implementing pre-set alarm mechanisms to rapidly initiate emergency response procedures if the risk level continues to rise." These measures are implemented through the software's dynamic scheduling system and early warning mechanisms. First, the software system dynamically adjusts output power based on the device's real-time load information and power trends, maintaining it within a relatively stable range to avoid excessive fluctuations and reduce the impact of load on the device. Furthermore, the software can set safety protection thresholds in the system. When the device approaches these thresholds, an alarm is automatically triggered and protective strategies are initiated, such as limiting power or increasing the cooling system's workload. To ensure the device's continued safe operation, the software also continuously tracks key operating parameters (such as temperature, load, and output power) and automatically optimizes system resource allocation based on pre-set rules to reduce the impact of high-load tasks on the amplifier. In this way, the software enables real-time scheduling and optimization, ensuring timely adjustments to the device when it is in a medium-risk state and preventing it from entering a high-risk state.

[0101] For power amplifier equipment classified as high-risk, the specific measures taken are: immediately start the overload protection program, limit the output power of the equipment, reduce its workload, and conduct emergency inspections and shutdowns of the equipment to prevent damage due to overload; report the risk status of the equipment to the system management platform, and automatically adjust system resources to optimize the load distribution of the power amplifier equipment to ensure that performances or work tasks are not affected, repair faults in a timely manner, and restore normal operation.

[0102] For amplifiers with high risk ratings, measures include "immediately initiating overload protection, limiting output power, reducing workload, and conducting emergency repairs and shutting down the equipment to prevent damage due to overload. The risk status of the equipment is reported to the system management platform, which automatically adjusts system resources and optimizes load distribution across the amplifier to ensure performances or tasks are not impacted, promptly repairs the fault, and restores normal operation." These measures can be implemented through automated control and remote monitoring mechanisms within the software system. First, the software needs to be tightly integrated with the amplifier hardware system to immediately initiate overload protection when an overload risk is detected. For example, if the system identifies that an amplifier exceeds a preset safe load, the software will automatically adjust the device's output power, limit its workload, activate cooling systems, or restrict certain tasks. Furthermore, the software can generate and upload equipment risk reports to the management platform, providing operators with real-time risk status feedback. In these cases, the system management platform can remotely control or schedule equipment and optimize resource allocation to ensure that equipment failures do not impact the stable operation of the entire system. The software also automatically records overload events, generates equipment maintenance requests, and dispatches specialized personnel for urgent repairs. Through these measures, the software system automatically implements effective response measures in high-risk situations, ensuring timely equipment shutdown and repairs, preventing damage and safeguarding the overall system safety.

[0103] The operation status monitoring and optimization module continuously monitors the operation status of the power amplifier equipment and dynamically adjusts the operating parameters and protection strategies based on the real-time monitoring results to ensure the safety of the power amplifier equipment operation and the stability of the system.

[0104] In the "Operation Status Monitoring and Optimization Module," continuous monitoring of the operating status of power amplifier equipment is achieved through the integration of multiple real-time monitoring systems. Specifically, by integrating hardware sensors (such as power sensors, temperature sensors, and load sensors) with the software system, various operational data of the power amplifier equipment can be obtained in real time, including power output, load changes, temperature, and device health status. The software system can continuously collect this data in the background and process it in real time, judging the device status based on set safety thresholds and early warning mechanisms. Any abnormal fluctuations (such as excessive load or temperature) will trigger a system alarm and automatically record the abnormal event. This process ensures that every operating status of the equipment is tracked in real time, ensuring that any potential faults or anomalies are detected and addressed immediately. The purpose of this approach is to ensure the continuous and stable operation of the power amplifier equipment, promptly detect and address unstable factors in operation, and reduce system failures or performance degradation caused by equipment failures.

[0105] Dynamic adjustment of operating parameters is achieved through software algorithms and real-time data feedback. Specifically, the software system automatically adjusts the operating parameters of the power amplifier based on real-time monitored device status data to maintain optimal system performance. For example, when the load or power output of the power amplifier approaches a preset safety threshold, the software can automatically reduce power output or adjust load distribution to prevent overload. This process relies on intelligent algorithms, such as dynamic optimization algorithms, which combine multiple factors such as the device's current load status, historical data, and temperature to calculate and optimize the device's operating strategy in real time. The software system must quickly respond and adjust device parameters based on preset safety protection strategies and real-time data. This effectively prevents damage to the device caused by prolonged high load conditions and ensures the long-term stable operation of the power amplifier. The purpose of dynamic adjustment is to improve device reliability and operational efficiency through flexible parameter optimization.

