CN117425183A - Trusted WLAN (wireless local area network) non-inductive switching method, system, equipment and readable storage medium - Google Patents
Trusted WLAN (wireless local area network) non-inductive switching method, system, equipment and readable storage medium Download PDFInfo
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Abstract
The invention belongs to the technical field of power systems, and discloses a trusted WLAN (wireless local area network) non-inductive switching method, which comprises the following steps: detecting a topological neighborhood, and calculating an optimal network according to a plurality of detected parameters of the neighborhood network; after selecting an optimal network, establishing a plurality of transmission paths to transmit data by adopting an improved MPTCP protocol, and vertically switching the network; triggering a switching process, triggering switching when the predicted received signal strength is lower than a threshold value, taking the current received signal strength and the predicted received signal strength as switching parameters as switching judgment, and completing non-inductive switching. The invention can realize the noninductive switching of the mobile terminal among different APs, and ensure that the network keeps higher throughput and lower time delay in the switching process.
Description
Technical Field
The invention belongs to the technical field of power communication, and particularly relates to a trusted WLAN (wireless local area network) non-inductive switching method, a system, equipment and a readable storage medium.
Background
Along with popularization of trusted WLAN networks in novel power systems, mobile applications such as mobile operation and mobile security supervision are more and more, and a large number of mobile terminals such as inspection robots, inspection unmanned aerial vehicles and mobile operation terminals are deployed in the intelligent and digital process of a power grid, and the services require high bandwidth, low time delay and no packet loss. However, in the use process, especially in the network roaming switching process, the problems of unstable network, overlong switching time, packet loss and the like often occur, so that the power service system is blocked or even interrupted, and the user experience is poor.
The Multi-path transmission control protocol (Multi-Path Transfer Control Protocol, MPTCP) can simultaneously perform data transmission in multiple networks, aggregate the transmission capacities of the multiple networks, and effectively improve the transmission throughput, which is one of the main solutions for performing vertical handover between heterogeneous networks. In heterogeneous networks, however, there is a large difference in quality of service index such as bandwidth, round trip delay, etc. for each link. In the vertical switching deployment of heterogeneous networks based on MPTCP, the performance problems of buffer expansion, low bandwidth utilization rate, queue head blocking and the like are faced, and the effective congestion control scheme can solve the problem of queue head blocking faced by the MPTCP in the deployment process.
Therefore, a lightweight module device can be provided based on an improved mobile switching mechanism of the MPTCP protocol, and the noninductive vertical switching of a trusted WLAN network is realized through a switching algorithm, so that the use experience of the network is improved.
Disclosure of Invention
The invention provides a trusted WLAN network non-inductive switching method, a system, equipment and a readable storage medium, which are used for solving the technical problem that the use experience of a terminal user is affected due to the fact that the conventional congestion control scheme cannot be effectively adapted to the characteristic of heterogeneous network attribute time variation.
The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed embodiments. This summary is not an extensive overview and is intended to neither identify key/critical elements nor delineate the scope of such embodiments. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later.
According to a first aspect of an embodiment of the present invention, there is provided a trusted WLAN network non-inductive handover method.
In one embodiment, a trusted WLAN network non-inductive handoff method includes:
detecting a topological neighborhood, and calculating an optimal network according to a plurality of detected parameters of the neighborhood network;
after selecting an optimal network, establishing a plurality of transmission paths to transmit data by adopting an improved MPTCP protocol, and vertically switching the network;
triggering a switching process, namely triggering switching when the predicted received signal strength is lower than a threshold value, and taking the current received signal strength and the predicted received signal strength as switching parameters to finish noninductive switching.
In one embodiment, the plurality of parameters of the neighborhood network include received signal strength, congestion signal, available bandwidth.
In one embodiment, the improved MPTCP protocol includes constructing a congestion control method that is autonomously optimized based on time-varying properties of the wireless network.
In one embodiment, the congestion control method is accomplished by reinforcement learning.
