Active noise reduction intelligent control method and system for expressway sound wave interference

Abstract

The invention discloses an active noise reduction intelligent control method and system for expressway sound wave interference, and belongs to the technical field of sound wave noise reduction control. The method comprises the following steps: mapping highway noise reduction equipment through a digital twin technology, establishing a digital twin sound wave database and generating a noise reduction control parameter flow sample set; a historical noise reduction behavior library is constructed to analyze parameter state changes, and parameter recontrol degrees are quantified to determine key recontrol parameters; the effective action range of the equipment is delimited, an associated equipment set is generated by calculating the noise reduction control relevancy between the equipment, and the effective action radius is evaluated and dynamically adjusted to optimize the noise reduction coverage degree. According to the invention, the problems of poor collaboration, unreasonable coverage, blind parameter adjustment and the like of noise reduction equipment in the prior art are solved, intelligent noise reduction control based on data driving is realized, and the accuracy and efficiency of sound wave interference noise reduction of the expressway are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of sound wave noise reduction control, and in particular to an active noise reduction intelligent control method and system for sound wave interference on highways. Background Art

[0002] As traffic on highways increases, the acoustic interference generated by vehicles (such as engine roar, tire friction, and horns) has become a major source of environmental noise pollution along the route. Existing active noise reduction technologies often rely on the independent control of a single device, which has the following limitations:

[0003] Noise reduction equipment on different road sections lacks linkage and cannot coordinate processing based on the continuity of sound waves propagating along the road, resulting in noise reduction blind spots or repeated noise reduction.

[0004] The setting of control parameters (such as sound wave frequency and power) relies on experience and is not optimized based on historical data, which can easily lead to "over-noise reduction" (interfering with the surrounding environment) or "under-noise reduction" (noise is not effectively offset);

[0005] The effective radius of the equipment cannot be adjusted dynamically after being preset, making it difficult to adapt to changes in noise characteristics at different times (such as peak / valley traffic) and on different road sections (such as bridges / tunnels);

[0006] Historical noise reduction operations have not formed a structured database, and it is impossible to extract key control logic by analyzing the patterns of parameter changes, resulting in slow technological iteration.

[0007] Therefore, there is an urgent need for an intelligent control method that can achieve equipment coordination, parameter optimization, and dynamic range adjustment to solve the problem of precise noise reduction caused by sound wave interference on highways. Summary of the Invention

[0008] The present invention aims to provide an intelligent control method and system for active noise reduction using acoustic interference on highways to address the problems raised in the above-mentioned background art. The method aims to implement intelligent control of active noise reduction based on the logic of "data modeling-rule mining-cooperative optimization", including:

[0009] Digital twin modeling: Digital twin mapping of noise reduction equipment is performed, converting the control parameters of the physical equipment (such as frequency, power, and direction) into digital parameter streams, establishing a sample set containing all possible control strategies, and providing basic data for subsequent analysis;

[0010] Historical pattern mining: Parameter changes are recorded through the historical noise reduction behavior library, and the "complex control degree" (the probability of a parameter appearing repeatedly in multiple noise reduction processes) is calculated to screen out the complex control parameters that play a key role in the noise reduction effect and reduce redundant parameter interference;

[0011] Collaborative scope delineation: Delineate the effective scope of action with a single device as the center, identify associated devices by calculating the "noise reduction control correlation" (based on the commonalities and differences of complex control parameters) between devices, and generate an associated set;

[0012] Dynamically optimize coverage: By evaluating "noise reduction coverage" (the coverage ratio of associated devices within the effective range), the effective radius is dynamically adjusted to ensure that the coverage range matches the sound wave propagation characteristics and avoid blind spots or overlaps.

[0013] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0014] An intelligent active noise reduction control system for highway acoustic interference. This system includes: a digital twin acoustic wave database module, a historical noise reduction behavior analysis module, a complex control parameter and range delineation module, and a correlation and coverage adjustment module.

[0015] The digital twin sound wave database module maps the noise reduction device based on the digital twin technology, stores the noise reduction control parameters when the simulated noise reduction device responds to sound wave interference, encodes the noise reduction device and the noise reduction control parameters, and generates a noise reduction control parameter stream sample set;

[0016] The historical noise reduction behavior analysis module is used to build a historical noise reduction behavior library, store historical noise reduction control parameters, generate a historical noise reduction control parameter stream set, and analyze the state changes of the noise reduction control parameters;

[0017] The complex control parameter and range delineation module quantifies the complex control degree of the noise reduction control parameter based on the noise reduction control parameter stream sample set, the historical noise reduction control parameter stream set and the state change to determine the complex control noise reduction control parameter, delineates the effective range of the noise reduction device, and generates a sample set to be optimized and evaluated;

[0018] The correlation and coverage adjustment module is used to determine the control correlation between noise reduction devices within the effective range, evaluate the noise reduction coverage, adjust the effective radius to meet the preset requirements, and send the results to the remote control platform.

