Load resource real-time data access processing method based on Internet of Things system

Abstract

The invention provides a load resource real-time data access processing method based on an Internet of Things system. The method comprises the steps of obtaining real-time working data through each mobile load in a mobile load cluster, sending the real-time working data to an equipment management platform to generate a real-time working data set, obtaining predicted power generation data through renewable energy power generation equipment, and sending the predicted power generation data to the equipment management platform, the method comprises the following steps: acquiring charge state data through energy storage equipment, and sending the charge state data to an equipment management platform, so that the equipment management platform generates a mobile load control instruction according to real-time working data, predicted power generation data and the charge state data, the mobile load control instruction is used for instructing at least one mobile load in the mobile load cluster to move to the charging and discharging interface to perform charging and discharging operation, so that dynamic scheduling of load resources is realized, and the utilization rate of renewable energy sources by the power management system is effectively improved.

Description

Technical Field

[0001] The present application relates to data processing technology, and in particular to a method for accessing and processing real-time data of load resources based on an Internet of Things system. Background Art

[0002] With the rapid development of smart grid and Internet of Things technologies, the intelligence level of site power management systems is increasing. Traditional power management systems often have problems such as information islands, data non-sharing, and inflexible scheduling, which makes it difficult to meet the needs of modern power management for efficiency, real-time, and intelligence. Especially in complex power systems that include equipment management platforms, mobile load clusters, renewable energy power generation equipment, and energy storage equipment, how to achieve real-time monitoring and intelligent scheduling of load resources has become a problem that needs to be solved in the current power management field. Summary of the invention

[0003] The present application provides a method for real-time data access and processing of load resources based on the Internet of Things system to achieve dynamic scheduling of load resources, thereby effectively improving the utilization rate of renewable energy in the power management system.

[0004] In a first aspect, the present application provides a method for accessing and processing real-time data of load resources based on an Internet of Things system, which is applied to a site power management system, wherein the site power management system includes a device management platform, a mobile load cluster, a renewable energy power generation device, and an energy storage device, wherein the device management platform is connected to the renewable energy power generation device, the energy storage device, and each mobile load in the mobile load cluster through an Internet of Things communication; the method includes:

[0005] Each mobile load in the mobile load cluster acquires real-time working data and sends the real-time working data to the device management platform to generate a real-time working data set, wherein the real-time working data includes real-time task data and real-time power data;

[0006] The renewable energy power generation equipment obtains predicted power generation data, and sends the predicted power generation data to the equipment management platform; the energy storage equipment obtains state of charge data, and sends the state of charge data to the equipment management platform;

[0007] The device management platform generates a mobile load control instruction based on the real-time working data, the predicted power generation data and the charge state data. The mobile load control instruction is used to instruct at least one mobile load in the mobile load cluster to move to the charging and discharging interface for charging and discharging operations. The renewable energy power generation equipment, the energy storage equipment and the charging and discharging interface are connected through a power grid.

[0008] In the above scheme, the mobile load cluster, renewable energy power generation equipment, energy storage equipment and equipment management platform are connected through the Internet of Things technology to realize real-time monitoring of load resources. The equipment management platform can receive real-time working data of mobile loads (such as real-time task data and real-time power data), predicted power generation data of renewable energy power generation equipment and state of charge data of energy storage equipment in real time, so as to fully grasp the operation status of the system. Based on these data, the equipment management platform can dynamically generate mobile load control instructions and adjust the working status of mobile loads in real time to realize dynamic scheduling of load resources, thereby effectively improving the utilization rate of renewable energy in the power management system. By combining renewable energy power generation equipment with energy storage equipment, the system can make full use of renewable energy for power generation and reduce dependence on traditional energy. At the same time, energy storage equipment can store electric energy when there is excess power and release electric energy when there is insufficient power, thereby balancing the load of the power grid and improving energy utilization efficiency. According to real-time data and predicted data, the equipment management platform intelligently schedules mobile loads for charging and discharging operations, ensuring that mobile loads are charged when there is excess power and providing power support when there is insufficient power, thereby further improving the overall utilization efficiency of energy. In addition, through real-time monitoring and dynamic scheduling, the system can avoid unnecessary energy waste and reduce operating costs. For example, when there is excess electricity, the system can guide mobile loads to charge to avoid wasting electricity; when there is insufficient electricity, the system can dispatch mobile loads with discharge capabilities to provide power support, reducing dependence on traditional power generation equipment and thus reducing operating costs.

[0009] Optionally, the device management platform generates a mobile load control instruction according to the real-time working data, the predicted power generation data and the charge state data, including:

[0010] The equipment management platform determines the predicted total power according to the predicted power generation in the predicted power generation data and the remaining power in the state of charge data;

[0011] If the device management platform determines that the predicted total power is greater than the upper limit threshold of the power charge of the energy storage device, the mobile load control instruction is generated, wherein the predicted power generation data includes the predicted power generation for the future period;

[0012] The equipment management platform determines at least one target mobile load from the mobile load cluster according to the real-time working data set, wherein the target mobile load is a mobile load that has completed a currently executed task before a starting time node of the future duration;

[0013] The device management platform sends the mobile load control instruction to the target mobile load, so that the target mobile load moves to the charging and discharging interface to perform a charging operation before the starting time node of the future time period.

[0014] In the above scheme, the equipment management platform calculates the predicted total power by comprehensively predicting the power generation and the remaining power of the energy storage device, so as to accurately judge the future power supply and demand situation. When the predicted total power exceeds the upper limit threshold of the charge of the energy storage device, it means that the power supply will exceed the current storage capacity of the system, and there is a risk of excess power. In this case, the equipment management platform intelligently generates mobile load control instructions, selects the mobile load that has completed the task before the start time node of the future duration as the target mobile load, and instructs it to move to the charging and discharging interface for charging, thereby effectively utilizing the excess power resources and avoiding the waste of power. At the same time, it ensures that the mobile load has sufficient power, which provides a guarantee for subsequent work tasks. That is, when there is excess power, the mobile load is dispatched in time for charging, which not only avoids the waste of power, but also improves the utilization rate of renewable energy. In addition, the equipment management platform can quickly generate mobile load control instructions based on real-time data and predicted data to realize dynamic scheduling of mobile loads. This flexibility and response speed ensure that the system can make rapid adjustments in the face of changes in power supply and demand, and maintain the stable operation of the system. At the same time, by selecting the mobile load that has completed its task before the start time node of the future duration as the target mobile load, the equipment management platform further improves the flexibility and efficiency of scheduling, avoiding the situation where the mobile load cannot be charged because it is performing a task. By optimizing the allocation of power resources and avoiding excess power waste, the equipment management platform helps to reduce the operating costs of the system. At the same time, through real-time monitoring and dynamic scheduling, the equipment management platform can promptly discover and solve potential imbalances in power supply and demand, avoid equipment damage or failures caused by power shortages or surpluses, and further reduce maintenance costs.

[0015] Optionally, after the device management platform determines the predicted total power of the predicted power generation in the predicted power generation data and the remaining power in the state of charge data, the method further includes:

[0016] If the device management platform determines that the predicted total power is less than the lower limit threshold of the power charge of the energy storage device, the mobile load control instruction is generated, wherein the predicted power generation data includes the predicted power generation for the future period;

[0017] The device management platform determines at least one target mobile load from the mobile load cluster according to the real-time working data set, wherein the target mobile load is a mobile load that has completed the current execution task before the starting time node of the future time period and whose estimated remaining power is greater than a preset power threshold;

[0018] The device management platform sends the mobile load control instruction to the target mobile load, so that the target mobile load moves to the charging and discharging interface to perform a discharging operation before the starting time node of the future time period.

