A hybrid clarifier stirring device remote communication method, device and equipment

Abstract

The application provides a kind of mixed clarifying tank stirring device remote communication method, device and equipment, belong to nuclear fuel reprocessing technical field, solve the control signal fluctuation problem caused by electromagnetic interference, cable aging and environmental mutation under complex industrial scene.The method comprises: obtaining signal quality data;Signal quality data is preprocessed, and the preprocessed signal quality data is obtained;According to the preprocessed signal quality data, the predicted signal attenuation data is determined;The predicted signal attenuation data is compared and analyzed with the preset threshold, and the control instruction is obtained;According to the control instruction, the mixed clarifying tank is communicated.The scheme can improve the anti-interference and prediction accuracy, realize the reduction of communication failure rate and the decline of energy consumption.

Description

Technical Field

[0001] This invention relates to the field of nuclear fuel reprocessing technology, and in particular to a remote communication method, apparatus and equipment for a mixing and clarification tank stirring device. Background Technology

[0002] As a key piece of equipment in the nuclear fuel reprocessing process, the stable operation of the mixing and clarification tank's agitation device directly affects the extraction efficiency and process safety of radioactive materials. The tank motor, as the core power unit driving the agitation device, is typically deployed in high-radiation, high-temperature, and highly corrosive environments such as spent fuel plants, posing a significant challenge to the reliability of its remote communication system. Currently, the industry commonly uses a remote control scheme based on the Profibus-DP protocol. This technology transmits control commands through voltage differential signals and relies on twisted-pair cables to construct a master-slave tree-like communication network. However, practical applications have shown that this scheme has significant shortcomings in complex industrial scenarios.

[0003] First, the voltage differential signal used in the Profibus-DP protocol is highly sensitive to electromagnetic interference. Broadband electromagnetic noise generated by the densely distributed inverters, high-voltage power cabinets, and radioactivity detection equipment within the spent fuel plant can easily couple into the communication system through cables, causing signal waveform distortion. Experimental data shows that when the cable length exceeds 800 meters, the signal attenuation rate can reach over 30%, and the control command error rate increases exponentially with distance. Second, the tree-like network topology has inherent vulnerabilities: communication interruption at any slave node will cause all downstream devices to go offline, and there is a lack of redundant path design between the master and slave stations. Furthermore, the extreme environment within the plant accelerates the aging of communication cables; for example, high temperatures cause insulation embrittlement, and gamma rays degrade the shielding layer, further exacerbating signal transmission quality degradation. Therefore, there is an urgent need to develop a remote communication method with strong anti-interference capabilities, a robust network structure, and the ability to adapt to environmental changes, to overcome existing technological bottlenecks and ensure the efficient and safe operation of the nuclear fuel reprocessing process. Summary of the Invention

[0004] This invention provides a remote communication method, device, and equipment for a mixing and clarification tank stirring device, which solves the problem of control signal fluctuation caused by electromagnetic interference, cable aging, and sudden environmental changes in complex industrial scenarios.

[0005] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows:

[0006] This invention provides a remote communication method for a mixing and clarification tank stirring device, comprising:

[0007] Acquire signal quality data;

[0008] The signal quality data is preprocessed to obtain preprocessed signal quality data;

[0009] Based on the preprocessed signal quality data, the predicted signal attenuation data is determined;

[0010] The predicted signal attenuation data is compared and analyzed with a preset threshold to obtain control commands;

[0011] The mixing and clarification tank is controlled to communicate according to the control instructions.

[0012] Optionally, acquiring signal quality data includes:

[0013] Signal quality data is acquired by sensors placed around the mixing and clarification tank; the sensors include at least one of a signal strength sensor, an environmental sensor, and a distance sensor.

[0014] Optionally, the signal quality data is preprocessed to obtain preprocessed signal quality data, including:

[0015] The signal quality data is cleaned to obtain cleaned signal quality data;

[0016] The cleaned signal quality data is then normalized to obtain normalized signal quality data.

[0017] The normalized signal quality data is subjected to time alignment processing to obtain preprocessed signal quality data.

[0018] Optionally, based on the preprocessed signal quality data, the predicted signal attenuation data is determined, including:

[0019] The preprocessed signal quality data is input into the communication control model for processing to obtain predicted signal attenuation data. The communication control model filters the preprocessed signal quality data to obtain intermediate results, and performs linear transformation on the intermediate results to obtain predicted signal attenuation data.

[0020] Optionally, the training and generation process of the communication control model includes:

[0021] Acquire multiple historical signal quality data;

[0022] The multiple historical signal quality data are preprocessed to obtain multiple preprocessed historical signal quality data.

