5G base station OFDM (Orthogonal Frequency Division Multiplexing) general inductance integrated waveform design and dynamic resource allocation strategy method

Abstract

The invention discloses a 5G base station OFDM (Orthogonal Frequency Division Multiplexing) sensing integration waveform design and dynamic resource allocation strategy method, and relates to the field of 5G communication sensing integration. The problems that communication performance and sensing performance are mutually limited and resources need to be efficiently allocated under a limited frequency spectrum due to competition existing in existing frequency spectrum and time slot resource occupation are solved. The method comprises the following steps: determining a subcarrier index set of a DMRS in an OFDM signal according to a 3GPP protocol, setting that one OFDM signal contains N subcarriers, and setting the DMRS subcarrier set as a specific set; selecting an optimal subset from the DMRS subcarrier set as a sensing pilot frequency, and reserving unselected DMRS subcarriers for communication channel estimation; adopting a BPSK (Binary Phase Shift Keying) or QPSK (Quadrature Phase Shift Keying) modulation mode to optimize a sensing pilot signal And a receiving end detects and senses the peak position of the pilot signal through discrete operation, calculates time delay, calculates a target distance and completes target parameter estimation.

Description

Technical Field

[0001] The present invention relates to the technical field of 5G communication perception integration, and in particular to a 5G base station OFDM synaesthesia integrated waveform design and dynamic resource allocation strategy method. Background Art

[0002] The existing 5G frame structure and OFDM waveform design are mainly designed for communication service optimization. However, when embedding sensing functions, they have the following defects: Competition exists for spectrum and time slot resource occupation, which results in mutual restrictions on communication and perception performance. Resources must be allocated efficiently within a limited spectrum to prevent performance degradation on one side from affecting the other.

[0003] The perception signal has high power and special modulation method, which can easily interfere with communication. For example, high-power perception signals may distort communication signals and affect communication quality.

[0004] Real-time resource scheduling is required in dynamic scenarios, and traditional high-complexity algorithms are unable to meet the system's needs for rapid adjustment of resource allocation under rapidly changing environments. Summary of the Invention

[0005] The present invention addresses the problem that there is competition in the existing spectrum and time slot resource occupation, which leads to mutual restriction of communication and perception performance. It is necessary to efficiently allocate resources under limited spectrum to avoid the performance degradation of one party affecting the other party.

[0006] To solve the above technical problems, the present invention is achieved through the following technical solutions: The present invention proposes a 5G base station OFDM synaesthesia integrated waveform design method, the method comprising: Step 1: Determine the subcarrier index set of DMRS in the OFDM signal according to the 3GPP protocol. Assume that an OFDM symbol contains N subcarriers, and the DMRS subcarrier set is a specific set. Step 2: Select an optimal subset from the DMRS subcarrier set described in step 1 as a sensing pilot, and the unselected DMRS subcarriers are reserved for communication channel estimation; Step 3: Optimize the perception pilot signal in step 2 by using BPSK or QPSK modulation; Step 4: The receiving end detects the peak position of the sensing pilot signal through discrete operations, calculates the time delay τ, and calculates the target distance to complete the target parameter estimation.

[0007] Furthermore, a preferred implementation is provided, wherein step 2 also includes the step of selecting the OFDM subcarrier spacing or adjusting the pilot density M using a pilot spacing optimization algorithm.

[0008] Furthermore, a preferred embodiment is provided, wherein the pilot spacing optimization algorithm is used to select the OFDM subcarrier spacing or adjust the pilot density M by calculating the distance resolution, Doppler resolution, and detecting the speed change. Among them, the distance resolution is: , where c is the speed of light and B is the signal bandwidth; Doppler resolution: , where T is the observation time; Detection speed change: Where λ is the carrier wavelength.

[0009] Furthermore, a preferred embodiment is provided, wherein step 3 further includes the step of establishing a fused zero subcarrier guard interval and edge weighting on both sides of the perception pilot; The fusion zero subcarrier guard interval is: insert j zero subcarriers on both sides of the perception pilot as a guard interval, assuming that the perception pilot is located at the i-th subcarrier, The position subcarrier is set to zero value to form a spectrum isolation band; Edge weighting is used to perform power weighting on the edge subcarriers of the OFDM signal.

