Energy management system and method based on embedded platform

Abstract

The invention relates to the technical field of energy management, in particular to an energy management system and method based on an embedded platform, and the system comprises an energy scheduling module, an energy optimization strategy module, a fault warning and processing module, a system configuration management module and a station operation analysis module. According to the invention, by monitoring and analyzing the power demand and supply state of the energy storage device and the power grid in real time, the operation mode and power output of the energy storage device are optimized, the energy utilization efficiency and the economical efficiency of device operation are improved, the charging and discharging state of the energy storage device is automatically adjusted, and the load demand of the power grid is effectively matched; the energy waste caused by overload or insufficient power is reduced, the abnormal state is quickly identified and marked by comprehensively analyzing the key performance index and the set threshold value of the equipment, the potential equipment failure rate and maintenance cost are reduced, the equipment configuration is updated in real time, and it is ensured that the operation parameters of the equipment are always in the optimal state.

Description

Technical Field

[0001] The present invention relates to the technical field of energy management, and particularly to an energy management system and method based on an embedded platform. Background Art

[0002] The technical field of energy management mainly studies the collection, distribution, conversion, storage and optimal utilization of energy, aiming to systematically improve the energy utilization efficiency, reduce energy waste and related operating costs. This field involves the design of energy system architectures, the dynamic regulation of energy flow, the optimal configuration of energy storage technologies, and the efficient conversion between multiple energy forms. It is widely applied in multiple scenarios such as power systems, industrial production, building energy conservation, and transportation, focusing on solving the problems of reliability, economy and environmental protection in energy use. In addition, this field also pays attention to the intelligent development of energy management systems. By combining advanced algorithms and information technologies, it realizes real-time monitoring and optimal control, and promotes the coordinated operation of clean energy and traditional energy.

[0003] An energy management system is a system used to monitor, analyze and optimize energy use. Its main functions include the real-time monitoring of energy flow, the diagnosis and evaluation of energy use patterns, and the execution of optimization strategies. Its purpose is to improve energy utilization efficiency, reduce energy consumption, and thus lower operating costs and environmental impacts through data-driven management methods. The system is widely applied in scenarios such as smart grids, industrial energy consumption management, and building intelligence, and can provide users with clear energy use information and optimization suggestions, supporting intelligent decision-making and resource scheduling.

[0004] Traditional management systems have deficiencies in dealing with the dynamic regulation of energy flow and the monitoring of equipment status. Traditional systems lack the ability to accurately match the status of energy storage devices with grid demands, resulting in low energy utilization efficiency and easy occurrence of resource waste. Traditional systems usually respond slowly in equipment anomaly detection and handling, lacking an effective early warning mechanism, which increases equipment maintenance costs and prolongs equipment fault recovery time. In addition, the capabilities of traditional systems in real-time data processing and equipment configuration update are also relatively limited, making it difficult to adapt to rapidly changing energy demands and complex operating environments, restricting the flexibility and response speed of energy management, and unable to fully exert the potential efficiency of the energy system, affecting the economy and environmental protection of the overall energy system. Summary of the Invention

[0005] The purpose of the present invention is to solve the deficiencies existing in the prior art, and to propose an energy management system and method based on an embedded platform.

[0006] To achieve the above purpose, the present invention adopts the following technical solution: An energy management system based on an embedded platform, the system includes,

[0007] The energy scheduling module is based on an embedded platform, compares the operating mode of the energy storage device with the power demand, determines whether the power demand matches the device status, switches the on / off state of the device, adjusts the operating mode, sets the charge / discharge power, and obtains the energy storage mode adjustment information;

[0008] The energy optimization strategy module, based on the energy storage mode adjustment information, extracts the real-time power demand of the site, the current power load of the energy storage device, and the remaining battery power, analyzes the matching of the power adjustment range with the grid power load, compares and reconfigures the energy storage device load, and obtains the energy scheduling optimization information;

[0009] The fault warning and handling module, based on the energy scheduling optimization information, compares the key operating parameter values with the standard thresholds, filters out abnormal parameters, records the operating status of the associated devices, marks the abnormal devices, cross-checks the operating records and time parameters, identifies the type and degree of the abnormality, and obtains the device abnormality information;

[0010] The system configuration management module, based on the device abnormality information, analyzes the operating parameters of the abnormal devices, corrects the operating mode parameters, reconfigures the site parameters, writes the corrected device parameters, and obtains the device configuration update information;

[0011] The site operation analysis module, based on the energy scheduling optimization information and the device configuration update information, extracts the total power input and output of the site and the device operating parameters, analyzes the deviation of the total power input and output, calculates the device power utilization rate, and obtains the site energy operation analysis result.

[0012] As a further solution of the present invention, the energy storage mode adjustment information includes device on / off state information, grid-connected state information, off-grid state information, and charge / discharge power. The energy scheduling optimization information is specifically the power distribution value, the remaining battery power level, and the grid load balance value. The device abnormality information includes the abnormal device number, the type of abnormality, and the operating state deviation value. The device configuration update information includes the updated device number, the corrected operating parameters, and the newly added site electricity price parameters. The site energy operation analysis result includes the operating power deviation value, the device power utilization rate, and the operating trend information.

[0013] As a further solution of the present invention, the energy scheduling module includes:

[0014] The operating mode switching sub-module, based on the embedded platform, extracts the current operating mode of the energy storage site, the battery power level, and the energy storage device status parameters, compares the current operating mode of the energy storage device with the power demand value one by one, determines whether the operating state of the energy storage device meets the requirements of the power demand value, switches the on / off state of the device, and adjusts the operating mode of the energy storage device in the grid-connected mode and the off-grid mode to obtain the device operating mode switching information;

[0015] Based on the device operation mode switching information, the power distribution adjustment sub-module calls the operating power parameter value and the supported power range value of the energy storage device, compares the power demand value with the supported power range of the energy storage device item by item, adjusts the charging power and discharging power parameters of the energy storage device, sets the power distribution parameter value of the energy storage device in the current operation mode, and obtains the energy storage power distribution parameter;

[0016] Based on the energy storage power distribution parameter, the status matching verification sub-module extracts the set values of the charging and discharging powers of the energy storage device and the current power demand parameter value, matches and verifies the set values of the charging and discharging powers of the energy storage device with the current power demand value, verifies whether the power status of the energy storage device meets the current operation requirements, and determines whether the adjustment result of the operation mode of the energy storage device meets the site power load requirements, so as to obtain the energy storage mode adjustment information.

[0017] As a further solution of the present invention, the energy optimization strategy module includes:

[0018] Based on the energy storage mode adjustment information, the power demand matching sub-module extracts the real-time power demand value of the site, the current power load value of the energy storage device and the remaining battery power level, analyzes the deviation range between the real-time power demand value and the power load value of the energy storage device, analyzes the adaptation degree between the power adjustment range of the energy storage device and the power load level of the power grid, and determines whether the energy storage device can meet the current real-time power demand of the site, so as to obtain the power demand adaptation result;

[0019] Based on the power demand adaptation result, the load distribution adjustment sub-module extracts the current power load value and the adjustment range value of the energy storage device, calculates the load distribution deviation value of the energy storage device, matches the load distribution value of the energy storage device with the real-time power demand, reconfigures the load distribution value of the energy storage device according to the matching result, and adjusts the power distribution priority, so as to obtain the load distribution configuration result;

[0020] Based on the load distribution configuration result, the energy storage strategy verification sub-module extracts the allocated load value of the energy storage device and the power demand value of the power grid, conducts item-by-item comparison and verification, and analyzes whether the adjusted energy storage strategy meets the overall energy scheduling requirements according to the change trend of the adjusted load value of the energy storage device, so as to obtain the energy scheduling optimization information.

