Industrial telephone communication signal enhancement method and system

Abstract

The invention provides an industrial telephone communication signal enhancement method and system. The method comprises the following steps: step 1, acquiring historical communication signal data, real-time communication signal data and environment data of an industrial telephone; according to the method, the signal interference recognition model is constructed, the interference signal features are accurately recognized, interference is removed through the intelligent filtering algorithm, the problems of signal strength weakening and call quality reduction caused by electromagnetic interference are effectively solved, meanwhile, the influence of environmental factors such as temperature and humidity on communication signals is considered, corresponding compensation measures are adopted, and the communication quality is improved. The adaptability and the stability of the system in a complex industrial environment are improved; signal relay equipment is arranged, so that the signal coverage range is effectively expanded, and efficient communication and coordination among departments in industrial production are ensured; dynamic adjustment is carried out according to the communication signal quality monitored in real time, it is guaranteed that the system is in the optimal working state all the time, and the intelligent level and stability of the system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of telephone communication, and in particular to an industrial telephone communication signal enhancement method and system. Background Art

[0002] In the industrial field, telephone communication plays a vital role. Industrial production usually involves complex processes and numerous links, and various departments need to exchange information and communicate in a timely and accurate manner. As a convenient and fast way of communication, telephone communication can quickly convey instructions, coordinate production progress, and solve unexpected problems in emergency situations. For example, when a production equipment fails, the operator can promptly notify the maintenance personnel by phone to carry out emergency repairs to avoid losses caused by production stagnation. In the daily management of industrial enterprises, telephone communication also facilitates communication and decision-making between personnel at all levels and improves management efficiency. In addition, for some large-scale industrial projects involving multiple partners and different work areas, telephone communication can achieve cross-regional coordination and cooperation to ensure the smooth progress of the project. Therefore, telephone communication plays an irreplaceable and important role in the industrial field. However, industrial telephone communication currently faces many challenges: On the one hand, the industrial environment is complex and changeable, and there are various electromagnetic interference sources. For example, electromagnetic radiation generated by large motors, inverters and other equipment can cause serious interference to telephone communication signals, resulting in weakened signal strength and reduced call quality. On the other hand, environmental factors such as temperature and humidity at industrial sites can also affect communication signals. For example, high temperature environments may reduce the performance of electronic components, thereby affecting the transmission quality of signals; high humidity environments may cause increased signal attenuation. In addition, industrial sites are usually large in area, with limited coverage of communication signals and signal blind spots, which affects efficient communication and coordination in industrial production; To this end, a method and system for enhancing industrial telephone communication signals are proposed. Summary of the invention

[0003] In view of this, the embodiments of the present invention hope to provide an industrial telephone communication signal enhancement method and system to solve or alleviate the technical problems existing in the prior art and at least provide a beneficial option.

[0004] In order to solve the above technical problems, a technical solution adopted in this application is: a method for enhancing industrial telephone communication signals, comprising the following steps: Step 1: Obtain historical communication signal data, real-time communication signal data and environmental data of industrial telephones; Step 2: Preprocess the acquired historical communication signal data, real-time communication signal data and environmental data based on signal processing technology; Step 3: Based on the processed historical communication signal data and environmental data, a signal interference recognition model is constructed based on a deep learning algorithm; Step 4: Based on the processed real-time communication signal data, use the signal interference recognition model to identify the characteristics of the interference signal; Step 5: Based on the adaptive frequency response technology, develop an intelligent filtering algorithm to filter the real-time signal data and remove interference signals; Step 6: Amplify the real-time signal data after filtering using a multi-stage amplifier circuit, and automatically adjust the amplification factor according to the signal strength; Step 7: Arrange signal relay equipment at the industrial site to relay and transmit the enhanced real-time signal data to expand the signal coverage; Step 8. According to the actual situation of the industrial site, monitor the communication signal quality in real time and dynamically adjust each link of signal enhancement.

[0005] As a further preferred embodiment of the present technical solution, in step one, the historical communication signal data, real-time communication signal data and environmental data are acquired from the industrial telephone communication network by a signal acquisition device installed at the industrial site, the signal acquisition device comprising a sensor, a signal conditioning circuit and a data acquisition card; the historical communication signal data and the real-time communication signal data comprise the strength and frequency of the communication signal; the environmental data comprises electromagnetic interference intensity, temperature and humidity.

[0006] As a further preferred embodiment of the present technical solution, in step 2, the historical communication signal data, the real-time communication signal data and the environmental data are preprocessed, including denoising, filtering, normalization and data cleaning.