[0010] Furthermore, a preferred embodiment is provided, in which the method for power-weighting the edge subcarriers of the OFDM signal is as follows: Let the edge subcarrier index be and , the subcarrier weights are adjusted to .

[0011] Furthermore, a preferred embodiment is provided, in step 4, the receiving end detects the peak position of the sensing pilot signal through discrete operation, and the method for calculating the time delay τ is: ,in To sense the pilot signal, is the delayed version of the target echo signal, is the delay corresponding to the target distance.

[0012] Solution 2: A dynamic resource allocation strategy method for a 5G base station OFDM synaesthesia-integrated waveform. The method is implemented based on the design method for a 5G base station OFDM synaesthesia-integrated waveform described in any one of Solution 1. The method includes the following steps: S1. Define user collection , IoT device collection , calculate the bandwidth requirement of a single user based on Shannon's formula , where B is the bandwidth; S2, based on the bandwidth requirements of a single user obtained in S1, uses a greedy algorithm to calculate the system-level minimum resource block (RB) requirement according to the number of users, average rate, and minimum signal-to-noise ratio; S3. Use reinforcement learning to dynamically optimize the allocation of remaining resource blocks (RBs) to achieve low-complexity real-time scheduling.

[0013] Solution 3: A computer device includes a memory and a processor, wherein the memory stores a computer program. When the processor runs the computer program stored in the memory, the processor executes any one of the methods described in Solution 1.

[0014] Solution 4: A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the steps of the method described in any one of Solution 1.

[0015] The present invention is beneficial in that: The 5G base station OFDM synaesthesia integrated waveform design and dynamic resource allocation strategy method described in the present invention realizes the orthogonal superposition of communication and perception signals in the OFDM waveform through DMRS subcarrier multiplexing, without the need for additional spectrum resources.

[0016] The 5G base station OFDM synaesthesia integrated waveform design and dynamic resource allocation strategy method described in the present invention uses dual frequency domain leakage suppression technology to combine zero subcarrier guard interval with edge weighting technology to effectively reduce the interference of perception signals on communication subcarriers.

[0017] The 5G base station OFDM synaesthesia integrated waveform design and dynamic resource allocation strategy method described in the present invention combines the greedy algorithm with reinforcement learning through a hybrid resource allocation strategy. The greedy algorithm is first used to allocate basic resources for communication, and then reinforcement learning is used to dynamically optimize the allocation of remaining resources to achieve low-complexity real-time scheduling.

[0018] The 5G base station OFDM interawareness-integrated waveform design and dynamic resource allocation strategy described in this paper reduce resource conflicts by effectively resolving spectrum and time slot competition through pilot reuse and dynamic resource allocation. Furthermore, the detection probability is increased by 15%, and range and Doppler resolution can be optimized through parameter adjustment. Communication rate fluctuations are reduced by 20%, significantly lowering the bit error rate in interference environments while maintaining a guaranteed minimum communication rate.

[0019] The 5G base station OFDM interaceptive integrated waveform design and dynamic resource allocation strategy method described in the present invention can achieve low-complexity real-time scheduling, that is, the algorithm convergence time is controlled within 10ms, adapting to the time-varying characteristics of the channel in 5G high-speed mobile scenarios.

[0020] The present invention is also applicable to fields such as intelligent transportation and industrial Internet of Things that require the integration of communication and environmental perception functions, realizing the sharing of spectrum, hardware and signal processing resources, improving system efficiency, reducing deployment costs, and providing intelligent services. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 This is a schematic diagram of how the distance resolution changes with bandwidth and how the velocity resolution changes with observation time in the eleventh embodiment.

[0022] Figure 2 Schematic diagram comparing spectrum utilization under different pilot densities in implementation mode eleven.

[0023] Figure 3 Schematic diagram for comparing different resolutions in the eleventh embodiment.

[0024] Figure 4 Schematic diagram comparing original edges and weighted edges in implementation mode eleven.

[0025] Figure 5 Schematic diagram of suppressing adjacent channel leakage by edge weighting in implementation mode eleven.

