Mobile base station self-powered monitoring system based on cloud computing

Abstract

The invention discloses a mobile base station self-energy-supply monitoring system based on cloud computing, which comprises a mobile base station data acquisition module, an energy storage scheduling strategy module and an operation state monitoring optimization module, and discloses a mobile base station self-energy-supply monitoring method based on cloud computing. The method comprises the following steps: acquiring mobile base station data and mobile base station position data in real time through information acquisition equipment, and uploading the mobile base station data to a cloud computing platform; evaluating the renewable energy source state of the mobile base station and the power demand of the mobile base station through the mobile base station data, and constructing an energy demand prediction model; formulating an energy storage battery storage demand and an energy storage scheduling strategy based on the energy demand prediction model; and the operation states of the mobile base station and the energy storage battery are detected in real time, new energy storage demands are compared, the energy demand prediction model is optimized and iterated, and the problems of unreasonable electric quantity allocation and renewable energy waste of the energy storage system are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of mobile base station self-powered monitoring, and in particular to a mobile base station self-powered monitoring system based on cloud computing. Background Art

[0002] Mobile base station self-powered technology refers to the use of renewable energy (such as solar energy, wind energy, etc.) to provide power for mobile communication base stations to reduce dependence on traditional power grids and improve the energy efficiency and reliability of base stations. However, the performance of the self-powered system depends largely on the energy availability of the surrounding environment, and there may be intermittent and unstable problems. At present, mobile base stations also need to be combined with traditional energy generation. Although the dependence on traditional energy is reduced, how to efficiently use renewable energy to further reduce the consumption of traditional energy needs to be considered. In addition, the consumption of traditional energy is alleviated through energy storage systems, but there is a lack of reasonable allocation of the power of energy storage systems, resulting in unnecessary consumption of traditional energy. Therefore, it is very necessary to design a cloud computing-based mobile base station self-powered monitoring system that optimizes the power allocation of energy storage systems and reduces the consumption of traditional energy. Summary of the invention

[0003] The purpose of the present invention is to provide a mobile base station self-powered monitoring system based on cloud computing to solve the problems raised in the above background technology.

[0004] In order to solve the above technical problems, the present invention provides the following technical solutions: a mobile base station self-powered monitoring system based on cloud computing, which is executed in a mobile base station self-powered monitoring method based on cloud computing, and the operation steps of the method include:

[0005] Step S1: collecting mobile base station data and mobile base station location data in real time through information collection equipment, and uploading the mobile base station data to a cloud computing platform;

[0006] Step S2: Evaluate the renewable energy state of the mobile base station and the power demand of the mobile base station through the mobile base station data, and build an energy demand prediction model;

[0007] Step S3: Formulate energy storage battery storage requirements and energy storage scheduling strategies based on the energy demand prediction model;

[0008] Step S4: Detect the operating status of the mobile base station and the energy storage battery in real time, compare the energy storage scheduling strategy with the new energy storage demand, and optimize and iterate the energy demand prediction model.

[0009] According to the above technical solution, step S1 further includes the following steps:

[0010] Step S11: the mobile base station data includes renewable energy power generation, energy storage battery power, and base station power load; the mobile base station location data includes the mobile base station geographical location and mobile base station environment; the mobile base station data is pre-processed and cleaned before being uploaded to the cloud computing platform;

[0011] Step S12: Add a fixed unique number to each mobile base station based on the geographical location of the mobile base station, wherein the fixed unique number can be set to A1, A2, ..., A i , i is a natural number greater than 0, and a mobile base station distribution map is constructed, in which mobile base station data corresponding to each mobile base station is added and the corresponding number is synchronously marked;

[0012] Step S13: Aggregate the mobile base station data according to the time granularity to retain key time series features.

[0013] According to the above technical solution, step S2 further includes the following steps:

[0014] Step S21: Energy supply and demand analysis is performed based on the mobile base station data and the key time series characteristics. Each base station in the mobile base station distribution map is associated with the natural factors of its environment. The fluctuation pattern of the renewable energy power generation under different environmental conditions is identified through the historical data of the renewable energy power generation and the base station power load. Except for special circumstances that cause the mobile base station data deviation to be greater than a threshold, the mobile base station data of the special circumstances are separately extracted with characteristic values ​​and cause identifiers, and the special circumstances cause identifiers are stored in a special circumstances historical reference library;

[0015] Step S22: further integrating the mobile base station data and the mobile base station location data to construct an energy supply and demand balance framework, wherein the energy supply and demand balance framework refers to analyzing the energy supply and demand status of each mobile base station in different time periods and under different environmental conditions, spatially associating each base station in the mobile base station distribution map with its corresponding energy supply and demand data, and forming a visualized energy supply and demand distribution map;

[0016] Step S23: Based on the energy supply and demand distribution diagram, different colors are used to identify the difference between the renewable energy power generation and the base station power load. When the difference is a positive number, it is identified with orange, and the orange color becomes darker as the difference becomes larger. When the difference is a negative number, blue is used, and the blue color becomes darker as the absolute value of the difference becomes larger. The changing trends of the environmental factors corresponding to each mobile base station are monitored in real time, the difference within a set period in the future for each base station is predicted, and the time period is divided to identify the corresponding difference vertical color gradient bars to construct an energy demand prediction model.