[0007] As a further preferred embodiment of the present technical solution, in step three, the deep learning algorithm includes a convolutional neural network, a recurrent neural network and a deep neural network; and the method for constructing a signal interference recognition model includes the following steps: Step 1. Choose a convolutional neural network, recurrent neural network, or deep neural network as the deep learning architecture. Step 2: Design the structure of the signal interference recognition model based on the deep learning architecture; Step 3: Divide the collected historical communication signal data into a training set, a verification set, and a test set; Step 4: Use the training set to train the signal interference recognition model, and continuously adjust the weight and bias of the signal interference recognition model through the back propagation algorithm; Step 5: During the training process, the signal interference recognition model is evaluated using the validation set. If the performance of the signal interference recognition model on the validation set does not improve any more, the training is stopped to prevent overfitting.

[0008] As a further preferred embodiment of the present technical solution, in step 4, the signal interference recognition model has the function of storing interference signal characteristics and the ability of real-time online learning.

[0009] As a further preferred embodiment of the present technical solution, in step five, the intelligent filtering algorithm adaptively adjusts the parameters of the filter according to the characteristics of the interference signal.

[0010] As a further preferred embodiment of the present technical solution, in step 6, the automatic adjustment of the magnification factor uses an exponential weighted average algorithm, and the calculation formula is: ; in, is the magnification at the current moment, is the smoothing coefficient, is the magnification of the previous moment, is the signal strength at the current moment.

[0011] As a further preferred embodiment of the present technical solution, in step seven, the location and quantity of the relay devices are set according to the layout of the industrial site and the signal coverage requirements.

[0012] As a further preferred embodiment of the present technical solution, in step eight, the real-time monitoring of the communication signal quality uses a signal-to-noise ratio indicator, the signal-to-noise ratio The calculation formula is: ; in, is the signal power, The signal enhancement process is dynamically adjusted according to the signal-to-noise ratio.

[0013] To solve the above technical problems, another technical solution adopted by the present application is: an industrial telephone communication signal enhancement system, comprising: a data acquisition module, a data preprocessing module, a model building module, an interference identification module, an intelligent filtering module, a signal amplification module, a signal relay module and a monitoring and adjustment module; The data acquisition module is used to obtain historical communication signal data, real-time communication signal data and environmental data of the industrial phone; The data preprocessing module is used to preprocess the collected historical communication signal data, real-time communication signal data and environmental data, including denoising, filtering, normalization and data cleaning; The model building module is used to build a signal interference recognition model based on the processed historical communication signal data and environmental data and based on a deep learning algorithm; The interference identification module is used to identify interference signal characteristics of the processed real-time communication signal data; The intelligent filtering module is used to develop an intelligent filtering algorithm to filter the real-time signal and remove the identified interference signal; The signal amplification module is used to amplify the filtered signal by automatically adjusting the amplification factor according to the signal strength using a multi-stage amplification circuit; The signal relay module is used to relay the enhanced signal to expand the signal coverage; The monitoring and adjustment module is used to monitor the signal quality in real time and dynamically adjust each link of signal enhancement according to the monitoring results.

[0014] The embodiment of the present invention has the following advantages due to the adoption of the above technical solution: 1. The present invention constructs a signal interference recognition model to accurately identify the characteristics of interference signals, and then removes interference through an intelligent filtering algorithm, effectively solving the problem of signal strength weakening and call quality degradation caused by electromagnetic interference. At the same time, considering the impact of environmental factors such as temperature and humidity on communication signals, corresponding compensation measures are taken to improve the adaptability and stability of the system in complex industrial environments; 2. The present invention arranges signal relay equipment to relay the enhanced signal, effectively expanding the signal coverage and ensuring efficient communication and coordination between various departments in industrial production; 3. The present invention adaptively adjusts the filter parameters and automatically adjusts the amplification factor according to the real-time monitored communication signal quality, thereby ensuring that the system is always in the best working state, thereby improving the intelligence level and stability of the system.

[0015] The above summary is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments and features described above, further aspects, embodiments and features of the present invention will be readily apparent by reference to the accompanying drawings and the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 A schematic diagram of a process of enhancing an industrial telephone communication signal according to the present invention; Figure 2 A schematic diagram of a flow chart of a method for constructing a signal interference identification model according to the present invention; Figure 3 The present invention is a schematic diagram of the functional modules of an industrial telephone communication signal enhancement system. DETAILED DESCRIPTION

[0018] The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

[0019] It should be clear that the following embodiments of the present disclosure are described by specific specific examples, and those skilled in the art can easily understand other advantages and effects of the present disclosure from the contents disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all of the embodiments. The present disclosure can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present disclosure. It should be noted that the following embodiments and features in the embodiments can be combined with each other in the absence of conflict. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present disclosure.

