Power supply control system for a hay gathering system

Abstract

This invention provides a power control system for a grass collection system, comprising: a data acquisition module for acquiring state performance-related characteristic data of the power supply within the power control system; a power state performance evaluation module for generating power state performance indicators based on preprocessed relevant characteristic data; a usage load evaluation module for acquiring operating data of the power system during use and generating power usage load indicators based on preprocessed operating data; and a risk level evaluation module for generating a power risk level based on the power state performance indicators and usage load indicators. This invention, by generating power risk levels from power state performance indicators and usage load indicators, can identify high-risk power supplies in advance, provide early warnings of potential faults, and, based on quantitative analysis, offer users personalized maintenance guidance and operation optimization suggestions, thereby significantly improving equipment reliability, extending power supply lifespan, and reducing sudden safety risks.

Description

Technical Field

[0001] This invention relates to the field of power control technology, specifically a power control system for a grass collection system. Background Technology

[0002] As a key component of outdoor power equipment, the reliability, safety, and lifespan of the power supply system of the grass collection system directly affect the overall working efficiency and operating costs of the machine. In existing technologies, power status monitoring often only focuses on basic parameters such as real-time voltage and current, or issues alarms after a single threshold is exceeded, such as a significant decrease in capacity or a significant increase in internal resistance. This is a passive and reactive management approach. Therefore, it is necessary to consider the cumulative impact of actual user behavior on power supply lifespan, which leads to battery micro-damage and battery performance degradation. This would enable a shift from post-fault handling to pre-fault prevention, providing users with precise maintenance decision support.

[0003] The prior art, disclosed in publication number CN114444569B, presents a health status assessment algorithm for a power control system. This technique includes: acquiring positive and negative sample datasets of the power control system; training a support vector machine (SVM) using the positive and negative sample datasets for classification; establishing a health level cloud model based on the classification model and a first support vector; acquiring the sample dataset to be assessed from the power control system and inputting it into the classification model to obtain a second distance dataset; inputting the second distance dataset into the health level cloud model to obtain a health membership vector set of the samples to be assessed; and calculating the health status vector of the power control system. This paper first transforms the health assessment problem of the power control system performance into a classification problem using a support vector machine, reducing the influence of subjective factors during the assessment process.

[0004] However, the existing technologies mentioned above do not fully consider the cumulative impact of users' actual usage behavior on the lifespan of the power supply. The micro-damage to the battery caused by abnormal operating conditions such as frequent deep discharge, high load operation, and high temperature environment operation cannot be quantitatively assessed. Therefore, it is difficult to provide users with targeted operation guidance and preventive maintenance suggestions. At the same time, most existing solutions lack a risk assessment mechanism that combines the aging state of the power supply itself with the real-time usage load, and cannot effectively warn and grade the response to performance degradation or safety failures that may occur in the short term.

[0005] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] The purpose of this invention is to provide a power control system for a grass collection system to solve the problems mentioned in the background art. This invention utilizes a risk assessment mechanism that combines the aging state of the power supply with the real-time load to effectively warn and provide tiered responses to potential short-term performance degradation or safety failures. This achieves a shift from post-fault handling to pre-fault prevention, providing users with precise maintenance decision support and improving the overall reliability, lifespan, and safety level of the system.

[0007] To achieve the above objectives, the present invention provides the following technical solution: A power control system for a grass collection system, comprising: Data acquisition module: Acquires state performance-related characteristic data of the power supply within the power control system. The relevant characteristic data includes: initial nominal capacity of the power supply, current available capacity of the power supply, initial internal impedance of the power supply, and current internal impedance of the power supply. Power Status Performance Evaluation Module: The module preprocesses the raw data collected from relevant feature data, including calibration, filtering, and operating condition compensation of the relevant feature data, removing outliers and standardizing the units; it generates power status performance indicators based on the preprocessed relevant feature data to quantify the cumulative aging performance of the power supply, and establishes a hierarchical threshold system to issue an early warning when the power status performance indicators exceed the threshold.

[0008] The load assessment module collects operating data of the power system during use, including baseline discharge, average discharge, baseline temperature, and average operating temperature. It preprocesses the collected raw operating data, including cleaning, outlier removal, unit standardization, and time series alignment, to generate standardized input data. Based on the preprocessed operating data, it generates a power supply load index, which quantifies the stress exerted on the power supply by user operating modes.