[0019] Furthermore, the digital twin acoustic wave database module also includes a storage unit and an encoding unit;

[0020] The storage unit performs digital twin mapping on all noise reduction devices on the highway, simulates the noise reduction control parameters of each noise reduction device when responding to sound wave interference, establishes a digital twin sound wave database and stores relevant parameters;

[0021] The encoding unit encodes and configures the noise reduction devices and noise reduction control parameters, coordinates all noise reduction control parameters of different noise reduction devices, generates a noise reduction control parameter stream sample set, and records it in a database.

[0022] Furthermore, the historical noise reduction behavior analysis module also includes a history library construction unit and a state change analysis unit;

[0023] The history library construction unit constructs a history noise reduction behavior library, stores the noise reduction control parameters of each time the noise reduction device responds to sound wave interference in history, and generates a history noise reduction control parameter stream set;

[0024] The state change analysis unit analyzes and generates a state change set of the noise reduction control parameters according to the noise reduction control parameter stream sample set and the historical noise reduction control parameter stream set.

[0025] Furthermore, the complex control parameter and range delineation module further includes a complex control degree calculation unit and an effective range delineation unit;

[0026] The complex control degree calculation unit calculates the complex control degree of each noise reduction control parameter based on the noise reduction control parameter stream sample set, the historical noise reduction control parameter stream set and the state change set, marks the parameter whose complex control degree reaches the threshold as the complex control noise reduction control parameter, and comprehensively generates the complex control noise reduction control parameter stream set;

[0027] The effective range demarcation unit sets an effective action radius with a single noise reduction device as the center to demarcate the effective action range, and all noise reduction devices within the range constitute a sample set to be optimized and evaluated.

[0028] Furthermore, the correlation and coverage adjustment module further includes a correlation determination unit and a coverage evaluation adjustment unit;

[0029] The correlation determination unit retrieves the complex noise reduction control parameter stream set of different noise reduction devices within the effective range, calculates the control correlation between the devices, determines that the devices with the correlation reaching a threshold have control correlation, and comprehensively generates a set of associated devices;

[0030] The coverage evaluation and adjustment unit calculates the noise reduction coverage of the noise reduction device, adjusts the effective action radius through a fixed scale and updates the evaluation sample set to be optimized until the coverage reaches a threshold, locks the radius and sends it to the remote control platform.

[0031] An intelligent control method for active noise reduction for highway acoustic interference includes the following steps:

[0032] Step S1: Perform digital twin mapping on the noise reduction equipment on the highway to establish a digital twin sound wave database, and encode the noise reduction equipment and noise reduction control parameters to generate a noise reduction control parameter stream sample set;

[0033] Step S2: Build a historical noise reduction behavior library, store historical noise reduction control parameters and analyze the state changes of the noise reduction control parameters;

[0034] Step S3: quantifying the complex control degree of the noise reduction control parameter to determine the complex control noise reduction control parameter, defining the effective range of the noise reduction device and generating a sample set to be optimized;

[0035] Step S4: Determine the noise reduction control correlation between the noise reduction devices within the effective range, evaluate the noise reduction coverage and adjust the effective radius to meet the preset requirements.

[0036] Furthermore, the specific implementation process of step S1 includes:

[0037] Perform digital twin mapping of all noise reduction equipment on the highway, simulate the noise reduction control parameters of each noise reduction device when responding to sound wave interference, form a noise reduction control parameter stream, and record it in the digital twin sound wave database;

[0038] The noise reduction equipment and noise reduction control parameters are encoded and configured to coordinate all the noise reduction control parameters corresponding to different noise reduction equipment, and generate a noise reduction control parameter stream sample set, which is recorded in the digital twin sound wave database. The noise reduction control parameter stream sample set is represented as L i ={C r |r∈[1, R]}, i is the code of the noise reduction device, C r represents the rth noise reduction control parameter.

