A 4diac-based wind-solar-storage multi-energy complementary layered autonomous coordination control and ai prediction service integration method and system

Abstract

The application discloses a kind of based on 4diac's wind and light storage multi-energy complementary layered autonomous coordination control and AI prediction service integration method and system, the method includes: obtaining multi-source operating condition data, the multi-source operating condition data is preprocessed, and unified state vector is obtained, the unified state vector is input into prediction model, and the prediction sequence output by the prediction model is obtained;According to the unified state vector, prediction sequence determines current operating mode, optimization is solved based on operating mode, and strategy vector is obtained;According to the strategy vector, power distribution is executed, and equipment control instruction is generated, and the application is embedded 4diac / IEC61499 function block network by embedding multi-time scale scheduling thought, so that prediction, pattern recognition, strategy generation and device level execution are connected by standardized event and data interface, so that it is more suitable for distributed deployment, online update and equipment expansion in engineering field.

Description

Technical Field

[0001] This invention relates to a method and system for integrating hierarchical autonomous coordinated control and AI prediction services based on 4diac-based wind-solar-storage multi-energy complementarity, belonging to the field of new energy coordinated control technology. Background Technology

[0002] As the installed capacity of new energy sources such as wind power and photovoltaics continues to increase, the power output on the power supply side is affected by meteorological conditions, exhibiting randomness, intermittency, and fluctuation. The load side is also constantly changing due to production plans, electricity prices, and user behavior. Traditional PLCs, DCSs, or centralized EMSs typically tightly couple data acquisition, strategy calculation, and command issuance within the same control logic. This results in problems such as difficulty in adapting new equipment, the inability of predictive services to directly participate in real-time control, reliance on manual mode switching, high costs for expanding control links, and insufficient real-time performance for batch adjustments of multiple devices.

[0003] It is evident that, in order to solve the above-mentioned technical problems, there is an urgent need for a method and system for integrating multi-energy complementary hierarchical autonomous coordinated control and AI prediction services based on 4diac. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method and system for integrating multi-energy complementary hierarchical autonomous coordinated control and AI prediction services based on 4diac. It embeds the concept of multi-timescale scheduling into the 4diac / IEC61499 functional block network, enabling prediction, pattern recognition, strategy generation and device-level execution to be connected through standardized event and data interfaces, thus making it more suitable for distributed deployment, online updates and device expansion in engineering sites.

[0005] To achieve the above objectives, the present invention is implemented using the following technical solution:

[0006] In a first aspect, the present invention provides a method for integrating hierarchical autonomous coordinated control and AI prediction services for wind-solar-storage multi-energy complementary systems based on 4diac, comprising:

[0007] Acquire multi-source operating condition data, preprocess the multi-source operating condition data to obtain a unified state vector, input the unified state vector into the prediction model, and obtain the prediction sequence output by the prediction model.

[0008] The current operating mode is determined based on the unified state vector and the prediction sequence. The strategy vector is obtained by optimizing the solution based on the operating mode.

[0009] Power allocation is performed based on the strategy vector, and device control commands are generated.

[0010] Furthermore, the multi-source operating condition data includes wind power, photovoltaic, energy storage, load, grid connection point, electricity price, and environmental parameters;

[0011] The preprocessing includes dimensional unification, range verification, time scale alignment, duplicate value filtering, missing value imputation, abnormal peak removal, and dead zone processing.

[0012] The prediction sequence includes photovoltaic power prediction, wind power prediction, load prediction, and electricity price prediction.

[0013] Furthermore, after obtaining the prediction sequence output by the prediction model, the method further includes:

[0014] The predicted sequence is quality evaluated to obtain a quality score;

[0015] The quality score is compared with a preset threshold. If the quality score is greater than the preset threshold, the current prediction sequence is determined to be valid.

[0016] If the quality score is lower than the preset threshold, the current prediction sequence is deemed invalid, and a historically valid prediction sequence is selected.

[0017] If the historical effective prediction sequence is missing, a moving average or linear extrapolation algorithm is used to generate a prediction value based on multi-source operating condition data.

[0018] Furthermore, based on the unified state vector and the prediction sequence, the current operating mode is determined, and optimization is performed based on the operating mode to obtain the policy vector, including:

[0019] The fault level is determined by weighted comprehensive evaluation based on the multi-source operating condition data, and the current operating mode is determined based on the fault level and the prediction sequence.

[0020] Based on the current operating mode, the pre-constructed multi-objective function is solved to obtain the optimization result;

[0021] The optimization results are then converted into policy vectors.

[0022] If the solution to the multi-objective function fails or times out, a safety net strategy is generated based on a pre-built rule base, and the safety net strategy is used as a strategy vector.

[0023] Furthermore, the multi-objective function includes:

[0024] J= | - | + ·|Δ | + Cost + · + · + · ;

[0025] In the formula, J is the objective function. To track weights, To smooth the weights, Δ The SOC regression weights for energy storage are used, and Cost is the operating cost. For the target state of charge, As the weight of abandoned electricity, For abandoned electricity , For load shedding weight, This refers to the load shedding power.

[0026] Furthermore, power allocation is performed according to the strategy vector, and device control commands are generated, including:

[0027] The adjustable boundaries for each device are determined based on the strategy vector.

[0028] The adjustable margin is determined based on the adjustable upper and lower boundaries and the pre-configured Count parameter;

[0029] Based on the adjustable margin, power allocation is performed in conjunction with the maximum power array, minimum power array, ramp rate array, energy storage state of charge array, and priority array to generate equipment control commands.

[0030] Furthermore, after generating the device control commands, it also includes:

[0031] Obtain the actual operating parameters of the equipment to determine the execution error;

[0032] If the execution error exceeds the preset threshold or the device remains without feedback for more than the preset time, the strategy vector generation step will be re-executed.

[0033] Secondly, this invention provides an integrated system for hierarchical autonomous coordinated control and AI predictive services based on 4diac-based multi-energy complementary wind-solar-storage system, comprising:

[0034] Perception and prediction module: used to acquire multi-source operating condition data, preprocess the multi-source operating condition data to obtain a unified state vector, input the unified state vector into the prediction model, and obtain the prediction sequence output by the prediction model.