[0023] The preprocessed historical signal quality data are randomly sampled to obtain a training dataset;

[0024] The data in the training dataset are input into the communication control model in time sequence. The hyperparameters of the communication control model include 5 input features, 128 hidden layer dimensions, 1 LSTM layer, 1 output feature, a learning rate of 0.001, an adaptive mean absolute error loss function, and a Nadam optimizer.

[0025] The training of the communication control model is terminated when the loss error value of the communication control model is less than a preset threshold after 1000 iterations or 20 consecutive iterations, and the trained communication control model is obtained.

[0026] Optionally, the predicted signal attenuation data is compared and analyzed with a preset threshold to obtain control commands, including:

[0027] When the predicted signal attenuation data is greater than a first threshold and less than a second threshold, a first control command is generated, which is used to adjust the cutoff frequency of the filter.

[0028] When the predicted signal attenuation data is greater than or equal to a second threshold, a second control command is generated, which is used to switch the backup communication channel.

[0029] Optionally, the first control command is based on:

[0030] f c ′=f c ×(1+kΔ), adjust the cutoff frequency of the filter;

[0031] Among them, f c ′ is the cutoff frequency of the filter before adjustment, f c The cutoff frequency of the adjusted filter is given by k, the scaling factor is given by Δ, and the predicted signal attenuation data is given by Δ.

[0032] This invention also provides a remote communication device for a mixing and clarification tank stirring apparatus, comprising:

[0033] The acquisition module is used to acquire signal quality data;

[0034] The processing module is used to preprocess the signal quality data to obtain preprocessed signal quality data; determine predicted signal attenuation data based on the preprocessed signal quality data; and compare and analyze the predicted signal attenuation data with a preset threshold to obtain control commands.

[0035] The control module is used to control the mixing and clarification tank to communicate according to the control instructions.

[0036] This invention also provides a computing device, including: a processor and a memory storing a computer program, wherein the computer program, when run by the processor, executes the above-described method.

[0037] This invention also provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the above-described method.

[0038] The technical solution of the present invention has at least the following effects:

[0039] The above-mentioned solution of the present invention obtains signal quality data; preprocesses the signal quality data to obtain preprocessed signal quality data; determines predicted signal attenuation data based on the preprocessed signal quality data; compares and analyzes the predicted signal attenuation data with a preset threshold to obtain control commands; and controls the mixing clarifier to communicate according to the control commands, thereby improving anti-interference capability and prediction accuracy, and reducing communication failure rate and energy consumption. Attached Figure Description

[0040] Figure 1 This is a flowchart of the remote communication method for the mixing and clarification tank stirring device provided in an embodiment of the present invention;

[0041] Figure 2 This is a structural diagram of the remote communication system for the mixing and clarification tank stirring device provided in an embodiment of the present invention;

[0042] Figure 3 This is a structural diagram of the remote communication device for the mixing and clarification tank stirring apparatus provided in an embodiment of the present invention;

[0043] Figure 4 This is a schematic diagram of the structure of the computing device provided in an embodiment of the present invention. Detailed Implementation

[0044] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this invention will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.

[0045] like Figure 1 As shown, an embodiment of the present invention proposes a remote communication method for a mixing and clarification tank stirring device, comprising:

[0046] Step 11, acquire signal quality data;

[0047] Step 12: Preprocess the signal quality data to obtain preprocessed signal quality data;

[0048] Step 13: Determine the predicted signal attenuation data based on the preprocessed signal quality data;

[0049] Step 14: Compare and analyze the predicted signal attenuation data with a preset threshold to obtain control commands;

[0050] Step 15: Control the mixing and clarification tank to communicate according to the control command.

[0051] In this embodiment, signal quality sensors are first deployed in the communication system of the mixing and clarification tank agitator. These sensors can be installed at the signal transmitter, along the transmission path, and at the receiver; the signal transmitter is the front-end equipment of the remote monitoring center, and the signal receiver is the communication module of the agitator; through multi-point acquisition, comprehensive signal status information at different locations can be obtained. The signal quality parameters to be collected include signal strength, electromagnetic noise intensity, temperature, humidity, and signal transmission path length; these parameters can reflect the signal quality status from different perspectives, providing a basis for accurately assessing the signal status.