[0026] Figure 6 This is a comparative diagram of zero subcarrier protection in implementation mode eleven.

[0027] Figure 7 Schematic diagram of suppressing spectrum leakage by zero subcarrier protection in implementation mode eleven.

[0028] Figure 8 This is a schematic diagram of the results of dynamic allocation of sensing resources under the greedy algorithm in implementation mode eleven.

[0029] Figure 9 This is a schematic diagram of the clustering effect of reinforcement learning in a multi-target scenario in a dense urban area in implementation mode eleven. DETAILED DESCRIPTION

[0030] In order to make the purpose, technical solutions and advantages of the implementation methods of this application clearer, the technical solutions in the implementation methods of this application will be clearly and completely described below in combination with the drawings in the implementation methods of this application. Obviously, the described implementation methods are only part of the implementation methods of this application, not all of the implementation methods.

[0031] Implementation method 1: This implementation method provides a 5G base station OFDM synaesthesia integrated waveform design method, the method comprising: Step 1: Determine the subcarrier index set of DMRS in the OFDM signal according to the 3GPP protocol. Assume that an OFDM symbol contains N subcarriers, and the DMRS subcarrier set is a specific set. Step 2: Select an optimal subset from the DMRS subcarrier set described in step 1 as a sensing pilot, and the unselected DMRS subcarriers are reserved for communication channel estimation; Step 3: Optimize the perception pilot signal in step 2 by using BPSK or QPSK modulation; Step 4: The receiving end detects the peak position of the sensing pilot signal through discrete operations, calculates the time delay τ, and calculates the target distance to complete the target parameter estimation.

[0032] Implementation method 2. This implementation method further limits the 5G base station OFDM synaesthesia integrated waveform design method described in implementation method 1. Step 2 also includes the step of using a pilot spacing optimization algorithm to select the OFDM subcarrier spacing or adjust the pilot density M.

[0033] Implementation method 3: This implementation method further limits the 5G base station OFDM synaesthesia integrated waveform design method described in implementation method 1. The method of selecting the OFDM subcarrier spacing or adjusting the pilot density M by using the pilot spacing optimization algorithm is obtained by calculating the distance resolution, Doppler resolution, and detecting the speed change. Among them, the distance resolution is: , where c is the speed of light and B is the signal bandwidth; Doppler resolution: , where T is the observation time; Detection speed change: Where λ is the carrier wavelength.

[0034] Implementation method 4: This implementation method further limits the 5G base station OFDM synaesthesia integrated waveform design method described in implementation method 1; step 3 also includes the step of establishing a fusion zero subcarrier guard interval and edge weighting on both sides of the perception pilot; The fusion zero subcarrier guard interval is: insert j zero subcarriers on both sides of the perception pilot as a guard interval, assuming that the perception pilot is located at the i-th subcarrier. to The position subcarrier is set to zero value to form a spectrum isolation band; Edge weighting is used to perform power weighting on the edge subcarriers of the OFDM signal.

[0035] Implementation method 5. This implementation method further limits the 5G base station OFDM synaesthesia integrated waveform design method described in implementation method 4. The method for power weighting the OFDM signal edge subcarriers is as follows: Let the edge subcarrier index be and , the subcarrier weights are adjusted to .

[0036] Implementation method 6. This implementation method further limits the 5G base station OFDM synaesthesia integrated waveform design method described in implementation method 1. In step 4, the receiving end detects the peak position of the perception pilot signal through discrete operation, and the method for calculating the delay τ is: ,in To sense the pilot signal, is the delayed version of the target echo signal, is the delay corresponding to the target distance.

[0037] Implementation method 7: This implementation method proposes a 5G base station OFDM synaesthesia integrated waveform dynamic resource allocation strategy method, which is based on the 5G base station OFDM synaesthesia integrated waveform design method described in any one of implementation methods 1 to 6. The method includes the following steps: S1. Define user collection , IoT device collection , calculate the bandwidth requirement of a single user based on Shannon's formula , where B is the bandwidth; S2, based on the bandwidth requirements of a single user obtained in S1, uses a greedy algorithm to calculate the system-level minimum resource block (RB) requirement according to the number of users, average rate, and minimum signal-to-noise ratio; S3. Use reinforcement learning to dynamically optimize the allocation of remaining resource blocks (RBs) to achieve low-complexity real-time scheduling.

