Method for adjusting emission light power of optical module

Abstract

The invention discloses a method for adjusting transmitting optical power of an optical module, which relates to the technical field of optical communication and comprises the following steps of: evaluating multi-dimensional attention demand vector preference of a historical network state of a communication base station based on historical communication data of the communication base station; acquiring operation parameters of a real-time communication base station, and analyzing a multi-dimensional attention vector of a real-time network state of the communication base station; and judging whether the multi-dimensional attention vector of the real-time network state of the communication base station meets the multi-dimensional attention demand vector preference of the historical network state of the communication base station, if not, judging that the emission optical power needs to be adjusted, and if not, judging that the emission optical power needs to be adjusted. And according to the deviation between the multi-dimensional attention demand vector preference of the historical network state of the communication base station and the multi-dimensional attention vector of the real-time network state of the communication base station, establishing a dynamic emission optical power adjustment model, and generating a dynamic emission optical power adjustment value of the communication base station. The method has the advantages that the communication quality is improved, the delay is reduced, and the network stability is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of optical communications, and in particular to a method for adjusting the transmitted optical power of an optical module. Background Art

[0002] Existing 5G optical power adjustment methods primarily rely on fixed algorithms and static thresholds, failing to fully account for dynamic changes in real-time network conditions and regional variations in demand. This results in inaccurate adjustment of transmitted optical power in situations such as fluctuating network load, varying user density, or signal interference. This can lead to overly strong or weak signals, impacting network quality and energy efficiency optimization. Therefore, how to flexibly adjust optical power based on real-time network conditions and changing demand remains an unresolved issue. Summary of the Invention

[0003] To address the aforementioned technical issues, a method for adjusting the transmitted optical power of an optical module is provided. This technical solution addresses the problem that existing 5G optical power adjustment methods primarily rely on fixed algorithms and static thresholds, failing to fully account for dynamic changes in real-time network status and varying demands across different regions. This results in the inability to accurately adjust the transmitted optical power in situations such as network load fluctuations, changes in user density, or signal interference. This can lead to overly strong or weak signals, impacting network quality and energy efficiency optimization. Therefore, how to flexibly adjust optical power based on real-time network status and demand changes remains an unresolved issue.

[0004] In order to achieve the above objects, the technical solution adopted by the present invention is:

[0005] The method for adjusting the transmit optical power of an optical module includes:

[0006] Based on the historical communication data of the communication base station, the transmission request timing parameters of the historical communication data are analyzed to evaluate the multi-dimensional attention demand vector preference of the historical network status of the communication base station; the multi-dimensional attention vector includes: the communication data quality dimension, the communication data delay dimension and the communication network stability dimension;

[0007] Based on the built-in sensors of the optical modules in the communication base stations, the real-time operating parameters of the communication base stations are obtained, and the multi-dimensional attention vectors of the real-time network status of the communication base stations are analyzed;

[0008] Determine whether the multi-dimensional attention vector of the real-time network state of the communication base station meets the multi-dimensional attention demand vector preference of the historical network state of the communication base station; if so, determine that there is no need to adjust the transmit optical power; if not, determine that the transmit optical power needs to be adjusted;

[0009] If it is determined that the transmitted optical power needs to be adjusted, a dynamic transmitted optical power adjustment model is established according to the deviation between the multi-dimensional attention demand vector preference of the historical network status of the communication base station and the multi-dimensional attention vector of the real-time network status of the communication base station, and the dynamic transmitted optical power adjustment value of the communication base station is generated.

