A data signal transmission method for wellbore maintenance

Abstract

The present invention relates to a data signal transmission method for shaft maintenance, belonging to the technical field of mine signal transmission, and includes: through a first communication device arranged on the derrick and a second communication device arranged on the cage in the shaft, transmitting the maintenance data in the shaft to obtain a transmission data sequence, predicting the cage sway to obtain predicted sway parameters; according to the predicted sway parameters, obtaining the communication deviation angle and the sway time, predicting the influence on the data transmission quality according to the communication deviation angle to obtain transmission quality influence parameters; according to the transmission quality influence parameters, the sway time and the transmission data sequence, supplementing the transmission data within the sway time to obtain a generated transmission data sequence; generating data identification information for identification according to the transmission quality influence parameters, and transmitting the data signal within the sway time. The present invention solves the technical problem that the stability and transmission quality of shaft maintenance data transmission in the prior art are affected.

Description

Technical Field

[0001] The present invention relates to the technical field of mine signal transmission, and particularly to a data signal transmission method for shaft maintenance. Background Art

[0002] Shaft maintenance is an important link in the safe operation of mines, involving multiple aspects such as shaft structure detection, equipment maintenance, and signal monitoring. During shaft maintenance, the cage is usually used as the main vertical transportation tool for transporting personnel, equipment, and monitoring instruments. At the same time, in order to achieve remote monitoring and real-time data transmission, wireless communication technology is usually adopted during shaft maintenance to enable data transmission between the cage inside the shaft and the headframe outside the shaft, and then communicate with the hoist room. However, during the maintenance process, there will be technical problems that the data transmission quality is interfered, affecting the stability and transmission quality of data transmission inside the shaft. Summary of the Invention

[0003] Aiming at the technical problem in the prior art that the data transmission quality during shaft maintenance is interfered, affecting the stability and transmission quality of data transmission inside the shaft, the present invention provides a data signal transmission method for shaft maintenance to solve this problem.

[0004] The technical solution of the present invention to solve the above technical problems is as follows:

[0005] In a first aspect, the present invention provides a data signal transmission method for shaft maintenance, including: through a first communication device disposed on the headframe and a second communication device disposed on the cage inside the shaft, performing the transmission of maintenance data inside the shaft to obtain a transmission data sequence, and performing cage sway prediction to obtain predicted sway parameters, wherein the transmission data includes transmission images;

[0006] According to the predicted sway parameters, obtaining the communication deviation angle and sway time of the first communication device and the second communication device, and performing a prediction of the influence on data transmission quality according to the communication deviation angle to obtain a transmission quality influence parameter;

[0007] According to the transmission quality influence parameter, the sway time, and the transmission data sequence, performing the supplementation of the transmission data during the sway time to obtain a generated transmission data sequence;

[0008] According to the transmission quality influence parameter, generating data identification information, identifying the generated transmission data sequence, and performing the display of the generated data during the sway time, and continuing to perform data signal transmission.

[0009] The beneficial effects of the present invention are as follows: By predicting the shaking of the cage and supplementing and optimizing the data, the stability and reliability of the data transmission for shaft maintenance are improved. Through the first communication device arranged on the derrick and the second communication device arranged on the cage in the shaft, the maintenance data in the shaft is transmitted to obtain a transmission data sequence, and the shaking of the cage is predicted, enabling real-time collection of the operating state of the cage and the data transmission situation. According to the predicted shaking parameters, the communication deviation angle and the shaking time of the first communication device and the second communication device are obtained, and based on the communication deviation angle, the influence on the data transmission quality is predicted to obtain a transmission quality influence parameter, which can accurately analyze the data transmission influence under different shaking conditions. Further, according to the transmission quality influence parameter, the shaking time, and the transmission data sequence, the transmission data is supplemented during the shaking time to obtain a generated transmission data sequence. According to the transmission quality influence parameter, data identification information is generated to identify the generated transmission data sequence, and data signal transmission is performed during the shaking time. Through the data identification, the data transmission influence situation can be known, enabling the receiving end to intelligently adjust the data processing strategy to ensure the real-time nature and reliability of the maintenance operation. Through technical means such as cage shaking prediction, communication deviation angle calculation, transmission quality influence assessment, data supplementation, and identification optimization, the present invention improves the stability of the shaft maintenance data transmission, reduces problems such as signal attenuation, data loss, and transmission quality degradation caused by cage shaking, and thus improves the efficiency and safety of the shaft maintenance work. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 It is a schematic flow chart of a data signal transmission method for shaft maintenance provided by the present invention;

[0011] Figure 2 It is a schematic flow chart of obtaining predicted shaking parameters in a data signal transmission method for shaft maintenance provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0012] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts fall within the protection scope of the present invention.