[0019] In the above scheme, when the device management platform determines that the predicted total power is lower than the lower limit threshold of the power storage device through comprehensive analysis of the predicted power generation and the remaining power of the energy storage device, it means that the power supply may be insufficient in the future period and there is a risk of power shortage. At this time, the device management platform will intelligently generate mobile load control instructions and dispatch mobile loads with discharge capabilities to perform discharge operations to supplement the power of the power grid and ensure the balance of power supply and demand, thereby effectively avoiding system instability or interruption caused by power shortage, ensuring the continuous and stable operation of the power system, and improving the reliability and safety of the system. That is, in the case of power shortage, the device management platform can quickly identify and dispatch mobile loads with discharge capabilities, especially those mobile loads that have completed their tasks before the start time node of the future time and are expected to have sufficient remaining power. This scheduling method not only ensures the timely replenishment of power, but also maximizes the energy utilization efficiency of mobile loads. Through this intelligent energy management method, the device management platform can manage load resources more efficiently, reduce energy waste, and improve the overall energy management efficiency. In other words, in the case of power shortage, by dispatching mobile loads for discharge operations, not only the power of the power grid is supplemented, but also the utilization of renewable energy is promoted. Because these mobile loads are likely to have been charged by renewable energy before, the discharge process is actually indirectly using renewable energy to power the grid.

[0020] Optionally, the device management platform determines the predicted total power according to the predicted power generation in the predicted power generation data and the remaining power in the state of charge data, including:

[0021] The device management platform determines at least one mobile load to be charged from the mobile load cluster according to the real-time working data set, where the mobile load to be charged is a mobile load to be charged within the future time period;

[0022] The device management platform determines the charging power consumption in the future time period according to the mobile load to be charged;

[0023] The device management platform determines the predicted total power based on the predicted power generation, the remaining power in the charge status data, the charging consumption power and the predicted device power consumption. The predicted device power consumption is determined based on the future duration and historical device power consumption data.

[0024] In the above scheme, when predicting the total power, the device management platform not only considers the predicted power generation of renewable energy power generation equipment and the remaining power of energy storage equipment, but also adds the charging power consumption of the mobile load to be charged in the future and the power consumption of the equipment predicted based on historical data. This comprehensive consideration makes the power supply and demand forecast more accurate and helps the system make more accurate power dispatching decisions. In addition, by comprehensively considering the charging needs of the mobile load to be charged, the device management platform can arrange the charging time more reasonably and avoid large-scale charging operations during peak power periods, thereby reducing the impact on the power grid and reducing the load on the power grid. At the same time, accurate power supply and demand forecasts help avoid energy waste caused by excess or shortage of power and improve energy utilization efficiency. Accurate power supply and demand forecasts and optimized power dispatch strategies help maintain the stable operation of the power grid. The device management platform can identify potential power supply and demand imbalance problems in advance and take corresponding preventive measures, such as dispatching mobile loads for charging and discharging operations, thereby avoiding system instability or interruption caused by power shortage or surplus. In addition, by comprehensively considering multiple factors for power supply and demand forecasts, the device management platform can more accurately judge the power supply and demand status in future periods, so as to formulate strategies to deal with emergencies in advance. For example, when it is predicted that there may be a power shortage in the future, the device management platform can dispatch mobile loads with discharge capabilities to perform discharge operations in advance, provide additional power support for the power grid, and enhance the system's ability to respond to sudden power shortages. Through accurate power supply and demand forecasts and optimized power dispatch strategies, the device management platform can better coordinate the relationship between renewable energy power generation equipment and the power grid. When the power generation of renewable energy is sufficient, mobile loads are preferentially dispatched for charging operations to make full use of renewable energy; when the power generation of renewable energy is insufficient, energy storage equipment and mobile loads are dispatched for discharge operations to supplement the power of the power grid, thereby ensuring the effective use of renewable energy and the stable operation of the power grid.

[0025] Optionally, before the renewable energy power generation equipment obtains predicted power generation data and sends the predicted power generation data to the equipment management platform, the method further includes:

[0026] The equipment management platform determines the current task intensive feature quantity according to the real-time working data set;

[0027] If the device management platform determines that the current task-intensive feature quantity is greater than a preset task-intensive feature upper limit threshold, a duration shortening operation is performed based on the preset calibration duration to determine the future duration;

[0028] If the device management platform determines that the current task-intensive feature quantity is less than a preset task-intensive feature lower limit threshold, an increase duration operation is performed based on a preset calibration duration to determine the future duration.

[0029] In the above scheme, the equipment management platform dynamically analyzes the current task-intensive feature quantity based on the real-time work data set, and flexibly adjusts the setting of the future duration based on the comparison result between the feature quantity and the preset threshold. When the task-intensive feature quantity is high, the future duration is shortened to more accurately predict the changes in power supply and demand in the short term, respond to the fluctuation of power load in a timely manner, and avoid the accumulation of prediction errors caused by too long a prediction cycle. On the contrary, when the task-intensive feature quantity is low, the future duration is increased to more comprehensively consider the power supply and demand trends for a longer period of time in the future, provide a more accurate reference for long-term power dispatching, and optimize the configuration and utilization of power resources.

[0030] By dynamically adjusting the prediction duration, the equipment management platform can better adapt to changes in power demand in different time periods and task intensities. During mission peak periods, by shortening the prediction duration, the system can respond more quickly to instantaneous changes in power load, ensure power supply for critical tasks, and avoid mission interruptions or delays caused by power shortages. During mission trough periods, by extending the prediction duration, the system can more reasonably plan the long-term configuration of power resources, such as optimizing the charging and discharging plans of energy storage equipment, improving the utilization rate of renewable energy, and reducing operating costs.

[0031] The strategy of dynamically adjusting the prediction duration gives the power dispatch system greater flexibility. The system can quickly adjust the power dispatch plan according to the actual situation of different time periods and task intensity to ensure the real-time balance of power supply and demand. This not only improves the efficiency of power dispatch, but also enhances the system's ability to respond to emergencies. For example, when an emergency causes a sharp increase in power load, the system can quickly shorten the prediction duration, re-evaluate the power supply and demand situation, and quickly adjust the dispatch strategy to meet the challenge of power shortage. By dynamically adjusting the prediction duration, the equipment management platform can more accurately predict the power generation of renewable energy and optimize the power dispatch plan based on the prediction results. When the power generation of renewable energy is sufficient, the system can extend the prediction duration to create favorable conditions for the large-scale grid connection and consumption of renewable energy; when the power generation of renewable energy is insufficient, the system can shorten the prediction duration, quickly respond to changes in power load, and ensure the stable operation of the power grid.

[0032] Optionally, the device management platform determines the current task-intensive feature quantity according to the real-time work data set, including:

[0033] The equipment management platform determines the number of real-time execution tasks according to the real-time work data set, so as to determine the ratio between the number of real-time execution tasks and the number of mobile loads in the mobile load cluster as the current task intensive characteristic value.

[0034] In the above scheme, through the real-time work data set, the equipment management platform can count the number of tasks currently being executed and compare it with the total number of mobile loads in the mobile load cluster, so as to calculate the current task intensive feature quantity. This method directly reflects the density of tasks in the system and the utilization rate of mobile loads, and provides accurate data support for subsequent prediction and scheduling. Accurate evaluation of the current task intensive feature quantity helps the equipment management platform to formulate a more reasonable power scheduling strategy. When the task intensive feature quantity is high, the system can predict possible peak power demand in the future, so as to reserve and schedule power in advance and ensure the power supply of critical tasks. This can not only improve the response speed of the system, but also effectively avoid task interruption or delay caused by power shortage.

[0035] Optionally, the site power management system is applied to a logistics warehousing scenario, the mobile load cluster is a picking mobile robot cluster, and the renewable energy power generation equipment is a photovoltaic power generation equipment.

[0036] In the above scheme, the real-time data access and processing method of load resources based on the Internet of Things system is applied to logistics and warehousing scenarios, especially for picking mobile robot clusters and photovoltaic power generation equipment, which can significantly improve the operational efficiency and energy utilization of logistics and warehousing. Through real-time monitoring and intelligent scheduling of the working status and power status of the picking mobile robot, combined with the predicted power generation of photovoltaic power generation equipment, the system can optimize the robot's charging and picking path.

[0037] In a second aspect, the present application provides a site power management system, including: a device management platform, a mobile load cluster, a renewable energy power generation device, and an energy storage device, wherein the device management platform is connected to the renewable energy power generation device, the energy storage device, and each mobile load in the mobile load cluster via an Internet of Things communication;

[0038] Each mobile load in the mobile load cluster acquires real-time working data and sends the real-time working data to the device management platform to generate a real-time working data set, wherein the real-time working data includes real-time task data and real-time power data;

[0039] The renewable energy power generation equipment obtains predicted power generation data, and sends the predicted power generation data to the equipment management platform; the energy storage equipment obtains state of charge data, and sends the state of charge data to the equipment management platform;

[0040] The device management platform generates a mobile load control instruction based on the real-time working data, the predicted power generation data and the charge state data. The mobile load control instruction is used to instruct at least one mobile load in the mobile load cluster to move to the charging and discharging interface for charging and discharging operations. The renewable energy power generation equipment, the energy storage equipment and the charging and discharging interface are connected through a power grid.