[0021] As a further solution of the present invention, the fault warning and processing module includes:

[0022] Based on the energy scheduling optimization information, the abnormal parameter screening sub-module extracts the key parameter values recorded during the operation of the device. The key parameter values include the PCS output power, the battery voltage and the battery temperature. It compares the key parameter values with the standard thresholds of the energy storage device item by item, screens out the abnormal parameter values exceeding the threshold range, and records the associated device number and operation status information, so as to obtain the abnormal parameter record information;

[0023] Based on the abnormal parameter record information, the equipment fault determination sub-module classifies the abnormal parameter values according to the running time, load level and threshold deviation amplitude information of the equipment corresponding to the abnormal parameter values, and combines the equipment running records to determine whether it belongs to the equipment running fault, marks the fault equipment number and fault type, and obtains the equipment fault characteristic information;

[0024] Based on the equipment fault characteristic information, the abnormal type analysis sub-module analyzes the fault type and scope of the equipment with abnormal operation according to the running records and time parameter values of the marked equipment, analyzes the type of equipment running abnormality, and evaluates the severity of the equipment running abnormality, and obtains the equipment abnormality information.

[0025] As a further solution of the present invention, the formula for evaluating the severity of the equipment running abnormality is:

[0026]

[0027] Among them, S represents the severity score of the equipment running abnormality, n represents the total number of key parameters, w i represents the weight coefficient of the i-th key parameter, P i represents the actual running value of the i-th key parameter, T i represents the standard threshold of the i-th key parameter, R i represents the running time of the equipment corresponding to the i-th key parameter, k is an adjustment coefficient, Δ i represents the load level deviation corresponding to the i-th key parameter.

[0028] As a further solution of the present invention, the system configuration management module includes:

[0029] Based on the equipment abnormality information, the parameter comparison and update sub-module extracts the configuration information of the current equipment at the site, including the equipment number, running mode parameter value and site electricity price parameter, compares the running parameters of the abnormal equipment with the site equipment configuration information item by item, screens the abnormal equipment with different running parameters, and marks the difference content to obtain the abnormal parameter comparison result;

[0030] Based on the abnormal parameter comparison result, the running mode correction sub-module extracts the running mode parameter value and difference content of the marked equipment, corrects the different running mode parameter values, updates the corrected running mode parameters by resetting the equipment running parameter range, and generates a set of corrected running parameters;

[0031] Based on the set of corrected running parameters, the equipment configuration writing sub-module extracts the associated parameters between the corrected equipment parameters and the site configuration table, writes the corrected equipment parameters into the site parameter configuration table, and reconfigures the equipment running mode to obtain the equipment configuration update information.

[0032] As a further solution of the present invention, the site operation analysis module includes:

[0033] Based on the energy scheduling optimization information and the device configuration update information, the trend comparison and analysis sub-module extracts the total power input of the site, the power output, and the device operation parameters, classifies and arranges the device operation parameters according to the input power and the output power, compares them item by item with the normal operation record data, analyzes the change trend of the operation parameters, and obtains the operation trend change characteristics;

[0034] Based on the operation trend change characteristics, the power deviation operation sub-module calls the overall power input value and the power output value of the site, calculates the deviation value between the input power and the output power, extracts the deviation amplitude and the associated device number, and obtains the power deviation characteristic set;

[0035] Based on the power deviation characteristic set, the utilization rate calculation sub-module extracts the actual power output value and the operation time of the device, analyzes the power utilization of the device operation by using the power output and the operation time of the device, and obtains the site energy operation analysis result.

[0036] As a further solution of the present invention, the formula for analyzing the change trend of the operation parameters is:

[0037]

[0038] Wherein, I represents the change trend index of the operation parameter, w j represents the weight coefficient of the jth operation parameter, U j,t represents the actual measured value of the jth operation parameter at time t, U j,t-1 represents the actual measured value of the jth operation parameter at time t-1, Z j,t represents the deviation value of the jth operation parameter at time t, c is the adjustment coefficient, T is the time span, and m is the total number of operation parameters.

[0039] An energy management method based on an embedded platform, the energy management method based on the embedded platform is executed based on the above-mentioned energy management system based on the embedded platform, and includes the following steps:

[0040] S1: Based on the embedded platform, compare the operation mode of the energy storage device with the power demand, determine whether the power demand matches the device state, switch the on / off state of the device, adjust the operation mode, set the charge / discharge power, and obtain the energy storage mode adjustment information;

[0041] S2: Based on the energy storage mode adjustment information, extract the real-time power demand of the site, the current power load of the energy storage device, and the remaining battery power. Analyze the power adjustment range to match the grid power load, compare and reconfigure the load of the energy storage device to obtain the energy scheduling optimization information;

[0042] S3: Based on the energy scheduling optimization information, compare the values of the key operation parameters with the standard thresholds, screen out the abnormal parameters, record the operation status of the associated devices, mark the abnormal devices, cross-check the operation records and time parameters, identify the types and degrees of abnormalities to obtain the device abnormality information;

[0043] S4: Based on the device abnormality information, analyze the operation parameters of the abnormal devices, correct the operation mode parameters, reconfigure the site parameters, and write the corrected device parameters to obtain the device configuration update information;

[0044] S5: Based on the energy scheduling optimization information and the device configuration update information, extract the total power input and output of the site and the device operation parameters, analyze the deviation of the total power input and output, calculate the device power utilization rate to obtain the site energy operation analysis result.

[0045] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0046] In the present invention, by real-time monitoring and analyzing the power demand and supply status of the energy storage device and the power grid, optimizing the operation mode and power output of the energy storage device, the energy utilization efficiency and the economy of device operation are improved. Automatically adjust the charge and discharge status of the energy storage device, effectively match the grid load demand, reduce energy waste caused by power overload or deficiency. In terms of abnormal situation prediction and handling, by comprehensively analyzing the key performance indicators of the device and the set thresholds, quickly identify and mark the abnormal status, reduce the potential device failure rate and maintenance cost, and update the device configuration in real time to ensure that the device operation parameters are always in the best state, extend the device life and keep the system running efficiently for a long time, optimize the accuracy of energy management and scheduling, and enhance the management of the device health status. Brief Description of the Drawings

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0048] Figure 1 is the system flow chart of the present invention;

[0049] Figure 2 is the schematic diagram of the system framework of the present invention;

[0050] Figure 3 It is a flowchart of the energy scheduling module of the present invention;

[0051] Figure 4 It is a flowchart of the energy optimization strategy module of the present invention;

[0052] Figure 5 It is a flowchart of the fault warning and handling module of the present invention;

[0053] Figure 6 It is a flowchart of the system configuration management module of the present invention;

[0054] Figure 7 It is a flowchart of the site operation analysis module of the present invention;

[0055] Figure 8 It is a schematic diagram of the method steps of the present invention. Specific embodiments

[0056] Next, in conjunction with the accompanying drawings, the technical solutions in the present invention will be described.

