Multi-machine system power distribution method, system, equipment and medium

Abstract

The invention relates to the technical field of gravity energy storage multi-machine system power distribution, in particular to a multi-machine system power distribution method, system and device and a medium. Calculating the total efficiency of each stand-alone system based on an efficiency model; constructing a power distribution optimization model based on the total efficiency of each single-machine system; and distributing the power of each unit according to the power distribution optimization model. The method has the beneficial effects that an intelligent power scheduling strategy can be provided for the renewable energy storage system, the energy loss and the equipment overload risk are reduced, and the peak regulation capability and the economic benefit of the power grid are improved.

Description

Technical Field

[0001] This invention relates to the field of power distribution technology for multi-machine gravity energy storage systems, and in particular to a power distribution method, system, device and medium for multi-machine systems. Background Technology

[0002] With the increasing global demand for clean energy, hillside tracked gravity energy storage multi-unit systems have received widespread attention in recent years as an emerging energy storage technology. This system utilizes the natural terrain of the mountain to convert gravitational potential energy into electrical energy, offering advantages such as simple structure, low cost, and environmental friendliness.

[0003] However, current multi-unit gravity energy storage systems based on mountain slopes face several pressing issues regarding power distribution. Firstly, the complex and varied terrain of mountains leads to significant differences in the operating conditions of each unit, resulting in inconsistent output characteristics. Secondly, traditional power distribution methods often involve average power allocation, which fails to adequately consider the efficiency differences of key components within each unit. Summary of the Invention

[0004] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0005] In a first aspect, the present invention provides a power allocation method for a multi-machine system, including establishing an efficiency model based on the key components of each single-machine system;

[0006] Calculate the overall efficiency of each stand-alone system based on the efficiency model;

[0007] A power allocation optimization model is constructed based on the overall efficiency of each individual system.

[0008] The power of each unit is allocated according to the power allocation optimization model.

[0009] As a preferred embodiment of the multi-machine system power allocation method of the present invention, a power allocation optimization model is constructed based on the overall efficiency of each individual machine system, including:

[0010] A power allocation optimization model is constructed with the energy efficiency of multi-machine systems as the optimization objective.

[0011] As a preferred embodiment of the multi-machine system power allocation method of the present invention, wherein: the energy efficiency of the multi-machine system is the optimization objective, including,

[0012] The optimization objective during the discharge phase is to maximize the ratio of total electrical power to total mechanical power of the system.

[0013] The optimization objective during the charging phase is to minimize the total electrical power input of the system.

[0014] As a preferred embodiment of the multi-machine system power allocation method of the present invention, wherein: an efficiency model is established based on the key components of each individual machine system, including,

[0015] Establish a mass block efficiency model based on the first objective parameters;

[0016] A chain efficiency model is established based on the second objective parameter;

[0017] A motor efficiency model is established based on the third objective parameter;

[0018] And to establish an efficiency model for sprockets and gearboxes.

[0019] As a preferred embodiment of the multi-machine system power allocation method of the present invention, the power allocation optimization model is constructed, including:

[0020] The objective function during the discharge phase is to maximize the ratio of total electrical power to total mechanical power, with the constraint that the sum of the power of each unit equals the total power demand.

[0021] The objective function of the charging phase is to minimize the total electrical power input, and the constraint is that the sum of the power of each unit equals the total power demand.

[0022] As a preferred embodiment of the multi-machine system power distribution method of the present invention, the first target parameters include the ramp angle, friction coefficient, mass block running speed, and mass parameters.

[0023] The second target parameters include the slack side friction coefficient of the chain, the friction coefficient between chain links, the unit mass of the chain, the sprocket tooth angle, and the chain length;

[0024] The third target parameters include motor copper loss, iron loss, wind friction loss, and electrical parameters.

[0025] In a preferred embodiment of the multi-machine system power allocation method of the present invention, the total efficiency of each individual machine system is the product of the efficiency models of each key component.

[0026] Secondly, the present invention provides a multi-machine system power allocation system, including: a modeling module for establishing an efficiency model based on the key components of each single-machine system;

[0027] The calculation module is used to calculate the overall efficiency of each individual system based on the efficiency model;

[0028] A building module is used to construct a power allocation optimization model based on the overall efficiency of each individual system.

[0029] The allocation module is used to allocate the power of each unit according to the power allocation optimization model.

[0030] Thirdly, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described above.

[0031] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method described above.