[0035] Coordination and decision-making module: used to determine the current operating mode based on the unified state vector and prediction sequence, and to perform optimization and solution based on the operating mode to obtain the strategy vector;

[0036] Execution control module: used to perform power allocation according to the strategy vector and generate device control commands.

[0037] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the methods described above.

[0038] Fourthly, the present invention provides a computer device, comprising:

[0039] Memory, used to store computer programs / instructions;

[0040] A processor for executing the computer program / instructions to implement the steps of any of the methods described above.

[0041] Fifthly, the present invention provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the steps of any of the methods described above.

[0042] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0043] This invention provides a method and system for integrating multi-energy complementary hierarchical autonomous coordinated control and AI prediction services based on 4diac. It embeds the above-mentioned multi-timescale scheduling concept into the 4diac / IEC61499 functional block network, enabling prediction, pattern recognition, strategy generation and device-level execution to be connected through standardized event and data interfaces, thus making it more suitable for distributed deployment, online updates and device expansion in engineering sites. Attached Figure Description

[0044] Figure 1 This is a flowchart of a method for integrating hierarchical autonomous coordinated control and AI prediction services based on 4diac-based wind-solar-storage multi-energy complementary systems, provided by an embodiment of the present invention.

[0045] Figure 2 This is a schematic diagram of the event and data pins of each functional block in a 4diac-based multi-energy complementary hierarchical autonomous coordinated control and AI prediction service integration method provided in this embodiment of the invention.

[0046] Figure 3 This is a schematic diagram of the structure of each functional block in a method for integrating wind, solar and energy storage multi-energy complementary hierarchical autonomous coordinated control and AI prediction services based on 4diac provided in an embodiment of the present invention. Detailed Implementation

[0047] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments and specific features in the embodiments are detailed descriptions of the technical solution of the present application, rather than limitations thereof. In the absence of conflict, the embodiments and technical features in the embodiments can be combined with each other.

[0048] In this article, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0049] Example 1:

[0050] Figure 1 This is a flowchart of a method for integrating hierarchical autonomous coordinated control and AI predictive services based on 4diac-based multi-energy complementary wind-solar-storage multi-energy system, as described in Embodiment 1 of the present invention. The method provided in this embodiment can be applied to a terminal and can be executed by a 4diac-based hierarchical autonomous coordinated control and AI predictive service integration system. This system can be implemented in software and / or hardware and can be integrated into the terminal, such as any smartphone, tablet, or computer device with communication capabilities. See also... Figure 1 The method implemented in this way specifically includes the following steps:

[0051] Acquire multi-source operating condition data, preprocess the multi-source operating condition data to obtain a unified state vector, input the unified state vector into the prediction model, and obtain the prediction sequence output by the prediction model.

[0052] The current operating mode is determined based on the unified state vector and the prediction sequence. The strategy vector is obtained by optimizing the solution based on the operating mode.

[0053] Power allocation is performed based on the strategy vector, and device control commands are generated.

[0054] The multi-source operating condition data includes wind power, photovoltaic, energy storage, load, grid connection point, electricity price, and environmental parameters;

[0055] The preprocessing includes dimensional unification, range verification, time scale alignment, duplicate value filtering, missing value imputation, abnormal peak removal, and dead zone processing.

[0056] The prediction sequence includes photovoltaic power prediction, wind power prediction, load prediction, and electricity price prediction.

[0057] After obtaining the predicted sequence output by the prediction model, the method further includes:

[0058] The predicted sequence is quality evaluated to obtain a quality score;

[0059] The quality score is compared with a preset threshold. If the quality score is greater than the preset threshold, the current prediction sequence is determined to be valid.

[0060] If the quality score is lower than the preset threshold, the current prediction sequence is deemed invalid, and a historically valid prediction sequence is selected.

[0061] If the historical effective prediction sequence is missing, a moving average or linear extrapolation algorithm is used to generate a prediction value based on multi-source operating condition data.

[0062] Based on the unified state vector and the prediction sequence, the current operating mode is determined. Optimization is then performed based on this operating mode to obtain the policy vector, which includes:

[0063] The fault level is determined by weighted comprehensive evaluation based on the multi-source operating condition data, and the current operating mode is determined based on the fault level and the prediction sequence.

[0064] Based on the current operating mode, the pre-constructed multi-objective function is solved to obtain the optimization result;

[0065] The optimization results are then converted into policy vectors.

[0066] If the solution to the multi-objective function fails or times out, a safety net strategy is generated based on a pre-built rule base, and the safety net strategy is used as a strategy vector.

[0067] The multi-objective function includes:

[0068] J= | - | + ·|Δ | + Cost + · + · + · ;

[0069] In the formula, J is the objective function. To track weights, To smooth the weights, Δ The SOC regression weights for energy storage are used, and Cost is the operating cost. For the target state of charge, As the weight of abandoned electricity, For abandoned electricity , For load shedding weight, This refers to the load shedding power.

[0070] Power allocation is performed according to the strategy vector, and device control commands are generated, including:

[0071] The adjustable boundaries for each device are determined based on the strategy vector.

[0072] The adjustable margin is determined based on the adjustable upper and lower boundaries and the pre-configured Count parameter;

[0073] Based on the adjustable margin, power allocation is performed in conjunction with the maximum power array, minimum power array, ramp rate array, energy storage state of charge array, and priority array to generate equipment control commands.

[0074] After generating the device control commands, the following is also included:

[0075] Obtain the actual operating parameters of the equipment to determine the execution error;

[0076] If the execution error exceeds the preset threshold or the device remains without feedback for more than the preset time, the strategy vector generation step will be re-executed.

