A photovoltaic energy storage configuration system

Abstract

The present invention relates to the field of photovoltaic energy storage technology, and specifically discloses a photovoltaic energy storage configuration system, which is used to solve the problem that when a photovoltaic gravity energy storage system is constructed in an existing abandoned mine, stones often slip during the process of lifting or releasing heavy objects, causing the generator to be unable to maintain a stable rotation speed and output power, thereby causing a large fluctuation in power output; the present invention detects and analyzes the stone bearing weight on the bottom of a heavy object elevator, obtains a first weight detection value, a second weight detection value, and a third weight detection value, respectively, calculates a stone slip risk value based on the above detection values, monitors data changes during the operation of the heavy object elevator, obtains a power output fluctuation value based on a power output fluctuation value analysis model, and can quickly identify and adjust influencing factors, thereby issuing an early warning when the risk of stone slip is high to remind operators to take countermeasures and reduce the occurrence of slip events.

Description

Technical Field

[0001] The present invention relates to the field of photovoltaic energy storage technology, and more specifically, to a photovoltaic energy storage configuration system. Background Art

[0002] Using abandoned mines to build photovoltaic gravity energy storage systems is an innovative and sustainable energy storage solution. Photovoltaic panels installed above or near the mines collect solar energy and convert it into electrical energy. When wind and solar power generation exceeds demand, excess electrical energy is used to drive motors to lift heavy objects (such as concrete blocks and stones) to the top of the mine and store them as potential energy. When power demand peaks or wind and solar power is insufficient, the heavy objects are released to fall, and the potential energy of the heavy objects is used to drive generators to generate electricity, which is converted into electrical energy and provided to power grids or industrial users. Combining the advantages of photovoltaic power generation and gravity energy storage, it can not only efficiently utilize natural resources, but also realize the reuse of abandoned mines. However, stones often slip during the process of lifting or releasing heavy objects, causing the generator to be unable to maintain a stable speed and output power, resulting in large fluctuations in power output, making it difficult to accurately control the gravity energy storage capacity of the mine and the degree of matching with photovoltaic power generation, making it difficult to ensure that there is enough capacity to store energy during the peak period of photovoltaic power generation, and resulting in the inability to fully release energy during the low period of photovoltaic power generation. In order to solve the above problems, a technical solution is now provided. Summary of the invention

[0003] In order to overcome the above-mentioned defects of the prior art, the present invention provides a photovoltaic energy storage configuration system, which detects and analyzes the stone bearing weight on the bottom of the weight lift, calculates the risk value of stone sliding, and then monitors the data changes during the operation of the weight lift. The power output fluctuation value is obtained based on the power output fluctuation value analysis model, and the influencing factors can be quickly identified and adjusted to solve the problems raised in the above-mentioned background technology.

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] A photovoltaic energy storage configuration system includes a photovoltaic power generation center, a weight elevator and a gravity energy storage center; the photovoltaic power generation center and the weight elevator are respectively connected to the gravity energy storage center; the gravity energy storage center includes a stone sliding detection module and a primary risk detection module; the stone sliding detection module is used to detect and analyze the stone bearing weight on the bottom surface of the weight elevator: the bottom surface of the weight elevator is divided into a plurality of detection areas, a plurality of detection target points are set in each detection area, a weight sensor is set at each detection target point, and a first weight detection value, a second weight detection value and a third weight detection value are obtained respectively; the primary risk detection module is used to obtain the first weight detection value, the second weight detection value and the third weight detection value, and the first weight detection value, the second weight detection value and the third weight detection value are introduced into the stone sliding risk value calculation formula to calculate the primary risk value, and the stone sliding risk value calculation formula is:

[0006]

[0007] Where: F sk is the first-level risk value, N is the number of target points detected on the bottom surface of the heavy object lift, is the first weight detection value of the i-th detection target point, is the second weight detection value of the i-th detection target point, T is the total number of monitoring moments in the operation monitoring cycle of the heavy object lift, is the third weight detection value of the i-th detection target point at time t, is the maximum third weight detection value of all detection target points at time t, is the minimum third weight detection value of all detection target points at time t, and ε is the adjustment parameter to prevent the numerator from being zero.