[0032] Compared with existing technologies, the beneficial effects of this invention are as follows: It clarifies the optimal power allocation law for multi-unit systems, constructs a dynamic optimization framework based on the efficiency models of key components of each unit, and achieves precise power allocation under charging and discharging conditions; it determines the energy efficiency threshold of multi-unit systems under complex terrain and unit differences, solving the efficiency loss problem caused by traditional average allocation; it enables real-time dynamic adjustment of the power of each unit, dynamically calculates the optimal allocation weight based on component efficiency parameters, and achieves adaptive optimization for abnormal energy efficiency fluctuations; and it provides long-term stable control of the overall system operating efficiency based on a hierarchical optimization model and remaining capacity prediction during the charging and discharging stages. It can provide intelligent power dispatch strategies for renewable energy storage systems, reduce energy loss and equipment overload risks, and improve grid peak-shaving capacity and economic benefits. Attached Figure Description

[0033] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1 This is a flowchart illustrating the power allocation method for a multi-machine system.

[0035] Figure 2 This is a schematic diagram of a multi-machine gravity energy storage system based on a mountain slope track.

[0036] Figure 3 The diagram shows the discharge efficiency of a multi-machine system under different discharge requirements.

[0037] Figure 4 This is a diagram showing the power allocation of each unit under different discharge requirements.

[0038] Figure 5 Charging efficiency diagrams for multi-machine systems under different charging requirements.

[0039] Figure 6 Power allocation diagram for each unit under different charging requirements. Detailed Implementation

[0040] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0041] Example 1, referring to Figure 1 This is the first embodiment of the present invention, which provides a power allocation method for a multi-machine system, including:

[0042] S100: Establish efficiency models based on the key components of each stand-alone system;

[0043] S200: Calculates the overall efficiency of each stand-alone system based on the efficiency model;

[0044] S300: Construct a power allocation optimization model based on the overall efficiency of each individual system;

[0045] S400: Allocates power to each unit according to the power allocation optimization model.

[0046] It should be noted that slope-based tracked gravity energy storage multi-unit systems typically operate in complex mountainous environments, where individual units exhibit variations in slope angle, chain-track friction coefficient, and motor parameters. Traditional average power distribution methods do not adequately consider these factors, often resulting in unreasonable power allocation and low overall system efficiency. Furthermore, during system operation, the efficiency of key components varies with operating conditions, such as the efficiency of the mass block at different slope angles and the frictional losses of the chain at different operating speeds. These dynamic characteristics increase the complexity of power distribution. Existing power distribution strategies often struggle to adapt to these changes accurately and in real time, failing to achieve optimal system energy efficiency.

[0047] Therefore, to address the aforementioned issues, the steps S100-S400 are as follows: First, an efficiency model is established based on the key components of each individual system to accurately quantify the efficiency characteristics of each component under different operating conditions. Next, the overall efficiency of each individual system is calculated based on these efficiency models to comprehensively evaluate the overall performance of the individual system. Then, a power allocation optimization model is constructed based on the overall efficiency of each individual system to scientifically and rationally plan the power allocation scheme with the goal of optimizing system energy efficiency. Finally, the power of each unit is allocated according to the power allocation optimization model to achieve coordinated and efficient operation of each individual system under charging and discharging conditions, effectively improving the energy conversion efficiency and operational stability of the entire multi-machine system.

[0048] Example 2, refer to Figures 1-6As an embodiment of the present invention, a power allocation method for a multi-machine system is provided based on the above embodiment.

[0049] In this embodiment of the application, step S100, which establishes an efficiency model based on the key components of each standalone system, includes the following steps A1-A4:

[0050] It should be noted that the key components include the mass block, chain, motor, sprocket, and gearbox.

[0051] A1: Establish a mass block efficiency model based on the first objective parameter;

[0052] It should be noted that the first target parameters include the slope angle, friction coefficient, mass block running speed, and mass parameters.

[0053] A2: Establish a chain efficiency model based on the second objective parameter;

[0054] It should be noted that the second target parameters include the slack side friction coefficient of the chain, the friction coefficient between chain links, the unit mass of the chain, the sprocket tooth angle, and the chain length.

[0055] A3: Establish a motor efficiency model based on the third objective parameter;

[0056] It should be noted that the third target parameters include motor copper loss, iron loss, wind friction loss, and electrical parameters.

[0057] A4: And establish an efficiency model for sprockets and gearboxes.

[0058] Specifically, in step A1, the mass block efficiency model η mass-d The specific manifestations are as follows:

[0059] (1) Under stable operating conditions during the discharge phase, the efficiency expression of the mass block is as follows:

[0060]

[0061] In the formula: m is the mass of a single mass block, v is the running speed of the mass block, θ is the slope angle, and μ is the coefficient of friction between the slope track and the mass block.