[0077] The method for integrating hierarchical autonomous coordinated control and AI prediction services based on 4diac-based wind-solar-storage multi-energy complementary hierarchical autonomous coordinated control with AI prediction services in this embodiment involves the following steps in its application process:

[0078] exist Figure 1 In the diagram, solid arrows represent data flows, while dashed arrows represent event flows or feedback trigger flows. The perception and prediction layer completes data acquisition and prediction after the request event (REQ) is triggered, and then triggers the coordination and decision-making layer with the confirmation event (CNF). The coordination and decision-making layer outputs a policy vector and triggers the execution and control layer. After issuing instructions, the execution and control layer collects on-site feedback, generating either a feedback indication event (FEEDBACK_IND) or a rollback request event (ROLL_REQ), driving the next cycle of rolling correction.

[0079] The perception and prediction layer is used to convert real-time field quantities, environmental quantities, electricity prices, and equipment status into a unified state vector that can be directly used in control, and calls on AI models to generate prediction results for future control cycles. This layer includes the data function module Data_FB, the data cleaning module Clean_FB, the prediction function module Forecast_FB, and the anomaly diagnosis module Diagnose_FB.

[0080] The parameters involved in the perception prediction layer are shown in Table 1 below:

[0081] Table 1

[0082] Data input PV_P Real-time photovoltaic power, WT_P Real-time wind power, ESS_P Real-time energy storage power, ESS_SOC Energy storage state of charge, ESS_SOH Health status, LOAD_P Load power, GRID_P Grid connection point power, GRID_TARGET Grid connection plan / schedule target, PRICE Electricity price, ENV_IRR Irradiance, ENV_TEMP Temperature, WIND_SPEED Wind speed, Grid_Status On / off grid status, Quality_Code Raw quality code, Time_Stamp Time stamp. Event Input INIT initialization; REQ periodic acquisition trigger; AI_REQ prediction call trigger; RESET clears exceptions. Data output State_Raw: Original state vector; State_Vector: Cleaned unified state vector; PV_Forecast: Photovoltaic forecast sequence; WT_Forecast: Wind power forecast sequence; LOAD_Forecast: Load forecast sequence; PRICE_Forecast: Electricity price forecast sequence; ESS_Avail: Energy storage availability status; Diagnose_Result: Diagnostic results; Fault_Level: Fault level; Data_Quality: Data quality score. Event Output INITO initialization complete; CNF data collection / prediction complete; AI_CNF prediction service returns; WARN data quality alert; ERR data collection or prediction failed. Key parameters Sample_DT sampling period 100ms-1s; Forecast_Horizon prediction steps 4-96; Forecast_DT prediction step size 1s, 5s, 15min configurable; AI_Timeout prediction service timeout 100-1000ms; Deadband power dead zone 0.1%-1% rated; Max_Invalid_Count consecutive invalid point threshold 3-5; Fallback_Mode degradation mode.

[0083] exist Figure 2 The Chinese meanings of the contents involved in the Data_FB function module are shown in Table 1-1 below:

[0084] Table 1-1

[0085] Event Input INIT Initialize communication parameters, measurement point table, and buffer. Event Input REQ Periodic acquisition trigger Event Output INITO Initialization complete Event Output CNF Data collection completed Event Output ERR Data acquisition failed or communication error Data input PV_P Real-time power of photovoltaics Data input WT_P Real-time wind power Data input ESS_SOC Energy storage state of charge Data input LOAD_P Real-time load power Data input GRID_P Real-time power at grid connection point Data input PRICE Current electricity price Data input ENV An array of environmental parameters, including irradiance, temperature, and wind speed. Data output State_Raw Original state vector Data output Quality Code Measurement point quality code array Data output Time_Stamp Collection time scale

[0086] exist Figure 2 The Chinese meanings of the contents involved in the data cleaning module Clean_FB are shown in Table 1-2 below:

[0087] Table 1-2

[0088] Event Input REQ Data cleaning trigger Event Output CNF Cleaning complete Event Output WARN The quality score is low, but it can continue to operate. Event Output ERR Critical data unavailable Data input State_Raw Original state vector Data input Quality Code Original quality code Data input Deadband Power dead zone threshold Data input Range_Limit Upper and lower limits of measuring points Data output State_Vector Unified state vector after cleaning Data output Data_Quality Data quality score 0-1 Data output Clean_Flag Cleaning success sign

[0089] exist Figure 2 The Chinese meanings of the contents involved in the Forecast_FB prediction function module are shown in Table 1-3 below:

[0090] Table 1-3

[0091] Event Input INIT Initialize AI service address and model parameters Event Input REQ Predictive call triggering Event Output CNF Prediction complete Event Output ERR Prediction failed and downgraded Data input State_Vector Cleaning state vector Data input AI_URL AI service address Data input Forecast_Horizon Predicted steps Data input Forecast_DT Predicted step size Data input AI_Timeout Predicted timeout Data output PV_Forecast Photovoltaic predicted power series Data output WT_Forecast Wind power forecast power series Data output LOAD_Forecast Load forecast sequence Data output PRICE_Forecast Electricity price forecast series Data output Forecast_Ready Validity indicator of prediction results

[0092] exist Figure 2 In the middle, the anomaly diagnosis module Diagnose_FB is in Figure 2 The Chinese meanings of the corresponding content are shown in Table 1-4 below:

[0093] Table 1-4

[0094] Event Input REQ Diagnostic trigger Event Output CNF Diagnosis completed Event Output WARN General alarm Event Output ERR Serious Fault Data input State_Vector Unified state vector Data input Feedback_Vector On-site feedback vector Data input Alarm_Raw Original alarm codes of the equipment Data output Diagnose_Result Diagnostic result coding Data output Fault_Level Fault levels: 0 Normal, 1 Alarm, 2 Degradation, 3 Outage / Orphan

[0095] As shown in Tables 1, 1-1, 1-2, 1-3, 1-4 and Figure 2 As shown, the internal algorithm steps involved in the perception prediction layer are as follows:

[0096] 1) Upon receiving the REQ event, the Data Function Module (Data_FB) reads the wind power, photovoltaic, energy storage, load, grid connection point, electricity price, and environmental parameters (ENV) according to the unified measurement point table. This includes real-time photovoltaic power (PV_P), real-time wind power (WT_P), energy storage state of charge (ESS_SOC), load power (LOAD_P), grid connection point power (GRID_P), electricity price (PRICE), ambient irradiance (ENV_IRR), and ambient temperature (ENV_TEMP). It also adds a time stamp (Time_Stamp) and a raw quality code (Quality_Code) to each measurement point and outputs the raw state vector (State_Raw) and the corresponding time stamp (Time_Stamp) and raw quality code (Quality_Code) to the Clean Module (Clean_FB).

