Unmanned file library automatic management method and management system thereof

Abstract

The invention discloses an automatic management method and management system for an unmanned file library, and relates to the technical field of intelligent file management, and the method comprises the steps: continuously monitoring and recording the access time period, frequency and importance mark score of each file through a sensor; by analyzing the use frequency, the time period and the importance mark of different archives, the access requirements of which archives will be increased within a period of time in the future are predicted; a prediction model is generated according to the access frequency trend, the access demand change of each file in a certain time in the future is obtained, a self-adaptive optimization mechanism is established, and the files which are frequently accessed are automatically moved to a position which is easier to access through a self-adaptive path optimization algorithm. According to the invention, full-process automation from data acquisition and analysis to path optimization and archive access is realized, manual intervention is greatly reduced, and the unmanned trend of modern archive management is met; and the change of file access requirements can be adapted.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent archive management, and in particular to an automated management method for an unmanned archive library and a management system thereof. Background Art

[0002] With the development of information technology and the continuous improvement of archive management needs, the traditional manual archive management model can no longer meet the needs of modern efficient management. In large archives, traditional archive access usually relies on manual operations, which is time-consuming and prone to errors. At the same time, the storage capacity of archives is increasing, and how to effectively classify, manage and retrieve archives has become an important issue in the field of archive management. In recent years, with the gradual application of automation technology, archive management systems are developing in the direction of intelligence and automation.

[0003] Existing archive management systems mainly include simple access record functions, but most lack the ability to analyze and dynamically adjust the frequency of archive use. In addition, the optimization of archive access paths is rarely considered in the existing technology, and most systems still use fixed storage locations, resulting in low access efficiency for frequently used archives, increasing operational redundancy and workload. At the same time, existing archive management systems are generally insufficient in terms of automation, and are unable to make adaptive adjustments based on the importance of archives, frequency of use, etc., making it difficult to achieve truly unmanned management. Summary of the invention

[0004] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] In view of the problems existing in the existing unmanned archive library automated management method and management system, the present invention is proposed.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] An automated management method for an unmanned archive library, the method comprising the following steps:

[0008] Step 1: Use sensors to continuously monitor and record the access period, frequency, and importance score of each file;

[0009] Step 2: By analyzing the usage frequency, time period, and importance marks of different archives, predict which archives will have an increased access demand in the future; generate a prediction model based on the access frequency trend to obtain the access demand changes of each archive in the future;

[0010] The outputs of the forecast include: high-frequency access to archives, seasonal or cyclical demand changes, and peak access periods for specific archive categories;

[0011] Step 3: Establish an adaptive optimization mechanism. Through the adaptive path optimization algorithm, frequently accessed files will be automatically moved to a more easily accessible location, that is, frequently used files or hot files will be stored in the location closest to the borrowing and returning window or robot, reducing access time.

[0012] As a preferred solution of the unmanned archive library automatic management method of the present invention, the step 1 specifically includes:

[0013] S101: Calculate the access time period of each file x in the past period of time based on the collected access time period data.

[0014] Where Δt is the time period, usually set to hours, days or weeks;

[0015] S102: Record the importance of each file according to its characteristics. x (t) and normalize to ensure that the importance scores of all files are within the same range;

[0016] Respectively represent the lowest and highest importance tag values ​​of all archives.

[0017] As a preferred solution of the unmanned archive library automatic management method described in the present invention, wherein:

[0018] In the step 2, a storage demand prediction model is constructed, and the storage demand in the future is predicted by historical data, which specifically includes the following steps:

[0019] S201: Construct a historical prediction function of access frequency, based on the access frequency f x (t′) predicts future access needs and uses integrals to represent the time interval [t 0 ,t], and uses an exponential decay function to consider the impact of past frequencies on current predictions; its expression is:

[0020] Find,α represents the attenuation coefficient, which controls the influence of past access data on current demand forecast, t, t′ represent the current time and historical time respectively;

[0021] S202: According to the periodic change of access frequency, a sine function is introduced to represent the periodic fluctuation of access demand; the sine function describes the influence of different time periods on demand, and the periodic effect is weighted by integration, and its expression is:

[0022]

[0023] Where: P x (t) periodic represents the access demand of file x at time t based on periodic factors, w k Represents the frequency factor of periodic fluctuations, t k Indicates the starting point of the period of periodic change, Δt indicates the time span of periodic change;

[0024] S203: Use importance marking score I x (t) The access demand for archives is weighted to indicate that the demand for archives will be overestimated during periods of high importance, and its expression is:

[0025]

[0026] Where: P x (t) importance represents the access demand prediction of file x at time t based on the weighted importance tag score, γ k represents the weight coefficient, and γ represents the attenuation factor that the impact of the importance of the archive on future access needs will decay over time.