[0041] Optionally, the device management platform generates a mobile load control instruction according to the real-time working data, the predicted power generation data and the charge state data, including:

[0042] The equipment management platform determines the predicted total power according to the predicted power generation in the predicted power generation data and the remaining power in the state of charge data;

[0043] If the device management platform determines that the predicted total power is greater than the upper limit threshold of the power charge of the energy storage device, the mobile load control instruction is generated, wherein the predicted power generation data includes the predicted power generation for the future period;

[0044] The equipment management platform determines at least one target mobile load from the mobile load cluster according to the real-time working data set, wherein the target mobile load is a mobile load that has completed a currently executed task before a starting time node of the future duration;

[0045] The device management platform sends the mobile load control instruction to the target mobile load, so that the target mobile load moves to the charging and discharging interface to perform a charging operation before the starting time node of the future time period.

[0046] Optionally, after the device management platform determines the predicted total power of the predicted power generation in the predicted power generation data and the remaining power in the state of charge data, the method further includes:

[0047] If the device management platform determines that the predicted total power is less than the lower limit threshold of the power charge of the energy storage device, the mobile load control instruction is generated, wherein the predicted power generation data includes the predicted power generation for the future period;

[0048] The device management platform determines at least one target mobile load from the mobile load cluster according to the real-time working data set, wherein the target mobile load is a mobile load that has completed the current execution task before the starting time node of the future time period and whose estimated remaining power is greater than a preset power threshold;

[0049] The device management platform sends the mobile load control instruction to the target mobile load, so that the target mobile load moves to the charging and discharging interface to perform a discharging operation before the starting time node of the future time period.

[0050] Optionally, the device management platform determines the predicted total power according to the predicted power generation in the predicted power generation data and the remaining power in the state of charge data, including:

[0051] The device management platform determines at least one mobile load to be charged from the mobile load cluster according to the real-time working data set, where the mobile load to be charged is a mobile load to be charged within the future time period;

[0052] The device management platform determines the charging power consumption in the future time period according to the mobile load to be charged;

[0053] The device management platform determines the predicted total power based on the predicted power generation, the remaining power in the charge status data, the charging consumption power and the predicted device power consumption. The predicted device power consumption is determined based on the future duration and historical device power consumption data.

[0054] Optionally, before the renewable energy power generation equipment obtains predicted power generation data and sends the predicted power generation data to the equipment management platform, the method further includes:

[0055] The equipment management platform determines the current task intensive feature quantity according to the real-time working data set;

[0056] If the device management platform determines that the current task-intensive feature quantity is greater than a preset task-intensive feature upper limit threshold, a duration shortening operation is performed based on the preset calibration duration to determine the future duration;

[0057] If the device management platform determines that the current task-intensive feature quantity is less than a preset task-intensive feature lower limit threshold, an increase duration operation is performed based on a preset calibration duration to determine the future duration.

[0058] Optionally, the device management platform determines the current task-intensive feature quantity according to the real-time work data set, including:

[0059] The equipment management platform determines the number of real-time execution tasks according to the real-time work data set, so as to determine the ratio between the number of real-time execution tasks and the number of mobile loads in the mobile load cluster as the current task intensive characteristic value.

[0060] Optionally, the site power management system is applied to a logistics warehousing scenario, the mobile load cluster is a picking mobile robot cluster, and the renewable energy power generation equipment is a photovoltaic power generation equipment.

[0061] In a third aspect, the present application provides an electronic device, including:

[0062] processor; and,

[0063] A memory, configured to store executable instructions of the processor;

[0064] The processor is configured to perform any possible method described in the first aspect by executing the executable instructions.

[0065] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, they are used to implement any possible method described in the first aspect.

[0066] The real-time data access and processing method for load resources based on the Internet of Things system provided in the present application obtains real-time working data through each mobile load in the mobile load cluster, and sends the real-time working data to the equipment management platform to generate a real-time working data set, obtains predicted power generation data through renewable energy power generation equipment, and sends the predicted power generation data to the equipment management platform, obtains charge state data through energy storage equipment, and sends the charge state data to the equipment management platform, so that the equipment management platform generates a mobile load control instruction according to the real-time working data, predicted power generation data and charge state data, and the mobile load control instruction is used to instruct at least one mobile load in the mobile load cluster to move to the charge and discharge interface for charge and discharge operations, so as to realize dynamic scheduling of load resources, thereby effectively improving the utilization rate of renewable energy for the power management system. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0068] Figure 1 It is a flowchart of a method for real-time data access and processing of load resources based on an Internet of Things system according to an exemplary embodiment of the present application;

[0069] Figure 2 is a flow chart of a method for real-time data access and processing of load resources based on an Internet of Things system according to another exemplary embodiment of the present application;

[0070] Figure 3 is a schematic diagram of a site power management system according to an exemplary embodiment of the present application;

[0071] Figure 4 It is a schematic diagram of the structure of an electronic device according to an exemplary embodiment of the present application.

[0072] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0073] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0074] In order to solve the above problems, the embodiments provided in this application aim to provide a method for real-time data access and processing of load resources based on the Internet of Things system. This method is mainly used in site power management systems, especially for complex power systems including equipment management platforms, mobile load clusters, renewable energy power generation equipment, and energy storage equipment. The purpose of the embodiments provided in this application is to improve the flexibility and response speed of the power system, optimize energy utilization, reduce costs, and ensure efficient and stable operation of the system through real-time monitoring and intelligent scheduling.

[0075] First, the embodiment provided in this application closely connects the mobile load cluster, renewable energy power generation equipment, energy storage equipment and equipment management platform through the Internet of Things technology to form a highly integrated power management system. This connection not only realizes the real-time transmission of data, but also provides a basis for subsequent data analysis and intelligent scheduling.

[0076] In terms of data acquisition, each mobile load in the mobile load cluster acquires and sends its working data to the equipment management platform in real time, including real-time task data and real-time power data. At the same time, renewable energy power generation equipment sends predicted power generation data, and energy storage equipment sends its charge state data. These data together constitute the real-time data collection of the power management system, providing comprehensive and accurate information support for subsequent decision-making.

[0077] The device management platform, as the "brain" of the system, conducts comprehensive analysis based on the real-time data set received. It first determines the predicted total power based on the predicted power generation and the remaining power of the energy storage device to evaluate the power supply and demand in the future period. Then, the device management platform intelligently generates mobile load control instructions based on real-time task data and power data.

[0078] In the case of excess electricity, the equipment management platform will select the mobile load that has completed its task before the start time of the future period as the target load, and instruct it to move to the charging and discharging interface for charging. This strategy not only avoids the waste of electricity, but also ensures that the mobile load has sufficient power, providing a guarantee for subsequent tasks.

[0079] On the contrary, in the case of power shortage, the equipment management platform will dispatch mobile loads with discharge capabilities to perform discharge operations to supplement the power of the grid. In particular, it will give priority to discharging mobile loads that have completed their tasks before the start time of the future period and are expected to have sufficient remaining power, thereby maximizing the use of existing resources and ensuring the stable operation of the power system.

[0080] In addition, the embodiments provided in this application also consider the dynamic adjustment of future durations. The device management platform flexibly sets future durations based on real-time task-intensive feature quantities to adapt to changes in power demand in different time periods and task intensities. This dynamic adjustment strategy enables the system to more accurately predict and dispatch power resources, improving response speed and energy efficiency.

[0081] In summary, the specific technical concept of the embodiments provided in this application is to realize real-time monitoring and intelligent scheduling of load resources through Internet of Things technology. By comprehensively analyzing real-time data and predicted data, the equipment management platform can dynamically generate mobile load control instructions and optimize the configuration and utilization of power resources. This method of real-time data access and processing of load resources based on the Internet of Things system not only improves the flexibility and response speed of the power system, but also reduces operating costs and ensures the efficient and stable operation of the system.