In one embodiment, the reinforcement learning approach is as follows: when the network environment changes, the intelligent agent acquires the whole network state, calculates all paths reaching the destination address according to the state, selects the optimal path, and then performs the action of switching or not; accessing the network state, evaluating according to the acquired rewards, and searching the optimal scheme by the control agent.
In one embodiment, the step of reinforcement learning includes: a stochastic process of satisfying markov for reinforcement learning and providing prize values for state transitions, consisting of five tuples (S, a, T, p, r);
s is the state space in which the process occurs; a is all action sets controlling state changes; t is the set of time steps needed to make a decision; p is a state transition probability function; r is the bonus function assigned for state transitions;
at each time step t, action a t State s applied at time t t State s reaching the next time t+1 And obtains a prize value; will r t Positive value of r is taken as benefit t As a cost, or to replace the bonus function r with a cost function c.
In one embodiment, the step of reinforcement learning further comprises: determining a strategy in a given set maximizes the desired value function, which is the following:
wherein E represents an expected value, gamma represents a discount factor and satisfies 0.ltoreq.gamma.ltoreq.1, h is the number of planning steps, t is time, R t Is the prize value at time t.
According to a second aspect of the embodiment of the present invention, there is provided a trusted WLAN network non-inductive switching system, including a mobile terminal module, performing topology neighborhood detection, and calculating an optimal network according to a plurality of detected parameters of a neighborhood network;
the control module is used for establishing a plurality of transmission paths to transmit data by adopting an improved MPTCP protocol after selecting an optimal network, and vertically switching the network;
and the server module is used for triggering a switching process, triggering switching when the predicted received signal strength is lower than a threshold value, taking the current received signal strength and the predicted received signal strength as switching parameters as switching judgment, and completing non-inductive switching.
In one embodiment, improving the MPTCP protocol includes constructing a congestion control method that is autonomously optimized based on time-varying properties of the wireless network to accomplish a network-aware vertical handover.
In one embodiment, the control module further comprises:
the northbound service interface is in butt joint with the service platform through a Restful interface;
SDN/NFV orchestrator, orchestrate the business and network cooperate;
a southbound network resource interface acquires network information;
and the SDN controller realizes configuration issuing and network control through openflow.
In one embodiment, the congestion control method is implemented by reinforcement learning, wherein an agent in reinforcement learning senses environmental information and directly changes the environment by making decisions; the wireless network time-varying attribute constructs an interaction environment of the intelligent agent, and the intelligent agent generates a corresponding congestion control method through interaction with the environment.
In one embodiment, the reinforcement learning approach is as follows: when the network environment changes, the intelligent agent acquires the whole network state, calculates all paths reaching the destination address according to the state, selects the optimal path, and then performs the action of switching or not; accessing the network state, evaluating according to the acquired rewards, and searching the optimal scheme by the control agent.
In one embodiment, a stochastic process that satisfies markov for reinforcement learning and provides prize values for state transitions, consisting of five tuples (S, a, T, p, r);
s is the state space in which the process occurs; a is all action sets controlling state changes; t is the set of time steps needed to make a decision; p is a state transition probability function; r is the bonus function assigned for state transitions;
at each time step t, action a t State s applied at time t t State s reaching the next time t+1 And obtains a prize value; will r t Positive value of r is taken as benefit t As a cost, or to replace the bonus function r with a cost function c.
In one embodiment, determining a policy in a given set maximizes a desired value function, which is the following:
wherein E represents an expected value, gamma represents a discount factor and satisfies 0.ltoreq.gamma.ltoreq.1, h is the number of planning steps, t is time, R t Is the prize value at time t.
In one embodiment, the system of the invention provides various interfaces to the outside, and can be conveniently accessed into the power mobile terminal, so that the power mobile terminal supports the improved MPTCP protocol.
According to a third aspect of embodiments of the present invention, a computer device is provided.