[0106] In the "Operational Status Monitoring and Optimization Module," dynamic optimization of protection strategies is achieved by combining risk assessments with real-time monitoring results. Based on the real-time status of the power amplifier, the software system determines whether additional protective measures need to be activated. For example, if the equipment is overloaded, overheated, or exhibits other fault indicators, the system automatically initiates protective mechanisms, such as increasing cooling system operation, limiting the amplifier's output power, or even automatically shutting down the equipment. These protection strategies are implemented through software logic control and automated decision-making. In practice, the system combines input data from multiple monitoring points for multi-dimensional analysis, triggering different levels of protection strategies as risk levels increase. This dynamic optimization process ensures that the power amplifier automatically adapts and adjusts to its status in all situations, preventing overload or damage, thereby ensuring system stability and safety. This ensures a rapid response to equipment failures or anomalies, minimizing the risk of damage and preventing overall system failures from impacting performances or missions.

[0107] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0108] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0109] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0110] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0111] In the several embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0112] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0113] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0114] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A full life cycle management system for audio and video equipment, characterized in that: It includes dynamic load monitoring module, dynamic operation information collection and analysis module, overload risk assessment module, risk response and protection strategy module, and operation status monitoring and optimization module; The dynamic load monitoring module performs real-time detection on the power amplifiers in the concert hall and establishes a dynamic load monitoring framework for the power amplifiers when it detects that the output power of the power amplifiers needs to be adjusted. The dynamic operation information collection and analysis module acquires the dynamic operation information generated by the power amplifier device during the output power adjustment process in real time, analyzes it after acquisition, and generates the output power variation index and load cumulative impact index of the power amplifier device; The overload risk assessment module constructs an overload risk assessment model for the power amplifier equipment based on the generated output power variation index and load cumulative impact index, generates an overload risk assessment coefficient, and analyzes it after generation to assess the risk level of equipment overload during the output power adjustment process of the power amplifier equipment. Based on the assessment results, the equipment status is classified into three levels: low risk, medium risk, and high risk. The risk response and protection strategy module takes corresponding measures for power amplifier equipment in three risk categories: low risk, medium risk, and high risk. The operation status monitoring and optimization module continuously monitors the operation status of the power amplifier equipment and dynamically adjusts the operating parameters and protection strategies based on the real-time monitoring results to ensure the safety of the power amplifier equipment operation and the stability of the system.

2. The audio and video equipment full life cycle management system according to claim 1, characterized in that: In the dynamic operation information acquisition and analysis module, the dynamic operation information generated by the power amplifier device during the output power adjustment process is obtained in real time and preprocessed after acquisition; the output power fluctuation information and load change history information in the preprocessed dynamic operation information are extracted and analyzed after extraction to generate the output power variation index and load cumulative impact index of the power amplifier device respectively.

3. The audio and video equipment full life cycle management system according to claim 2, characterized in that: The logic for obtaining the output power variation index of the power amplifier device is as follows: Extract the output power fluctuation information from the pre-processed dynamic operation information, including the output power change of the power amplifier device at different times during a period of time during the output power adjustment process, the number of power adjustments, the duration of power adjustment, and the average output power of the power amplifier device during the period, the average frequency of power adjustment, and the average power change duration, and calibrate them as ΔP respectively. m 、fA m TA m , PP, fP and TP, ΔP m Indicates the change in output power of the power amplifier device at time m during the process of adjusting the output power, fA m Indicates the number of power adjustments at time m during the process of adjusting the output power of the power amplifier. m represents the duration of power adjustment at time m during a period of time when the power amplifier device is adjusting its output power. PP represents the average output power of the power amplifier device during this period. fP represents the average frequency of power adjustment of the power amplifier device during this period. TP represents the average duration of power change of the power amplifier device during this period. m = 1, 2, 3, ..., g, where g is a positive integer. Calculate the output power variation index of the power amplifier equipment. The specific calculation formula is as follows: Where PVI is the output power variation index of the power amplifier equipment.

