Accelerometer data real-time storage and transmission method, system, device and medium

Abstract

The present invention relates to the technical field of data processing, and discloses a method, system, device and medium for real-time storage and transmission of accelerometer data, including: obtaining the original data collected by the accelerometer through an embedded accelerometer; obtaining the target signal after processing the original data using an amplifier and a filter; performing spectrum analysis on the target signal based on a sliding window mechanism to identify the divisible intervals of the spectrum; clipping the signal segments according to the divisible intervals; performing analog-to-digital conversion processing on each clipped signal segment, and performing error correction on the conversion result through forward verification; caching the corrected acceleration data segment in real time, and synchronously performing segmented storage and wireless transmission. The accuracy and effectiveness of data transmission are further improved by the dual-state structure design of the original value and the corrected value, supplemented by the final similarity selection mechanism. The overall solution has high robustness, high fidelity and intelligent processing capabilities.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method, system, device and medium for real-time storage and transmission of accelerometer data. Background Art

[0002] With the widespread adoption of wearable devices, IoT sensor systems, and smart terminals, accelerometers, as key sensing components, are widely used in a variety of fields, including motion monitoring, behavior recognition, industrial vibration detection, and health assessment. In embedded systems, in particular, accelerometers continuously acquire triaxial acceleration signals to achieve real-time perception of state changes in target objects. However, the raw signals output by accelerometers are typically in analog form, characterized by weak amplitude, high noise interference, and nonlinear waveforms. These signals require a series of conditioning, analysis, and processing before they can be used for subsequent recognition, prediction, or storage and transmission.

[0003] Most existing systems use fixed time window sampling and regularly spaced analog-to-digital conversion, making it difficult to accurately capture sudden or continuous changes in real-world motion. This can lead to problems such as unreasonable signal truncation and a high proportion of invalid data. Furthermore, spectrum analysis is typically limited to coarse-grained energy determination and lacks the ability to extract deeper features such as signal continuity, separability, and mutation points, making it difficult to support more sophisticated data segmentation and encoding strategies.

[0004] On the other hand, the actual acquisition and transmission of acceleration data can be subject to signal distortion, sampling interference, or packet loss. Existing solutions are significantly deficient in data repair, accuracy preservation, and contextual semantics preservation, failing to meet the combined requirements of high reliability, real-time performance, and predictability. Therefore, the key challenge awaits a breakthrough: building a segment-level rationality judgment mechanism that intelligently repairs and optimizes the current signal based on the context of previous data. Summary of the Invention

[0005] In view of the above-mentioned problems, the present invention is proposed.

[0006] To solve the above technical problems, the present invention provides the following technical solution: a method for real-time storage and transmission of accelerometer data, comprising:

[0007] Obtain the raw data collected by the embedded accelerometer;

[0008] After processing the raw data using an amplifier and a filter, a target signal is obtained; a spectrum analysis is performed on the target signal based on a sliding window mechanism to identify splittable intervals of the spectrum;

[0009] Cutting the signal segments according to the splittable intervals;

[0010] Perform analog-to-digital conversion on each clipped signal segment and perform error correction on the conversion result through forward verification;

[0011] The corrected acceleration data segments are cached in real time and synchronously stored in segments and transmitted wirelessly.

[0012] As a preferred solution of the method for real-time storage and transmission of accelerometer data described in the present invention, the raw data includes a three-axis analog signal in the form of voltage as an analog quantity.

[0013] As a preferred embodiment of the method for real-time storage and transmission of accelerometer data of the present invention, the spectrum analysis includes sliding a variable time window backward at the end or initial position of the previous interval as the interval starting point, and performing three-dimensional spectrum analysis on the target signal within the time window to determine the interval end point in the variable time window;

[0014] The three-dimensional spectrum analysis includes energy dimension, split dimension, and size dimension;

[0015] The analysis of the energy dimension includes obtaining the energy spectrum of the window through discrete Fourier transform and accumulating the total energy in the window; the analysis of the split dimension includes analyzing the spectrum mutation of the information before and after the end point of the interval to obtain the mutation measurement between the before and after information; the analysis of the size dimension includes setting the maximum length between the start and end points of the interval.