[0038] Implementation 8: This implementation further limits the 5G base station OFDM synaesthesia integrated waveform dynamic resource allocation strategy method described in Implementation 7. The method for calculating the system-level minimum resource block RB requirement in S2 is: For a single bandwidth, is the average rate requirement, is the time slot length.

[0039] Embodiment 9: A computer device includes a memory and a processor, wherein the memory stores a computer program. When the processor runs the computer program stored in the memory, the processor executes the method described in any one of embodiments 1 to 6.

[0040] Embodiment 10: A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method described in any one of embodiments 1 to 6 are implemented.

[0041] Implementation 11: The examples provided in this implementation are used to explain the above implementations 1 to 10. Implementation 1 includes the following contents: See also Figures 1 to 9 This embodiment describes a 5G base station OFDM synaesthesia integrated waveform design method described in this embodiment, which specifically includes the following steps: 1. Pilot reuse perception mechanism Step 1: Determine the DMRS subcarrier position: Determine the DMRS subcarrier index set in the OFDM symbol according to the 3GPP protocol (such as TS38.211). Assume that an OFDM symbol contains N subcarriers, and the DMRS subcarrier set is a specific set.

[0042] Step 2: Construct a set of sensing pilots: Based on the sensing accuracy requirements (such as range resolution), select a suitable subset from the DMRS subcarrier set as sensing pilots. The unselected DMRS subcarriers are reserved for communication channel estimation.

[0043] Step 3: Select the modulation method: Use BPSK or QPSK modulation for the sensing pilot, and avoid using high-order modulation (such as 16QAM and 64QAM) to avoid introducing phase noise that affects the accuracy of communication channel estimation.

[0044] Step 4: Receiver processing: The receiver detects the peak position of the perception pilot signal through discrete correlation operation, estimates the time delay τ, further calculates the target distance, and completes the target parameter estimation.

[0045] Mathematical model / formula: The receiver detects the target echo signal delay through correlation operation, and the formula is ,in To sense the pilot signal, is the delayed version of the target echo signal, is the delay corresponding to the target distance.

[0046] 2. Principle of the Perception Resolution Optimization Model: Perception resolution is affected by signal bandwidth B and observation time T. Range resolution is dominated by signal bandwidth B. As bandwidth increases, range resolution increases. Doppler resolution is determined by observation time T. As observation time increases, Doppler resolution improves.

[0047] Step 1: Analyze the relationship between pilot spacing and spectrum utilization: Dense pilot spacing consumes more system resources and reduces spectrum efficiency. A balance must be found between resolution and resource usage.

[0048] Step 2: Pilot spacing optimization algorithm: By reasonably selecting the OFDM subcarrier spacing or adjusting the pilot density M (inserting one perceptual pilot for every M subcarriers), resource usage is minimized while ensuring resolution.

[0049] Distance resolution: , where c is the speed of light and B is the signal bandwidth; Doppler resolution: , where T is the observation time; Detection speed change: Where λ is the carrier wavelength.

[0050] 3. Frequency domain leakage suppression technology zero subcarrier guard interval: Steps: Insert j zero subcarriers on both sides of the perception pilot as a guard interval. Assume that the perception pilot is located at the i-th subcarrier. to The position subcarriers are set to zero value to form a spectrum isolation band.

[0051] Parameters: Inserting two zero subcarriers can reduce the adjacent channel interference power by at least 15dB. Edge subcarrier weighting technology: Steps: Power weight the edge subcarriers (lowest and highest subcarriers) of the OFDM signal. Let the edge subcarrier index be and , the subcarrier weights are adjusted to

[0052] Parameters: When the weighting coefficient is 4.75, the adjacent channel interference power can be further reduced by 5-8dB.

[0053] Dual Isolation System: Integrates zero subcarrier guard interval and edge weighting technology to form a "guard interval + edge enhancement" system to improve anti-interference capabilities.

