Method and device for improving energy efficiency of slope type gravity energy storage system

Abstract

The invention discloses an energy efficiency improving method and device for a slope type gravity energy storage system, and relates to the technical field of electric energy storage systems. The method and device for improving the energy efficiency of the slope type gravity energy storage system comprise the following steps that S1, energy storage operation data are obtained through multi-source sensor fusion, and data cleaning and normalization processing are conducted on the energy storage operation data; s2, extracting a multi-dimensional state factor, carrying out quantitative evaluation on the operation state of the energy storage unit, constructing a state grading mechanism based on an evaluation result, and generating an operation state label; s3, calling a running state label to screen candidate units, constructing a scheduling priority ranking index, and dynamically allocating tasks and adjusting an execution sequence according to the index; and S4, analyzing structural wear and energy consumption load characteristics, establishing a scheduling correction mechanism, and dynamically adjusting a scheduling priority ranking index. The problem that the scheduling self-adaption cannot be realized according to the load, loss and wear difference of each unit in the parallel operation process of a plurality of energy storage units is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric energy storage systems, in particular to an energy efficiency improvement method and device for a slope type gravity energy storage system. BACKGROUND

[0002] The slope type gravity energy storage technology realizes the bidirectional conversion of electric energy and potential energy by driving a heavy object to ascend and descend along a slope track, and has the characteristics of being clean, efficient and fast-responding, and has become an important form of energy storage for addressing the volatility and regulation requirements of renewable energy. In particular, in Chongqing and other southwest regions, the complex mountainous terrain and high drop resources provide rich natural layout conditions and system integration advantages for slope type gravity energy storage, and have good engineering adaptability and promotion potential.

[0003] For example, the application with the publication number CN119109091A discloses a gravity energy storage system and a control method and device thereof. The gravity energy storage system includes a first gravity energy storage device, a first motor, a frequency converter, a plurality of second gravity energy storage devices, and a plurality of second motors. The first gravity energy storage device is connected with the first motor, and the first motor is further connected with the frequency converter; the plurality of second gravity energy storage devices are connected with the plurality of second motors in a one-to-one correspondence, and the frequency converter and the plurality of second motors are connected with the power grid. In this application, the frequency converter can adjust the speed of the first motor by adjusting the voltage and frequency of the first motor. In combination with the first motor, the frequency converter and the second motor, the precise control of the output electric power of the entire gravity energy storage system can be realized, and the frequency support capability of the gravity energy storage system is further improved.

[0004] However, the existing technology still has significant limitations: the system control strategy is mostly statically configured, and it is difficult to dynamically schedule according to the differences in load capacity, response performance and wear state of the energy storage units, resulting in uneven resource allocation and low overall efficiency; the energy efficiency optimization means often focuses on single-point improvement, lacks a whole-process state evaluation mechanism that integrates multi-source data, and is difficult to realize the collaborative management of operation scheduling and life evolution; at the same time, there is a lack of prediction and control strategy for performance degradation caused by temperature rise, friction and fatigue, which easily causes excessive scheduling and non-planned loss, affecting operation stability and system life.

[0005] Therefore, in view of the above problems, there is an urgent need for an energy efficiency improvement method and device for a slope type gravity energy storage system. SUMMARY

[0006] Technical problems solved In view of the deficiencies of the prior art, the present application provides an energy efficiency improvement method and device for a slope type gravity energy storage system, which solves the problem that multiple energy storage units cannot be adaptively scheduled according to the load, loss and wear differences of each unit during parallel operation.

[0007] Technical scheme To achieve the above objectives, the present invention provides the following technical solution: a method for improving the energy efficiency of a slope-type gravity energy storage system, comprising the following steps: S1, acquiring energy storage operation data through multi-source sensor fusion, cleaning and normalizing the energy storage operation data to obtain preprocessed energy storage operation data; S2, extracting multi-dimensional state factors from the preprocessed energy storage operation data, quantitatively evaluating the operation status of the energy storage unit, and constructing a state classification mechanism based on the evaluation results to generate operation status labels; S3, using the operation status labels to screen candidate units, analyzing the regulation capability characteristics in conjunction with the energy storage operation data, constructing a scheduling priority ranking index, and dynamically allocating tasks and adjusting the execution order based on this index; S4, tracking relevant operation variables based on task execution feedback, analyzing structural wear and energy load characteristics, establishing a scheduling correction mechanism, dynamically adjusting the scheduling priority ranking index, and achieving coordinated optimization of scheduling behavior and lifespan control.

[0008] Furthermore, the specific steps for obtaining preprocessed energy storage operation data through multi-source sensor fusion and data cleaning and normalization are as follows: Energy storage operation data is acquired by real-time data acquisition and processing of the drive equipment, monitoring sensors, and operation control system of multiple energy storage units in the slope gravity energy storage system. This data includes motor input power, motor output torque, speed, acceleration, control response time, slide rail friction resistance, lifting stroke, unit power consumption, cumulative number of operations, equipment temperature, and ambient temperature. Abrupt values ​​in the energy storage operation data are identified and removed using a median residual detection method based on a sliding window. Missing fields in the energy storage operation data are completed using a time series linear interpolation method. Continuously changing indicators are smoothed and high-frequency fluctuations are suppressed using a Gaussian kernel weighted moving average. Distribution correction and scaling are performed on the energy storage operation data by combining logarithmic transformation and maximum-minimum normalization methods, thus completing the normalization process of the energy storage operation data.

[0009] Furthermore, the specific steps for extracting multidimensional state factors using preprocessed energy storage operation data are as follows: Based on the preprocessed energy storage operation data, the state factors of each energy storage unit are constructed: the energy efficiency factor is calculated by multiplying the ratio of motor output torque to input power by the rotational speed; the response factor is calculated by summing the reciprocal of the control response time and the acceleration; the operating pressure factor is calculated by adding the lifting stroke to the cumulative number of operations; the temperature difference factor is calculated by the absolute value of the difference between the equipment temperature and the ambient temperature; and the stability factor is calculated by multiplying the rotational speed change amplitude by the acceleration change amplitude.

[0010] Furthermore, the specific steps for quantitatively evaluating the operating status of energy storage units are as follows: Using the state factors of energy storage units, the operating status of each energy storage unit under the current energy storage task is quantitatively evaluated: Calculate twice the product of the energy efficiency factor and the response factor, divide the result by the stability factor plus 1 to obtain the first term; calculate the square of the energy efficiency factor divided by the operating pressure factor plus 1 to obtain the second term; add the first and second terms to obtain the efficiency response enhancement term; calculate the square of the temperature difference factor plus the operating pressure factor, and use the sum as the numerator; calculate the stability factor plus 2 as the denominator; calculate 1 minus the ratio of the numerator to the denominator to obtain the load penalty term; multiply the efficiency response enhancement term and the load penalty term to obtain the unit operation evaluation value.