[0020] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein may be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on the present disclosure, it should be understood by those skilled in the art that an aspect described herein may be implemented independently of any other aspect, and two or more of these aspects may be combined in various ways. For example, any number of aspects described herein may be used to implement the device and / or practice the method. In addition, other structures and / or functionalities other than one or more of the aspects described herein may be used to implement this device and / or practice this method.

[0021] It should also be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present disclosure. The drawings only show components related to the present disclosure rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0022] Additionally, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, it will be understood by those skilled in the art that the aspects described may be practiced without these specific details.

[0023] Figure 1 1 is a flow chart of an industrial telephone communication signal enhancement method according to an embodiment of the present invention. It should be noted that if there are substantially the same results, the method of the present application is not limited to the method of FIG. Figure 1The process sequence shown is limited. Figure 1-2 As shown: A method for enhancing industrial telephone communication signals, comprising the following steps: Step 1: Obtain historical communication signal data, real-time communication signal data, and environmental data of industrial telephones; specifically: Acquisition of historical communication signal data: by accessing existing data storage systems or historical databases, querying and exporting industrial telephone communication signal data over a period of time (such as one month, one year, etc.), which may exist in the form of files, database records or data streams; Real-time communication signal data acquisition, using signal acquisition devices to capture industrial telephone communication signals in real time, the analog signal output by the sensor is processed by the signal conditioning circuit, converted into a digital signal by the data acquisition card, and transmitted to the data processing center or storage system in real time. Similarly, real-time data also includes key parameters such as the strength and frequency of the communication signal; Environmental data acquisition: using specialized environmental sensors (such as electromagnetic field sensors, temperature sensors, humidity sensors, etc.) to synchronously measure environmental parameters at industrial sites. Environmental data include electromagnetic interference intensity, temperature, and humidity, etc. These data are of great significance for analyzing the degree to which communication signals are affected by the environment. Step 2: Based on signal processing technology, preprocess the acquired historical communication signal data, real-time communication signal data and environmental data; the purpose of preprocessing is to improve data quality, reduce noise and interference, and provide a reliable data basis for subsequent signal analysis, feature extraction and model building; the preprocessing process usually includes signal denoising, filtering, sampling rate adjustment, data standardization and other steps to ensure the accuracy, consistency and availability of the data; Step 3: Based on the processed historical communication signal data and environmental data, a signal interference recognition model is constructed based on a deep learning algorithm. The goal of the signal interference recognition model is to identify and classify interference components in communication signals to improve the stability and reliability of the communication system. The deep learning algorithm has become the preferred method for constructing such a model due to its powerful feature extraction and pattern recognition capabilities. Step 4: Based on the processed real-time communication signal data, use the signal interference recognition model to identify the characteristics of the interference signal; specifically: First, the processed real-time communication signal data is passed as input to the signal interference recognition model. The format and dimension of the input data should be consistent with the data used in model training. Then, the loaded model is used to infer the input real-time signal (also called forward propagation). During the inference process, the model will output the corresponding prediction result through a series of calculations and transformations based on the characteristics of the input signal. For the signal interference identification task, the prediction result may be the category label, probability distribution or other form of representation of one or more interference signals; Finally, based on the prediction results of the model, the interference signal characteristics in the real-time communication signal are identified, which usually involves parsing and interpreting the prediction results to extract useful information; Step 5. Based on the adaptive frequency response technology, develop an intelligent filtering algorithm to filter the real-time signal data and remove the interference signal. Among them, the adaptive frequency response technology is a technology that can automatically adjust the filter parameters according to the signal characteristics. It allows the filter to dynamically change its frequency response characteristics during operation to better adapt to the changes in the interference components in the signal. This technology usually relies on real-time analysis and prediction of signal characteristics. According to the principle of adaptive frequency response technology, design an intelligent filtering algorithm that can automatically adjust the filter parameters. The algorithm needs to be able to analyze the spectral characteristics of the signal in real time, identify the frequency range of the interference signal, and adjust the filter's cutoff frequency, bandwidth and other parameters accordingly. Use machine learning or optimization algorithms to optimize the filter parameters to improve the filtering effect. This can be achieved by minimizing the distortion of the filtered signal and maximizing the signal-to-noise ratio. Objective functions such as achieving this can be achieved. Since real-time signal data needs to be processed quickly, the intelligent filtering algorithm must have efficient computing power to ensure that the filtering process is completed within a limited time. Among them, the method of real-time signal data filtering processing is specifically as follows: First, the real-time signal data that has been pre-processed (such as identifying interference signal characteristics in step 4) is input into the intelligent filtering algorithm; Then, based on the spectrum characteristics of the real-time signal data, the intelligent filtering algorithm automatically adjusts the filter parameters to match the frequency range of the interference signal; Next, the real-time signal data is filtered using the adjusted filter to remove the interference signal components; Finally, the filtered signal is output to the subsequent processing module or system for use.