[0027] As a preferred solution of the unmanned archive library automatic management method described in the present invention, the final access demand forecast of archive x at time t is obtained by combining historical trends, periodic changes and importance influences. Its expression is:

[0028]

[0029] in, represents the comprehensive access demand forecast value of file x at time t, is a positive real number representing the intensity of access demand.

[0030] As a preferred solution of the unmanned archive library automatic management method of the present invention, the adaptive optimization mechanism includes the following steps:

[0031] S301: define file x at position l x The access cost C on

[0032]

[0033] Where d represents the file location l x Distance from access window or robot position;

[0034] S302: Define the total system access cost C total , and in order to achieve overall optimization, the system hopes to reduce the total cost C total minimize;

[0035]

[0036] Where n represents the total number of files, and the total cost C total The objective function to be minimized is:

[0037]

[0038] S303: Then, the storage location of the archive is gradually optimized through the simulated annealing algorithm.

[0039] As a preferred solution of the unmanned archive library automatic management method of the present invention, the simulated annealing algorithm includes the following steps:

[0040] S3031: Initially randomly select the location combination of the archive {l 1 ,l 2 ,l 3 ...l n};

[0041] S3032: Set the current total access cost C total is the energy function;

[0042] S3033: Randomly select a file pair (l x ,l y ), swap their positions and calculate the new total access cost;

[0043] S3034: According to the Metropolis criterion, the probability of receiving a new state is:

[0044]

[0045] Among them, T is the temperature in the simulated annealing process, which gradually decreases with the number of iterations until the temperature change of T is less than the set threshold and the iteration is stopped.

[0046] As a preferred solution of the unmanned archive library automatic management method described in the present invention, when C new <C old , that is, when the new state is better than the old state, P is close to 1, and the new state is always accepted;

[0047] When C new >Cold That is, when the new state is worse than the old state, the acceptance probability is determined by P, which depends on the temperature T and the cost difference C new -C old size.

[0048] The management system of the automated management method of an unmanned archive library as described above comprises: a data collection and monitoring module, which monitors and records the access information of each archive in real time, including the access time period, frequency, and importance mark;

[0049] The data analysis and prediction module performs data analysis and modeling based on the access frequency, time period, importance and other data provided by the data collection module to generate a prediction model for future access needs of archives;

[0050] The adaptive path optimization module uses the path optimization algorithm to dynamically adjust the storage location of files according to the changes in the file usage frequency. It moves high-frequency or important files to the optimal storage area;

[0051] The archive storage and retrieval module is responsible for the physical storage and retrieval of archives. It cooperates with the manipulator and motion module to achieve accurate storage and retrieval of archives;

[0052] and a robot control module that controls the movement and execution of operations by the robot, including grabbing files from the storage area, moving them to the transfer area, returning the files to their original locations, and other operations.

[0053] A computer device includes a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, the steps of the above-mentioned unmanned file library automatic management method are implemented.

[0054] A computer-readable storage medium having a computer program stored thereon, characterized in that: when the computer program is executed by a processor, the steps of the above-mentioned unmanned file library automated management method are implemented.

[0055] Beneficial effects of the present invention:

[0056] 1. By monitoring the access frequency of archives and predicting future demand changes, the system can dynamically adjust the storage location of archives, reduce access time and improve efficiency; by using an adaptive path optimization algorithm, the storage location of archives is no longer fixed, and the system automatically optimizes the storage area according to actual needs, thereby improving the intelligent level of access.

[0057] 2. The present invention realizes the automation of the entire process from data collection and analysis to path optimization and archive access, greatly reducing manual intervention and conforming to the unmanned trend of modern archive management; it can adapt to changes in archive access needs, ensuring that important archives or high-frequency archives can be stored in convenient locations in priority during different time periods, thus ensuring the flexibility and efficiency of access. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0059] Figure 1 This is a schematic diagram of the overall process of an unmanned file library automated management method proposed by the present invention;

[0060] Figure 2 This is a comparison diagram between the unmanned file library automated management method proposed by the present invention and the traditional method. DETAILED DESCRIPTION

[0061] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0062] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0063] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0064] Reference Figure 1 , which is an embodiment of the present invention, provides an automated management method for an unmanned archive library and a management system thereof.

