Region classification method and system under crowdsourcing environment with multi-level task allocation

Abstract

The invention relates to the technical field of task allocation, in particular to a regional classification method and system for multi-level task allocation in a crowdsourcing environment. The method comprises the steps of obtaining crowdsourcing worker historical data; utilizing a random forest model to predict the number of workers in each grid after grid division according to historical data of crowdsourcing workers; a density ratio is obtained based on the predicted number of workers, a task area is divided into a low-ratio area, a medium-ratio area and a high-ratio area, and based on a cost benefit maximization target, a delay strategy is adopted in the low-ratio area, a balance strategy is adopted in the medium-ratio area, and a cooperation strategy is adopted in the high-ratio area; and carrying out cross-grid region recruitment on the remaining tasks to complete task allocation. The invention provides a multi-level task allocation strategy of a region classification method in a crowdsourcing environment, and the scheme divides a whole gridded sensing region into a low-ratio region, a medium-ratio region and a high-ratio region through corresponding density ratios, and fully considers the characteristics of different regions.

Description

Technical Field

[0001] The present invention relates to the technical field of task allocation, and in particular to a method and system for regional classification in a crowdsourcing environment for multi-level task allocation. Background Art

[0002] Task allocation is a crucial technical issue in mobile crowdsourcing perception systems. Its core lies in how to effectively schedule workers to complete various tasks while finding the best balance between resource utilization and service quality. With the increase in task complexity, traditional task allocation methods face many challenges, such as low allocation efficiency, long response time, and insufficient resource allocation. In order to cope with these challenges, a variety of emerging task allocation strategies have emerged in the research field, but these methods are still insufficient in dynamically changing environments and complex task scenarios, lacking flexibility and intelligent processing capabilities. In traditional crowdsourcing systems, task allocation often relies on fixed rules and static information, which makes it difficult to achieve optimal resource allocation when facing changes in the number of workers and task requirements. In particular, when the spatial and temporal characteristics of tasks have an increasingly obvious impact on resource scheduling, traditional methods are difficult to effectively cope with this challenge. Therefore, there is an urgent need for a new task allocation framework that can dynamically adjust strategies to adapt to changes in participants and tasks, thereby improving overall execution efficiency. Summary of the invention

[0003] In order to solve the above-mentioned problems, the present invention provides a method and system for regional classification in a crowdsourcing environment for multi-level task allocation. Based on the regional ratio classification of the number of workers' predictions, the task area is divided into low ratio, medium ratio and high ratio areas, and corresponding strategies are adopted for areas of different classifications to allocate tasks within the area. After the task allocation in each area is completed, the remaining cost is used to allocate tasks across grid areas, thereby ensuring a high task perception quality while ensuring the task completion rate.

[0004] In a first aspect, the present invention provides a method for regional classification in a crowdsourcing environment with multi-level task allocation, which adopts the following technical solution: A method for region classification in a crowdsourcing environment with multi-level task allocation, including: Obtain historical data of crowdsourcing workers; The random forest model is used to predict the number of workers in each grid after grid division based on the historical data of crowdsourcing workers; Based on the predicted number of workers, the density ratio is obtained, and the task area is divided into low ratio, medium ratio and high ratio areas. Based on the cost-effectiveness maximization goal, a delay strategy is adopted in the low ratio area, a balance strategy is adopted in the medium ratio area, and a collaborative strategy is adopted in the high ratio area. For the remaining tasks, cross-grid area recruitment is adopted to complete the task allocation.

[0005] Furthermore, the random forest model is used to predict the number of workers in each grid after grid division based on the historical data of crowdsourcing workers, including grid division of the entire perception area and using the random forest algorithm to predict the number of workers in each grid, wherein the attribute set of crowdsourcing workers is ,in Represent the horizontal and vertical coordinate positions of the workers, Represents the perceived quality ability level of crowdsourcing workers.

[0006] Furthermore, the density ratio is obtained based on the predicted number of workers, including the ratio of the predicted number of workers in each grid to the actual number of tasks in each grid after the task is released, as the density ratio, and the entire perception area is divided into low ratio, medium ratio and high ratio areas through the density ratio.