[0097] 2) The data cleaning module Clean_FB performs unit unification, range verification, time stamp alignment, duplicate value filtering, missing value imputation, abnormal peak removal, and dead zone processing based on the state vector State_Raw, the corresponding time stamp Time_Stamp, and the original quality code Quality_Code, forming a unified state vector State_Vector, a data quality score Data_Quality, and a data cleaning flag Clean_Flag after cleaning.

[0098] 3) The Forecast_FB module encapsulates the cleaned unified state vector (State_Vector), historical window data, and prediction parameters into an AI service request. It obtains the prediction sequence through the HTTP / gRPC / local model interface AI_URL, including: photovoltaic prediction sequence PV_Forecast, wind power prediction sequence WT_Forecast, load prediction sequence LOAD_Forecast, and electricity price prediction sequence PRICE_Forecast, and generates the prediction ready signal Forecast_ready.

[0099] 4) When the AI ​​request timeout (AI_Timeout) exceeds the preset threshold and is determined to be an AI service timeout, or when the prediction steps (Forecast_Horizon) and prediction step size (Forecast_DT) return an anomaly, or when the data quality score (Data_Quality) is lower than the threshold, the prediction function module (Forecast_FB) will initiate a degradation strategy: it will prioritize using the previous valid prediction sequence, and then use a moving average or linear extrapolation to ensure that the control link remains unbroken.

[0100] 5) The Diagnose_FB module generates a fault level (Fault_Level) based on limit exceedance, mutation rate, communication status, SOC / SOH, and device alarms. If the fault level (Fault_Level) reaches the preset level, it outputs a WARN or ERR event and triggers the coordination decision layer to enter the safety priority or isolated network supply guarantee strategy.

[0101] The coordination and decision-making layer is used to generate autonomous coordinated control strategies based on current operating conditions, forecast results, and control objectives. This layer includes a pattern recognition module (Mode_FB), an objective optimization module (Optimize_FB), and a strategy generation module (Strategy_FB).

[0102] The content involved in coordinating the decision-making level is shown in Table 2 below:

[0103] Table 2

[0104] Data input After cleaning, the unified state vector is defined as: State_Vector, PV_Forecast, WT_Forecast, LOAD_Forecast, PRICE_Forecast, GRID_TARGET, ESS_Avail, Fault_Level, Mode_Set (manual mode setting), Weight_Set (weight setting), SOC_Min (minimum state of charge) / SOC_Max (maximum state of charge), and PCC_Limit (grid connection point power limit). Event Input REQ policy calculation trigger; MODE_CHG mode switching trigger; ROLL_REQ rolling correction trigger; FAULT_IND fault trigger. Data output Mode_ID: Current mode; Opt_Target: Optimization target; P_Grid_Target: Grid connection point power target; P_ESS_Req: Total energy storage charging and discharging demand; P_PV_Limit: Photovoltaic power curtailment suggestion; P_WT_Limit: Wind power curtailment suggestion; Reserve_Up: Increase reserve; Reserve_Down: Decrease reserve; Strategy_Vector: Strategy vector. Event Output CNF policy generation complete; MODE_CNF mode switching complete; SAFE_CNF security policy generation complete; ERR optimization failed. Key parameters W_track tracking weight; W_smooth smoothing weight; W_cost economic weight; W_soc energy storage SOC regression weight; SOC_Ref target SOC is generally taken as 50%-60%; SOC_Low / SOC_High safety threshold; Ramp_Limit ramp limit; Opt_Cycle rolling optimization cycle 1s-15min; Mode_Priority mode priority.

[0105] exist Figure 2In the middle, the pattern recognition module Mode_FB is in Figure 2 The Chinese meanings of the corresponding content are shown in Table 2-1 below:

[0106] Table 2-1

[0107] Event Input REQ Pattern recognition trigger Event Input MODE_CHG External mode switching trigger Event Output CNF Pattern recognition complete Event Output SAFE_CNF Safe Mode Confirmation Data input State_Vector Unified state vector Data input Forecast_Vector Predicted composite vector Data input Grid_Status Grid-connected / Off-grid status Data input Mode_Set Manual setting mode Data input Fault_Level Fault Level Data output Mode_ID Current running mode encoding Data output Mode_Reason Reason for mode switching

[0108] exist Figure 2 In the middle, the target optimization module Optimize_FB is in Figure 2 The Chinese meanings of the corresponding content are shown in Table 2-2 below:

[0109] Table 2-2

[0110] Event Input REQ Optimization solution trigger Event Output CNF Optimization complete Event Output ERR Optimization failed or timed out Data input Mode_ID Current operating mode Data input State_Vector Current status Data input Forecast_Vector Predicted sequence Data input GRID_TARGET Grid-connected power target Data input Weight_Set Target weight array Data output P_Grid_Target Revised grid connection point target Data output P_ESS_Req Total energy storage power demand: positive value for discharging, negative value for charging Data output P_PV_Limit Recommendations for limiting solar power generation Data output P_WT_Limit Wind power generation restriction recommendations Data output Reserve_Up The margin can be increased. Data output Reserve_Down The margin can be reduced.