[0054] Example 1: Pilot density optimization: In an OFDM system, the number of subcarriers N is set to 1024, and the observation time is 40ms. When the pilot density M = 600, the spectrum utilization rate is 0.17%, and the range resolution is 5 meters. When M = 300, the spectrum utilization rate is increased to 0.33%, and the Doppler resolution is improved to 50Hz.

[0055] Frequency domain leakage suppression: In a 64-subcarrier simulation system, the traditional NC-OFDM leakage power ratio is -12dB. After edge weighting (weighting factor 4.75), it is reduced to -20dB, the isolation effect is improved by 8dB, and the sidelobe peak is reduced by 10dB.

[0056] Implementation method 2: The 5G base station OFDM synaesthesia integrated waveform dynamic resource allocation strategy method described in this implementation method includes the following steps: System Parameter Modeling Communication Requirements: Defining the User Set , IoT device collection , calculate the bandwidth requirement of a single user based on Shannon's formula , where B is the bandwidth. Perception requirements: define the target set , priority , the objective function is to maximize the weighted detection probability .

[0057] The greedy algorithm framework includes the guaranteed allocation of communication resources: according to the number of users, average rate and minimum signal-to-noise ratio, the system-level minimum resource block (RB) requirement is calculated: For a single bandwidth, is the average rate requirement, The length of the time slot is rounded up using the floor function to ensure sufficient resources.

[0058] Dynamic allocation of perception resources: The millimeter wave frequency band (above 24 GHz) is used as the perception resource pool, and the remaining RBs are allocated according to the principle of "high frequency band first". Its high bandwidth (single RB bandwidth is 100 MHz) and narrow beam (beamwidth less than 10°) characteristics are utilized to meet the perception requirements of continuous spectrum and high resolution.

[0059] Among them, the reinforcement learning optimization strategy includes: State space: The core state variable is the resource block occupancy:

[0060] It is discretized into 10 intervals, and the auxiliary state variables are the channel change rate (estimated by Doppler frequency shift) and the number of sensed targets, reflecting the time-varying characteristics of the channel and the scale of the sensing task.

[0061] Action Space: Action Indicates the RB allocation combination of communication and perception, which needs to be satisfied To ensure that communication can meet the minimum requirements, the action set includes possible.

[0062] Reward function: balancing communication rate and perception performance By using a greedy strategy to select actions, the Q-value table is continuously updated iteratively until the corresponding strategy reaches a convergence state, thereby achieving low-complexity scheduling results in dynamic scenarios (the algorithm takes no more than 10ms to converge). is the weight parameter (its default value is 0.6, with the emphasis on ensuring communication) is the detection probability of the target, calculated by the radar equation: , where is the target signal-to-noise ratio, is the detection threshold, is the sensitivity parameter.

[0063] Iterative optimization process: Update the Q value table through the Bellman equation: After 1000 iterations, the convergence time can be stably controlled within 8ms.

[0064] Example 2, hybrid algorithm performance: Compared with the pure greedy algorithm, the perception detection probability is increased by 15%, and the communication rate fluctuation is reduced by 20%; compared with the traditional convex optimization algorithm, the computational efficiency is increased by 300 times and the memory usage is reduced by 60%.

[0065] Anti-interference capability: When adjacent channel power leakage increases by 10dB, the reinforcement learning module adjusts the resource allocation strategy within two time slots, reducing the communication bit error rate from 5% to 1.2%, and maintaining the perception detection probability above 90%.

[0066] High-Doppler scenarios: In scenarios with a vehicle speed of 120 km / h (coherence time 3ms), the resource reallocation cycle is reduced from 10ms to 5ms, ensuring timely resource allocation. Multi-target scenarios: When the number of perceived targets exceeds 20, a target clustering algorithm is used to merge spatially similar targets, reducing the single-step decision time from 4ms to 2.5ms, improving algorithm throughput.

[0067] In summary, the method described in this embodiment effectively resolves spectrum and time slot resource competition through pilot reuse and dynamic resource allocation. Furthermore, the detection probability is increased by 15%, range resolution and Doppler resolution can be optimized through parameter adjustment, and communication rate fluctuation is reduced by 20%. Bit error rates are significantly reduced in interference environments, while maintaining a guaranteed minimum communication rate.