[0024] Step 6: Use a multi-stage amplifier circuit to amplify the real-time signal data after filtering, and automatically adjust the amplification factor according to the signal strength; specifically: First, the real-time signal data after filtering is input into the input end of the multi-stage amplification circuit; Then, the AGC circuit monitors the strength of the output signal in real time and automatically adjusts the gain of the VGA according to a preset algorithm or rule. When the signal strength is low, the AGC increases the gain to improve the signal strength; when the signal strength is too high, the gain is reduced to avoid signal distortion or damage; Finally, the signal after multi-stage amplification and automatic gain control will be output to the subsequent processing module or system for use.

[0025] Step 7: Arrange signal relay equipment at the industrial site to relay the enhanced real-time signal data and expand the signal coverage. Specifically: First, it is necessary to clarify the specific needs of industrial sites for signal coverage, including key indicators such as coverage range, signal strength, and transmission rate. The industrial site environment should be evaluated to understand possible signal interference sources (such as electromagnetic interference, physical obstacles, etc.) and difficult areas for signal transmission. Then, according to the signal type (such as wireless signal, wired signal, etc.) and transmission requirements, select appropriate signal relay equipment, which may include wireless repeaters, optical fiber amplifiers, signal regenerators, etc. Pay attention to key performance parameters such as gain, bandwidth, noise coefficient, etc. of the relay equipment to ensure that it can meet the signal transmission requirements of the industrial site; Next, according to the layout of the industrial site and the difficult areas of signal transmission, the layout locations of the relay equipment should be reasonably selected. These locations should be as close as possible to the areas with greater signal attenuation in order to effectively relay the signal. The equipment should be installed and debugged according to the equipment manual to ensure that the relay equipment can work normally and seamlessly connect and synchronize with the existing signal transmission system. Provide a stable power supply for the relay equipment and configure appropriate communication interfaces for connection with the upper or lower system; Finally, after the relay equipment is deployed, a signal test is conducted to verify its transmission effect. The test content may include signal strength, coverage, transmission rate, etc., and the parameters of the relay equipment are adjusted and optimized according to the test results to improve the efficiency and stability of signal transmission. During the signal transmission process, possible faults and problems are discovered and solved in a timely manner to ensure that the relay equipment can work continuously and stably. Step 8: According to the actual situation of the industrial site, monitor the communication signal quality in real time and dynamically adjust each link of signal enhancement. Specifically: First, signal quality monitoring equipment, such as signal strength meters and bit error rate testers, are installed at key locations on industrial sites to obtain key indicators such as signal strength, bit error rate, and delay in real time. Data from monitoring equipment is collected regularly or in real time through a data acquisition system, and data analysis tools are used to process and analyze the data to evaluate the overall signal quality. Then, based on the data analysis results, identify problems with signal quality, such as insufficient signal strength, high bit error rate, and excessive latency. Further analyze the causes of the identified problems, which may be caused by environmental factors (such as electromagnetic interference, physical obstruction), equipment failure (such as repeater damage, cable aging), or improper configuration (such as unreasonable gain setting, wrong antenna pointing), etc. Next, according to the cause of the signal quality problem, adjust the parameters of the relay equipment, such as increasing the gain, adjusting the antenna direction, replacing the faulty equipment, etc., to improve the signal transmission effect, and further optimize the filtered signal, adjust the filter cutoff frequency and the gain of the amplifier circuit to reduce noise and interference, and improve the signal-to-noise ratio and clarity. If the signal quality problem is caused by obstacles or interference sources on the transmission path, consider adjusting the transmission path or adding additional relay equipment to bypass the obstacles or reduce interference; Finally, after completing the adjustment, use the monitoring equipment to monitor the signal quality again to verify whether the adjustment effect meets the expectations. After verifying the adjustment effect, continue to observe the stability of the signal quality to ensure that the problem is fundamentally resolved.