[0011] Furthermore, based on the evaluation results, a state classification mechanism is constructed, and the specific steps for generating operation status labels are as follows: After each energy storage task is completed, the unit operation evaluation values ​​of all energy storage units are summarized and compared with the operation adaptation threshold: When the unit operation evaluation value is greater than or equal to the secondary operation adaptation threshold, the energy storage unit is marked as a main response unit, given priority to participate in high-load energy regulation tasks, and allowed to enter continuous operation state; when the unit operation evaluation value is less than the secondary operation adaptation threshold but greater than the primary operation adaptation threshold, the energy storage unit is marked as a normal operation unit, taking turns to replace the energy regulation tasks of the main response unit, controlling the operation frequency, and avoiding centralized scheduling; when the unit operation evaluation value is less than or equal to the primary operation adaptation threshold, the energy storage unit is marked as a warning unit, not assigned a main task, only reserved for backup, and automatically triggers health diagnosis checks; the operation status label, unit operation evaluation value, and status factor of each energy storage unit are input into the operation parameter management interface, supporting real-time reading at the control layer.

[0012] Furthermore, the specific steps for constructing a scheduling priority index by calling the operating status label to filter candidate units and combining the energy storage operation data analysis and adjustment capability characteristics are as follows: Before the start of a new round of energy storage tasks, the latest operating status label and unit operation evaluation value are called to filter out qualified energy storage units as candidate units; among them, the main response units are included in the candidate scheduling sequence, ordinary operating units are used as supplementary resources, and early warning units do not participate in the current task allocation, but only retain the status monitoring channel; the energy storage operation data of the candidate units in the candidate scheduling sequence are obtained, and the energy storage task adaptability index is constructed: the product of motor output torque and acceleration is calculated, and the result is divided by the product of motor input power and slide rail friction resistance plus 1 to obtain the execution efficiency item; the reciprocal of lifting stroke plus 1 is calculated to obtain the position sensitivity item; the control response time is calculated and divided by acceleration plus 1 as the penalty item; the execution efficiency item and the position sensitivity item are added together and the penalty item is subtracted to obtain the energy storage scheduling priority value.

[0013] Furthermore, the specific steps for dynamically allocating tasks and adjusting the execution order based on this indicator are as follows: The main response units are sorted in descending order according to their energy storage scheduling priority values, and scheduling tasks are allocated sequentially based on their numerical values. When the adjustment capacity corresponding to a main response unit is insufficient for the current task requirements, the ordinary operating unit with the highest energy storage scheduling priority value is automatically called to participate in the supplementary scheduling. After the scheduling command is issued, the execution status of the energy storage task is continuously monitored in conjunction with feedback information. When problems such as response delay, increased path resistance, power anomalies, and sliding rail conflicts occur, the latest energy storage operation data is called to update the energy storage scheduling priority value, and the scheduling objects and resource allocation order are readjusted to ensure that the energy storage task execution process is continuous and controllable. After each round of energy storage tasks is completed, the energy storage scheduling priority value, scheduling command, execution response, and abnormal events generated during the scheduling process are recorded and uniformly packaged and stored to provide basic data support for subsequent strategy adjustments and behavior optimization.

[0014] Furthermore, based on the task execution feedback, the specific steps for tracking relevant operational variables and analyzing structural wear and energy load characteristics are as follows: After each round of energy storage tasks, extract energy storage operation data, calculate the standard deviation of the energy storage unit's rotational speed, the average difference between the equipment temperature and the ambient temperature, the total unit power consumption, and the average motor output torque during the task, and obtain the maximum equipment temperature to comprehensively analyze the lifespan decline trend of the energy storage unit; calculate the product of the cumulative number of operations and the standard deviation of the rotational speed as the numerator, calculate the product of the maximum equipment temperature and the average difference between the ambient temperature plus 1 as the denominator, and divide the numerator by the denominator to obtain the loss fluctuation term; calculate the total unit power consumption divided by the product of the average motor output torque and the energy efficiency factor to obtain the energy load term; add the loss fluctuation term and the energy load term to obtain the energy storage attenuation assessment value.

[0015] Furthermore, a scheduling correction mechanism is established to dynamically adjust scheduling priority ranking indicators. The specific steps to achieve coordinated optimization of scheduling behavior and lifetime control are as follows: When the energy storage attenuation assessment value shows an upward trend in three consecutive energy storage tasks and the energy storage attenuation assessment value of any task exceeds the energy storage attenuation threshold, the corresponding energy storage unit is marked as a lifetime-sensitive unit. In subsequent task allocation, the call frequency and adjustment range are restricted, and tasks with small load changes, low friction levels, and slow operating pace are only allocated. The energy storage attenuation assessment value is introduced as a correction factor into the energy storage scheduling priority value calculation structure to dynamically lower the energy storage scheduling priority value of lifetime-sensitive units, forming a mechanism for coordinated adjustment of lifetime control and scheduling ranking. After each round of energy storage tasks, the energy storage attenuation assessment value, lifetime marking status, scheduling correction record, and lifetime trend prediction results of each energy storage unit are archived and stored, and uniformly connected to the operation parameter management interface. Before the start of the next energy storage task, the data generated by the previous energy storage task is read and used as an initial parameter reference to achieve closed-loop feedback update of scheduling assessment, task allocation, and lifetime control.

[0016] The second aspect of this invention provides an energy efficiency improvement device for a sloped gravity energy storage system, comprising: an energy storage operation data acquisition and preprocessing module, an operation status identification and hierarchical evaluation module, a collaborative control and dynamic task allocation module, and an operation loss monitoring and lifespan control module. The energy storage operation data acquisition and preprocessing module is used to acquire energy storage operation data through multi-source sensor fusion, perform data cleaning and normalization on the energy storage operation data to obtain preprocessed energy storage operation data. The operation status identification and hierarchical evaluation module is used to extract multi-dimensional state factors from the preprocessed energy storage operation data, quantitatively evaluate the operation status of the energy storage unit, and construct a state hierarchical mechanism based on the evaluation results to generate operation status labels. The collaborative control and dynamic task allocation module is used to call the operation status labels to screen candidate units, analyze the adjustment capability characteristics in conjunction with the energy storage operation data, construct a scheduling priority ranking index, and dynamically allocate tasks and adjust the execution order according to this index. The operation loss monitoring and lifespan control module is used to track relevant operation variables based on task execution feedback, analyze structural wear and energy load characteristics, establish a scheduling correction mechanism, dynamically adjust the scheduling priority ranking index, and achieve collaborative optimization of scheduling behavior and lifespan control.

