Method for processing real-time data access of load resources based on internet of things system

Abstract

The application provides a load resource real-time data access processing method based on an Internet of Things system. The method acquires real-time working data through each mobile load in a mobile load cluster, and sends the real-time working data to a device management platform to generate a real-time working data set. The method acquires predicted power generation data through a renewable energy power generation device, and sends the predicted power generation data to the device management platform. The method acquires state of charge data through an energy storage device, and sends the state of charge data to the device management platform. The device management platform generates a mobile load control instruction according to the real-time working data, the predicted power generation data and the state of charge data. The mobile load control instruction is used to instruct at least one mobile load in the mobile load cluster to move to a charging and discharging interface to perform a charging and discharging operation, so as to realize dynamic scheduling of load resources, thereby effectively improving the utilization rate of renewable energy of the power management system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, and particularly relates to a load resource real-time data access processing method based on an Internet of Things system. BACKGROUND

[0002] With the rapid development of smart grid and Internet of Things technology, the intelligent level of site power management systems is increasingly improved. Traditional power management systems often have problems such as information island, data not sharing, and inflexible scheduling, and are difficult to meet the needs of modern power management for efficiency, real-time, and intelligence. Especially in a complex power system including a device management platform, a mobile load cluster, a renewable energy power generation device, and an energy storage device, how to realize real-time monitoring and intelligent scheduling of load resources has become a problem to be solved in the current power management field. SUMMARY

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

[0004] In a first aspect, the present application provides a load resource real-time data access processing method based on an Internet of Things system, applied to a site power management system, the site power management system including a device management platform, a mobile load cluster, a renewable energy power generation device, and an energy storage device, the device management platform being connected to each mobile load in the mobile load cluster, the renewable energy power generation device, the energy storage device, and the mobile load cluster through Internet of Things communication; the method comprising:

[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, the real-time working data including real-time task data and real-time power data;

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

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

[0008] In the above scheme, through the Internet of Things technology, the mobile load cluster, renewable energy power generation equipment, energy storage equipment and equipment management platform are connected 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 comprehensively master the operation state of the system. Based on these data, the equipment management platform can dynamically generate mobile load control instructions to adjust the working state of the mobile load in real time to realize dynamic scheduling of load resources, thereby effectively improving the utilization rate of renewable energy of the power management system. By combining renewable energy power generation equipment with energy storage equipment, the system can fully utilize renewable energy for power generation and reduce dependence on traditional energy. At the same time, the energy storage equipment can store electric energy when there is excess power and release electric energy when there is insufficient power, thereby balancing the power grid load and improving energy utilization efficiency. The equipment management platform intelligently schedules the mobile load for charging and discharging operations according to real-time data and predicted data to ensure that the mobile load is charged when there is excess power and provides power support when there is insufficient power, thereby further improving the overall energy utilization efficiency. 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 power, the system can guide the mobile load to perform charging operations to avoid waste of electric energy; when there is insufficient power, the system can schedule mobile loads with discharging capabilities to provide power support to reduce dependence on traditional power generation equipment, thereby reducing operating costs.

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

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

[0011] If the equipment management platform determines that the predicted total power is greater than an upper threshold of the state of charge of the energy storage equipment, the mobile load control instruction is generated, wherein the predicted power generation data includes a predicted power generation in a future time period;

[0012] The equipment management platform determines at least one target mobile load from the mobile load cluster according to the set of real-time working data, the target mobile load being a mobile load that has ended a current execution task before a starting time node of the future time period;

[0013] The equipment management platform sends the mobile load control instruction to the target mobile load to make the target mobile load move to the charging and discharging interface to perform charging operations before the starting time node of the future time period.

[0014] In the above scheme, the device management platform calculates the predicted total power by comprehensively predicting the power generation and the remaining power of the energy storage device, thereby accurately determining the future power supply and demand situation. When the predicted total power exceeds the upper limit threshold of the charge capacity 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 power surplus. In this case, the device management platform intelligently generates a mobile load control instruction, selects a mobile load that has completed its task before the starting time node of the future time period as the target mobile load, and instructs it to move to the charging interface for charging operation, thereby effectively utilizing the excess power resources and avoiding the waste of power, while ensuring the sufficient power of the mobile load and providing protection for subsequent work tasks. That is, when there is power surplus, the mobile load is promptly dispatched for charging, not only avoiding the waste of power, but also improving the utilization rate of renewable energy. In addition, the device management platform can quickly generate a mobile load control instruction according to real-time data and predicted data, and realize dynamic scheduling of the mobile load. This flexibility and response speed ensures that the system can quickly adjust when facing changes in power supply and demand, maintaining the stable operation of the system. At the same time, by selecting a mobile load that has completed its task before the starting time node of the future time period as the target mobile load, the device management platform further improves the flexibility and efficiency of scheduling, avoiding the situation where the mobile load is executing a task and cannot perform charging operations. By optimizing power resource allocation and avoiding power surplus and waste, the device management platform helps to reduce the operating cost of the system. At the same time, through real-time monitoring and dynamic scheduling, the device management platform can timely discover and solve potential power supply and demand imbalance problems, avoid equipment damage or failure caused by power shortage or surplus, 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, it further comprises:

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

[0017] The device management platform determines at least one target mobile load from the mobile load cluster according to the real-time work 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 time period and is expected to have a remaining power 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 charge-discharge interface to perform a discharging operation before the starting time node of the future time length.

[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 state of charge of the energy storage device by comprehensively analyzing and predicting the power generation and the remaining power of the energy storage device, it means that there may be a power shortage in the future period. At this time, the device management platform intelligently generates a mobile load control instruction to dispatch the mobile load with discharging capability to perform a discharging operation to supplement the power grid power, so as to ensure the balance between 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 the mobile load with discharging capability, especially those mobile loads that have completed tasks before the starting time node of the future time length and are expected to have sufficient remaining power. This scheduling method not only ensures the timely supplement of power, but also maximizes the energy utilization efficiency of the mobile load. Through this intelligent energy management method, the device management platform can more efficiently manage load resources, reduce energy waste, and improve overall energy management efficiency. In other words, in the case of power shortage, by dispatching the mobile load to perform a discharging operation, not only is the power grid power 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 discharging process is actually an indirect use of renewable energy to supply power to the power grid.

[0020] Optionally, the device management platform determines a 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 to-be-charged mobile load from the mobile load cluster according to the real-time working data set, and the to-be-charged mobile load is a mobile load that performs charging in the future time length.