[0008] As a further solution of the present invention, the gravity energy storage center also includes a power output fluctuation analysis module; the power output fluctuation analysis module is used to extract the first-level risk value, the weight elevator load change value at each moment of the operation monitoring period, the weight elevator speed change value and the humidity change value in the mine, and import the first-level risk value, the weight elevator load change value, the weight elevator speed change value and the humidity change value in the mine into the power output fluctuation value analysis model to calculate the power output fluctuation value. The calculation formula of the power output fluctuation value analysis model is:

[0009]

[0010] Where: b sf is the power output fluctuation value, F sk is the first-level risk value, v j+1 is the change in speed of the weight lifter at the j+1th moment, v j is the change in speed of the weight lifter at the jth moment, fj+1 is the load change value of the heavy object elevator at the j+1th moment, f j is the load change of the heavy object elevator at the jth moment, s j+1 is the humidity change value in the mine at the j+1th moment, s j is the humidity change value in the mine at the jth moment, v b is the average value of the speed change of the heavy object lift during the operation monitoring period, is the initial load value of the heavy object lift, s b It is the average change of humidity in the mine during the operation monitoring period.

[0011] As a further solution of the present invention, the gravity energy storage center also includes an elevator monitoring module; the elevator monitoring module is used to monitor the data changes during the operation of the heavy object elevator: an operation monitoring cycle is generated for the operation process of the heavy object elevator, and the load change value of the heavy object elevator, the speed change value of the heavy object elevator and the humidity change value in the mine are obtained at each moment of the operation monitoring cycle.

[0012] As a further solution of the present invention, the load change value of the heavy object elevator is the difference between the initial load value of the heavy object elevator at the moment and the final load value of the heavy object elevator. The second weight detection value of the heavy object elevator is obtained based on the stone sliding detection module, and the initial load value of the heavy object elevator is obtained after summarizing; the third weight detection value of the heavy object elevator is obtained based on the stone sliding detection module, and the final load value of the heavy object elevator at each moment in the operation monitoring cycle is obtained after summarizing, so the calculation formula of the load change value of the heavy object elevator is:

[0013]

[0014] Where: f j is the load change value of the heavy object elevator at the jth moment, is the initial load value of the heavy lift, is the final load value of the heavy object elevator at the jth moment, is the weight detection value of the i-th detection target point after the heavy object lift is loaded. It is the real-time weight detection value of the i-th detection target point at the i-th moment during the operation of the heavy object lift.

[0015] As a further solution of the present invention, the first weight detection value is the weight detection value of each detection target point before the heavy object elevator is loaded, the second weight detection value is the weight detection value of each detection target point after the heavy object elevator is loaded, and the third weight detection value is the real-time weight detection value of each detection target point during the operation of the heavy object elevator.

[0016] As a further solution of the present invention, the stone fall detection module is also used to perform variance calculation on the first weight detection values ​​of all detection target points to obtain the basic distribution coefficient of the heavy object elevator, perform variance calculation on the second weight detection values ​​of all detection target points to obtain the secondary distribution coefficient of the heavy object elevator, and perform variance calculation on the third weight detection values ​​of all detection target points at the same time to obtain the tertiary distribution coefficient of the heavy object elevator.

[0017] The technical effects and advantages of a photovoltaic energy storage configuration system of the present invention are as follows: the present invention detects and analyzes the stone bearing weight on the bottom of the weight lift, which helps to avoid mechanical failures or safety accidents caused by stone sliding, thereby ensuring the overall safety of the system; respectively obtains a first weight detection value, a second weight detection value, and a third weight detection value, and calculates the stone sliding risk value based on the above detection values, which helps to store more energy during the peak period of photovoltaic power generation and release energy more efficiently during the power generation trough period; then monitors the data changes during the operation of the weight lift, and obtains the power output fluctuation value based on the power output fluctuation value analysis model, which can quickly identify and adjust the influencing factors, thereby issuing an early warning when the risk of stone sliding is high to remind the operator to take countermeasures and reduce the occurrence of sliding events. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A schematic diagram of the structure of a photovoltaic energy storage configuration system provided in Example 1 of the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the accompanying drawings in the present invention to clearly and completely describe the technical solution in the present invention. Obviously, the described technical solution is only a part of the present invention, not all of it. Based on the technical solution in the present invention, all other technical solutions obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0020] Figure 1 FIG. 1 shows a schematic diagram of the structure of a photovoltaic energy storage configuration system provided by Embodiment 1 of the present invention. Figure 1 As shown, a photovoltaic energy storage configuration system in this embodiment includes a photovoltaic power generation center, a heavy object elevator and a gravity energy storage center; the photovoltaic power generation center and the heavy object elevator are respectively connected to the gravity energy storage center;

[0021] Photovoltaic power generation centers are used to collect solar energy and convert it into electricity;

[0022] The gravity energy storage center is used to analyze the impact of rock fall risks on power output fluctuations;

[0023] Heavy-duty lifts are used to lift or release heavy objects.