[0017] Beneficial effects The present invention has the following beneficial effects: (1) The energy efficiency improvement method and device of the slope gravity energy storage system, by introducing an operation evaluation model and labeling method based on state factors, innovatively establishes a multi-index fusion evaluation system from energy efficiency response to structural pressure, realizes dynamic quantification of the scheduling adaptability of energy storage units, and improves the comprehensiveness and discrimination ability of evaluation dimensions.

[0018] (2) The energy efficiency improvement method and device of the slope gravity energy storage system, by designing the energy storage scheduling priority value and collaborative sorting mechanism, combined with the three elements of execution efficiency, location sensitivity and response penalty, for the first time constructs the regulation capability index oriented to actual execution conditions, realizes the distributed dynamic scheduling among multiple units, and solves the problem that traditional methods cannot adapt to the performance differences of heterogeneous units.

[0019] (3) The energy efficiency improvement method and device of the slope gravity energy storage system establishes an energy storage attenuation assessment value and a life-sensitive labeling strategy, and embeds them into the scheduling decision process to form a closed-loop control logic that links the operating status, task allocation and structural life, effectively delaying the equipment wear process and filling the defect of life management and scheduling decoupling in the existing scheme.

[0020] (4) The energy efficiency improvement method and device of the slope gravity energy storage system, by constructing a multi-parameter energy state identification method based on normalized feature vectors, transforms the operating variables into high-dimensional state factors that can be used for subsequent scheduling modeling, realizes the effective mapping from the original operating parameters to the structured evaluation model, and improves the modeling depth and system adaptability of scheduling analysis. Attached Figure Description

[0021] Figure 1 A flowchart of a method for improving the energy efficiency of a slope-type gravity energy storage system; Figure 2 This is a structural diagram of an energy efficiency improvement device for a slope-type gravity energy storage system. Figure 3 A tiered bar chart of energy storage unit operation assessment values; Figure 4 This is a distribution diagram of scheduling priority values ​​for energy storage units. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] Please see Figures 1-4 This invention provides a technical solution: a method for improving the energy efficiency of a slope-type gravity energy storage system, comprising the following steps: S1, acquiring energy storage operation data through multi-source sensor fusion, cleaning and normalizing the energy storage operation data to obtain preprocessed energy storage operation data; S2, extracting multi-dimensional state factors from the preprocessed energy storage operation data, quantitatively evaluating the operation status of the energy storage unit, and constructing a state classification mechanism based on the evaluation results to generate operation status labels; S3, calling the operation status labels to screen candidate units, combining the energy storage operation data to analyze the regulation capability characteristics, constructing a scheduling priority ranking index, and dynamically allocating tasks and adjusting the execution order according to this index; S4, tracking relevant operation variables based on task execution feedback, analyzing structural wear and energy load characteristics, establishing a scheduling correction mechanism, dynamically adjusting the scheduling priority ranking index, and achieving coordinated optimization of scheduling behavior and lifespan control.

[0024] Specifically, the steps for obtaining preprocessed energy storage operation data through multi-source sensor fusion, followed by data cleaning and normalization, are as follows: Energy storage operation data is acquired through real-time data acquisition and processing of the drive equipment, monitoring sensors, and operation control systems of multiple energy storage units in the slope gravity energy storage system. This data includes motor input power, motor output torque, speed, acceleration, control response time, slide rail friction resistance, lifting stroke, unit power consumption, cumulative number of operations, equipment temperature, and ambient temperature. Specifically, the motor input power is obtained by reading voltage and current sensor data recorded in the power control system; feedback is obtained from the torque sensor at the motor shaft end. The system acquires the following parameters: motor output torque; rotational speed via the angle signal fed back from the motor encoder; acceleration via an accelerometer on the slide rail; control response time via monitoring the time difference between control system command signals and actual action responses; slide rail friction resistance via a friction monitoring unit in the slide rail system; lifting stroke via displacement information recorded by a position sensor; unit power consumption via voltage and current sampling over time in the power metering module; cumulative number of runs via cycle counter data recorded in the programmable logic controller system; equipment temperature via temperature sensors attached to key parts of the equipment; and ambient temperature via environmental monitoring sensors deployed at the site. The system also employs a sliding window-based median residual detection method to identify and remove abrupt changes in the energy storage operation data; time series linear interpolation to complete missing fields in the energy storage operation data; Gaussian kernel weighted moving average to smooth continuously changing indicators and suppress high-frequency fluctuations; and a combination of logarithmic transformation and maximum-minimum normalization to perform distribution correction and scale unification on the energy storage operation data, thus completing the normalization process.

[0025] This implementation plan integrates data sources from drive devices, sensors, and operation control systems across multiple energy storage units. It comprehensively collects energy storage operation data, including motor input power, motor output torque, speed, acceleration, control response time, slide rail friction resistance, lifting stroke, unit power consumption, cumulative number of operations, equipment temperature, and ambient temperature. Furthermore, it employs median residual detection to remove abrupt changes, time-series linear interpolation to fill in missing fields, Gaussian kernel-weighted moving average to smooth fluctuating signals, and a combination of logarithmic transformation and maximum-minimum normalization to achieve distribution correction and scale uniformity. This significantly improves data integrity, stability, and comparability, providing high-quality foundational data support for subsequent operational status assessment and scheduling strategy development.

[0026] Specifically, the steps for extracting multi-dimensional state factors from preprocessed energy storage operation data are as follows: Based on the preprocessed energy storage operation data, state factors are constructed for each energy storage unit. These state factors include energy efficiency factor, response factor, operating pressure factor, temperature difference factor, and stability factor. The energy efficiency factor is calculated by multiplying the ratio of motor output torque to input power by the rotational speed. This factor reflects the mechanical output efficiency level under unit power input. The response factor is calculated by summing the reciprocal of the control response time and the acceleration, characterizing the dynamic response sensitivity of the energy storage unit to control commands. The operating pressure factor is calculated by adding the lifting stroke to the cumulative number of operations, used to measure the displacement load and structural fatigue degree of the energy storage unit within the scheduling cycle. The temperature difference factor is calculated by the absolute value of the difference between the equipment temperature and the ambient temperature. This indicator reveals the influence of the coupling degree between the system's thermal load and the environment on the unit's operating state. The stability factor is calculated by multiplying the rotational speed change amplitude and the acceleration change amplitude, used to identify the coupling characteristics of speed and inertial fluctuations during the operation of the energy storage unit, thereby characterizing its overall dynamic stability level.