[0057] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to represent examples, illustrations or explanations. Any embodiment or design solution described as an "example" in the present invention should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of the word "example" is intended to present concepts in a specific manner. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one of the two.

[0058] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when their differences are not emphasized, their intended meanings are the same. "(of)", "corresponding", and "corresponding" can sometimes be used interchangeably. It should be noted that when their differences are not emphasized, their intended meanings are the same.

[0059] In the embodiments of the present invention, sometimes subscripts such as W1 may be written in a non-subscript form such as W1. When their differences are not emphasized, their intended meanings are the same.

[0060] To make the technical problems, technical solutions and advantages to be solved by the present invention clearer, the following will be described in detail in conjunction with the accompanying drawings and specific embodiments.

[0061] Please refer to Figure 1 , the present invention provides a technical solution: an energy management system based on an embedded platform, the system includes:

[0062] Based on the embedded platform, the energy scheduling module calls the current operating mode of the energy storage site, the battery power level, and the status parameter of the energy storage device, compares the operating mode of the energy storage device with the power demand value, determines whether the current power demand matches the status of the energy storage device, switches the on / off state of the device, adjusts the operating mode of the energy storage device in combination with the grid-connected and off-grid states, and sets the charge / discharge power according to the range of power values supported by the energy storage device, and obtains the energy storage mode adjustment information;

[0063] Based on the energy storage mode adjustment information, the energy optimization strategy module extracts the real-time power demand value of the site, the current power load value of the energy storage device, and the remaining battery power level, analyzes the matching situation between the power adjustment range value of the energy storage device and the grid power load level, compares and analyzes the load value of the energy storage device with the grid power demand value, reconfigures the load distribution value of the energy storage device, and obtains the energy scheduling optimization information;

[0064] Based on the energy scheduling optimization information, the fault warning and handling module compares the key parameter values during operation with the standard thresholds. The key parameter values include the PCS output power, battery voltage, and battery temperature, filters out the abnormal parameter values, records the operating status of the associated devices, marks the devices with abnormal operation in combination with the exceeded range of the abnormal parameters, and calls the operating records of the marked devices for cross-checking with the associated time parameter values to identify the type and degree of abnormal device operation, and obtains the device abnormal information;

[0065] Based on the device abnormal information, the system configuration management module calls the configuration information of the current devices at the site, including device numbers, operating mode parameter values, and site electricity price parameters, analyzes the operating parameters and configuration information of the abnormal devices, corrects the operating mode parameter of the abnormal devices, and writes the corrected device parameters by reconfiguring the site parameters, and obtains the device configuration update information;

[0066] Based on the energy scheduling optimization information and the device configuration update information, the site operation analysis module extracts the total power input power, power output power, and device operating parameters of the site, classifies and compares the operating parameters with the normal operation data, combines the deviation value of the overall power input and output of the site, analyzes the trend of the deviation value, and calculates the device power utilization rate, and obtains the site energy operation analysis result;

[0067] The energy storage mode adjustment information includes device on / off state information, grid-connected state information, off-grid state information, and charge / discharge power. The energy scheduling optimization information is specifically the power distribution value, remaining battery power level, and grid load balance value. The device abnormal information includes the abnormal device number, abnormal type, and operating status deviation value. The device configuration update information includes the updated device number, corrected operating parameters, and newly added site electricity price parameters. The site energy operation analysis result includes the operating power deviation value, device power utilization rate, and operating trend information.

[0068] Please refer to Figure 2 and Figure 3 , the energy scheduling module includes an operation mode switching sub-module, a power distribution adjustment sub-module, and a status matching verification sub-module;

[0069] Based on the embedded platform, the operation mode switching sub-module extracts the current operation mode, battery power level, and energy storage device status parameters of the energy storage site, calls the operation status parameter values of the energy storage device, extracts the grid-connected and off-grid mode parameters of the device, compares the current operation mode of the energy storage device with the power demand value one by one, determines whether the operation status of the energy storage device meets the requirements of the power demand value, switches the on / off state of the device according to the matching result, adjusts the operation mode of the energy storage device in the grid-connected mode and the off-grid mode, and obtains the device operation mode switching information;

[0070] Based on the embedded platform, extract the current operation mode data of the energy storage site, including the mode status of device grid connection and off-grid, battery power level, and energy storage device status parameters, and call the power demand allocation value. Compare it with the grid-connected and off-grid modes in the device operation status parameters one by one. Compare the power demand value with the grid-connected state operation power range and the off-grid state operation power range respectively to determine whether the device operation mode status matches the power demand. Switch the device switch state through the device control interface. For example, when the device output power in the grid-connected state is lower than the power demand value, the device needs to switch to the off-grid state to meet the load power demand. At the same time, call the device battery status to analyze whether its power level is sufficient to support the off-grid mode. When the off-grid operation requirements are met, complete the device mode switch and confirm that the current operation mode of the device is updated to the off-grid state. If the device mode status does not meet the operation requirements, keep the current operation state, record the relevant adjustment results in the device management table by recording the device mode adjustment parameters, and obtain the device operation mode switching information.

[0071] Based on the device operation mode switching information, the power distribution adjustment sub-module calls the operation power parameter value and the supported power range value of the energy storage device, compares the power demand value with the supported power range of the energy storage device item by item, adjusts the charging power and discharging power parameters of the energy storage device according to the comparison result, and sets the power distribution parameter value of the energy storage device in the current operation mode to obtain the energy storage power distribution parameter;

[0072] Based on the device operation mode switching information, call the operating power parameter values of the energy storage device, including the current charge and discharge power, the maximum charging power, the minimum discharge power, and the power demand value. Compare the power demand value with the device support power range value one by one to determine whether the current power range of the device can meet the power demand. For example, when the power demand is higher than the maximum charging power of the device, calculate the power range to be adjusted. By adjusting the charge or discharge power parameters of the device, such as reducing the discharge power or increasing the charging power to the upper limit of the device operating power range, make it meet the power demand, and record the adjusted charge power and discharge power values of the device. Set the power distribution parameter values of the device under the current operating mode, and file the operating power adjustment situation of the energy storage device through the power distribution result table to obtain the energy storage power distribution parameters.

[0073] The status matching verification sub-module extracts the charge and discharge power setting values and the current power demand parameter values of the energy storage device based on the energy storage power distribution parameters, matches and verifies the charge and discharge power setting values of the energy storage device with the current power demand value, verifies whether the power status of the energy storage device conforms to the current operating requirements, and determines whether the adjustment result of the energy storage device operating mode meets the site power load requirements to obtain the energy storage mode adjustment information.

