A power dispatching management system

Abstract

This application relates to the field of power technology and discloses a power dispatch management system. The system includes a data acquisition module for acquiring real-time data, which includes one or more of the following: wind and solar power forecast data, power generation information, load demand information, and operating parameters of power grid generating equipment; a preliminary dispatch module for selecting a matching dispatch model from a model library based on the real-time data and determining the target dispatch strategy corresponding to the optimal solution of the dispatch model; a control module for issuing the dispatch strategy to power equipment, which includes one or more of the following: distributed photovoltaic systems, electric vehicle charging piles, and user energy storage devices; and a real-time optimization module for adding and adjusting constraints in the dispatch model in real-time based on influencing factors, and adjusting the target dispatch strategy according to the constraints. This system significantly improves the power grid's ability to absorb renewable energy and enhances its adaptability to fluctuations and complex load demands.

Description

Technical Field

[0001] This invention relates to the field of power technology, and in particular to a power dispatching and management system. Background Technology

[0002] The power dispatching system plays a crucial role in ensuring the safe, stable, and economical operation of the power system. Its functions encompass data acquisition, generation control, transmission management, and consumption control. Data acquisition and monitoring require real-time and accurate acquisition of electrical parameters such as voltage, current, and power from all nodes in the network to comprehensively understand the system's operating status. Generation control necessitates precise adjustment of generator unit operations based on load forecasts and system operating conditions to ensure supply and demand balance. Transmission management requires close monitoring and dispatching of transmission lines and substations to ensure efficient power transmission and prevent anomalies such as line overload. Therefore, power dispatching involves numerous factors, and how to rationally, safely, and efficiently dispatch power resources is critical to the normal operation of the power system.

[0003] In the process of implementing the embodiments of this application, at least the following problems were found in the related technology:

[0004] Existing power dispatching methods typically formulate dispatching strategies by integrating various factors and data. However, during the power consumption process, dispatching strategies are often static and immutable. Dispatch strategies that rely on static rules lack dynamic adaptability and intelligent decision-making capabilities, making it difficult to effectively cope with the increasingly complex and ever-changing power grid dispatching needs.

[0005] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this application, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] To provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended as a general commentary, nor is it intended to identify key / important components or describe the scope of protection of these embodiments, but rather as a prelude to the detailed description that follows.

[0007] This application provides a power dispatching and management system to improve the efficiency and rationality of power dispatching.

[0008] In some embodiments, the power dispatch management system includes:

[0009] The data acquisition module is used to acquire real-time data, which includes one or more of the following: wind and solar power prediction data, power generation information, load demand information, and operating parameters of power grid generating equipment.

[0010] The preliminary scheduling module is used to select a matching scheduling model from the model library based on the real-time data, and determine the target scheduling strategy corresponding to the optimal solution of the scheduling model.

[0011] The control module is used to send the target scheduling strategy to the power equipment, which includes one or more of distributed photovoltaic, electric vehicle charging piles, and user energy storage terminals.

[0012] The real-time optimization module is used to add and adjust constraints in the scheduling model in real time according to the influencing factors, and adjust the target scheduling strategy according to the constraints.

[0013] Optionally, the wind and solar power prediction data includes weather prediction data, which is obtained by the following method: constructing an LSTM prediction model; training the LSTM prediction model using historical wind speed, wind direction, irradiance, cloud cover, and temperature information; and updating the LSTM prediction model at preset intervals.

[0014] Optionally, the preliminary scheduling module includes: a robust optimization module, which establishes an objective function consisting of wind and solar curtailment costs, load shedding penalties, and reserve capacity costs; a generator, which generates typical scenarios for new energy processing under extreme weather conditions based on a GAN network; and a safety verification unit, which uses an improved DC power flow model to quickly calculate the line over-limit risk under N-1 faults.

[0015] Optionally, the influencing factors include wind curtailment cost and voltage deviation, and the real-time optimization module includes adding the following constraint to the objective function: K1*α (wind curtailment cost) + K2*β (voltage deviation) ≤ threshold, where α and β are parameters for different risk confidence levels.