[0017] According to the above technical solution, step S3 further includes the following steps:

[0018] Step S31: Based on the energy demand prediction model, the energy supply and demand trend of each mobile base station in the future period can be analyzed, and the energy supply and demand trend includes the expected change of the renewable energy power generation, the predicted value of the power load of the base station and the energy storage battery storage demand, wherein the energy storage battery storage demand calculation formula is:

[0019]

[0020] In the formula, SD t represents the energy storage battery storage demand at time step t, t represents a specific time point t, N represents the length of the predicted time window, FL t+n represents the predicted value of the base station power load at time step t+n, FR t+n Represents the predicted value of renewable energy power generation at time step t+n, () + Indicates taking the positive part, RC t Represents the remaining capacity of the energy storage battery at time step t, M t Represents a safety margin to account for forecast errors, system fluctuations, and other uncertainties;

[0021] Step S32: monitoring the maximum capacity threshold, health status parameters, and renewable energy power generation of the energy storage battery in each mobile base station, evaluating whether the energy storage battery can meet the energy storage battery storage demand, and formulating the energy storage scheduling strategy;

[0022] Step S33: Based on the energy storage scheduling strategy, the energy storage battery storage demand is maximized. For mobile base stations that do not meet the energy storage battery storage demand, a mobile base station with sufficient renewable energy power generation is matched nearby as a power supply base station according to the energy supply and demand distribution map, or a mobile base station with sufficient storage battery capacity temporarily borrows the storage battery capacity to meet the energy storage battery storage demand.

[0023] According to the above technical solution, the step S32 further includes the following steps:

[0024] Step S321: When the maximum capacity threshold and the renewable energy power generation can both meet the energy storage battery storage demand, charging the energy storage battery until the energy storage battery storage demand is met;

[0025] Step S322: when the maximum capacity threshold can reach the energy storage battery storage demand, but the renewable energy power generation cannot reach the energy storage battery storage demand, the power supply base station with sufficient energy generation is identified nearby according to the energy supply and demand distribution map until the energy storage battery storage demand is reached;

[0026] Step S323: when the maximum capacity threshold cannot meet the storage demand of the energy storage battery, but the renewable energy power generation can meet the storage demand of the energy storage battery, the energy storage battery is charged until the maximum capacity threshold is reached, and the mobile base station is marked as being able to supply power, waiting for other mobile base stations to match and charge;

[0027] Step S324: When the maximum capacity threshold and the renewable energy power generation cannot meet the storage demand of the energy storage battery, the energy storage battery is charged until the maximum capacity threshold is reached, and the power supply base station with sufficient renewable energy power generation is identified nearby according to the energy supply and demand distribution map.

[0028] According to the above technical solution, the system includes a mobile base station data acquisition module, an energy storage scheduling strategy module, and an operation status monitoring and optimization module:

[0029] The mobile base station data acquisition module is used to collect mobile base station data and mobile base station location data in real time through information acquisition equipment, and upload the mobile base station data to the cloud computing platform;

[0030] The energy status evaluation module is used to evaluate the renewable energy status of the mobile base station and the power demand of the mobile base station through the mobile base station data, and to build an energy demand prediction model;

[0031] The energy storage scheduling strategy module is used to formulate energy storage battery storage requirements and energy storage scheduling strategies based on the energy demand prediction model;

[0032] The operation status monitoring and optimization module is used to detect the operation status of the mobile base station and the energy storage battery in real time, compare the energy storage scheduling strategy with the new energy storage demand, and optimize and iterate the energy demand prediction model.

[0033] According to the above technical solution, the mobile base station data acquisition module includes a mobile base station data preprocessing module, a mobile base station distribution map construction module, and a mobile base station data aggregation module:

[0034] The mobile base station data preprocessing module is used for the mobile base station data including renewable energy power generation, energy storage battery power, base station power load, and the mobile base station location data including the mobile base station geographical location and mobile base station environmental conditions, and preprocessing and cleaning the mobile base station data before uploading it to the cloud computing platform;

[0035] The mobile base station distribution map construction module is used to add a fixed unique number to each mobile base station based on the geographical location of the mobile base station, wherein the fixed unique number can be set to A1, A2, ..., A i , i is a natural number greater than 0, and a mobile base station distribution map is constructed, in which mobile base station data corresponding to each mobile base station is added and the corresponding number is synchronously marked;

[0036] The mobile base station data aggregation module is used to aggregate the mobile base station data according to time granularity to retain key time series features.