[0111] exist Figure 2 In the middle, the strategy generation module Strategy_FB is Figure 2 The Chinese meanings of the corresponding content are shown in Table 2-3 below:

[0112] Table 2-3

[0113] Event Input REQ Strategy generation trigger Event Output CNF Strategy generation complete Event Output ERR The strategy is not executable. Data input Mode_ID Current operating mode Data input P_Grid_Target Grid connection target Data input P_ESS_Req Energy storage demand Data input Reserve_Up Increase margin Data input Reserve_Down Reduce margin Data output Strategy_Vector Policy Vector Data output Strategy_Valid Strategy validity indicator

[0114] As shown in Tables 2, 2-1, 2-2, and 2-3, Figure 2 As shown, the internal algorithm steps of the coordination decision-making layer are as follows:

[0115] 1) The Mode recognition module Mode_FB first identifies the operating mode based on the grid status (Grid_Status), fault level (Fault_Level), mode setting (Mode_Set), price forecast (PRICE_Forecast) in the forecast sequence (Forecast_Vector), energy storage state of charge (SOC_State) in the cleaned unified state vector (State_Vector), and grid connection point deviation. If the grid fault or disconnection signal is valid, the islanded operation mode has the highest priority; if the scheduling target deviation exceeds the threshold, it enters the power tracking mode; if the short-term volatility exceeds the threshold, it enters the power smoothing mode; if the peak-valley price difference exceeds the threshold, it enters the economic operation mode. The specific modes are shown in Table 2-4 below:

[0116] Table 2-4

[0117] Power Point Tracking Mode The scheduling target GRID_TARGET is valid and the difference between the active power GRID_P at the grid connection point and the scheduling target exceeds the dead zone. Prioritize minimizing grid connection tracking deviations, with energy storage handling rapid shortfalls, and wind and solar power curtailment serving as a secondary measure. Grid connection point power target P_Grid_Target, total energy storage charge and discharge demand P_ESS_Req, solar power curtailment recommendation P_PV_Limit, wind power curtailment recommendation P_WT_Limit Peak shaving and valley filling mode Load or grid-connected power exceeds the peak threshold, or a trough is predicted to be approaching. Discharge during peak periods, charge during off-peak periods, and keep the SOC within a safe range. ESS discharge / charge plan, peak-valley strategy Economic operation mode Peak-valley electricity price difference or real-time electricity price exceeding a threshold Minimize electricity purchase costs and energy storage loss costs, and combine charging and discharging with SOC constraints. High-priced discharge, low-priced charging Power smoothing mode Wind and solar forecast volatility or real-time ΔP exceeding the threshold Minimize grid-connected power variation rate; energy storage quickly compensates for short-term fluctuations in wind and solar power. ESS Quick Compensation Command Self-use mode Priority is given to absorbing the load in the industrial park, and local absorption targets are effective. Minimize external power purchases and reverse power transmission, and prioritize anti-reverse current constraints. Energy storage absorbs surplus renewable energy or supplements load. Isolated network operation mode The grid status Grid_Status indicates either off-grid or grid fault. Maintain power balance and ensure supply to primary loads, and disconnect low-priority loads according to load priority. Energy storage system active power command ESS_Cmd, load shedding command Load_Shed, solar power curtailment suggestion P_PV_Limit, wind power curtailment suggestion P_WT_Limit

[0118] 2) The Optimize_FB module constructs a multi-objective function to solve for the energy storage power demand and wind and solar curtailment under equipment constraints, grid connection point constraints, SOC constraints, and ramp-up constraints. This yields the grid connection point power target P_Grid_Target; the total energy storage charge / discharge demand P_ESS_Req; the solar curtailment recommendation P_PV_Limit; the wind curtailment recommendation P_WT_Limit; and adjustments to the reserve allocation (Reserve_Up and Reserve_Down). The multi-objective function is as follows:

[0119] J= | - | + ·|Δ | + Cost + · + · + · ;

[0120] In the formula, J is the objective function. To track weights, To smooth the weights, Δ The SOC regression weights for energy storage are used, and Cost is the operating cost. For the target state of charge, As the weight of abandoned electricity, For abandoned electricity , For load shedding weight, This refers to the load shedding power.

[0121] 3) When changes in wind and solar power output and load within the prediction window lead to an increase in the grid connection point deviation, the target optimization module Optimize_FB adopts a rolling correction mechanism, which only executes the first step result of the current control cycle and recalculates in the next cycle in combination with the feedback vector Feedback_Vector.

[0122] 4) The strategy generation module Strategy_FB converts the photovoltaic curtailment suggestion P_PV_Limit, wind power curtailment suggestion P_WT_Limit, reserve increase Reserve_Up and reserve decrease Reserve_Down in the optimization results into a strategy vector that can be recognized by the execution layer, and generates a strategy validity flag Strategy_Valid, which includes equipment category, target power, priority, reserve margin, curtailment amount, load shedding level and validity period.

[0123] 5) If the target optimization module Optimize_FB fails to solve or times out, the strategy generation module Strategy_FB uses the rule base to generate a backup strategy: energy storage is limited according to the safe SOC boundary, new energy generation is limited to protect grid connection points from exceeding the limit, and isolated grid scenarios prioritize the protection of primary loads.

[0124] The execution control layer is used to convert coordination strategies into device-level instructions that can be issued to PCS, inverters, wind turbine controllers, and load controllers, forming a feedback loop. This layer includes the constraint processing module Limit_FB, the power allocation module Dispatch_FB, the instruction output module Cmd_FB, and the feedback module Feedback_FB.

[0125] The contents involved in the execution control layer are shown in Table 3 below:

[0126] Table 3

[0127] Data input The system contains the following arrays: Strategy Vector (for issuing commands), Device Count (for total number of devices), Device ID Array (for device identifiers), Device Type Array (for device types), P_Max Array (for maximum active power limits), P_Min Array (for minimum active power limits), Ramp Array (for power ramp rate limits), SOC Array (for energy storage state of charge), SOH Array (for energy storage state of health), Enable Array (for device switching enable flags), Priority Array (for device control priority), Protocol Set (for device communication protocol family configuration), and Feedback Array (for raw feedback data). Event Input REQ execution trigger; LIMIT_REQ limit check trigger; SEND_REQ command issuance trigger; FB_REQ feedback acquisition trigger; RESET reset. Data output Limit_Vector: Device adjustable boundary; P_Cmd_Array: Device power command array; StartStop_Array: Start / stop commands; Cmd_Status: Command issuance status; Feedback_Vector: Feedback vector; Track_Error: Tracking error; Alarm_Code: Alarm code. Event Output CNF execution completed; LIMIT_CNF limit completed; SEND_CNF delivery completed; FEEDBACK_IND feedback valid; ERR delivery failed or feedback error. Key parameters Count: Number of participating devices, recommended upper limit 500; Dispatch_Mode: Dispatch mode; Cmd_Timeout: Command timeout; Ramp_DT: Ramp calculation cycle; P_Deadband: Power dead zone; Retry_Count: Number of retransmissions; Protocol_ID: Protocol type; Anti_Backflow_Enable: Anti-backflow enable.