[0052] The acquired raw signals typically contain outliers and noise data, necessitating preprocessing to remove these. Outliers, caused by sensor malfunctions, transient interference, etc., can severely impact the accuracy of subsequent analysis. Noise data can be processed using filtering algorithms, such as moving average filtering and median filtering, to effectively smooth the data and reduce the influence of random noise. Since different signal quality parameters have different value ranges and dimensions, direct analysis can amplify or diminish the influence of certain parameters. Therefore, the cleaned data needs to be normalized to map the values ​​of each parameter to a uniform range.

[0053] After obtaining the preprocessed signal quality data, a suitable prediction model needs to be selected to determine the signal attenuation data. For example, if signal attenuation is related to multiple factors such as the operating status of the stirring device and ambient temperature, and there are complex nonlinear relationships among these factors, a neural network model can be chosen for processing. The neural network model needs to be trained using historical preprocessed signal quality data. During training, the dataset is divided into a training set and a test set, usually in a certain ratio (e.g., 7:3 or 8:2). The training set is used for learning and adjusting the model parameters, while the test set is used to evaluate the model's predictive performance. By continuously adjusting the model's parameters, the prediction error on the training set is minimized, and the model's generalization ability is verified on the test set. The preprocessed signal quality data at the current moment is input into the trained prediction model to obtain the predicted signal attenuation data for a future period. The prediction time span can be set according to actual needs, such as predicting signal attenuation in the next 5, 10, or 30 seconds.

[0054] After obtaining the predicted signal attenuation data, it is compared and analyzed with a preset threshold. The preset threshold is determined based on the performance requirements and actual operating experience of the remote communication system for the mixing and clarification tank agitator. Generally, the preset threshold can be determined through experimental testing, expert experience, or historical data analysis, and is dynamically adjusted according to actual conditions. During the comparative analysis, if the predicted signal attenuation data does not exceed the preset threshold, it indicates that the current communication signal quality is good and can meet the remote communication needs of the mixing and clarification tank agitator; in this case, no special control measures are required. If the predicted signal attenuation data exceeds the preset threshold, it indicates a problem with the communication signal quality, and corresponding control commands need to be generated based on the results of the comparative analysis. Control commands can be divided into: communication parameter adjustment commands and communication link switching commands.

[0055] The generated control commands are transmitted to the control system of the mixing and clarification tank agitator via a communication link. Upon receiving the control commands, the control system parses them, identifying their type and specific content. Based on the command type, corresponding control actions are taken. For example, if it's a communication parameter adjustment command, the control system will adjust the relevant parameter settings of the communication module; if it's a communication link switching command, the control system will initiate a link switching process, switching the communication connection from the current link to the target link.

[0056] The structure of the remote communication system for the mixing and clarification tank agitator is as follows: Figure 2As shown, the controller's signal input terminal is electrically connected to the signal output terminals of multiple control panels, the controller's signal output terminal is electrically connected to the signal input terminals of multiple frequency converters, and the frequency converters' signal output terminals are electrically connected to the motors of the mixing and clarification tank. Each frequency converter is electrically connected to only one mixing and clarification tank motor. Specifically, the control panel is the interface for personnel to interact with the system, typically equipped with a display screen, buttons, knobs, and other operating elements. Operators input control commands through the control panel, such as setting parameters like stirring speed and running time, and can also view the real-time operating status information of the mixing and clarification tank, such as motor speed, stirring time, and operating mode. The controller undertakes the core information processing and command forwarding tasks, receiving signal inputs from multiple control panels, analyzing, judging, and integrating these signals, and generating corresponding control commands based on preset control logic. Subsequently, the commands are accurately sent to the corresponding frequency converters. The frequency converter is the key device connecting the controller and the motor, playing a role in signal conversion and motor speed regulation. It receives signals from the controller, converts them into voltage and frequency signals suitable for driving the motor, and achieves precise adjustment of the mixing and clarification tank motor speed. Each frequency converter is connected one-to-one to a motor in the mixing and clarification tank. This dedicated connection ensures the stability and accuracy of signal transmission, allowing for independent and precise control of the corresponding motor's operation according to control commands, meeting the diverse speed requirements of different mixing processes. The motor is the power source of the mixing device, driving the agitator blades to rotate and ensuring thorough mixing and clarification of the materials in the mixing and clarification tank. Under the control of the frequency converter, the motor can operate stably at the set speed, providing reliable power support for the mixing process and ensuring that the mixing and clarification tank efficiently and stably completes its production tasks.

[0057] The above embodiments of the present invention have the following technical effects:

[0058] (1) Ensures communication stability: By acquiring and preprocessing signal quality data, the current communication signal status can be accurately grasped, and interference factors such as noise can be eliminated, providing a reliable basis for subsequent analysis. Based on the preprocessed data, the predicted signal attenuation data can be determined, and the signal change trend can be predicted in advance, avoiding communication interruption or data loss due to sudden signal attenuation, effectively ensuring the stability of remote communication of the mixing and clarification tank stirring device.