[0016] As a preferred solution of the real-time storage and transmission method of accelerometer data described in the present invention, the splittable interval includes: when the total energy in the window reaches a threshold, the end point of the pending interval is generated; through two fixed-length time windows, the mutation metrics before and after the end point of the pending interval are analyzed respectively; if the mutation metric is less than the product of the energy density at the end point of the pending interval and the mutation metric threshold, the end point of the pending interval is confirmed as the actual interval end point; if the mutation metric is not less than the product of the energy density at the end point of the pending interval and the mutation metric threshold, the end point of the pending interval is slid forward until the mutation metric is less than the product of the energy density at the end point of the pending interval and the mutation metric threshold; and the interval length is constrained by setting a maximum length between the start and end points of the interval.

[0017] As a preferred solution of the real-time storage and transmission method of accelerometer data described in the present invention, the clipping of the signal segments includes dividing the signal segments at each actual interval end point, and using the divided position as the starting point of the next interval, sliding the time window backward again to find the next interval end point.

[0018] As a preferred solution of the method for real-time storage and transmission of accelerometer data of the present invention, wherein: after analog-to-digital conversion processing is performed on the signal segments, interval data of each segment is obtained;

[0019] The forward verification includes: assuming that the current interval data is d1, and the interval data before d1 is represented as a set D, where D1 represents the interval data set before correction, and D2 represents the interval data set after correction; inputting D2 and d1 into the trained LSTM, and outputting the correction result d2 of d1;

[0020] Merge d1 into D1 to get D1+; merge d2 into D2 to get D2+;

[0021] Input D1+ into the trained LSTM and output the re-corrected result D1++ for each interval data.

[0022] As a preferred embodiment of the method for real-time storage and transmission of accelerometer data of the present invention, the segmented storage and wireless transmission includes respectively analyzing the data similarity between D1++ and D1+, and between D2+ and D1+; selecting the interval data in the set with the highest similarity as the final corrected interval data, and updating the stored content;

[0023] At the same time, d1 is transmitted to the host computer.

[0024] A real-time storage and transmission system for accelerometer data, wherein:

[0025] The acquisition unit acquires the raw data collected by the accelerometer through the embedded accelerometer;

[0026] The recognition unit processes the raw data using an amplifier and a filter to obtain a target signal; performs spectrum analysis on the target signal based on a sliding window mechanism to identify splittable intervals of the spectrum;

[0027] a splitting unit, which cuts the signal segments according to the splittable intervals;

[0028] Perform analog-to-digital conversion on each clipped signal segment and perform error correction on the conversion result through forward verification;

[0029] The cache unit caches the corrected acceleration data segments in real time, and simultaneously performs segmented storage and wireless transmission.

[0030] A computer device comprises: a memory and a processor; the memory stores a computer program, wherein: when the processor executes the computer program, the steps of any one of the methods of the present invention are implemented.

[0031] A computer-readable storage medium stores a computer program, wherein: when the computer program is executed by a processor, the steps of any one of the methods of the present invention are implemented.

[0032] Beneficial effects of the present invention: The real-time storage and transmission method of accelerometer data provided by the present invention realizes the precise identification of the divisible intervals of the acceleration signal by introducing a multi-dimensional spectrum analysis mechanism under a sliding window, thereby improving the accuracy of signal clipping and the expressive ability of the data structure; combined with the "forward verification" mechanism after analog-to-digital conversion, the rationality analysis and correction of the current interval are performed using the previous interval data, thereby enhancing the system's fault tolerance in the case of signal mutation, interference or loss. The fragment-level dynamic correction framework based on the LSTM neural network supports the learning and reconstruction of fragment continuity and predictability, so that the data is still interpretable and reducible in the presence of noise or interruption. At the same time, the accuracy and effectiveness of data transmission are further improved through the dual-state structure design of the original value and the corrected value, supplemented by the final similarity selection mechanism. The overall solution has high robustness, high fidelity and intelligent processing capabilities, is suitable for resource-constrained embedded scenarios, and has good practical value and promotion prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of 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 paying any creative work.

[0034] Figure 1 This is an overall flow chart of a method for real-time storage and transmission of accelerometer data provided by the first embodiment of the present invention. DETAILED DESCRIPTION

[0035] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.

[0036] Example 1, with reference to Figure 1 , as one embodiment of the present invention, provides a method for real-time storage and transmission of accelerometer data, comprising:

[0037] S1: Obtain the raw data collected by the embedded accelerometer.

[0038] In embedded systems, MEMS (Micro-Electro-Mechanical Systems) accelerometer modules are often used to collect triaxial acceleration signals. These accelerometers contain a micromass, a spring structure, and a capacitance measuring element, sensing changes in inertia caused by external motion. When the device moves along any axis, the mass displaces in the corresponding direction, altering the capacitance or charge distribution of the sensing structure, which is converted into a corresponding voltage output.