[0062] (2) Under stable operating conditions during the charging phase, the efficiency expression of the mass block is as follows:

[0063]

[0064] In the formula: θ is the slope angle, and μ is the friction coefficient between the slope track and the mass block.

[0065] In step A2, the chain efficiency model η chain The specific manifestations are as follows:

[0066]

[0067] Where: μ c1 μ is the coefficient of friction between the slack side of the chain and the track. c2 Let α be the coefficient of friction between the links in the chain, m0 be the mass per unit length of the chain, and α be the coefficient of friction between the links. st Let l1 be the chain angle corresponding to each tooth of the sprocket, l2 be the length of the inclined chain segment, and P be the length of the horizontal chain segment. i p represents the power allocated to the motors in a multi-machine system. fe p is expressed as the iron loss of the motor. fw Represented as the wind friction loss of the motor, r a This is represented as stator resistance, and U is the motor stator voltage. The power factor angle of the motor.

[0068] In step A3, the motor efficiency model η e The specific manifestations are as follows:

[0069]

[0070] In the formula: P i r represents the power of each individual unit. a U is the stator resistance, and U is the motor stator voltage. p is the power factor angle of the motor. fe p is the iron loss of the motor. fw This refers to the wind wear of the motor.

[0071] Specifically, in step A4, since the gearbox and sprocket have the same influence on the final optimized model during charging and discharging, for ease of comparison, the efficiency of the gearbox and sprocket is considered a constant value as shown in the following formula:

[0072]

[0073] In the formula: η sprocket The efficiency of the sprocket; η gearbox This refers to the efficiency of the gearbox.

[0074] In an optional implementation, the efficiency model established in step S100 can also be established through machine learning-driven efficiency modeling. That is, real-time operating data of each component is collected by sensors, combined with historical fault and energy efficiency data, and a predictive model of component efficiency is built by using supervised learning algorithms (such as random forests and neural networks) to train.

[0075] In another optional implementation, the efficiency model established in step S100 can also be based on virtual calibration modeling of digital twins, that is, to build a high-precision digital twin model for the single-machine system, verify the efficiency of components under different working conditions (such as the friction loss of sprockets at different speeds) through virtual simulation, and combine real-time data to calibrate the model.

[0076] In this embodiment of the application, step S200, which calculates the overall efficiency of each individual system based on the efficiency model, includes the following step B1:

[0077] B1: The overall efficiency of each stand-alone system is the product of the efficiency models of each key component.

[0078] Specifically, the overall efficiency of each individual system is as follows:

[0079] η i =η mass ·η chain ·η sprocket ·η gearbox ·η e

[0080] In the formula: η i For the overall efficiency; η mass For the efficiency of the mass block and the efficiency of the chain η chain sprocket efficiency η sprocket Gearbox efficiency η gearbox and motor efficiency η e .

[0081] In an optional implementation, the overall efficiency of each individual system in step S200 can also be calculated using a dynamic weight adaptive method, that is, the weighting coefficient of each component's efficiency is dynamically adjusted according to the importance of the components (e.g., motor efficiency has a greater impact on the system) and real-time operating conditions (e.g., high temperature causes chain efficiency to decrease).

[0082] In another optional implementation, the overall efficiency of each individual system in step S200 can also be calculated using energy flow analysis, that is, by performing energy flow analysis on the individual systems from the perspective of energy input and output. The total energy input to the individual systems and the effective energy output are calculated, and the overall efficiency of the individual systems is obtained by the ratio of the two.

[0083] In this embodiment of the application, step S300, which constructs a power allocation optimization model based on the overall efficiency of each individual system, includes the following steps C1-C2:

[0084] It should be noted that the power allocation optimization model is constructed with the energy efficiency of the multi-machine system as the optimization objective. Specifically, the optimization objective of the multi-machine system energy efficiency includes maximizing the ratio of the total electrical power to the total mechanical power of the system during the discharge stage, and minimizing the total electrical power input of the system during the charging stage.

[0085] C1: The objective function of the discharge phase is to maximize the ratio of total electrical power to total mechanical power, with the constraint that the sum of the power of each unit equals the total power demand.

[0086] C2: The objective function of the charging phase is to minimize the total electrical power input, and the constraint is that the sum of the power of each unit is equal to the total power demand.

[0087] Specifically, the efficiency of a multi-machine system under two different operating conditions, charging and discharging, is defined as follows:

[0088]

[0089] In the formula, P i Let η represent the power supplied by the i-th generating unit. i Let be the total efficiency of the i-th unit.