[0059] (2) Intelligent control is achieved: The system compares and analyzes the predicted signal attenuation data with preset thresholds to generate control commands, thus realizing intelligent communication control. The system can automatically make decisions based on the actual signal conditions without frequent manual intervention, improving control efficiency and accuracy.

[0060] (3) Improved communication performance: Based on the comparison results of predicted signal attenuation data and threshold, the communication of the mixing clarifier is controlled in a targeted manner. This can avoid data errors or communication failures caused by forced communication when the signal quality is poor, and can also make full use of resources when the signal is good, thereby optimizing communication performance and improving the timeliness and accuracy of data transmission.

[0061] (4) Improved system reliability: The entire method forms a complete closed loop from signal monitoring and analysis to control, enabling the remote communication system of the mixing and clarification tank stirring device to have self-sensing, judgment and adjustment capabilities, and to operate stably in complex environments, thereby improving the system's reliability and adaptability and ensuring the continuity and stability of the production process.

[0062] In an optional embodiment of the present invention, step 11 may include:

[0063] Step 111: Acquire signal quality data using sensors located around the mixing and clarification tank; the sensors include at least one of a signal strength sensor, an environmental sensor, and a distance sensor.

[0064] In this embodiment, sensors are arranged around the mixing and clarification tank agitator. These sensors include signal strength sensors, environmental sensors, and distance sensors. The signal strength sensors measure the voltage differential signal strength in real time, the environmental sensors collect interference parameters such as electromagnetic noise intensity, temperature, and humidity, and the distance sensors record the signal transmission path length. Remote data acquisition is achieved through a monitoring and data acquisition system, ensuring that the timestamps of the data from each sensor are aligned. The acquired signal quality data is transmitted to the remote control console at a fixed frequency (e.g., once per second) to form a raw dataset.

[0065] In an optional embodiment of the present invention, step 12 may include:

[0066] Step 121: Clean the signal quality data to obtain cleaned signal quality data;

[0067] Step 122: Normalize the cleaned signal quality data to obtain normalized signal quality data;

[0068] Step 123: Perform time-series alignment processing on the normalized signal quality data to obtain preprocessed signal quality data.

[0069] In this embodiment, the purpose of data cleaning is to remove invalid and abnormal data to ensure data quality. For missing data caused by sensor malfunction or transmission interruption, linear interpolation is used to supplement it.

[0070]

[0071] Where, x t For missing data, x t-1 x represents the valid data before the missing points. t+1 The valid data is defined after the missing points, and Δt is the time window for the valid data. t This is a missing time window.

[0072] When processing abnormal data, thresholds can be set based on physical constraints, such as a signal strength range of 0-5V. Data outside this range is marked as abnormal.

[0073] To improve the efficiency of model training and data processing, the max-min normalization method can be used:

[0074]

[0075] Where, x norm The signal quality data is normalized. x min x is the minimum value of the signal quality data. max denoted as the maximum value of the signal quality data, and x represents the signal quality data after cleaning.

[0076] After obtaining the normalized signal quality data, the data is aligned according to a unified timestamp to generate a time series matrix, which is the preprocessed signal quality data; each row of the matrix contains parameters such as signal strength, distance, and noise.

[0077] In an optional embodiment of the present invention, step 13 may include:

[0078] Step 131: Input the preprocessed signal quality data into the communication control model for processing to obtain predicted signal attenuation data; The communication control model filters the preprocessed signal quality data to obtain intermediate results, and performs linear transformation on the intermediate results to obtain predicted signal attenuation data.

[0079] In this embodiment, the communication control model is essentially an LSTM model, a special type of recurrent neural network specifically designed for processing sequential data. It can capture long-term dependencies in the data and is well-suited for predicting time-varying processes such as signal decay. The LSTM model consists of multiple LSTM units, each containing key components such as an input gate, a forget gate, an output gate, and a cell state. The input gate controls how much information from the current input data can enter the cell state; the forget gate determines how much information from the previous cell state needs to be forgotten; and the output gate controls how much information from the cell state can be output to the current hidden state. This unique gating mechanism allows the LSTM model to effectively and selectively remember and forget information, thus avoiding the vanishing or exploding gradient problems when processing long sequences of data and better capturing long-term dependencies in the data.