[0022] The device management platform determines the charging consumption power of the to-be-charged mobile load according to the to-be-charged mobile load.

[0023] The device management platform determines the predicted total power according to the predicted power generation, the remaining power in the state of charge data, the charging consumption power, and a predicted device power consumption, and the predicted device power consumption is determined according to the future time length and historical device power consumption data.

[0024] In the above scheme, the device management platform not only considers the predicted power generation of the renewable energy power generation device and the remaining power of the energy storage device when predicting the total power, but also adds the charging consumption power of the to-be-charged mobile load within the future time length and the device power consumption predicted based on historical data. This comprehensive consideration makes the power supply and demand prediction more accurate, helping the system to make more accurate power dispatching decisions. Moreover, by comprehensively considering the charging demand of the to-be-charged mobile load, the device management platform can more reasonably arrange the charging time, avoid large-scale charging operation during the peak power period, thereby reducing the impact on the power grid and reducing the power grid load. At the same time, accurate power supply and demand prediction helps to avoid energy waste caused by power surplus or shortage, and improves energy utilization efficiency. Accurate power supply and demand prediction and optimized power dispatching strategy help to 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, to avoid system instability or interruption caused by power shortage or surplus. In addition, by comprehensively considering various factors for power supply and demand prediction, the device management platform can more accurately judge the power supply and demand situation in the future period, so as to formulate strategies in advance to deal with unexpected situations. For example, when predicting that there may be power shortage in the future period, the device management platform can dispatch mobile loads with discharging capability to discharge in advance to provide additional power support for the power grid, enhancing the system's ability to respond to unexpected power shortage. Through accurate power supply and demand prediction and optimized power dispatching strategy, the device management platform can better coordinate the relationship between renewable energy power generation devices and the power grid. When the renewable energy power generation is sufficient, the mobile load is preferentially dispatched for charging operation to fully utilize renewable energy; when the renewable energy power generation is insufficient, the energy storage device and the mobile load are dispatched for discharging operation to supplement the power grid, thereby ensuring the effective utilization of renewable energy and the stable operation of the power grid.

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

[0026] The device 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 the preset upper limit threshold of the task-intensive feature, a shortened time length operation is performed based on the preset calibration time length to determine the future time length;

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

[0029] In the above scheme, the device management platform dynamically analyzes the current task intensive characteristic quantity according to the real-time working data set, and flexibly adjusts the setting of the future time length according to the comparison result of the characteristic quantity and the preset threshold value. When the task intensive characteristic quantity is high, the future time length is shortened, so as to more accurately predict the power supply and demand change in the short term, timely respond to the fluctuation of power load, and avoid the accumulation of prediction error caused by too long prediction period. On the contrary, when the task intensive characteristic quantity is low, the future time length is increased, so as to more comprehensively consider the power supply and demand trend in the future longer period, provide more accurate reference for long-term power dispatching, and optimize the configuration and utilization of power resources.

[0030] By dynamically adjusting the prediction time length, the device management platform can better adapt to the power demand changes in different time periods and task intensities. During the task peak period, by shortening the prediction time length, the system can respond more quickly to the instantaneous changes of power load, ensure the power supply of critical tasks, and avoid task interruption or delay caused by power shortage. During the task valley period, by prolonging the prediction time length, the system can more reasonably plan the long-term configuration of power resources, such as optimizing the charge and discharge plan of energy storage devices, improving the utilization rate of renewable energy, and reducing operating costs.

[0031] The strategy of dynamically adjusting the prediction time length gives the power dispatching system higher flexibility. The system can quickly adjust the power dispatching scheme according to the actual situation of different time periods and task intensities, and ensure the real-time balance of power supply and demand. Thus, not only the efficiency of power dispatching is improved, but also the system's response ability to sudden situations is enhanced. For example, in the case of sudden events leading to a sharp increase in power load, the system can quickly shorten the prediction time length, re-evaluate the power supply and demand situation, and quickly adjust the dispatching strategy to cope with the challenge of power shortage. By dynamically adjusting the prediction time length, the device management platform can more accurately predict the power generation of renewable energy, and optimize the power dispatching scheme according to the prediction result. When the renewable energy generation is sufficient, the system can prolong the prediction time length to create favorable conditions for large-scale grid connection and consumption of renewable energy; when the renewable energy generation is insufficient, the system can shorten the prediction time length to quickly respond to the changes of power load, and ensure the stable operation of the power grid.

[0032] Optionally, the device management platform determines the current task intensive characteristic quantity according to the real-time working data set, comprising:

[0033] The device management platform determines the real-time execution task quantity according to the real-time working data set, and determines the ratio between the real-time execution task quantity and the number of mobile loads in the mobile load cluster as the current task intensive characteristic quantity.

[0034] In the above scheme, by using real-time work data sets, the equipment management platform can count the number of currently executing tasks and compare it with the total number of mobile loads in the mobile load cluster, thereby calculating the current task intensity characteristic. This method directly reflects the task intensity and mobile load utilization rate in the system, providing accurate data support for subsequent prediction and scheduling. Accurate assessment of the current task intensity characteristic helps the equipment management platform formulate more reasonable power dispatch strategies. When the task intensity characteristic is high, the system can predict potential future power demand peaks, thereby enabling advance power reserves and scheduling to ensure power supply for critical tasks. This not only improves the system's response speed but also effectively avoids task interruptions or delays caused by power shortages.

[0035] Optionally, the site power management system is applied to a logistics and 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 solution, the real-time data access and processing method for load resources based on the Internet of Things (IoT) is applied to logistics and warehousing scenarios, particularly for picking mobile robot clusters and photovoltaic power generation equipment. This significantly improves the operational efficiency and energy utilization of logistics and warehousing. By real-time monitoring and intelligent scheduling of the working status and power consumption of picking mobile robots, combined with the predicted power generation of photovoltaic power generation equipment, the system can optimize the charging and picking paths of the robots.

[0037] Secondly, this application provides a site power management system, including: an equipment management platform, a mobile load cluster, renewable energy power generation equipment, and energy storage equipment, wherein the equipment management platform is connected to the renewable energy power generation equipment, the energy storage equipment, and each mobile load in the mobile load cluster via 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, which includes real-time task data and real-time power data.