[0037] According to the above technical solution, the energy status assessment module includes an energy supply and demand analysis module, an energy supply and demand balance framework module, and an energy demand prediction model construction module:

[0038] The energy supply and demand analysis module is used to perform energy supply and demand analysis based on the mobile base station data and the key time series characteristics, each base station in the mobile base station distribution map is associated with the natural factors of its environment, and the fluctuation pattern of the renewable energy power generation under different environmental conditions is identified through the historical data of the renewable energy power generation and the base station power load, wherein, except for special circumstances causing the deviation of the mobile base station data to be greater than a threshold, the characteristic value and cause identifier of the special situation are separately extracted from the mobile base station data, and the cause identifier of the special situation is stored in the special situation history reference library;

[0039] The energy supply and demand balance framework module is used to further integrate the mobile base station data and the mobile base station location data to construct an energy supply and demand balance framework. The energy supply and demand balance framework refers to analyzing the energy supply and demand status of each mobile base station in different time periods and different environmental conditions, and spatially associating each base station in the mobile base station distribution map with its corresponding energy supply and demand data to form a visual energy supply and demand distribution map;

[0040] The energy demand prediction model construction module is used to identify the difference between the renewable energy power generation and the base station power load with different colors based on the energy supply and demand distribution diagram. When the difference is a positive number, it is identified with orange, and the orange color becomes darker as the difference becomes larger. When the difference is a negative number, it is represented by blue, and the blue color becomes darker as the absolute value of the difference becomes larger. The changing trends of the environmental factors corresponding to each mobile base station are monitored in real time, the difference within a set period in the future for each base station is predicted, and the time period is divided to identify the corresponding difference vertical color gradient bars to construct an energy demand prediction model.

[0041] According to the above technical solution, the energy storage scheduling strategy module includes an energy storage battery storage demand evaluation module, an energy storage battery maximization satisfaction module, and an energy storage battery storage demand optimization module:

[0042] The energy storage battery saving demand assessment module is used to analyze the energy supply and demand trends of each mobile base station in the future based on the energy demand prediction model. The energy supply and demand trends include the expected changes in the renewable energy generation, the predicted value of the power load of the base station and the energy storage battery saving demand, wherein the energy storage battery saving demand calculation formula is:

[0043]

[0044] In the formula, SD t represents the energy storage battery storage demand at time step t, t represents a specific time point t, N represents the length of the predicted time window, FL t+n represents the predicted value of the base station power load at time step t+n, FR t+n Represents the predicted value of renewable energy power generation at time step t+n, () + Indicates taking the positive part, RC t Represents the remaining capacity of the energy storage battery at time step t, M t Represents a safety margin to account for forecast errors, system fluctuations, and other uncertainties;

[0045] The energy storage battery maximization satisfaction module is used to monitor the maximum capacity threshold, health status parameters, and renewable energy power generation of the energy storage battery in each mobile base station, evaluate whether the energy storage battery can meet the energy storage battery storage demand, and formulate the energy storage scheduling strategy;

[0046] The energy storage battery storage demand optimization module is used to maximize the satisfaction of the energy storage battery storage demand based on the energy storage scheduling strategy. For mobile base stations that do not meet the energy storage battery storage demand, the energy supply and demand distribution map is used to match the nearest mobile base station with sufficient renewable energy power generation as a power supply base station, or a mobile base station with sufficient storage battery capacity temporarily borrows the storage battery capacity to meet the energy storage battery storage demand.

[0047] According to the above technical solution, the energy storage battery maximization satisfaction module includes a charging strategy implementation submodule, a power supply base station identification submodule, a power supply base station marking submodule, and a power supply base station matching submodule:

[0048] The charging strategy implementation submodule is used for charging the energy storage battery until the energy storage battery storage demand is met when the maximum capacity threshold and the renewable energy power generation can both meet the energy storage battery storage demand;

[0049] The power supply base station identification submodule is used to identify the power supply base station with sufficient energy generation capacity nearby according to the energy supply and demand distribution map when the maximum capacity threshold can reach the energy storage battery storage demand, but the renewable energy power generation cannot reach the energy storage battery storage demand, so as to meet the energy storage battery storage demand;

[0050] The power supplyable base station marking submodule is used to charge the energy storage battery until the maximum capacity threshold is reached when the maximum capacity threshold cannot meet the energy storage battery storage demand, but the renewable energy power generation can meet the energy storage battery storage demand, and mark it as a power supplyable mobile base station, waiting for other mobile base stations to match and charge;

[0051] The power supply base station matching submodule is used to charge the energy storage battery until the maximum capacity threshold is reached when the maximum capacity threshold and the renewable energy power generation cannot meet the storage demand of the energy storage battery, and to identify the power supply base station with sufficient renewable energy power generation nearby according to the energy supply and demand distribution map.

[0052] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: the present invention, by collecting energy data and location data of mobile base stations in real time and uploading them to a cloud computing platform for processing, constructs an energy supply and demand analysis framework and an energy demand prediction model, can predict the energy supply and demand conditions under different environmental conditions, thereby achieving more effective energy management, and dynamically adjusts energy supply and demand according to real-time monitoring data and energy storage scheduling strategies to cope with prediction errors, system fluctuations and other uncertain factors, formulates energy storage battery storage requirements and energy storage scheduling strategies, and can timely discover and solve potential energy supply problems by real-time detection of the operating status of mobile base stations and energy storage batteries, and compares the energy storage scheduling strategies with the new energy storage requirements based on the energy storage scheduling strategies, thereby optimizing energy use and reducing energy waste, and maximizing the use of renewable energy, which helps to reduce the impact on the environment, thereby optimizing the power allocation of the energy storage system and reducing traditional energy consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0054] Figure 1 A schematic diagram of a flow chart of a mobile base station self-powered monitoring system based on cloud computing and a method for storing and allocating renewable energy based on a mobile base station provided in Embodiment 1 of the present invention;