[0128] exist Figure 2 In the constraint processing module Limit_FB, Figure 2 The Chinese meanings of the corresponding content are shown in Table 3-1 below:

[0129] Table 3-1

[0130] Event Input REQ Constraint check trigger Event Output CNF Constraint verification completed Event Output WARN Insufficient margin warning Data input Strategy_Vector Policy Vector Data input Count Number of devices involved in control Data input P_Max_Array Device maximum power array Data input P_Min_Array Minimum power array of devices Data input Ramp_Array Equipment ramp rate array Data input SOC_Array Energy Storage SOC Array Data output Limit_Vector The device can execute upper and lower limits Data output Available_Margin Total adjustable margin

[0131] exist Figure 2 In the middle, the power distribution module Dispatch_FB is in Figure 2 The Chinese meanings of the corresponding content are shown in Table 3-2 below:

[0132] Table 3-2

[0133] Event Input REQ Power distribution trigger Event Output CNF Allocation complete Event Output ERR No available device or allocation failed Data input Count Number of participating devices Data input P_Req Total power to be allocated Data input Limit_Vector Device Execution Boundaries Data input Priority_Array Device priority array Data input Dispatch_Mode Allocation mode Data output P_Cmd_Array Device-level power instruction array Data output Dispatch_Status Assign status code

[0134] exist Figure 2 In the command output module Cmd_FB, Figure 2 The Chinese meanings of the corresponding content are shown in Table 3-3 below:

[0135] Table 3-3

[0136] Event Input REQ Command issued and triggered Event Output CNF Distribution completed Event Output ERR Distribution failed Data input P_Cmd_Array Device-level power command Data input Device_ID_Array Device number array Data input Protocol_Set Protocol type array Data input Cmd_Timeout Instruction timeout Data output Cmd_Status Status updates for each device Data output Protocol_Frame Protocol message / message digest

[0137] exist Figure 2 In the middle, the feedback module Feedback_FB is in Figure 2 The Chinese meanings of the corresponding content are shown in Table 3-4 below:

[0138] Table 3-4

[0139] Event Input REQ Feedback collection trigger Event Output CNF Feedback completed Event Output FEEDBACK_IND Feedback triggers scroll correction Event Output ERR Feedback error Data input P_Cmd_Array Previous cycle instruction Data input Feedback_Raw On-site feedback of raw values Data input Track_Threshold Tracking error threshold Data output Feedback_Vector Feedback vector Data output Track_Error Grid connection point / equipment power tracking error Data output Alarm_Code Feedback Alarm Code

[0140] As shown in Tables 3, 3-1, 3-2, 3-3, 3-4 and Figure 2 As shown, the steps of the algorithm within the control layer are as follows:

[0141] 1) The Limit_FB constraint verification module calculates the adjustable limits for each device based on the device's rated power, real-time availability, SOC / SOH, communication status, start / stop status, ramp rate, and grid connection point limit.

[0142] 2) The power distribution module Dispatch_FB iterates through the device array according to the number of participating devices Count parameter, filters out unavailable devices, and calculates the total adjustable margin; when the total adjustable margin is insufficient, it outputs the limited executable power and triggers WARN.

[0143] 3) Power allocation can employ algorithms such as proportional allocation, equal margin allocation, priority allocation, SOC regression, or economic priority allocation. In proportional allocation, a single device command... = · / When assigning SOC during regression, the deviation from SOC is considered. Larger energy storage capacity results in a higher charge / discharge weight.

[0144] 4) The instruction output module Cmd_FB converts the device-level power instruction P_Cmd_Array into field protocol messages, supporting Modbus TCP, IEC 60870-5-104, MQTT and OPC UA, and records the message sequence number, timestamp, quality code and acknowledgment status.

[0145] 5) The Feedback_FB module reads the actual power of the device. SOC, execution status, and alarm codes, based on the actual power of the device. With device power command The difference is used to calculate the device power tracking error, Track_Error. When the error exceeds the threshold or the number of consecutive retry attempts exceeds Retry_Count, an ERR (Error Feedback Exception) or a FEEDBACK_IND (Feedback Trigger Rolling Correction) event is output, triggering a recalculation at the coordination decision layer.

[0146] Existing PLCs / DCS / EMS systems often embed data acquisition, decision-making, and execution into a centralized program. This invention separates sensing and prediction, coordination and decision-making, and execution control into standard IEC61499 functional block layers, with coupling between layers only through event and data interfaces. When adding new equipment, it mainly involves adding or replacing device-level functional blocks and array measurement points, without needing to rewrite the entire control program. In prototype engineering, adding similar energy storage devices can be reduced from modifying global logic to configuring Count and device arrays.

[0147] Existing systems often rely on manual switching or single-target control. This invention automatically identifies power tracking, smoothing, economy, peak shaving and valley filling, self-consumption, and islanded grid modes based on Grid_Status, Fault_Level, price, SOC, and power deviation. It can automatically switch strategies in scenarios such as cloud cover, peak-valley pricing, and islanded grid failures, reducing manual intervention. Mode switching is triggered by the MODE_CHG event, avoiding delays caused by polling.

[0148] Existing control systems typically allocate power according to fixed rules. This invention incorporates tracking deviation, smoothing, electricity price, SOC regression, power limiting, and load shedding into a unified objective function, and only executes the first step of the rolling window. In the 10MW photovoltaic, 15MW wind power, and 5MW / 10MWh energy storage embodiment, when the photovoltaic output drops from 8MW to 5MW within 2 seconds, the energy storage output provides 2.5MW compensation, the wind power output is increased by 0.5MW, and the grid connection fluctuation can be reduced from the 3MW level to within the 0.5MW level.