[0082] Figure 1 FIG. 1 is a flow chart of a method for accessing and processing real-time data of load resources based on an IoT system according to an exemplary embodiment of the present application. Figure 1 As shown, the load resource real-time data access processing method based on the Internet of Things system provided in this embodiment includes:

[0083] S101. Each mobile load in the mobile load cluster obtains real-time working data and sends the real-time working data to the equipment management platform.

[0084] In this step, each mobile load in the mobile load cluster obtains real-time working data and sends the real-time working data to the device management platform to generate a real-time working data set. The real-time working data includes real-time task data and real-time power data.

[0085] Specifically, each mobile load in the mobile load cluster (such as logistics vehicles, logistics sorting robots, etc.) is equipped with an IoT communication module. While performing tasks, these mobile loads will collect their working status and power information in real time to form real-time working data. These data include the type of task currently performed by the mobile load, task progress, remaining power, etc. Through the IoT communication connection, these data are sent to the device management platform in real time. After receiving these real-time working data, the device management platform will summarize and preliminarily process them to form a real-time working data set. This step provides basic data support for subsequent data analysis and control instruction generation.

[0086] S102: The renewable energy power generation equipment obtains predicted power generation data and sends the predicted power generation data to the equipment management platform.

[0087] Specifically, renewable energy generation equipment (such as photovoltaic panels, wind turbines, etc.) obtains future power generation forecast data through built-in forecasting algorithms or external forecasting services. These forecast data reflect the expected power generation of renewable energy generation equipment in a specific time period. Through the IoT communication connection, these forecast power generation data are sent to the device management platform. The device management platform uses this data to evaluate the potential of future power supply and provide a basis for subsequent power dispatch and load control.

[0088] It is worth noting that when the renewable energy power generation equipment is a photovoltaic panel, the determination of the predicted power generation data can be determined by collecting historical meteorological data of the area where the photovoltaic power station is located, including sunshine intensity, irradiance, temperature, humidity, wind speed, etc. These data can be obtained through the meteorological department, solar energy database or professional photovoltaic simulation software. Then, determine the type, conversion efficiency, tilt angle, direction and other parameters of the photovoltaic panel. And consider the system configuration of the photovoltaic power station, such as inverter efficiency, cable loss, transformer loss, etc. Then choose professional photovoltaic simulation software, such as PVsyst, HOMER, Partridge Cloud, etc. These software can comprehensively consider multiple factors such as geographical location, climatic conditions, photovoltaic panel model, system configuration, etc., and provide more accurate power generation estimation results. Establish a model of the photovoltaic power station in the software, input the parameters of the photovoltaic panel, system configuration and historical meteorological data. Through the simulation function of the software, simulate the power generation of the photovoltaic power station in the future.

[0089] S103: The energy storage device obtains charge state data and sends the charge state data to the device management platform.

[0090] Specifically, energy storage devices (such as battery packs, supercapacitors, etc.) are important means of power regulation, and their state of charge is a key indicator that the device management platform needs to pay attention to in real time. The energy storage device sends its current state of charge data to the device management platform through the Internet of Things communication module. The device management platform evaluates the available capacity and charging and discharging capabilities of the energy storage device based on this data, and provides decision support for subsequent power dispatch and load control.

[0091] S104. The equipment management platform generates a mobile load control instruction according to the real-time working data, the predicted power generation data and the charge state data.

[0092] In this step, the equipment management platform generates a mobile load control instruction based on the real-time working data, predicted power generation data and charge status data. The mobile load control instruction is used to instruct at least one mobile load in the mobile load cluster to move to the charging and discharging interface for charging and discharging operations. The renewable energy power generation equipment, energy storage equipment and the charging and discharging interface are connected through the power grid.

[0093] Specifically, after receiving the real-time working data set, predicted power generation data and energy storage device charge state data, the device management platform generates mobile load control instructions based on the real-time working data, predicted power generation data and charge state data. These instructions include instructing specific mobile loads to move to the charging and discharging interface for charging or discharging operations to balance power supply and demand, optimize power use and reduce operating costs. After receiving the control instructions sent by the device management platform, the mobile loads will perform the corresponding charging and discharging operations. At the same time, they will feed back the execution results to the device management platform through the Internet of Things communication module. The device management platform evaluates the execution effect of the control instructions based on the feedback results and adjusts the subsequent control strategy as needed. This closed-loop feedback mechanism ensures the accuracy and effectiveness of power dispatching and load control.

[0094] In one possible implementation, the device management platform determines the predicted total power based on the predicted power generation in the predicted power generation data and the remaining power in the state of charge data. If the device management platform determines that the predicted total power is greater than the upper limit threshold of the charge of the energy storage device, a mobile load control instruction is generated, wherein the predicted power generation data includes the predicted power generation for the future duration. The device management platform determines at least one target mobile load from the mobile load cluster based on the real-time working data set, and the target mobile load is a mobile load that has completed the current execution task before the starting time node of the future duration. The device management platform sends the mobile load control instruction to the target mobile load so that the target mobile load moves to the charging and discharging interface for charging before the starting time node of the future duration.

[0095] First, the device management platform calculates the predicted total power based on the predicted power generation data received from the renewable energy power generation equipment, including the predicted power generation for the future period, and the remaining power in the charge status data received from the energy storage device.

[0096] Next, the device management platform will compare the calculated predicted total power with the upper limit threshold of the energy storage device's charge. This threshold is pre-set and is used to determine whether the power supply is likely to exceed the storage capacity of the energy storage device. If the predicted total power is greater than the upper limit threshold of the energy storage device's charge, it means that the power supply in the future period will exceed the current storage capacity of the system, and there is a risk of excess power.

[0097] When excess power is confirmed, the device management platform will further select target mobile loads from the mobile load cluster. These target mobile loads are mobile loads that have completed their current tasks before the start time node of the future duration. Through the real-time working data collection, the device management platform can accurately grasp the working status of each mobile load, and thus accurately select mobile loads that meet the conditions.

[0098] Once the target mobile load is determined, the device management platform will generate the corresponding mobile load control instructions and send these instructions to the target mobile load through the IoT communication connection. These instructions will instruct the target mobile load to move to the charging and discharging interface for charging before the start time node of the future duration. In this way, the device management platform can ensure that the mobile load is charged in time when there is excess power, which not only avoids the waste of electricity, but also ensures that the mobile load has sufficient power, providing a guarantee for subsequent work tasks.

[0099] In addition, in order to ensure that the mobile load can smoothly reach the charging and discharging interface and charge, the equipment management platform can also be integrated with the navigation system or path planning system in the venue to provide the best charging path planning for the mobile load. In this way, the mobile load can reach the charging and discharging interface more efficiently and accurately, further improving the operating efficiency of the entire power management system.

[0100] In the above scheme, the equipment management platform calculates the predicted total power by comprehensively predicting the power generation and the remaining power of the energy storage device, so as to accurately judge the future power supply and demand situation. When the predicted total power exceeds the upper limit threshold of the charge of the energy storage device, it means that the power supply will exceed the current storage capacity of the system, and there is a risk of excess power. In this case, the equipment management platform intelligently generates mobile load control instructions, selects the mobile load that has completed the task before the start time node of the future duration as the target mobile load, and instructs it to move to the charging and discharging interface for charging, thereby effectively utilizing the excess power resources and avoiding the waste of power. At the same time, it ensures that the mobile load has sufficient power, which provides a guarantee for subsequent work tasks. That is, when there is excess power, the mobile load is dispatched in time for charging, which not only avoids the waste of power, but also improves the utilization rate of renewable energy. In addition, the equipment management platform can quickly generate mobile load control instructions based on real-time data and predicted data to realize dynamic scheduling of mobile loads. This flexibility and response speed ensure that the system can make rapid adjustments in the face of changes in power supply and demand, and maintain the stable operation of the system. At the same time, by selecting the mobile load that has completed its task before the start time node of the future duration as the target mobile load, the equipment management platform further improves the flexibility and efficiency of scheduling, avoiding the situation where the mobile load cannot be charged because it is performing a task. By optimizing the allocation of power resources and avoiding excess power waste, the equipment management platform helps to reduce the operating costs of the system. At the same time, through real-time monitoring and dynamic scheduling, the equipment management platform can promptly discover and solve potential imbalances in power supply and demand, avoid equipment damage or failures caused by power shortages or surpluses, and further reduce maintenance costs.