[0065] An automated management method for an unmanned archive library, the method comprising the following steps:

[0066] Step 1: Use sensors to continuously monitor and record the access period, frequency, and importance score of each file;

[0067] Step 1: When collecting archive access data, it specifically includes:

[0068] S101: Calculate the access time period of each file x in the past period of time based on the collected access time period data.

[0069] Where Δt is the time period, usually set to hours, days or weeks;

[0070] S102: Record the importance of each file according to its characteristics. x (t) and normalize to ensure that the importance scores of all files are within the same range;

[0071] Respectively represent the lowest and highest importance tag values ​​of all archives.

[0072] Step 2: By analyzing the usage frequency, time period, and importance tags of different archives, predict which archives will have increased access demand in the future; generate a prediction model based on the access frequency trend to derive the changes in access demand for each archive in the future.

[0073] Forecast outputs include: frequently accessed archives, seasonal or cyclical demand changes, and peak access times for specific archive categories.

[0074] Specifically, building an access demand prediction model and predicting access demand in the future based on historical data includes the following steps:

[0075] S201: Construct a historical prediction function of access frequency, based on the access frequency f x (t′) predicts future access needs and uses integrals to represent the time interval [t 0 ,t], and uses an exponential decay function to consider the impact of past frequencies on current predictions; its expression is:

[0076] Find,α represents the attenuation coefficient, which controls the influence of past access data on current demand forecast, t, t′ represent the current time and historical time respectively;

[0077] S202: According to the periodic change of access frequency, a sine function is introduced to represent the periodic fluctuation of access demand; the sine function describes the influence of different time periods on demand, and the periodic effect is weighted by integration, and its expression is:

[0078]

[0079] Where: P x (t) periodicrepresents the access demand of file x at time t based on periodic factors, w k Represents the frequency factor of periodic fluctuations, t k Indicates the starting point of the period of periodic change, Δt indicates the time span of periodic change;

[0080] S203: Use importance marking score I x (t) The access demand for archives is weighted to indicate that the demand for archives will be overestimated during periods of high importance, and its expression is:

[0081]

[0082] Where: P x (t) importance represents the access demand prediction of file x at time t based on the weighted importance tag score, γ k represents the weight coefficient, and γ represents the attenuation factor that the impact of the importance of the archive on future access needs will decay over time.

[0083] Combining historical trends, periodic changes, and importance impacts, we can derive the final access demand forecast for file x at time t. Its expression is:

[0084]

[0085] in, represents the comprehensive access demand forecast value of file x at time t, is a positive real number representing the intensity of access demand.

[0086] Step 3: Establish an adaptive optimization mechanism. Through the adaptive path optimization algorithm, frequently accessed files will be automatically moved to a more easily accessible location, that is, frequently used files or hot files will be stored in the location closest to the borrowing and returning window or robot, reducing access time.

[0087] Specifically, the adaptive optimization mechanism includes the following steps:

[0088] S301: define file x at position l x The access cost C on

[0089]

[0090] Where d represents the file location l x Distance from access window or robot position;

[0091] S302: Define the total system access cost C total , and in order to achieve overall optimization, the system hopes to reduce the total cost C totalminimize;

[0092]

[0093] Where n represents the total number of files, and the total cost C total The objective function to be minimized is:

[0094]

[0095] S303: Then, the storage location of the archive is gradually optimized through the simulated annealing algorithm.

[0096] The above-mentioned simulated annealing algorithm includes the following steps:

[0097] S3031: Initially randomly select the location combination of the archive {l 1 ,l 2 ,l 3 ...l n};

[0098] S3032: Set the current total access cost C total is the energy function;

[0099] S3033: Randomly select a file pair (l x ,l y ), swap their positions and calculate the new total access cost;

[0100] S3034: According to the Metropolis criterion, the probability of receiving a new state is:

[0101]

[0102] Where T is the temperature in the simulated annealing process, which gradually decreases with the number of iterations until the temperature change of T is less than the set threshold and the iteration is stopped. new <C old , that is, when the new state is better than the old state, P is close to 1, and the new state is always accepted;

[0103] When C new >C old That is, when the new state is worse than the old state, the acceptance probability is determined by P, which depends on the temperature T and the cost difference C new -C old size.

