An event-driven rapid simulation method for low-altitude transportation systems

Abstract

This invention relates to the field of urban traffic planning simulation technology, specifically disclosing a rapid simulation method for low-altitude transportation systems based on event-driven principles. The method includes the following steps: S1, the simulator parses and preloads the configuration file and method modules, loading the first data from the configuration file into the simulation environment; S2, the simulator configures the first data and travel calculation dependency modules for the simulation model, calculating the initial travel plan for the simulation; S3, the simulator initializes the simulation time and the state of the simulation object; S4, the simulation model simulates the travel plan of the simulation object according to the simulation steps, derives the simulation results, and updates the state and records the time of the simulation object based on the simulation results; S5, the simulation model outputs the simulation results in file format to the corresponding address according to the output directory in the configuration file. This invention enables parallel loading and computation for large-scale simulations while reducing unnecessary simulation process calculations, effectively improving simulation efficiency.

Description

Technical Field

[0001] This invention relates to the field of urban traffic planning simulation technology, specifically to an event-driven rapid simulation method for low-altitude transportation systems. Background Technology

[0002] Compared to traditional ground transportation, low-altitude transportation offers advantages such as fewer obstacles, fewer restrictions, and more spatial levels, but it also places higher demands on communication, scheduling, and safety. Currently available low-altitude transportation planning and simulation software suffers from the following problems:

[0003] (1) Simulation based on a preset time step requires processing a lot of invalid intermediate process information, which is time-consuming and inefficient, and cannot meet the high timeliness requirements of urban traffic operation.

[0004] (2) Path calculation: Most simulation path calculation processes use Dijkstra's algorithm, which traverses all nodes in the network to find the shortest path. The solution process consumes a lot of time and computing power.

[0005] (3) Traditional simulation is limited by road space and rules, and cannot realize parallel simulation of traffic behavior on a large scale. The simulation calculation process is time-consuming and inefficient. Summary of the Invention

[0006] To address the problems existing in the prior art, this invention designs an event-driven rapid simulation method for low-altitude transportation systems. This method enables parallel loading operations for large-scale simulations while reducing unnecessary simulation calculations, effectively improving simulation efficiency. The specific technical solution is as follows:

[0007] An event-driven rapid simulation method for low-altitude transportation systems includes the following steps:

[0008] S1. The simulator parses and preloads the configuration file and method module, loading the first data in the configuration file into the simulation environment. The configuration file includes at least the output directory and network data files, vehicle data files, infrastructure data files, travel plan data files, and control parameter data files for the simulation object running the simulation model. The first data includes at least network data, vehicle data, infrastructure data, travel plan data, and control parameter data. The simulation environment has a variable parameter table corresponding to the first data. The variable parameter table includes at least a network attribute table, a vehicle attribute table, and a simulation parameter table. The network attribute table corresponds to the network data of the simulation object, the vehicle attribute table corresponds to the vehicle data of the simulation object, and the simulation parameter table corresponds to the control parameter data of the simulation object.

[0009] S2. The simulator configures the first data and the travel calculation dependency module for the simulation model based on the first data, and calculates the initial travel plan for the simulation.

[0010] S3. The simulator initializes the simulation time and the state of the simulation object, and activates the listening and calling function at the same time.

[0011] S4. The simulation model simulates the travel plan of the simulation object according to the simulation steps to obtain simulation results, and updates the status and records the time of the simulation object according to the simulation results.

[0012] S5. The simulation model outputs the simulation results to the corresponding address in file format according to the output directory in the configuration file.

[0013] Preferably, S1 specifically includes:

[0014] S11. Input the configuration file address into the simulation model as a command-line parameter. The simulation framework automatically parses the configuration file at the corresponding address. After parsing, it puts the first data in the configuration file into the corresponding scene container and loads it into memory. The scene container is a collection of variables in the configuration file.

[0015] S12. The simulation framework parses the parameters of the running module in the configuration file and loads them into the running controller of the simulation model;

[0016] S13. The simulation framework verifies the field names and data formats in the configuration file according to the pre-set data format;

[0017] S14. The simulation framework initializes the parameters and behavior of the listener module and integrates it into the simulation model.