[0074] Based on the energy storage power distribution parameters, extract the charge and discharge power setting values of the energy storage device, including the power demand value and the current device operating power range. Compare the charge and discharge power setting values with the current power demand parameter values, and verify the compliance of the energy storage device operating power status with the current demand through item-by-item matching. For example, when the device discharge power exceeds the power demand value, determine whether the device operating status needs to be adjusted to reduce the discharge power. Through the adjusted device operating power setting value, verify the status requirements of the device to ensure that the adjusted power meets the current power load requirements. Update the energy storage device operating parameter table and record the relevant matching verification parameters, and associate and match the adjusted device operating power parameters with the actual demand to obtain the energy storage mode adjustment information.

[0075] Please refer to Figure 2 and Figure 4 , the energy optimization strategy module includes a power demand matching sub-module, a load distribution adjustment sub-module, and an energy storage strategy verification sub-module;

[0076] The power demand matching sub-module extracts the site real-time power demand value, the current power load value of the energy storage device, and the remaining battery power level based on the energy storage mode adjustment information, analyzes the deviation range between the real-time power demand value and the power load value of the energy storage device, analyzes the adaptation degree of the energy storage device power adjustment range to the grid power load level, and determines whether the energy storage device can meet the site current real-time power demand to obtain the power demand adaptation result;

[0077] Based on the energy storage mode adjustment information, extract the real-time power demand value of the site, including the current power consumption demand curve and load fluctuation data of the site, call the current power load value of the energy storage device, and by monitoring the output power of the energy storage device in real time, compare the power load value of the energy storage device with the real-time power demand value. During the comparison process, record the deviation value between the two, and statistically classify the deviation value in hours. According to the fluctuation range of the deviation value in different time intervals, analyze the matching degree between the power adjustment range of the energy storage device and the power load level of the power grid. By matching the power output range of the energy storage device with the power demand fluctuation range of the power grid, record the time period with a higher adaptation degree of the energy storage device, and at the same time judge whether the energy storage device can meet the real-time power demand of the site during the current period. For example, when the deviation value is negative and the fluctuation amplitude is large, the energy storage device cannot meet the current power demand; when the deviation value is positive and the fluctuation amplitude is stable, it means that the operating power of the device can adapt to the load demand, and write this data into the adaptation result table to obtain the power demand adaptation result.

[0078] Based on the power demand adaptation result, the load distribution adjustment sub-module extracts the current power load value and adjustment range value of the energy storage device, calculates the load distribution deviation value of the energy storage device, matches the load distribution value of the energy storage device with the real-time power demand, and reconfigures the load distribution value of the energy storage device according to the matching result, and adjusts the power distribution priority to obtain the load distribution configuration result;

[0079] Based on the power demand adaptation result, extract the current power load value of the energy storage device, including the charging power and discharge power setting values, call the adjustment range value of the energy storage device, including the maximum adjustable charging power and minimum discharge power of the device, and calculate the load distribution deviation value of the energy storage device by comparing the current power load value of the energy storage device with the data in the time interval with a large deviation in the power demand adaptation result. For example, if the current device load value is 5MW and the power demand adaptation value is 8MW, the deviation value is 3MW. Calculate the adjusted load distribution parameters according to the deviation value, reconfigure the power distribution value of the energy storage device, and allocate it to the time period with a higher priority to preferentially meet the power demand in the high-load time period, and at the same time reduce the power output in the low-load time period. Record the reconfigured device power through the power distribution table to generate the load distribution configuration result.

[0080] Based on the load distribution configuration result, the energy storage strategy verification sub-module extracts the allocated load value of the energy storage device and the power demand value of the power grid, conducts one-by-one comparison and verification, and analyzes whether the adjusted energy storage strategy meets the overall energy scheduling requirements according to the change trend of the adjusted load value of the energy storage device to obtain the energy scheduling optimization information.

[0081] Based on the load distribution configuration result, extract the allocated load value of the energy storage device, including the charge and discharge power parameters reconfigured by time period, and call the power grid power demand value, including the real-time load demand curve of the site and the adjusted power supply fluctuation value of the power grid. Compare and verify the allocated load value of the energy storage device with the power grid power demand value one by one. By recording the fluctuation range and change trend of the energy storage device load after adjustment by time period, analyze the adaptability of the adjusted energy storage strategy to the overall energy scheduling. For example, when the adjusted allocated load value can completely match the power grid demand curve, it is considered that the strategy verification passes; when the adjusted load value deviates from the demand value by more than 10%, relevant load parameters need to be marked and the energy storage strategy needs to be readjusted. Write the verified data into the strategy adjustment table to obtain the energy scheduling optimization information.

[0082] Please refer to Figure 2 and Figure 5 , the fault alarm and handling module includes an abnormal parameter screening sub-module, a device fault determination sub-module, and an abnormal type analysis sub-module;

[0083] The abnormal parameter screening sub-module extracts the key parameter values recorded during the operation of the device based on the energy scheduling optimization information. The key parameter values include the PCS output power, battery voltage, and battery temperature. Compare each key parameter value with the standard threshold of the energy storage device item by item, screen out the abnormal parameter values that exceed the threshold range, and record the associated device number and operation status information to obtain the abnormal parameter record information.

[0084] Based on the energy scheduling optimization information, extract the key parameter values recorded during the operation of the device. The parameters include the PCS output power, battery voltage, and battery temperature. Arrange the key parameter values recorded for each device during operation in chronological order. Then obtain the standard threshold range of the energy storage device. For example, for the PCS output power, the standard range is set from 1 MW to 5 MW; for the battery voltage, the range is 3.2 V to 4.2 V; for the battery temperature, the range is 10 °C to 40 °C. Compare each parameter value with the corresponding standard threshold range one by one. For the parameter values that exceed the threshold range, mark their abnormal status. For example, when the PCS output power reaches 6 MW during a certain period, this power value is marked as an abnormal value. At the same time, record the device number associated with this abnormal parameter and the operation status of the device at this time point, such as recording the operation mode (grid-connected or off-grid), power load level, and battery power of the device. All abnormal data is classified and stored in the abnormal parameter record table to obtain the abnormal parameter record information.

[0085] Based on the abnormal parameter record information, according to the operating time, load level, and threshold deviation amplitude information of the device corresponding to the abnormal parameter value, classify the abnormal parameter value, and combine with the device operation record to determine whether it belongs to the device operation failure, mark the failure device number and failure type, and obtain the device failure characteristic information;

[0086] Based on the abnormal parameter record information, according to the operating time, load level, and threshold deviation amplitude information of the device corresponding to the abnormal parameter value, classify the abnormal parameter value. Call each abnormal parameter value stored in the abnormal parameter record table, extract the device number and operating time corresponding to each parameter value, classify the abnormal parameter values according to the device number, and sort them in combination with the operating time. At the same time, analyze the deviation amplitude between each abnormal parameter value and the standard threshold. For example, when the abnormal value of the battery temperature is 50°C and exceeds the standard range of 40°C, the deviation amplitude is 10°C. Classify the abnormal data according to the deviation amplitude. For example, those with a deviation amplitude less than 10% are classified as minor abnormalities, those with a deviation amplitude between 10% and 30% are classified as moderate abnormalities, and those exceeding 30% are classified as severe abnormalities. By combining the device operation record, compare whether there is a trend of abnormal operation status. For example, if the same type of abnormal parameter exists in multiple consecutive time periods, mark it as a potential failure device, record the failure device number and its failure type, including power abnormality, too high battery temperature, or too low voltage, and finally form the device failure characteristic information.