[0007] Furthermore, the delay strategy in the low ratio area includes adopting a binary integer programming method to select the most cost-effective worker based on the perceived quality of the worker and the distance between the tasks, so as to maximize the cost-effectiveness ratio in each grid of the low ratio area, which is expressed as: , in Indicates workers The perceived quality of Indicates that workers Assign to task The cost of The value is 1 or 0. When the value is 1, it means that the task Assign to workers , when the value is 0, it means that the allocation is not successful.

[0008] Furthermore, the described balanced strategy adopted in the medium ratio area includes a method of allocating multiple rounds of packaged tasks, wherein the tasks in the area are first preliminarily packaged, and then allocated according to the emergency priority of the task packages. After the first round of packaged allocation is completed, the task packages that have not been successfully allocated are unpacked and repackaged to improve the matching rate and flexibility of task allocation.

[0009] Furthermore, the strategy of adopting collaboration in the high ratio area includes selecting a worker combination for each task through a greedy algorithm, wherein all possible worker combinations for each task are first arranged, and the total cost and perceived quality of each combination are calculated one by one. When a combination has the lowest total cost or the highest perceived quality at the same cost, the system sets the combination as the current optimal choice. As the allocation is completed, workers who have been assigned to a specific task will not be repeatedly assigned to other tasks, and the recruitment cost distance of the workers is calculated by the following formula: in, Representing the workers and mission points The Haversine distance between them is expressed as: in: in, is the radius of the earth. In the present invention, the Haversine distance is used for the relevant distance calculation.

[0010] Furthermore, the allocation of the remaining tasks is completed by recruiting across grid areas, including analyzing the grid where each unallocated task is located, detecting all directly adjacent grid areas, and screening qualified workers in adjacent grids according to the urgency of the task after completing the task allocation in the low ratio, medium ratio and high ratio areas, generating a set of optional workers for each task, and achieving global optimal allocation through a linear programming method while maximizing the cost-effectiveness ratio of the objective function.

[0011] Second, a regional classification system in a crowdsourcing environment with multi-level task allocation, including: The data acquisition module is configured to acquire historical data of crowdsourcing workers; The prediction module is configured to use a random forest model to predict the number of workers in each grid after grid division based on the historical data of crowdsourcing workers; The strategy module is configured to obtain a density ratio based on the predicted number of workers, divide the task area into low ratio, medium ratio and high ratio areas, wherein based on the cost-effectiveness maximization goal, a delay strategy is adopted in the low ratio area, a balance strategy is adopted in the medium ratio area, and a collaborative strategy is adopted in the high ratio area; The allocation module is configured to adopt cross-grid area recruitment for the remaining tasks to complete the task allocation.

[0012] In a third aspect, the present invention provides a computer-readable storage medium storing a plurality of instructions, wherein the instructions are suitable for being loaded and executed by a processor of a terminal device, for example, a method for regional classification in a crowdsourcing environment with multi-level task allocation.

[0013] In a fourth aspect, the present invention provides a terminal device comprising a processor and a computer-readable storage medium, wherein the processor is used to implement various instructions; and the computer-readable storage medium is used to store multiple instructions, wherein the instructions are suitable for being loaded by the processor and executing the method for regional classification in a crowdsourcing environment with multi-level task allocation.

[0014] In summary, the present invention has the following beneficial technical effects: (1) The present invention provides a multi-level task allocation strategy for a regional classification method in a crowdsourcing environment. The scheme divides the entire perception area after gridding into low-ratio, medium-ratio and high-ratio areas according to corresponding density ratios, fully considering the characteristics of different areas.

[0015] (2) Different delay, balance and collaboration strategies are adopted according to the three ratio areas. After the three ratio areas are divided, the division of cross-grid areas is considered to make full use of resources. While improving the task perception quality, it also greatly improves the task completion rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is the distance threshold in the ratio region of embodiment 1 of the present invention Schematic diagram of internal task packaging; Figure 2 is a schematic diagram of recruiting workers across grid areas according to Example 1 of the present invention; Figure 3 is a schematic diagram of the comparison of the perceptual qualities corresponding to different costs on different data sets in embodiment 1 of the present invention; Figure 4 It is a schematic diagram comparing the task completion rates corresponding to different costs in different cities according to Example 1 of the present invention. DETAILED DESCRIPTION

[0017] The present invention is further described in detail below in conjunction with the accompanying drawings.