[0009] Risk level assessment module: Generates power risk level based on power status performance indicators and usage load indicators. The power risk level is used to quantitatively assess the urgency of its failure in the short term. Establishes a threshold system and multi-level alarm mechanism to prioritize the identification of high-risk power sources. When the power risk level exceeds the threshold, it provides risk level indication, predictive maintenance decisions, and generates clear operation instructions.

[0010] Furthermore, the raw data of the relevant feature data needs to undergo systematic preprocessing, including: calibrating the raw data based on the sensor calibration curve to correct its inherent zero-point drift and gain error; filtering the data sequence using a digital filtering algorithm to suppress instantaneous noise and extract stable trends; compensating the data to standard operating conditions according to the physical model to ensure that the data are at the same comparison benchmark; then, using statistical methods and rules to identify and remove outliers that significantly deviate from the normal range to avoid interfering with the analysis; finally, unifying the units of all data to the International System of Units (SI).

[0011] Furthermore, the power state performance index is calculated using the following formula:

[0012] Where Ci is the initial nominal capacity of the power supply; Cc represents the current available capacity of the power supply; Ri is the initial internal impedance of the power supply; Rc is the current internal impedance of the power supply; SOP stands for State of Power Performance Index. α is the weighting coefficient for the impact of internal resistance. α is a weighting coefficient between 0 and 1, which quantifies the degree of impact of internal resistance growth on overall health. The larger the value of α, the higher the weight of the negative impact of internal resistance changes on SOP.

[0013] Furthermore, the power state performance index maps the complex physicochemical aging process into a normalized value through the fusion calculation of relevant characteristic data of the power supply, thereby characterizing the degree of degradation of the power supply from its initial performance to its current state. Based on the power state performance index, the system establishes a well-defined hierarchical threshold system, setting multiple key threshold nodes according to the power supply's safe operation boundary and performance requirements. When the power state performance index monitored in real time exceeds any preset threshold, the system will immediately trigger the corresponding hierarchical early warning mechanism, issuing status abnormality alarms with different degrees of urgency to the operator.

[0014] Furthermore, a three-tiered threshold system is established for the critical thresholds: the first-level warning threshold indicates significant performance degradation, requiring planned maintenance; the second-level alarm threshold indicates that performance is nearing the safety boundary, requiring immediate measures and limiting output power; the third-level fault threshold indicates an immediate risk of failure, triggering mandatory protective commands. The determination of critical thresholds requires comprehensive consideration of the end-of-life specifications provided by the power supply manufacturer, statistical analysis of historical failures of the same model of power supply, and specialized aging test data, and ultimately, corrections and fine-tuning based on the actual application scenario.

[0015] Furthermore, the operational data undergoes preprocessing, which includes: first, data cleaning, by identifying and repairing missing values ​​and correcting erroneous readings to ensure the integrity and rationality of the data sequence; second, outlier removal, based on statistical methods and domain knowledge rules, filtering out outliers caused by transient interference or atypical operating conditions to prevent distortion of subsequent statistical feature calculations; third, unit unification, converting all operational data to a consistent international standard unit system and performing necessary dimensional conversions to ensure comparability and calculability between data; and fourth, time series alignment, using timestamp synchronization and resampling techniques to unify operational data sequences from different acquisition frequencies and clock sources to the same time base and sampling points, forming strictly spatiotemporally aligned data alignment; after preprocessing, the original operational data is transformed into standardized input data.

[0016] Furthermore, the usage load index is calculated using the following formula:

[0017] Where: AD is the average discharge quantity; Di is the baseline discharge quantity; AT represents the average operating temperature. Ti is the reference temperature; UI is a usage load indicator; k is the temperature influence coefficient, which represents the percentage increase in intensity for every 1°C increase in temperature above the reference temperature.

[0018] Furthermore, the usage load index collects and analyzes the operating data monitored in real time during system use, and integrates it with preset benchmark parameters to quantify the cumulative micro-damage to the battery caused by unconventional discharge depth, continuous high-rate load, and abnormal temperature rise operation modes, as reflected by the user's actual usage behavior. By using the load index for quantitative evaluation, the system transforms the battery life management mode from passive state monitoring that only issues alarms after the performance threshold is exceeded to proactive behavior guidance that is forward-looking and interventionist.

[0019] Furthermore, the power supply risk level is calculated using the following formula:

[0020] Wherein: BRI is the power risk level. The higher the value, the higher the risk of the battery failing in the near future or experiencing a sharp decline in performance.