[0039] Specifically, an accelerometer chip outputs three analog voltage signals corresponding to acceleration changes along the X, Y, and Z axes. The signal amplitudes are proportional to the acceleration. To ensure signal accuracy and stability, the chip typically operates at a specific sampling frequency (such as 100Hz, 200Hz, or higher) and incorporates basic anti-interference circuitry.

[0040] In embedded host chips (such as the STM32, ESP32, and ARM Cortex-M series), the chip periodically reads the accelerometer's output voltage via its on-chip analog sampling interface (ADC). The sampling frequency must be consistent with the system's sliding window and spectrum analysis period. To ensure accuracy, a signal conditioning module is typically connected in series before the ADC. This includes an amplifier (to boost weak signal amplitude) and a filter (to suppress high-frequency noise and power ripple), ensuring signal quality for the subsequent analog-to-digital conversion.

[0041] Finally, the collected three-axis analog voltage signal will be used as the original input of acceleration data for further processing by subsequent spectrum analysis, interval clipping and error correction modules.

[0042] S2: After processing the raw data using an amplifier and a filter, a target signal is obtained; a spectrum analysis is performed on the target signal based on a sliding window mechanism to identify splittable intervals of the spectrum.

[0043] The spectrum analysis includes sliding a variable time window backward at the end or initial position of the previous interval as the interval starting point, and performing three-dimensional spectrum analysis on the target signal in the time window to determine the interval end point in the variable time window.

[0044] The three-dimensional spectrum analysis includes energy, splitting, and size. The energy dimension analysis involves performing a discrete Fourier transform to obtain the energy spectrum of a window and accumulating the total energy within the window. The splitting dimension analysis involves analyzing the spectrum mutations of the information before and after the interval endpoint to obtain a mutation metric between the information before and after. The size dimension analysis involves setting a maximum length between the start and end points of an interval.

[0045] Specifically, the spectrum energy index , use the discrete Fourier transform (DFT) to get the energy spectrum of the window:

[0046]

[0047] in, Display window spectrum; Indicates frequency Energy on Indicates the maximum sampling frequency (such as 500Hz); Display window The total energy reflects the intensity.

[0048] Mutation metrics:

[0049]

[0050] in, Display window The parameter value of the mutation metric at the end of the identified interval. Display window The total energy of the identified interval end point and the length of the forward t intervals. Display window The spectrum of the identified interval end point and the length of t intervals forward; the same applies to i+t.

[0051] When the total energy within the window reaches a threshold (preset value), the endpoint of the pending interval is generated. The mutation metrics before and after the endpoint of the pending interval are analyzed using two fixed-length time windows. If the mutation metric is less than the product of the energy density at the endpoint of the pending interval and the mutation metric threshold (also a preset value), the endpoint of the pending interval is confirmed as the actual interval endpoint. If the mutation metric is not less than the product of the energy density at the endpoint of the pending interval and the mutation metric threshold, the endpoint of the pending interval is moved forward until the mutation metric is less than the product of the energy density at the endpoint of the pending interval and the mutation metric threshold. This ensures that the information content at the disconnected position of each data segment is relatively small. During data storage, if the front or back end of the interval is missing, it can be supplemented through data analysis.

[0052] By setting a maximum length between the start and end points of the interval, the interval length is constrained.

[0053] In practical applications, acceleration data is characterized by alternating sudden events (such as falls and vibrations) and stable states (such as standing and walking). If fixed-length segmentation is used, it is very likely that: incorrect segmentation occurs at high-information-density locations, resulting in semantic breaks; important information is prematurely truncated before it is completed, affecting time series modeling; or information jumps drastically at interval breakpoints, and missing this point will seriously affect signal restoration.

[0054] Therefore, the present invention sets the following: the energy dimension is used to ensure that the signal strength at the division point is relatively weak, that is, the information expression is low, and it is less likely to cause segmentation distortion. The split dimension is used to measure the similarity of the spectrum before and after the endpoint, ensuring that the "semantic mutation" at the interval boundary is small, and the previous and next segments have logical coherence. The size dimension is used to prevent certain motion states from lasting too long, resulting in large segments that are difficult to cache and push, and to control the modeling granularity.

[0055] By combining these three elements into a model, the following effects are achieved: A continuous signal is automatically broken down into semantically complete, spectrally stable, and low-energy segmented units. If storage or transmission loss occurs, the information at the breakpoint is weaker and less variable, making it easier to compensate or reconstruct it using surrounding segments. Sufficient information density is retained within the interval to meet the needs of subsequent downstream tasks such as feature extraction, behavior recognition, and fault detection. In edge device scenarios, this reduces storage space pressure and improves bandwidth utilization.