[0104] Temperature T is a dynamically changing parameter. Usually in the simulated annealing algorithm, it starts from a higher initial value and gradually decreases with the number of iterations, simulating the cooling process during the annealing process. 0 It is usually set to a larger value to allow the system to explore the search space more extensively in the early stages.

[0105] The simulated annealing algorithm gradually reduces the temperature T as the number of iterations t or time passes, which is usually controlled by a cooling schedule function: T(t) = T 0 ×α t .

[0106] α is a constant less than 1 (usually between 0.8 and 0.99), and t is the current iteration number. As t increases, the temperature T will continue to decrease.

[0107] When files need to be stored, the system will automatically send the files to the designated location through the file manipulator and motion module. The storage location has been adjusted according to the previous path optimization steps to ensure that the storage process is efficient and accurate. For this reason, when users enter query requirements through the multimodal retrieval system (such as text, voice, image input, etc.), the system quickly locates the required files based on the storage path adjusted by path optimization.

[0108] In summary, by monitoring the access frequency of archives and predicting future changes in demand, the system can dynamically adjust the storage location of archives, reduce access time, and improve efficiency; using the adaptive path optimization algorithm, the storage location of archives is no longer fixed, and the system automatically optimizes the storage area according to actual needs, thereby improving the intelligent level of access. The present invention realizes the automation of the entire process from data collection and analysis to path optimization and archive access, greatly reducing manual intervention, and conforming to the unmanned trend of modern archive management; it can adapt to changes in archive access needs, ensuring that important archives or high-frequency archives can be stored in convenient locations in priority during different time periods, ensuring the flexibility and efficiency of access.

[0109] In order to verify the beneficial effects of the present invention, an experiment was conducted in a medium-sized archive environment to verify whether sensor monitoring, data analysis and adaptive path optimization algorithm can effectively improve the efficiency of archive access, reduce manual intervention, and improve the intelligence and automation level of archive management.

[0110] The experiment was conducted in an unmanned archive library with 10,000 archive storage locations. The library includes:

[0111] Sensor system: used to monitor data such as access time, frequency, importance mark, etc. of each file.

[0112] Adaptive path optimization module: Through the data analysis and prediction module, the file location is optimized based on the access demand prediction algorithm.

[0113] Robot and automated storage and retrieval system: automatically adjust the file position and achieve fast access based on the results of path optimization.

[0114] Experimental setup: The experimental period is 30 days and is divided into two stages: Stage 1: Traditional storage management method, that is, all files are stored in a fixed location. Stage 2: The automated management method of the present invention, that is, adjusting the file location based on access demand prediction and path optimization. The results are shown in the following table:

[0115]

[0116] Through the data of this embodiment, it can be clearly seen that by introducing the unmanned file library automated management method of the present invention, the access efficiency is significantly improved, especially for the optimization effect of high-frequency access files. The automated path optimization and data analysis model can accurately predict access needs, dynamically adjust the file storage location, reduce access time, improve the overall efficiency of the system, and effectively reduce the access error rate, ensuring the stable operation of the system.

[0117] This embodiment also provides a computer device, which is suitable for an unmanned file library automated management method, including: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement an unmanned file library automated management method as proposed in the above embodiment.

[0118] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0119] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, an unmanned file library automated management method as proposed in the above embodiment is implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0120] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An automated management method for an unmanned archive library, characterized in that: The method comprises the following steps: Step 1: Use sensors to continuously monitor and record the access period, frequency, and importance score of each file; Step 2: By analyzing the usage frequency, time period, and importance marks of different archives, predict which archives will have an increased access demand in the future; generate a prediction model based on the access frequency trend to obtain the access demand changes of each archive in the future; The outputs of the forecast include: high-frequency access to archives, seasonal or cyclical demand changes, and peak access periods for specific archive categories; Step 3: Establish an adaptive optimization mechanism. Through the adaptive path optimization algorithm, frequently accessed files will be automatically moved to a more easily accessible location, that is, frequently used files or hot files will be stored in the location closest to the borrowing and returning window or robot, reducing access time.

2. The method for automated management of an unmanned archive library according to claim 1, characterized in that: The step 1 specifically includes: S101: Calculate the access frequency f of each file x in the past certain period of time based on the collected access period data x (t′), which will be used for subsequent demand forecasting; Where Δt is the time period, usually set to hours, days or weeks; S102: Record the importance of each file according to its characteristics. x (t) and normalize to ensure that the importance scores of all files are within the same range; in, represents the original importance label score of file x at time t, I max and I min Respectively represent the lowest and highest importance tag values ​​of all archives.