[0021] Furthermore, the power supply risk level integrates the power supply's physical performance and real-time load, mapping the electrochemical aging process and external stress effects into a comprehensive risk value for dynamic, quantitative, and proactive management of power system operation risks. The system establishes a graded threshold system, automatically triggering matching multi-level alarms and response mechanisms when the power supply risk level exceeds different thresholds, generating executable maintenance instructions and work orders to guide operation and maintenance actions.

[0022] Compared with existing technologies, the beneficial effects of this invention are: by integrating the aging state of the power supply itself with the actual operating load of the user, a two-dimensional accurate assessment is achieved, and a hierarchical early warning and proactive intervention mechanism is established. This not only identifies high-risk power supplies in advance and warns of potential faults, but also provides users with personalized maintenance guidance and operation optimization suggestions based on quantitative analysis, thereby significantly improving equipment reliability, extending power supply life, and reducing maintenance costs and safety risks caused by sudden failures. Attached Figure Description

[0023] Fig. 1 This is a system block diagram of the power control system of a grass collection system according to the present invention; Fig. 2 This is a schematic diagram of the operation flow of the power control system of the grass collection system of the present invention. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0025] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0026] Example: Please see Figs. 1-2 The present invention provides a technical solution: A power control system for a grass collection system, comprising: Data Acquisition Module: Acquires state performance-related characteristic data of the power supply within the power control system. The relevant characteristic data is divided into capacity characteristic data and impedance characteristic data. Capacity characteristic data includes: the initial nominal capacity of the power supply, that is, the total energy storage capacity calibrated by the power supply under standard test conditions at the time of manufacture, which serves as the benchmark value for performance degradation assessment; the current available capacity of the power supply: that is, the effective amount of energy that the power supply can actually release under the current state and specific operating conditions, which is obtained through standard charge and discharge cycle tests or online estimation based on models. Impedance characteristic data includes: initial internal impedance of the power supply: the baseline values ​​of its internal equivalent DC resistance and AC impedance in a brand-new state, reflecting its initial power transmission characteristics; current internal impedance of the power supply: the real-time or recent measurement value of its internal equivalent impedance after a certain period of aging and use. An increase in this value is a core indicator reflecting key failure modes such as power supply health degradation, connector aging, or electrolyte deterioration.

[0027] Power supply status performance evaluation module: By preprocessing the raw data collected from relevant feature data, the preprocessing aims to eliminate interference caused by measurement errors, environmental noise and operating condition fluctuations, thereby extracting effective information that truly reflects the performance degradation of the power supply itself. The preprocessing includes: Data calibration: To address the inherent system errors of sensors and measurement circuits, the raw readings are compared with a higher-precision reference source. By applying pre-stored calibration curves or offset compensation coefficients, the data is corrected in the first order to ensure alignment with the physical true value. Data filtering: To suppress random noise and transient interference mixed in during the acquisition process, a digital filtering algorithm is used to smooth the data stream in order to extract stable trend signals; Operating condition compensation: In order to make the data comparable under different environments and loads, the data is uniformly compensated to the standard reference operating condition, and the capacity readings at different discharge rates are compensated to the values ​​at the standard discharge rate. Outlier removal: Based on the statistical principle of the 3σ criterion and physical constraint rules, outlier data points caused by acquisition failures, communication interruptions, and extreme random events are automatically identified and removed to prevent them from misleading subsequent analysis and calculations.

[0028] Dimensional unification and standardization: The units of all feature data are unified to the International System of Units (SI), and the numerical values ​​are scaled to eliminate the impact of differences in the dimensions and orders of magnitude of different features on subsequent fusion calculations.

[0029] The power state performance index is calculated using the following formula:

[0030] Where Ci is the initial nominal capacity of the power supply, which is obtained from the nominal value on the power label and serves as the base value for the power supply capacity. Cc represents the current available capacity of the power supply, which is measured through standard charge and discharge tests, and indicates how much energy the power supply can still store. Ri is the initial internal impedance of the power supply, which is the reference value of the internal impedance of the power supply at the factory. Rc is the current internal impedance of the power supply, which is measured by methods such as pulse testing. SOP stands for State of Power Performance Index. α is the weighting coefficient for the impact of internal resistance, which is a weighting coefficient between 0 and 1. It quantifies the degree of impact of internal resistance growth on overall health. The larger the value of α, the higher the weight of the negative impact of internal resistance change on SOP. It directly reflects the capacity retention rate and is the core indicator of SOP (Start of Production), reflecting the battery's range. The internal resistance growth rate is calculated, reflecting the degree of decrease in peak power output and increased heat generation of the power supply. Calculate the performance discount caused by the increase in internal resistance, which means that the overall performance of the battery decreases due to internal resistance. Then the internal resistance health coefficient is obtained.