[0055] Figure 2A schematic diagram of the module composition of a mobile base station self-powered monitoring system based on cloud computing provided in Embodiment 2 of the present invention. DETAILED DESCRIPTION

[0056] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0057] Embodiment 1: This embodiment can be applied to the scenario of mobile base station renewable energy storage and deployment. The method can be executed by a mobile base station self-powered monitoring system based on cloud computing provided in this embodiment. Figure 1 A flowchart diagram of a mobile base station self-powered monitoring system based on cloud computing according to the first embodiment of the present invention executing a method for storing and allocating renewable energy based on a mobile base station is provided. The method specifically includes the following steps:

[0058] Step S1: collecting mobile base station data and mobile base station location data in real time through information collection equipment, and uploading the mobile base station data to a cloud computing platform;

[0059] Step S2: Evaluate the renewable energy state of the mobile base station and the power demand of the mobile base station through the mobile base station data, and build an energy demand prediction model;

[0060] Step S3: Formulate energy storage battery storage requirements and energy storage scheduling strategies based on the energy demand prediction model;

[0061] Step S4: Detect the operating status of the mobile base station and the energy storage battery in real time, compare the energy storage scheduling strategy with the new energy storage demand, and optimize and iterate the energy demand prediction model.

[0062] In an embodiment of the present invention, the step S1 further includes the following steps:

[0063] Step S11: the mobile base station data includes renewable energy power generation, energy storage battery power, and base station power load; the mobile base station location data includes the mobile base station geographical location and mobile base station environment; the mobile base station data is pre-processed and cleaned before being uploaded to the cloud computing platform;

[0064] Step S12: Add a fixed unique number to each mobile base station based on the geographical location of the mobile base station, wherein the fixed unique number can be set to A1, A2, ..., A i, i is a natural number greater than 0, and a mobile base station distribution map is constructed, in which mobile base station data corresponding to each mobile base station is added and the corresponding number is synchronously marked;

[0065] Step S13: Aggregate the mobile base station data according to the time granularity to retain key time series features.

[0066] In an embodiment of the present invention, the step S2 further includes the following steps:

[0067] Step S21: Energy supply and demand analysis is performed based on the mobile base station data and the key time series characteristics. Each base station in the mobile base station distribution map is associated with the natural factors of its environment. The fluctuation pattern of the renewable energy power generation under different environmental conditions is identified through the historical data of the renewable energy power generation and the base station power load. Except for special circumstances that cause the mobile base station data deviation to be greater than a threshold, the mobile base station data of the special circumstances are separately extracted with characteristic values ​​and cause identifiers, and the special circumstances cause identifiers are stored in a special circumstances historical reference library;

[0068] Step S22: further integrating the mobile base station data and the mobile base station location data to construct an energy supply and demand balance framework, wherein the energy supply and demand balance framework refers to analyzing the energy supply and demand status of each mobile base station in different time periods and under different environmental conditions, spatially associating each base station in the mobile base station distribution map with its corresponding energy supply and demand data, and forming a visualized energy supply and demand distribution map;

[0069] Step S23: Based on the energy supply and demand distribution diagram, different colors are used to identify the difference between the renewable energy power generation and the base station power load. When the difference is a positive number, it is identified with orange, and the orange color becomes darker as the difference becomes larger. When the difference is a negative number, blue is used, and the blue color becomes darker as the absolute value of the difference becomes larger. The changing trends of the environmental factors corresponding to each mobile base station are monitored in real time, the difference within a set period in the future for each base station is predicted, and the time period is divided to identify the corresponding difference vertical color gradient bars to construct an energy demand prediction model.

[0070] In an embodiment of the present invention, step S3 further includes the following steps:

[0071] Step S31: Based on the energy demand prediction model, the energy supply and demand trend of each mobile base station in the future period can be analyzed, and the energy supply and demand trend includes the expected change of the renewable energy power generation, the predicted value of the power load of the base station and the energy storage battery storage demand, wherein the energy storage battery storage demand calculation formula is:

[0072]

[0073] In the formula, SD t represents the energy storage battery storage demand at time step t, t represents a specific time point t, N represents the length of the predicted time window, FL t+n represents the predicted value of the base station power load at time step t+n, FR t+n Represents the predicted value of renewable energy power generation at time step t+n, () + Indicates taking the positive part, RC t Represents the remaining capacity of the energy storage battery at time step t, M t Represents a safety margin to account for forecast errors, system fluctuations, and other uncertainties;

[0074] Step S32: monitoring the maximum capacity threshold, health status parameters, and renewable energy power generation of the energy storage battery in each mobile base station, evaluating whether the energy storage battery can meet the energy storage battery storage demand, and formulating the energy storage scheduling strategy;

[0075] Step S33: Based on the energy storage scheduling strategy, the energy storage battery storage demand is maximized. For mobile base stations that do not meet the energy storage battery storage demand, a mobile base station with sufficient renewable energy power generation is matched nearby as a power supply base station according to the energy supply and demand distribution map, or a mobile base station with sufficient storage battery capacity temporarily borrows the storage battery capacity to meet the energy storage battery storage demand.