In an embodiment, the computer device comprises a memory storing a computer program and a processor implementing the steps of the above method when the processor executes the computer program.
According to a fourth aspect of embodiments of the present invention, a computer-readable storage medium is provided.
In an embodiment, the computer readable storage medium has stored thereon a computer program which, when executed by a processor, implements the steps of the above method.
The technical scheme provided by the embodiment of the invention can have the following beneficial effects:
the method for the non-inductive switching of the trusted WLAN network can enable the mobile terminal to realize the non-inductive switching among different APs, and ensure that the network keeps higher throughput and lower time delay in the switching process.
Experimental results show that the method avoids data break points in the heterogeneous network switching process, effectively reduces throughput fluctuation conditions in the heterogeneous network switching process, and achieves seamless switching of the heterogeneous network. Through practical application test verification on the inspection robot, the method has the advantages that the monitoring video can be obviously blocked and sometimes even dropped in the past when the network is switched, the monitoring video is smooth and has no blocking when the network is switched, the switching time delay is less than 20ms, the packet loss rate is less than 0.5%, and the communication requirement of the inspection robot is completely met.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
Fig. 1 is a block diagram illustrating a trusted WLAN network non-inductive handoff system in accordance with an exemplary embodiment;
fig. 2 is a flow chart illustrating a trusted WLAN network non-inductive handoff method according to an exemplary embodiment.
Detailed Description
The following description and the drawings sufficiently illustrate specific embodiments herein to enable those skilled in the art to practice them. Portions and features of some embodiments may be included in, or substituted for, those of others. The scope of the embodiments herein includes the full scope of the claims, as well as all available equivalents of the claims. The terms "first," "second," and the like herein are used merely to distinguish one element from another element and do not require or imply any actual relationship or order between the elements. Indeed the first element could also be termed a second element and vice versa. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a structure, apparatus, or device that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such structure, apparatus, or device. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a structure, apparatus or device comprising the element. Various embodiments are described herein in a progressive manner, each embodiment focusing on differences from other embodiments, and identical and similar parts between the various embodiments are sufficient to be seen with each other.
The terms "longitudinal," "transverse," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like herein refer to an orientation or positional relationship based on that shown in the drawings, merely for ease of description herein and to simplify the description, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operate in a particular orientation, and thus are not to be construed as limiting the invention. In the description herein, unless otherwise specified and limited, the terms "mounted," "connected," and "coupled" are to be construed broadly, and may be, for example, mechanically or electrically coupled, may be in communication with each other within two elements, may be directly coupled, or may be indirectly coupled through an intermediary, as would be apparent to one of ordinary skill in the art.
Herein, unless otherwise indicated, the term "plurality" means two or more.
Herein, the character "/" indicates that the front and rear objects are an or relationship. For example, A/B represents: a or B.
Herein, the term "and/or" is an association relation describing an object, meaning that three relations may exist. For example, a and/or B, represent: a or B, or, A and B.
It should be understood that, although the steps in the flowchart are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the figures may include multiple sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor does the order in which the sub-steps or stages are performed necessarily performed in sequence, but may be performed alternately or alternately with at least a portion of other steps or other steps.
The various modules in the apparatus or systems of the present application may be implemented in whole or in part in software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
Embodiments of the invention and features of the embodiments may be combined with each other without conflict.
Fig. 1 illustrates one embodiment of a trusted WLAN network non-inductive handoff method of the present invention.
In this alternative embodiment, the trusted WLAN network non-inductive handover method includes:
step S101, detecting a topological neighborhood, and calculating an optimal network according to a plurality of detected parameters of the neighborhood network;
step S102, after selecting an optimal network, establishing a plurality of transmission paths to transmit data by adopting an improved MPTCP protocol, and vertically switching the network;
step S103, triggering a switching process, triggering switching when the predicted received signal strength is lower than a threshold value, taking the current received signal strength and the predicted received signal strength as switching parameters as switching judgment, and completing non-inductive switching.