[0010] Preferably, based on the historical communication data of the communication base station, analyzing the transmission request timing parameters of the historical communication data, and evaluating the multi-dimensional attention demand vector preference of the historical network status of the communication base station specifically include:

[0011] Based on the standardized processing of the historical communication data of the communication base station, a sliding window is established according to the unit time. The transmission request timing parameters of the historical communication data are used as variable attributes to obtain the multi-dimensional demand characteristic values of the historical network status of the communication base station;

[0012] Using the K-means clustering algorithm, the multi-dimensional demand characteristic values of the historical network status of the communication base station are clustered according to the various dimensional demands under unit time, and the multi-dimensional demand characteristic parameter data set A of the historical network status of the communication base station is obtained. i (t)}; where X i (t) is the demand characteristic parameter of the i-th dimension of the historical network status of the communication base station under the t-th unit time;

[0013] Based on AR time series analysis-autoregression, a multi-dimensional preference analysis model of historical network status is constructed;

[0014] Stabilize the multi-dimensional demand characteristic parameter dataset based on the historical network status of the communication base station and substitute it into the multi-dimensional preference analysis model of the historical network status. The multi-dimensional demand characteristic parameters are used as input, and the multi-dimensional attention demand vector preference of the historical network status of the communication base station is used as output.

[0015] The multi-dimensional preference analysis model of the historical network status is specifically as follows:

[0016]

[0017] Where, P i (t) is the preference value of the attention demand vector of the i-th dimension under the historical network status of the communication base station at the t-th unit time, X i (ti) is X i (t) is the i-th dimension demand characteristic parameter under the ti-th unit time autoregressive order of the historical network state of the communication base station, ∈ i (t) is the error term of the i-th dimension of the historical network status of the communication base station at the t-th unit time, and n is the total number of multi-dimensional demand characteristic parameters.

[0018] Preferably, based on the built-in sensor of the optical module in the communication base station, the operating parameters of the real-time communication base station are obtained, and the multi-dimensional attention vector of the real-time network status of the communication base station is analyzed specifically includes:

[0019] Based on the normalization of the operating parameters of the real-time communication base station, the multi-dimensional demand feature data of the real-time network status of the communication base station is marked according to the sliding window;

[0020] Based on multi-factor linear regression, a multi-factor linear regression model of real-time network status is established;

[0021] According to the multi-factor linear regression model of real-time network status, the multi-dimensional demand feature data of the real-time network status of the communication base station is substituted into the original training set, the multi-dimensional demand feature data is input as the independent variable, and the multi-dimensional attention vector index of the real-time network status of the communication base station is output as the dependent variable;

[0022] The multi-factor linear regression model of real-time network status is specifically as follows:

[0023] G i (t)=β0+β1Y1(t)+β2Y2(t)+…+β i Y i (t)

[0024] Where G i (t) is the attention vector index of the i-th dimension under the historical network status of the communication base station at the t-th unit time, Y i (t) is the demand characteristic data of the i-th dimension under the historical network status of the communication base station at the t-th unit time, β0, β1, β2, β i All are linear regression coefficients.

[0025] Preferably, if it is determined that the transmitted optical power needs to be adjusted, a dynamic transmitted optical power adjustment model is established according to the deviation between the multi-dimensional attention demand vector preference of the historical network state of the communication base station and the multi-dimensional attention vector of the real-time network state of the communication base station, and the dynamic transmitted optical power adjustment value of the communication base station is generated. Specifically, the following steps are performed:

[0026] Normalizing the multi-dimensional attention demand vector preference index based on the historical network status of the communication base station and the multi-dimensional attention vector of the real-time network status of the communication base station;

[0027] Aligning the multi-dimensional attention demand vector preference index of the historical network status of the communication base station with the multi-dimensional attention vector of the real-time network status of the communication base station based on the time attribute;

[0028] Using the Euclidean distance formula, the spatial distance between the multi-dimensional attention demand vector preference index of the historical network state of the communication base station and the multi-dimensional attention vector of the real-time network state of the communication base station per unit time is calculated to determine the multi-dimensional attention demand deviation vector of the real-time network state of the communication base station;

[0029] Based on the multi-dimensional demand deviation vector of the real-time network status of the communication base station, a dynamic transmit optical power adjustment model is established according to weighted linear regression to generate the dynamic transmit optical power adjustment value of the communication base station;

[0030] The Euclidean distance formula is specifically:

[0031]

[0032] Where, d i (t) is the attention demand deviation vector of the i-th dimension under the t-th unit time of the real-time network status of the communication base station.