[0101] In another possible implementation, if the device management platform determines that the predicted total power is less than the lower limit threshold of the charge of the energy storage device, a mobile load control instruction is generated, wherein the predicted power generation data includes the predicted power generation of the future duration. The device management platform determines at least one target mobile load from the mobile load cluster based on the real-time working data set, and the target mobile load is a mobile load that has completed the current execution task before the starting time node of the future duration and the estimated remaining power is greater than the preset power threshold. The device management platform sends the mobile load control instruction to the target mobile load so that the target mobile load moves to the charge and discharge interface for discharge operation before the starting time node of the future duration.

[0102] After the device management platform calculates the predicted total power based on the predicted power generation and the remaining power of the energy storage device, if it finds that the predicted total power is less than the lower limit of the energy storage device's charge, this means that the power supply may be insufficient in the future period and there is a risk of power shortage. At this time, the device management platform will start another set of processing mechanisms to deal with this situation.

[0103] First, the equipment management platform will select qualified target mobile loads from the mobile load cluster based on the real-time working data set. These target mobile loads must not only have completed their current execution tasks before the start time node of the future duration, but also have an estimated remaining power greater than the preset power threshold. This screening process ensures that the selected mobile loads have both discharge capabilities and will not affect their subsequent work tasks due to discharge operations.

[0104] Once the target mobile load is determined, the device management platform will generate the corresponding mobile load control instructions and send these instructions to the target mobile load through the IoT communication connection. These instructions will instruct the target mobile load to move to the charge and discharge interface for discharge operation before the start time node of the future time period. In this way, the device management platform can respond quickly to power shortages and supplement the power grid by dispatching mobile loads with discharge capabilities to ensure the balance of supply and demand in the power system.

[0105] It is worth noting that when dispatching mobile loads for discharge operations, the equipment management platform will also comprehensively consider factors such as the discharge capacity of the mobile loads, the remaining power, and the impact of discharge on the stability of the power grid, ensuring that the discharge operation can effectively supplement the power grid without adversely affecting the stable operation of the power grid.

[0106] In addition, in order to ensure that the mobile load can smoothly reach the charging and discharging interface and perform the discharge operation, the equipment management platform can also be integrated with the navigation system or path planning system in the venue to provide the best discharge path planning for the mobile load. In this way, the mobile load can reach the charging and discharging interface more efficiently and accurately, further improving the emergency response capability and operating efficiency of the entire power management system.

[0107] In the above scheme, when the device management platform determines that the predicted total power is lower than the lower limit threshold of the power storage device through comprehensive analysis of the predicted power generation and the remaining power of the energy storage device, it means that the power supply may be insufficient in the future period and there is a risk of power shortage. At this time, the device management platform will intelligently generate mobile load control instructions and dispatch mobile loads with discharge capabilities to perform discharge operations to supplement the power of the power grid and ensure the balance of power supply and demand, thereby effectively avoiding system instability or interruption caused by power shortage, ensuring the continuous and stable operation of the power system, and improving the reliability and safety of the system. That is, in the case of power shortage, the device management platform can quickly identify and dispatch mobile loads with discharge capabilities, especially those mobile loads that have completed their tasks before the start time node of the future time and are expected to have sufficient remaining power. This scheduling method not only ensures the timely replenishment of power, but also maximizes the energy utilization efficiency of mobile loads. Through this intelligent energy management method, the device management platform can manage load resources more efficiently, reduce energy waste, and improve the overall energy management efficiency. In other words, in the case of power shortage, by dispatching mobile loads for discharge operations, not only the power of the power grid is supplemented, but also the utilization of renewable energy is promoted. Because these mobile loads are likely to have been charged by renewable energy before, the discharge process is actually indirectly using renewable energy to power the grid.

[0108] In this embodiment, real-time working data is obtained through each mobile load in the mobile load cluster, and the real-time working data is sent to the equipment management platform to generate a real-time working data set, predicted power generation data is obtained through renewable energy power generation equipment, and the predicted power generation data is sent to the equipment management platform, and charge state data is obtained through energy storage equipment, and the charge state data is sent to the equipment management platform, so that the equipment management platform generates a mobile load control instruction according to the real-time working data, predicted power generation data and charge state data. The mobile load control instruction is used to instruct at least one mobile load in the mobile load cluster to move to the charge and discharge interface for charge and discharge operations, so as to realize dynamic scheduling of load resources, thereby effectively improving the utilization rate of renewable energy by the power management system.

[0109] Figure 2 FIG. 1 is a flow chart of a method for real-time data access and processing of load resources based on an IoT system according to another exemplary embodiment of the present application. Figure 2 As shown, the load resource real-time data access processing method based on the Internet of Things system provided in this embodiment includes:

[0110] S201. Each mobile load in the mobile load cluster obtains real-time working data and sends the real-time working data to the equipment management platform.

[0111] In this step, each mobile load in the mobile load cluster obtains real-time working data and sends the real-time working data to the device management platform to generate a real-time working data set. The real-time working data includes real-time task data and real-time power data.

[0112] Optionally, the above-mentioned site power management system can be applied to logistics warehousing scenarios, the mobile load cluster is a picking mobile robot cluster, and the renewable energy power generation equipment is a photovoltaic power generation equipment.

[0113] Specifically, the equipment management platform is responsible for collecting real-time working data (such as task status, power level, etc.) of the picking mobile robot cluster, predicted power generation data of photovoltaic power generation equipment, and charge state data of energy storage equipment. Based on the collected data, the equipment management platform dynamically generates control instructions for the picking mobile robot through algorithm analysis to optimize its charging and picking path.

[0114] The picking mobile robot cluster consists of multiple autonomously navigated mobile robots, each equipped with an IoT communication module that can upload its working status and power information in real time. When the robot is performing a picking task, the device management platform will dynamically adjust its work plan based on its real-time power and task status. For example, when the robot's power is low and the task allows, it will be given priority to return to the charging station for charging. When the photovoltaic power generation is sufficient, the device management platform will intelligently dispatch robots with low power to the charging station to charge with green energy, reducing dependence on traditional power grids. Photovoltaic power generation equipment can be equipped with an intelligent monitoring system that can predict power generation in future periods based on weather forecasts and historical power generation data. The device management platform will formulate an overall energy management strategy based on the predicted data of photovoltaic power generation and the status of energy storage equipment. When the power generation exceeds the demand of the storage center, the excess power will be used to charge the energy storage equipment or the picking robot.

[0115] Energy storage equipment stores electricity when there is excess photovoltaic power generation and releases electricity when there is insufficient power to balance the load on the power grid and ensure the stable operation of the logistics and warehousing center. The equipment management platform will intelligently dispatch the charging and discharging operations of energy storage equipment based on real-time power demand and photovoltaic power generation forecasts to achieve optimal configuration of electricity.

[0116] S202: The renewable energy power generation equipment obtains predicted power generation data and sends the predicted power generation data to the equipment management platform.

[0117] Specifically, renewable energy generation equipment (such as photovoltaic panels, wind turbines, etc.) obtains future power generation forecast data through built-in forecasting algorithms or external forecasting services. These forecast data reflect the expected power generation of renewable energy generation equipment in a specific time period. Through the IoT communication connection, these forecast power generation data are sent to the device management platform. The device management platform uses this data to evaluate the potential of future power supply and provide a basis for subsequent power dispatch and load control.

[0118] S203: The energy storage device obtains charge state data and sends the charge state data to the device management platform.

[0119] Specifically, energy storage devices (such as battery packs, supercapacitors, etc.) are important means of power regulation, and their state of charge is a key indicator that the device management platform needs to pay attention to in real time. The energy storage device sends its current state of charge data to the device management platform through the IoT communication module. The device management platform evaluates the available capacity and charging and discharging capabilities of the energy storage device based on this data, and provides decision support for subsequent power dispatch and load control.

[0120] S204: The device management platform determines at least one mobile load to be charged from the mobile load cluster according to the real-time working data set.