[0018] Preferably, the network data file includes at least a road network data file and a low-altitude airway network data file;

[0019] The vehicle data file includes at least the type, performance parameters, serial number, and capacity of cars, buses, ferries, subways, trains, and aircraft;

[0020] The infrastructure data file contains various behavioral parameters within the infrastructure, including at least passenger entry and exit, waiting and boarding, and aircraft takeoff, landing and cruising.

[0021] The travel plan data file shall include at least the travel number, origin and destination, mode of transport, and time.

[0022] The control parameter data file includes at least the total simulation duration, simulation step size, simulation random seed, simulation start time, simulation end time, vehicle ticket cost, distance cost, and waiting cost.

[0023] Preferably, the calculation steps for the initial travel plan are as follows:

[0024] S21. Inject the simulation method modules and parameter configurations into the simulation model;

[0025] S22. Inject the first data in the configuration file into the unified scene container;

[0026] S23. Call the activity planner of the simulation model, and calculate the specific travel path in the travel plan based on the A* algorithm, and configure the specific travel path into the scene container.

[0027] Furthermore, S3 specifically includes:

[0028] S31. Initialize the simulation time according to the first data in the configuration file;

[0029] S32. Perform spatiotemporal correlation judgment on the travel plans calculated by the activity planner: First, divide the travel plans according to time periods, and perform cruise route interweaving judgment, cruise route overlap judgment, and take-off and landing shared station judgment on the travel plans within the same time period. Then, allocate the travel plans without spatiotemporal correlation to different plan pools for initial classification.

[0030] S33. Initialize the state of objects that are constantly being updated in the simulation.

[0031] S34. Activate the listener module.

[0032] Preferably, S4 specifically includes:

[0033] S41. A simulation step is one object state update cycle in the simulation. The implementation of the travel plan consists of events in the simulation step. The object state is updated according to the events in the simulation step. Each time the simulation is performed, the state of the simulation object is updated and it is advanced to the next simulation step. At the same time, the update of the simulation step will filter and use the events in the corresponding simulation step stage.

[0034] S42. After determining the current simulation step, the simulation model judges whether to execute the simulation based on the current state of the object, obtains a judgment result, and updates or maintains the object state based on the judgment result:

[0035] S43. The simulation model calls the event manager in the simulator to check whether all travel plans have been simulated at the end of the simulation step, and calls the thread synchronization tool in the simulator after the simulation ends.

[0036] S44. The simulation model judges the travel status of all objects. First, it judges whether the simulation process of all travel plans has ended. At the same time, it judges whether the simulation has reached the preset deadline. If both are satisfied, the simulation ends. Otherwise, the simulation step is updated and the next simulation is performed.

[0037] Furthermore, the specific steps for simulation execution are as follows:

[0038] S421. The simulation model obtains the vehicle initialization state, calls the state judgment module in the simulator, judges the vehicle state based on the current vehicle parameters, and calls the standard state list in the simulation environment to obtain the current vehicle state.

[0039] S422. The simulation model obtains the network initialization status, calculates the road network capacity, and determines whether other vehicles are allowed to enter at present.

[0040] S423. Call the network admission judgment result in the network attribute table to judge the next state of the vehicle: If the network is admitted, the vehicle enters the new network and updates the node queue state, vehicle state and network state at the same time; if the network is saturated, the vehicle waits at the current node, the state of the vehicle and the network remains unchanged, and the simulation model will determine the state advancement of the vehicle in the next simulation step according to the new state of the target network.

[0041] Preferably, the simulation results include at least the first data input into the configuration file of the simulation model and the travel time, activity time and travel route data output by the activity planner.

[0042] Preferably, after the simulation ends, the listener module is shut down, and anomaly checks and memory release are performed.

[0043] A computer-readable storage medium is also disclosed, wherein a computer program is stored therein, and when the computer program is executed by a processor, it implements the event-driven rapid simulation method for low-altitude transportation systems.