[0039] The renewable energy power generation equipment acquires predicted power generation data and sends the predicted power generation data to the equipment management platform; the energy storage device acquires 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 according to the real-time working data, the predicted power generation data, and the state of charge data, the mobile load control instruction being used to instruct at least one mobile load in the mobile load cluster to move to the charging and discharging interface to perform a charging and discharging operation, and the renewable power generation device, the energy storage device, and the charging and discharging interface being connected through a power grid.

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

[0042] The device management platform determines a predicted total power according to a predicted power generation amount in the predicted power generation data and a remaining power amount in the state of charge data.

[0043] If the device management platform determines that the predicted total power is greater than an upper limit threshold of the state of charge of the energy storage device, the mobile load control instruction is generated, wherein the predicted power generation data includes a predicted power generation amount in a future time length.

[0044] The device management platform determines at least one target mobile load from the mobile load cluster according to the real-time working data set, the target mobile load being a mobile load that has ended a current execution task before a starting time node of the future time length.

[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 length.

[0046] Optionally, after the device management platform determines the predicted total power according to the predicted power generation amount in the predicted power generation data and the remaining power amount 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 a lower limit threshold of the state of charge of the energy storage device, the mobile load control instruction is generated, wherein the predicted power generation data includes a predicted power generation amount in a future time length.

[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, the target mobile load being a mobile load that has ended a current execution task before a starting time node of the future time length and is predicted to have a remaining power amount 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 charge-discharge interface to perform discharging operation before the starting time node of the future time length.

[0050] Optionally, the device management platform determines a predicted total power according to the predicted power generation amount in the predicted power generation data and the residual power amount 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, the mobile load to be charged being a mobile load to be charged in the future time length;

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

[0053] The device management platform determines the predicted total power according to the predicted power generation amount, the residual power amount in the state of charge data, the charging consumption power, and a predicted device power consumption amount, the predicted device power consumption amount being determined according to the future time length and historical device power consumption data.

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

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

[0056] If the device management platform determines that the current task intensive feature amount is greater than a preset upper limit threshold of task intensive feature, a shortened time length operation is performed on the basis of a preset designated time length to determine the future time length;

[0057] If the device management platform determines that the current task intensive feature amount is less than a preset lower limit threshold of task intensive feature, a lengthened time length operation is performed on the basis of a preset designated time length to determine the future time length.

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

[0059] The device management platform determines a real-time execution task number according to the real-time working data set, so that a ratio between the real-time execution task number and a number of mobile loads in the mobile load cluster is the current task intensive feature amount.

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

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

[0062] a processor; and

[0063] a memory for storing executable instructions of the processor;

[0064] The processor is configured to execute any possible method 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 the computer-executable instructions are executed by a processor to implement any possible method in the first aspect.

[0066] The load resource real-time data access processing method based on the Internet of Things system provided by the present application acquires real-time working data of 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, acquires predicted power generation data of the renewable energy power generation device and sends the predicted power generation data to the equipment management platform, acquires state of charge data of the energy storage device and sends the state of charge 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, the predicted power generation data and the state of charge data. The mobile load control instruction is used to instruct at least one mobile load in the mobile load cluster to move to a charging and discharging interface to perform charging and discharging operation, so as to realize dynamic scheduling of the load resource, thereby effectively improving the utilization rate of the renewable energy by the power management system. BRIEF DESCRIPTION OF DRAWINGS

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

[0068] Figure 1 FIG. 1 is a flowchart of a load resource real-time data access processing method based on an Internet of Things system according to an example embodiment of the present application;

[0069] Figure 2 FIG. 2 is a flowchart of a load resource real-time data access processing method based on an Internet of Things system according to another example embodiment of the present application;

[0070] Figure 3 FIG. 3 is a structural diagram of a site power management system according to an example embodiment of the present application;

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

[0072] The specific embodiments of the present application have been shown and described in the above-described drawings and text. These drawings and text are not intended to limit the scope of the present application in any way, but rather to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0073] The exemplary embodiments will be described in detail herein with reference to the attached drawings. The same numbers are used in different drawings to represent the same or similar elements. The embodiments described in the following exemplary embodiments are not meant to represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present application as detailed in the appended claims.

[0074] To solve the above problems, the embodiments provided by the present application aim to provide a load resource real-time data access processing method based on the Internet of Things system. This method is mainly applied to a site power management system, especially for a complex power system containing a device management platform, a mobile load cluster, a renewable energy power generation device, and an energy storage device. The purpose of the embodiments provided by the present application is to improve the flexibility and response speed of the power system, optimize energy utilization, reduce costs, and ensure the efficient and stable operation of the system through real-time monitoring and intelligent scheduling.

[0075] Firstly, the embodiments provided by the present application closely connect the mobile load cluster, the renewable energy power generation device, the energy storage device, and the device management platform through the Internet of Things technology, forming a highly integrated power management system. This connection not only realizes 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, including real-time task data and real-time power data, to the device management platform in real time. At the same time, the renewable energy power generation device sends predicted power generation data, and the energy storage device sends its state of charge data. These data collectively constitute the real-time data set 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 received real-time data set. It first determines the predicted total power based on the predicted power generation and the remaining power of the energy storage device to assess the power supply and demand situation 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 power surplus, the device management platform selects mobile loads that have completed tasks before the start time node of the future period as target loads and instructs them to move to the charging interface for charging operation. This strategy not only avoids waste of electricity, but also ensures sufficient power for mobile loads, providing a guarantee for subsequent tasks.

[0079] On the contrary, in the case of power shortage, the device management platform dispatches mobile loads with discharging capability for discharging operation to supplement the power grid. In particular, it prioritizes mobile loads that have completed tasks before the start time node of the future period and are expected to have sufficient remaining power for discharging, thereby maximizing the use of existing resources and ensuring stable operation of the power system.

[0080] In addition, the embodiments provided by the present application also consider dynamic adjustment of the future time length. The device management platform flexibly sets the future time length based on real-time task intensity features 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 utilization efficiency.

[0081] In summary, the specific technical concept of the embodiments provided by the present 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 prediction data, the device management platform can dynamically generate mobile load control instructions to optimize the configuration and utilization of power resources. This real-time data access processing method for 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 efficient and stable operation of the system.

[0082] Figure 1 is a flowchart of the real-time data access processing method for load resources based on the Internet of Things system according to an example embodiment of the present application. As shown in Figure 1 the real-time data access processing method for load resources based on the Internet of Things system provided by the present embodiment includes:

[0083] S101, 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.