[0121] In this step, the device management platform determines at least one mobile load to be charged from the mobile load cluster according to the real-time working data set, and the mobile load to be charged is a mobile load to be charged in the future.

[0122] Specifically, the device management platform first selects at least one mobile load to be charged from the mobile load cluster according to the real-time working data set. These mobile loads to be charged refer to mobile loads that have charging requirements in the future, such as electric vehicles, mobile devices or other devices that need to be charged regularly.

[0123] The device management platform collects real-time working data of each device in the mobile load cluster through the Internet of Things technology, including but not limited to device type, current power, charging plan, etc. According to the user's charging needs or the device's charging strategy, set screening conditions, such as power below a certain threshold, appointment for charging in the future, etc. Match the real-time working data set with the screening conditions to determine the mobile loads to be charged that meet the conditions.

[0124] S205: The device management platform determines the charging power consumption in the future according to the mobile load to be charged.

[0125] Specifically, the device management platform calculates the charging power consumption of these loads in the future based on the selected mobile loads to be charged. The battery capacity, charging efficiency and other parameters of the mobile loads to be charged can be obtained from the device information database. According to the current power and target power of the device (such as fully charged or reaching a preset power), combined with the charging efficiency, the required charging amount of each mobile load to be charged in the future is calculated. The charging requirements of all mobile loads to be charged are summarized to obtain the total charging power consumption in the future.

[0126] S206. The equipment management platform determines the predicted total power according to the predicted power generation, the remaining power in the charge state data, the charging power consumption, and the predicted equipment power consumption.

[0127] In this step, the equipment management platform determines the predicted total power based on the predicted power generation, the remaining power in the charge status data, the charging consumption power and the predicted equipment power consumption. The predicted equipment power consumption is determined based on the future duration and historical equipment power consumption data.

[0128] Specifically, the equipment management platform comprehensively considers the predicted power generation, the remaining power in the state of charge data, the charging power consumption, and the predicted equipment power consumption to calculate the predicted total power. The predicted power generation for the future duration can be obtained through weather forecasts, power generation equipment status monitoring, etc. The remaining power of the current power grid or energy storage system is obtained from the state of charge data. According to the future duration and historical equipment power consumption data, time series analysis, machine learning and other methods are used to predict the power consumption of the equipment in the future. The predicted power generation, remaining power, charging power consumption and predicted equipment power consumption are added up, and the possible power loss and conversion efficiency are considered to obtain the predicted total power.

[0129] S207. The equipment management platform generates a mobile load control instruction according to the real-time working data, the predicted power generation data and the charge state data.

[0130] In this step, the equipment management platform generates a mobile load control instruction based on the real-time working data, predicted power generation data and charge state data. The mobile load control instruction is used to instruct at least one mobile load in the mobile load cluster to move to the charging and discharging interface for charging and discharging operations. The renewable energy power generation equipment, energy storage equipment and the charging and discharging interface are connected through the power grid.

[0131] On the basis of the above embodiment, before the renewable energy power generation equipment obtains the predicted power generation data and sends the predicted power generation data to the equipment management platform, it also includes: the equipment management platform determines the current task intensive feature quantity based on the real-time working data set; if the equipment management platform determines that the current task intensive feature quantity is greater than the preset task intensive feature upper limit threshold, then the duration is shortened based on the preset calibration duration to determine the future duration; if the equipment management platform determines that the current task intensive feature quantity is less than the preset task intensive feature lower limit threshold, then the duration is increased based on the preset calibration duration to determine the future duration.

[0132] In the above scheme, the equipment management platform dynamically analyzes the current task-intensive feature quantity based on the real-time work data set, and flexibly adjusts the setting of the future duration based on the comparison result between the feature quantity and the preset threshold. When the task-intensive feature quantity is high, the future duration is shortened to more accurately predict the changes in power supply and demand in the short term, respond to the fluctuation of power load in a timely manner, and avoid the accumulation of prediction errors caused by too long a prediction cycle. On the contrary, when the task-intensive feature quantity is low, the future duration is increased to more comprehensively consider the power supply and demand trends for a longer period of time in the future, provide a more accurate reference for long-term power dispatching, and optimize the configuration and utilization of power resources.

[0133] By dynamically adjusting the prediction duration, the equipment management platform can better adapt to changes in power demand in different time periods and task intensities. During mission peak periods, by shortening the prediction duration, the system can respond more quickly to instantaneous changes in power load, ensure power supply for critical tasks, and avoid mission interruptions or delays caused by power shortages. During mission trough periods, by extending the prediction duration, the system can more reasonably plan the long-term configuration of power resources, such as optimizing the charging and discharging plans of energy storage equipment, improving the utilization rate of renewable energy, and reducing operating costs.

[0134] The strategy of dynamically adjusting the prediction duration gives the power dispatch system greater flexibility. The system can quickly adjust the power dispatch plan according to the actual situation of different time periods and task intensity to ensure the real-time balance of power supply and demand. This not only improves the efficiency of power dispatch, but also enhances the system's ability to respond to emergencies. For example, when an emergency causes a sharp increase in power load, the system can quickly shorten the prediction duration, re-evaluate the power supply and demand situation, and quickly adjust the dispatch strategy to meet the challenge of power shortage. By dynamically adjusting the prediction duration, the equipment management platform can more accurately predict the power generation of renewable energy and optimize the power dispatch plan based on the prediction results. When the power generation of renewable energy is sufficient, the system can extend the prediction duration to create favorable conditions for the large-scale grid connection and consumption of renewable energy; when the power generation of renewable energy is insufficient, the system can shorten the prediction duration, quickly respond to changes in power load, and ensure the stable operation of the power grid.

[0135] It is worth noting that in a further specific implementation, the device management platform determines the current task-intensive feature quantity Dt based on the real-time work data set. If the device management platform determines that the current task-intensive feature quantity Dt is greater than the preset task-intensive feature upper limit threshold Du, then Formula 1 is used to determine the future duration Tf based on the preset calibration duration T0. Formula 1 is:

[0136]

[0137] Wherein, α is the first adjustment parameter, 0<α<1, and Dmax is the preset maximum task intensive feature quantity;

[0138] If the device management platform determines that the current task intensive feature quantity Dt is less than the preset task intensive feature lower limit threshold Dw, then Formula 2 is used to determine the future duration Tf according to the preset calibration duration T0. Formula 2 is:

[0139]

[0140] Wherein, β is the second adjustment parameter, 0<β<1.

[0141] In the above scheme, in order to more accurately predict the power supply and demand in the future period, the equipment management platform will dynamically adjust the setting of the future duration according to the current task-intensive feature quantity. This strategy is implemented by introducing Formula 1 and Formula 2, which aims to flexibly adjust the prediction period according to the task intensity, thereby ensuring the accuracy and response speed of power dispatch. Among them, during the peak period of the task, the power load changes rapidly and frequently. By shortening the prediction duration, the equipment management platform can respond to the instantaneous changes of the power load more quickly, thereby improving the accuracy and timeliness of the prediction. A shorter prediction cycle means more frequent power dispatch decisions. This helps the equipment management platform to adjust power distribution in a timely manner according to real-time data, ensure power supply for critical tasks, and avoid task interruptions or delays caused by power shortages. Faced with a sudden increase in task intensity, the system can quickly adjust the prediction duration to adapt to the new power demand pattern, thereby enhancing the flexibility and adaptability of the system.

[0142] During the mission trough period, the power load is relatively stable. By extending the prediction time, the equipment management platform can more comprehensively consider the power supply and demand trends for a longer period of time in the future, and provide a more accurate reference for long-term power dispatch. In periods when power demand is relatively stable, the system can plan the configuration and utilization of power resources more calmly. For example, the charging and discharging plans of energy storage equipment can be optimized to improve the utilization rate of renewable energy and reduce operating costs. By considering the power supply and demand situation for a longer period of time, the system can identify potential power supply and demand imbalances in advance and take corresponding preventive measures, such as dispatching mobile loads for charging and discharging operations, thereby avoiding system instability or interruptions caused by power shortages or surpluses.

[0143] In summary, by introducing formula 1 and formula 2 to dynamically adjust the future duration, the equipment management platform can flexibly adapt to different power demand patterns according to the current task-intensive feature quantity, thereby improving the accuracy and timeliness of the prediction, optimizing power scheduling and resource allocation, and enhancing the flexibility and stability of the system. This strategy is of great significance for improving the overall performance of the power management system in logistics warehousing scenarios.