[0076] In an embodiment of the present invention, the step S32 further includes the following steps:

[0077] Step S321: When the maximum capacity threshold and the renewable energy power generation can both meet the energy storage battery storage demand, charging the energy storage battery until the energy storage battery storage demand is met;

[0078] Step S322: when the maximum capacity threshold can reach the energy storage battery storage demand, but the renewable energy power generation cannot reach the energy storage battery storage demand, the power supply base station with sufficient energy generation is identified nearby according to the energy supply and demand distribution map until the energy storage battery storage demand is reached;

[0079] Step S323: when the maximum capacity threshold cannot meet the storage demand of the energy storage battery, but the renewable energy power generation can meet the storage demand of the energy storage battery, the energy storage battery is charged until the maximum capacity threshold is reached, and the mobile base station is marked as being able to supply power, waiting for other mobile base stations to match and charge;

[0080] Step S324: When the maximum capacity threshold and the renewable energy power generation cannot meet the storage demand of the energy storage battery, the energy storage battery is charged until the maximum capacity threshold is reached, and the power supply base station with sufficient renewable energy power generation is identified nearby according to the energy supply and demand distribution map.

[0081] Embodiment 2: Embodiment 2 of the present invention provides a mobile base station self-powered monitoring system based on cloud computing. Figure 2 A schematic diagram of the module composition of a mobile base station self-powered monitoring system based on cloud computing provided in Embodiment 2 of the present invention is shown in FIG. Figure 2 As shown, the system includes a mobile base station data acquisition module, an energy storage scheduling strategy module, and an operation status monitoring and optimization module:

[0082] The mobile base station data acquisition module is used to collect mobile base station data and mobile base station location data in real time through information acquisition equipment, and upload the mobile base station data to the cloud computing platform;

[0083] The energy status evaluation module is used to evaluate the renewable energy status of the mobile base station and the power demand of the mobile base station through the mobile base station data, and to build an energy demand prediction model;

[0084] The energy storage scheduling strategy module is used to formulate energy storage battery storage requirements and energy storage scheduling strategies based on the energy demand prediction model;

[0085] The operation status monitoring and optimization module is used to detect the operation status of the mobile base station and the energy storage battery in real time, compare the energy storage scheduling strategy with the new energy storage demand, and optimize and iterate the energy demand prediction model.

[0086] In some embodiments of the present invention, the mobile base station data acquisition module includes a mobile base station data preprocessing module, a mobile base station distribution map construction module, and a mobile base station data aggregation module:

[0087] The mobile base station data preprocessing module is used for the mobile base station data including renewable energy power generation, energy storage battery power, base station power load, and the mobile base station location data including the mobile base station geographical location and mobile base station environmental conditions, and preprocessing and cleaning the mobile base station data before uploading it to the cloud computing platform;

[0088] The mobile base station distribution map construction module is used to add a fixed unique number to each mobile base station based on the geographical location of the mobile base station, wherein the fixed unique number can be set to A1, A2, ..., A i, i is a natural number greater than 0, and a mobile base station distribution map is constructed, in which mobile base station data corresponding to each mobile base station is added and the corresponding number is synchronously marked;

[0089] The mobile base station data aggregation module is used to aggregate the mobile base station data according to time granularity to retain key time series features.

[0090] In some embodiments of the present invention, the energy status assessment module includes an energy supply and demand analysis module, an energy supply and demand balance framework module, and an energy demand prediction model construction module:

[0091] The energy supply and demand analysis module is used to perform energy supply and demand analysis based on the mobile base station data and the key time series characteristics, each base station in the mobile base station distribution map is associated with the natural factors of its environment, and the fluctuation pattern of the renewable energy power generation under different environmental conditions is identified through the historical data of the renewable energy power generation and the base station power load, wherein, except for special circumstances causing the deviation of the mobile base station data to be greater than a threshold, the characteristic value and cause identifier of the special situation are separately extracted from the mobile base station data, and the cause identifier of the special situation is stored in the special situation history reference library;

[0092] The energy supply and demand balance framework module is used to further integrate the mobile base station data and the mobile base station location data to construct an energy supply and demand balance framework. The energy supply and demand balance framework refers to analyzing the energy supply and demand status of each mobile base station in different time periods and different environmental conditions, and spatially associating each base station in the mobile base station distribution map with its corresponding energy supply and demand data to form a visual energy supply and demand distribution map;

[0093] The energy demand prediction model construction module is used to identify the difference between the renewable energy power generation and the base station power load with different colors based on the energy supply and demand distribution diagram. When the difference is a positive number, it is identified with orange, and the orange color becomes darker as the difference becomes larger. When the difference is a negative number, it is represented by blue, and the blue color becomes darker as the absolute value of the difference becomes larger. The changing trends of the environmental factors corresponding to each mobile base station are monitored in real time, the difference within a set period in the future for each base station is predicted, and the time period is divided to identify the corresponding difference vertical color gradient bars to construct an energy demand prediction model.