[0090] Furthermore, the constraints are as follows:

[0091]

[0092] Furthermore, the specific forms of the power allocation optimization model include:

[0093] ①The mathematical model for power allocation optimization in a multi-machine system during the discharge phase is as follows:

[0094] min P T AP+b T P+α T c

[0095]

[0096] In the formula: P = (P1, P2, ..., P n ) T This represents the power allocation vector for each individual machine, where P n Let A be the power of the nth single machine; A = diag(a1, a2, ..., a n Let be a diagonal matrix with diagonal elements a1, a2, ..., a3. n , where a n Let b be the coefficient of the quadratic term of the nth single machine in the objective function; b = (b1, b2, ..., bn) n ) T This represents a column vector with elements b1, b2, ..., b n, where b n Let be the coefficient of the linear term of the nth single machine in the objective function;

[0097] c = (c1, c2, ..., c n ) T This represents a column vector with elements c1, c2, ..., c3. n , where c n Let α be the constant term of the nth single machine in the objective function; α = (1, 1, ..., 1) T P represents a column vector where all elements are 1; demand P represents the total power requirement of the system under the current operating conditions. n This indicates the maximum rated power of a single unit, that is, the maximum power that each unit can safely operate at; P T AP is a quadratic term, representing the sum of the squares of the power of each individual unit and the products of the corresponding coefficients in matrix A, reflecting the nonlinear losses or costs of power distribution; b T P is a linear term, representing the sum of the products of the power of each individual unit and the corresponding coefficients in vector b, reflecting the linear loss or cost of power allocation; α T c is a constant term, representing the dot product of vectors α and c, which is the sum of the fixed losses or costs of all individual machines.

[0098] ②The mathematical model for power allocation optimization in a multi-machine system during the charging phase is as follows:

[0099] max P T AP+b T P+α T c

[0100]

[0101] In the formula: P demand P represents the total power requirement of the system under the current operating conditions. n This indicates the maximum rated power of a single unit, that is, the maximum power that each unit can safely operate at; P T AP is a quadratic term, representing the sum of the squares of the power of each individual unit and the products of the corresponding coefficients in matrix A, reflecting the nonlinear losses or costs of power distribution; b T P is a linear term, representing the sum of the products of the power of each individual unit and the corresponding coefficients in vector b, reflecting the linear loss or cost of power allocation; α T c is a constant term, representing the dot product of vectors α and c, which is the sum of the fixed losses or costs of all individual machines.

[0102] In an optional implementation, the power allocation optimization model in step S300 can also be constructed based on a multi-objective optimization method, that is, considering multiple optimization objectives, such as system efficiency, response speed, stability, etc., to construct a multi-objective power allocation optimization model.

[0103] In another optional implementation, the power allocation optimization model in step S300 can also be built based on big data analytics. This involves collecting a large amount of historical operational data, including information such as the overall efficiency, power allocation, and system performance of each individual system. Data mining and machine learning algorithms are then used to analyze the inherent relationships and patterns between the data, constructing a power allocation optimization model based on big data analytics. By training the model to learn patterns and trends, the system performance under different power allocations can be predicted, thereby finding the optimal power allocation scheme, suitable for scenarios with abundant data.

[0104] In this embodiment of the application, step S400, which allocates the power of each unit according to the power allocation optimization model, includes the following steps D1-D2:

[0105] D1: Solve the power allocation optimization model using numerical calculation methods such as programming to obtain the power that should be allocated to each unit under different charging and discharging requirements;

[0106] D2: Based on the solution results, perform actual power distribution control on each individual machine in the multi-machine system.

[0107] In an optional implementation, the power allocation of each unit in step S400 can also be optimized based on a genetic algorithm. That is, by simulating natural evolution, an initial power allocation scheme is randomly generated, and the optimal allocation scheme is finally obtained through fitness evaluation, selection, crossover mutation and iterative optimization.

[0108] In another optional implementation, the power allocation for each unit in step S400 can also be based on a particle swarm optimization algorithm. This involves simulating a flock of birds foraging using swarm intelligence, and updating the velocity and position of each particle based on its own optimal position and the global optimal position. The search is iterative until a stopping condition is met, such as reaching the maximum number of iterations or finding a satisfactory power allocation scheme.

[0109] Furthermore, to verify the effectiveness of the mathematical model of this method, a multi-unit gravity energy storage system consisting of eight 5MW rated power units was set up, and the system parameters are shown in Table 1.

[0110] Table 1. System parameters of the inclined track-type gravity energy storage unit 8

[0111]

[0112] Substitute the parameters in the table into the power allocation optimization model of this method for programming and solve, and the resulting effect curve is shown below. Figure 3-6 As shown in the figure, the power allocation method for multi-machine systems proposed in this patent can improve the overall charging and discharging efficiency of multi-machine systems by 0.6%.