[0084] In this step, 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. The real-time working data includes real-time task data and real-time power data.

[0085] Specifically, each mobile load (such as a logistics vehicle, a logistics sorting robot, etc.) in the mobile load cluster is equipped with an Internet of Things communication module. These mobile loads will collect their working status and power information in real time while performing tasks, forming real-time working data. These data include the type of task currently performed by the mobile load, the task progress, the remaining power, etc. Through the Internet of Things 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 device acquires predicted power generation data and sends the predicted power generation data to the device management platform.

[0087] Specifically, the renewable energy power generation device (such as a photovoltaic power generation panel, a wind power generator, etc.) acquires future power generation prediction data through a built-in prediction algorithm or an external prediction service. These prediction data reflect the predicted power generation of the renewable energy power generation device within a specific time period. Through the Internet of Things communication connection, these prediction power generation data are sent to the device management platform. The device management platform uses these data to evaluate the potential of future power supply, providing a basis for subsequent power dispatching and load control.

[0088] It is worth noting that when the renewable energy power generation device is a photovoltaic power generation panel, the determination of the prediction power generation data can be through the collection of historical meteorological data of the region where the photovoltaic power station is located, including sunshine intensity, irradiance, temperature, humidity, wind speed, etc. These data can be obtained through meteorological departments, solar databases, or professional photovoltaic simulation software. Then, determine the type, conversion efficiency, inclination angle, direction, etc. of the photovoltaic panel. And consider the system configuration of the photovoltaic power station, such as inverter efficiency, cable loss, transformer loss, etc. Then select professional photovoltaic simulation software such as PVsyst, HOMER, and Cuckoo Cloud, etc. These software can comprehensively consider geographical location, climate condition, photovoltaic panel model, system configuration, etc. to provide more accurate power generation estimation results. In the software, a model of the photovoltaic power station is established, and the parameters of the photovoltaic panel, the system configuration, and the historical meteorological data are input. Through the simulation running function of the software, the power generation of the photovoltaic power station in the future period of time is simulated.

[0089] S103, the energy storage device acquires state of charge data and sends the state of charge data to the device management platform.

[0090] Specifically, the state of charge of energy storage devices (such as battery packs, supercapacitors, etc.) is a key indicator that the device management platform needs to pay attention to in real time as an important means of power regulation. 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 assesses the available capacity and charge-discharge capacity of the energy storage device based on these data to provide decision support for subsequent power dispatching and load control.

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

[0092] In this step, the device management platform generates a mobile load control instruction according to the real-time working data, the predicted power generation data, and the state of charge 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-discharge interface for charge-discharge operation. The renewable energy power generation device, the energy storage device, and the charge-discharge interface are connected through the power grid.

[0093] Specifically, the device management platform generates a mobile load control instruction according to the real-time working data, the predicted power generation data, and the state of charge data after receiving the real-time working data set, the predicted power generation data, and the state of charge data of the energy storage device. These instructions include instructions for instructing specific mobile loads to move to the charge-discharge interface for charging or discharging operations to balance power supply and demand, optimize power use, and reduce operating costs. After receiving the control instruction sent by the device management platform, the mobile load will perform the corresponding charge-discharge operation. 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 instruction according to 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 a predicted total power generation 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 generation is greater than the upper threshold of the state of charge of the energy storage device, it generates a mobile load control instruction. The predicted power generation data includes the predicted power generation in the future time 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. The target mobile load is a mobile load that has completed its current execution task before the start time node of the future time period. The device management platform sends the mobile load control instruction to the target mobile load to make the target mobile load move to the charge-discharge interface for charging operation before the start time node of the future time period.

[0095] Firstly, the device management platform calculates the predicted total power based on the predicted power generation data received from the renewable energy power generation device, including the predicted power generation in the future time period, and the remaining power in the state of charge data received from the energy storage device.

[0096] Next, the device management platform compares the calculated predicted total power with the upper limit threshold of the state of charge of the energy storage device. This threshold is pre-set 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 state of charge of the energy storage device, it means that the power supply in the future time period will exceed the current storage capacity of the system, and there is a risk of power surplus.

[0097] In the case of power surplus, the device management platform further selects 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 time period. Through the real-time work data set, the device management platform can accurately grasp the working state of each mobile load, so as to accurately select the mobile loads that meet the conditions.

[0098] Once the target mobile loads are determined, the device management platform generates corresponding mobile load control instructions and sends them to the target mobile loads through the Internet of Things communication connection. These instructions instruct the target mobile loads to move to the charging interface for charging operation before the start time node of the future time period. In this way, the device management platform can ensure that the mobile loads are charged in time when there is power surplus, avoiding waste of electric energy and ensuring sufficient power for the mobile loads, providing guarantee for subsequent work tasks.

[0099] In addition, in order to ensure that the mobile loads can smoothly reach the charging interface and perform charging, the device management platform can also be integrated with the navigation system or path planning system in the place to provide the mobile loads with optimal charging path planning. In this way, the mobile loads can more efficiently and accurately reach the charging interface, further improving the operation efficiency of the entire power management system.

[0100] In the above scheme, the device management platform calculates the predicted total power by comprehensively predicting the power generation and the remaining power of the energy storage device, thereby accurately judging the future power supply and demand situation. When the predicted total power exceeds the upper limit threshold of the charge capacity 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 power surplus. In this case, the device management platform intelligently generates a mobile load control instruction, selects a mobile load that has completed its task before the starting time node of the future time period as the target mobile load, and instructs it to move to the charging and discharging interface for charging operation, thereby effectively utilizing the excess power resources and avoiding waste of electricity, while ensuring sufficient power for the mobile load and providing protection for subsequent work tasks. That is, when there is a power surplus, the mobile load is promptly dispatched for charging, not only avoiding waste of electricity, but also improving the utilization rate of renewable energy. In addition, the device management platform can quickly generate a mobile load control instruction based on real-time data and prediction data to dynamically schedule the mobile load. This flexibility and response speed ensures that the system can quickly adjust when facing changes in power supply and demand, maintaining stable operation of the system. At the same time, by selecting a mobile load that has completed its task before the starting time node of the future time period as the target mobile load, the device management platform further improves the flexibility and efficiency of scheduling, avoiding the situation where the mobile load is executing a task and cannot perform charging operations. By optimizing power resource allocation and avoiding power waste, the device management platform helps reduce the operating cost of the system. At the same time, through real-time monitoring and dynamic scheduling, the device management platform can timely discover and solve potential power supply and demand imbalance problems, avoiding damage or failure of devices due to power shortage or surplus, further reducing 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 capacity of the energy storage device, it generates a mobile load control instruction, wherein the predicted power generation data includes the predicted power generation of the future time period. The device management platform determines at least one target mobile load from the mobile load cluster based on the real-time work 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 time period and is expected to have a remaining power greater than a preset power threshold. The device management platform sends the mobile load control instruction to the target mobile load to make the target mobile load move to the charging and discharging interface for discharging operation before the starting time node of the future time period.