[0026] In one embodiment, specifically, in step 1, historical communication signal data, real-time communication signal data and environmental data are acquired from the industrial telephone communication network by a signal acquisition device installed at the industrial site. The signal acquisition device should be installed at a key node of the industrial telephone communication network so as to capture representative communication signal data. Meanwhile, considering the impact of environmental data on communication signals, sensors should also be placed at locations that can accurately reflect the characteristics of the on-site environment. The signal acquisition device includes a sensor, a signal conditioning circuit and a data acquisition card, wherein the sensor is used to sense and measure industrial telephone communication signals and environmental parameters. For communication signals, a special signal receiver or probe may be required to capture the signal strength and frequency. For environmental data, such as electromagnetic interference intensity, temperature and humidity, corresponding sensors are used for measurement respectively; the signal conditioning circuit is used to pre-process the original signal output by the sensor, such as amplification, filtering, analog-to-digital conversion, etc., to ensure that the signal quality meets the requirements of subsequent processing; the data acquisition card is used to convert the conditioned signal into a digital signal and store it in a local or remote data storage system. The data acquisition card has high-speed and high-precision acquisition capabilities to ensure the integrity and accuracy of the data; The historical communication signal data and the real-time communication signal data include the strength and frequency of the communication signal; the environmental data include the electromagnetic interference strength, temperature and humidity.

[0027] In one embodiment, specifically, in step 2, the historical communication signal data, real-time communication signal data and environmental data are preprocessed, including denoising, filtering, normalization and data cleaning, wherein the denoising uses wavelet transform or median filtering to remove high-frequency noise in the signal; the filtering further smoothes the signal through a low-pass or band-pass filter; the normalization is used to map all data to the same numerical range (such as 0-1) for subsequent processing; and the data cleaning is used to identify and eliminate outliers to ensure the accuracy and reliability of the data.

[0028] In one embodiment, specifically, in step three, the deep learning algorithm includes a convolutional neural network, a recurrent neural network, and a deep neural network; and the method for constructing a signal interference recognition model includes the following steps: Step 1. Choose a convolutional neural network (CNN), a recurrent neural network (RNN, especially variants such as LSTM or GRU), or a deep neural network (DNN, also known as a multi-layer perceptron MLP) as the deep learning architecture; the selection is usually based on: Data characteristics: If the signal data has significant spatial features (such as spectrograms), CNN may be a good choice. If the data is a time series and needs to capture long-term dependencies, RNN (especially LSTM) may be more suitable. DNN is suitable for situations where the feature relationship is not clear but needs to be extracted through multiple layers of nonlinear transformations to extract advanced features. Computational resources: Different architectures have different requirements for computing resources. CNN and RNN (especially multi-layer LSTM) usually consume more computing resources than DNN. Task requirements: Select the most appropriate architecture based on the requirements of specific tasks (such as real-time performance, accuracy, etc.); Step 2: Based on the deep learning architecture, design the structure of the signal interference recognition model; wherein the structure of the signal interference recognition model includes an input layer, a hidden layer, and an output layer, specifically: Input layer: Design the input layer according to the dimension and type of the signal data. For example, if the input is a spectrogram, the input layer may need to receive a two-dimensional array; if it is time series data, the input layer may receive a one-dimensional array.

[0029] Hidden layers: Design the number, type, and parameters of hidden layers. For CNN, you need to determine the number and parameters of convolutional layers and pooling layers (such as convolution kernel size, step size, padding method, etc.); for RNN, you need to determine the number of LSTM / GRU units, the number of layers, and whether to use bidirectional RNN, etc.; for DNN, you need to determine the number of fully connected layers and the number of neurons in each layer; Output layer: Design the output layer according to the task requirements. If it is a multi-classification problem (identifying different types of interference), the output layer may be a softmax layer that outputs the probability of each category. Step 3: Divide the collected historical communication signal data into a training set, a validation set, and a test set; the training set is used for model training, the validation set is used for model selection and parameter adjustment (such as early stopping method to prevent overfitting), and the test set is used to evaluate the final performance of the model. The division ratio can be determined according to the specific task and data volume. Common division ratios are 70% training set, 15% validation set, and 15% test set. Step 4: Use the training set to train the signal interference recognition model. Use the back propagation algorithm to continuously adjust the weights and biases of the signal interference recognition model to minimize the loss function. During the training process, optimization algorithms (such as Adam, SGD, etc.) need to be used to accelerate the convergence process.

[0030] Step 5. During the training process, the validation set is used to evaluate the signal interference recognition model. If the performance of the signal interference recognition model on the validation set no longer improves, the training is stopped to prevent overfitting. Stopping training is a simple and effective strategy to prevent overfitting. In addition, other strategies can be considered to further prevent overfitting, such as adding regularization terms (such as L1 and L2 regularization), using dropout technology, data enhancement (although it may not be applicable to communication signal data), or adjusting the model structure.