[0016] Optionally, the influencing factors include market prices and seasonal parameters, and the real-time optimization module includes adding the following constraints to the objective function:

[0017] G(X)=a1*(P_market-P_threshold)²

[0018] +a2*|S_season-S_optimal|+a3*max(0, E_actual - E_limit)≤C_tolerance;

[0019] Wherein, P_market is the current market price fluctuation coefficient, dynamically reflecting the market premium level of raw materials / products; P_threshold represents the price trigger threshold, when the market price exceeds this value, strong constraints are activated; S_season is the quantified value of seasonal parameters (such as the normalized result of environmental factors such as temperature and humidity); S_optimal is the optimal operating condition baseline value of the equipment under the seasonal parameters; E_actual represents real-time environmental indicators (such as carbon emissions per unit output); E_limit is the emission limit required by environmental regulations; a1, a2, and a3 are dynamic weighting coefficients, which are automatically adjusted according to the importance of operating conditions through an online learning algorithm; and C_tolerance is the system's allowable constraint deviation tolerance.

[0020] Optionally, the influencing factor includes a real-time carbon price signal, and the real-time optimization module includes adding a carbon quota constraint to the objective function, expressed as:

[0021] min Σ(λ1*power generation cost + λ2*risk cost + λ3*carbon trading cost); where, regional carbon quota ≥ actual carbon emissions × (1-green electricity deduction ratio).

[0022] Optionally, each constraint in the real-time optimization module also includes an admission parameter, which is the difference between the current value and the preset value of the influencing factor. If the difference is less than zero, the influence of the constraint corresponding to the influencing factor in the scheduling model is excluded.

[0023] Optionally, the system further includes a carbon flow tracking visualization platform, comprising: a carbon flow calculation module for generating carbon flow density corresponding to the power of each branch in real time according to the power flow tracking algorithm; a binding module for confirming the rights of wind and solar power generation and carbon emission reduction on the blockchain through smart contracts; and a display module, including a three-dimensional power grid carbon flow heat map for displaying the clean energy penetration rate of the power grid in different regions according to the color gradient.

[0024] The power dispatching and management system provided in this application embodiment can achieve the following technical effects:

[0025] This system collects multi-dimensional data in real time, including wind and solar power forecasts, power generation information, load demand, and grid equipment parameters. Combined with matching optimization and dynamic constraint adjustment from an intelligent dispatch model library, it achieves precise and coordinated control of power equipment such as distributed photovoltaics, electric vehicle charging piles, and user energy storage terminals. This significantly improves the grid's ability to absorb renewable energy, dispatch response speed, and system operation economy, while also enhancing the grid's adaptability to fluctuations and complex load demands.

[0026] The above general description and the description below are exemplary and illustrative only and are not intended to limit this application. Attached Figure Description

[0027] One or more embodiments are illustrated by way of example with reference to the accompanying drawings. These illustrations and drawings do not constitute a limitation on the embodiments. Elements having the same reference numerals in the drawings are shown as similar elements. The drawings are not to be scaled. And wherein:

[0028] Figure 1 This is a schematic diagram of the composition and structure of a power system;

[0029] Figure 2 This is a schematic diagram of the structure of a power dispatching and management system according to an embodiment of this application;

[0030] Figure 3 This is a schematic diagram of the structure of a power dispatch optimization management system provided in an embodiment of this application. Detailed Implementation

[0031] To provide a more detailed understanding of the features and technical content of the embodiments of this application, the implementation of the embodiments of this application will be described in detail below with reference to the accompanying drawings. The accompanying drawings are for illustrative purposes only and are not intended to limit the embodiments of this application. In the following technical description, for ease of explanation, several details are used to provide a full understanding of the disclosed embodiments. However, one or more embodiments may still be implemented without these details. In other cases, well-known structures and devices may be simplified in their depiction to simplify the drawings.

[0032] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this application described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion.

[0033] Unless otherwise stated, the term "multiple" means two or more.

[0034] In this embodiment, the character " / " indicates that the objects before and after it are in an "or" relationship. For example, A / B means: A or B.

[0035] The term "and / or" describes an association between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or A and B.

[0036] Combination Figure 1As shown, a power plant dispatching system typically includes master station and substation equipment. The master station includes core components such as computers and dual-machine switching components, external storage (disk drives / tape drives), input / output devices (control console terminals / printers), data transmission channel interfaces, dispatching control consoles, and user terminals. The substation is equipped with microcomputer-controlled remote control units (RTUs), dedicated remote transmitters, power amplifiers and their cabinets, as well as data acquisition and execution equipment such as remote control / remote signaling relay panels.

[0037] The system also relies on dedicated communication infrastructure, such as remote control channels consisting of power line carrier, fiber optic, or digital microwave, as well as uninterruptible power supplies (UPS) to ensure continuous power supply. Data exchange between the master station and the plant is achieved through standardized protocols (such as CDT or POLLING protocols), and analog disks and recording and printing equipment are provided for real-time status visualization.