[0094] In some embodiments of the present invention, the energy storage scheduling strategy module includes an energy storage battery storage demand evaluation module, an energy storage battery maximization satisfaction module, and an energy storage battery storage demand optimization module:

[0095] The energy storage battery saving demand assessment module is used to analyze the energy supply and demand trends of each mobile base station in the future based on the energy demand prediction model. The energy supply and demand trends include the expected changes in the renewable energy generation, the predicted value of the power load of the base station and the energy storage battery saving demand, wherein the energy storage battery saving demand calculation formula is:

[0096]

[0097] In the formula, SD t represents the energy storage battery storage demand at time step t, t represents a specific time point t, N represents the length of the predicted time window, FL t+n represents the predicted value of the base station power load at time step t+n, FR t+n Represents the predicted value of renewable energy power generation at time step t+n, () + Indicates taking the positive part, RC t Represents the remaining capacity of the energy storage battery at time step t, M t Represents a safety margin to account for forecast errors, system fluctuations, and other uncertainties;

[0098] The energy storage battery maximization satisfaction module is used to monitor the maximum capacity threshold, health status parameters, and renewable energy power generation of the energy storage battery in each mobile base station, evaluate whether the energy storage battery can meet the energy storage battery storage demand, and formulate the energy storage scheduling strategy;

[0099] The energy storage battery storage demand optimization module is used to maximize the satisfaction of the energy storage battery storage demand based on the energy storage scheduling strategy. For mobile base stations that do not meet the energy storage battery storage demand, the energy supply and demand distribution map is used to match the nearest mobile base station with sufficient renewable energy power generation as a power supply base station, or a mobile base station with sufficient storage battery capacity temporarily borrows the storage battery capacity to meet the energy storage battery storage demand.

[0100] In some embodiments of the present invention, the energy storage battery maximization satisfaction module includes a charging strategy implementation submodule, a power supply base station identification submodule, a power supply base station marking submodule, and a power supply base station matching submodule:

[0101] The charging strategy implementation submodule is used for charging the energy storage battery until the energy storage battery storage demand is met when the maximum capacity threshold and the renewable energy power generation can both meet the energy storage battery storage demand;

[0102] The power supply base station identification submodule is used to identify the power supply base station with sufficient energy generation capacity nearby according to the energy supply and demand distribution map when the maximum capacity threshold can reach the energy storage battery storage demand, but the renewable energy power generation cannot reach the energy storage battery storage demand, so as to meet the energy storage battery storage demand;

[0103] The power supplyable base station marking submodule is used to charge the energy storage battery until the maximum capacity threshold is reached when the maximum capacity threshold cannot meet the energy storage battery storage demand, but the renewable energy power generation can meet the energy storage battery storage demand, and mark it as a power supplyable mobile base station, waiting for other mobile base stations to match and charge;

[0104] The power supply base station matching submodule is used to charge the energy storage battery until the maximum capacity threshold is reached when the maximum capacity threshold and the renewable energy power generation cannot meet the storage demand of the energy storage battery, and to identify the power supply base station with sufficient renewable energy power generation nearby according to the energy supply and demand distribution map.

[0105] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0106] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A mobile base station self-powered monitoring system based on cloud computing, characterized in that: The system includes a mobile base station data acquisition module, an energy storage scheduling strategy module, and an operation status monitoring and optimization module: The mobile base station data acquisition module is used to collect mobile base station data and mobile base station location data in real time through information acquisition equipment, and upload the mobile base station data to the cloud computing platform; The energy status evaluation module is used to evaluate the renewable energy status of the mobile base station and the power demand of the mobile base station through the mobile base station data, and to build an energy demand prediction model; The energy storage scheduling strategy module is used to formulate energy storage battery storage requirements and energy storage scheduling strategies based on the energy demand prediction model; The operation status monitoring and optimization module is used to detect the operation status of the mobile base station and the energy storage battery in real time, compare the energy storage scheduling strategy with the new energy storage demand, and optimize and iterate the energy demand prediction model.

2. According to the cloud computing-based mobile base station self-powered monitoring system of claim 1, it is characterized by: The mobile base station data acquisition module includes a mobile base station data preprocessing module, a mobile base station distribution map construction module, and a mobile base station data aggregation module: The mobile base station data preprocessing module is used for the mobile base station data including renewable energy power generation, energy storage battery power, base station power load, and the mobile base station location data including the mobile base station geographical location and mobile base station environmental conditions, and preprocessing and cleaning the mobile base station data before uploading it to the cloud computing platform; The mobile base station distribution map construction module is used to add a fixed unique number to each mobile base station based on the geographical location of the mobile base station, wherein the fixed unique number can be set to A1, A2, ..., A i , i is a natural number greater than 0, and a mobile base station distribution map is constructed, in which mobile base station data corresponding to each mobile base station is added and the corresponding number is synchronously marked; The mobile base station data aggregation module is used to aggregate the mobile base station data according to time granularity to retain key time series features.