[0087] Based on the device failure characteristic information, according to the operation record and time parameter value of the marked device, analyze the failure type and scope of the device with abnormal operation, analyze the type of device operation abnormality, and evaluate the severity of the device operation abnormality to obtain the device abnormality information.

[0088] Based on the device failure characteristic information, according to the operation record and time parameter value of the marked device, analyze the failure type and scope of the device with abnormal operation. Call the failure device characteristic table, extract the failure type of each device and its associated abnormal time period. For example, when a device continuously shows an abnormal PCS output power exceeding the limit within 3 hours, the failure type is marked as power overload. Combine the abnormal time period with the device operation status record for analysis. For example, record the operation mode and load level of the device during this time period, and evaluate the severity of the device operation abnormality to obtain the device abnormality information.

[0089] The formula for evaluating the severity of device operation abnormality is:

[0090]

[0091] Among them, S represents the severity score of device operation abnormality, n represents the total number of key parameters, w i represents the weight coefficient of the i-th key parameter, Pi Represents the actual operating value of the i-th key parameter, T i Represents the standard threshold of the i-th key parameter, R i Represents the device operating time corresponding to the i-th key parameter, k is the adjustment coefficient, Δ i Represents the load level deviation corresponding to the i-th key parameter.

[0092] Formula:

[0093]

[0094] Parameter meaning and acquisition method:

[0095] w i : The weight coefficient of the i-th key parameter. The weight coefficient can be calculated by the fuzzy analytic hierarchy process. The fuzzy analytic hierarchy process constructs a fuzzy complementary judgment matrix, calculates the eigenmatrix, and performs consistency verification to finally determine the weights of each parameter.

[0096] P i : The actual operating value of the i-th key parameter, such as the current PCS output power, battery voltage, and battery temperature of the device. These values are collected in real time through the device's sensors and monitoring system.

[0097] T i : The standard threshold of the i-th key parameter, such as the standard values of the PCS output power, battery voltage, and battery temperature specified for the energy storage device. The threshold is usually provided by the device manufacturer or set according to industry standards and device operating requirements.

[0098] R i : The device operating time corresponding to the i-th key parameter, indicating the duration of the device operating in an abnormal state, in hours. This parameter is obtained from the device's operation log and time record.

[0099] k: Adjustment coefficient, used to adjust the influence degree of the load deviation on the score in the formula, usually set according to system design experience or experimental data. The value of this coefficient should be within a reasonable range, such as between 0.1 and 1, to ensure the accuracy of the calculation result.

[0100] Δ i : The load level deviation corresponding to the i-th key parameter, indicating the difference between the current load power and the device design load power. This parameter is calculated by real-time monitoring of the device's load condition and comparing it with the design load.

[0101] Calculation example:

[0102] The following parameters are set:

[0103] Key Parameter 1 (PCS Output Power): Actual Operating Value (P1): 55 kW, Standard Threshold (T1): 50 kW, Weighting Coefficient (w1): 0.4, Operating Time (R1): 2 hours, Load Level Deviation (Δ1): 5 kW;

[0104] Key Parameter 2 (Battery Voltage): Actual Operating Value (P2): 48 V, Standard Threshold (T2): 50 V, Weighting Coefficient (w2): 0.3, Operating Time (R2): 3 hours, Load Level Deviation (Δ2): -2 V;

[0105] Key Parameter 3 (Battery Temperature): Actual Operating Value (P3): 35 °C, Standard Threshold (T3): 30 °C, Weighting Coefficient (w3): 0.3, Operating Time (R3): 1 hour, Load Level Deviation (Δ3): 5 °C, Regulation Coefficient (k): 0.5.

[0106] Calculate the Severity Score (S) of Equipment Operating Abnormality:

[0107] Calculate the absolute value of the deviation of key parameters:

[0108] |P1 - T1| = |55 - 50| = 5;

[0109] |P2 - T2| = |48 - 50| = 2;

[0110] |P3 - T3| = |35 - 30| = 5;

[0111] Calculate the weighted deviation sum:

[0112] w1 × |P1 - T1| = 0.4 × 5 = 2;

[0113] w2 × |P2 - T2| = 0.3 × 2 = 0.6;

[0114] w3 × |P3 - T3| = 0.3 × 5 = 1.5;

[0115] Weighted deviation sum: 2 + 0.6 + 1.5 = 4.1;

[0116] Calculate the square root of the sum of squares of operating times:

[0117]

[0118] Sum of squares of operating times: 4 + 9 + 1 = 14;

[0119] Square root:

[0120] Calculate the sum of absolute values of load level deviations:

[0121] |Δ1| = |5| = 5; |Δ2| = |-2| = 2; |Δ3| = |5| = 5;

[0122] Sum of absolute deviations: 5 + 2 + 5 = 12;

[0123] Calculate the denominator part:

[0124] Set the adjustment coefficient k = 0.5

[0125]

[0126] Calculate the severity score (S):

[0127]

[0128] The severity score of the abnormal device operation is calculated by the formula as S ≈ 0.421. This score indicates the degree of influence of the device abnormality on the overall system operation. The larger the value, the higher the severity of the abnormality. In this example, although the deviations of some key parameters are relatively large (such as PCS output power and battery temperature), due to the short operation time and relatively low deviation of the load level, the final score is at a medium - low level, indicating that this abnormality belongs to a minor degree of abnormality.

[0129] Please refer to Figure 2 and Figure 6 , the system configuration management module includes a parameter comparison and update sub - module, an operation mode correction sub - module, and a device configuration writing sub - module;

[0130] Based on the device abnormality information, the parameter comparison and update sub - module extracts the configuration information of the current devices at the site, including device numbers, operation mode parameter values, and site electricity price parameters. It compares the operation parameters of the abnormal devices item - by - item with the site device configuration information, screens out the abnormal devices with different operation parameters, and marks the different contents to obtain the abnormal parameter comparison result;

[0131] Based on the device abnormality information, extract the configuration information of the current devices at the site, including device numbers, operation mode parameter values, and site electricity price parameters. Call the operation parameter record table of the abnormal devices to extract the current operation mode parameter values of each abnormal device. For example, the operation mode parameter values of the device with the device number "E001" show a grid - connected mode, the current charging power is 1 MW, and the discharging power is 0.8 MW. At the same time, call the standard operation parameters of the corresponding devices in the site device configuration table, compare the two sets of data item - by - item, and the comparison contents include the operation mode status, the set value of the charging power, and the set value of the discharging power. Mark the parameters with deviations. For example, the current charging power deviation of the operation parameters of "E001" is - 0.2 MW, which exceeds the allowable range. Record the different contents in the abnormal parameter comparison result table, screen out all the devices with differences and mark their corresponding different contents to obtain the abnormal parameter comparison result.

[0132] Based on the abnormal parameter comparison result, the operation mode correction sub-module extracts the operation mode parameter values and difference contents of the tagged device, corrects the different operation mode parameter values, updates the corrected operation mode parameters by resetting the device operation parameter range, and generates a set of corrected operation parameters.