[0102] When 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 threshold of the charge capacity of the energy storage device, it means that there may be a power shortage 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 mechanism to deal with this situation.

[0103] First, the device management platform will filter out target mobile loads that meet the conditions from the mobile load cluster according to the real-time working data set. These target mobile loads not only need to have ended the current execution task before the future time length starting time node, but also their estimated remaining power must be greater than the preset power threshold. This screening process ensures that the selected mobile loads not only have discharge capability, but also will not affect their subsequent work tasks due to discharge operation.

[0104] Once the target mobile load is determined, the device management platform will generate corresponding mobile load control instructions and send them to the target mobile load through the Internet of Things communication connection. These instructions will instruct the target mobile load to move to the charging and discharging interface for discharge operation before the future time length starting time node. In this way, the device management platform can quickly respond to power shortages and supplement the power grid power by scheduling mobile loads with discharge capability to ensure the balance between supply and demand of the power system.

[0105] It is worth noting that when scheduling mobile loads for discharge operation, the device management platform will also consider factors such as the discharge capacity, remaining power of the mobile load, and the impact of discharge on the stability of the power grid. Ensure that the discharge operation can effectively supplement the power grid power and will not adversely affect 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 discharge operation, the device management platform can also be integrated with the navigation system or path planning system in the place to provide the mobile load with the optimal discharge path planning. In this way, the mobile load can more efficiently and accurately reach the charging and discharging interface, 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 threshold of the charge level of the energy storage device by comprehensively analyzing and predicting the power generation and the remaining power of the energy storage device, it means that there may be a power shortage in the future period. At this time, the device management platform intelligently generates a mobile load control instruction to dispatch the mobile load with discharging capability to perform discharging operation to supplement the power grid power and ensure the balance between 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 the mobile load with discharging capability, especially those mobile loads that have completed the task before the start time node of the future time period and are expected to have sufficient remaining power. This scheduling method not only ensures the timely supplement of power, but also maximizes the energy utilization efficiency of the mobile load. Through this intelligent energy management method, the device management platform can more efficiently manage load resources, reduce energy waste, and improve overall energy management efficiency. In other words, in the case of power shortage, by scheduling the mobile load to perform discharging operation, not only the power grid power is supplemented, but also the utilization of renewable energy is promoted. Because these mobile loads may have been charged by renewable energy before, the discharging process actually indirectly utilizes renewable energy to supply power to the power grid.

[0108] In the present embodiment, 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, obtains predicted power generation data from the renewable energy power generation device and sends the predicted power generation data to the device management platform, obtains state of charge data from the energy storage device and sends the state of charge data to the device management platform, so 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 state of charge 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 to perform charging and discharging operation, so as to realize dynamic scheduling of load resources, thereby effectively improving the utilization rate of renewable energy of the power management system.

[0109] Figure 2 is a flowchart of a load resource real-time data access processing method based on an Internet of Things system according to another example embodiment of the present application. As shown in Figure 2 the load resource real-time data access processing method based on the Internet of Things system provided by the present embodiment comprises:

[0110] S201, 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.

[0111] In this step, 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, the real-time working data including real-time task data and real-time power data.

[0112] Optionally, the above-mentioned site power management system can be applied to a logistics storage scene, the mobile load cluster being a picking mobile robot cluster, and the renewable energy power generation device being a photovoltaic power generation device.

[0113] Specifically, the device 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 the photovoltaic power generation device, and state of charge data of the energy storage device in real time. Based on the collected data, the device management platform dynamically generates control instructions for the picking mobile robots through algorithm analysis to optimize their charging and picking paths.

[0114] The picking mobile robot cluster is composed of multiple self-navigating mobile robots, each of which is equipped with an Internet of Things communication module and can upload its working status and power information in real time. When the robots perform picking tasks, the device management platform dynamically adjusts their work plans according to their real-time power and task status. For example, when the robot has low power and the task allows, it will be preferentially arranged to return to the charging station for charging. When the photovoltaic power generation capacity is sufficient, the device management platform will intelligently schedule the robots with low power to go to the charging station to charge using green energy, reducing dependence on the traditional power grid. The photovoltaic power generation device can be equipped with an intelligent monitoring system that can predict the power generation capacity in the future period according to weather forecasts and historical power generation data. The device management platform will develop an overall energy management strategy based on the predicted data of photovoltaic power generation and the state of the energy storage device. When the power generation capacity is higher than the demand of the warehouse center, the excess power will be used to charge the energy storage device or charge the picking robots.

[0115] The energy storage device stores electrical energy when the photovoltaic power generation capacity is excessive and releases electrical energy when the power is insufficient to balance the power grid load and ensure the stable operation of the logistics and storage center. The device management platform will intelligently schedule the charging and discharging operations of the energy storage device according to the real-time power demand and photovoltaic power generation prediction to achieve the optimal allocation of electrical energy.

[0116] S202, the renewable energy power generation device acquires predicted power generation data and sends the predicted power generation data to the device management platform.

[0117] Specifically, renewable energy power generation devices such as photovoltaic panels and wind turbines obtain future power generation prediction data through built-in prediction algorithms or external prediction services. These prediction data reflect the expected power generation of renewable energy power generation devices within a certain time period. Through Internet of Things communication connection, these predicted power generation data are sent to the device management platform. The device management platform uses these data to assess the potential of future power supply, providing a basis for subsequent power dispatching and load control.

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

[0119] Specifically, energy storage devices such as battery packs and supercapacitors are important means of power regulation, and their state of charge is a key indicator that the device management platform needs to monitor 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 assesses the available capacity and charge-discharge capacity of the energy storage device based on these data, providing decision support for subsequent power dispatching 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. The mobile load to be charged is a mobile load that needs to be charged within a future time period.