[0039] Furthermore, the specific implementation process of step S2 includes:

[0040] Construct a historical noise reduction behavior library to store the noise reduction control parameters of each time the noise reduction equipment responds to the sound wave interference in the highway sound wave environment, and generate a historical noise reduction control parameter flow set, recorded as i k ,and Among them, i k represents the historical noise reduction control parameter stream set of the noise reduction device i when responding to the sound wave interference for the kth time in history;

[0041] According to the noise reduction control parameter flow sample set and the historical noise reduction control flow set, the state change set of the noise reduction control parameter is obtained, which is recorded as U(k→k+1), and U(k→k+1)=i k ∩i k+1 , where U(k→k+1) represents the state change set of noise reduction control parameters generated when the noise reduction device i responds to the sound wave interference from the kth to the k+1th time, i k+1 represents the historical noise reduction control parameter stream set of the noise reduction device i when responding to the sound wave interference for the k+1th time in history, and

[0042] Furthermore, the specific implementation process of step S3 includes:

[0043] According to the noise reduction control parameter flow sample set, the historical noise reduction control parameter flow set and the state change set, a noise reduction control parameter C is randomly selected from the noise reduction control parameter flow sample set. r , calculate the noise reduction control parameter C r Complex control degree Where N represents the total number of times noise reduction device i has responded to sound wave interference in history;

[0044] If the noise reduction control parameter C r Satisfy C r ∈U(k→k+1), then let f[C r ∈U(k→k+1)]=1, otherwise, let f[C r ∈U(k→k+1)]=0; if the noise reduction control parameter C r Satisfy C r ∈i k , then let F[C r ∈i k ]=1, otherwise, let F[C r ∈i k ]=0;

[0045] Preset complex control threshold, if the noise reduction control parameter C r If the complex control degree is greater than or equal to the complex control degree threshold, the noise reduction control parameter C r Marked as complex control noise reduction control parameters; coordinate all complex control noise reduction control parameters of noise reduction device i and generate a complex control noise reduction control parameter flow set, recorded as V i ;

[0046] Among the noise reduction devices deployed in a fixed highway section, the effective radius r of the noise reduction device i is set with the noise reduction device i as the center point, and the effective boundary range of the noise reduction device i is generated. All noise reduction devices within the effective boundary range of the noise reduction device i constitute the sample set to be optimized and evaluated, which is recorded as S r .

[0047] Furthermore, the specific implementation process of step S4 includes:

[0048] Within the effective boundary of noise reduction device i, call the complex control noise reduction control parameter flow set of noise reduction device i and noise reduction device j, and i≠j, i, j∈S r , determine whether there is a noise reduction control correlation of sound wave interference between noise reduction device i and noise reduction device j:

[0049] Quantify the noise reduction control correlation between noise reduction device i and noise reduction device j Where α represents the common value of parameter characteristics and α=|V i ∩V j|+1, β represents the parameter characteristic difference value and β=|V i -V j |+|V j -V i |+1, V j represents the set of complex noise reduction control parameter flows corresponding to noise reduction device j, |V i ∩V j | represents the set of complex noise reduction control parameter flows V i and the complex control noise reduction control parameter flow set V j The number of noise reduction control parameters contained in the intersection set between |V i -V j | represents the set of complex noise reduction control parameter flows V i does not belong to the complex control noise reduction control parameter flow set V j The number of noise reduction control parameters, |V j -V i | represents the set of complex noise reduction control parameter flows V j does not belong to the complex control noise reduction control parameter flow set V i The number of noise reduction control parameters;

[0050] A correlation threshold is preset. If the noise reduction control correlation DA(i, j) is greater than or equal to the correlation threshold, it is determined that there is a noise reduction control correlation of acoustic interference between noise reduction devices i and noise reduction devices j. Among the noise reduction devices deployed in a fixed highway section, all noise reduction devices that are correlated with noise reduction device i are coordinated to generate a set of associated acoustic interference devices, which is recorded as W. i ;

[0051] Evaluate the noise reduction coverage of noise reduction equipment i Where, |S r ∩W i | represents the evaluation sample set S to be optimized r Associated with the sound wave interference device set W i The number of noise reduction devices included in the intersection set, |S r ∪W i | represents the evaluation sample set S to be optimized r Associated with the sound wave interference device set W i The number of noise reduction devices included in the union set;

[0052] Adjust the effective radius of the noise reduction device i to r = r + Δr, Δr is a fixed adjustment scale value, and treat the optimization evaluation sample set S r Update until the noise reduction coverage RC i When the preset noise reduction coverage threshold is met, the optimization evaluation sample set S is stopped. r The update will lock the effective radius of the noise reduction device i and send it to the remote control platform.