[0113] In summary, this invention clarifies the optimal power allocation law for multi-unit systems, constructs a dynamic optimization framework based on the efficiency models of key components of each unit, and achieves precise power allocation under charging and discharging conditions. It determines the energy efficiency threshold of multi-unit systems under complex terrain and unit differences, solving the efficiency loss problem caused by traditional average allocation. It enables real-time dynamic adjustment of the power of each unit, dynamically calculates the optimal allocation weight based on component efficiency parameters, and achieves adaptive optimization for abnormal energy efficiency fluctuations. Based on a hierarchical optimization model and remaining capacity prediction during the charging and discharging stages, it provides long-term stable control of the overall system operating efficiency. This invention can provide intelligent power scheduling strategies for renewable energy storage systems, reducing energy loss and equipment overload risks, and improving grid peak-shaving capacity and economic benefits.

[0114] Example 3 illustrates a schematic scheme for a multi-machine system power allocation method. It should be noted that the technical solution of this multi-machine system power allocation system belongs to the same concept as the technical solution of the multi-machine system power allocation method described above. Details not described in detail in this example can be found in the description of the technical solution of the multi-machine system power allocation method described above.

[0115] This embodiment also provides a multi-machine system power distribution system, including:

[0116] The acquisition module is used to acquire pipeline parameters and build pipeline models;

[0117] Establish a module to build efficiency models based on the key components of each standalone system;

[0118] The calculation module is used to calculate the overall efficiency of each individual system based on the efficiency model;

[0119] A building module is used to construct a power allocation optimization model based on the overall efficiency of each individual system.

[0120] The allocation module is used to allocate the power of each unit according to the power allocation optimization model.

[0121] This embodiment also provides an electronic device suitable for power allocation in a multi-machine system, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the power allocation method for a multi-machine system as proposed in the above embodiment.

[0122] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the power allocation method for a multi-machine system as proposed in the above embodiments.

[0123] The storage medium proposed in this embodiment and the method for implementing power allocation in a multi-machine system proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0124] Based on the above description of the implementation methods, those skilled in the art will clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0125] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A power allocation method for a multi-machine system, characterized in that: include, Efficiency models are established based on the key components of each stand-alone system; Calculate the overall efficiency of each individual system based on the efficiency model described above; A power allocation optimization model is constructed based on the overall efficiency of each individual system. The power of each unit is allocated according to the power allocation optimization model.

2. The power allocation method for a multi-machine system as described in claim 1, characterized in that: The power allocation optimization model constructed based on the overall efficiency of each individual system includes... A power allocation optimization model is constructed with the energy efficiency of multi-machine systems as the optimization objective.

3. The power allocation method for a multi-machine system as described in claim 2, characterized in that: The optimization objective of multi-machine system energy efficiency includes, The optimization objective during the discharge phase is to maximize the ratio of total electrical power to total mechanical power of the system. The optimization objective during the charging phase is to minimize the total electrical power input of the system.

4. The power allocation method for a multi-machine system as described in claim 3, characterized in that: The efficiency model established based on the key components of each standalone system includes... Establish a mass block efficiency model based on the first objective parameters; A chain efficiency model is established based on the second objective parameter; A motor efficiency model is established based on the third objective parameter; And to establish an efficiency model for sprockets and gearboxes.

5. The power allocation method for a multi-machine system as described in claim 4, characterized in that: The construction of the power allocation optimization model includes, The objective function during the discharge phase is to maximize the ratio of total electrical power to total mechanical power, with the constraint that the sum of the power of each unit equals the total power demand. The objective function of the charging phase is to minimize the total electrical power input, and the constraint is that the sum of the power of each unit equals the total power demand.

6. A power allocation method for a multi-machine system as described in claim 4 or 5, characterized in that: The first target parameters include the slope angle, friction coefficient, mass block running speed, and mass parameters; The second target parameters include the slack side friction coefficient of the chain, the friction coefficient between chain links, the unit mass of the chain, the sprocket tooth angle, and the chain length; The third target parameter includes motor copper loss, iron loss, wind friction loss, and electrical parameters.

7. A power allocation method for a multi-machine system as described in any one of claims 1 to 5, characterized in that: The overall efficiency of each individual system is the product of the efficiency models of each key component.

8. A multi-machine system power distribution system, using the method described in any one of claims 1-7, characterized in that, include: Establish a module to build efficiency models based on the key components of each standalone system; The calculation module is used to calculate the overall efficiency of each stand-alone system based on the efficiency model. The module is used to build a power allocation optimization model based on the overall efficiency of each individual system. The allocation module is used to allocate the power of each unit according to the power allocation optimization model.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

Images