[0031] The power supply state performance index (PSPI) integrates and calculates multiple relevant characteristic data of the power supply. It comprehensively maps and quantifies the physicochemical processes such as electrochemical aging, material fatigue, and interface degradation that are not directly observable inside the power supply into a numerical index with clear physical meaning. This effectively abstracts and characterizes the comprehensive degree of degradation and remaining lifespan potential of the power supply from its initial performance state at the factory to its current operating performance state. Based on the PSPI, the system establishes a hierarchical dynamic threshold system with clear levels and rigorous logic. According to the power supply's design safety boundary, performance guarantee requirements, and reliability requirements of the application scenario, multiple key threshold nodes are scientifically preset.

[0032] When the system detects that the power status performance index exceeds any preset key threshold node in real time, it will immediately trigger a strictly corresponding, automated hierarchical early warning and response mechanism. While issuing status anomaly alarms with different levels of urgency to the operator, it can also execute predefined proactive control strategies in conjunction with the alarm level. This achieves closed-loop management from status monitoring and risk warning to initial intervention, significantly improving the system's proactive security and intelligent operation and maintenance level.

[0033] The critical threshold system adopts a three-tiered architecture to achieve segmented and precise management of the power state degradation process. Each threshold level corresponds to a clear technical state definition and automatic response strategy, which together form a progressive early warning and protection network. Level 1: Warning Threshold. When the power supply status performance index exceeds this threshold for the first time, it indicates that its key performance parameters have shown a identifiable decline that exceeds the normal attenuation curve. This threshold is intended to indicate that the power supply has left the healthy operating range and entered the attention phase. The system will trigger a planned maintenance prompt. Its response mechanism includes: generating a maintenance suggestion log on the operation interface, generating a preliminary work order on the remote monitoring platform, and possibly prompting a deep inspection to be carried out in the next scheduled maintenance cycle. The core objective is to achieve early warning and reserve a time window for orderly arrangement of preventive maintenance. Level Two: Alarm Threshold. When the indicator exceeds this threshold, it indicates that the power supply performance is approaching the design safety boundary. Continued full-load operation may lead to performance instability or affect the overall system reliability; this threshold marks the power supply entering a risky stage. The system will immediately trigger real-time alarms and proactive intervention. The response mechanism includes: issuing an emergency alarm signal combining sound and light to the operator; automatically executing the preset power limiting strategy; and simultaneously sending a high-priority work order to the maintenance management system, requesting immediate inspection or intervention. Its core objective is to prevent further deterioration and mitigate operational risks.

[0034] Level 3: Fault Threshold, the highest level threshold. Exceeding this threshold indicates an extremely high risk of immediate power supply failure, potentially accompanied by a sharp increase in internal resistance, thermal runaway, or voltage collapse. This threshold marks the power supply entering a crisis phase, triggering mandatory protection commands from the system. The response mechanism has the highest priority and typically includes: immediately disconnecting the power supply's main load, initiating a seamless backup power supply switching process, and placing the power supply itself in an electrically isolated safe state. Simultaneously, the system will lock the fault event and send the highest-level alarm. The core objective is to ensure personal and equipment safety and continuous system operation, preventing catastrophic failures.

[0035] The specific values ​​of the critical thresholds are not fixed, but are dynamically determined through a calibration process that integrates multi-source data and adapts to different scenarios. This process follows these steps: Benchmark acquisition: The performance boundaries specified in the end-of-life specifications and safety data sheets provided by the power supply manufacturer are used as theoretical benchmarks; Empirical correction: Statistical analysis of historical operation and failure cases of the same model of power supply is incorporated, and actual field data is used to correct deviations in the theoretical model and identify common pre-failure characteristics; Accelerated verification: A performance accelerated degradation model is obtained by referring to specialized aging test data to verify the effectiveness of the thresholds under extreme conditions; Scenario fine-tuning: Finally, the power supply is fine-tuned in an engineering manner according to the severity of the application scenarios.