[0053] Compared with the prior art, the present invention has the following beneficial effects:

[0054] By identifying collaborative devices on the sound wave propagation path through a collection of associated devices, multi-device relay noise reduction can be achieved (for example, devices on bridge sections and straight road sections can be started simultaneously), solving the problem of insufficient coverage by a single device.

[0055] By screening key parameters based on complex control, invalid parameter adjustments are avoided, and control parameters are precisely matched with noise characteristics (such as low-frequency noise from trucks and high-frequency horns from cars), improving noise reduction efficiency by more than 30%;

[0056] The effective radius can be adjusted according to the real-time noise coverage (e.g., the radius can be expanded to 100 meters during peak hours and reduced to 50 meters during off-peak hours), reducing energy waste and interference with the surrounding environment.

[0057] The historical behavior library and complex control degree analysis form a closed-loop optimization mechanism, which enables the control strategy to be continuously optimized as historical data accumulates, adapting to the noise variation patterns in different road sections and seasons. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.

[0059] Figure 1 It is a schematic diagram of the steps of the active noise reduction intelligent control method for highway sound wave interference of the present invention. DETAILED DESCRIPTION

[0060] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0061] Embodiment 1: Provide an active noise reduction intelligent control system for highway acoustic wave interference, the system comprising: a digital twin acoustic wave database module, a historical noise reduction behavior analysis module, a complex control parameter and range delineation module, and a correlation and coverage adjustment module;

[0062] The digital twin sound wave database module maps the noise reduction device based on the digital twin technology, stores the noise reduction control parameters when the simulated noise reduction device responds to sound wave interference, encodes the noise reduction device and the noise reduction control parameters, and generates a noise reduction control parameter stream sample set;

[0063] Wherein, the digital twin sound wave database module also includes a storage unit and an encoding unit;

[0064] The storage unit performs digital twin mapping on all noise reduction devices on the highway, simulates the noise reduction control parameters of each noise reduction device when responding to sound wave interference, establishes a digital twin sound wave database and stores relevant parameters;

[0065] The encoding unit encodes and configures the noise reduction devices and noise reduction control parameters, coordinates all noise reduction control parameters of different noise reduction devices, generates a noise reduction control parameter stream sample set, and records it in a database;

[0066] The historical noise reduction behavior analysis module is used to build a historical noise reduction behavior library, store historical noise reduction control parameters, generate a historical noise reduction control parameter stream set, and analyze the state changes of the noise reduction control parameters;

[0067] Wherein, the historical noise reduction behavior analysis module also includes a history library construction unit and a state change analysis unit;

[0068] The history library construction unit constructs a history noise reduction behavior library, stores the noise reduction control parameters of each time the noise reduction device responds to sound wave interference in history, and generates a history noise reduction control parameter stream set;

[0069] The state change analysis unit analyzes and generates a state change set of the noise reduction control parameters based on the noise reduction control parameter stream sample set and the historical noise reduction control parameter stream set;

[0070] The complex control parameter and range delineation module quantifies the complex control degree of the noise reduction control parameter based on the noise reduction control parameter stream sample set, the historical noise reduction control parameter stream set and the state change to determine the complex control noise reduction control parameter, delineates the effective range of the noise reduction device, and generates a sample set to be optimized and evaluated;

[0071] Wherein, the complex control parameter and range delineation module further includes a complex control degree calculation unit and an effective range delineation unit;

[0072] The complex control degree calculation unit calculates the complex control degree of each noise reduction control parameter based on the noise reduction control parameter stream sample set, the historical noise reduction control parameter stream set and the state change set, marks the parameter whose complex control degree reaches the threshold as the complex control noise reduction control parameter, and comprehensively generates the complex control noise reduction control parameter stream set;

[0073] The effective range demarcation unit sets an effective action radius with a single noise reduction device as the center to demarcate the effective action range, and all noise reduction devices within the range constitute a sample set to be optimized and evaluated;

[0074] The correlation and coverage adjustment module is used to determine the control correlation between noise reduction devices within the effective range, evaluate the noise reduction coverage, adjust the effective radius to meet the preset requirements, and send the results to the remote control platform;

[0075] Wherein, the correlation and coverage adjustment module further includes a correlation judgment unit and a coverage evaluation adjustment unit;

[0076] The correlation determination unit retrieves the complex noise reduction control parameter stream set of different noise reduction devices within the effective range, calculates the control correlation between the devices, determines that the devices with the correlation reaching a threshold have control correlation, and comprehensively generates a set of associated devices;

[0077] The coverage evaluation and adjustment unit calculates the noise reduction coverage of the noise reduction device, adjusts the effective action radius through a fixed scale and updates the evaluation sample set to be optimized until the coverage reaches a threshold, locks the radius and sends it to the remote control platform.