[0018] Glossary: Density ratio: The ratio of the predicted number of workers in a grid block to the actual number of tasks in the task release process is called the density ratio, which is expressed as: in represents the predicted number of workers for a grid block, Represents the actual number of tasks in the grid block. When is the low ratio area, When is the middle ratio area, It is a high ratio area.

[0019] Cost-effectiveness ratio: The cost-effectiveness ratio (CER) is the ratio between the benefit and cost of each task assignment. It is used to measure the relative ratio between the quality of the workers assigned to the task and the corresponding cost, so the calculation of CER can be expressed as: in Indicates workers The perceived quality of Indicates that workers Assign to task cost.

[0020] Task completion rate: Task completion rate It refers to the ratio of the number of completed tasks to the total number of tasks within a specific period of time. It is used to measure the efficiency of task allocation and execution and reflects the degree of success in task completion. It is defined as: in Indicates the number of completed tasks. Indicates the total number of tasks.

[0021] Example 1 Reference Figure 1 , a method for regional classification in a crowdsourcing environment with multi-level task allocation in this embodiment, Obtain historical data of crowdsourcing workers; The random forest model is used to predict the number of workers in each grid after grid division based on the historical data of crowdsourcing workers; Based on the predicted number of workers, the density ratio is obtained, and the task area is divided into low ratio, medium ratio and high ratio areas. Based on the cost-effectiveness maximization goal, a delay strategy is adopted in the low ratio area, a balance strategy is adopted in the medium ratio area, and a collaborative strategy is adopted in the high ratio area. For the remaining tasks, cross-grid area recruitment is adopted to complete the task allocation.

[0022] Specifically: S1. Obtain historical data of crowdsourcing workers, record the workers' task participation history through mobile applications or crowdsourcing platforms, and extract the geographical location, time information and task-related features (such as weather, traffic, holidays, etc.) related to the workers. Then, based on these data, map the workers' historical activity trajectories to the grids in the task area, count the number of historical workers in each grid, and provide data support for subsequent regional division, task allocation and strategy formulation.

[0023] S2. Use the random forest model to predict the number of workers in each grid after grid division based on the historical data of crowdsourcing workers. Among them, the entire perception area (the geographical scope of task execution and data collection in the mobile crowdsourcing perception system. It is usually a specific target area that covers all locations where the perception task needs to be completed) is first gridded. Then, the random forest algorithm is used to predict the number of workers in each grid. The prediction is based on historical data, including the historical number of workers in each grid, and considers influencing factors such as economic level, traffic conditions, and weather to improve the accuracy of the prediction. Through the comprehensive analysis of these factors, the model can more accurately estimate the distribution of workers in different regions and provide reliable data support for subsequent task allocation strategies. In the prediction process, data preprocessing is first performed to process category features (weather) and generate lagged features (such as the number of workers in the past 7 days, 14 days, etc.) to capture the impact of time series. Subsequently, the entire dataset is divided into training and testing sets, and hyperparameters are tuned through grid search and cross-validation to optimize model performance. Finally, multiple decision trees are trained and the prediction results of each tree are combined to give the final prediction value, reducing overfitting and improving generalization ability. With the help of the relevant data of the first 39 days in the gMission dataset, the number of workers on the 40th day is predicted; at the same time, the relevant data of the first 42 days in the EverySender dataset is used to predict the data on the 43rd day.

[0024] In the specific implementation process, the attribute set of crowdsourcing workers is ,in Represent the horizontal and vertical coordinate positions of the workers, Indicates the worker's level, that is, the worker's ability to perceive the quality of the task. There are three levels of workers: senior workers (level A), with a perceived quality of 50; intermediate workers (level B), with a perceived quality of 30; and junior workers (level C), with a perceived quality of 20.

[0025] S3. Get the density ratio based on the predicted number of workers, Among them, the predicted number of workers in each grid and the number of tasks in each grid after the actual task is released are used according to the density ratio formula Calculate the value .when When is the low ratio area, When is the middle ratio area, The entire perception area is divided into low ratio, medium ratio and high ratio areas.