[0122] Specifically, the device management platform first filters 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 are mobile loads that have charging needs within a future time period, such as electric vehicles, mobile devices, or other devices that need regular charging.

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

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

[0125] Specifically, the device management platform calculates the charging consumption power of the mobile loads to be charged in the future time length according to the screened 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 (such as full power or reaching a certain preset power) of the device, combined with the charging efficiency, the charging power required by each mobile load to be charged in the future time length is calculated. The charging demands of all mobile loads to be charged are summarized to obtain the total charging consumption power in the future time length.

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

[0127] In this step, the device management platform determines the predicted total power according to the predicted power generation, the remaining power in the state of charge data, the charging consumption power and the predicted device power consumption. The predicted device power consumption is determined according to the future time length and historical device power consumption data.

[0128] Specifically, the device management platform comprehensively considers the predicted power generation, the remaining power in the state of charge data, the charging consumption power and the predicted device power consumption to calculate the predicted total power. The predicted power generation in the future time length can be obtained by means of weather prediction, power generation device state monitoring and the like. The remaining power of the current power grid or energy storage system is obtained from the state of charge data. The device power consumption in the future time length is predicted by using time series analysis, machine learning and the like according to the future time length and historical device power consumption data. The predicted power generation, the remaining power, the charging consumption power and the predicted device power consumption are added up, and possible power loss and conversion efficiency are considered to obtain the predicted total power.

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

[0130] In this step, the device management platform generates a mobile load control instruction according to the real-time working data, the predicted power generation data and the state of charge 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 operation. The renewable energy power generation device, the energy storage device and the charging and discharging interface are connected through the power grid.

[0131] On the basis of the above-mentioned embodiments, before the renewable energy power generation device obtains the predicted power generation data and sends the predicted power generation data to the device management platform, further comprising: the device 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 the preset upper limit threshold of the task intensive feature, the preset calibration time length is shortened to determine the future time length; if the device management platform determines that the current task intensive feature quantity is less than the preset lower limit threshold of the task intensive feature, the preset calibration time length is increased to determine the future time length.

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

[0133] By dynamically adjusting the prediction time length, the device management platform can better adapt to the changes of power demand in different time periods and task intensities. During the task peak period, by shortening the prediction time length, the system can respond more quickly to the instantaneous changes of power load, ensure the power supply of critical tasks, and avoid task interruption or delay caused by power shortage. During the task valley period, by extending the prediction time length, the system can more reasonably plan the long-term configuration of power resources, such as optimizing the charging and discharging plan of energy storage devices, improving the utilization rate of renewable energy, and reducing operating costs.

[0134] The strategy of dynamically adjusting the prediction time length gives the power dispatching system higher flexibility. The system can quickly adjust the power dispatching scheme according to the actual situation of different time periods and task intensities, and ensure the real-time balance of power supply and demand. Thus, not only the efficiency of power dispatching is improved, but also the system's response ability to sudden situations is enhanced. For example, in the case of sudden events leading to a sharp increase in power load, the system can quickly shorten the prediction time length, re-evaluate the power supply and demand situation, and quickly adjust the dispatching strategy to cope with the challenge of power shortage. By dynamically adjusting the prediction time length, the device management platform can more accurately predict the power generation of renewable energy, and optimize the power dispatching scheme according to the prediction result. When the renewable energy generation is sufficient, the system can extend the prediction time length to create favorable conditions for large-scale grid connection and consumption of renewable energy; when the renewable energy generation is insufficient, the system can shorten the prediction time length to quickly respond to changes in power load and ensure the stable operation of the power grid.

[0135] Notably, in further specific implementations, the device management platform determines the current task-intensive feature quantity Dt based on the real-time working data set. If the device management platform determines that the current task-intensive feature quantity Dt is greater than the preset upper limit threshold Du of the task-intensive feature, formula 1 is used, and the future time length Tf is determined according to the preset calibration time length T0, and formula 1 is:

[0136]

[0137] wherein a is the first adjustment parameter, 0 < a < 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 lower limit threshold Dw of the task-intensive feature, formula 2 is used, and the future time length Tf is determined according to the preset calibration time length T0, and 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 situation in the future period, the device management platform will dynamically adjust the setting of the future time length according to the current task-intensive feature quantity. This strategy is realized by introducing formula 1 and formula 2, aiming to flexibly adjust the prediction period according to the task-intensive degree, so as to ensure the accuracy and response speed of power dispatching. Among them, in the task peak period, the power load changes rapidly and frequently. By shortening the prediction time length, the device management platform can respond more quickly to the instantaneous changes of power load, thereby improving the accuracy and timeliness of prediction. A shorter prediction period means more frequent power dispatching decisions. This helps the device management platform to adjust the power distribution in a timely manner according to real-time data, to ensure the power supply of key tasks, and to avoid task interruption or delay caused by power shortage. In the face of sudden increase in task-intensive degree, the system can quickly adjust the prediction time length to adapt to the new power demand mode, thereby enhancing the flexibility and adaptability of the system.

[0142] In the task trough period, the power load is relatively stable. By extending the prediction time length, the device management platform can more comprehensively consider the power supply and demand trend in the future for a longer period of time, providing more accurate reference for long-term power dispatching. In periods of relatively stable power demand, the system can plan the configuration and utilization of power resources more calmly. For example, the charging and discharging plan of energy storage devices can be optimized, the utilization rate of renewable energy can be improved, and the operating cost can be reduced. By considering the power supply and demand situation for a longer period of time, the system 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, so as to avoid system instability or interruption caused by power shortage or excess.

[0143] In summary, by introducing formula 1 and formula 2 to dynamically adjust the future time length, the device management platform can flexibly adapt to different power demand modes 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 the logistics and warehouse scenario.

[0144] Among them, the device management platform determines the current task intensive feature quantity Dt according to the real-time working data set can include: the device management platform determines the real-time execution task quantity according to the real-time working data set, and determines the ratio between the real-time execution task quantity 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 intensity of tasks and the utilization rate of mobile loads in the system, providing accurate data support for subsequent prediction and scheduling. Accurate current task intensive feature quantity evaluation helps the device management platform to develop more reasonable power scheduling strategies. When the task intensive feature quantity is high, the system can predict the possible power demand peak in the future, so as to make power reserves and scheduling in advance, and ensure the power supply of key tasks. This not only can improve the response speed of the system, but also can effectively avoid the task interruption or delay caused by power shortage.