[0027] The specific calculation formulas for each state factor are as follows: ; ; ; ; ; In the formula, A represents the energy efficiency factor, B represents the response factor, C represents the operating pressure factor, D represents the temperature difference factor, F represents the stability factor, τ represents the motor output torque, P represents the motor input power, v represents the speed, t represents the control response time, a represents the acceleration, s represents the lifting stroke, N represents the cumulative number of runs, and T represents the equipment temperature. Indicates ambient temperature. Indicates the range of speed change. It indicates the magnitude of the change in acceleration.

[0028] In this implementation plan, this step constructs energy efficiency factors, response factors, operating pressure factors, temperature difference factors, and stability factors to achieve structured analysis and refined extraction of multi-dimensional state characteristics from energy storage operation data. This enables energy storage units to obtain comprehensive quantitative support in key dimensions such as energy conversion efficiency, control response performance, structural load level, thermal boundary changes, and dynamic stability. By maintaining consistency with the variable names in the original energy storage operation data, this process not only enhances the specificity and comparability of operation state identification but also provides standardized input for subsequent operation evaluation and scheduling control.

[0029] Specifically, the steps for quantitatively evaluating the operating status of energy storage units are as follows: Using the state factors of the energy storage units, the operating status of each unit under the current energy storage task is quantitatively evaluated: The product of the energy efficiency factor and the response factor is calculated as twice the result, reflecting the synergistic performance of unit input power and response speed in energy efficiency conversion. This result is then divided by the stability factor plus 1 to obtain the first term, which characterizes the comprehensive energy efficiency capability under dynamic fluctuation conditions; the square of the energy efficiency factor is calculated and divided by the operating pressure factor plus 1 to obtain the second term, reflecting the ability to maintain high-efficiency operation under structural load constraints; the first term and... The second addition yields the efficiency response enhancement term, used to evaluate the comprehensive adaptability under multiple coupled operating conditions. Simultaneously, the square of the temperature difference factor is calculated and added to the operating pressure factor as an indicator of the combined effect of thermodynamics and structure in the load response. This result is used as the numerator, and the stability factor plus 2 is used as the denominator. The ratio of the numerator to the denominator is calculated by subtracting 1 from the numerator to obtain the load penalty term, which reflects the negative impact of unfavorable thermal conditions and unstable operation on adaptability. Finally, the efficiency response enhancement term and the load penalty term are multiplied to obtain the unit operation evaluation value, comprehensively quantifying the comprehensive adaptability level of the energy storage unit under the current operating conditions.

[0030] The specific formula for calculating the unit operation evaluation value is as follows: ; In the formula, Z represents the unit operation evaluation value, A represents the energy efficiency factor, B represents the response factor, C represents the operating pressure factor, D represents the temperature difference factor, and F represents the stability factor.

[0031] In this embodiment, Table 1 is a data table of unit operation evaluation values, which records in detail the state factors extracted during the operation status identification and modeling process of the five energy storage units and the corresponding unit operation evaluation value calculation results, used to measure the comprehensive operation performance of each energy storage unit under the current scheduling cycle. Specifically: for Unit 1, the energy efficiency factor is 4.50, the response factor is 4.60, the operating pressure factor is 1.20, the temperature difference factor is 0.40, and the stability factor is 1.00, resulting in a calculated unit operation evaluation value of 9.12; for Unit 2, the energy efficiency factor is 3.80, the response factor is 3.70, the operating pressure factor is 1.10, the temperature difference factor is 0.45, and the stability factor is 1.20, resulting in a calculated unit operation evaluation value of 8.34; for Unit 3, the energy efficiency factor is 2.10, the response factor is 2.00, and the operating pressure factor is 0.95. In Unit 1, the temperature difference factor is 0.60, the stability factor is 1.50, and the calculated unit operation evaluation value is 6.85; in Unit 2, the energy efficiency factor is 3.60, the response factor is 3.90, the operating pressure factor is 1.05, the temperature difference factor is 0.42, the stability factor is 1.10, and the calculated unit operation evaluation value is 7.92; in Unit 3, the energy efficiency factor is 1.90, the response factor is 1.80, the operating pressure factor is 0.90, the temperature difference factor is 0.70, the stability factor is 1.60, and the calculated unit operation evaluation value is 5.67.

[0032] Table 1 Unit Operation Evaluation Values ​​Data Table

[0033] like Figure 3 The chart shown is a tiered bar chart of energy storage unit operation assessment values, displaying the unit operation assessment values ​​and their tiers for five energy storage units. The color of each bar represents its corresponding operation status label in the current scheduling cycle, as follows: Unit 1 has an operation assessment value of 9.12, which is above the upper limit of the assessment value, and is marked as a main response unit with priority scheduling qualification; Units 2 and 4 have assessment values ​​of 8.34 and 7.92 respectively, which are in the middle assessment range, and are marked as ordinary operating units, which can be used as supplementary resources for scheduling; Units 3 and 5 have operation assessment values ​​of 6.85 and 5.67 respectively, which are below the lower limit of the operation adaptability, and are marked as warning units, requiring limited call frequency and priority scheduling to light-load, slow-paced task sequences. The chart uses color to distinguish label categories, and the assessment values ​​are directly displayed numerically at the top of the bars, clearly reflecting the correspondence between the current operation status, scheduling capability, and health level of each energy storage unit, providing quantitative support for subsequent task allocation and lifespan control strategies.

[0034] This implementation plan constructs a multi-dimensional evaluation structure by integrating energy efficiency factors, response factors, operating pressure factors, temperature difference factors, and stability factors. This structure comprehensively analyzes the operating status of energy storage units from multiple aspects, including energy conversion capability, dynamic response characteristics, structural load level, thermal state fluctuations, and operational stability. By introducing a comparison mechanism between efficiency response enhancement terms and load penalty terms, a detailed quantification of the comprehensive adaptability of energy storage units is achieved. This provides accurate performance data for subsequent operating status label generation and task scheduling, effectively supporting scientific decision-making in energy storage task allocation.