[0080] When preprocessed signal quality data is input into the LSTM model, the model processes each data point sequentially over time. During processing, the model dynamically updates the cell and hidden states based on the current input data and the hidden and cell states from the previous time step, through calculations by each gating unit. This process is similar to "filtering" the input data; the model can automatically learn useful features and patterns in the signal quality data, ignoring noise and irrelevant information, and transforming the raw data into more representative and interpretable intermediate results. For example, the model might learn that there is a correlation between signal strength and bit error rate within a specific time period; when the signal strength decreases to a certain level, the bit error rate increases significantly. Through filtering, the model can highlight this key information, providing strong support for subsequent predictions.

[0081] While the intermediate results obtained after filtering contain important features of the signal quality data, they may not directly correspond to the predicted signal attenuation data. The main purpose of linear transformation is to further transform the dimensions and extract features from the intermediate results, mapping them to the feature space where the predicted signal attenuation data resides, thereby obtaining prediction results that better meet actual needs. Linear transformation is typically implemented through a fully connected layer. Each neuron in the fully connected layer is connected to all neurons in the previous layer, linearly combining the input data through weight matrices and bias terms, and applying an activation function to finally output the predicted signal attenuation data.

[0082] In an optional embodiment of the present invention, the training and generation process of the communication control model includes:

[0083] Step 1311: Obtain multiple historical signal quality data;

[0084] Step 1312: Preprocess the multiple historical signal quality data to obtain preprocessed multiple historical signal quality data;

[0085] Step 1313: Randomly extract data from the preprocessed historical signal quality data to obtain a training dataset;

[0086] Step 1314: Input the data in the training dataset into the communication control model in time sequence. The hyperparameters of the communication control model include 5 input features, 128 hidden layer dimensions, 1 LSTM layer, 1 output feature, a learning rate of 0.001, an adaptive mean absolute error loss function, and a Nadam optimizer.

[0087] Step 1315: The training of the communication control model is terminated when the loss error value of the communication control model is less than a preset threshold after 1000 iterations or 20 consecutive iterations, and the trained communication control model is obtained.

[0088] In this embodiment, multiple historical signal quality data are first preprocessed according to the method described in step 12 to obtain multiple preprocessed historical signal quality data.

[0089] After obtaining multiple preprocessed historical signal quality data, all data are treated as a population, and each data point is assigned a unique number. Then, a random number generator is used to generate random numbers equal to the number of training samples required. Based on these numbers, corresponding data points are extracted from the population to form the training dataset. For example, if there are 1000 preprocessed data points and 200 need to be extracted as training samples, 200 random integers between 1 and 1000 are generated, and the data points corresponding to these integers are selected.

[0090] If the data contains subgroups with different characteristics (such as data from different time periods or under different communication environments), stratified sampling can be used to ensure that the proportion of these subgroups in the training dataset is consistent with the overall population. First, the data is stratified according to these characteristics. Then, random sampling is performed in each stratum. Finally, the samples from each stratum are merged to obtain the training dataset. The data in the training dataset is then input into the communication control model in time series. At each time step, five features related to signal quality are received as input. These features are one row of elements in the time series matrix, including signal strength, electromagnetic interference intensity, temperature, humidity, and transmission path length.

[0091] The dimensionality of the hidden layer determines the size of the cell states and hidden states within an LSTM unit. A larger hidden layer dimension allows the model to learn more complex feature representations and patterns, but it also increases the number of parameters and computational complexity. A 128-dimensional hidden layer achieves a good balance between model complexity and computational efficiency, enabling the model to learn long-term dependencies in signal quality data.

[0092] Using a single-layer LSTM structure can reduce model complexity to some extent, and decrease training time and the risk of overfitting. Although multi-layer LSTMs can learn more abstract feature levels, in this scenario, a single-layer LSTM combined with appropriate hidden layer dimensions and hyperparameter settings is already able to effectively capture key information in signal quality data.

[0093] The model's output is a prediction, specifically the predicted signal attenuation data. For example, it can predict the signal strength attenuation at a future point in time, thus the output feature count is 1.

[0094] The learning rate controls the step size of model parameter updates in each iteration. A smaller learning rate (e.g., 0.001) can make the model more stable during training, avoiding excessive parameter updates that could cause model oscillations and prevent convergence. Although a smaller learning rate makes the training process relatively slower, it can improve the model's ability to converge to the global optimum.

[0095] Mean absolute error (MAE) measures the average absolute difference between model predictions and actual values. Adaptive MAE improves upon traditional MAE by automatically adjusting the weights of the loss based on different samples or features, paying more attention to samples or features that have a greater impact on the prediction results, thereby improving the model's prediction accuracy.