[0145] Further, before the above-mentioned utilization of formula 1 and determination of the future time length Tf according to the preset calibration time length T0, it can further include:

[0146] The device management platform utilizes formula 3 and determines the first adjustment parameter α according to the historical task intensive feature quantity set D={D1, D2, …, Di, …, Dn}, wherein the historical task intensive feature quantity set D includes the historical task intensive feature quantities corresponding to the preset number of monitoring time points before the current time, and formula 3 is:

[0147]

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

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

[0150]

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

[0152] In the above scheme, to more accurately adjust the future duration setting based on the current task-intensive features, the device management platform uses a set of historical task-intensive features to determine the first and second adjustment parameters. This strategy is implemented by introducing Formulas 3 and 4, aiming to provide a more scientific and reasonable basis for dynamically adjusting the predicted duration through historical data analysis.

[0153] Formula 3 is used to determine the first adjustment parameter, representing the degree of fluctuation in historical data; the task-intensive feature volatility threshold is used to define the normal range of data fluctuation; and the preset first dynamic adjustment factor is used to adjust the final value of the first adjustment parameter. Formula 3 ensures that the setting of the first adjustment parameter considers both the actual situation of historical data and 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 future duration. The value calculated by Formula 3 can be dynamically adjusted according to the fluctuation of historical data, allowing the system to adjust the prediction duration more flexibly when facing different task intensities, thereby enhancing the system's adaptability and response speed. In addition, a reasonable first adjustment parameter helps the equipment management platform to more accurately predict the power supply and demand situation in future periods, thereby formulating more optimized power dispatch strategies. 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 a preset fluctuation factor and a preset second dynamic adjustment factor, Formula 4 ensures that the second adjustment parameter fluctuates within a reasonable range, avoiding parameter setting errors caused by historical data anomalies, and helping to enhance the robustness and reliability of the system. Furthermore, the second adjustment parameter affects the extent of future duration extension. The second adjustment parameter calculated by Formula 4 ensures that the system appropriately extends the prediction duration when task intensity is low, thus more comprehensively considering the power supply and demand trends over a longer period. This helps the system to more rationally plan the long-term allocation and utilization of power resources, improving energy efficiency. A reasonable second adjustment parameter helps the equipment management platform formulate more scientific and reasonable power dispatch plans when facing different task intensities. This not only improves the system's response speed and flexibility but also reduces operating costs and improves energy efficiency, thereby comprehensively improving the overall performance of the system.

[0155] Figure 3 This is a schematic diagram of the structure of a site power management system according to an example embodiment of this application. For example... Figure 3As shown, the site power management system 300 provided by the embodiment includes:

[0156] The device management platform 310 is connected with each mobile load in the mobile load cluster 320, the renewable energy power generation device 330, and the energy storage device 340 through Internet of Things communication.

[0157] Each mobile load in the mobile load cluster 320 acquires 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 acquires predicted power generation data and sends the predicted power generation data to the device management platform 310, and the energy storage device 340 acquires 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 according to the real-time working data, the predicted power generation data, and the state of charge data, wherein the mobile load control instruction is used to instruct at least one mobile load in the mobile load cluster 320 to move to a charging and discharging interface to perform charging and discharging operation, and the renewable energy power generation device 330, the energy storage device 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 state of charge data, including:

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

[0162] If the device management platform 310 determines that the predicted total power is greater than an upper threshold of the state of charge of the energy storage device 340, the mobile load control instruction is generated, wherein the predicted power generation data includes a predicted power generation amount in a future time length.

[0163] 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 ended a current execution task before a starting time node of the future time length.

[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 charging operation before the starting time node of the future time length.

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

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

[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, the target mobile load being a mobile load that has ended a current execution task and is expected to have a residual power amount greater than a preset power threshold before a starting time node of the future time length;

[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 discharging operation before the starting time node of the future time length.

[0169] Optionally, the device management platform 310 determines a predicted total power from a predicted power generation amount in the predicted power generation data and a residual power amount in the state of charge data, comprising:

[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, the mobile load to be charged being a mobile load that performs charging within the future time length;

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

[0172] The device management platform 310 determines the predicted total power from the predicted power generation amount, the residual power amount in the state of charge data, the charging consumption power amount, and a predicted device power consumption amount, the predicted device power consumption amount being determined according to the future time length and historical device power consumption data.

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

[0174] The device management platform 310 determines a 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 upper limit threshold of the task-intensive feature, a shortened time length operation is performed on the basis of a preset calibration time length to determine the future time length;

[0176] If the device management platform 310 determines that the current task-intensive feature quantity is less than a preset lower limit threshold of the task-intensive feature, a lengthened time length operation is performed on the basis of a preset calibration time length to determine the future time length.

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

[0178] The device management platform 310 determines a real-time execution task quantity according to the real-time working data set, and determines a ratio between the real-time execution task quantity and a quantity of mobile loads in the mobile load cluster 320 as the current task-intensive feature quantity.

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

[0180] Figure 4 is a structural schematic diagram of an electronic device according to an example embodiment. As shown in Figure 4 The electronic device 400 provided in this embodiment includes a processor 401 and a memory 402; wherein:

[0181] The memory 402 is used for storing a computer program, and the memory can also be a flash memory.

[0182] The processor 401 is used for executing an execution instruction stored in the memory to realize each step in the above method. For details, refer to the related description in the foregoing method embodiment.

[0183] Optionally, the memory 402 can 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 can further include:

[0185] A bus 403 is used for connecting the memory 402 and the processor 401.

[0186] The embodiment also provides a readable storage medium, and the readable storage medium stores a computer program. When at least one processor of an electronic device executes the computer program, the electronic device executes the method provided by various embodiments.

[0187] The embodiment also provides a program product, and the program product includes a computer program stored in a readable storage medium. At least one processor of an electronic device can read the computer program from the readable storage medium, and the at least one processor executes the computer program to enable the electronic device to implement the method provided by various embodiments.

[0188] Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. It is intended that the specification and examples be considered as exemplary only, with the true scope and spirit of the application being indicated by the following claims.

[0189] It will be understood that the application is not limited to the precise structures hereinbefore described and illustrated in the drawings, and that various modifications and changes can be made without departing from the scope thereof. The scope of the application is indicated by the appended claims, only.