[0036] Using the load assessment module: collect the operating data of the power system during use, including the reference discharge quantity, average discharge quantity, reference temperature, and average operating temperature.

[0037] Using the load assessment module: This module collects operational data of the power system during operation. This data includes baseline discharge rate, average discharge rate, baseline temperature, and average operating temperature. Simultaneously, the collected raw operational data undergoes preprocessing. The preprocessing process specifically includes: First, the original running data sequence is cleaned by using algorithms to identify missing values, non-numerical entries, and erroneous readings that clearly do not conform to physical laws. For missing values, the system uses time series-based interpolation and regression-based imputation based on correlation parameters to repair them. For errors, automatic correction or labeling is performed based on sensor range and prior knowledge, thereby ensuring the logical integrity and basic rationality of the data sequence.

[0038] Secondly, the cleaned data undergoes outlier removal. The system comprehensively applies statistical outlier detection methods and a pre-set domain knowledge rule base to effectively filter out statistical outliers and non-representative condition data caused by electromagnetic pulses, instantaneous sensor failures, or atypical short-term operating conditions. This prevents these noisy data from distorting the subsequent calculation of key statistical characteristic values ​​such as average discharge and average operating temperature.

[0039] Subsequently, a strict unit unification and standardization conversion was performed. The system uniformly converted all possible different unit representations in the operational data to the International System of Units (SI) and performed the necessary dimensional conversions, thereby fundamentally ensuring the absolute comparability of all data in the physical dimension and its direct mathematical computability.

[0040] Finally, high-precision time series alignment is performed for multi-source asynchronous data streams. Since different sensors or acquisition channels may have different sampling frequencies, clock sources and initial timestamps, the system uses a high-precision master clock as a reference and unifies all data sequences to the same, equally spaced time reference axis and sampling points through timestamp synchronization correction and resampling technology, ensuring that data of different dimensions are strictly aligned on the time axis.

[0041] The load index is calculated using the following formula:

[0042] Where: AD is the average discharge amount, which is the average amount of electricity actually released by the power supply in a single typical working cycle or a specific statistical period in actual use scenarios. It is obtained by integrating the real-time monitored current over time and averaging the data. Di is the baseline discharge capacity, which refers to the rated amount of electricity released by the power supply from a fully charged state to the cutoff voltage under the standard test conditions specified by the manufacturer. The data comes from the power supply technical specifications. AT stands for Average Operating Temperature, which is the average temperature of the power supply during its operating period. Ti is the reference temperature, the standard ambient temperature on which power supply performance calibration and life testing are based; UI is a usage load indicator; k is the temperature influence coefficient, which represents the percentage increase in intensity for every 1°C increase in temperature above the reference temperature; Used to assess the stress caused by the usage habit of depth of discharge; Used to assess the stress caused by the environmental factor of operating temperature.

[0043] By fusing operational data to generate a comprehensive usage load index, the system quantifies and characterizes the micro-damage inside the battery caused by actual user operations that is difficult to observe directly. This reflects the cumulative impact of stress exceeding ideal design conditions on battery health. Based on this quantified usage load index, the system achieves a fundamental shift in battery life management. Traditional models typically trigger alarms only after battery performance degrades to a preset threshold, representing passive status monitoring and reactive responses. In contrast, this system proactively calculates and displays the usage intensity index, providing early warnings during the damage accumulation phase and alerting users that their current usage patterns may accelerate battery aging. It generates specific operational suggestions, proactively guiding and intervening in user behavior. This provides key input for predictive maintenance, upgrading maintenance strategies from fixed-cycle or remedial measures after severe degradation to personalized, preventative planning based on the severity of actual use. Therefore, battery management is elevated from passive status monitoring to proactive behavior guidance and health management.

[0044] Risk level assessment module: The power supply status performance index reflects the inherent performance degradation of the power supply due to natural aging and historical use, that is, its current degradation state; while the usage load index quantifies the current and recent pressure imposed by the operating mode and environment. The system uses a predefined risk fusion algorithm to fuse the two dimensions of the index and finally output a power supply risk level. The power supply risk level is calculated using the following formula:

[0045] Wherein: BRI is the power risk level, the higher the value, the higher the risk of the battery failing or its performance deteriorating sharply in the near future; the value of the power state performance index is negatively correlated with the value of the power risk level, when the value of the power state performance index increases, the value of the power risk level will decrease; the usage load index is positively correlated with the power risk level, when the value of the usage load index increases, the value of the power risk level will increase.