[0133] Based on the abnormal parameter comparison result, extract the operation mode parameter values and difference contents of the tagged device. Determine the correction method by analyzing the current operation parameter range and the difference value. For example, for the device numbered "E001", the current operation mode is grid-connected mode and the charging power deviation is -0.2 MW. Call the historical operation records of the device to analyze the possible reasons for the deviation, including the device load status and operation time data. Reset the device operation parameter range. For example, adjust the charging power range to 0.9 MW to 1.2 MW, and the discharging power range to 0.7 MW to 1.0 MW. Overwrite and update each corrected parameter value with the original parameter value item by item, and record the adjustment basis and device number during the correction process to generate a set of corrected operation parameters.

[0134] Based on the set of corrected operation parameters, the device configuration writing sub-module extracts the corrected device parameters and the associated parameters in the site configuration table, writes the corrected device parameters into the site parameter configuration table, and reconfigures the device operation mode to obtain device configuration update information.

[0135] Based on the set of corrected operation parameters, extract the corrected device operation parameters, including the device number, operation mode, and corrected charge and discharge power ranges. By calling the associated parameters in the site configuration table, compare each corrected parameter value with the current parameter value in the site configuration table to ensure that the corrected parameters are correctly overwritten. For example, update the charging power range of the "E001" device from the original parameter of 0.8 MW to 1.0 MW to the corrected 0.9 MW to 1.2 MW, write it into the site parameter configuration table, generate a record log for the updated configuration parameters at the same time, and synchronize it to the site device management module to complete the reconfiguration of the device operation mode and obtain device configuration update information.

[0136] Please refer to Figure 2 and Figure 7 , the site operation analysis module includes a trend comparison analysis sub-module, a power deviation calculation sub-module, and a utilization rate calculation sub-module.

[0137] Based on the energy scheduling optimization information and the device configuration update information, the trend comparison analysis sub-module extracts the total power input of the site, the power output, and the device operation parameters, classifies and organizes the device operation parameters according to the input power and the output power, compares them item by item with the normal operation record data, analyzes the change trend of the operation parameters, and obtains the change characteristics of the operation trend.

[0138] Based on the energy scheduling optimization information and equipment configuration update information, extract the total site power input, power output, and equipment operation parameters. Classify and organize the equipment operation parameters according to the input power and output power. By calling the historical operation record data of the site, classify the power input and output according to time intervals. For example, count the input power and output power data in hours, and sort out the equipment operation parameters for the corresponding time periods, including equipment numbers, operation modes, and load statuses. Compare the input power and output power for each time period item by item with the normal operation record data. By calculating the power fluctuation range for each time period, extract and record the time periods with fluctuations exceeding the threshold. For example, if the input power rises from 5 MW to 7 MW in a certain time period while the output power remains at 6 MW, record the power fluctuation range as 2 MW. Combine the equipment operation parameters to analyze the change trend of the operation parameters and obtain the characteristics of the change trend of the operation trend.

[0139] The formula for analyzing the change trend of operation parameters is:

[0140]

[0141] Among them, I represents the change trend index of the operation parameters, w j represents the weight coefficient of the jth operation parameter, U j,t represents the actual measured value of the jth operation parameter at time t, U j,t-1 represents the actual measured value of the jth operation parameter at time t - 1, Z j,t represents the deviation value of the jth operation parameter at time t, c is the adjustment coefficient, T is the time span, and m is the total number of operation parameters.

[0142] Formula:

[0143]

[0144] Meaning and acquisition method of parameters:

[0145] w j : The weight coefficient of the jth operation parameter, used to measure the importance of different operation parameters in the change trend analysis. The weight value can be calculated through the analytic hierarchy process or empirical analysis. The weight of each parameter is set according to its degree of influence on the overall operation of the equipment. For example, the weight of the input power is usually greater than that of the current fluctuation.

[0146] U j,t and U j,t-1 : U j,t represents the measured value of the jth operation parameter at time t, such as the real-time value of the total site power input, power output, equipment voltage, or current, U j,t-1Represents the measured value of the same parameter at time t - 1, used to compare with the current value U j,t for calculating the absolute change. The data is obtained through real-time monitoring of the device, such as power measurement sensors, data collectors, or system operation logs.

[0147] Z j,t : The difference between the j-th operating parameter at time t and its normal operating record value, i.e., Z j,t = U j,t - U j,norm , where U j,norm is the normal operating value of the j-th operating parameter. The normal value is usually obtained from the technical documents provided by the device manufacturer or the statistical data of long-term operation.

[0148] c: The adjustment coefficient, used to adjust the influence degree of the time span on the change trend index. The adjustment coefficient c is usually set by the device operation engineer according to the system characteristics, and its value range is from 0.1 to 1, and the empirical value is generally taken as 0.5.

[0149] T: The time span, representing the time range involved in the calculation (such as hours, minutes, days). The device operation system records the time interval in hours, and the time span can be extracted from the system operation log.

[0150] Calculation example:

[0151] Set the following operating parameter values:

[0152] Total power input: U 1,t = 500kW, U 1,t-1 = 480kW, deviation value Z 1,t = U 1,t - U 1,norm = 500 - 490 = 10kW, weight coefficient w1 = 0.4;

[0153] Total power output: U 2,t = 300kW, U 2,t-1 = 290kW, deviation value Z 2,t = U 2,t - U 2,norm = 300 - 295 = 5kW, weight coefficient w2 = 0.3;

[0154] Device current: U 3,t = 1000A, U 3,t-1 = 950A, deviation value Z 3,t = U 3,t - U 3,norm = 1000 - 980 = 20A, weight coefficient w3 = 0.3;

[0155] Set the adjustment coefficient c = 0.5 and the time span T = 1 hour.

[0156] Calculate the absolute change of each operating parameter:

[0157] |U 1,t -U 1,t-1 | = |500 - 480| = 20 kW;

[0158] |U 2,t -U 2,t-1 | = |300 - 290| = 10 kW;

[0159] |U 3,t -U 3,t-1 | = |1000 - 950| = 50 A;

[0160] Sum up with weights:

[0161] w1·|U 1,t -U 1,t-1 | = 0.4·20 = 8;

[0162] w2·|U 2,t -U 2,t-1 | = 0.3·10 = 3;

[0163] w3·|U 3,t -U 3,t-1 | = 0.3·50 = 15;

[0164] Weighted sum: 8 + 3 + 15 = 26;

[0165] Calculate the square root of the sum of squared deviations:

[0166] (Z 1,t ) 2 = (10) 2 = 100;

[0167] (Z 2,t ) 2 = (5) 2 = 25;

[0168] (Z 3,t ) 2 = (20) 2 = 400;

[0169] Sum of squared deviations: 100 + 25 + 400 = 525;

[0170] Square root:

[0171] Calculate the denominator part:

[0172]

[0173] Calculate the change trend index I:

[0174]

[0175] The change trend index of the operating parameters is calculated by the formula to be I≈1.11. This result indicates that the operating parameters of the equipment have a significant change trend within the current time range, suggesting a certain degree of volatility. It is necessary to analyze the relevant operating parameters to confirm whether it is necessary to adjust the energy scheduling strategy or optimize the equipment operation mode.