[0044] Compared with the prior art, the beneficial effects of the present invention are:

[0045] This invention employs an event-responsive simulation design, constructing an efficient event-based simulation framework. During the simulation startup phase, the A* algorithm is used to accelerate pathfinding calculations, supporting flexible adjustments to flight plans. Simultaneously, based on the independent hierarchical characteristics of flight routes, sub-task groups are divided through task spatiotemporal correlation judgment, enabling parallel loading and computation for large-scale simulations. Through these improvements, this invention can achieve rapid simulation and evaluation of low-altitude, high-frequency, heterogeneous, and highly complex large-scale systems at any time point, reducing unnecessary simulation calculations, lowering the spatial and temporal complexity of the search, and significantly improving simulation efficiency. Attached Figure Description

[0046] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0047] Figure 1 This is a flowchart of the simulation method of the present invention.

[0048] Figure 2 This is the overall flowchart of the present invention.

[0049] Figure 3 This is a flowchart of the simulation operation of the present invention.

[0050] Figure 4 This is a flowchart of the travel plan spatiotemporal correlation judgment process of the present invention.

[0051] Figure 5 This is a flowchart of the simulation execution process of the present invention. Detailed Implementation

[0052] The present invention will be further described below through specific embodiments, but this is not a limitation of the present invention. Those skilled in the art can make various modifications or improvements based on the basic idea of ​​the present invention, but as long as they do not depart from the basic idea of ​​the present invention, they are all within the protection scope of the present invention.

[0053] This invention aims to achieve simulation modeling of urban low-altitude transportation systems by constructing a clear "input-simulation-output" process framework. This framework mainly consists of three processes: input, simulation, and output. Input is responsible for configuring and controlling the simulation input information and model operation; the simulation process is responsible for pre-configuration, simulation step iteration, and information monitoring and collection during the simulation process; and output is responsible for cleaning, slicing, and standardizing the output data.

[0054] Overall simulation process description: Standard input format data is passed to the configuration file via a data file path. This data, along with key simulation parameters stored in the controller, is read and parsed by the simulator and then invoked during the simulation. Each intermediate step in the simulation process is monitored by a listener and recorded by the event manager. Intermediate results are updated in the form of logs. After the simulation runs, the simulation results generate corresponding output files according to the controller's instructions and are generated in the corresponding locations according to the configured paths. The following section describes this process and the specific steps involved.

[0055] See Figures 1 to 5 One embodiment provided by the present invention is as follows:

[0056] This invention discloses a rapid simulation method for low-altitude transportation systems based on event-driven approaches, comprising the following steps:

[0057] S1. Simulation Preloading: The simulator parses and preloads the configuration file and method modules, loading the first data from the configuration file into the simulation environment. The method modules include program modules for simulation preprocessing, input, execution, and output. The configuration file includes at least the output directory and network data files, vehicle data files, infrastructure data files, travel plan data files, and control parameter data files for the simulation object running the simulation model. The first data includes at least network data, vehicle data, infrastructure data, travel plan data, and control parameter data. The simulation environment has a variable parameter table corresponding to the first data. The variable parameter table includes at least a network attribute table, a vehicle attribute table, and a simulation parameter table. The network attribute table corresponds to the network data of the simulation object, the vehicle attribute table corresponds to the vehicle data of the simulation object, and the simulation parameter table corresponds to the control parameter data of the simulation object.

[0058] The simulation process involves repeatedly updating these tables, requiring multiple data refreshes, creation of new tables, and overwriting of old ones. This is a process of efficiently updating, iterating, transferring, and storing variables. The configuration file specifies everything used in the simulation, including the path to input data, simulation control, traffic decisions, benefit calculations, mode selection, and other parameters. The simulator is a complex library of code methods that processes and calculates the input files, using various parameters derived from the configuration file.

[0059] Network data files are spatial carrier files that various modes of transportation in urban traffic rely on to complete their journeys. They consist of nodes with spatial attributes and the connections between nodes, and form the basis for traffic simulation. Network data files include at least road network data files and low-altitude airway network data files. This invention integrates the road network and low-altitude airway network into a unified network data file. In addition to its role in ground traffic, the road network is also designed to address low-altitude travel demand and serve air-to-ground transfer processes.

[0060] Low-altitude transportation requires the use of aircraft of different types and parameters as means of transportation. The vehicle data file should include at least the type, performance parameters, serial number, and capacity of cars, buses, ferries, subways, trains, and aircraft.