[0033] The generating of the dynamic transmission optical power adjustment value of the communication base station is specifically as follows:

[0034]

[0035] Where Δr is the dynamic transmission optical power adjustment value of the communication base station, w i is the i-th dimension weight of the real-time network status of the communication base station, and b is the bias term.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] This invention proposes a scheme for regulating the transmitted optical power of optical modules. This scheme analyzes historical communication data to assess the base station's network demand preferences. This analysis, combined with real-time operational parameters acquired by the base station's built-in sensors, determines whether the current network status meets these historical demand preferences. If not, a dynamic transmitted optical power regulation model is established to adjust the transmitted optical power based on the deviation between the two. This scheme has the beneficial effect of optimizing transmitted optical power regulation based on the base station's real-time needs, improving communication quality, reducing latency, and ensuring network stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 Flowchart of a method for adjusting the transmitted optical power of an optical module;

[0039] Figure 2 Flowchart of the multi-dimensional attention demand vector preference method for evaluating the historical network status of communication base stations;

[0040] Figure 3 Flowchart of a multi-dimensional attention vector method for analyzing the real-time network status of a communication base station;

[0041] Figure 4 A flow chart of a method for generating a dynamic transmission optical power adjustment value for a communication base station. DETAILED DESCRIPTION

[0042] The following description is intended to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are merely examples, and those skilled in the art may conceive of other obvious variations.

[0043] Reference Figure 1 As shown in FIG, the method for adjusting the transmit optical power of an optical module includes:

[0044] Based on the historical communication data of the communication base station, the transmission request timing parameters of the historical communication data are analyzed to evaluate the multi-dimensional attention demand vector preference of the historical network status of the communication base station; the multi-dimensional attention vector includes: the communication data quality dimension, the communication data delay dimension and the communication network stability dimension;

[0045] Based on the built-in sensors of the optical modules in the communication base stations, the real-time operating parameters of the communication base stations are obtained, and the multi-dimensional attention vectors of the real-time network status of the communication base stations are analyzed;

[0046] Determine whether the multi-dimensional attention vector of the real-time network state of the communication base station meets the multi-dimensional attention demand vector preference of the historical network state of the communication base station; if so, determine that there is no need to adjust the transmit optical power; if not, determine that the transmit optical power needs to be adjusted;

[0047] If it is determined that the transmitted optical power needs to be adjusted, a dynamic transmitted optical power adjustment model is established according to the deviation between the multi-dimensional attention demand vector preference of the historical network status of the communication base station and the multi-dimensional attention vector of the real-time network status of the communication base station, and the dynamic transmitted optical power adjustment value of the communication base station is generated.

[0048] This solution analyzes historical communication data to assess the base station's network demand preferences. Combined with real-time operating parameters acquired by the base station's built-in sensors, it determines whether the current network status meets these historical preferences. If not, a dynamic transmit optical power adjustment model is established to adjust transmit optical power based on the deviation between the two. This solution's beneficial effects include optimizing transmit optical power adjustment based on the base station's real-time needs, improving communication quality, reducing latency, and ensuring network stability.

[0049] Reference Figure 2 As shown, based on the historical communication data of the communication base station, the transmission request timing parameters of the historical communication data are analyzed, and the multi-dimensional attention demand vector preference of the historical network status of the communication base station is evaluated. Specifically, the preference includes:

[0050] Based on the standardized processing of the historical communication data of the communication base station, a sliding window is established according to the unit time. The transmission request timing parameters of the historical communication data are used as variable attributes to obtain the multi-dimensional demand characteristic values of the historical network status of the communication base station;

[0051] Using the K-means clustering algorithm, the multi-dimensional demand characteristic values of the historical network status of the communication base station are clustered according to the various dimensional demands under unit time, and the multi-dimensional demand characteristic parameter data set A of the historical network status of the communication base station is obtained. i (t)}; where X i (t) is the demand characteristic parameter of the i-th dimension of the historical network status of the communication base station under the t-th unit time;

[0052] Based on AR time series analysis-autoregression, a multi-dimensional preference analysis model of historical network status is constructed;

[0053] Stabilize the multi-dimensional demand characteristic parameter dataset based on the historical network status of the communication base station and substitute it into the multi-dimensional preference analysis model of the historical network status. The multi-dimensional demand characteristic parameters are used as input, and the multi-dimensional attention demand vector preference of the historical network status of the communication base station is used as output.