[0024] The gravity energy storage center includes a stone sliding detection module, a primary risk detection module, an elevator monitoring module and a power output fluctuation analysis module; the stone sliding detection module is connected to the primary risk detection module, the stone sliding detection module is connected to the elevator monitoring module, and the primary risk detection module and the elevator monitoring module are respectively connected to the power output fluctuation analysis module.

[0025] The stone slide detection module is used to detect and analyze the stone bearing weight on the bottom surface of the heavy object elevator; the bottom surface of the heavy object elevator is divided into several detection areas, several detection target points are set in each detection area, and a weight sensor is set at each detection target point to obtain a first weight detection value, a second weight detection value and a third weight detection value respectively, and the first weight detection value of all detection target points is calculated by variance to obtain the basic distribution coefficient of the heavy object elevator, the second weight detection value of all detection target points is calculated by variance to obtain the secondary distribution coefficient of the heavy object elevator, and the third weight detection value of all detection target points is calculated by variance to obtain the tertiary distribution coefficient of the heavy object elevator;

[0026] The first weight detection value is the weight detection value of each detection target point before the heavy object elevator is loaded, the second weight detection value is the weight detection value of each detection target point after the heavy object elevator is loaded, and the third weight detection value is the real-time weight detection value of each detection target point during the operation of the heavy object elevator;

[0027] By acquiring the third weight detection value in real time and calculating the three-level distribution coefficient, the system can quickly identify the risk of stone movement or sliding during the operation of the heavy lift. Once an abnormal change in weight distribution is found, an early warning can be issued in time to avoid mechanical failure or safety accidents caused by sliding. The distribution state of stones during the lifting process can be continuously monitored, and the control strategy can be adjusted in real time to ensure that the stones are always evenly distributed on the bottom of the lift, reducing the risk of tilting or sliding due to imbalance; by analyzing the distribution coefficient, the system can timely detect and correct the overload or tilt that may occur during the operation of the heavy lift, which not only improves the stability of the system, but also reduces mechanical wear and energy consumption; the uniform distribution and real-time monitoring of stones help reduce the operational risks caused by sliding or imbalance, and ensure the safety of the lift during operation; the uniform distribution of stones reduces the additional stress on the lift during operation, avoids local overload or frequent stress fluctuations, helps to extend the service life of the lift and related mechanical components, and avoids equipment damage caused by stone sliding or uneven load, thereby reducing the maintenance frequency and cost of the equipment.

[0028] The basic distribution coefficient reflects the initial uniformity of the weight of the stones on the bottom of the heavy object lift. Based on the variance of the first weight detection value, by evaluating the weight difference between different detection points, it is possible to determine whether the weight value of the bottom of the lift in the initial state is evenly distributed. If the basic distribution coefficient is greater than the preset basic distribution coefficient threshold, the initial weight value of the bottom of the lift is unevenly distributed, and there are local areas that are overloaded or too light, which will affect the balance of the lift and increase the risk of stones slipping or tilting. Otherwise, the initial weight value of the bottom of the lift is evenly distributed, and there is no local area that is overloaded or too light.

[0029] The secondary distribution coefficient reflects the initial uniformity of the weight of the stones on the bottom surface of the heavy object lift; based on the variance of the second weight detection value, by evaluating the weight difference between different detection points, it is possible to determine whether the stones on the bottom surface of the lift are evenly distributed in the initial state; if the secondary distribution coefficient is greater than the preset basic distribution coefficient threshold, the initial distribution of the stones is uneven, and there are local areas that are overloaded or too light, which will affect the balance of the lift and increase the risk of slipping or tilting; otherwise, the initial distribution of the stones is uniform, and there is no situation where the local areas are overloaded or too light.

[0030] The three-level distribution coefficient reflects the instantaneous distribution uniformity of stones at a specific moment; the variance of the weight detection values ​​of each detection target point is obtained during the operation of the heavy object elevator. By calculating the three-level distribution coefficient at each moment during the operation of the heavy object elevator, it is possible to determine whether the distribution state of the stones on the bottom surface of the elevator at a specific moment is uniform; if the three-level distribution coefficient is greater than the preset basic distribution coefficient threshold, it means that there is a significant uneven distribution of stones in the heavy object elevator at a certain moment, which will affect the stability and safety of the elevator; otherwise, the distribution of stones in the heavy object elevator is uniform, and there is no situation where local areas are overloaded or too light.