[0068] Those skilled in the art will understand that the above description is only a preferred embodiment of the present invention, and the features described in the various embodiments and / or claims of the present disclosure may be combined or coupled in various ways, even if such a combination or coupling is not explicitly described in the present disclosure. It is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art may still modify the technical solutions described in the aforementioned embodiments or make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included in the scope of protection of the present invention.

[0069] Although preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they are aware of the basic inventive concepts. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention. Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, the present invention is intended to include such changes and modifications as fall within the scope of the claims and their equivalents.

Claims

The 1.5G base station OFDM synaesthesia integrated waveform design method is characterized by: The method comprises: Step 1: Determine the subcarrier index set of DMRS in the OFDM signal according to the 3GPP protocol. Assume that an OFDM symbol contains N subcarriers, and the DMRS subcarrier set is a specific set. Step 2: Select an optimal subset from the DMRS subcarrier set described in step 1 as a sensing pilot, and the unselected DMRS subcarriers are reserved for communication channel estimation; Step 3: Optimize the perception pilot signal in step 2 by using BPSK or QPSK modulation; Step 4: The receiving end detects the peak position of the pilot signal through discrete operations and calculates the delay , and calculate the target distance to complete the target parameter estimation.

2. The 5G base station OFDM synaesthesia integrated waveform design method according to claim 1 is characterized in that: Step 2 also includes the step of selecting the OFDM subcarrier spacing or adjusting the pilot density M by using a pilot spacing optimization algorithm.

3. The 5G base station OFDM synaesthesia integrated waveform design method according to claim 3 is characterized in that: The method of selecting OFDM subcarrier spacing or adjusting pilot density M by using pilot spacing optimization algorithm is obtained by calculating distance resolution, Doppler resolution, and detecting speed change. Among them, the distance resolution is: , where c is the speed of light and B is the signal bandwidth; Doppler resolution: , where T is the observation time; Detection speed change: , where λ is the carrier wavelength.

4. The 5G base station OFDM synaesthesia integrated waveform design method according to claim 1 is characterized in that: Step 3 also includes the steps of establishing a fused zero subcarrier guard interval and edge weighting on both sides of the perception pilot; The fusion zero subcarrier guard interval is: insert j zero subcarriers on both sides of the perception pilot as a guard interval, assuming that the perception pilot is located at the i-th subcarrier. to The position subcarrier is set to zero value to form a spectrum isolation band; Edge weighting is used to perform power weighting on the edge subcarriers of the OFDM signal.

5. The 5G base station OFDM synaesthesia integrated waveform design method according to claim 4 is characterized in that: The method for power weighting the edge subcarriers of the OFDM signal is: Let the edge subcarrier index be and , the subcarrier weights are adjusted to .

6. The 5G base station OFDM synaesthesia integrated waveform design method according to claim 1, characterized in that: In step 4, the receiving end detects the peak position of the sensing pilot signal through discrete operations and calculates the delay The method is: ,in To sense the pilot signal, is the delayed version of the target echo signal, is the delay corresponding to the target distance. 7.5G base station OFDM synaesthesia integrated waveform dynamic resource allocation strategy method, characterized by: The method is implemented based on the design method of the 5G base station OFDM synaesthesia integrated waveform according to any one of claims 1 to 6, and the method comprises the following steps: S1. Define user collection , IoT device collection , calculate the bandwidth requirement of a single user based on Shannon's formula , where B is the bandwidth; S2, based on the bandwidth requirements of a single user obtained in S1, uses a greedy algorithm to calculate the system-level minimum resource block (RB) requirement according to the number of users, average rate, and minimum signal-to-noise ratio; S3. Use reinforcement learning to dynamically optimize the allocation of remaining resource blocks (RBs) to achieve low-complexity real-time scheduling.

8. The 5G base station OFDM synaesthesia integrated waveform dynamic resource allocation strategy method according to claim 7 is characterized in that: The method for calculating the system-level minimum resource block RB requirement in S2 is: For a single bandwidth, is the average rate requirement, is the time slot length.

9. A computer device comprising a memory and a processor, characterized in that A computer program is stored in the memory. When the processor runs the computer program stored in the memory, the processor executes the method according to any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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