[0176] Based on the characteristics of the change in the operating trend, the power deviation operator module calls the overall power input value and power output value of the site, calculates the deviation value between the input power and the output power, extracts the deviation amplitude and the associated equipment number, and obtains the power deviation characteristic set.

[0177] Based on the characteristics of the change in the operating trend, the overall power input value and power output value of the site are called, and the input power and output power data for each time period are extracted. The deviation value is calculated by comparing the input power and output power for each time period. For example, if the input power is 7 MW and the output power is 6 MW in a certain time period, the calculated deviation value is 1 MW. While recording the deviation value, the associated equipment number is extracted, and the deviation values are classified and stored according to the equipment number. By statistically analyzing the deviation amplitude of each equipment in multiple time periods, the equipment with a larger deviation amplitude is selected. For example, if the deviations of a certain equipment in three time periods are 1 MW, 1.5 MW, and 2 MW respectively, its average deviation amplitude is calculated to be 1.5 MW. While recording the deviation value, the associated equipment number is marked, and the data is integrated into the power deviation characteristic table to obtain the power deviation characteristic set.

[0178] The utilization rate calculation sub-module, based on the power deviation characteristic set, extracts the actual power output value and operating time of the equipment, and analyzes the power utilization situation of the equipment operation by using the power output and operating time of the equipment to obtain the analysis result of the site energy operation.

[0179] Based on the power deviation characteristic set, the actual power output value and operating time of the equipment are extracted. By calling the operation record table of the equipment, the actual power output data of each equipment is extracted, and combined with the operating time parameter of the equipment. For example, if a certain equipment operates for 10 hours in a day and the total power output is 60 MW, its average power output is calculated to be 6 MW. At the same time, through the data in the power deviation characteristic set, it is judged whether the power output of the equipment is stable. For example, by statistically analyzing the power fluctuation range during the operating time, it is judged whether the fluctuation is within the allowable range. If the power output fluctuation range is large, it is recorded that the utilization rate of this equipment is low. Finally, the power utilization situation of each equipment is sorted into the equipment power utilization rate table, and statistical analysis is carried out in combination with the time period data to obtain the analysis result of the site energy operation.

[0180] Please refer to Figure 8 Figure 8 , an energy management method based on an embedded platform, which is executed based on the energy management system based on the embedded platform described above, and includes the following steps:

[0181] S1: Based on the embedded platform, compare the operating mode of the energy storage device with the power demand, determine whether the power demand matches the device status, switch the on / off status of the device, adjust the operating mode, set the charge / discharge power, and obtain the energy storage mode adjustment information;

[0182] S2: Based on the energy storage mode adjustment information, extract the real-time power demand of the site, the current power load of the energy storage device, and the remaining battery power, analyze the matching of the power adjustment range with the grid power load, compare and reconfigure the energy storage device load, and obtain the energy scheduling optimization information;

[0183] S3: Based on the energy scheduling optimization information, compare the values of the key operating parameters with the standard thresholds, filter out the abnormal parameters, record the operating status of the associated devices, mark the abnormal devices, cross-check the operating records and time parameters, identify the type and degree of the abnormality, and obtain the device abnormality information;

[0184] S4: Based on the device abnormality information, analyze the operating parameters of the abnormal devices, correct the operating mode parameters, reconfigure the site parameters, and write the corrected device parameters to obtain the device configuration update information;

[0185] S5: Based on the energy scheduling optimization information and the device configuration update information, extract the total power input and output of the site and the device operating parameters, analyze the deviation of the total power input and output, calculate the device power utilization rate, and obtain the site energy operation analysis result.

[0186] It should be understood that the term "and / or" in this article is merely a description of the association relationship between associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Here, A and B can be singular or plural. In addition, the character " / " in this article generally represents an "or" relationship between the associated objects before and after, but it may also represent an "and / or" relationship, which can be specifically understood by referring to the context before and after.

[0187] In the present invention, "at least one" means one or more, and "a plurality" means two or more. "At least one of the following items (pieces)" or its similar expressions refer to any combination of these items, including any combination of single items (pieces) or plural items (pieces). For example, at least one of a, b, or c can represent: a, b, c, a - b, a - c, b - c, or a - b - c, where a, b, and c can be single or multiple.

[0188] It should be understood that in various embodiments of the present invention, the sequence numbers of the above processes do not imply the order of execution, and the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0189] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professionals can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0190] Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the devices, apparatuses, and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0191] In several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there can be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

[0192] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place, or can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0193] In addition, the functional units in various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit.

[0194] When the above-mentioned functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs.

[0195] As described above, the above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. An energy management system based on an embedded platform, characterized in that, The system includes The energy scheduling module is based on an embedded platform, compares the operating mode of the energy storage device with the power demand, determines whether the power demand matches the device status, switches the on / off state of the device, adjusts the operating mode, sets the charge / discharge power, and obtains the energy storage mode adjustment information; The energy optimization strategy module, based on the energy storage mode adjustment information, extracts the real-time power demand of the site, the current power load of the energy storage device, and the remaining battery power, analyzes the matching of the power adjustment range with the grid power load, compares and reconfigures the energy storage device load, and obtains the energy scheduling optimization information; The fault warning and handling module, based on the energy scheduling optimization information, compares the key operating parameter values with the standard thresholds, filters out abnormal parameters, records the operating status of the associated devices, marks the abnormal devices, cross-checks the operating records and time parameters, and identifies the type and degree of the abnormality to obtain the device abnormality information; The system configuration management module, based on the device abnormality information, analyzes the operating parameters of the abnormal devices, corrects the operating mode parameters, reconfigures the site parameters, and writes the corrected device parameters to obtain the device configuration update information; The site operation analysis module, based on the energy scheduling optimization information and the device configuration update information, extracts the total power input / output power of the site and the device operating parameters, analyzes the deviation of the total power input / output, calculates the device power utilization rate, and obtains the site energy operation analysis result.

2. The energy management system based on an embedded platform according to claim 1, wherein The energy storage mode adjustment information includes the device on / off state information, grid-connected state information, off-grid state information, and charge / discharge power. The energy scheduling optimization information is specifically the power distribution value, the remaining battery power level, and the grid load balance value. The device abnormality information includes the abnormal device number, the type of abnormality, and the operating status deviation value. The device configuration update information includes the updated device number, the corrected operating parameters, and the newly added site electricity price parameters. The site energy operation analysis result includes the operating power deviation value, the device power utilization rate, and the operating trend information.