[0035] Specifically, the steps for constructing a state classification mechanism based on the evaluation results and generating operational status labels are as follows: After each energy storage task is completed, the unit operation evaluation values ​​of all energy storage units are summarized and compared with the Level 1 and Level 2 operational adaptation thresholds. Classification is then performed based on the relative position of the evaluation value within the threshold range. When the unit operation evaluation value is greater than or equal to the Level 2 operational adaptation threshold, the energy storage unit is marked as a primary response unit. These units possess strong energy efficiency conversion capabilities, rapid response capabilities, and structural stability, and are given priority in participating in high-load energy regulation tasks, allowing them to enter continuous operation and execute high-frequency task scheduling. When the unit operation evaluation value is less than the Level 2 operational adaptation threshold but greater than the Level 1 operational adaptation threshold, the energy storage unit is marked as a normal operation unit. These units are generally in good condition but have some limiting factors. They are mainly used to replace the primary response unit in energy regulation tasks, and their operating frequency is controlled during scheduling to prevent accelerated wear caused by load concentration. When a unit's operational evaluation value is less than or equal to the Level 1 operational adaptation threshold, the energy storage unit is marked as an early warning unit, indicating that it has issues with decreased structural stability and low energy efficiency during the current task cycle. No major tasks are scheduled for this unit; only its status monitoring channel is retained, and a health diagnostic check process is automatically triggered. Finally, the operational status tags, operational evaluation values, and their corresponding energy efficiency factors, response factors, operational pressure factors, temperature difference factors, and stability factors of each energy storage unit are input into the operational parameter management interface. This supports real-time invocation and periodic reading by the control layer, providing fundamental support for the generation of subsequent energy storage task scheduling strategies.

[0036] This implementation scheme constructs a state-level mechanism based on unit operation evaluation values, combined with a dual-threshold comparison strategy of first-level and second-level operation adaptation thresholds, to achieve refined identification and dynamic classification of the operating status of each energy storage unit, effectively distinguishing the scheduling roles of main response units, ordinary operating units, and early warning units. By combining the analysis results of energy efficiency factors, response factors, operating pressure factors, temperature difference factors, and stability factors, the scheduling strategy's ability to perceive performance differences among energy storage units is significantly improved, avoiding the allocation of high-load tasks to potentially faulty units, and enhancing the reliability of task scheduling and the safety of system operation. The synchronous output of operating status tags and the integration of parameter interfaces further ensure the real-time response and periodic optimization of the control layer, realizing the deep integration of energy storage task scheduling, health management, and energy efficiency control.

[0037] Specifically, the steps for using operational status tags to filter candidate units and combining energy storage operation data to analyze adjustment capability characteristics and construct scheduling priority ranking indicators are as follows: Before the start of a new round of energy storage tasks, the latest operational status tags and unit operation evaluation values ​​are used to filter out qualified energy storage units as candidate units; among them, operational status tags indicate that the main response units are included in the candidate scheduling sequence for priority matching of high-load tasks, ordinary operating units are dynamically allocated as backup resources, and early warning units, because their unit operation evaluation values ​​are lower than the first-level operation adaptation threshold, only retain the status monitoring channel to avoid their participation in task scheduling and causing failure risks; obtain the energy storage operation data of each candidate unit in the candidate scheduling sequence, including motor input power, motor... Output torque, acceleration, slide rail friction resistance, lifting stroke, and control response time are used to construct an energy storage task adaptability index: The product of motor output torque and acceleration is calculated, and the result is divided by the product of motor input power and slide rail friction resistance plus 1 to obtain the execution efficiency term, which measures the instantaneous driving capability of the unit; the reciprocal of the lifting stroke plus 1 is calculated to reflect the impact of its displacement on scheduling sensitivity, serving as a position sensitivity term; the control response time is calculated and divided by acceleration plus 1 to quantify the inhibitory effect of execution lag on the overall response, serving as a penalty term; the execution efficiency term and the position sensitivity term are added together and the penalty term is subtracted to obtain the energy storage scheduling priority value that comprehensively reflects the regulation capability, response efficiency, and load adaptability, providing a quantitative basis for subsequent task allocation and ranking.

[0038] The specific formula for calculating the priority value of energy storage dispatch is as follows: ; In the formula, X represents the energy storage scheduling priority value, τ represents the motor output torque, a represents the acceleration, P represents the motor input power, μ represents the slide rail friction resistance, s represents the lifting stroke, and t represents the control response time.

[0039] In this embodiment, Table 2 is a data table of energy storage scheduling priority values, which records in detail the key state variables and corresponding energy storage scheduling priority value calculation results of the five candidate energy storage units during the scheduling modeling and analysis process. This is used to measure the adaptability and execution advantages of each unit in undertaking the regulation task within the current cycle. Specifically: In candidate unit 1, the motor output torque is 220 Nm, acceleration is 0.6 Nm, motor input power is 5.5 Nm, slide rail friction resistance is 0.15 Nm (dimensionless), lifting stroke is 15.0 Nm, control response time is 0.9 Nm, and the calculated energy storage scheduling priority value is 71.83; In candidate unit 2, the motor output torque is 180 Nm, acceleration is 0.5 Nm, motor input power is 4.8 Nm, slide rail friction resistance is 0.18 Nm, lifting stroke is 12.0 Nm, control response time is 1.1 Nm, and the calculated energy storage scheduling priority value is 47.63; In candidate unit 3, the motor output torque is 160 Nm, acceleration is 0.4 Nm, motor input power is 4.2 Nm, slide rail friction resistance is 0.15 Nm (dimensionless), lifting stroke is 15.0 Nm, control response time is 0.9 Nm, and the calculated energy storage scheduling priority value is 71.8 ...2, the motor output torque is 180 Nm, acceleration is 0.5 Nm, motor input power is 4.2 Nm, slide rail friction resistance is 0.18 Nm, lifting stroke is 12.0 Nm With a resistance of 0.20, a lifting stroke of 18.0, and a control response time of 1.3, the calculated energy storage scheduling priority value is 33.91. In candidate unit 4, the motor output torque is 200, the acceleration is 0.7, the motor input power is 5.0, the slide rail friction resistance is 0.16, the lifting stroke is 14.5, and the control response time is 0.8, the calculated energy storage scheduling priority value is 77.37. In candidate unit 5, the motor output torque is 150, the acceleration is 0.45, the motor input power is 4.5, the slide rail friction resistance is 0.19, the lifting stroke is 16.0, and the control response time is 1.2, the calculated energy storage scheduling priority value is 35.62.

[0040] Table 2 Energy Storage Dispatch Priority Data Table

[0041] like Figure 4 The chart shown presents the priority distribution of energy storage units, displaying the priority values ​​of five candidate energy storage units. The bar chart visually reflects the task adaptability of each unit in the current scheduling cycle. Higher values ​​indicate better overall performance in execution efficiency, location response, and control timeliness, resulting in a higher scheduling priority. Each bar is labeled with its corresponding value to facilitate quick identification of priority scheduling targets, providing decision support for subsequent task allocation and resource coordination.