[0054] The multi-dimensional preference analysis model of the historical network status is specifically as follows:

[0055]

[0056] Where, P i (t) is the preference value of the attention demand vector of the i-th dimension under the historical network status of the communication base station at the t-th unit time, X i (ti) is X i (t) is the i-th dimension demand characteristic parameter under the ti-th unit time autoregressive order of the historical network state of the communication base station, ∈ i (t) is the error term of the i-th dimension of the historical network status of the communication base station at the t-th unit time, and n is the total number of multi-dimensional demand characteristic parameters.

[0057] This solution extracts multi-dimensional network demand characteristics through in-depth analysis of historical base station communication data. Combining K-means clustering with AR time series analysis, it accurately assesses base station network status. Through standardized processing and sliding window technology, it accurately captures demand changes over time periods, providing data support for adjusting the transmit optical power of optical modules. This allows for dynamic optimization of optical power based on network load, improving communication quality, reducing energy consumption, and ensuring network stability and efficiency.

[0058] Reference Figure 3 The multi-dimensional attention vector of obtaining the real-time operating parameters of the communication base station based on the built-in sensor of the optical module in the communication base station and analyzing the real-time network status of the communication base station specifically includes:

[0059] Based on the normalization of the operating parameters of the real-time communication base station, the multi-dimensional demand feature data of the real-time network status of the communication base station is marked according to the sliding window;

[0060] Based on multi-factor linear regression, a multi-factor linear regression model of real-time network status is established;

[0061] According to the multi-factor linear regression model of real-time network status, the multi-dimensional demand feature data of the real-time network status of the communication base station is substituted into the original training set, the multi-dimensional demand feature data is input as the independent variable, and the multi-dimensional attention vector index of the real-time network status of the communication base station is output as the dependent variable;

[0062] The multi-factor linear regression model of real-time network status is specifically as follows:

[0063] G i (t)=β0+β1Y1(t)+β2Y2(t)+…+β i Y i (t)

[0064] Where G i (t) is the attention vector index of the i-th dimension under the historical network status of the communication base station at the t-th unit time, Y i (t) is the demand characteristic data of the i-th dimension under the historical network status of the communication base station at the t-th unit time, β0, β1, β2, β i All are linear regression coefficients.

[0065] It is understood that the optimal linear regression coefficient in the model can be obtained using the least squares method, which is well known to those skilled in the art and will not be described in detail here.

[0066] This solution uses the built-in sensors of the communication base station optical module to obtain real-time operating parameters, and combines sliding window technology to normalize multi-dimensional demand feature data. It can analyze the base station network status in real time and establish an accurate network status prediction mechanism through a multi-factor linear regression model. The model can use real-time network status data as a training set and, by inputting multi-dimensional feature data, predict the multi-dimensional attention vector indicators of the real-time network status of the communication base station, providing key data for subsequent adjustment of the optical module transmission light power of the communication base station.

[0067] Reference Figure 4As shown, if it is determined that the transmitted optical power needs to be adjusted, a dynamic transmitted optical power adjustment model is established according to the deviation between the multi-dimensional attention demand vector preference of the historical network status of the communication base station and the multi-dimensional attention vector of the real-time network status of the communication base station. The dynamic transmitted optical power adjustment value of the communication base station is generated specifically including:

[0068] Normalizing the multi-dimensional attention demand vector preference index based on the historical network status of the communication base station and the multi-dimensional attention vector of the real-time network status of the communication base station;

[0069] Aligning the multi-dimensional attention demand vector preference index of the historical network status of the communication base station with the multi-dimensional attention vector of the real-time network status of the communication base station based on the time attribute;