[0056] S3: Cutting the signal segments according to the divisible intervals.

[0057] The signal segment is segmented at each actual interval end point, and the segmented position is used as the starting point of the next interval. The time window is slid backward again to find the next interval end point.

[0058] S4: Perform analog-to-digital conversion on each of the cropped signal segments, and perform error correction on the conversion results through forward verification.

[0059] After the signal segments are subjected to analog-to-digital conversion processing, interval data of each segment is obtained.

[0060] The forward validation involves assuming that the current interval data is d1 and the interval data before d1 is represented as set D, where D1 represents the interval data set before correction and D2 represents the interval data set after correction. The trained LSTM is fed with D2 and d1 as inputs and outputs the correction result d2 for d1.

[0061] Merge d1 into D1 to get D1+; merge d2 into D2 to get D2+.

[0062] Input D1+ into the trained LSTM and output the re-corrected result D1++ for each interval data.

[0063] In embedded systems, collected acceleration data may be subject to interference from hardware jitter, sampling errors, and other factors. It can also lead to missing or distorted data segments due to storage interruptions, transmission failures, and other factors. Simply relying on single-frame or single-segment data to determine accuracy often fails to guarantee the stability and semantic continuity of the overall data sequence. Therefore, after completing signal segment cropping and analog-to-digital conversion, the present invention constructs a forward verification and correction mechanism based on temporal context.

[0064] This mechanism introduces a lightweight LSTM neural network model and leverages the combined features of the previous corrected segment data D2 and the current segment d1 to generate a corrected prediction value d2 for the current segment, thereby increasing the confidence level of the current data. Furthermore, by grouping the original data and the corrected data into the pre-correction set D1+ and the post-correction set D2+, respectively, the entire sequence is used as input for a second round of global optimization, outputting the corrected result D1++ for the historical segment. This process not only enhances the ability to judge the rationality of the current segment but also makes the entire data structure more traceable, interpretable, and predictable for future data.

[0065] Through the forward verification mechanism, the present invention realizes secondary correction, trend alignment and information enhancement of analog-to-digital conversion results while ensuring the real-time performance of data, effectively improving the ability to reconstruct and compensate signal data under conditions of loss, fragmentation or offset, and providing a structurally stable data foundation for subsequent behavioral analysis, anomaly detection and system identification.

[0066] S5: Cache the corrected acceleration data segments in real time, and simultaneously perform segmented storage and wireless transmission.

[0067] Analyze the data similarity between D1++ and D1+, and D2+ and D1+ respectively; select the interval data in the set with the highest similarity as the final corrected interval data and update the stored content. Simultaneously, transmit d1 to the host computer.

[0068] During the signal correction process, the neural network model may be affected by changes in the preceding fragment, sample generalization ability, or interference from dynamic features, resulting in multiple possibilities for the corrected data it generates. Simply using the first correction value can introduce cumulative errors or weaken the signal authenticity due to excessive correction strategies. Therefore, after completing the original correction (D2+) and sequence re-correction (D1++) of the signal fragment, this solution further introduces a multi-version similarity assessment mechanism to accurately screen multiple correction candidates.

[0069] Specifically, D1++ and D1+, and D2+ and D1+, are compared at the interval level. The consistency and structural preservation of the results from different correction schemes are measured using metrics such as cosine similarity, mean square error, or correlation coefficient. The set of intervals with the highest similarity is then stored as the most reliable correction result. This mechanism preserves the system's ability to correct error segments while avoiding excessive segment modification. It also introduces a competition mechanism between different versions, improving the stability and structural fidelity of the correction data.

[0070] In addition, by synchronously transmitting the original fragment d1 to the host computer, the complete preservation of the original signal is ensured, providing the original data basis for future analysis tasks (such as playback, model retraining, diagnostic comparison, etc.), and realizing a data closed loop of complementary edge optimization and central intelligence.

[0071] Example 2: This embodiment further provides a real-time storage and transmission system for accelerometer data, which includes:

[0072] The acquisition unit obtains the raw data collected by the accelerometer through the embedded accelerometer.

[0073] The recognition unit processes the raw data using an amplifier and a filter to obtain a target signal; performs spectrum analysis on the target signal based on a sliding window mechanism to identify splittable intervals of the spectrum.

[0074] The splitting unit cuts the signal segments according to the splittable intervals.