[0096] Nadam is a variant of the Adam optimizer, combining the advantages of Nesterov momentum and the Adam algorithm. The Adam optimizer effectively handles problems with sparse gradients and non-stationary objective functions by adaptively adjusting the learning rate of each parameter. Nesterov momentum helps the optimizer anticipate gradient changes during parameter updates, thereby accelerating convergence and improving the model's generalization ability.

[0097] Because signal quality data is time-series data, the model needs to input each sample in the training dataset sequentially over time. At each time step, the model receives the input features of the current time step and combines them with the hidden state and cell state of the previous time step to calculate and update the hidden state and cell state of the current time step. This time-series input method enables the model to learn the temporal dependencies in the signal quality data, such as the continuous trend of signal strength changes over a period of time or the cumulative effect of bit error rate over time.

[0098] The iterative training process includes forward propagation, back propagation, and parameter update.

[0099] Forward propagation: In each iteration, the data in the training dataset is input into the model in time sequence. After the calculation by the LSTM layer, the hidden state and cell state at each time step are obtained. Then, the hidden state is mapped to the output space through the fully connected layer to obtain the model's prediction value.

[0100] Backpropagation and parameter update: The loss between the predicted and true values ​​is calculated (using the AMAE loss function). Then, the loss gradient is propagated from the output layer to the input layer via backpropagation, calculating the gradient for each parameter. Finally, the Nadam optimizer is used to update the model parameters based on the gradient information to minimize the loss function.

[0101] The training termination conditions include two situations:

[0102] (1) Reach a fixed number of iterations, i.e., set the model to undergo 1000 iterations of training. During training, as the number of iterations increases, the model parameters are continuously adjusted, and the loss function value usually gradually decreases. When 1000 iterations are reached, training stops regardless of whether the loss function value has converged to a very small value. This method is suitable for scenarios with clear requirements on training time or preliminary expectations for model performance.

[0103] (2) Meeting the loss error threshold: Training ends when the loss error value is less than a preset threshold for 20 consecutive iterations. The preset threshold is a small value set according to actual needs and model performance requirements. When the loss function value of the model is less than this threshold in 20 consecutive iterations, it indicates that the model parameters have basically converged, and further training will have very limited effect on improving the model performance. At this point, training can be stopped, and the trained communication control model can be obtained. This method ensures that the model stops training only after it has reached good performance, avoiding unnecessary iterations.

[0104] In an optional embodiment of the present invention, step 14 may include:

[0105] Step 141: When the predicted signal attenuation data is greater than a first threshold and less than a second threshold, a first control command is generated, which is used to adjust the cutoff frequency of the filter.

[0106] Step 142: When the predicted signal attenuation data is greater than or equal to the second threshold, a second control command is generated, which is used to switch the backup communication channel.

[0107] In this embodiment, when the predicted signal attenuation data exceeds a first threshold, it indicates that the signal quality has begun to decline to some extent. However, communication can still remain basically normal at this time, although there may be some slight interference or signal fluctuations. If no measures are taken, the signal attenuation may further worsen. At this point, the cutoff frequency of the filter can be adjusted. By adjusting the cutoff frequency, the range of signal frequencies allowed by the filter can be changed, filtering out more interference signals while retaining more effective communication signals, thereby improving the signal-to-noise ratio and communication quality. The first control command is based on:

[0108] f c ′=f c ×(1+kΔ), adjust the cutoff frequency of the filter;

[0109] Among them, f c ′ is the cutoff frequency of the filter before adjustment, f c The cutoff frequency of the adjusted filter is given by k, the scaling factor is given by Δ, and the predicted signal attenuation data is given by Δ.

[0110] When the predicted signal attenuation data is greater than or equal to the second threshold, it indicates that the signal quality of the current communication channel can no longer meet the requirements of normal communication. At this point, simply adjusting the filter's cutoff frequency is insufficient to solve the problem, as the signal attenuation may have reached the limit that the communication protocol or equipment can withstand. Continuing to use the channel may lead to a complete communication interruption, making remote control of the stirring device impossible. Therefore, a more thorough measure must be taken, namely, switching to a backup communication channel: First, the availability of the backup communication channel is checked, including parameters such as the channel's connection status, signal strength, and bit error rate; if the backup channel is available, the system will switch the data stream originally transmitted through the main channel to the backup channel according to a preset switching strategy.