[0031] In one embodiment, specifically, in step 4, the signal interference recognition model has the function of storing interference signal features and the ability of real-time online learning, specifically: An interference signal feature database is maintained in the signal interference identification model or in an external storage system associated therewith. The database is used to store feature information of identified interference signals, including but not limited to interference type, frequency range, intensity, duration, etc.; with the continuous input of real-time communication signal data and the continuous reasoning of the model, newly identified interference signal features will be dynamically added to the feature database. At the same time, existing features can also be updated or optimized based on factors such as their frequency of occurrence and importance; The signal interference recognition model has the ability of incremental learning, that is, it can use new real-time communication signal data to update and optimize model parameters without retraining the entire model. This is usually achieved through online gradient descent, small batch learning or other incremental learning algorithms. The signal interference recognition model obtains accuracy evaluations of its recognition results through some form of feedback mechanism (such as user feedback, system logs, etc.). Based on these feedbacks, the model can automatically adjust its learning strategy and parameters to improve future recognition performance. The signal interference recognition model can adaptively adjust its internal structure and parameters according to the results of its online learning and changes in the real-time communication environment. For example, when a new type of interference signal is discovered, the model can automatically add new neurons or layers to better represent these features. When the communication environment becomes complex, the model can adjust its learning rate or regularization strength to prevent overfitting. By storing known interference signal features and real-time online learning, the signal interference recognition model can more accurately identify new and complex interference signals, and can adaptively adjust its structure and parameters to adapt to the ever-changing communication environment, thereby improving its stability and reliability in practical applications. Through feature retrieval and incremental learning, the model can identify known interference signals more quickly and optimize its learning process, thereby improving overall recognition efficiency. At the same time, real-time online learning capabilities reduce the need to retrain the entire model, thereby reducing the cost of model maintenance and updates.

[0032] In one embodiment, specifically, in step five, the intelligent filtering algorithm adaptively adjusts the parameters of the filter according to the characteristics of the interference signal. Through real-time monitoring and parameter adjustment, the intelligent filtering algorithm can ensure that the filtering effect is always maintained in the optimal state, effectively remove the interference signal, and improve the signal quality. The adaptive adjustment mechanism reduces the need for manual adjustment of the filter parameters, reduces the maintenance cost and complexity of the system, and at the same time, the signal after filtering is purer and more stable, which helps to improve the performance and reliability of the entire communication system.

[0033] In one embodiment, specifically, in step six, the magnification is automatically adjusted using an exponential weighted average algorithm, and the calculation formula is: ; in, is the magnification at the current moment, is the smoothing coefficient, is the magnification of the previous moment, is the signal strength at the current moment; For example, in an industrial telephone communication system, the smoothing coefficient is set At the initial moment , assuming the magnification at the previous moment for , a new real-time signal is received at this time, and the signal strength at the current moment is measured ; According to the formula: ; We can get: ; ; ; That is, the magnification at the current moment is adjusted to .

[0034] In one embodiment, specifically, in step seven, the position and number of relay devices are set according to the layout of the industrial site and the signal coverage requirements. By setting the position and number of relay devices according to the layout of the industrial site and the signal coverage requirements, accurate signal coverage is achieved, signal waste and blind spots are avoided, and the signal transmission efficiency and stability are improved through reasonable layout and parameter adjustment of relay devices, ensuring the reliability and quality of signal transmission. At the same time, through precise calculation and optimized design, unnecessary equipment investment and maintenance costs are reduced, and the cost-effectiveness of the overall system is improved.

[0035] In one embodiment, specifically, in step eight, the signal-to-noise ratio indicator is used to monitor the quality of the communication signal in real time. The calculation formula is: ; in, is the signal power, The signal enhancement is dynamically adjusted according to the signal-to-noise ratio to reduce the noise power. When the signal-to-noise ratio is lower than the set threshold, the signal processing intensity is increased, such as increasing the filtering intensity and increasing the amplification factor. When the signal-to-noise ratio is higher than the set threshold, the signal processing intensity is appropriately reduced to avoid signal distortion caused by excessive processing.