[0042] This implementation scheme constructs an energy storage task adaptability index by incorporating motor input power, motor output torque, acceleration, slide rail friction resistance, lifting stroke, and control response time from energy storage operation data, and generates energy storage scheduling priority values. This effectively quantifies the adjustment capability and response efficiency of each energy storage unit under current task conditions. Compared to static rules and single-parameter scheduling strategies, this method fully integrates execution effectiveness, location sensitivity, and penalty terms, enabling accurate assessment of the comprehensive scheduling value of energy storage units. This improves the flexibility and matching degree of scheduling decisions, ensuring the real-time performance and efficiency of energy allocation.

[0043] Specifically, the steps for dynamically allocating tasks and adjusting the execution order based on this indicator are as follows: The main response units are ranked in descending order according to their energy storage scheduling priority value. Scheduling tasks are allocated sequentially according to the magnitude of the energy storage scheduling priority value, prioritizing energy storage units with higher overall task adaptability to undertake the main regulation load. When the regulation capacity corresponding to a main response unit is insufficient to cover the current task requirements, the unit with the highest energy storage scheduling priority value among the ordinary operating units is automatically called to participate in supplementary scheduling, ensuring complete coverage of regulation capacity and smooth connection of the response path. After the scheduling command is issued, the system combines speed, acceleration, motor output torque, and slide rail friction... The system continuously monitors the execution of energy storage tasks based on feedback information such as resistance. When deviations in scheduling occur, such as response delays, increased path resistance, abnormal motor input power, and rail conflicts, the system calls up the latest energy storage operation data in real time, updates the energy storage scheduling priority values ​​of relevant energy storage units, and dynamically adjusts the scheduling objects and resource allocation order to ensure that the energy storage task execution process is continuous, efficient, and controllable. After each round of energy storage tasks is completed, the system records the energy storage scheduling priority values, scheduling instructions, execution responses, and abnormal events generated during the scheduling process, and packages and stores them in a unified manner in the operation parameter management interface to provide basic data support for subsequent scheduling strategy adjustments and operation behavior optimization.

[0044] In this implementation plan, by introducing energy storage scheduling priority values ​​as a dynamic sorting basis for task allocation and execution order, the scheduling coordination between the main response unit and ordinary operating units is improved, and the task adaptation based on key energy storage operation data such as motor output torque, acceleration, motor input power, and slide rail friction resistance is refined. During execution, the energy storage scheduling priority values ​​are dynamically adjusted in conjunction with real-time feedback, making task allocation adaptive and interference robust. At the same time, the archiving and management of scheduling results and process data provides traceable data support for subsequent strategy optimization and behavior evolution modeling, significantly enhancing the transparency of scheduling behavior and the closed-loop nature of control.

[0045] Specifically, the steps for tracking relevant operational variables and analyzing structural wear and energy load characteristics based on task execution feedback are as follows: After each round of energy storage tasks, energy storage operation data is extracted, and key variables are obtained from the motor, transmission structure, and environmental monitoring channels. During the task, the standard deviation of the energy storage unit's rotational speed, the average difference between the equipment temperature and the ambient temperature, the total unit power consumption, and the average motor output torque are calculated. The maximum equipment temperature is also obtained simultaneously as a basic parameter characterizing the unit's thermal load and structural stress fluctuations. The lifespan decline trend of the energy storage unit is comprehensively analyzed. Furthermore, the product of the cumulative number of operations and the standard deviation of the rotational speed is calculated as the numerator, and the product of the maximum equipment temperature and the average difference between the ambient temperature plus 1 is used as the denominator. The ratio of the two yields the loss fluctuation term, which is used to characterize the dynamic wear level of the mechanical structure. At the same time, the total unit power consumption is calculated and divided by the product of the average motor output torque and the energy efficiency factor to obtain the energy load term, which is used to assess the energy utilization pressure of the electric drive system. Finally, the loss fluctuation term and the energy load term are added together to form a comprehensive energy storage attenuation assessment value characterizing structural fatigue and energy accumulation, which serves as the core reference indicator for subsequent lifespan control and task scheduling adjustments.

[0046] The specific formula for calculating the energy storage degradation assessment value is as follows: ; In the formula, L represents the energy storage degradation assessment value, and N represents the cumulative number of operations. Indicates the standard deviation of rotational speed. Indicates the maximum equipment temperature. E represents the average difference between the equipment temperature and the ambient temperature, and E represents the total power consumption of the unit. This represents the average output torque of the motor, and A represents the energy efficiency factor.

[0047] This implementation plan, by tracking key operational variables during energy storage task execution and combining them with the standard deviation of rotational speed, the average difference between equipment temperature and ambient temperature, total unit power consumption, average motor output torque, and maximum equipment temperature, constructs an energy storage degradation assessment index. This index comprehensively characterizes the structural wear characteristics and energy consumption pressure levels of energy storage units under different regulating loads. This method effectively enhances the interpretability of energy storage operation data in lifetime trend analysis, realizing the transformation from operational status monitoring to lifetime degradation identification, and providing a quantitative basis for subsequent scheduling constraints and maintenance strategies.

[0048] Specifically, the following steps are taken to establish a scheduling correction mechanism to dynamically adjust scheduling priority ranking indicators and achieve coordinated optimization of scheduling behavior and lifespan control: When the energy storage degradation assessment value shows an upward trend in three consecutive energy storage tasks, and the energy storage degradation assessment value exceeds the set energy storage degradation threshold in any task, the energy storage unit is marked as a lifespan-sensitive unit, and its participation in high-frequency, large-amplitude energy regulation behavior is automatically restricted. It is prioritized for allocation to task types with small load changes, low rail friction resistance, and slower operating pace to reduce wear risk. Based on this, the energy storage degradation assessment value is introduced as a correction factor into the energy storage scheduling priority value calculation structure. By dynamically lowering the energy storage scheduling priority value of lifespan-sensitive units, their position in the scheduling ranking is adjusted in real time, achieving linked control of scheduling priority and health status. After each round of energy storage tasks is completed, the energy storage degradation assessment value, lifetime marker status, scheduling correction record and lifetime trend prediction result of each energy storage unit are archived and stored, and connected to the operation parameter management interface to form a data-driven status update mechanism. Before the next energy storage task is started, the archived data in the previous round of tasks is automatically read and used as the evaluation input in the initialization phase to realize closed-loop feedback and collaborative optimization of energy storage task scheduling, resource allocation and lifetime control.