[0046] The power supply risk level is essentially a collaborative modeling of the real-time operating data of the power supply system and its accumulated historical health status. This combines abstract, long-term aging trends with specific, short-term stress shocks, and accurately quantifies the comprehensive probability and urgency of the power supply experiencing a sudden performance drop, functional failure, or safety malfunction in a short period of time under specific operating conditions. To achieve precise application of risk levels, the system has set up a threshold judgment system. Based on the continuous numerical spectrum of the power risk index, the system scientifically divides multiple discrete risk state intervals: normal, attention, warning, high risk, and emergency. Clear upper and lower limit thresholds are set for each interval, and each risk level strictly corresponds to a set of preset, differentiated multi-level alarm and response strategies. When the real-time calculated power risk index exceeds the concern level threshold for the first time, the system will activate the primary warning mechanism, which may be manifested in the form of visual status prompts on the operation interface and log recording, aiming to remind the operator to pay attention to the performance trend changes; when the risk index further climbs to the warning level threshold, the system will activate the intermediate alarm mechanism, trigger obvious audible and visual warnings, and may automatically generate preliminary maintenance suggestions or restrict non-critical loads. When the risk index reaches the high-risk threshold, the system initiates a high-level emergency response, including: forcing the power system into a derated operation mode to ensure safety, sending real-time alarm information to the remote monitoring center, and automatically creating a high-priority maintenance work order; when the risk index exceeds the highest level emergency threshold, the system will immediately execute the highest-priority protective instructions, instantly disconnect the load, initiate the backup power switching sequence, and electrically isolate the faulty unit to prevent accidents, while sending the highest-level emergency notification to the relevant responsible persons.

[0047] The system can automatically scan and sort all power units in real time, quickly identify and prioritize high-risk power units. When the risk level of any power unit exceeds the preset threshold, the system will immediately activate a multimodal visual alarm interface, which will intuitively indicate the risk level through color coding, flashing icons and graded progress bars. At the same time, the alarm signal will automatically trigger the predictive maintenance decision engine in the backend. This engine integrates equipment knowledge graph, maintenance history and real-time operating conditions to automatically generate an executable closed-loop operation instruction set, including immediate automated control instructions, structured maintenance work orders and forward-looking management suggestions, thereby realizing intelligent closed-loop management of the entire chain from risk perception to proactive intervention.

[0048] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset data in the formulas are set by those skilled in the art according to the actual situation.

[0049] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0050] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0051] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A power control system for a grass collection system, characterized in that, include: Data acquisition module: Acquires state performance-related characteristic data of the power supply within the power control system. The relevant characteristic data includes: initial nominal capacity of the power supply, current available capacity of the power supply, initial internal impedance of the power supply, and current internal impedance of the power supply. Power Status Performance Evaluation Module: The module preprocesses the raw data collected from relevant feature data, including calibration, filtering, and operating condition compensation of the relevant feature data, removing outliers and standardizing the units; it generates power status performance indicators based on the preprocessed relevant feature data to quantify the cumulative aging performance of the power supply, and establishes a hierarchical threshold system to issue an early warning when the power status performance indicators exceed the threshold. The load assessment module collects operating data of the power system during use, including baseline discharge, average discharge, baseline temperature, and average operating temperature. It preprocesses the collected raw operating data, including cleaning, outlier removal, unit standardization, and time series alignment, to generate standardized input data. Based on the preprocessed operating data, it generates a power supply load index, which quantifies the stress exerted on the power supply by user operating modes. Risk level assessment module: Generates power risk level based on power status performance indicators and usage load indicators. The power risk level is used to quantitatively assess the urgency of its failure in the short term. Establishes a threshold system and multi-level alarm mechanism to prioritize the identification of high-risk power sources. When the power risk level exceeds the threshold, it provides risk level indication, predictive maintenance decisions, and generates clear operation instructions.

2. The power control system for a grass collection system according to claim 1, characterized in that: The raw data of the relevant feature data needs to undergo systematic preprocessing, including: calibrating the raw data based on the sensor calibration curve to correct its inherent zero-point drift and gain error; filtering the data sequence using a digital filtering algorithm to suppress instantaneous noise and extract stable trends; compensating the data to standard operating conditions according to the physical model to ensure that the data are at the same comparison benchmark; furthermore, using statistical methods and rules to identify and remove outliers that significantly deviate from the normal range to avoid interfering with the analysis; and finally, unifying the units of all data to the International System of Units (SI).