[0111] like Figure 3 As shown, this embodiment of the invention also provides a remote communication device 30 for a mixing and clarification tank stirring apparatus, comprising:

[0112] Acquisition module 31 is used to acquire signal quality data;

[0113] Processing module 32 is used to preprocess the signal quality data to obtain preprocessed signal quality data; determine predicted signal attenuation data based on the preprocessed signal quality data; and compare and analyze the predicted signal attenuation data with a preset threshold to obtain control commands.

[0114] The control module 33 is used to control the mixing and clarification tank to communicate according to the control instructions.

[0115] Optionally, module 31 is specifically used for:

[0116] Signal quality data is acquired by sensors placed around the mixing and clarification tank; the sensors include at least one of a signal strength sensor, an environmental sensor, and a distance sensor.

[0117] Optionally, processing module 32 is specifically used for:

[0118] The signal quality data is cleaned to obtain cleaned signal quality data;

[0119] The cleaned signal quality data is then normalized to obtain normalized signal quality data.

[0120] The normalized signal quality data is subjected to time alignment processing to obtain preprocessed signal quality data.

[0121] Optionally, the processing module 32 is also specifically used for:

[0122] The preprocessed signal quality data is input into the communication control model for processing to obtain predicted signal attenuation data. The communication control model filters the preprocessed signal quality data to obtain intermediate results, and performs linear transformation on the intermediate results to obtain predicted signal attenuation data.

[0123] Optionally, the training and generation process of the communication control model includes:

[0124] Acquire multiple historical signal quality data;

[0125] The multiple historical signal quality data are preprocessed to obtain multiple preprocessed historical signal quality data.

[0126] The preprocessed historical signal quality data are randomly sampled to obtain a training dataset;

[0127] The data in the training dataset are input into the communication control model in time sequence. The hyperparameters of the communication control model include 5 input features, 128 hidden layer dimensions, 1 LSTM layer, 1 output feature, a learning rate of 0.001, an adaptive mean absolute error loss function, and a Nadam optimizer.

[0128] The training of the communication control model is terminated when the loss error value of the communication control model is less than a preset threshold after 1000 iterations or 20 consecutive iterations, and the trained communication control model is obtained.

[0129] Optionally, the processing module 32 is also specifically used for:

[0130] When the predicted signal attenuation data is greater than a first threshold and less than a second threshold, a first control command is generated, which is used to adjust the cutoff frequency of the filter.

[0131] When the predicted signal attenuation data is greater than or equal to a second threshold, a second control command is generated, which is used to switch the backup communication channel.

[0132] Optionally, the first control command is based on:

[0133] f c ′=f c ×(1+kΔ), adjust the cutoff frequency of the filter;

[0134] Among them, f c ′ is the cutoff frequency of the filter before adjustment, f c The cutoff frequency of the adjusted filter is given by k, the scaling factor is given by Δ, and the predicted signal attenuation data is given by Δ.

[0135] It should be noted that this device is a device corresponding to the above method. All implementation methods in the above method embodiments are applicable to this embodiment and can achieve the same technical effect.

[0136] like Figure 4 As shown, this embodiment of the invention also provides a computing device 40, including a processor 41, a memory 42, and a program or instructions stored in the memory 42 and executable on the processor 41. When the program or instructions are executed by the processor 41, they implement the various processes of the above-described embodiment of the remote communication method for the mixing and clarification tank stirring device, and achieve the same technical effects. To avoid repetition, further details are omitted here. It should be noted that the computing device in this embodiment includes the aforementioned mobile electronic devices and non-mobile electronic devices.

[0137] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0138] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0139] In the embodiments provided by this invention, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0140] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0141] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0142] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0143] Furthermore, it should be noted that in the apparatus and method of the present invention, it is obvious that the components or steps can be decomposed and / or recombined. These decompositions and / or recombinations should be considered equivalent solutions of the present invention. Moreover, the steps performing the above-described series of processes can naturally be executed in the order described, but are not necessarily required to be executed in chronological order; some steps can be executed in parallel or independently of each other. Those skilled in the art will understand that all or any step or component of the method and apparatus of the present invention can be implemented in any computing device (including processors, storage media, etc.) or network of computing devices, in hardware, firmware, software, or a combination thereof. This is something that those skilled in the art can achieve by using their basic programming skills after reading the description of the present invention.