[0026] In the specific implementation process, the attribute set of the crowdsourcing task is ,in Respectively represent the horizontal and vertical coordinate positions of the task. Indicates the urgency of the task, that is, how long it takes to complete the task after it is issued. The urgency is divided into three categories: Need to be completed within half an hour after the task is released, secondary task Need to complete within one hour, level three task There is no time limit. The ratio of the predicted number of workers per grid to the actual number of tasks released is less than 1, which is a low ratio area, between 1 and 1.2, which is a medium ratio area, and greater than 1.2, which is a high ratio area.

[0027] (1) In the process of task allocation in low-ratio areas, the scheme described in this example adopts a binary integer programming method to select the most cost-effective workers based on the workers' perceived quality and the distance between tasks, so as to maximize the cost-effectiveness ratio in each grid of low-ratio areas. This process can be expressed as follows: (3) The constraints are: (4) (5) in When it is equal to 1, it means that the task Assign to workers , when it is equal to 0, it means that the allocation is not successful. The distance cost calculation of recruiting workers in this process is expressed as: (6) in Represents the Haversine distance between the worker and the task.

[0028] (2) In the task allocation process in the medium ratio area, this example adopts a multi-round task allocation strategy. First, the tasks in the area are initially packaged, and then allocated according to the emergency priority of the task packages. After the first round of package allocation is completed, the task packages that failed to be successfully allocated are unpacked and repacked to improve the matching rate and flexibility of task allocation. Throughout the process, the goal is always to maximize the cost-effectiveness ratio to ensure the efficiency and benefits of resource allocation.

[0029] The constraints include: The first round of task packaging rules and constraints: the packaging group follows the combination strategy of "first-level task-second-level task" or "second-level task-third-level task". There can be only one first-level task and one second-level task in each packaging group, but there can be multiple third-level tasks.

[0030] Second round task packaging rule constraints: In the second round, tasks are allocated according to the strategy of "three-level task - three-level task". Multiple three-level tasks are allowed to be included in the packaging group.

[0031] Distance constraint between packaging tasks: The distance between any two tasks in a group must not exceed .

[0032] In the specific implementation, according to the number and density of data in the data set, The value range is set to between 5 km and 8 km. Understandably, The value can be adjusted according to actual needs.

[0033] (3) In the task allocation process in the high ratio area, this example selects a worker combination with higher perceived quality for each task through a greedy algorithm. The system will consider all possible worker combinations (such as AC, AB, BC, and CC) and calculate the total recruitment cost and perceived quality of each combination. Based on the greedy algorithm, the system gives priority to the combination with the lowest total cost and the highest perceived quality. The specific steps are as follows: The system first arranges all possible worker combinations for each task and calculates the total cost and perceived quality of each combination one by one. When the total cost of a combination is lower or the perceived quality is higher at the same cost, the system sets the combination as the current optimal choice. As the allocation is completed, workers who have been assigned to a specific task will not be repeatedly assigned to other tasks. During this process, the system calculates the recruitment cost distance of the worker using the following formula: (7) S4. After completing the task allocation in the low ratio, medium ratio, and high ratio regions, this example uses a cross-grid region partitioning method for the remaining unallocated tasks. Specifically, the system searches for the available worker sets in adjacent grid regions to form a selectable worker set for each task, such as Figure 2As shown in Figure 1. On this basis, the system further applies the linear programming method to find the global optimal solution by maximizing the cost-effectiveness ratio. This method comprehensively considers the perceived quality of workers and the recruitment cost to ensure that the remaining tasks are effectively allocated under the premise of minimizing costs and maximizing quality. In this way, even in different grid areas, the system can make reasonable use of resources and achieve cross-regional global optimization. Specifically, after completing the task allocation in the low ratio, medium ratio and high ratio areas, the grid where each unassigned task is located is analyzed to detect all its directly adjacent grid areas. These adjacent grid areas are regarded as potential candidate resources to further expand the range of optional workers for the task. According to the urgency of the task, the system screens qualified workers in the adjacent grids within the reachable distance of the workers and generates a set of optional workers for each task. After determining the set of optional workers for each task, the system uses the linear programming method to maximize the cost-effectiveness ratio of the objective function while satisfying the corresponding constraints to achieve global optimal allocation. After the linear programming solution is completed, the system assigns an optimal worker to each unassigned task to ensure that the task allocation result not only meets the quality requirements but also minimizes the resource cost.