[0149] Existing single-device control requires writing logic loop by loop; this invention achieves parallel allocation of multiple devices through Count, device maximum active power limit array P_Max_Array, device minimum active power limit array P_Min_Array, energy storage state of charge array SOC_Array, device control priority array Priority_Array, and device power command array P_Cmd_Array, supporting a unified allocation model for up to 500 devices; proportional / marginal allocation of 100 devices can be completed within one control cycle on a regular industrial computer, facilitating the expansion of power plant and park equipment scale.

[0150] Upgrading existing control programs often requires system downtime or a complete download. This invention utilizes the runtime redeployment capability of 4diac and sets interface version numbers, state retention areas, and rollback events to replace the Forecast_FB prediction module or the Dispatch_FB power allocation module algorithm without changing the main control link. A rollback strategy is triggered when the upgrade fails, reducing the risk of online upgrades.

[0151] The following examples illustrate the content involved in the above embodiments.

[0152] Application Case 1: Fast and smooth control in cloud-occluded scenarios

[0153] System configuration: 10MW photovoltaic, 15MW wind power, 5MW / 10MWh energy storage

[0154] Current input:

[0155] PV_P = 8MW

[0156] WT_P = 9MW

[0157] ESS_SOC = 68%

[0158] LOAD_P = 20MW

[0159] GRID_P = 19MW

[0160] AI prediction:

[0161] The photovoltaic capacity will drop to 5MW within the next 2 seconds.

[0162] The coordination and decision-making level determined that the system had entered a "smooth mode".

[0163] Output:

[0164] PV_Cmd = 0MW

[0165] WT_Cmd = +0.5MW

[0166] ESS_Cmd = +2.5MW

[0167] result:

[0168] Power fluctuations at the grid connection point are controlled within the allowable range.

[0169] Note: The system is configured with 10MW of photovoltaic (PV), 15MW of wind power, and 5MW / 10MWh of energy storage. Current inputs are PV_P=8MW, WT_P=9MW, ESS_SOC=68%, LOAD_P=20MW, and GRID_P=19MW. Forecast_FB predicts that PV will drop to 5MW within the next 2 seconds, and Mode_FB determines to enter power smoothing mode. Optimize_FB outputs P_ESS_Req=+2.5MW, WT increased by 0.5MW, and PV limited to 0MW. Dispatch_FB allocates energy storage commands based on SOC and adjustable margin, and Cmd_FB sends them to the PCS. The result is that the power fluctuation at the grid connection point is controlled within the allowable range, avoiding a sudden drop in grid connection point caused by a rapid decline in PV power.

[0170] Application Case 2: Economic Operation Control under Peak-Valley Electricity Pricing Scenarios

[0171] Current electricity price: PRICE = 1.25 yuan / kWh

[0172] enter:

[0173] ESS_SOC = 80%

[0174] LOAD_P = 10MW

[0175] The system automatically switches to "economic operation mode".

[0176] Output:

[0177] ESS_Cmd = +3MW (discharge)

[0178] result:

[0179] Reduce the cost of purchasing electricity at high prices.

[0180] Off-peak electricity price: PRICE = 0.32 yuan / kWh

[0181] Output:

[0182] ESS_Cmd = -2MW (charging)

[0183] This forms a closed-loop operation for peak-valley arbitrage.

[0184] Note: When PRICE = 1.25 yuan / kWh and ESS_SOC = 80% and LOAD_P = 10MW, Mode_FB enters the economic operation mode, and Optimize_FB outputs ESS_Cmd = +3MW for discharge during high-price periods to reduce external power purchases; when PRICE = 0.32 yuan / kWh and SOC is lower than SOC_High, it outputs ESS_Cmd = -2MW for off-peak charging, forming a peak-valley arbitrage closed-loop operation.

[0185] Application Case 3: Autonomous Power Supply Control in Isolated Grid Operation Scenarios

[0186] After the external power grid is disconnected due to a fault, the system enters islanded mode.

[0187] enter:

[0188] PV_P = 4MW

[0189] WT_P = 6MW

[0190] ESS_SOC = 55%

[0191] LOAD_P = 13MW

[0192] The strategy layer performs load priority control:

[0193] Level 1 load is guaranteed, and Level 3 load is cut off.

[0194] Output:

[0195] ESS_Cmd = +3MW

[0196] Load_Shed = Low-priority load shedding

[0197] result:

[0198] The system maintains stable isolated network operation.

[0199] Note: After an external power grid fault is resolved, Grid_Status switches to islanded mode. Inputs: PV_P=4MW, WT_P=6MW, ESS_SOC=55%, LOAD_P=13MW. Mode_FB sets the islanded mode to the highest priority, Strategy_FB performs load priority control, outputs ESS_Cmd=+3MW, and disconnects low-priority loads. The system maintains power supply to primary loads and maintains islanded power balance.

[0200] This invention does not limit the specific algorithm type of the AI ​​prediction service. Forecast_FB can call Long Short-Term Memory Networks, Random Forests, Gradient Boosting Trees, Temporal Convolutional Networks, Transformer prediction models, or other prediction models. It also does not limit the type of field protocol; Cmd_FB can select Modbus TCP, IEC 60870-5-104, MQTT, OPC UA, or vendor-specific protocols depending on the project. Any system that uses the three-level functional layer, event-driven closed loop, and standardized functional block pins described in this invention to implement multi-energy complementary hierarchical autonomous coordinated control of wind, solar, and energy storage falls within the protection scope of this invention.

[0201] Example 2:

[0202] This embodiment provides an integrated system for hierarchical autonomous coordinated control and AI predictive services based on 4diac-based multi-energy complementary wind-solar-storage system, including:

[0203] Perception and prediction module: used to acquire multi-source operating condition data, preprocess the multi-source operating condition data to obtain a unified state vector, input the unified state vector into the prediction model, and obtain the prediction sequence output by the prediction model.