[0049] In this implementation plan, an energy storage degradation assessment value is introduced as a correction factor for scheduling ranking. Combined with lifetime marker status and trend prediction results from historical energy storage task data, a dynamic correlation mechanism between operating status and scheduling behavior is established. By lowering the scheduling priority of lifetime-sensitive units, proactive intervention is achieved for frequently called, high-intensity operating units, effectively mitigating operational fatigue and lifetime degradation caused by the accumulation of motor output torque, motor input power, and slide rail friction resistance. Finally, by archiving energy storage operating data and status marker information, an initialization assessment reference is constructed before scheduling, achieving a closed-loop linkage between scheduling assessment, task allocation, and lifetime control. This helps improve the flexibility of the system scheduling strategy and the accuracy of lifetime management.

[0050] like Figure 2As shown, the second aspect of the present invention provides an energy efficiency improvement device for a slope-type gravity energy storage system, comprising: an energy storage operation data acquisition and preprocessing module, an operation status identification and hierarchical evaluation module, a collaborative control and dynamic task allocation module, and an operation loss monitoring and lifespan control module. The energy storage operation data acquisition and preprocessing module is used to acquire energy storage operation data through multi-source sensor fusion, perform data cleaning and normalization on the energy storage operation data to obtain preprocessed energy storage operation data. The operation status identification and hierarchical evaluation module is used to extract multi-dimensional state factors from the preprocessed energy storage operation data, quantitatively evaluate the operation status of the energy storage unit, and construct a state hierarchical mechanism based on the evaluation results to generate operation status labels. The collaborative control and dynamic task allocation module is used to call the operation status labels to screen candidate units, analyze the adjustment capability characteristics in conjunction with the energy storage operation data, construct a scheduling priority ranking index, and dynamically allocate tasks and adjust the execution order according to this index. The operation loss monitoring and lifespan control module is used to track relevant operation variables based on task execution feedback, analyze structural wear and energy load characteristics, establish a scheduling correction mechanism, dynamically adjust the scheduling priority ranking index, and achieve collaborative optimization of scheduling behavior and lifespan control.

[0051] This implementation scheme achieves closed-loop management of energy storage units in a sloped gravity energy storage system from data acquisition and status identification to task allocation and lifespan control by constructing a complete functional chain including a data acquisition and preprocessing module for energy storage operation, a status identification and hierarchical evaluation module, a collaborative control and dynamic task allocation module, and a loss monitoring and lifespan control module. Energy storage operation data acquired through multi-source sensor fusion is cleaned and normalized to improve the accuracy of status factor extraction and operation evaluation. The dynamic hierarchical and task allocation mechanism based on operation status tags enhances the matching capability of regulatory resources. Structural wear and energy consumption analysis combined with scheduling execution feedback enables dynamic correction of energy storage scheduling priorities and risk management of lifespan-sensitive units, thereby effectively improving the operating efficiency of energy storage units and the overall energy efficiency of the system, and enhancing the safety, stability, and economy of system operation.

[0052] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0053] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for improving the energy efficiency of a slope-type gravity energy storage system, characterized in that, Includes the following steps: S1. Energy storage operation data is acquired by multi-source sensor fusion, and the energy storage operation data is cleaned and normalized to obtain pre-processed energy storage operation data. S2. Multidimensional state factors are extracted from the preprocessed energy storage operation data to quantitatively evaluate the operation status of the energy storage unit, and a state classification mechanism is constructed based on the evaluation results to generate operation status labels. S3, call the running status label to filter candidate units, combine energy storage operation data to analyze the regulation capability characteristics, construct scheduling priority ranking index, and dynamically allocate tasks and adjust the execution order according to the scheduling priority ranking index; S4 tracks relevant operational variables based on task execution feedback, analyzes structural wear and energy load characteristics, establishes a scheduling correction mechanism, dynamically adjusts scheduling priority ranking indicators, and achieves coordinated optimization of scheduling behavior and lifespan control.

2. The method for improving the energy efficiency of a slope-type gravity energy storage system according to claim 1, characterized in that: The specific steps for obtaining preprocessed energy storage operation data by fusing multi-source sensor data, cleaning and normalizing the data are as follows: By acquiring and processing real-time data from the drive equipment, monitoring sensors, and operation control system of multiple energy storage units in the slope gravity energy storage system, energy storage operation data is obtained. The energy storage operation data includes motor input power, motor output torque, speed, acceleration, control response time, slide rail friction resistance, lifting stroke, unit power consumption, cumulative number of operations, equipment temperature, and ambient temperature. The data undergoes normalization by identifying and removing abrupt changes in energy storage operation data using a sliding window-based median residual detection method; completing missing fields in the data using time series linear interpolation; smoothing continuously changing indicators and suppressing high-frequency fluctuations using Gaussian kernel-weighted moving average; and performing distribution correction and scaling on the energy storage operation data by combining logarithmic transformation and maximum-minimum normalization methods.

3. The method for improving the energy efficiency of a slope-type gravity energy storage system according to claim 1, characterized in that: The specific steps for extracting multidimensional state factors from preprocessed energy storage operation data are as follows: Based on the preprocessed energy storage operation data, the state factors of each energy storage unit are constructed as follows: the energy efficiency factor is calculated by multiplying the ratio of motor output torque to input power by the rotational speed; the response factor is calculated by summing the reciprocal of the control response time and the acceleration; the operating pressure factor is calculated by adding the lifting stroke to the cumulative number of operations; the temperature difference factor is calculated by the absolute value of the difference between the equipment temperature and the ambient temperature; and the stability factor is calculated by multiplying the rotational speed change amplitude and the acceleration change amplitude.

4. The method for improving the energy efficiency of a slope-type gravity energy storage system according to claim 1, characterized in that: The specific steps for quantitatively evaluating the operating status of the energy storage unit are as follows: The operating status of each energy storage unit under the current energy storage task is quantitatively evaluated using the state factor of the energy storage unit: the product of the energy efficiency factor and the response factor is multiplied by two, and the result is divided by the stability factor plus 1 to obtain the first term; the square of the energy efficiency factor is divided by the operating pressure factor plus 1 to obtain the second term; the first term and the second term are added together to obtain the efficiency response enhancement term. Calculate the squared value of the temperature difference factor plus the operating pressure factor, and use the sum as the numerator; Calculate the stability factor and add 2 to the denominator; Calculate 1 and subtract the ratio of the numerator to the denominator to obtain the load penalty term; multiply the efficiency response enhancement term by the load penalty term to obtain the unit operation evaluation value.