[0036] Figure 3 Schematic diagram of the functional modules of an industrial telephone communication signal enhancement system according to an embodiment of the present application. Figure 3 As shown, an industrial telephone communication signal enhancement system includes: a data acquisition module, a data preprocessing module, a model building module, an interference identification module, an intelligent filtering module, a signal amplification module, a signal relay module and a monitoring and adjustment module; Data acquisition module, used to obtain historical communication signal data, real-time communication signal data and environmental data of industrial telephones. The data acquisition module is a vital part of the industrial telephone communication signal enhancement system. It is responsible for accurately capturing and collecting communication signal data from various sources, providing a solid foundation for subsequent data processing, analysis and optimization; The data preprocessing module is used to preprocess the collected historical communication signal data, real-time communication signal data and environmental data, including denoising, filtering, normalization and data cleaning, providing a high-quality data source for subsequent model building and signal processing; The model building module is used to build a signal interference recognition model based on the deep learning algorithm according to the processed historical communication signal data and environmental data. The model building module uses the deep learning algorithm and rich historical communication signal data and environmental data to build an efficient and accurate signal interference recognition model, which provides strong support for the overall performance improvement of the industrial telephone communication signal enhancement system; Interference identification module is used to identify interference signal characteristics of processed real-time communication signal data. The interference identification module uses deep learning and other advanced technologies to accurately identify interference signals by deeply analyzing the characteristics of real-time communication signal data, providing a strong guarantee for the stable operation of the industrial telephone communication signal enhancement system; Intelligent filtering module is used to develop intelligent filtering algorithms, filter real-time signals, and remove identified interference signals. The intelligent filtering module plays an important role in the signal processing system. It develops efficient intelligent filtering algorithms to perform deep filtering on real-time signals, remove identified interference signals, and improve signal quality. With the continuous advancement of technology and the continuous expansion of application scenarios, the intelligent filtering module will be widely used and developed in more fields; The signal amplification module is used to use a multi-stage amplification circuit to automatically adjust the amplification factor according to the signal strength, amplify the filtered signal, avoid signal distortion or overload, reduce power consumption while ensuring the amplification effect, and extend the service life of the equipment; The signal relay module is used to relay the enhanced signal to expand the signal coverage. The signal relay module expands the signal coverage through multi-level relay transmission, ensuring that the signal can be stably transmitted in complex industrial environments. The application of multi-path transmission and intelligent routing technology further improves the reliability and stability of signal transmission and reduces the risk of signal interruption or loss. The monitoring and adjustment module is used to monitor the signal quality in real time and dynamically adjust each link of signal enhancement according to the monitoring results, thus realizing the adaptive optimization of the system.

[0037] In summary, the industrial telephone communication signal enhancement system provides strong support for telephone communications in industrial environments with its efficient signal processing capabilities, intelligent identification and adaptive adjustment, wide coverage and reliability, highly integrated and modular design, low power consumption and environmental protection, easy deployment and maintenance, and high cost-effectiveness.

[0038] For other details about the technical solutions for implementing each module in the system of the above embodiment, please refer to the description of an industrial telephone communication signal enhancement method and system in the above embodiment, which will not be repeated here.

[0039] It should be noted that each embodiment in this specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments, and the same or similar parts between the embodiments can be referred to each other. For system embodiments, since they are basically similar to method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0040] The basic principles of the present disclosure are described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, effects, etc. mentioned in the present disclosure are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. are required by each embodiment of the present disclosure. In addition, the specific details disclosed above are only for the purpose of illustration and ease of understanding, and are not limitations. The above details do not limit the present disclosure to the necessity of adopting the above specific details to be implemented.

[0041] In the present disclosure, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. The block diagrams of the devices, devices, equipment, and systems involved in the present disclosure are only illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagram. As will be appreciated by those skilled in the art, these devices, devices, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including", "comprising", "having", etc. are open words, referring to "including but not limited to", and can be used interchangeably with them. The words "or" and "and" used here refer to the words "and / or" and can be used interchangeably with them, unless the context clearly indicates otherwise. The words "such as" used here refer to the phrase "such as but not limited to", and can be used interchangeably with them.

[0042] Additionally, as used herein, "or" used in a list of items beginning with "at least one" indicates a separate list, so that, for example, a list of "at least one of A, B, or C" means A or B or C, or AB or AC or BC, or ABC (i.e., A and B and C). Furthermore, the word "exemplary" does not mean that the example described is preferred or better than other examples.

[0043] It should also be noted that in the system and method of the present disclosure, each component or each step can be decomposed and / or recombined. Such decomposition and / or recombination should be regarded as equivalent solutions of the present disclosure.

[0044] Various changes, substitutions, and modifications of the techniques described herein may be made without departing from the teachings defined by the appended claims. Furthermore, the scope of the claims of the present disclosure is not limited to the specific aspects of the processes, machines, manufactures, compositions of events, means, methods, and actions described above. Currently existing or later to be developed processes, machines, manufactures, compositions of events, means, methods, or actions that perform substantially the same functions or achieve substantially the same results as the corresponding aspects described herein may be utilized. Thus, the appended claims include such processes, machines, manufactures, compositions of events, means, methods, or actions within their scope.

[0045] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present disclosure. Therefore, the present disclosure is not intended to be limited to the aspects shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

[0046] The above description has been given for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present disclosure to the forms disclosed herein. Although multiple example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions and sub-combinations thereof.