[0013] In the description of the present invention, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of the described features. In the description of the present invention, "plurality" means two or more, unless otherwise specifically defined.

[0014] In the description of the present invention, the term "for example" is used to mean "serving as an example, illustration, or explanation". Any embodiment described as "for example" in the present invention is not necessarily construed as being more preferred or advantageous than other embodiments. The following description is given to enable any person skilled in the art to implement and use the present invention. In the following description, details are set forth for purposes of explanation. It should be understood that those of ordinary skill in the art can recognize that the present invention can be implemented without the use of these specific details. In other instances, well-known structures and processes are not elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but rather to be in line with the broadest scope consistent with the principles and features disclosed in the present invention.

[0015] Embodiment 1, as Figure 1 shown, the embodiment of the present invention provides a data signal transmission method for shaft maintenance, specifically including the following steps:

[0016] S10: Through the first communication device disposed on the derrick and the second communication device disposed on the cage in the shaft, transmit the maintenance data in the shaft, obtain the transmission data sequence, and perform cage sway prediction to obtain the predicted sway parameters, where the transmission data includes transmission images.

[0017] In the embodiment of the present application, through the first communication device disposed on the derrick and the second communication device disposed on the cage in the shaft, wireless transmission of the maintenance data in the shaft is performed.

[0018] During the transmission process, the transmitted transmission data can be recorded to form a transmission data sequence. The transmission data includes images, which are used to reflect and record the maintenance progress and maintenance data in the shaft.

[0019] In the embodiment of the present application, the cage will inevitably sway, vibrate, or even swing during operation, which will in turn cause interference to the signals of the first communication device and the second communication device, affecting the data transmission quality and stability. The sway of the cage will be reflected in the transmission images in the transmission data. For example, the transmission image shows a trend of picture sway. Therefore, in the embodiment of the present application, the cage sway is predicted based on the transmission images in the transmission data sequence to predict the amplitude and time of the cage sway, and the predicted sway parameters are obtained as the data basis for subsequent data transmission processing.

[0020] As Figure 2 shown, step S10 in the method provided by the embodiment of the present application includes:

[0021] Through the first communication device installed on the derrick and the second communication device installed on the cage in the shaft, the maintenance data in the shaft is transmitted to the hoist house to obtain a transmission data sequence. Each transmission data includes a transmission image and a transmission voice.

[0022] Extract the transmission image sequence from the transmission data sequence.

[0023] According to the transmission image sequence, predict the cage swaying to obtain the predicted swaying parameters.

[0024] In the embodiment of the present application, the first communication device installed on the derrick is, for example, a mobile wireless base station installed on the derrick at the shaft opening, and a vertically downward directional antenna is installed. The second communication device installed on the cage in the shaft is, for example, a mobile wireless base station installed on the cage in the shaft, and a vertically upward directional antenna is installed. In this way, the directional antennas on the derrick and the cage are opposite to each other to achieve wireless data transmission. Moreover, after the first communication device on the derrick obtains the maintenance data transmission data in the shaft, it can transmit the maintenance data to the hoist house to form remote data transmission. Data can be transmitted between the derrick and the hoist house through wireless transmission or fiber optic communication. In this way, the transmission of maintenance data in the shaft and the video communication between maintenance personnel and ground personnel can be realized.

[0025] When the cage sways, it will cause the direction of the directional antennas on the derrick and the cage to change, from the vertical direction to a certain included angle, which will affect the stability and quality of the transmitted data.

[0026] During the data transmission process, the maintenance data is collected and transmitted in real time to form a transmission data sequence, which includes transmission images and transmission voices. The transmission images can be high-definition image data such as the internal structure of the shaft and the state of the cage guides, for example, image data with a resolution of 1920×1080 and a frame rate of 30 frames. The transmission voice is the voice communication data of the maintenance personnel, used for remote communication or issuing voice commands, usually with a sampling rate of 16 kHz. The transmitted transmission images and transmission voice data are arranged in chronological order to form a transmission data sequence. For example, the transmission images and transmission voice data within the most recent 3 seconds of transmission are arranged to form a transmission data sequence.