In this alternative embodiment, the parameters of the neighborhood network include received signal strength, congestion signal, available bandwidth. The system also comprises parameters such as transmission delay, bit error rate, packet loss rate, network cost and the like, and different parameters can be selected according to different application scenes and network requirements. Trusted WLANs mainly consider received signal strength, congestion signals, available bandwidth in a power application scenario.
The congestion control method is completed in a reinforcement learning mode, an intelligent agent in reinforcement learning senses environment information, and the environment is directly changed by making a decision; the wireless network time-varying attribute constructs an interaction environment of the intelligent agent, and the intelligent agent generates a corresponding congestion control method through interaction with the environment.
In this alternative embodiment, the reinforcement learning framework elements include the following:
an intelligent agent: the sender of the MPTCP connection is responsible for adjusting congestion windows, interacting with the network environment;
status: at the time node t of each received congestion signal, the sender uses the statuss t To represent the aggregate state of the network state it observes, and the MPTCP connection state;
the actions are as follows: in MPTCP congestion control, the action determines how the sender adjusts the congestion window size of each sub-stream when receiving the congestion signal, and the action performed at time t is denoted as a t ；
Bonus function: the α -fairness utility function may represent utility functions of different fairness criteria as a bonus function;
sequence: a sequence ofThe sequence length T is a fixed length, and the sequence of the data set has J total pieces.
In this alternative embodiment, a stochastic process of satisfying markov for reinforcement learning and providing prize values for state transitions, consisting of five tuples (S, a, T, p, r);
s is the state space in which the process occurs; a is all action sets controlling state changes; t is the set of time steps needed to make a decision; p is a state transition probability function; r is the bonus function assigned for state transitions;
at each time step t, action a t State s applied at time t t State s reaching the next time t+1 And obtains a prize value; will r t Positive value of r is taken as benefit t As a cost, or to replace the bonus function r with a cost function c.
In this alternative embodiment, determining a policy in a given set maximizes the desired value function, which is the following:
wherein E represents an expected value, gamma represents a discount factor and satisfies 0.ltoreq.gamma.ltoreq.1, h is the number of planning steps, t is time, R t Is the prize value at time t.
Additionally, in this alternative embodiment, improving the MPTCP protocol includes constructing a congestion control method that is autonomously optimized based on time-varying properties of the wireless network to accomplish a network-aware vertical handover.
Fig. 2 illustrates one embodiment of a trusted WLAN network non-inductive handoff system of the present invention.
In this alternative embodiment, the trusted WLAN network non-inductive switching system includes:
the mobile terminal module is used for detecting the topological neighborhood and calculating an optimal network according to a plurality of detected parameters of the neighborhood network;
the control module is used for establishing a plurality of transmission paths to transmit data by adopting an improved MPTCP protocol after selecting an optimal network, and vertically switching the network;
and the server module is used for triggering a switching process, triggering switching when the predicted received signal strength is lower than a threshold value, taking the current received signal strength and the predicted received signal strength as switching parameters as switching judgment, and completing non-inductive switching.
The congestion control method is completed in a reinforcement learning mode, an intelligent agent in reinforcement learning senses environment information, and the environment is directly changed by making a decision; the wireless network time-varying attribute constructs an interaction environment of the intelligent agent, and the intelligent agent generates a corresponding congestion control method through interaction with the environment.
In this alternative embodiment, the reinforcement learning framework elements include the following:
an intelligent agent: the system comprises mobile equipment, a server and a controller supporting the MPTCP protocol, wherein the mobile equipment, the server and the controller are senders of MPTCP connection and are responsible for adjusting congestion windows and interacting with a network environment;
status: at the time node t of each received congestion signal, the sender uses the state s t To represent the aggregate state of its observed network state and MPTCP connection state;
the actions are as follows: in MPTCP congestion control, the actions determine how the sender adjusts the congestion window size of each sub-flow when receiving the congestion signal, and the actions performed at time node t are denoted as a t ；
Bonus function: the α -fairness utility function represents a utility function of different fairness criteria as a bonus function;
sequence: a sequence ofT is the number of network devices and J is the number of transmission paths that can reach the destination address.