Claims

1. A method for enhancing industrial telephone communication signals, characterized in that: The following steps are involved: Step 1: Obtain historical communication signal data, real-time communication signal data and environmental data of industrial telephones; Step 2: Preprocess the acquired historical communication signal data, real-time communication signal data and environmental data based on signal processing technology; Step 3: Based on the processed historical communication signal data and environmental data, a signal interference recognition model is constructed based on a deep learning algorithm; Step 4: Based on the processed real-time communication signal data, use the signal interference recognition model to identify the characteristics of the interference signal; Step 5: Based on the adaptive frequency response technology, develop an intelligent filtering algorithm to filter the real-time signal data and remove interference signals; Step 6: Amplify the real-time signal data after filtering using a multi-stage amplifier circuit, and automatically adjust the amplification factor according to the signal strength; Step 7: Arrange signal relay equipment at the industrial site to relay and transmit the enhanced real-time signal data to expand the signal coverage; Step 8. According to the actual situation of the industrial site, monitor the communication signal quality in real time and dynamically adjust each link of signal enhancement.

2. The method for enhancing industrial telephone communication signals according to claim 1, characterized in that: In step one, the historical communication signal data, real-time communication signal data and environmental data are acquired from the industrial telephone communication network by a signal acquisition device installed at the industrial site, wherein the signal acquisition device comprises a sensor, a signal conditioning circuit and a data acquisition card; the historical communication signal data and the real-time communication signal data comprise the strength and frequency of the communication signal; the environmental data comprises electromagnetic interference strength, temperature and humidity.

3. The method for enhancing industrial telephone communication signals according to claim 1, characterized in that: In step 2, the historical communication signal data, the real-time communication signal data and the environmental data are preprocessed, including denoising, filtering, normalization and data cleaning.

4. The method for enhancing industrial telephone communication signals according to claim 1, characterized in that: In step three, the deep learning algorithm includes a convolutional neural network, a recurrent neural network and a deep neural network; the method for constructing a signal interference recognition model includes the following steps: Step 1. Choose a convolutional neural network, recurrent neural network, or deep neural network as the deep learning architecture. Step 2: Design the structure of the signal interference recognition model based on the deep learning architecture; Step 3: Divide the collected historical communication signal data into a training set, a verification set, and a test set; Step 4: Use the training set to train the signal interference recognition model, and continuously adjust the weight and bias of the signal interference recognition model through the back propagation algorithm; Step 5: During the training process, the signal interference recognition model is evaluated using the validation set. If the performance of the signal interference recognition model on the validation set does not improve any more, the training is stopped.

5. The method for enhancing industrial telephone communication signals according to claim 1, characterized in that: In step 4, the signal interference recognition model has the function of storing interference signal features and the ability of real-time online learning.

6. The method for enhancing industrial telephone communication signals according to claim 1, characterized in that: In step five, the intelligent filtering algorithm adaptively adjusts the parameters of the filter according to the characteristics of the interference signal.

7. The method for enhancing industrial telephone communication signals according to claim 1, characterized in that: In step six, the automatic adjustment of the magnification uses an exponentially weighted average algorithm, and the calculation formula is: ； in, is the magnification at the current moment, is the smoothing coefficient, is the magnification of the previous moment, is the signal strength at the current moment.

8. The method for enhancing industrial telephone communication signals according to claim 1, characterized in that: In step seven, the location and quantity of the relay devices are set according to the layout of the industrial site and the signal coverage requirements.

9. The method for enhancing industrial telephone communication signals according to claim 1, characterized in that: In step eight, the real-time monitoring of the communication signal quality uses a signal-to-noise ratio indicator, the signal-to-noise ratio The calculation formula is: ； in, is the signal power, The signal enhancement process is dynamically adjusted according to the signal-to-noise ratio.

10. An industrial telephone communication signal enhancement system, characterized in that: include: Data acquisition module, data preprocessing module, model building module, interference identification module, intelligent filtering module, signal amplification module, signal relay module and monitoring and adjustment module; The data acquisition module is used to obtain historical communication signal data, real-time communication signal data and environmental data of the industrial phone; The data preprocessing module is used to preprocess the collected historical communication signal data, real-time communication signal data and environmental data, including denoising, filtering, normalization and data cleaning; The model building module is used to build a signal interference recognition model based on the processed historical communication signal data and environmental data and based on a deep learning algorithm; The interference identification module is used to identify interference signal characteristics of the processed real-time communication signal data; The intelligent filtering module is used to develop an intelligent filtering algorithm to filter the real-time signal and remove the identified interference signal; The signal amplification module is used to amplify the filtered signal by automatically adjusting the amplification factor according to the signal strength using a multi-stage amplification circuit; The signal relay module is used to relay the enhanced signal to expand the signal coverage; The monitoring and adjustment module is used to monitor the signal quality in real time and dynamically adjust each link of signal enhancement according to the monitoring results.

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