[0075] Each clipped signal segment is converted into a digital form and the error of the conversion result is corrected through forward verification.

[0076] The cache unit caches the corrected acceleration data segments in real time, and simultaneously performs segmented storage and wireless transmission.

[0077] In Example 3, if the above functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0078] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0079] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, and then editing, interpreting, or processing in another suitable manner as necessary, and then storing it in a computer memory.

[0080] It should be understood that various components of the present invention may be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods may be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof may be used: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0081] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. 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. A method for real-time storage and transmission of accelerometer data, characterized in that: include: Obtain the raw data collected by the embedded accelerometer; After processing the raw data using an amplifier and a filter, a target signal is obtained; Perform spectrum analysis on the target signal based on the sliding window mechanism to identify the splittable intervals of the spectrum; Cutting the signal segments according to the splittable intervals; Perform analog-to-digital conversion on each clipped signal segment, and perform error correction on the conversion result through forward verification; The corrected acceleration data segments are cached in real time and stored in segments and transmitted wirelessly simultaneously; The spectrum analysis includes sliding a variable time window backward at the end or initial position of the previous interval as the interval starting point, and performing three-dimensional spectrum analysis on the target signal within the time window to determine the interval end point in the variable time window; The three-dimensional spectrum analysis includes energy dimension, split dimension, and size dimension; The energy dimension analysis includes obtaining the energy spectrum of the window through discrete Fourier transform and accumulating the total energy within the window; the split dimension analysis includes analyzing the spectrum mutation of the information before and after the interval end point to obtain the mutation metric between the information before and after; the size dimension analysis includes setting a maximum length between the start and end points of the interval; The splittable interval includes: when the total energy in the window reaches a threshold, generating a pending interval endpoint; through two fixed-length time windows, analyzing the mutation metrics before and after the pending interval endpoint respectively; if the mutation metric is less than the product of the energy density at the pending interval endpoint and the mutation metric threshold, then the pending interval endpoint is confirmed as the actual interval endpoint; if the mutation metric is not less than the product of the energy density at the pending interval endpoint and the mutation metric threshold, then sliding the pending interval endpoint forward until the mutation metric is less than the product of the energy density at the pending interval endpoint and the mutation metric threshold; and using the maximum length set between the start and end points of the interval to constrain the interval length.

2. The method for real-time storage and transmission of accelerometer data according to claim 1, wherein: The original data includes: A three-axis analog signal in the form of voltage.

3. The method for real-time storage and transmission of accelerometer data according to claim 2, wherein: The clipping of the signal segments includes dividing the signal segments at each actual interval end point, taking the divided position as the starting point of the next interval, and sliding the time window backward again to find the next interval end point.

4. The method for real-time storage and transmission of accelerometer data according to claim 3, wherein: After the signal segments are subjected to analog-to-digital conversion processing, interval data of each segment is obtained; The forward verification includes: assuming that the current interval data is d1, and the interval data before d1 is represented as a set D, where D1 represents the interval data set before correction, and D2 represents the interval data set after correction; inputting D2 and d1 into the trained LSTM, and outputting the correction result d2 of d1; Merge d1 into D1 to get D1+; Merge d2 into D2 to get D2+; Input D1+ into the trained LSTM and output the re-corrected result D1++ for each interval data.

5. The method for real-time storage and transmission of accelerometer data according to claim 4, wherein: The segmented storage and wireless transmission include respectively analyzing the data similarity between D1++ and D1+, and between D2+ and D1+; Select the interval data in the set with the highest similarity as the final corrected interval data and update the stored content; At the same time, d1 is transmitted to the host computer.

6. A real-time storage and transmission system for accelerometer data using the method according to any one of claims 1 to 5, characterized in that: The acquisition unit acquires the raw data collected by the accelerometer through the embedded accelerometer; The recognition unit processes the raw data using an amplifier and a filter to obtain a target signal; Perform spectrum analysis on the target signal based on the sliding window mechanism to identify the splittable intervals of the spectrum; a splitting unit, which cuts the signal segments according to the splittable intervals; Perform analog-to-digital conversion on each clipped signal segment, and perform error correction on the conversion result through forward verification; The cache unit caches the corrected acceleration data segments in real time, and simultaneously performs segmented storage and wireless transmission.

7. A computer device comprising: memory and processor; The memory stores a computer program, wherein the processor implements the steps of any one of the methods according to claims 1 to 5 when executing the computer program.

8. 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 method according to any one of claims 1 to 5 are implemented.

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