[0034] In the process of linear programming, by establishing a mathematical model, the goal is set to maximize the cost-effectiveness ratio, and the solution is combined with relevant constraints. Specifically, the objective function uses the cost-effectiveness ratio of each task and worker combination as a weight to select the optimal allocation scheme to achieve overall optimization. In the model: ① The decision variable is used to indicate whether each task is completed by a certain worker; ② The objective function is defined as the maximization of the sum of the cost-effectiveness ratios of the selected combination; ③ The constraints include that each task can only be assigned to one worker, each worker can only complete one task, and the distance between tasks and workers must be within a specified range. By introducing perceptual quality and distance data, linear programming tools (such as PuLP) generate standardized problem solving problems and apply solvers to calculate the global optimal solution for the optimal allocation of tasks and workers, thereby ensuring the highest perceptual quality coverage at the lowest cost.

[0035] In the specific implementation, it is assumed that the worker's travel speed is 60 km / h. According to the urgency of the task, the system sets the maximum distance range of the reachable worker for different levels of tasks. For the first-level task, the maximum distance of the reachable worker is 30km; for the second-level task, the maximum distance range is extended to 60 km; and for the third-level task, the maximum distance range of the reachable worker is set to 100-160 km.

[0036] In order to verify the effectiveness of the solution described in this embodiment, this embodiment carried out the following experimental verification: The simulation experiment is as follows: Using four sets of real data sets, experiments were carried out on the task allocation system on the EverySender and gMission data sets respectively: the regional ratio algorithm represents the method described in this embodiment, the random allocation algorithm mentioned in “A heterogeneous mobile crowd sensing system for urban public safety”, the minimum cost algorithm mentioned in “Participant quantity-aware online task allocation in mobile crowd sensing”, the random utility value algorithm mentioned in “Online dependent task assignment in preference awarespatial crowdsourcing”, and the two-stage online algorithm and two-stage greedy algorithm mentioned in “Two-sided online micro-taska assignment in spatial crowdsourcing,” represent comparative task allocation algorithms.

[0037] In the experiment, some data from the EverySender and gMission datasets were extracted for evaluation. The practicality and coverage of the algorithm were evaluated by observing the completion rate of tasks and the overall perceived quality of tasks with increasing labor costs. Figure 3 (a) to Figure 3 (b) shows the comparison of the overall perceived quality after matching crowdsourcing workers with tasks at different costs. Figure 4 (a) to Figure 4 (b) shows the comparison of task completion rates under different costs and different allocation methods. It can be seen from the figure that the method proposed in this implementation has a better allocation effect than other online algorithms.

[0038] Example 2 This embodiment provides a regional classification system in a crowdsourcing environment with multi-level task allocation, including: The data acquisition module is configured to acquire historical data of crowdsourcing workers; The prediction module is configured to use a random forest model to predict the number of workers in each grid after grid division based on the historical data of crowdsourcing workers; The strategy module is configured to obtain a density ratio based on the predicted number of workers, divide the task area into low ratio, medium ratio and high ratio areas, wherein based on the cost-effectiveness maximization goal, a delay strategy is adopted in the low ratio area, a balance strategy is adopted in the medium ratio area, and a collaborative strategy is adopted in the high ratio area; The allocation module is configured to adopt cross-grid area recruitment for the remaining tasks to complete the task allocation.

[0039] A computer-readable storage medium stores a plurality of instructions, wherein the instructions are suitable for being loaded and executed by a processor of a terminal device, and the method for regional classification in a crowdsourcing environment with multi-level task allocation is disclosed.

[0040] A terminal device includes a processor and a computer-readable storage medium, wherein the processor is used to implement various instructions; the computer-readable storage medium is used to store multiple instructions, wherein the instructions are suitable for being loaded by the processor and executing the method for regional classification in a crowdsourcing environment with multi-level task allocation.

[0041] The above are all preferred embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Therefore, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for regional classification in a crowdsourcing environment with multi-level task allocation, characterized in that: include: Obtain historical data of crowdsourcing workers; The random forest model is used to predict the number of workers in each grid after grid division based on the historical data of crowdsourcing workers; Based on the predicted number of workers, the density ratio is obtained, and the task area is divided into low ratio, medium ratio and high ratio areas. Based on the cost-effectiveness maximization goal, a delay strategy is adopted in the low ratio area, a balance strategy is adopted in the medium ratio area, and a collaborative strategy is adopted in the high ratio area. For the remaining tasks, cross-grid area recruitment is adopted to complete the task allocation.