When the network changes, for example, the detected network signal strength is lower than a certain threshold, at this time point, the intelligent agent acquires the whole network state including the wireless air interface side and the network side, then the intelligent agent calculates all paths reaching the destination address according to the state, selects the optimal path from the paths, and then performs the action of switching or not. The agent can not only perceive the surrounding environmental information, but can also directly change this environment by making decisions, in fig. 1, the mobile device and server supporting the MPTCP protocol, the controller make up the agent, access a series of states, and evaluate through observed rewards, the control agent looks for the optimal policy.
In this alternative embodiment, a stochastic process of satisfying markov for reinforcement learning and providing prize values for state transitions, consisting of five tuples (S, a, T, p, r);
s is the state space in which the process occurs; a is all action sets controlling state changes; t is the set of time steps needed to make a decision; p is a state transition probability function; r is the bonus function assigned for state transitions;
at each time step t, action a t State s applied at time t t State s reaching the next time t+1 And obtains a prize value; will r t Positive value of r is taken as benefit t As a cost, or to replace the bonus function r with a cost function c.
In this alternative embodiment, determining a policy in a given set maximizes the desired value function, which is the following:
wherein E represents an expected value, gamma represents a discount factor and satisfies 0.ltoreq.gamma.ltoreq.1, h is the number of planning steps, t is time, R t Is the prize value at time t.
In one embodiment, the method of the invention is used for testing the actual effect by using the application scene of the inspection robot in the transformer substation. In the inspection process of the inspection robot in the comparative embodiment, when network switching is encountered, the monitoring video can appear obvious blocking phenomenon, sometimes even the line is dropped, the switching time delay is more than 50ms, 31820 ping packets, 3250 packet loss and more than 10 packet loss rate; by using the method, when the network is switched, the monitoring video is smooth and has no blocking, the switching time delay is less than 20ms, 92165 ping packets are lost, 356 packets are lost, the packet loss rate is less than 0.5%, the performance is obviously improved, and the communication requirement of the inspection robot is completely met.
In an embodiment, a computer device is also provided, comprising a memory and a processor, the memory having stored therein a computer program, the processor performing the steps of the above-described method embodiments when the computer program is executed.
In one embodiment, a computer readable storage medium is provided, on which a computer program is stored which, when executed by a processor, carries out the steps of the method embodiments described above.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, or the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like.
The present invention is not limited to the structure that has been described above and shown in the drawings, and various modifications and changes can be made without departing from the scope thereof. The scope of the invention is limited only by the appended claims.
Claims (16)
1. A trusted WLAN network non-inductive handoff method, comprising:
detecting a topological neighborhood, and calculating an optimal network according to a plurality of detected parameters of the neighborhood network;
after selecting an optimal network, establishing a plurality of transmission paths to transmit data by adopting an improved MPTCP protocol, and vertically switching the network;
triggering a switching process, namely triggering switching when the predicted received signal strength is lower than a threshold value, and taking the current received signal strength and the predicted received signal strength as switching parameters to finish noninductive switching.
2. The method of claim 1, wherein the plurality of parameters of the neighbor network include received signal strength, congestion signal, available bandwidth.
3. The trusted WLAN network non-inductive handoff method of claim 1, wherein said modified MPTCP protocol comprises constructing a congestion control method that is autonomously optimized based on a time-varying attribute of the wireless network.
4. The method of claim 3, wherein the congestion control method is performed by reinforcement learning.
5. The method for non-inductive handoff of a trusted WLAN network of claim 4, wherein the reinforcement learning is as follows: when the network environment changes, the intelligent agent acquires the whole network state, calculates all paths reaching the destination address according to the state, selects the optimal path, and then performs the action of switching or not; accessing the network state, evaluating according to the acquired rewards, and searching the optimal scheme by the control agent.