[0027] Furthermore, extract the transmission image sequence from the transmission data sequence, that is, separate the image data from the continuous data packets for the analysis of cage swaying. Since the cage will sway due to inertia, changes in wire rope tension or external disturbances during the operation in the shaft, the transmission images include the characteristics before the cage swaying occurs. For example, the position changes of the fixed reference objects (such as cage guides and guide rails) in the transmission images. Based on this, according to the transmission image sequence, the cage swaying can be predicted to predict the angle and time of the cage swaying, forming the predicted swaying parameters.

[0028] The step of "performing cage sway prediction based on the transmitted image sequence to obtain predicted sway parameters" in the method provided by the embodiment of the present application includes:

[0029] Based on the shaft maintenance transmission data within the historical time, collect a set of sample transmitted image sequences, and collect the maximum sway angle and continuous sway time of the cage after different sample transmitted image sequences, and label them as a set of sample sway parameters;

[0030] Construct a sway predictor based on a convolutional neural network;

[0031] Use the set of sample transmitted image sequences and the set of sample sway parameters to perform supervised training and testing on the sway predictor until the accuracy meets the requirements;

[0032] Input the transmitted image sequence into the sway predictor, and predict and output to obtain the predicted sway parameters.

[0033] In the embodiment of the present application, a convolutional neural network in deep learning is used to predict the sway parameters of the cage according to the transmitted image sequence.

[0034] First, in the shaft maintenance transmission data within the historical time, for example, in the transmission data collected during shaft maintenance in the past year, collect multiple sample transmitted image sequences collected during previous maintenance to form a set of sample transmitted image sequences. For example, each sample transmitted image sequence includes 90 transmitted images in 3 seconds.

[0035] Furthermore, collect the maximum sway angle and continuous sway time of the cage within a preset time range after different sample transmitted image sequences. The preset time range is, for example, 5 seconds. That is, according to the data log recorded during the maintenance process within the historical time, obtain the maximum sway angle and continuous sway time of the cage within the preset time range after each sample transmitted image sequence. The maximum sway angle is the maximum angle between the axis of the cage and the vertical direction when the cage sways within the preset time range, for example, 15 degrees. The continuous sway time is, for example, the time from when the sway angle of the cage is greater than the sway angle threshold when it starts to sway to when the sway angle is less than the sway angle threshold and stops swaying. For example, it is 2s. The sway angle threshold is, for example, 5°. That is, the maximum sway angle that does not affect the data transmission quality can be obtained by performing data transmission quality tests when the cage sways. In this way, collect the corresponding multiple maximum sway angles and multiple continuous sway times, and label them as a set of sample sway parameters. Each sample sway parameter includes the maximum sway angle and the continuous sway time.

[0036] Furthermore, based on a convolutional neural network, a sway predictor is constructed, which includes an input layer, multiple convolutional layers, multiple pooling layers, a fully connected layer, and an output layer. Exemplarily, the convolutional layer includes 3 layers, which respectively include 32, 64, and 128 3×3 convolutional kernels for extracting features in the transmitted image. The pooling layer includes 3 layers with a pooling window size of 2×2. The fully connected layer structure is 256-128, using the ReLU activation function. During the training process, the mean square error loss function is adopted. The dimension of the output layer is 2, which are respectively the maximum sway angle and the continuous sway time within the sway parameters. During the training process, the sample transmitted image sequence is input, the output sway parameters are obtained, the error from the actual sway parameters is calculated, for example, the squares of the errors of the maximum sway angle and the continuous sway time are respectively calculated as the loss, and the network parameters are optimized according to the loss to reduce the loss, and iterative training is performed in this way. And it can be tested with the data divided from the sample transmitted image sequence set and the sample sway parameter set. After the test loss is less than the requirement, for example, the prediction error of the maximum sway angle < 0.5°, and the prediction error of the continuous sway time < 0.2s, the training is completed.

[0037] Based on the trained sway predictor, the current transmitted image sequence is input to obtain the predicted sway parameters of the predicted output, including the predicted maximum sway angle and the continuous sway time, and the sway prediction of the cage is completed.