[0038] Combination Figure 2 As shown in the figure, a power dispatch management system 200 provided in this application embodiment includes: a data acquisition module 201, a preliminary dispatch module 202, a control module 203, and a real-time optimization module 204. The data acquisition module 201 is used to acquire real-time data, which includes one or more of the following: wind and solar power forecast data, power generation information, load demand information, and operating parameters of power grid generating equipment. The preliminary dispatch module 202 is used to select a matching dispatch model from a model library based on the real-time data and calculate the target dispatch strategy corresponding to the optimal solution of the dispatch model. The control module 203 is used to issue the target dispatch strategy to power equipment, which includes one or more of the following: distributed photovoltaic systems, electric vehicle charging piles, and user energy storage terminals. The real-time optimization module 204 is used to add and adjust constraints in the dispatch model in real time according to influencing factors, and adjust the target dispatch strategy according to the constraints. This system can dynamically add constraints based on influencing factors, such as constraints related to weather data, economic costs and benefits, and regional information. It can also remove certain constraints or dynamically adjust their weights based on actual application scenarios. The system implements a dynamic adjustment mechanism for constraints through a real-time optimization module, flexibly adding, removing, or reweighting constraints (such as wind curtailment penalty coefficients and voltage deviation thresholds). For example, it automatically strengthens safety constraints in extreme weather scenarios and relaxes economic constraints during periods of low electricity prices, ensuring that the dispatch strategy always aligns with actual operational needs. Compared to traditional static dispatch models, this effectively improves the renewable energy absorption rate.

[0039] As an example, the initial state of a dynamic constraint can be represented as:

[0040] ；

[0041] in, The total system load demand at time t is the core input parameter for scheduling optimization. It represents the sensitivity of the nodal marginal price (LMP) to load changes, reflecting the trend of price changes when the load increases (economic constraints). This represents the power correction amount that is dynamically adjusted based on changes in load and electricity price. m1 and m2 are adjustment coefficients.

[0042] During the control process, adjustment coefficients can be dynamically adjusted, and constraint terms can be dynamically added, for example:

[0043]

[0044] in, P base For baseline load, P weather As a meteorologically sensitive load, P event For special event loads.

[0045] Furthermore, the aforementioned wind and solar power prediction data includes weather prediction data, which is obtained using the following method: constructing an LSTM prediction model; training the LSTM prediction model using historical wind speed, wind direction, irradiance, cloud cover, and temperature information; and updating the LSTM prediction model at preset intervals.

[0046] The LSTM (Long Short-Term Memory) prediction model is constructed through the following steps:

[0047] 1) Data processing: Collect historical weather data and exclude abnormal data, incomplete data and outliers based on data similarity;

[0048] 2) Data classification: Perform cluster analysis on the data and organize it into data types;

[0049] 3) Model Training: The LSTM model is trained using historical data, and prediction results are obtained based on evaluation metrics. Input historical meteorological data (wind speed, irradiance, cloud cover, etc.) and corresponding power generation data. The LSTM model is trained using the sliding window method, and the model weights are updated every 6 hours to adapt to sudden weather changes.

[0050] To more comprehensively evaluate the experimental prediction effect, this embodiment of the application utilizes multi-dimensional errors to construct an evaluation function, where the one-dimensional error is E1, the two-dimensional error is E2, the coefficient of determination is R, and the three-dimensional errors are weighted and summed to determine the evaluation function value. The specific formula is as follows:

[0051] E1=

[0052] E2 =

[0053] R 2 =

[0054] Where N is the number of samples, and Let be the predicted power and the actual power at point i on the day to be measured, respectively. This represents the average actual power.

[0055] The above model can accurately predict weather information and use this information as a constraint to update the target scheduling strategy.

[0056] Combination Figure 3 As shown, this is a power dispatch optimization management system 300 provided in an embodiment of this application. Figure 2 Based on the system shown, the preliminary scheduling module 202 of this system further includes: a robust optimization module 2001, which establishes an objective function consisting of wind and solar curtailment costs, load shedding penalties, and reserve capacity costs; a generator 2002, which generates new energy power application scenarios under extreme weather conditions based on a GAN network; and a safety verification unit 2003, which uses an improved DC power flow model to quickly calculate the line over-limit risk under N-1 faults. The safety verification process is a calculation module used in the power system to assess the operational safety of the power grid, mainly checking for risks such as overload and voltage over-limits. The method in this embodiment uses an optimized simplified model to quickly predict whether other lines will experience overload when any component in the power grid fails. Compared to traditional methods, this embodiment, by combining robust optimization of the objective function, GAN generation of extreme scenarios, and rapid safety verification, significantly improves the economy and reliability of high-proportion new energy power grid scheduling, ensuring system safety under N-1 faults while reducing wind and solar curtailment costs.