3. According to the cloud computing-based mobile base station self-powered monitoring system of claim 2, it is characterized by: The energy status assessment module includes an energy supply and demand analysis module, an energy supply and demand balance framework module, and an energy demand prediction model construction module: The energy supply and demand analysis module is used to perform energy supply and demand analysis based on the mobile base station data and the key time series characteristics, each base station in the mobile base station distribution map is associated with the natural factors of its environment, and the fluctuation pattern of the renewable energy power generation under different environmental conditions is identified through the historical data of the renewable energy power generation and the base station power load, wherein, except for special circumstances causing the deviation of the mobile base station data to be greater than a threshold, the characteristic value and cause identifier of the special situation are separately extracted from the mobile base station data, and the cause identifier of the special situation is stored in the special situation history reference library; The energy supply and demand balance framework module is used to further integrate the mobile base station data and the mobile base station location data to construct an energy supply and demand balance framework. The energy supply and demand balance framework refers to analyzing the energy supply and demand status of each mobile base station in different time periods and different environmental conditions, and spatially associating each base station in the mobile base station distribution map with its corresponding energy supply and demand data to form a visual energy supply and demand distribution map; The energy demand prediction model construction module is used to identify the difference between the renewable energy power generation and the base station power load with different colors based on the energy supply and demand distribution diagram. When the difference is a positive number, it is identified with orange, and the orange color becomes darker as the difference becomes larger. When the difference is a negative number, it is represented by blue, and the blue color becomes darker as the absolute value of the difference becomes larger. The changing trends of the environmental factors corresponding to each mobile base station are monitored in real time, the difference within a set period in the future for each base station is predicted, and the time period is divided to identify the corresponding difference vertical color gradient bars to construct an energy demand prediction model.

4. According to claim 3, a mobile base station self-powered monitoring system based on cloud computing is characterized in that: The energy storage scheduling strategy module includes an energy storage battery storage demand assessment module, an energy storage battery maximization satisfaction module, and an energy storage battery storage demand optimization module: The energy storage battery saving demand assessment module is used to analyze the energy supply and demand trends of each mobile base station in the future based on the energy demand prediction model. The energy supply and demand trends include the expected changes in the renewable energy generation, the predicted value of the power load of the base station and the energy storage battery saving demand, wherein the energy storage battery saving demand calculation formula is: In the formula, SD t represents the energy storage battery storage demand at time step t, t represents a specific time point t, N represents the length of the predicted time window, FL t+n represents the predicted value of the base station power load at time step t+n, FR t+n Represents the predicted value of renewable energy power generation at time step t+n, () + Indicates taking the positive part, RC t Represents the remaining capacity of the energy storage battery at time step t, M t Represents a safety margin to account for forecast errors, system fluctuations, and other uncertainties; The energy storage battery maximization satisfaction module is used to monitor the maximum capacity threshold, health status parameters, and renewable energy power generation of the energy storage battery in each mobile base station, evaluate whether the energy storage battery can meet the energy storage battery storage demand, and formulate the energy storage scheduling strategy; The energy storage battery storage demand optimization module is used to maximize the satisfaction of the energy storage battery storage demand based on the energy storage scheduling strategy. For mobile base stations that do not meet the energy storage battery storage demand, the energy supply and demand distribution map is used to match the nearest mobile base station with sufficient renewable energy power generation as a power supply base station, or a mobile base station with sufficient storage battery capacity temporarily borrows the storage battery capacity to meet the energy storage battery storage demand.

5. The mobile base station self-powered monitoring system based on cloud computing according to claim 4, characterized in that: The energy storage battery maximization satisfaction module includes a charging strategy implementation submodule, a power supply base station identification submodule, a power supply base station marking submodule, and a power supply base station matching submodule: The charging strategy implementation submodule is used for charging the energy storage battery until the energy storage battery storage demand is met when the maximum capacity threshold and the renewable energy power generation can both meet the energy storage battery storage demand; The power supply base station identification submodule is used to identify the power supply base station with sufficient energy generation capacity nearby according to the energy supply and demand distribution map when the maximum capacity threshold can reach the energy storage battery storage demand, but the renewable energy power generation cannot reach the energy storage battery storage demand, so as to meet the energy storage battery storage demand; The power supplyable base station marking submodule is used to charge the energy storage battery until the maximum capacity threshold is reached when the maximum capacity threshold cannot meet the energy storage battery storage demand, but the renewable energy power generation can meet the energy storage battery storage demand, and mark it as a power supplyable mobile base station, waiting for other mobile base stations to match and charge; The power supply base station matching submodule is used to charge the energy storage battery until the maximum capacity threshold is reached when the maximum capacity threshold and the renewable energy power generation cannot meet the storage demand of the energy storage battery, and to identify the power supply base station with sufficient renewable energy power generation nearby according to the energy supply and demand distribution map.

6. A method for operating a mobile base station self-powered monitoring system based on cloud computing, characterized in that: The operating steps of the method include: Step S1: collecting mobile base station data and mobile base station location data in real time through information collection equipment, and uploading the mobile base station data to a cloud computing platform; Step S2: Evaluate the renewable energy state of the mobile base station and the power demand of the mobile base station through the mobile base station data, and build an energy demand prediction model; Step S3: formulating energy storage battery storage requirements and energy storage scheduling strategies based on the energy demand prediction model; Step S4: Detect the operating status of the mobile base station and the energy storage battery in real time, compare the energy storage scheduling strategy with the new energy storage demand, and optimize and iterate the energy demand prediction model.