[0078] See also Figure 1 Embodiment 2: Provides an intelligent control method for active noise reduction for highway acoustic interference, the method comprising the following steps:

[0079] Step S1: Perform digital twin mapping on the noise reduction equipment on the highway to establish a digital twin sound wave database, and encode the noise reduction equipment and noise reduction control parameters to generate a noise reduction control parameter stream sample set;

[0080] For example, a digital twin mapping is performed on all noise reduction devices on the highway, and the noise reduction control parameters of each noise reduction device when responding to sound wave interference are simulated to form a noise reduction control parameter stream, which is recorded in the digital twin sound wave database;

[0081] The noise reduction equipment and noise reduction control parameters are encoded and configured to coordinate all the noise reduction control parameters corresponding to different noise reduction equipment, and generate a noise reduction control parameter stream sample set, which is recorded in the digital twin sound wave database. The noise reduction control parameter stream sample set is represented as L i ={C r |r∈[1, R]}, i is the code of the noise reduction device, C r represents the rth noise reduction control parameter;

[0082] For example, a two-way four-lane expressway section K10-K12 (including one bridge and a 2-kilometer straight section) was selected and 10 active noise reduction loudspeakers (coded i = 1 to 10) were deployed. The target noise reduction frequency range was 50-2000 Hz, covering residential areas within 100 meters along the expressway.

[0083] Digital twin mapping was performed on 10 devices to simulate their control parameters for different noises (e.g., C1 = frequency (Hz), C2 = power (W), C3 = direction angle (°)).

[0084] Generate parameter flow sample set L i ={C1, C2, C3}, for example, the sample set L1 of device 1 ={100 Hz, 30 W, 0°; 200 Hz, 25 W, 15°; ...}.

[0085] Step S2: Build a historical noise reduction behavior library, store historical noise reduction control parameters and analyze the state changes of the noise reduction control parameters;

[0086] For example, a historical noise reduction behavior library is constructed to store the noise reduction control parameters of each time the noise reduction device responds to the sound wave interference in the highway sound wave environment in history, and generate a historical noise reduction control parameter stream set, which is recorded as i k ,and Among them, i k represents the historical noise reduction control parameter stream set of the noise reduction device i when responding to the sound wave interference for the kth time in history;

[0087] According to the noise reduction control parameter flow sample set and the historical noise reduction control flow set, the state change set of the noise reduction control parameter is obtained, which is recorded as U(k→k+1), and U(k→k+1)=i k ∩i k+1 , where U(k→k+1) represents the state change set of noise reduction control parameters generated when the noise reduction device i responds to the sound wave interference from the kth to the k+1th time, i k+1 represents the historical noise reduction control parameter stream set of the noise reduction device i when responding to the sound wave interference for the k+1th time in history, and

[0088] For example, a history library is constructed to store the past 100 noise reduction data and generate a historical parameter stream set ik (e.g., the k-th noise reduction parameter of device 1 is ik = {100 Hz, 30 W, 0°});

[0089] When calculating the state change set, for example, the intersection of the 5th and 6th noise reduction operations of device 1 is {100 Hz, 30 W}, which means that these two parameters appear repeatedly in the two operations.

[0090] Step S3: quantifying the complex control degree of the noise reduction control parameter to determine the complex control noise reduction control parameter, defining the effective range of the noise reduction device and generating a sample set to be optimized;

[0091] For example, according to the noise reduction control parameter stream sample set, the historical noise reduction control parameter stream set and the state change set, a noise reduction control parameter C is randomly selected from the noise reduction control parameter stream sample set. r , calculate the noise reduction control parameter Cr Complex control degree Where N represents the total number of times noise reduction device i has responded to sound wave interference in history;

[0092] If the noise reduction control parameter C r Satisfy C r ∈U(k→k+1), then let f[C r ∈U(k→k+1)]=1, otherwise, let f[C r ∈U(k→k+1)]=0; if the noise reduction control parameter C r Satisfy C r ∈i k , then let F[C r ∈i k ]=1, otherwise, let F[C r ∈i k ]=0;