[0070] Using the Euclidean distance formula, the spatial distance between the multi-dimensional attention demand vector preference index of the historical network state of the communication base station and the multi-dimensional attention vector of the real-time network state of the communication base station per unit time is calculated to determine the multi-dimensional attention demand deviation vector of the real-time network state of the communication base station;

[0071] Based on the multi-dimensional demand deviation vector of the real-time network status of the communication base station, a dynamic transmit optical power adjustment model is established according to weighted linear regression to generate the dynamic transmit optical power adjustment value of the communication base station;

[0072] The Euclidean distance formula is specifically:

[0073]

[0074] Where, d i (t) is the attention demand deviation vector of the i-th dimension under the t-th unit time of the real-time network status of the communication base station;

[0075] The generating of the dynamic transmission optical power adjustment value of the communication base station is specifically as follows:

[0076]

[0077] Where Δr is the dynamic transmission optical power adjustment value of the communication base station, w i is the i-th dimension weight of the real-time network status of the communication base station, and b is the bias term.

[0078] It is understandable that the distribution of dimension weights is based on the gradient descent method or other optimization algorithms, which is well known to those skilled in the art and will not be described in detail here.

[0079] This solution normalizes and aligns multi-dimensional attention vectors of historical and real-time network states. It uses the Euclidean distance formula to calculate the deviation vector, and constructs a dynamic transmit optical power adjustment model based on the deviation vector to dynamically generate transmit optical power adjustment values for communication base stations. By quantifying the difference between historical preferences and real-time status, it accurately reflects changes in network demand and optimizes the transmit optical power adjustment of communication base station optical modules. This improves the real-time and accuracy of optical power adjustment, enhances the network adaptability of communication base stations, reduces signal quality degradation caused by improper power, and improves the performance and efficiency of the overall communication system.

[0080] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions merely illustrate the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the invention as claimed. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for adjusting the transmitted optical power of an optical module, characterized in that: include: Based on the historical communication data of the communication base station, the transmission request timing parameters of the historical communication data are analyzed to evaluate the multi-dimensional attention demand vector preference of the historical network status of the communication base station; The multi-dimensional attention vector includes: communication data quality dimension, communication data delay dimension and communication network stability dimension; Based on the built-in sensors of the optical modules in the communication base stations, the real-time operating parameters of the communication base stations are obtained, and the multi-dimensional attention vectors of the real-time network status of the communication base stations are analyzed; Determine whether the multi-dimensional attention vector of the real-time network state of the communication base station meets the multi-dimensional attention demand vector preference of the historical network state of the communication base station; if so, determine that there is no need to adjust the transmit optical power; if not, determine that the transmit optical power needs to be adjusted; If it is determined that the transmitted optical power needs to be adjusted, a dynamic transmitted optical power adjustment model is established according to the deviation between the multi-dimensional attention demand vector preference of the historical network status of the communication base station and the multi-dimensional attention vector of the real-time network status of the communication base station, and the dynamic transmitted optical power adjustment value of the communication base station is generated.

2. The method for adjusting the transmitted optical power of an optical module according to claim 1, wherein: Based on the historical communication data of the communication base station, the transmission request timing parameters of the historical communication data are analyzed, and the multi-dimensional attention demand vector preferences of the historical network status of the communication base station are evaluated. Specifically, the following are included: Based on the standardized processing of the historical communication data of the communication base station, a sliding window is established according to the unit time. The transmission request timing parameters of the historical communication data are used as variable attributes to obtain the multi-dimensional demand characteristic values of the historical network status of the communication base station; Using the K-means clustering algorithm, the multi-dimensional demand characteristic values of the historical network status of the communication base station are clustered according to the various dimensional demands under unit time, and the multi-dimensional demand characteristic parameter data set A of the historical network status of the communication base station is obtained. i (t)}; where X i (t) is the demand characteristic parameter of the i-th dimension of the historical network status of the communication base station under the t-th unit time; Based on AR time series analysis-autoregression, a multi-dimensional preference analysis model of historical network status is constructed; The multi-dimensional demand characteristic parameter dataset of the historical network status of the communication base station is stabilized and substituted into the multi-dimensional preference analysis model of the historical network status. The multi-dimensional demand characteristic parameters are used as input, and the multi-dimensional attention demand vector preference of the historical network status of the communication base station is used as output.