2. The method for regional classification in a crowdsourcing environment with multi-level task allocation according to claim 1 is characterized in that: The random forest model is used to predict the number of workers in each grid after grid division based on the historical data of crowdsourcing workers, including grid division of the entire perception area and using the random forest algorithm to predict the number of workers in each grid, where the attribute set of crowdsourcing workers is ,in Represent the horizontal and vertical coordinate positions of the workers, Represents the perceived quality ability level of crowdsourcing workers.

3. The method for regional classification in a crowdsourcing environment with multi-level task allocation according to claim 2 is characterized in that: The density ratio is obtained based on the predicted number of workers, including the ratio of the predicted number of workers in each grid to the actual number of tasks in each grid after the task is released, as the density ratio. The entire perception area is divided into low ratio, medium ratio and high ratio areas through the density ratio.

4. The method for regional classification in a crowdsourcing environment with multi-level task allocation according to claim 3 is characterized in that: The delay strategy in the low ratio area includes adopting a binary integer programming method to select the most cost-effective worker based on the perceived quality of the worker and the distance between the tasks to maximize the cost-effectiveness ratio in each grid of the low ratio area, which is expressed as: ， in Indicates workers The perceived quality of Indicates that workers Assign to task The cost of The value is 1 or 0. When the value is 1, it means that the task Assign to workers , when the value is 0, it means that the allocation is not successful.

5. The method for regional classification in a crowdsourcing environment with multi-level task allocation according to claim 4 is characterized in that: The described balanced strategy is adopted in the medium ratio area, including a multi-round packaged task allocation method, firstly the tasks in the area are preliminarily packaged, and then allocated according to the emergency priority of the task packages. After the first round of package allocation is completed, the task packages that have not been successfully allocated are unpacked and repacked to improve the matching rate and the flexibility of task allocation.

6. The method for regional classification in a crowdsourcing environment with multi-level task allocation according to claim 5, characterized in that: The strategy of adopting collaboration in the high ratio area includes selecting a worker combination for each task through a greedy algorithm, wherein all possible worker combinations for each task are first arranged, and the total cost and perceived quality of each combination are calculated one by one. When a combination has the lowest total cost or the highest perceived quality at the same cost, the system sets the combination as the current optimal choice. With the completion of the allocation, workers who have been assigned to a specific task will not be repeatedly assigned to other tasks, and the recruitment cost distance of the workers is calculated by the following formula: in, Representing the workers and mission points The Haversine distance between them is expressed as: in: in, is the radius of the Earth.

7. The method for regional classification in a crowdsourcing environment with multi-level task allocation according to claim 6, characterized in that: The method of allocating the remaining tasks by recruiting across grid areas includes analyzing the grid where each unassigned task is located, detecting all directly adjacent grid areas, screening qualified workers in adjacent grids according to the urgency of the task, generating a set of optional workers for each task, and achieving global optimal allocation while maximizing the cost-effectiveness ratio of the objective function through a linear programming method.

8. A regional classification system in a crowdsourcing environment with multi-level task allocation, characterized in that: include: The data acquisition module is configured to acquire historical data of crowdsourcing workers; The prediction module is configured to use a random forest model to predict the number of workers in each grid after grid division based on the crowdsourcing workers' historical data; The strategy module is configured to obtain a density ratio based on the predicted number of workers, divide the task area into low ratio, medium ratio and high ratio areas, wherein based on the cost-effectiveness maximization goal, a delay strategy is adopted in the low ratio area, a balance strategy is adopted in the medium ratio area, and a collaborative strategy is adopted in the high ratio area; The allocation module is configured to adopt cross-grid area recruitment for the remaining tasks to complete the task allocation.

9. A computer-readable storage medium storing a plurality of instructions, characterized in that: The instructions are suitable for being loaded by a processor of a terminal device and executing the method according to claim 1 .

10. A terminal device, comprising a processor and a computer-readable storage medium, wherein the processor is used to implement each instruction; and the computer-readable storage medium is used to store multiple instructions, characterized in that: The instructions are suitable for being loaded by a processor and executing the method as claimed in claim 1 .

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