[0204] Coordination and decision-making module: used to determine the current operating mode based on the unified state vector and prediction sequence, and to perform optimization and solution based on the operating mode to obtain the strategy vector;

[0205] Execution control module: used to perform power allocation according to the strategy vector and generate device control commands.

[0206] The specific functions of each module described above are explained in the relevant content of the method in Embodiment 1, and will not be repeated here.

[0207] Example 3:

[0208] This embodiment provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in Embodiment 1.

[0209] Example 4:

[0210] This embodiment provides a computer device, including:

[0211] Memory, used to store computer programs / instructions;

[0212] A processor for executing the computer program / instructions to implement the steps of the method described in Embodiment 1.

[0213] Example 5:

[0214] This embodiment provides a computer program product, including a computer program / instructions, which, when executed by a processor, implement the steps of the method described in Embodiment 1.

[0215] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

[0216] Those skilled in the art will understand that embodiments of this disclosure can be provided as methods, systems, or computer program products. Therefore, this disclosure can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this disclosure can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0217] This disclosure is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1A device that provides the functions specified in one or more boxes.

[0218] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0219] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0220] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this disclosure and not to limit its protection scope. Although this disclosure has been described in detail with reference to the above embodiments, those skilled in the art should understand that after reading this disclosure, they can still make various changes, modifications or equivalent substitutions to the specific implementation of the invention, but these changes, modifications or equivalent substitutions are all within the protection scope of the pending claims.

Claims

1. A method for integrating hierarchical autonomous coordinated control and AI predictive services of wind-solar-storage multi-energy complementary systems based on 4diac, characterized in that, include: Acquire multi-source operating condition data, preprocess the multi-source operating condition data to obtain a unified state vector, input the unified state vector into the prediction model, and obtain the prediction sequence output by the prediction model. The current operating mode is determined based on the unified state vector and the prediction sequence. The strategy vector is obtained by optimizing the solution based on the operating mode. Power allocation is performed based on the strategy vector, and device control commands are generated.

2. The method for integrating hierarchical autonomous coordinated control and AI predictive services based on 4diac-based wind-solar-storage multi-energy complementarity according to claim 1, characterized in that, The multi-source operating condition data includes wind power, photovoltaic, energy storage, load, grid connection point, electricity price, and environmental parameters; The preprocessing includes dimensional unification, range verification, time scale alignment, duplicate value filtering, missing value imputation, abnormal peak removal, and dead zone processing. The prediction sequence includes photovoltaic power prediction, wind power prediction, load prediction, and electricity price prediction.

3. The method for integrating hierarchical autonomous coordinated control and AI predictive services based on 4diac-based wind-solar-storage multi-energy complementarity according to claim 2, characterized in that, After obtaining the predicted sequence output by the prediction model, the method further includes: The predicted sequence is quality evaluated to obtain a quality score; The quality score is compared with a preset threshold. If the quality score is greater than the preset threshold, the current prediction sequence is determined to be valid. If the quality score is lower than the preset threshold, the current prediction sequence is deemed invalid, and a historically valid prediction sequence is selected. If the historical effective prediction sequence is missing, a moving average or linear extrapolation algorithm is used to generate a prediction value based on multi-source operating condition data.

4. The method for integrating hierarchical autonomous coordinated control and AI predictive services based on 4diac-based wind-solar-storage multi-energy complementarity according to claim 3, characterized in that, Based on the unified state vector and the prediction sequence, the current operating mode is determined. Optimization is then performed based on this operating mode to obtain the policy vector, which includes: The fault level is determined by weighted comprehensive evaluation based on the multi-source operating condition data, and the current operating mode is determined based on the fault level and the prediction sequence. Based on the current operating mode, the pre-constructed multi-objective function is solved to obtain the optimization result; The optimization results are then converted into policy vectors. If the solution to the multi-objective function fails or times out, a safety net strategy is generated based on a pre-built rule base, and the safety net strategy is used as a strategy vector.

5. The method for integrating hierarchical autonomous coordinated control and AI predictive services based on 4diac-based multi-energy complementarity of wind, solar, and energy storage, characterized in that, The multi-objective function includes: J= | - | + ·|Δ | + ·Cost + · + · + · ; In the formula, J is the objective function. To track weights, To smooth the weights, Δ The SOC regression weights for energy storage are used, and Cost is the operating cost. For the target state of charge, As the weight of abandoned electricity, For abandoned electricity , For load shedding weight, This refers to the load shedding power.

6. The method for integrating hierarchical autonomous coordinated control and AI predictive services based on 4diac-based wind-solar-storage multi-energy complementarity according to claim 5, characterized in that, Power allocation is performed according to the strategy vector, and device control commands are generated, including: The adjustable boundaries for each device are determined based on the strategy vector. The adjustable margin is determined based on the adjustable upper and lower boundaries and the pre-configured Count parameter; Based on the adjustable margin, power allocation is performed in conjunction with the maximum power array, minimum power array, ramp rate array, energy storage state of charge array, and priority array to generate equipment control commands.

7. The method for integrating hierarchical autonomous coordinated control and AI predictive services based on 4diac-based wind-solar-storage multi-energy complementarity according to claim 6, characterized in that, After generating the device control commands, the following is also included: Obtain the actual operating parameters of the equipment to determine the execution error; If the execution error exceeds the preset threshold or the device remains without feedback for more than the preset time, the strategy vector generation step will be re-executed.

8. A 4Diac-based multi-energy complementary hierarchical autonomous coordinated control and AI predictive service integrated system, characterized in that, include: Perception and prediction module: used to acquire multi-source operating condition data, preprocess the multi-source operating condition data to obtain a unified state vector, input the unified state vector into the prediction model, and obtain the prediction sequence output by the prediction model. Coordination and decision-making module: used to determine the current operating mode based on the unified state vector and prediction sequence, and to perform optimization and solution based on the operating mode to obtain the strategy vector; Execution control module: used to perform power allocation according to the strategy vector and generate device control commands.

9. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the steps of the method described in any one of claims 1 to 7.

10. A computer device, characterized in that, include: Memory, used to store computer programs / instructions; A processor for executing the computer program / instructions to implement the steps of the method according to any one of claims 1 to 7.

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