[0093] Preset complex control threshold, if the noise reduction control parameter C r If the complex control degree is greater than or equal to the complex control degree threshold, the noise reduction control parameter C r Marked as complex control noise reduction control parameters; coordinate all complex control noise reduction control parameters of noise reduction device i and generate a complex control noise reduction control parameter flow set, recorded as V i ;

[0094] Among the noise reduction devices deployed in a fixed highway section, the effective radius r of the noise reduction device i is set with the noise reduction device i as the center point, and the effective boundary range of the noise reduction device i is generated. All noise reduction devices within the effective boundary range of the noise reduction device i constitute the sample set to be optimized and evaluated, which is recorded as S r ;

[0095] The effective radius r is a spatial data. The radius is generally set to 10 meters and can be determined by adjusting the operating power of the noise reduction equipment.

[0096] Step S4: Determine the noise reduction control correlation between the noise reduction devices within the effective range, evaluate the noise reduction coverage and adjust the effective radius to meet the preset requirements;

[0097] For example, within the effective boundary of the noise reduction device i, the complex control noise reduction control parameter stream set of the noise reduction device i and the noise reduction device j is retrieved, and i≠j, i, j∈S r , determine whether there is a noise reduction control correlation of sound wave interference between noise reduction device i and noise reduction device j:

[0098] Quantify the noise reduction control correlation between noise reduction device i and noise reduction device j Where α represents the common value of parameter characteristics and α=|V i ∩V j|+1, β represents the parameter characteristic difference value and β=|V i -V j |+|V j -V i |+1, V j represents the set of complex noise reduction control parameter flows corresponding to noise reduction device j, |V i ∩V j | represents the set of complex noise reduction control parameter flows V i and the complex control noise reduction control parameter flow set V j The number of noise reduction control parameters contained in the intersection set between |V i -V j | represents the set of complex noise reduction control parameter flows V i does not belong to the complex control noise reduction control parameter flow set V j The number of noise reduction control parameters, |V j -V i | represents the set of complex noise reduction control parameter flows V j does not belong to the complex control noise reduction control parameter flow set V i The number of noise reduction control parameters;

[0099] A correlation threshold is preset. If the noise reduction control correlation DA(i, j) is greater than or equal to the correlation threshold, it is determined that there is a noise reduction control correlation of acoustic interference between noise reduction devices i and noise reduction devices j. Among the noise reduction devices deployed in a fixed highway section, all noise reduction devices that are correlated with noise reduction device i are coordinated to generate a set of associated acoustic interference devices, which is recorded as W. i ;

[0100] Evaluate the noise reduction coverage of noise reduction equipment i Where, |S r ∩W i | represents the evaluation sample set S to be optimized r Associated with the sound wave interference device set W i The number of noise reduction devices included in the intersection set, |S r ∪W i | represents the evaluation sample set S to be optimized r Associated with the sound wave interference device set W i The number of noise reduction devices included in the union set;

[0101] Adjust the effective radius of the noise reduction device i to r = r + Δr, Δr is a fixed adjustment scale value, and treat the optimization evaluation sample set S r Update until the noise reduction coverage RC i When the preset noise reduction coverage threshold is met, the optimization evaluation sample set S is stopped. r The update will lock the effective radius of the noise reduction device i and send it to the remote control platform.

[0102] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0103] Finally, it should be noted that the above description is merely a preferred embodiment of the present invention and 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 will be able to modify the technical solutions described in the aforementioned embodiments or substitute equivalents for some of the technical features. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. An intelligent control method for active noise reduction using acoustic interference on highways, characterized in that: The method comprises the following steps: Step S1: Perform digital twin mapping on the noise reduction equipment on the highway to establish a digital twin sound wave database, and encode the noise reduction equipment and noise reduction control parameters to generate a noise reduction control parameter stream sample set; Step S2: Build a historical noise reduction behavior library, store historical noise reduction control parameters and analyze the state changes of the noise reduction control parameters; Step S3: quantifying the complex control degree of the noise reduction control parameter to determine the complex control noise reduction control parameter, defining the effective range of the noise reduction device and generating a sample set to be optimized; Step S4: Determine the noise reduction control correlation between the noise reduction devices within the effective range, evaluate the noise reduction coverage and adjust the effective radius to meet the preset requirements.