3. The power control system for a grass collection system according to claim 1, characterized in that: The power state performance index is calculated using the following formula: Where: Ci is the initial nominal capacity of the power supply; Cc represents the current available capacity of the power supply; Ri is the initial internal impedance of the power supply; Rc is the current internal impedance of the power supply; SOP stands for State of Power Performance Index. α is the weighting coefficient for the impact of internal resistance. α is a weighting coefficient between 0 and 1, which quantifies the degree of impact of internal resistance growth on overall health. The larger the value of α, the higher the weight of the negative impact of internal resistance changes on SOP.

4. The power control system for a grass collection system according to claim 1, characterized in that: The power state performance index maps the complex physicochemical aging process into a normalized value by fusing and calculating relevant characteristic data of the power supply, thereby characterizing the degree of degradation of the power supply from its initial performance to its current state. Based on power status performance indicators, the system establishes a well-defined hierarchical threshold system, setting multiple key threshold nodes according to the power supply's safe operation boundaries and performance requirements. When the power status performance indicators monitored in real time exceed any preset threshold, the system will immediately trigger the corresponding hierarchical early warning mechanism, issuing status anomaly alarms with different levels of urgency to the operator.

5. The power control system for a grass collection system according to claim 4, characterized in that: The critical thresholds establish a three-tiered threshold system: the first-level warning threshold indicates a significant performance degradation, requiring planned maintenance; the second-level alarm threshold indicates that the performance is nearing the safety boundary, requiring immediate measures to be taken and output power limited. The Level 3 fault threshold poses an immediate risk of failure, which will trigger a mandatory protective command. The critical thresholds are set based on the end-of-life specifications provided by the power supply manufacturer, statistical analysis of historical failures of the same model of power supply, and data from specialized aging tests, and are modified and fine-tuned according to the actual application scenarios of the power supply.

6. The power control system for a grass collection system according to claim 1, characterized in that: The operational data undergoes preprocessing, which includes: first, data cleaning, which identifies and repairs missing values ​​and corrects erroneous readings to ensure the integrity and rationality of the data sequence; second, outlier removal, which uses statistical methods and domain knowledge rules to filter out outliers caused by transient interference or atypical operating conditions, preventing distortion of subsequent statistical feature calculations; third, unit unification, which converts all operational data to a consistent international standard unit system and performs necessary dimensional conversions to ensure comparability and calculability between data; and fourth, time series alignment, which uses timestamp synchronization and resampling techniques to unify operational data sequences from different acquisition frequencies and clock sources to the same time base and sampling points, forming strictly spatiotemporally aligned data alignment. After preprocessing, the original operational data is transformed into standardized input data.

7. The power control system for a grass collection system according to claim 1, characterized in that: The usage load index is calculated using the following formula: Where: AD is the average discharge quantity; Di is the baseline discharge quantity; AT represents the average operating temperature. Ti is the reference temperature; UI is a usage load indicator; k is the temperature influence coefficient, which represents the percentage increase in intensity for every 1°C increase in temperature above the reference temperature.

8. The power control system for a grass collection system according to claim 7, characterized in that: The usage load index collects and analyzes the real-time operating data monitored by the system during use, and integrates it with preset benchmark parameters to quantify the cumulative micro-damage to the battery caused by unconventional discharge depth, continuous high-rate load, and abnormal temperature rise operation modes, as reflected by the user's actual usage behavior. By using the load index for quantitative evaluation, the system transforms the battery life management mode from a passive state monitoring that only issues alarms after the performance threshold is exceeded to a proactive and intervention-based behavior guidance.

9. The power control system for a grass collection system according to claim 8, characterized in that: The power supply risk level is calculated using the following formula: Wherein: BRI is the power risk level. The higher the value, the higher the risk of the battery failing in the near future or experiencing a sharp decline in performance.

10. The power control system for a grass collection system according to claim 9, characterized in that: The power supply risk level integrates the power supply's physical performance and real-time load, mapping the electrochemical aging process and external stress effects into a comprehensive risk value for dynamic, quantitative, and proactive management of power system operation risks. The system establishes a graded threshold system, automatically triggering matching multi-level alarms and response mechanisms when the power supply risk level exceeds different thresholds, generating executable maintenance instructions and work orders to guide operation and maintenance actions.

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