[0038] In the embodiment of the present application, through historical data collection, deep learning model training, and real-time prediction, the intelligent prediction of the cage sway is realized, and then it is used as the data basis for subsequent optimization and adjustment of data transmission, improving the stability and quality of wireless transmission of maintenance data in the shaft.

[0039] S20: According to the predicted sway parameters, obtain the communication deviation angle and sway time of the first communication device and the second communication device, and predict the impact on data transmission quality according to the communication deviation angle to obtain the transmission quality impact parameter.

[0040] In the embodiment of the present application, it is necessary to quantitatively analyze the impact on data transmission between the first communication device and the second communication device according to the predicted sway parameters. Specifically, according to the predicted sway parameters, obtain the communication deviation angle and sway time of the first communication device and the second communication device, and then predict the impact on data transmission quality according to the communication deviation angle to obtain the transmission quality impact parameter, quantifying the degree of impact on data transmission quality, and then adjusting the subsequent data transmission strategy.

[0041] Step S20 in the method provided by the embodiment of the present application includes:

[0042] Obtain the maximum sway angle and the continuous sway time within the predicted sway parameters;

[0043] According to the maximum swaying angle, calculate the communication deviation angle between the first communication device and the second communication device, and use the continuous swaying time as the swaying time.

[0044] In the embodiment of the present application, based on the predicted swaying parameters obtained from the foregoing content, extract the maximum swaying angle and the continuous swaying time therein, for example, 25° and 4s respectively.

[0045] Further, it is possible to determine whether the maximum swaying angle is greater than or equal to the swaying angle threshold. If so, it means that the currently predicted swaying parameters will affect data transmission, and then proceed to the next step. If not, it means that the currently predicted swaying parameters will not affect data transmission. At this time, the continuous swaying time is output as 0, and the next step is not performed, and data transmission and monitoring continue.

[0046] Further, according to this maximum swaying angle, calculate the communication deviation angle between the first communication device and the second communication device. Among them, the swaying of the cage will cause the direction of the antenna of the communication device to change, thereby generating a communication deviation angle and affecting the signal quality.

[0047] The cage is in a vertical state under normal conditions. When it sways and generates the maximum swaying angle, the directional antenna in the second communication device also sways with it, thereby causing an angle between its direction and the direction of the directional antenna in the first communication device. This angle is the communication deviation angle. Since the second communication device sways with the cage, the communication deviation angle is the maximum swaying angle, for example, 25°.

[0048] Further, use the continuous swaying time in the predicted swaying parameters as the swaying time. The communication deviation angle and the swaying time are respectively used as the data basis for subsequent analysis of the impact on data transmission quality and supplementary generation of transmission data.

[0049] Step S20 in the method provided by the embodiment of the present application further includes:

[0050] According to the wellbore data transmission historical data, collect the sample communication deviation angle set, and collect the data transmission quality impact amplitude under different sample communication deviation angles, and label it as the sample transmission quality impact parameter set;

[0051] Use a feedforward neural network to construct a data transmission impact predictor;

[0052] Use the sample communication deviation angle set and the sample transmission quality impact parameter set to perform supervised training on the data transmission impact predictor until the accuracy meets the requirements;

[0053] Input the communication deviation angle into the data transmission impact predictor, and predict and output the transmission quality impact parameter.

[0054] In the embodiments of this application, the swaying of the cage will cause changes in the communication deviation angle, thereby affecting the quality of data transmission. In order to accurately predict the impact of the communication deviation angle on the data transmission quality, a data transmission impact predictor is constructed based on the historical data of wellbore data transmission, so as to perform corresponding processing when the cage sways, such as supplementing and generating transmission data or giving early warnings. Specifically, the impact of the currently predicted cage swaying on data transmission is analyzed according to the communication deviation angle.

[0055] Among them, according to the data transmission historical data during wellbore maintenance within the historical time, multiple communication deviation angles monitored when the cage swayed previously are collected to obtain a sample communication deviation angle set. Exemplarily, the sample communication deviation angles are 10°, 15°, 17°, etc. The sample communication deviation angles can be obtained by actually measuring the cage swaying angle with a gyroscope installed in the cage.