2. The intelligent control method for active noise reduction of highway sound wave interference according to claim 1 is characterized in that: The specific implementation process of step S1 includes: Perform digital twin mapping of all noise reduction equipment on the highway, simulate the noise reduction control parameters of each noise reduction device when responding to sound wave interference, form a noise reduction control parameter stream, and record it in the digital twin sound wave database; The noise reduction equipment and noise reduction control parameters are encoded and configured to coordinate all the noise reduction control parameters corresponding to different noise reduction equipment, and generate a noise reduction control parameter stream sample set, which is recorded in the digital twin sound wave database. The noise reduction control parameter stream sample set is represented as L i ={C r |r∈[1, R]}, i is the code of the noise reduction device, C r represents the rth noise reduction control parameter.

3. The active noise reduction intelligent control method for highway acoustic interference according to claim 2 is characterized in that: The specific implementation process of step S2 includes: Construct a historical noise reduction behavior library to store the noise reduction control parameters of each time the noise reduction equipment responds to the sound wave interference in the highway sound wave environment, and generate a historical noise reduction control parameter flow set, recorded as i k ,and Among them, i k represents the historical noise reduction control parameter stream set of the noise reduction device i when responding to the sound wave interference for the kth time in history; According to the noise reduction control parameter flow sample set and the historical noise reduction control flow set, the state change set of the noise reduction control parameter is obtained, which is recorded as U(k→k+1), and U(k→k+1)=i k ∩i k+1 , where U(k→k+1) represents the state change set of noise reduction control parameters generated when the noise reduction device i responds to the sound wave interference from the kth to the k+1th time, i k+1 represents the historical noise reduction control parameter stream set of the noise reduction device i when responding to the sound wave interference for the k+1th time in history, and 4. The intelligent control method for active noise reduction of highway sound wave interference according to claim 3 is characterized in that: The specific implementation process of step S3 includes: According to the noise reduction control parameter flow sample set, the historical noise reduction control parameter flow set and the state change set, a noise reduction control parameter C is randomly selected from the noise reduction control parameter flow sample set. r , calculate the noise reduction control parameter C r Complex control degree Where N represents the total number of times noise reduction device i has responded to sound wave interference in history; If the noise reduction control parameter C r Satisfy C r ∈U(k→k+1), then let f[C r ∈U(k→k+1)]=1, otherwise, let f[C r ∈U(k→k+1)]=0; if the noise reduction control parameter C r Satisfy C r ∈i k , then let F[C r ∈i k ]=1, otherwise, let F[C r ∈i k ]=0; Preset complex control threshold, if the noise reduction control parameter C r If the complex control degree is greater than or equal to the complex control degree threshold, the noise reduction control parameter C r Marked as complex control noise reduction control parameters; coordinate all complex control noise reduction control parameters of noise reduction device i and generate a complex control noise reduction control parameter flow set, recorded as V i ; Among the noise reduction devices deployed in a fixed highway section, the effective radius r of the noise reduction device i is set with the noise reduction device i as the center point, and the effective boundary range of the noise reduction device i is generated. All noise reduction devices within the effective boundary range of the noise reduction device i constitute the sample set to be optimized and evaluated, which is recorded as S r .

5. The intelligent control method for active noise reduction of highway sound wave interference according to claim 4 is characterized in that: The specific implementation process of step S4 includes: Within the effective boundary of noise reduction device i, call the complex control noise reduction control parameter flow set of noise reduction device i and noise reduction device j, and i≠j, i, j∈S r , determine whether there is a noise reduction control correlation of sound wave interference between noise reduction device i and noise reduction device j: Quantify the noise reduction control correlation between noise reduction device i and noise reduction device j Where α represents the common value of parameter characteristics and α=|V i ∩V j |+1, β represents the parameter characteristic difference value and β=|V i -V j |+|V j -V i |+1, V j represents the set of complex noise reduction control parameter flows corresponding to noise reduction device j, |V i ∩V j | represents the set of complex noise reduction control parameter flows V i and the complex control noise reduction control parameter flow set V j The number of noise reduction control parameters contained in the intersection set between |V i -V j | represents the set of complex noise reduction control parameter flows V i does not belong to the complex control noise reduction control parameter flow set V j The number of noise reduction control parameters, |V j -V i | represents the set of complex noise reduction control parameter flows V j does not belong to the complex control noise reduction control parameter flow set V i The number of noise reduction control parameters; A correlation threshold is preset. If the noise reduction control correlation DA(i, j) is greater than or equal to the correlation threshold, it is determined that there is a noise reduction control correlation of acoustic interference between noise reduction devices i and noise reduction devices j. Among the noise reduction devices deployed in a fixed highway section, all noise reduction devices that are correlated with noise reduction device i are coordinated to generate a set of associated acoustic interference devices, which is recorded as W. i ; Evaluate the noise reduction coverage of noise reduction equipment i Where, |S r ∩W i | represents the evaluation sample set S to be optimized r Associated with the sound wave interference device set W i The number of noise reduction devices included in the intersection set, |S r ∪W i | represents the evaluation sample set S to be optimized r Associated with the sound wave interference device set W i The number of noise reduction devices included in the union set; Adjust the effective radius of the noise reduction device i to r = r + Δr, Δr is a fixed adjustment scale value, and treat the optimization evaluation sample set S r Update until the noise reduction coverage RC i When the preset noise reduction coverage threshold is met, the optimization evaluation sample set S is stopped. r The update will lock the effective radius of the noise reduction device i and send it to the remote control platform.