[0057] The objective function is designed to minimize the total cost:

[0058] min(C 弃风 +C 切负荷 +C 备用 )

[0059] Among them, C 弃风 =P1 × Curtailed Air Volume, C 切负荷 =P2 × load shearing amount, C 备用 =P3 × Reserve Capacity, where P1, P2, and P3 are weighting coefficients.

[0060] Furthermore, the influencing factors include wind curtailment cost and voltage deviation, and the real-time optimization module includes adding constraints to the objective function, the constraints including:

[0061] K1*α + K2*β ≤ threshold;

[0062] Here, α is the risk confidence level parameter, reflecting the tolerance for wind curtailment probability. For example, α=95% means allowing a 5% wind curtailment risk. β is the voltage safety margin confidence parameter, which is related to the grid N-1 fault verification. K1 and K2 are weighting coefficients used to adjust the priority of wind curtailment costs and voltage deviation in the overall objective.

[0063] In some embodiments, the influencing factors include market prices, seasonal parameters, and environmental protection requirements. The real-time optimization module includes adding constraints to the objective function, the constraints including:

[0064] G(X)=a1*(P_market-P_threshold)²+a2*|S_season-S_optimal|+a3*max(0, E_actual - E_limit)≤C_tolerance

[0065] Wherein, P_market is the current market price fluctuation coefficient, dynamically reflecting the market premium level of raw materials or products; P_threshold represents the price trigger threshold, when the market price exceeds this value, strong constraints are activated; S_season is the quantified value of seasonal parameters (such as the normalized result of environmental factors such as temperature and humidity); S_optimal is the optimal operating condition baseline value of the equipment under the seasonal parameters; E_actual represents real-time environmental indicators (such as carbon emissions per unit output); E_limit is the emission limit required by environmental regulations; a1, a2, and a3 are dynamic weight coefficients, which are automatically adjusted according to the importance of the operating condition through an online learning algorithm; and C_tolerance is the system's allowable constraint deviation tolerance.

[0066] In some embodiments, the real-time optimization module includes carbon quota constraints, embedding real-time carbon price signals into the objective function, as shown in the following expression:

[0067] Real-time carbon price signal = min Σ(λ1*power generation cost + λ2*risk cost + λ3*carbon trading cost).

[0068] Among them, the regional carbon quota must be ≥ actual carbon emissions * (1 - green electricity deduction ratio).

[0069] In some embodiments, the constraints in the real-time optimization module further include admission parameters. These admission parameters include the difference between the current value and a preset value of the influencing factor. If the difference between the current value and the preset value is less than zero, the constraint corresponding to that influencing factor is excluded from the scheduling model. For example, if the difference Δ between the current value and the preset value of the influencing factor is less than 0, then that constraint is excluded to avoid over-optimization.

[0070] In some embodiments, the system further includes a carbon flow tracking visualization platform, which comprises: a carbon flow calculation module for generating carbon flow density corresponding to the power of each branch in real time according to a power flow tracking algorithm; a binding module for on-chain confirmation of rights between wind and solar power generation and carbon emission reduction through smart contracts; and a display module, including a three-dimensional grid carbon flow heat map for displaying the clean energy penetration rate of different areas of the grid according to color gradients. This system has a visualization platform that can monitor the environmental status of the power grid in real time, like a "carbon emission map".

[0071] Specifically, the carbon flow calculation module calculates the "carbon concentration" of each line in the power grid. When wind and solar power are generated, the system automatically uses blockchain technology to calculate and display the carbon emissions reduced by this clean electricity. Finally, a heat map is used to display which areas are more environmentally friendly on a three-dimensional power grid map using different shades of color (e.g., darker green represents more clean energy). This allows for accurate calculation of pollution sources and a visual understanding of the distribution effect of green energy.

[0072] The foregoing description and accompanying drawings fully illustrate embodiments of this application to enable those skilled in the art to practice them. Other embodiments may include structural, logical, electrical, procedural, and other changes. The embodiments represent only possible variations. Individual components and functions are optional unless explicitly required, and the order of operation may vary. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. Moreover, the terminology used in this application is for describing embodiments only and is not intended to limit the claims. As used in the description of embodiments and claims, the singular forms “a,” “an,” and “the” are intended to equally include the plural forms unless the context clearly indicates otherwise. Similarly, the term “and / or” as used in this application means including one or more of the associated listed items and all possible combinations thereof. Additionally, when used in this application, the term "comprise" and its variations "comprises" and / or "comprising" refer to the presence of stated features, integrals, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof. Without further limitations, an element defined by the phrase "comprises a..." does not exclude the presence of other identical elements in the process, method, or apparatus that includes said element. In this document, each embodiment may focus on the differences from other embodiments, and similar or identical parts between embodiments can be referred to mutually. For methods, products, etc., disclosed in the embodiments, if they correspond to the method section disclosed in the embodiments, the relevant parts can be referred to the description of the method section.