[0031] The first-level risk detection module is used to obtain the first weight detection value, the second weight detection value, and the third weight detection value, and import the first weight detection value, the second weight detection value, and the third weight detection value into the stone sliding risk value calculation formula to calculate the first-level risk value. The stone sliding risk value calculation formula is:

[0032]

[0033] Where: F sk is the first-level risk value, N is the number of target points detected on the bottom surface of the heavy object lift, is the first weight detection value of the i-th detection target point, is the second weight detection value of the i-th detection target point, T is the total number of monitoring moments in the operation monitoring cycle of the heavy object lift, is the third weight detection value of the i-th detection target point at time t, is the maximum third weight detection value of all detection target points at time t, is the minimum third weight detection value of all detection target points at time t, and ε is the adjustment parameter to prevent the numerator from being zero.

[0034] The elevator monitoring module is used to monitor the data changes during the operation of the heavy object elevator: generate an operation monitoring cycle for the operation process of the heavy object elevator, and obtain the load change value of the heavy object elevator, the speed change value of the heavy object elevator, and the humidity change value in the mine at each moment of the operation monitoring cycle;

[0035] It should be noted that the operation monitoring cycle is generated for the operation process of the heavy object lift, and the operation process of the heavy object lift refers to the process of the heavy object lift lifting the heavy object or the process of releasing the heavy object.

[0036] By introducing the first-level risk detection module and the elevator monitoring module, the problem of power output fluctuation caused by stone sliding can be effectively solved, and comprehensive monitoring and intelligent management can be achieved during operation, which not only improves the safety and stability of the system, but also optimizes energy management, extends the service life of equipment, and improves economic and environmental benefits. Overall, it provides a strong guarantee for the stable and efficient operation of photovoltaic energy storage applications. Through the first-level risk detection module, the system can calculate the risk value of stone sliding in real time, so that it can warn and take preventive measures before the problem occurs, significantly reducing the possibility of stone sliding, thereby ensuring the safety of the entire system; through the elevator monitoring module, the system can comprehensively monitor the load changes, speed changes, and environmental humidity changes of the heavy object elevator when lifting or releasing heavy objects, which helps to ensure the stability of the elevator at each stage of operation. By accurately monitoring and analyzing the distribution of stones and the load conditions of the heavy object elevator, the system can prevent mechanical wear and equipment failure caused by uneven load or stone sliding, thereby extending the service life of the equipment and reducing maintenance costs; it optimizes the energy management strategy based on real-time load changes and speed changes to ensure that the heavy object elevator achieves the best balance between energy consumption and power generation efficiency. By stabilizing the load, more efficient energy utilization can be achieved. Humidity changes will affect the friction characteristics of mechanical components and the sliding tendency of stones. By real-time monitoring of humidity changes in the mine, the elevator's operating strategy can be dynamically adjusted to ensure that the system can still operate stably and efficiently under different humidity conditions.

[0037] It should be noted that the load change value of the weight elevator, the speed change value of the weight elevator and the humidity change value in the mine are obtained at each moment of the operation monitoring period. The load change value of the weight elevator is the difference between the initial load value of the weight elevator at the moment and the final load value of the weight elevator. The second weight detection value of the weight elevator is obtained based on the stone sliding detection module, and the initial load value of the weight elevator is obtained after summarizing; the third weight detection value of the weight elevator is obtained based on the stone sliding detection module, and the final load value of the weight elevator at each moment in the operation monitoring period is obtained after summarizing. Therefore, the calculation formula of the load change value of the weight elevator is:

[0038]

[0039] Where: f j is the load change value of the heavy object elevator at the jth moment, is the initial load value of the heavy lift, is the final load value of the heavy object elevator at the jth moment, is the weight detection value of the i-th detection target point after the heavy object lift is loaded. It is the real-time weight detection value of the i-th detection target point at the i-th moment during the operation of the heavy object lift.