[0061] Low-altitude transportation requires the use of road infrastructure design to simulate docking and transfer. The infrastructure data file contains various behavioral parameters within the infrastructure, including at least passenger entry and exit, waiting, and boarding, as well as aircraft takeoff, landing, and cruising.

[0062] The travel plan data file is a file used by the simulator to initialize travel route parameter information during the simulation process. The travel plan data file includes at least the travel number, origin and destination, mode of transport, and time.

[0063] The control parameter data file should include at least the total simulation duration, simulation step size, simulation random seed, simulation start time, simulation end time, vehicle ticket cost, distance cost, and waiting cost.

[0064] S1 specifically includes:

[0065] S11, Configuration Injection: Input the configuration file address into the simulation model as a command-line parameter. The simulation framework automatically parses the configuration file at the corresponding address. After parsing, it puts the first data in the configuration file into the corresponding scene container and loads it into memory for the simulation model to run and call. The scene container is a collection of variables in the configuration file.

[0066] S12, Controller Loading: The simulation framework parses the parameters of the running module in the configuration file and loads them into the running controller of the simulation model to control the operation of the simulation model;

[0067] S13. Input Validation: The simulation framework validates the field names and data formats in the configuration file according to the pre-set data format to ensure that the input data meets the requirements of the simulation model and eliminates missing or erroneous data.

[0068] S14. Listener Loading: The simulation framework initializes the parameters and behaviors of the listener module and integrates it into the simulation model, ensuring that the listener can correctly capture and process events during the simulation process.

[0069] S2. Simulation Startup: Based on the initial data, the simulator configures the initial data and travel calculation dependency modules for the simulation model, and calculates the initial travel plan for the simulation. The calculation steps for the initial travel plan are as follows:

[0070] S21. Simulation resource configuration: Inject the method modules and parameter configurations used in the simulation into the simulation model to ensure the smooth calling of the simulation model;

[0071] S22. Simulation Input Configuration: Inject the first data in the configuration file into a unified scene container for easy unified calling;

[0072] S23. Trip Route Calculation: Call the activity planner of the simulation model, calculate the specific route of the trip in the trip plan based on the A* algorithm, and configure the specific route of the trip into the scene container as the direct input data of the simulation model.

[0073] The A* search algorithm, commonly known as the A-star algorithm, is a popular heuristic search algorithm widely used in path optimization. Its unique feature is that it introduces global information when examining each possible node in the shortest path, estimating the distance from the current node to the destination and using this estimate as a measure of the probability that the node is on the shortest path.

[0074] S3. State Initialization: The simulation model simulates the travel plan according to the simulation steps to obtain simulation results, and updates the state and records the time of the simulation object according to the simulation results.

[0075] S3 specifically includes:

[0076] S31. Simulation time initialization: Initialize the simulation time according to the first data in the configuration file to ensure that the simulation runs according to the start time configured in the simulation model;

[0077] S32, Subtask Initialization: Perform spatiotemporal correlation judgment on the travel plans calculated by the activity planner: First, divide the travel plans according to time periods, and perform cruise route interweaving judgment, cruise route overlap judgment, and take-off and landing shared station judgment on the travel plans within the same time period. Then, allocate the travel plans without spatiotemporal correlation to different plan pools for initialization and classification, so as to facilitate the use of different threads for simulation calculation.

[0078] S33. Object state initialization: Initialize the state of objects that are continuously updated in the simulation to ensure that all simulation objects are in the state at the end of the previous simulation activity when each simulation starts.

[0079] S34. Listener Activation: Activate the listener module to ensure that the simulation can call the listener at any stage to monitor the simulation environment and record events.

[0080] S4. Simulation Run: The simulation model simulates the travel plan according to the simulation steps, obtains simulation results, and updates the status and records the time of the simulation object based on the simulation results;

[0081] S4 specifically includes:

[0082] S41, Simulation Step Update: A simulation step is one object state update cycle in the simulation. The implementation of the travel plan consists of events in the simulation step. The object state is updated according to the events in the simulation step. Each time the simulation is performed, the state of the simulation object is updated and it advances to the next simulation step. At the same time, the simulation step update will filter and use the events in the corresponding simulation step stage.