[0144] Therefore, the object of the present invention can also be achieved by running a program or a set of programs on any computing device. The computing device can be a known general-purpose device. Therefore, the object of the present invention can also be achieved simply by providing a program product containing program code for implementing the method or apparatus. That is, such a program product also constitutes the present invention, and the storage medium storing such a program product also constitutes the present invention. Obviously, the storage medium can be any known storage medium or any storage medium developed in the future. It should also be noted that in the apparatus and method of the present invention, it is obvious that the components or steps can be decomposed and / or recombined. These decompositions and / or recombinations should be considered equivalent to the present invention. Furthermore, the steps for performing the above series of processes can naturally be performed in the order described, but are not necessarily required to be performed in chronological order. Some steps can be performed in parallel or independently of each other.

[0145] The above are preferred embodiments of the present invention. It should be noted that, for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A remote communication method for a mixing and clarification tank stirring device, characterized in that, include: Acquire signal quality data; The signal quality data is preprocessed to obtain preprocessed signal quality data; Based on the preprocessed signal quality data, the predicted signal attenuation data is determined; The predicted signal attenuation data is compared and analyzed with a preset threshold to obtain control commands; According to the control instructions, the mixing and clarification tank is controlled to communicate; The control command is obtained by comparing and analyzing the predicted signal attenuation data with a preset threshold, including: When the predicted signal attenuation data is greater than a first threshold and less than a second threshold, a first control command is generated, which is used to adjust the cutoff frequency of the filter. When the predicted signal attenuation data is greater than or equal to a second threshold, a second control command is generated, which is used to switch the backup communication channel.

2. The remote communication method for the mixing and clarification tank stirring device according to claim 1, characterized in that, The acquisition of signal quality data includes: Signal quality data is acquired by sensors placed around the mixing and clarification tank; the sensors include at least one of a signal strength sensor, an environmental sensor, and a distance sensor.

3. The remote communication method for the mixing and clarification tank stirring device according to claim 1, characterized in that, The signal quality data is preprocessed to obtain preprocessed signal quality data, including: The signal quality data is cleaned to obtain cleaned signal quality data; The cleaned signal quality data is then normalized to obtain normalized signal quality data. The normalized signal quality data is subjected to time alignment processing to obtain preprocessed signal quality data.

4. The remote communication method for the mixing and clarification tank stirring device according to claim 1, characterized in that, Based on the preprocessed signal quality data, the predicted signal attenuation data is determined, including: The preprocessed signal quality data is input into the communication control model for processing to obtain predicted signal attenuation data. The communication control model filters the preprocessed signal quality data to obtain intermediate results, and performs linear transformation on the intermediate results to obtain predicted signal attenuation data.

5. The remote communication method for the mixing and clarification tank stirring device according to claim 4, characterized in that, The training and generation process of the communication control model includes: Acquire multiple historical signal quality data; The multiple historical signal quality data are preprocessed to obtain multiple preprocessed historical signal quality data. The preprocessed historical signal quality data are randomly sampled to obtain a training dataset; The data in the training dataset are input into the communication control model in time sequence. The hyperparameters of the communication control model include 5 input features, 128 hidden layer dimensions, 1 LSTM layer, 1 output feature, a learning rate of 0.001, an adaptive mean absolute error loss function, and a Nadam optimizer. The training of the communication control model is terminated when the loss error value of the communication control model is less than a preset threshold after 1000 iterations or 20 consecutive iterations, and the trained communication control model is obtained.

6. The remote communication method for the mixing and clarification tank stirring device according to claim 1, characterized in that, The first control command is based on: f c '= f c ×(1+ kΔ Adjust the filter's cutoff frequency; in, f c To adjust the cutoff frequency of the filter before adjustment, f c To adjust the cutoff frequency of the filter, k This is the proportionality coefficient. Δ For predicting signal attenuation data.

7. A remote communication device for a mixing and clarification tank stirring apparatus, characterized in that, include: The acquisition module is used to acquire signal quality data; The processing module is used to preprocess the signal quality data to obtain preprocessed signal quality data; Based on the preprocessed signal quality data, the predicted signal attenuation data is determined; The predicted signal attenuation data is compared and analyzed with a preset threshold to obtain control commands; The control module is used to control the mixing and clarification tank to communicate according to the control instructions; The control command is obtained by comparing and analyzing the predicted signal attenuation data with a preset threshold, including: When the predicted signal attenuation data is greater than a first threshold and less than a second threshold, a first control command is generated, which is used to adjust the cutoff frequency of the filter. When the predicted signal attenuation data is greater than or equal to a second threshold, a second control command is generated, which is used to switch the backup communication channel.

8. A computing device, characterized in that, include: A processor, a memory storing a computer program, wherein the computer program, when executed by the processor, performs the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The system stores instructions that, when executed on a computer, cause the computer to perform the method as described in any one of claims 1 to 6.

Images