[0144] Among them, for the device management platform to determine the current task intensive feature quantity Dt according to the real-time working data set, it can include: the device management platform determines the number of real-time execution tasks according to the real-time working data set, so as to determine the ratio between the number of real-time execution tasks and the number of mobile loads in the mobile load cluster as the current task intensive feature quantity Dt. Through the real-time working data set, the device management platform can count the number of tasks currently being executed and compare it with the total number of mobile loads in the mobile load cluster, so as to calculate the current task intensive feature quantity. This method directly reflects the density of tasks in the system and the utilization rate of mobile loads, and provides accurate data support for subsequent prediction and scheduling. Accurate evaluation of the current task intensive feature quantity helps the device management platform to formulate a more reasonable power scheduling strategy. When the task intensive feature quantity is high, the system can predict the possible peak of power demand in the future, so as to reserve and schedule power in advance and ensure the power supply of key tasks. This can not only improve the response speed of the system, but also effectively avoid task interruption or delay caused by power shortage.

[0145] Furthermore, before using Formula 1 and determining the future duration Tf according to the preset calibration duration T0, the following may also be included:

[0146] The equipment management platform uses formula 3 and determines the first adjustment parameter α according to the historical task intensive feature set D = {D1, D2, ..., Di, ..., Dn}, where the historical task intensive feature set D includes the historical task intensive feature corresponding to a preset number of monitoring moments before the current moment. Formula 3 is:

[0147]

[0148] Wherein, n is the preset number, σth is the task-intensive feature quantity fluctuation threshold, ε is the preset first constant, 0<ε<0.1, γ is the preset first dynamic adjustment factor, 0<γ≤1;

[0149] The equipment management platform uses formula 4 and determines the second adjustment parameter β according to the historical task intensive feature quantity set D = {D1, D2, ..., Di, ..., Dn}. Formula 4 is:

[0150]

[0151] in, is the preset volatility factor, δ is a preset second dynamic adjustment factor, 0≤γ<1.

[0152] In the above scheme, in order to more accurately dynamically adjust the future duration according to the current task-intensive feature quantity, the device management platform will use the historical task-intensive feature quantity set to determine the first adjustment parameter and the second adjustment parameter. This strategy is implemented by introducing Formula 3 and Formula 4, aiming to provide a more scientific and reasonable basis for dynamically adjusting the predicted duration through historical data analysis.

[0153] Among them, Formula 3 is used to determine the first adjustment parameter, which represents the degree of fluctuation of historical data; the task-intensive characteristic quantity fluctuation rate threshold is used to define the normal range of data fluctuation; the first dynamic adjustment factor is preset to adjust the final value of the first adjustment parameter. Formula 3 can ensure that the setting of the first adjustment parameter not only takes into account the actual situation of historical data, but also avoids the influence of extreme values, making the parameter setting more scientific and reasonable. The first adjustment parameter directly affects the degree of shortening of the future duration. The value calculated by Formula 3 can be dynamically adjusted according to the fluctuation of historical data, so that the system can more flexibly adjust the predicted duration when facing different task intensities, thereby enhancing the adaptability and response speed of the system. In addition, a reasonable first adjustment parameter helps the equipment management platform to more accurately predict the power supply and demand in future time periods, so as to formulate a more optimized power dispatching strategy. This can not only improve the utilization efficiency of power resources, but also reduce operating costs and improve the overall performance of the system.

[0154] Formula 4 is used to determine the second adjustment parameter. By setting the preset fluctuation factor and the preset second dynamic adjustment factor, Formula 4 can ensure that the second adjustment parameter fluctuates within a reasonable range, avoiding parameter setting errors caused by abnormal historical data, and helping to enhance the robustness and reliability of the system. In addition, the second adjustment parameter affects the degree of extension of the future duration. The second adjustment parameter calculated by Formula 4 can ensure that the system appropriately extends the forecast duration when the task intensity is low, thereby more comprehensively considering the power supply and demand trends for a longer period of time in the future. This helps the system to more reasonably plan the long-term configuration and utilization of power resources and improve energy utilization efficiency. Reasonable second adjustment parameters help the equipment management platform to formulate more scientific and reasonable power dispatch plans when facing different task intensities. This can not only improve the response speed and flexibility of the system, but also reduce operating costs and improve energy utilization efficiency, thereby comprehensively improving the overall performance of the system.

[0155] Figure 3 FIG. 1 is a schematic diagram of a site power management system according to an exemplary embodiment of the present application. Figure 3As shown, the site power management system 300 provided in this embodiment includes:

[0156] A device management platform 310, a mobile load cluster 320, a renewable energy power generation device 330, and an energy storage device 340, wherein the device management platform 310 is connected to the renewable energy power generation device 330, the energy storage device 340, and each mobile load in the mobile load cluster 320 via Internet of Things communication;

[0157] Each mobile load in the mobile load cluster 320 obtains real-time working data and sends the real-time working data to the device management platform 310 to generate a real-time working data set, wherein the real-time working data includes real-time task data and real-time power data;

[0158] The renewable energy power generation device 330 obtains predicted power generation data and sends the predicted power generation data to the device management platform 310; the energy storage device 340 obtains state of charge data and sends the state of charge data to the device management platform 310;

[0159] The device management platform 310 generates a mobile load control instruction based on the real-time working data, the predicted power generation data and the charge state data. The mobile load control instruction is used to instruct at least one mobile load in the mobile load cluster 320 to move to the charging and discharging interface for charging and discharging operations. The renewable energy power generation equipment 330, the energy storage equipment 340 and the charging and discharging interface are connected through a power grid.

[0160] Optionally, the device management platform 310 generates a mobile load control instruction according to the real-time working data, the predicted power generation data and the charge state data, including:

[0161] The device management platform 310 determines the predicted total power according to the predicted power generation in the predicted power generation data and the remaining power in the charge state data;

[0162] If the device management platform 310 determines that the predicted total power is greater than the upper limit threshold of the power charge of the energy storage device 340, the mobile load control instruction is generated, wherein the predicted power generation data includes the predicted power generation for the future time period;

[0163] The equipment management platform 310 determines at least one target mobile load from the mobile load cluster 320 according to the real-time working data set, wherein the target mobile load is a mobile load that has completed the current execution task before the starting time node of the future duration;

[0164] The device management platform 310 sends the mobile load control instruction to the target mobile load, so that the target mobile load moves to the charging and discharging interface to perform a charging operation before the starting time node of the future time period.

[0165] Optionally, after the device management platform 310 determines the predicted total power of the predicted power generation in the predicted power generation data and the remaining power in the state of charge data, the method further includes:

[0166] If the device management platform 310 determines that the predicted total power is less than the lower limit threshold of the charge of the energy storage device 340, the mobile load control instruction is generated, wherein the predicted power generation data includes the predicted power generation for the future time period;

[0167] The device management platform 310 determines at least one target mobile load from the mobile load cluster 320 according to the real-time working data set, wherein the target mobile load is a mobile load that has completed the current execution task before the start time node of the future duration and whose estimated remaining power is greater than a preset power threshold;

[0168] The device management platform 310 sends the mobile load control instruction to the target mobile load, so that the target mobile load moves to the charging and discharging interface to perform a discharging operation before the starting time node of the future time period.

[0169] Optionally, the device management platform 310 determines the predicted total power according to the predicted power generation in the predicted power generation data and the remaining power in the state of charge data, including:

[0170] The device management platform 310 determines at least one mobile load to be charged from the mobile load cluster 320 according to the real-time working data set, where the mobile load to be charged is a mobile load to be charged within the future time period;

[0171] The device management platform 310 determines the charging power consumption in the future time period according to the mobile load to be charged;

[0172] The device management platform 310 determines the predicted total power based on the predicted power generation, the remaining power in the charge status data, the charging power consumption and the predicted device power consumption, and the predicted device power consumption is determined based on the future duration and historical device power consumption data.