3. The method for adjusting the transmitted optical power of an optical module according to claim 2, wherein: The multi-dimensional preference analysis model of the historical network status is specifically: Where, P i (t) is the preference value of the attention demand vector of the i-th dimension under the historical network status of the communication base station at the t-th unit time, X i (ti) is X i (t) is the i-th dimension demand characteristic parameter under the ti-th unit time autoregressive order of the historical network state of the communication base station, ∈ i (t) is the error term of the i-th dimension of the historical network status of the communication base station at the t-th unit time, and n is the total number of multi-dimensional demand characteristic parameters.

4. The method for adjusting the transmitted optical power of an optical module according to claim 3, wherein: Based on the built-in sensors of the optical modules in the communication base stations, we can obtain the real-time operating parameters of the communication base stations and analyze the multi-dimensional attention vectors of the real-time network status of the communication base stations. Specifically, the following are included: Based on the normalization of the operating parameters of the real-time communication base station, the multi-dimensional demand feature data of the real-time network status of the communication base station is marked according to the sliding window; Based on multi-factor linear regression, a multi-factor linear regression model of real-time network status is established; According to the multi-factor linear regression model of real-time network status, the multi-dimensional demand feature data of the real-time network status of the communication base station is substituted as the original training set, the multi-dimensional demand feature data is input as the independent variable, and the multi-dimensional attention vector index of the real-time network status of the communication base station is output as the dependent variable.

5. The method for adjusting the transmitted optical power of an optical module according to claim 4, wherein: The multi-factor linear regression model of real-time network status is as follows: G i (t)=β0+β1Y1(t)+β2Y2(t)+…+β i Y i (t) Where G i (t) is the attention vector index of the i-th dimension under the historical network status of the communication base station at the t-th unit time, Y i (t) is the demand characteristic data of the i-th dimension under the historical network status of the communication base station at the t-th unit time, β0, β1, β2, β i All are linear regression coefficients.

6. The method for adjusting the transmitted optical power of an optical module according to claim 5, wherein: If it is determined that the transmit optical power needs to be adjusted, a dynamic transmit optical power adjustment model is established based on the deviation between the multi-dimensional attention demand vector preference of the historical network status of the communication base station and the multi-dimensional attention vector of the real-time network status of the communication base station. The dynamic transmit optical power adjustment value of the communication base station is generated, specifically including: Normalizing the multi-dimensional attention demand vector preference index based on the historical network status of the communication base station and the multi-dimensional attention vector of the real-time network status of the communication base station; Aligning the multi-dimensional attention demand vector preference index of the historical network status of the communication base station with the multi-dimensional attention vector of the real-time network status of the communication base station based on the time attribute; Using the Euclidean distance formula, the spatial distance between the multi-dimensional attention demand vector preference index of the historical network state of the communication base station and the multi-dimensional attention vector of the real-time network state of the communication base station per unit time is calculated to determine the multi-dimensional attention demand deviation vector of the real-time network state of the communication base station; Based on the multi-dimensional attention demand deviation vector of the real-time network status of the communication base station, a dynamic transmission optical power adjustment model is established according to weighted linear regression to generate the dynamic transmission optical power adjustment value of the communication base station.

7. The method for adjusting the transmitted optical power of an optical module according to claim 6, wherein: The Euclidean distance formula is specifically: Where, d i (t) is the attention demand deviation vector of the i-th dimension under the t-th unit time of the real-time network status of the communication base station.

8. The method for adjusting the transmitted optical power of an optical module according to claim 7, wherein: The specific method of generating the dynamic transmission optical power adjustment value of the communication base station is: Where Δr is the dynamic transmission optical power adjustment value of the communication base station, w i is the i-th dimension weight of the real-time network status of the communication base station, and b is the bias term.

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