6. The method of trusted WLAN network non-inductive handoff of claim 5, wherein said step of reinforcement learning comprises: a stochastic process of satisfying markov for reinforcement learning and providing prize values for state transitions, consisting of five tuples (S, a, T, p, r);
s is the state space in which the process occurs; a is all action sets controlling state changes; t is the set of time steps needed to make a decision; p is a state transition probability function; r is the bonus function assigned for state transitions;
at each time step t, action a t State s applied at time t t State s reaching the next time t+1 And obtains a prize value; will r t Positive value of r is taken as benefit t As a cost, or to replace the bonus function r with a cost function c.
7. The method of trusted WLAN network non-inductive handoff of claim 6, wherein said step of reinforcement learning further comprises: determining a strategy in a given set maximizes the desired value function, which is the following:
wherein E represents an expected value, gamma represents a discount factor and satisfies 0.ltoreq.gamma.ltoreq.1, h is the number of planning steps, t is time, R t Is the prize value at time t.
8. A trusted WLAN network non-inductive handoff system, comprising:
the mobile terminal module is used for detecting the topological neighborhood and calculating an optimal network according to a plurality of detected parameters of the neighborhood network;
the control module is used for establishing a plurality of transmission paths to transmit data by adopting an improved MPTCP protocol after selecting an optimal network, and vertically switching the network;
and the server module is used for triggering a switching process, triggering switching when the predicted received signal strength is lower than a threshold value, taking the current received signal strength and the predicted received signal strength as switching parameters as switching judgment, and completing non-inductive switching.
9. The trusted WLAN network non-inductive switching system of claim 8, wherein improving the MPTCP protocol includes constructing a congestion control method that is autonomously optimized based on time-varying properties of the wireless network to complete the network non-inductive vertical switch.
10. The trusted WLAN network non-inductive switching system of claim 9, wherein said control module further comprises:
the northbound service interface is in butt joint with the service platform through a Restful interface;
SDN/NFV orchestrator, orchestrate the business and network cooperate;
a southbound network resource interface acquires network information;
and the SDN controller realizes configuration issuing and network control through openflow.
11. The system of claim 10, wherein the congestion control method is implemented by reinforcement learning, wherein the agents in reinforcement learning sense environmental information, and wherein the decision is made to directly change the environment; the wireless network time-varying attribute constructs an interaction environment of the intelligent agent, and the intelligent agent generates a corresponding congestion control method through interaction with the environment.
12. The trusted WLAN network non-inductive switching system of claim 11, wherein the reinforcement learning is as follows: when the network environment changes, the intelligent agent acquires the whole network state, calculates all paths reaching the destination address according to the state, selects the optimal path, and then performs the action of switching or not; accessing the network state, evaluating according to the acquired rewards, and searching the optimal scheme by the control agent.
13. The trusted WLAN network non-inductive switching system of claim 12, wherein the stochastic process of causing reinforcement learning to meet markov and providing prize values for state transitions is comprised of five tuples (S, a, T, p, r);
s is the state space in which the process occurs; a is all action sets controlling state changes; t is the set of time steps needed to make a decision; p is a state transition probability function; r is the bonus function assigned for state transitions;
at each time step t, action a t State s applied at time t t State s reaching the next time t+1 And obtains a prize value; will r t Positive value of r is taken as benefit t As a cost, or to replace the bonus function r with a cost function c.
14. The trusted WLAN network non-inductive switching system of claim 13, wherein determining a policy in a given set maximizes a desired value function, which is:
wherein E represents an expected value, gamma represents a discount factor and satisfies 0.ltoreq.gamma.ltoreq.1, h is the number of planning steps, t is time, R t Is the prize value at time t.
15. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 7 when the computer program is executed.
16. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any one of claims 1 to 7.
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