5. The method for improving the energy efficiency of a slope-type gravity energy storage system according to claim 1, characterized in that: The specific steps for constructing a state classification mechanism based on the evaluation results and generating operational status labels are as follows: After each energy storage task is completed, the unit operation evaluation values ​​of all energy storage units are summarized and compared with the operation adaptation threshold: when the unit operation evaluation value is greater than or equal to the secondary operation adaptation threshold, the energy storage unit is marked as the main response unit, given priority to participate in high-load energy regulation tasks, and allowed to enter the continuous operation state. When the unit operation assessment value is less than the level 2 operation adaptation threshold but greater than the level 1 operation adaptation threshold, the energy storage unit is marked as a normal operation unit, and its energy regulation task is rotated to replace the main response unit, the operation frequency is controlled, and centralized scheduling is avoided; when the unit operation assessment value is less than or equal to the level 1 operation adaptation threshold, the energy storage unit is marked as an early warning unit, no main task is assigned, only standby is reserved, and a health diagnosis check is automatically triggered. The operating status tags, unit operating evaluation values, and status factors of each energy storage unit are input into the operating parameter management interface, which supports real-time reading at the control layer.

6. The method for improving the energy efficiency of a slope-type gravity energy storage system according to claim 1, characterized in that: The specific steps for filtering candidate units by calling up operational status tags, analyzing regulation capability characteristics based on energy storage operation data, and constructing scheduling priority ranking indicators are as follows: Before a new round of energy storage tasks begins, the latest operating status tags and unit operating evaluation values ​​are used to screen out qualified energy storage units as candidate units. Among them, the main response units are included in the candidate scheduling sequence, ordinary operating units are used as supplementary resources, and early warning units do not participate in the current task allocation, but only retain the status monitoring channel. Obtain energy storage operation data of candidate units in the candidate scheduling sequence and construct energy storage task adaptability index: calculate the product of motor output torque and acceleration, divide the result by the product of motor input power and slide rail friction resistance plus 1 to obtain the execution efficiency item; calculate the reciprocal of lifting stroke plus 1 to obtain the position sensitivity item; calculate the control response time divided by acceleration plus 1 as the penalty item; add the execution efficiency item and the position sensitivity item and subtract the penalty item to obtain the energy storage scheduling priority value.

7. The method for improving the energy efficiency of a slope-type gravity energy storage system according to claim 1, characterized in that: The specific steps for dynamically allocating tasks and adjusting execution order based on scheduling priority indicators are as follows: The main response units are sorted in descending order according to their energy storage scheduling priority values, and scheduling tasks are assigned in order of their numerical values. When the regulation capacity of the main response unit is insufficient for the current task requirements, the ordinary operating unit with the highest energy storage scheduling priority value is automatically called to participate in the supplementary scheduling. After the dispatching command is issued, the execution of the energy storage task is continuously monitored in conjunction with the feedback information. When problems such as response delay, increased path resistance, power anomaly and sliding rail conflict occur, the latest energy storage operation data is called up, the energy storage dispatching priority value is updated, and the dispatching objects and resource allocation order are readjusted to ensure that the execution process of the energy storage task is continuous and controllable. After each round of energy storage tasks is completed, the energy storage scheduling priority value, scheduling instructions, execution response and abnormal events generated during the scheduling process are recorded, packaged and stored in a unified manner to provide basic data support for subsequent strategy adjustments and behavior optimization.

8. The method for improving the energy efficiency of a slope-type gravity energy storage system according to claim 1, characterized in that: The specific steps for tracking relevant operational variables and analyzing structural wear and energy load characteristics based on task execution feedback are as follows: After each round of energy storage missions, extract energy storage operation data, calculate the standard deviation of the rotational speed of the energy storage unit, the average difference between the equipment temperature and the ambient temperature, the total power consumption of the unit and the average output torque of the motor during the mission, and obtain the maximum equipment temperature to comprehensively analyze the declining trend of the energy storage unit's lifespan. The product of the cumulative number of runs and the standard deviation of the rotational speed is used as the numerator, and the product of the average difference between the maximum equipment temperature and the ambient temperature plus 1 is used as the denominator. The numerator is divided by the denominator to obtain the loss fluctuation term. The total power consumption of the calculation unit is divided by the product of the average output torque of the motor and the energy efficiency factor to obtain the energy load item; the loss fluctuation item is added to the energy load item to obtain the energy storage degradation assessment value.

9. The method for improving the energy efficiency of a slope-type gravity energy storage system according to claim 1, characterized in that: The specific steps for establishing a scheduling correction mechanism to dynamically adjust scheduling priority indicators and achieve coordinated optimization of scheduling behavior and lifetime control are as follows: When the energy storage degradation assessment value shows an upward trend in three consecutive energy storage tasks and the energy storage degradation assessment value of any task exceeds the energy storage degradation threshold, the corresponding energy storage unit will be marked as a lifespan-sensitive unit. In subsequent task allocation, the calling frequency and adjustment range will be restricted, and it will only be allocated to task types with small load changes, low friction levels, and slow operating pace. In the energy storage scheduling priority calculation structure, the energy storage decay assessment value is introduced as a correction factor to dynamically lower the energy storage scheduling priority value for lifetime-sensitive units, forming a mechanism for coordinated adjustment of lifetime control and scheduling ranking. After each round of energy storage tasks is completed, the energy storage degradation assessment value, lifetime marker status, scheduling correction record and lifetime trend prediction result of each energy storage unit are archived and stored, and uniformly connected to the operation parameter management interface. Before the next energy storage task begins, the data generated by the previous energy storage task is read and used as the initial parameter reference to realize the closed-loop feedback update of scheduling assessment, task allocation and lifetime control.

10. An energy efficiency improvement device for a sloped gravity energy storage system, employing the energy efficiency improvement method for a sloped gravity energy storage system as described in any one of claims 1-9, characterized in that: include: The energy storage operation data acquisition and preprocessing module, operation status identification and hierarchical assessment module, collaborative control and dynamic task allocation module, and operation loss monitoring and lifespan control module, among which: The energy storage operation data acquisition and preprocessing module is used to acquire energy storage operation data through multi-source sensor fusion, perform data cleaning and normalization on the energy storage operation data, and obtain preprocessed energy storage operation data. The operation status identification and hierarchical evaluation module is used to extract multi-dimensional state factors from preprocessed energy storage operation data, quantitatively evaluate the operation status of energy storage units, construct a state hierarchical mechanism based on the evaluation results, and generate operation status labels. The collaborative control and task dynamic allocation module is used to call the running status label to filter candidate units, combine energy storage operation data to analyze and adjust the adjustment capability characteristics, construct scheduling priority ranking indicators, and dynamically allocate tasks and adjust the execution order according to the scheduling priority ranking indicators. The operation loss monitoring and life control module is used to track relevant operation variables based on task execution feedback, analyze structural wear and energy load characteristics, establish a scheduling correction mechanism, dynamically adjust scheduling priority indicators, and achieve coordinated optimization of scheduling behavior and life control.

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