Claims

1. A load resource real-time data access processing method based on an Internet of Things system, characterized in that, The application is applied to a site power management system, the site power management system comprises 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 with each mobile load in the renewable energy power generation device, the energy storage device and the mobile load cluster through Internet of Things communication connection; the method comprises: 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, the real-time working data comprises real-time task data and real-time power data; The renewable energy power generation device acquires predicted power generation data and sends the predicted power generation data to the device management platform; the energy storage device acquires state of charge data and sends the state of charge data to the device management platform; The device management platform generates a mobile load control instruction according to the real-time working data, the predicted power generation data and the state of charge data, the mobile load control instruction is used for instructing at least one mobile load in the mobile load cluster to move to a charging and discharging interface for charging and discharging operation, the renewable energy power generation device, the energy storage device and the charging and discharging interface are connected through a power grid; The device management platform generates a mobile load control instruction according to the real-time working data, the predicted power generation data and the state of charge data, comprising: The device management platform determines a predicted total power according to predicted power generation in the predicted power generation data and residual power in the state of charge data; If the device management platform determines that the predicted total power is greater than an upper threshold of the state of charge of the energy storage device, the mobile load control instruction is generated, wherein the predicted power generation data comprises predicted power generation of a future time length; The device management platform determines at least one target mobile load from the mobile load cluster according to the real-time working data set, the target mobile load is a mobile load that has ended a current execution task before a starting time node of the future time length; 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 operation before the starting time node of the future time length; The device management platform determines a predicted total power according to predicted power generation in the predicted power generation data and residual power in the state of charge data, comprising: The device management platform determines at least one to-be-charged mobile load from the mobile load cluster according to the real-time working data set, the to-be-charged mobile load is a mobile load that charges within the future time length; The device management platform determines charging consumption power of the to-be-charged mobile load within the future time length; The device management platform determines the predicted total power according to the predicted power generation, the residual power in the state of charge data, the charging consumption power and predicted device power consumption, the predicted device power consumption is determined according to the future time length and historical device power consumption data; Before the renewable energy power generation device acquires the predicted power generation data and sends the predicted power generation data to the device management platform, further comprising: The device 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 the preset upper limit threshold of the task-intensive feature, a shortened time operation is performed on the basis of the preset calibration time length to determine the future time length; If the device management platform determines that the current task-intensive feature quantity is less than the preset lower limit threshold of the task-intensive feature, a lengthened time operation is performed on the basis of the preset calibration time length to determine the future time length. 2.The method of claim 1, wherein, After the device management platform determines the predicted total power of the predicted power generation quantity in the predicted power generation data and the residual power in the state of charge data, further comprising: If the device management platform determines that the predicted total power is less than the lower limit threshold of the charge quantity of the energy storage device, the mobile load control instruction is generated, wherein the predicted power generation data includes the predicted power generation quantity of the future time length; The device management platform determines at least one target mobile load from the mobile load cluster according to the real-time working data set, and the target mobile load is a mobile load that has ended the current execution task before the starting time node of the future time length and is expected to have a residual power greater than a preset power threshold; The device management platform sends the mobile load control instruction to the target mobile load to make the target mobile load move to the charging and discharging interface for discharging operation before the starting time node of the future time length. 3.The method of claim 1, wherein, The device management platform determines the current task-intensive feature quantity according to the real-time working data set, comprising: The device management platform determines the real-time execution task quantity according to the real-time working data set to determine the ratio between the real-time execution task quantity and the number of mobile loads in the mobile load cluster as the current task-intensive feature quantity. 4.The method of claim 1 or 2, wherein, The site power management system is applied to a logistics and warehousing scene, the mobile load cluster is a picking mobile robot cluster, and the renewable energy power generation device is a photovoltaic power generation device.

5. A site power management system, characterized by, Comprising: A device management platform, a mobile load cluster, a renewable energy power generation device, and an energy storage device, the device management platform and each mobile load in the mobile load cluster, the renewable energy power generation device, and the energy storage device are connected through 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, the real-time working data including real-time task data and real-time power data; The renewable energy power generation device acquires predicted power generation data and sends the predicted power generation data to the device management platform; the energy storage device acquires state of charge data and sends the state of charge data to the device management platform; The device management platform generates a mobile load control instruction according to the real-time working data, the predicted power generation data, and the state of charge data, the mobile load control instruction being used to instruct at least one mobile load in the mobile load cluster to move to the charging and discharging interface to perform a charging and discharging operation, and the renewable energy power generation device, the energy storage device, and the charging and discharging interface being connected through a power grid; The device management platform generates a mobile load control instruction according to the real-time working data, the predicted power generation data, and the state of charge data, including: The device management platform determines a predicted total power according to a predicted power generation amount in the predicted power generation data and a residual power amount in the state of charge data; If the device management platform determines that the predicted total power is greater than an upper threshold of the state of charge of the energy storage device, the mobile load control instruction is generated, wherein the predicted power generation data includes a predicted power generation amount in a future time length; The device management platform determines at least one target mobile load from the mobile load cluster according to the real-time working data set, the target mobile load being a mobile load that has ended a current execution task before a starting time node of the future time length; 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 length; The device management platform determines a predicted total power according to a predicted power generation amount in the predicted power generation data and a residual power amount 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, the mobile load to be charged being a mobile load that performs charging in the future time length; The device management platform determines a charging consumption power of the mobile load to be charged in the future time length; The device management platform determines the predicted total power according to the predicted power generation amount, the residual power amount in the state of charge data, the charging consumption power, and a predicted device power consumption amount, the predicted device power consumption amount being determined according to the future time length and historical device power consumption data; Before the renewable energy power generation device acquires predicted power generation data and sends the predicted power generation data to the device management platform, further including: The device management platform determines a current task intensive feature amount according to the real-time working data set; If the device management platform determines that the current task intensive feature amount is greater than a preset upper threshold of a task intensive feature, a shortened time length operation is performed on the basis of a preset calibration time length to determine the future time length; If the device management platform determines that the current task intensive feature amount is less than a preset lower threshold of a task intensive feature, a lengthened time length operation is performed on the basis of the preset calibration time length to determine the future time length.

6. An electronic device, comprising: including: a processor; and a memory for storing executable instructions of the processor; The processor is configured to execute the method of any one of claims 1 to 4 via execution of the executable instructions.

7. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer execution instructions, and the computer execution instructions are executed by the processor to implement the method of any one of claims 1 to 4.

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