[0083] S42. Simulation Execution: After determining the current simulation step, the simulation model judges whether to execute the simulation based on the current state of the object, obtains a judgment result, and updates or maintains the object state based on the judgment result.

[0084] S43. Simulation Synchronization: The simulation model calls the event manager in the simulator to check whether all travel plans have been simulated at the end of the simulation step, and calls the thread synchronization tool in the simulator after the simulation ends to ensure that all threads are in the initial state before the next simulation iteration begins.

[0085] S44. Iterative Judgment: The simulation model judges the travel status of all objects. First, it judges whether the simulation process of all travel plans has ended. At the same time, it judges whether the simulation has reached the preset deadline. If both are satisfied, the simulation ends. Otherwise, the simulation step is updated and the next simulation is performed.

[0086] Furthermore, the specific steps for simulation execution are as follows:

[0087] S421. The simulation model obtains the vehicle initialization state, calls the state judgment module in the simulator, judges the vehicle state based on the current vehicle parameters, and calls the standard state list in the simulation environment to obtain the current vehicle state.

[0088] S422. The simulation model obtains the network initialization status, calculates the road network capacity, and determines whether other vehicles are allowed to enter at present.

[0089] S423. Call the network admission judgment result in the network attribute table to judge the next state of the vehicle: If the network is admitted, the vehicle enters the new network and updates the node queue state, vehicle state and network state at the same time; if the network is saturated, the vehicle waits at the current node, the state of the vehicle and the network remains unchanged, and the simulation model will determine the state advancement of the vehicle in the next simulation step according to the new state of the target network.

[0090] This example only uses the interaction between vehicles and the network; other examples are similar and will not be elaborated upon here.

[0091] S5. Output Results: The simulation model outputs the simulation results in file format to the corresponding address according to the output directory in the configuration file, and performs cleaning, filtering, and statistical analysis on the simulation results. The simulation results include at least the data input into the configuration file of the simulation model, travel information with added details from the activity planner, and intermediate result events generated during the simulation. The specific output content and format of the simulation results are controlled by adjusting the corresponding parameters in the configuration file.

[0092] Finally, after the simulation ends, it checks whether there are any running simulation processes or remaining processes waiting to enter the simulator. If not, it closes the listener module, the iteration ends, the simulation process terminates, and anomaly investigation and memory release are performed.

[0093] Anomaly detection involves investigating and recording any anomalies that occur during the simulation process.

[0094] Memory release clears variables and instances during the simulation process, freeing up memory and ensuring a clean simulation environment for the next time.

[0095] The present invention also discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-mentioned event-driven rapid simulation method for low-altitude transportation systems.

[0096] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the present invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A rapid simulation method for low-altitude transportation systems based on event-driven principles, characterized in that, Includes the following steps: S1. The simulator parses and preloads the configuration file and method module, loading the first data in the configuration file into the simulation environment. The configuration file includes at least the output directory and network data files, vehicle data files, infrastructure data files, travel plan data files, and control parameter data files for the simulation object running the simulation model. The first data includes at least network data, vehicle data, infrastructure data, travel plan data, and control parameter data. The simulation environment has a variable parameter table corresponding to the first data. The variable parameter table includes at least a network attribute table, a vehicle attribute table, and a simulation parameter table. The network attribute table corresponds to the network data of the simulation object, the vehicle attribute table corresponds to the vehicle data of the simulation object, and the simulation parameter table corresponds to the control parameter data of the simulation object. S2. The simulator configures the first data and the travel calculation dependency module for the simulation model based on the first data, and calculates the initial travel plan for the simulation. S3. The simulator initializes the simulation time and the state of the simulation object, and activates the listening and calling function at the same time. S4. The simulation model simulates the travel plan of the simulation object according to the simulation steps to obtain simulation results, and updates the status and records the time of the simulation object according to the simulation results. S5. The simulation model outputs the simulation results to the corresponding address in file format according to the output directory in the configuration file; The calculation steps for the initial travel plan are as follows: S21. Inject the simulation method modules and parameter configurations into the simulation model; S22. Inject the first data in the configuration file into the unified scene container; S23. Call the activity planner of the simulation model, and calculate the specific travel path in the travel plan based on the A* algorithm, and configure the specific travel path into the scene container; S3 specifically includes: S31. Initialize the simulation time according to the first data in the configuration file; S32. Perform spatiotemporal correlation judgment on the travel plans calculated by the activity planner: First, divide the travel plans according to time periods, and perform cruise route interweaving judgment, cruise route overlap judgment, and take-off and landing shared station judgment on the travel plans within the same time period. Then, allocate the travel plans without spatiotemporal correlation to different plan pools for initial classification. S33. Initialize the state of objects that are constantly being updated in the simulation. S34. Activate the listener module.