[0040] The speed sensor is used to obtain the real-time speed of the heavy object lift during the operation monitoring period. The speed change value of the heavy object lift is the difference between the initial speed and the final speed at the moment, that is, Among them, v j is the change in speed of the weight lifter at the jth moment, is the initial speed of the weight lift, is the final velocity of the weight lift at the jth moment;

[0041] The real-time humidity of the mine during the operation monitoring period is obtained in real time through the humidity sensor. The humidity change value in the mine is the difference between the initial humidity and the final humidity at the moment, that is, Among them, s j is the humidity change value in the mine at the jth moment, is the initial humidity in the mine, is the final humidity in the mine at the jth moment;

[0042] By acquiring and analyzing the load change value, speed change value and humidity change value of the heavy object elevator, it provides strong support for the stability, safety and energy efficiency of photovoltaic gravity energy storage, which not only improves the intelligence and automation level of the system, but also significantly improves the overall economic and environmental benefits of the photovoltaic energy storage system by optimizing energy management, reducing maintenance costs and extending equipment life; by calculating the difference between the initial load value and the final load value of the heavy object elevator, it can accurately monitor the load change of the elevator at each moment, which helps to identify abnormal load changes in time and ensure the stability of the elevator operation; by acquiring the speed change value of the elevator in real time, it can quickly respond to abnormal speed conditions that may cause instability or failure, thereby optimizing the process of lifting and releasing heavy objects and ensuring the smooth operation of the system; the humidity change in the mine has a great impact on the operation of the elevator. Real-time monitoring of the humidity change value helps to prevent mechanical failures or stone sliding caused by humidity fluctuations, ensuring that the system maintains efficient operation under different environmental conditions; by calculating the load change value, it can detect and correct load imbalance problems caused by stone sliding or movement, reduce equipment wear and energy loss caused by unbalanced loading, and extend the service life of the equipment.

[0043] The power output fluctuation analysis module is used to extract the first-level risk value, the weight elevator load change value at each moment of the operation monitoring period, the weight elevator speed change value and the humidity change value in the mine, and import the first-level risk value, the weight elevator load change value, the weight elevator speed change value and the humidity change value in the mine into the power output fluctuation value analysis model to calculate the power output fluctuation value. The calculation formula of the power output fluctuation value analysis model is:

[0044]

[0045] Where: b sf is the power output fluctuation value, F sk is the first-level risk value, v j+1 is the change in speed of the weight lifter at the j+1th moment, v j is the change in speed of the weight lifter at the jth moment, f j+1 is the load change value of the heavy object elevator at the j+1th moment, f j is the load change of the heavy object elevator at the jth moment, s j+1 is the humidity change value in the mine at the j+1th moment, s j is the humidity change value in the mine at the jth moment, v b is the average value of the speed change of the heavy object lift during the operation monitoring period, is the initial load value of the heavy object lift, s b It is the average change of humidity in the mine during the operation monitoring period.

[0046] By introducing the power output fluctuation analysis module, it is possible to accurately predict and control power output fluctuations, enhance the robustness and reliability of the system, optimize energy management, improve economic benefits, and maintain efficient operation in complex environments. This not only improves the overall efficiency of the photovoltaic energy storage system, but also enhances the intelligence and automation level of the system, providing a solid guarantee for achieving stable power supply and efficient energy utilization; the power output fluctuation value analysis model comprehensively considers a variety of influencing factors, including the risk of stone sliding, elevator load changes, speed changes, and environmental humidity changes, and can more accurately predict and control power output fluctuations; by calculating the power output fluctuation value in real time, the operating parameters of the elevator and generator are dynamically adjusted during operation, and the power fluctuations caused by load changes or environmental changes are quickly responded to, thereby maintaining the stability of the power grid.

[0047] The embodiment of the present invention detects and analyzes the stone load-bearing weight on the bottom of the weight lift, which helps to avoid mechanical failures or safety accidents caused by stone sliding and ensure the overall safety of the system; respectively obtains a first weight detection value, a second weight detection value and a third weight detection value, and calculates the stone sliding risk value based on the above detection values, which helps to store more energy during the peak period of photovoltaic power generation and release energy more efficiently during the power generation trough period, thereby improving the overall energy utilization efficiency of the system; then monitors the data changes during the operation of the weight lift, and obtains the power output fluctuation value based on the power output fluctuation value analysis model, which can quickly identify and adjust the influencing factors, thereby issuing an early warning when the risk of stone sliding is high to remind the operator to take countermeasures and reduce the occurrence of sliding events.