7. The method for operating a mobile base station self-powered monitoring system based on cloud computing according to claim 6, characterized in that: The step S1 further comprises the following steps: Step S11: the mobile base station data includes renewable energy power generation, energy storage battery power, and base station power load; the mobile base station location data includes the mobile base station geographical location and mobile base station environment; the mobile base station data is pre-processed and cleaned before being uploaded to the cloud computing platform; Step S12: Add a fixed unique number to each mobile base station based on the geographical location of the mobile base station, wherein the fixed unique number can be set to A1, A2, ..., A i , i is a natural number greater than 0, and a mobile base station distribution map is constructed, in which mobile base station data corresponding to each mobile base station is added and the corresponding number is synchronously marked; Step S13: Aggregate the mobile base station data according to the time granularity to retain key time series features.

8. The method for operating a mobile base station self-powered monitoring system based on cloud computing according to claim 7, characterized in that: The step S2 further comprises the following steps: Step S21: Energy supply and demand analysis is performed based on the mobile base station data and the key time series characteristics. Each base station in the mobile base station distribution map is associated with the natural factors of its environment. The fluctuation pattern of the renewable energy power generation under different environmental conditions is identified through the historical data of the renewable energy power generation and the base station power load. Except for special circumstances that cause the mobile base station data deviation to be greater than a threshold, the mobile base station data of the special circumstances are separately extracted with characteristic values ​​and cause identifiers, and the special circumstances cause identifiers are stored in a special circumstances historical reference library; Step S22: further integrating the mobile base station data and the mobile base station location data to construct an energy supply and demand balance framework, wherein the energy supply and demand balance framework refers to analyzing the energy supply and demand status of each mobile base station in different time periods and under different environmental conditions, spatially associating each base station in the mobile base station distribution map with its corresponding energy supply and demand data, and forming a visualized energy supply and demand distribution map; Step S23: Based on the energy supply and demand distribution diagram, different colors are used to identify the difference between the renewable energy power generation and the base station power load. When the difference is a positive number, it is identified with orange, and the orange color becomes darker as the difference becomes larger. When the difference is a negative number, blue is used, and the blue color becomes darker as the absolute value of the difference becomes larger. The changing trends of the environmental factors corresponding to each mobile base station are monitored in real time, the difference within a set period in the future for each base station is predicted, and the time period is divided to identify the corresponding difference vertical color gradient bars to construct an energy demand prediction model.

9. The method for operating a mobile base station self-powered monitoring system based on cloud computing according to claim 8, characterized in that: The step S3 further comprises the following steps: Step S31: Based on the energy demand prediction model, the energy supply and demand trend of each mobile base station in the future period can be analyzed, and the energy supply and demand trend includes the expected change of the renewable energy power generation, the predicted value of the power load of the base station and the energy storage battery storage demand, wherein the energy storage battery storage demand calculation formula is: In the formula, SD t represents the energy storage battery storage demand at time step t, t represents a specific time point t, N represents the length of the predicted time window, FL t+n represents the predicted value of the base station power load at time step t+n, FR t+n Represents the predicted value of renewable energy power generation at time step t+n, () + Indicates taking the positive part, RC t Represents the remaining capacity of the energy storage battery at time step t, M t Represents a safety margin to account for forecast errors, system fluctuations, and other uncertainties; Step S32: monitoring the maximum capacity threshold, health status parameters, and renewable energy power generation of the energy storage battery in each mobile base station, evaluating whether the energy storage battery can meet the energy storage battery storage demand, and formulating the energy storage scheduling strategy; Step S33: Based on the energy storage scheduling strategy, the energy storage battery storage demand is maximized. For mobile base stations that do not meet the energy storage battery storage demand, a mobile base station with sufficient renewable energy power generation is matched nearby as a power supply base station according to the energy supply and demand distribution map, or a mobile base station with sufficient storage battery capacity temporarily borrows the storage battery capacity to meet the energy storage battery storage demand.

10. The method for operating a mobile base station self-powered monitoring system based on cloud computing according to claim 9, characterized in that: The step S32 further comprises the following steps: Step S321: When the maximum capacity threshold and the renewable energy power generation can both meet the energy storage battery storage demand, charging the energy storage battery until the energy storage battery storage demand is met; Step S322: when the maximum capacity threshold can reach the energy storage battery storage demand, but the renewable energy power generation cannot reach the energy storage battery storage demand, the power supply base station with sufficient energy generation is identified nearby according to the energy supply and demand distribution map until the energy storage battery storage demand is reached; Step S323: when the maximum capacity threshold cannot meet the storage demand of the energy storage battery, but the renewable energy power generation can meet the storage demand of the energy storage battery, the energy storage battery is charged until the maximum capacity threshold is reached, and the mobile base station is marked as being able to supply power, waiting for other mobile base stations to match and charge; Step S324: When the maximum capacity threshold and the renewable energy power generation cannot meet the storage demand of the energy storage battery, the energy storage battery is charged until the maximum capacity threshold is reached, and the power supply base station with sufficient renewable energy power generation is identified nearby according to the energy supply and demand distribution map.

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