3. The energy management system based on an embedded platform according to claim 1, wherein The energy scheduling module includes: The operating mode switching sub-module, based on the embedded platform, extracts the current operating mode of the energy storage site, the battery power level, and the energy storage device status parameters, compares the current operating mode of the energy storage device with the power demand value one by one, determines whether the operating status of the energy storage device meets the requirements of the power demand value, switches the on / off state of the device, and adjusts the operating mode of the energy storage device in the grid-connected mode and the off-grid mode to obtain the device operating mode switching information; The power distribution adjustment sub-module, based on the device operating mode switching information, calls the operating power parameter value and the supported power range value of the energy storage device, compares the power demand value with the supported power range of the energy storage device item by item, adjusts the charge power and discharge power parameters of the energy storage device, and sets the power distribution parameter value of the energy storage device in the current operating mode to obtain the energy storage power distribution parameter; The status matching and verification sub-module extracts the charge and discharge power set values and the current power demand parameter values of the energy storage device based on the energy storage power distribution parameters, performs matching verification on the charge and discharge power set values of the energy storage device and the current power demand values, verifies whether the power status of the energy storage device meets the current operation requirements, determines whether the adjustment result of the operation mode of the energy storage device meets the site power load requirements, and obtains the energy storage mode adjustment information.

4. The energy management system based on an embedded platform according to claim 1, wherein The energy optimization strategy module includes: The power demand matching sub-module extracts the real-time power demand value of the site, the current power load value of the energy storage device, and the remaining battery power level based on the energy storage mode adjustment information, analyzes the deviation range between the real-time power demand value and the power load value of the energy storage device, analyzes the adaptation degree between the power adjustment range of the energy storage device and the power load level of the power grid, determines whether the energy storage device can meet the current real-time power demand of the site, and obtains the power demand adaptation result; The load distribution adjustment sub-module extracts the current power load value and the adjustment range value of the energy storage device based on the power demand adaptation result, calculates the load distribution deviation value of the energy storage device, matches the load distribution value of the energy storage device with the real-time power demand, reconfigures the load distribution value of the energy storage device according to the matching result, and adjusts the power distribution priority to obtain the load distribution configuration result; The energy storage strategy verification sub-module extracts the allocated load value of the energy storage device and the power demand value of the power grid based on the load distribution configuration result, performs one-by-one comparison verification, analyzes whether the adjusted energy storage strategy meets the overall energy scheduling requirements according to the change trend of the adjusted load value of the energy storage device, and obtains the energy scheduling optimization information.

5. The energy management system based on an embedded platform according to claim 1, wherein The fault warning and handling module includes: The abnormal parameter screening sub-module extracts the key parameter values recorded during the operation of the device based on the energy scheduling optimization information. The key parameter values include the PCS output power, battery voltage, and battery temperature. It compares the key parameter values with the standard thresholds of the energy storage device item by item, screens out the abnormal parameter values exceeding the threshold range, and records the associated device number and operation status information to obtain the abnormal parameter record information; The device fault determination sub-module classifies the abnormal parameter values based on the operation time, load level, and threshold deviation amplitude information of the device corresponding to the abnormal parameter values according to the abnormal parameter record information, and combines the device operation records to determine whether it belongs to the device operation fault, marks the fault device number and fault type, and obtains the device fault characteristic information; The abnormal type analysis sub-module analyzes the operation abnormal device fault type and range based on the device fault characteristic information according to the operation record and time parameter value of the marked device, analyzes the type of device operation abnormality, and evaluates the severity of the device operation abnormality to obtain the device abnormality information.

6. The energy management system based on an embedded platform according to claim 5, wherein The formula for evaluating the severity of the device operation abnormality is: Among them, S represents the severity score of the abnormal operation of the device, n represents the total number of key parameters, w i represents the weight coefficient of the i-th key parameter, P i represents the actual operating value of the i-th key parameter, T i represents the standard threshold of the i-th key parameter, R i represents the device operation time corresponding to the i-th key parameter, k is the adjustment coefficient, Δ i represents the load level deviation corresponding to the i-th key parameter.

7. The energy management system based on an embedded platform according to claim 1, wherein The system configuration management module includes: The parameter comparison and update sub-module extracts the configuration information of the current devices at the site based on the device exception information, including device numbers, operating mode parameter values, and site electricity price parameters. It compares the operating parameters of the abnormal devices item by item with the site device configuration information, filters out the abnormal devices with different operating parameters, and marks the different contents to obtain the abnormal parameter comparison result; The operating mode correction sub-module extracts the operating mode parameter values and different contents of the marked devices based on the abnormal parameter comparison result, corrects the different operating mode parameter values, updates the corrected operating mode parameters by resetting the device operating parameter range, and generates a set of corrected operating parameters; The device configuration writing sub-module extracts the associated parameters between the corrected device parameters and the site configuration table based on the set of corrected operating parameters, writes the corrected device parameters into the site parameter configuration table, and reconfigures the device operating mode to obtain the device configuration update information.

8. The energy management system based on an embedded platform according to claim 1, characterized in that, The site operation analysis module includes: The trend comparison and analysis sub-module extracts the total power input power, power output power, and device operating parameters of the site based on the energy scheduling optimization information and the device configuration update information, classifies and organizes the device operating parameters according to the input power and output power, compares them item by item with the normal operation record data, and analyzes the change trend of the operating parameters to obtain the change characteristics of the operating trend; The power deviation calculation sub-module calls the overall power input value and power output value of the site based on the change characteristics of the operating trend, calculates the deviation value between the input power and the output power, and extracts the deviation amplitude and the associated device number to obtain a set of power deviation characteristics; The utilization rate calculation sub-module extracts the actual power output value and operating time of the device based on the set of power deviation characteristics, analyzes the power utilization of the device operation using the power output and operating time of the device, and obtains the site energy operation analysis result.

9. The energy management system based on an embedded platform according to claim 8, characterized in that The formula for analyzing the change trend of the operating parameters is: where I represents the change trend index of the operating parameters, w j represents the weight coefficient of the j-th operating parameter, U j,t represents the actual measured value of the j-th operating parameter at time t, U j,t-1 represents the actual measured value of the j-th operating parameter at time t-1, Z j,t represents the deviation value of the j-th operating parameter at time t, c is the adjustment coefficient, T is the time span, and m is the total number of operating parameters.

10. An energy management method based on an embedded platform, characterized in that, Execute according to the energy management system based on the embedded platform described in any one of claims 1-9, including the following steps: S1: Based on the embedded platform, compare the operating mode of the energy storage device with the power demand, determine whether the power demand matches the device status, switch the on / off status of the device, adjust the operating mode, set the charge / discharge power, and obtain the energy storage mode adjustment information; S2: Based on the energy storage mode adjustment information, extract the real-time power demand of the site, the current power load of the energy storage device, and the remaining battery power, analyze the matching of the power adjustment range with the grid power load, compare and reconfigure the energy storage device load to obtain the energy scheduling optimization information; S3: Based on the energy scheduling optimization information, compare the key operating parameter values with the standard thresholds, filter out the abnormal parameters, record the operating status of the associated devices, mark the abnormal devices, cross-check the operating records and time parameters, and identify the type and degree of the abnormality to obtain the device exception information; S4: Based on the device exception information, analyze the operating parameters of the abnormal devices, correct the operating mode parameters, reconfigure the site parameters, and write the corrected device parameters to obtain the device configuration update information. S5: Based on the energy scheduling optimization information and the device configuration update information, extract the total power input and output of the site and the device operation parameters, analyze the deviation of the total power input and output, calculate the device power utilization rate, and obtain the site energy operation analysis result.

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