[0048] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0049] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A photovoltaic energy storage configuration system, comprising a photovoltaic power generation center, a heavy object elevator and a gravity energy storage center; the photovoltaic power generation center and the heavy object elevator are respectively connected to the gravity energy storage center; characterized in that, The gravity energy storage center includes a stone sliding detection module and a first-level risk detection module; the stone sliding detection module is used to detect and analyze the stone bearing weight on the bottom of the heavy object elevator: the bottom of the heavy object elevator is divided into several detection areas, several detection target points are set in each detection area, and a weight sensor is set at each detection target point to obtain the first weight detection value, the second weight detection value and the third weight detection value respectively; the first-level risk detection module is used to obtain the first weight detection value, the second weight detection value and the third weight detection value, and import the first weight detection value, the second weight detection value and the third weight detection value into the stone sliding risk value calculation formula to calculate the first-level risk value. The stone sliding risk value calculation formula is: Where: F sk is the first-level risk value, N is the number of target points detected on the bottom surface of the heavy object lift, is the first weight detection value of the i-th detection target point, is the second weight detection value of the i-th detection target point, T is the total number of monitoring moments in the operation monitoring cycle of the heavy object lift, is the third weight detection value of the i-th detection target point at time t, is the maximum third weight detection value of all detection target points at time t, is the minimum third weight detection value of all detection target points at time t, and ε is the adjustment parameter to prevent the numerator from being zero.

2. A photovoltaic energy storage configuration system according to claim 1, characterized in that: The gravity energy storage center also includes a power output fluctuation analysis module; the power output fluctuation analysis module is used to extract the first-level risk value, the weight elevator load change value at each moment of the operation monitoring cycle, the weight elevator speed change value and the humidity change value in the mine, and import the first-level risk value, the weight elevator load change value, the weight elevator speed change value and the humidity change value in the mine into the power output fluctuation value analysis model to calculate the power output fluctuation value. The calculation formula of the power output fluctuation value analysis model is: Where: b sf is the power output fluctuation value, F sk is the first-level risk value, v j+1 is the change in speed of the weight lifter at the j+1th moment, v j is the change in speed of the weight lifter at the jth moment, f j+1 is the load change value of the heavy object elevator at the j+1th moment, f j is the load change of the heavy object elevator at the jth moment, s j+1 is the humidity change value in the mine at the j+1th moment, s j is the humidity change value in the mine at the jth moment, v b is the average value of the speed change of the heavy object lift during the operation monitoring period, is the initial load value of the heavy object lift, s b It is the average change of humidity in the mine during the operation monitoring period.

3. A photovoltaic energy storage configuration system according to claim 2, characterized in that: The gravity energy storage center also includes an elevator monitoring module; the elevator monitoring module is used to monitor data changes during the operation of the heavy object elevator: an operation monitoring cycle is generated for the operation process of the heavy object elevator, and the load change value of the heavy object elevator, the speed change value of the heavy object elevator and the humidity change value in the mine are obtained at each moment of the operation monitoring cycle.

4. A photovoltaic energy storage configuration system according to claim 3, characterized in that: The load change value of the heavy object elevator is the difference between the initial load value of the heavy object elevator at the moment and the final load value of the heavy object elevator. The second weight detection value of the heavy object elevator is obtained based on the stone sliding detection module, and the initial load value of the heavy object elevator is obtained after summarization; The third weight detection value of the heavy object elevator is obtained based on the stone sliding detection module, and the final load value of the heavy object elevator at each moment in the operation monitoring cycle is obtained after summarization. The calculation formula of the load change value of the heavy object elevator is: Where: f j is the load change value of the heavy object elevator at the jth moment, is the initial load value of the heavy lift, is the final load value of the heavy object elevator at the jth moment, is the weight detection value of the i-th detection target point after the heavy object lift is loaded. It is the real-time weight detection value of the i-th detection target point during the operation of the heavy object lift.

5. A photovoltaic energy storage configuration system according to claim 1, characterized in that: The first weight detection value is the weight detection value of each detection target point before the heavy object lift is loaded, the second weight detection value is the weight detection value of each detection target point after the heavy object lift is loaded, and the third weight detection value is the real-time weight detection value of each detection target point during the operation of the heavy object lift.

6. A photovoltaic energy storage configuration system according to claim 5, characterized in that: The stone fall detection module is also used to perform variance calculation on the first weight detection values ​​of all detection target points to obtain the basic distribution coefficient of the heavy object elevator, perform variance calculation on the second weight detection values ​​of all detection target points to obtain the secondary distribution coefficient of the heavy object elevator, and perform variance calculation on the third weight detection values ​​of all detection target points at the same time to obtain the tertiary distribution coefficient of the heavy object elevator.

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