[0073] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the embodiments of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0074] The methods and products (including but not limited to devices and equipment) disclosed in the embodiments herein can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of units may be merely a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, and the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to implement this embodiment according to actual needs. In addition, the functional units in the embodiments of this application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0075] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than that shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order than disclosed in the description; sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. Each block in a block diagram and / or flowchart, and combinations of blocks in a block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

Claims

1. A power dispatching and management system, characterized in that, include: The data acquisition module is used to acquire real-time data, which includes one or more of the following: wind and solar power prediction data, power generation information, load demand information, and operating parameters of power grid generating equipment. The preliminary scheduling module is used to select a matching scheduling model from the model library based on the real-time data, and determine the target scheduling strategy corresponding to the optimal solution of the scheduling model. The control module is used to send the target scheduling strategy to the power equipment, which includes one or more of distributed photovoltaic, electric vehicle charging piles, and user energy storage terminals. The real-time optimization module is used to add and adjust constraints in the scheduling model in real time according to the influencing factors, and to adjust the target scheduling strategy according to the constraints. The preliminary scheduling module includes: The robust optimization module establishes an objective function consisting of wind and solar curtailment costs, load shedding penalties, and reserve capacity costs. A generator that generates typical scenarios for new energy processing under extreme weather conditions based on a GAN network; The safety verification unit uses an improved DC power flow model to quickly calculate the line over-limit risk under N-1 fault conditions; The influencing factors include wind curtailment cost and voltage deviation, and the real-time optimization module includes adding the following constraints to the objective function: K1*α (cost of wind curtailment) + K2*β (voltage deviation) ≤ threshold; Where α and β are parameters for different risk confidence levels; The influencing factors include market prices and seasonal parameters, and the real-time optimization module includes adding the following constraints to the objective function: G(X)=a1 *(P_market-P_threshold)²+a2*|S_season-S_optimal|+a3*max(0, E_actual - E_limit)≤C_tolerance; Wherein, P_market is the current market price fluctuation coefficient, dynamically reflecting the market premium level of raw materials or products; P_threshold represents the price trigger threshold, when the market price exceeds the P_threshold value, strong constraints are activated; S_season is the quantified value of seasonal parameters (such as the normalized result of environmental factors such as temperature and humidity); S_optimal is the optimal operating condition baseline value of the equipment under the stated seasonal parameters; E_actual represents real-time environmental indicators (such as carbon emissions per unit output); E_limit is the emission limit required by environmental regulations; a1, a 2、 a3 is a dynamic weighting coefficient that is automatically adjusted based on the importance of the working condition through an online learning algorithm; C_tolerance is the system's allowable constraint deviation tolerance. The influencing factors include real-time carbon price signals, and the real-time optimization module includes adding the following carbon quota constraints to the objective function: min Σ(λ1*power generation cost + λ2*risk cost + λ3*carbon trading cost); Among them, the regional carbon quota is ≥ the actual carbon emissions × (1 - green electricity deduction ratio).

2. The system according to claim 1, characterized in that, The wind and solar power prediction data includes weather prediction data, which is obtained using the following method: Construct an LSTM prediction model; The LSTM prediction model was trained using historical wind speed, wind direction, irradiance, cloud cover, and temperature information. The LSTM prediction model is updated at preset time intervals.

3. The system according to claim 1, characterized in that, The constraints in the real-time optimization module also include an admission parameter, which is the difference between the current value and the preset value of the influence factor. If the difference is less than zero, the influence of the constraint corresponding to the influence factor in the scheduling model is excluded.

4. The system according to claim 1, characterized in that, The system also includes a carbon flow tracing visualization platform, comprising: The carbon flow calculation module is used to generate the carbon flow density corresponding to the power of each branch in real time based on the power flow tracing algorithm. The binding module uses smart contracts to establish on-chain ownership of wind and solar power generation and carbon emission reduction. The display module includes a three-dimensional heat map of the power grid carbon flow, which is used to display the clean energy penetration rate of the power grid in different regions according to the color gradient.

Images