2. The event-driven rapid simulation method for low-altitude transportation systems according to claim 1, characterized in that, S1 specifically includes: S11. Input the configuration file address into the simulation model as a command-line parameter. The simulation framework automatically parses the configuration file at the corresponding address. After parsing, it puts the first data in the configuration file into the corresponding scene container and loads it into memory. The scene container is a collection of variables in the configuration file. S12. The simulation framework parses the parameters of the running module in the configuration file and loads them into the running controller of the simulation model; S13. The simulation framework verifies the field names and data formats in the configuration file according to the pre-set data format; S14. The simulation framework initializes the parameters and behavior of the listener module and integrates it into the simulation model.

3. The event-driven rapid simulation method for low-altitude transportation systems according to claim 1, characterized in that, The network data files include at least road network data files and low-altitude airway network data files; The vehicle data file includes at least the type, performance parameters, serial number, and capacity of cars, buses, ferries, subways, trains, and aircraft; The infrastructure data file contains various behavioral parameters within the infrastructure, including at least passenger entry and exit, waiting and boarding, and aircraft takeoff, landing and cruising. The travel plan data file shall include at least the travel number, origin and destination, mode of transport, and time. The control parameter data file includes at least the total simulation duration, simulation step size, simulation random seed, simulation start time, simulation end time, vehicle ticket cost, distance cost, and waiting cost.

4. The event-driven rapid simulation method for low-altitude transportation systems according to claim 1, characterized in that, S4 specifically includes: S41. A simulation step is one object state update cycle in the simulation. The implementation of the travel plan consists of events in the simulation step. The object state is updated according to the events in the simulation step. Each time the simulation is performed, the state of the simulation object is updated and it is advanced to the next simulation step. At the same time, the update of the simulation step will filter and use the events in the corresponding simulation step stage. S42. After determining the current simulation step, the simulation model judges whether to execute the simulation based on the current state of the object, obtains a judgment result, and updates or maintains the object state based on the judgment result: S43. The simulation model calls the event manager in the simulator to check whether all travel plans have been simulated at the end of the simulation step, and calls the thread synchronization tool in the simulator after the simulation ends. S44. The simulation model judges the travel status of all objects. First, it judges whether the simulation process of all travel plans has ended. At the same time, it judges whether the simulation has reached the preset deadline. If both are satisfied, the simulation ends. Otherwise, the simulation step is updated and the next simulation is performed.

5. The event-driven rapid simulation method for low-altitude transportation systems according to claim 4, characterized in that, The specific steps for simulation execution are as follows: S421. The simulation model obtains the vehicle initialization state, calls the state judgment module in the simulator, judges the vehicle state based on the current vehicle parameters, and calls the standard state list in the simulation environment to obtain the current vehicle state. S422. The simulation model obtains the network initialization status, calculates the road network capacity, and determines whether other vehicles are allowed to enter at present; S423. Call the network admission judgment result in the network attribute table to judge the next state of the vehicle: If the network is admitted, the vehicle enters the new network and updates the node queue state, vehicle state and network state at the same time; if the network is saturated, the vehicle waits at the current node, the state of the vehicle and the network remains unchanged, and the simulation model will determine the state advancement of the vehicle in the next simulation step according to the new state of the target network.

6. The event-driven rapid simulation method for low-altitude transportation systems according to claim 1, characterized in that, The simulation results include at least the first data input into the configuration file of the simulation model and the travel time, activity time and travel route data output by the activity planner.

7. The event-driven rapid simulation method for low-altitude transportation systems according to claim 1, characterized in that, After the simulation is complete, shut down the listener module and perform troubleshooting and memory release.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the event-driven rapid simulation method for low-altitude transportation systems as described in any one of claims 1-7.

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