3. The method for automated management of an unmanned archive library according to claim 2, characterized in that: In the step 2, a storage demand prediction model is constructed, and the storage demand in the future is predicted by historical data, which specifically includes the following steps: S201: Construct a historical prediction function of access frequency, based on the access frequency f x (t′) predicts future access demand, uses integral to represent the cumulative effect of access frequency of file x in the time interval [t0, t], and uses exponential decay function to consider the impact of past frequency on current prediction; its expression is: Among them, P x (t) history represents the access demand of file x at time t based on the historical access frequency prediction, α represents the attenuation coefficient, which controls the influence of past access data on the current demand prediction, t and t′ represent the current time and historical time respectively; S202: According to the periodic change of access frequency, a sine function is introduced to represent the periodic fluctuation of access demand; the sine function describes the influence of different time periods on demand, and the periodic effect is weighted by integration, and its expression is: Where: P x (t) periodic represents the access demand of file x at time t based on periodic factors, w k Represents the frequency factor of periodic fluctuations, t k Indicates the starting point of the period of periodic change, Δt indicates the time span of periodic change; S203: Use importance marking score I x (t) The access demand for archives is weighted to indicate that the demand for archives will be overestimated during periods of high importance, and its expression is: Where: P x (t) importance represents the access demand prediction of file x at time t based on the weighted importance tag score, γ k represents the weight coefficient, and β represents the attenuation factor that the impact of the importance of the archive on future access needs will decay over time.

4. The method for automated management of an unmanned archive library according to claim 3, characterized in that: Combining historical trends, periodic changes, and importance impacts, we can derive the final access demand forecast for file x at time t. Its expression is: in, represents the comprehensive access demand forecast value of file x at time t, is a positive real number representing the intensity of access demand.

5. The method for automated management of an unmanned archive library according to claim 1, characterized in that: The adaptive optimization mechanism comprises the following steps: S301: define file x at position l x The access cost C on Where d represents the file location l x Distance from access window or robot position; S302: Define the total system access cost C total , and in order to achieve overall optimization, the system hopes to reduce the total cost C total minimize; Where n represents the total number of files, and the total cost C total The objective function to be minimized is: S303: Then, the storage location of the archive is gradually optimized through the simulated annealing algorithm.

6. The method for automated management of an unmanned archive library according to claim 5, characterized in that: The simulated annealing algorithm comprises the following steps: S3031: Initially randomly select the location combination of the archive {l1,l2,l3...l n }; S3032: Set the current total access cost C total is the energy function; S3033: Randomly select a file pair (l x ,l y ), swap their positions and calculate the new total access cost; S3034: According to the Metropolis criterion, the probability of receiving a new state is: Among them, T is the temperature in the simulated annealing process, which gradually decreases with the number of iterations until the temperature change of T is less than the set threshold and the iteration is stopped.

7. The method for automatic management of unmanned archives according to claim 6, characterized in that: When C new <C old , that is, when the new state is better than the old state, P is close to 1, and the new state is always accepted; When C new >C old That is, when the new state is worse than the old state, the acceptance probability is determined by P, which depends on the temperature T and the cost difference C new -C old size.

8. The management system of the unmanned archive library automatic management method according to claim 7, characterized in that: The system includes: a data collection and monitoring module, which monitors and records the access information of each file in real time, including the access period, frequency, and importance mark; The data analysis and prediction module performs data analysis and modeling based on the access frequency, time period, importance and other data provided by the data collection module to generate a prediction model for future access needs of archives; The adaptive path optimization module uses the path optimization algorithm to dynamically adjust the storage location of files according to the changes in the file usage frequency. It moves high-frequency or important files to the optimal storage area; The archive storage and retrieval module is responsible for the physical storage and retrieval of archives. It cooperates with the manipulator and motion module to achieve accurate storage and retrieval of archives; and a robot control module that controls the movement and execution of operations by the robot, including grabbing files from the storage area, moving them to the transfer area, returning the files to their original locations, and other operations.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the processor implements the steps of the unmanned archive library automated management method and management system as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the unmanned archive library automated management method and management system thereof are implemented as described in any one of claims 1 to 7.

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