6. An active noise reduction intelligent control system for highway acoustic interference, which implements the active noise reduction intelligent control method according to any one of claims 1 to 5, characterized in that: The system includes: a digital twin sound wave database module, a historical noise reduction behavior analysis module, a complex control parameter and range delineation module, and a correlation and coverage adjustment module; The digital twin sound wave database module maps the noise reduction device based on the digital twin technology, stores the noise reduction control parameters when the simulated noise reduction device responds to sound wave interference, encodes the noise reduction device and the noise reduction control parameters, and generates a noise reduction control parameter stream sample set; The historical noise reduction behavior analysis module is used to build a historical noise reduction behavior library, store historical noise reduction control parameters, generate a historical noise reduction control parameter stream set, and analyze the state changes of the noise reduction control parameters; The complex control parameter and range delineation module quantifies the complex control degree of the noise reduction control parameter based on the noise reduction control parameter stream sample set, the historical noise reduction control parameter stream set and the state change to determine the complex control noise reduction control parameter, delineates the effective range of the noise reduction device, and generates a sample set to be optimized and evaluated; The correlation and coverage adjustment module is used to determine the control correlation between noise reduction devices within the effective range, evaluate the noise reduction coverage, adjust the effective radius to meet the preset requirements, and send the results to the remote control platform.

7. The active noise reduction intelligent control system for highway acoustic interference according to claim 6 is characterized in that: The digital twin acoustic wave database module also includes a storage unit and an encoding unit; The storage unit performs digital twin mapping on all noise reduction devices on the highway, simulates the noise reduction control parameters of each noise reduction device when responding to sound wave interference, establishes a digital twin sound wave database and stores relevant parameters; The encoding unit encodes and configures the noise reduction devices and noise reduction control parameters, coordinates all noise reduction control parameters of different noise reduction devices, generates a noise reduction control parameter stream sample set, and records it in a database.

8. The active noise reduction intelligent control system for highway acoustic interference according to claim 6 is characterized by: The historical noise reduction behavior analysis module also includes a history library construction unit and a state change analysis unit; The history library construction unit constructs a history noise reduction behavior library, stores the noise reduction control parameters of each time the noise reduction device responds to sound wave interference in history, and generates a history noise reduction control parameter stream set; The state change analysis unit analyzes and generates a state change set of the noise reduction control parameters according to the noise reduction control parameter stream sample set and the historical noise reduction control parameter stream set.

9. The active noise reduction intelligent control system for highway acoustic interference according to claim 6 is characterized in that: The complex control parameter and range delineation module also includes a complex control degree calculation unit and an effective range delineation unit; The complex control degree calculation unit calculates the complex control degree of each noise reduction control parameter based on the noise reduction control parameter stream sample set, the historical noise reduction control parameter stream set and the state change set, marks the parameter whose complex control degree reaches the threshold as the complex control noise reduction control parameter, and comprehensively generates the complex control noise reduction control parameter stream set; The effective range demarcation unit sets an effective action radius with a single noise reduction device as the center to demarcate the effective action range, and all noise reduction devices within the range constitute a sample set to be optimized and evaluated.

10. The active noise reduction intelligent control system for highway acoustic interference according to claim 6 is characterized in that: The correlation and coverage adjustment module also includes a correlation judgment unit and a coverage evaluation adjustment unit; The correlation determination unit retrieves the complex noise reduction control parameter stream set of different noise reduction devices within the effective range, calculates the control correlation between the devices, determines that the devices with the correlation reaching a threshold have control correlation, and comprehensively generates a set of associated devices; The coverage evaluation and adjustment unit calculates the noise reduction coverage of the noise reduction device, adjusts the effective action radius through a fixed scale and updates the evaluation sample set to be optimized until the coverage reaches a threshold, locks the radius and sends it to the remote control platform.

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