4. The audio and video equipment full life cycle management system according to claim 3, characterized in that: The logic for obtaining the load cumulative impact index of the power amplifier device is as follows: Extract the load change history information from the pre-processed dynamic operation information, including the amplitude of the equipment load change at different times during a period of time when the power amplifier equipment adjusts the output power, the number of times the equipment load changes, and the standard deviation of the power amplifier equipment load during the period, and calibrate them as FD n 、FZB n and σFZ,FD n Indicates the amplitude of the equipment load change at time n during a period of time when the power amplifier equipment adjusts its output power. n represents the number of times the load of the power amplifier device changes at time n within a period of time during the process of adjusting the output power of the power amplifier device. σFZ represents the standard deviation of the load of the power amplifier device within this period of time. n = 1, 2, 3, ..., u, where u is a positive integer. Calculate the load cumulative impact index of the power amplifier equipment. The specific calculation formula is as follows: Where LAII is the load cumulative impact index of the power amplifier equipment.

5. The audio and video equipment full life cycle management system according to claim 4, characterized in that: In the overload risk assessment module, an overload risk assessment model for the power amplifier equipment is constructed based on the generated output power variation index and load cumulative impact index, and an overload risk assessment coefficient is generated. Specifically, the following steps are included: Collect several output power variation indexes, load cumulative impact indexes and corresponding overload risk assessment coefficients generated in the past period of time, and calibrate them as PVI x LAII x and ORAC x , x represents the number of output power variation indexes, load cumulative impact indexes, and corresponding overload risk assessment coefficients generated in the past period, x = 3, 4, 5, ..., d, d is a positive integer, and the data collected in the past period are formed into a historical data set; The multivariate regression model is selected as the power amplifier equipment overload risk assessment model, and is trained through historical data sets to determine the value of the regression coefficient according to the formula: ORAC x =β0+β1*PVI x +β2*LAII x Where β0, β1 and β2 are regression coefficients; By minimizing the error between the predicted value and the actual value, the regression coefficient is optimized and the values ​​of the regression coefficients β0, β1 and β2 are finally determined; The final regression coefficient is used to input the real-time generated output power variation index PVI and load cumulative impact index LAII into the constructed power amplifier equipment overload risk assessment model to generate the overload risk assessment coefficient ORAC in real time.

6. The audio and video equipment full life cycle management system according to claim 5, characterized in that: The generated overload risk assessment coefficient ORAC is compared with the pre-set overload risk assessment coefficient threshold interval [ORAC min , ORAC max ] to compare, and based on the comparison results, evaluate the risk level of equipment overload during the output power adjustment process of the power amplifier equipment. Based on the evaluation results, the equipment status is divided into three levels: low risk, medium risk and high risk. The specific comparison analysis is as follows: If ORAC <ORAC min The risk level of equipment overload during output power adjustment of the power amplifier equipment is low; If ORAC min ≤ORAC≤ORAC max The risk level of equipment overload during output power adjustment of the power amplifier equipment is medium risk level; If ORAC>ORAC max The risk level of equipment overload during the output power adjustment process of the power amplifier equipment is high.

7. The audio and video equipment full life cycle management system according to claim 6, characterized in that: In the risk response and protection strategy module, according to the classification results of the risk level of power amplifier equipment, corresponding measures are taken for power amplifier equipment in the three levels of low risk, medium risk and high risk, specifically: For amplifiers classified as low risk, the following measures should be taken: continue to operate the amplifiers normally, monitor load changes in real time, and ensure that the equipment is adjusted within the normal operating range. Regularly assess the health status and working environment of the equipment to ensure that risks in long-term operation are controllable. For power amplifiers classified as medium risk, the following measures are implemented: While operating normally, adjust the output power of the equipment to reduce load fluctuations and increase the safety protection threshold of the equipment; strengthen the tracking of the operating parameters of the power amplifier equipment through a real-time monitoring system, and set up an alarm mechanism. When the risk level continues to rise, quickly initiate emergency response procedures, such as increasing the operation of the cooling system or suspending high-load tasks of the equipment to ensure that the equipment is not overloaded; For power amplifier equipment classified as high-risk, the specific measures taken are: immediately start the overload protection program, limit the output power of the equipment, reduce its workload, and conduct emergency inspections and shutdowns of the equipment to prevent damage due to overload; report the risk status of the equipment to the system management platform, and automatically adjust system resources to optimize the load distribution of the power amplifier equipment, promptly repair faults and restore normal operation.

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