[0173] Optionally, before the renewable energy power generation device 330 acquires the predicted power generation data and sends the predicted power generation data to the device management platform 310, the process further includes:

[0174] The equipment management platform 310 determines the current task intensive feature quantity according to the real-time working data set;

[0175] If the device management platform 310 determines that the current task-intensive feature quantity is greater than a preset task-intensive feature upper limit threshold, a duration reduction operation is performed based on the preset calibration duration to determine the future duration;

[0176] If the device management platform 310 determines that the current task-intensive feature quantity is less than a preset task-intensive feature lower limit threshold, an increase duration operation is performed based on the preset calibration duration to determine the future duration.

[0177] Optionally, the device management platform 310 determines the current task-intensive feature quantity according to the real-time work data set, including:

[0178] The equipment management platform 310 determines the number of real-time execution tasks according to the real-time work data set, so as to determine the ratio between the number of real-time execution tasks and the number of mobile loads in the mobile load cluster 320 as the current task intensive feature value.

[0179] Optionally, the site power management system is applied to a logistics warehousing scenario, the mobile load cluster 320 is a picking mobile robot cluster, and the renewable energy power generation equipment 330 is a photovoltaic power generation equipment.

[0180] Figure 4 is a schematic diagram of the structure of an electronic device according to an exemplary embodiment of the present application. Figure 4 As shown, an electronic device 400 provided in this embodiment includes: a processor 401 and a memory 402; wherein:

[0181] The memory 402 is used to store computer programs, and the memory may also be a flash memory.

[0182] The processor 401 is used to execute the execution instructions stored in the memory to implement each step in the above method. For details, please refer to the relevant description in the above method embodiment.

[0183] Optionally, the memory 402 may be independent or integrated with the processor 401 .

[0184] When the memory 402 is a device independent of the processor 401, the electronic device 400 may further include:

[0185] The bus 403 is used to connect the memory 402 and the processor 401 .

[0186] This embodiment further provides a readable storage medium, in which a computer program is stored. When at least one processor of an electronic device executes the computer program, the electronic device executes the methods provided in the above-mentioned various implementation modes.

[0187] This embodiment also provides a program product, which includes a computer program stored in a readable storage medium. At least one processor of the electronic device can read the computer program from the readable storage medium, and at least one processor executes the computer program so that the electronic device implements the methods provided in the above various embodiments.

[0188] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. The present application is intended to cover any modification, use or adaptation of the present application, which follows the general principles of the present application and includes common knowledge or customary techniques in the art that are not disclosed in the present application. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present application are indicated by the claims.

[0189] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A method for real-time data access and processing of load resources based on the Internet of Things system, characterized in that: Applied to a site power management system, the site power management system includes a device management platform, a mobile load cluster, a renewable energy power generation device and an energy storage device, the device management platform is connected to the renewable energy power generation device, the energy storage device and each mobile load in the mobile load cluster through Internet of Things communication; the method includes: Each mobile load in the mobile load cluster acquires real-time working data and sends the real-time working data to the device management platform to generate a real-time working data set, wherein the real-time working data includes real-time task data and real-time power data; The renewable energy power generation equipment obtains predicted power generation data, and sends the predicted power generation data to the equipment management platform; the energy storage equipment obtains state of charge data, and sends the state of charge data to the equipment management platform; The device management platform generates a mobile load control instruction based on the real-time working data, the predicted power generation data and the charge state data. The mobile load control instruction is used to instruct at least one mobile load in the mobile load cluster to move to the charging and discharging interface for charging and discharging operations. The renewable energy power generation equipment, the energy storage equipment and the charging and discharging interface are connected through a power grid.

2. The method for real-time data access and processing of load resources based on the Internet of Things system according to claim 1 is characterized in that: The device management platform generates a mobile load control instruction according to the real-time working data, the predicted power generation data and the charge state data, including: The equipment management platform determines the predicted total power according to the predicted power generation in the predicted power generation data and the remaining power in the state of charge data; If the device management platform determines that the predicted total power is greater than the upper limit threshold of the power charge of the energy storage device, the mobile load control instruction is generated, wherein the predicted power generation data includes the predicted power generation for the future period; The equipment management platform determines at least one target mobile load from the mobile load cluster according to the real-time working data set, wherein the target mobile load is a mobile load that has completed a currently executed task before a starting time node of the future duration; The device management platform sends the mobile load control instruction to the target mobile load, so that the target mobile load moves to the charging and discharging interface to perform a charging operation before the starting time node of the future time period.

3. The method for real-time data access and processing of load resources based on the Internet of Things system according to claim 2 is characterized in that: After the device management platform determines the predicted total power of the predicted power generation in the predicted power generation data and the remaining power in the state of charge data, the method further includes: If the device management platform determines that the predicted total power is less than the lower limit threshold of the power charge of the energy storage device, the mobile load control instruction is generated, wherein the predicted power generation data includes the predicted power generation for the future period; The device management platform determines at least one target mobile load from the mobile load cluster according to the real-time working data set, wherein the target mobile load is a mobile load that has completed the current execution task before the starting time node of the future time period and whose estimated remaining power is greater than a preset power threshold; The device management platform sends the mobile load control instruction to the target mobile load, so that the target mobile load moves to the charging and discharging interface to perform a discharging operation before the starting time node of the future time period.

4. The method for real-time data access and processing of load resources based on the Internet of Things system according to claim 2 or 3, characterized in that: The device management platform determines the predicted total power according to the predicted power generation in the predicted power generation data and the remaining power in the state of charge data, including: The device management platform determines at least one mobile load to be charged from the mobile load cluster according to the real-time working data set, where the mobile load to be charged is a mobile load to be charged within the future time period; The device management platform determines the charging power consumption in the future time period according to the mobile load to be charged; The device management platform determines the predicted total power based on the predicted power generation, the remaining power in the charge status data, the charging power consumption and the predicted device power consumption. The predicted device power consumption is determined based on the future duration and historical device power consumption data.

5. The method for real-time data access and processing of load resources based on the Internet of Things system according to claim 4 is characterized in that: Before the renewable energy power generation equipment obtains predicted power generation data and sends the predicted power generation data to the equipment management platform, the method further includes: The equipment management platform determines the current task intensive feature quantity according to the real-time working data set; If the device management platform determines that the current task-intensive feature quantity is greater than a preset task-intensive feature upper limit threshold, a duration shortening operation is performed based on the preset calibration duration to determine the future duration; If the device management platform determines that the current task-intensive feature quantity is less than a preset task-intensive feature lower limit threshold, an increase duration operation is performed based on a preset calibration duration to determine the future duration.

6. The method for real-time data access and processing of load resources based on the Internet of Things system according to claim 5 is characterized in that: The device management platform determines the current task intensive feature quantity according to the real-time work data set, including: The equipment management platform determines the number of real-time execution tasks according to the real-time work data set, so as to determine the ratio between the number of real-time execution tasks and the number of mobile loads in the mobile load cluster as the current task intensive characteristic value.

7. The method for real-time data access and processing of load resources based on the Internet of Things system according to any one of claims 1 to 3, characterized in that: The site power management system is applied to logistics warehousing scenarios, the mobile load cluster is a picking mobile robot cluster, and the renewable energy power generation equipment is a photovoltaic power generation equipment.

8. A site power management system, characterized in that: include: A device management platform, a mobile load cluster, a renewable energy power generation device, and an energy storage device, wherein the device management platform is connected to the renewable energy power generation device, the energy storage device, and each mobile load in the mobile load cluster via Internet of Things communication; Each mobile load in the mobile load cluster acquires real-time working data and sends the real-time working data to the device management platform to generate a real-time working data set, wherein the real-time working data includes real-time task data and real-time power data; The renewable energy power generation equipment obtains predicted power generation data, and sends the predicted power generation data to the equipment management platform; the energy storage equipment obtains state of charge data, and sends the state of charge data to the equipment management platform; The device management platform generates a mobile load control instruction based on the real-time working data, the predicted power generation data and the charge state data. The mobile load control instruction is used to instruct at least one mobile load in the mobile load cluster to move to the charging and discharging interface for charging and discharging operations. The renewable energy power generation equipment, the energy storage equipment and the charging and discharging interface are connected through a power grid.

9. An electronic device, characterized in that: include: processor; as well as, A memory, configured to store executable instructions of the processor; The processor is configured to perform the method of any one of claims 1 to 7 by executing the executable instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 7 when executed by a processor.

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