[0056] Furthermore, the impact amplitude of data transmission quality under different sample communication deviation angles is collected, such as specifically the bit error rate in data transmission, which is marked as a sample transmission quality impact parameter to obtain a sample transmission quality impact parameter set. For example, the sample transmission quality impact parameters are 1.02%, 3.87%, 8.94%. The sample transmission quality impact parameters can be obtained by performing data transmission tests when the cage sways at different angles to generate different sample communication deviation angles.

[0057] Furthermore, a feedforward neural network in machine learning is used to construct a data transmission impact predictor, which includes an input layer, a hidden layer, and an output layer. The hidden layer includes two layers, containing 32 and 16 nodes respectively, and the ReLU activation function is used. During the training process, the sample communication deviation angles are input, the obtained transmission quality impact parameters of the output are acquired, the difference from the corresponding sample transmission quality impact parameters is calculated, the loss is calculated through the mean square error loss function, and the network parameters such as the weights of the nodes are tuned according to the loss to reduce the loss for training and optimization. Until the loss of testing the data transmission impact predictor is less than the requirement, such as less than 0.01%, the training is completed.

[0058] Based on the trained data transmission impact predictor, the current communication deviation angle is input, the obtained transmission quality impact parameters of the output are acquired, and the prediction of the impact of data transmission quality based on the communication deviation angle is completed.

[0059] Through the above steps, the embodiments of this application can achieve real-time prediction of the communication quality changes caused by the cage swaying, and further provide a reference for decision-making when the data transmission stability is affected, such as performing subsequent supplementary data generation or data transmission anomaly early warning, improving the safety of mine equipment maintenance and the stability of data transmission.

[0060] S30: According to the transmission quality influence parameter, the shaking time, and the transmission data sequence, perform the supplement of the transmission data within the shaking time to obtain the generated transmission data sequence.

[0061] In the embodiment of the present application, when the cage shakes and the data transmission is affected, in order to repair the coherence of the data transmission, for example, to repair the coherence of the video communication, according to the transmission quality influence parameter, the shaking time, and the transmission data sequence, supplement and generate the transmission data within the shaking time to form the generated transmission data sequence, perform the generation supplement on the transmission data within the shaking time, thereby improving the coherence and stability of the data transmission.

[0062] The step S30 in the method provided by the embodiment of the present application includes:

[0063] Construct a transmission data supplement channel;

[0064] Input the transmission data sequence, the transmission quality influence parameter, and the shaking time into the transmission data supplement channel, supplement and generate the generated transmission data within the future shaking time range to obtain the generated transmission data sequence.

[0065] In the embodiment of the present application, first construct a transmission data supplement channel for supplementing and generating transmission data. Among them, it is preferably to use a generative adversarial network to construct the transmission data supplement channel.

[0066] The step "Construct a transmission data supplement channel" in the method provided by the embodiment of the present application includes:

[0067] According to the local historical maintenance data and historical transmission data of the cage during the shaft maintenance, collect the sample transmission data sequence set, the sample transmission quality influence parameter set, the sample shaking time set, and collect the transmission data sequence within the sample shaking time after each sample transmission data sequence, and label it as the sample generated transmission data sequence set;

[0068] Use a generative adversarial network to construct a generator and a discriminator to obtain the transmission data supplement channel;

[0069] Use the sample transmission data sequence set, the sample transmission quality influence parameter set, the sample shaking time set, and the sample generated transmission data sequence set to perform supervised training on the transmission data supplement channel until the accuracy meets the requirements, where the generator and the discriminator are alternately trained.

[0070] In the embodiments of the present application, according to the local historical maintenance data and historical transmission data of the inner cage for shaft maintenance, that is, the historical maintenance image data collected by the local camera device of the cage and the historical transmission data transmitted, a set of sample transmission data sequences, a set of sample transmission quality influence parameters, and a set of sample shaking time when the cage shakes during previous data transmission are collected. The set of sample transmission quality influence parameters and the set of sample shaking time can be obtained through the above content for processing.

[0071] Further, the transmission data sequence within the sample shaking time after each sample transmission data sequence is collected. For example, if the sample shaking time is 3 s, the transmission data sequence within 3 s after the sample transmission data sequence is collected and labeled as the sample generated transmission data sequence, and the sample generated transmission data sequence is obtained. Among them, if data transmission loss occurs due to the loss of data transmission quality caused by shaking, it can be collected from the local historical maintenance data of the inner cage for shaft maintenance.

[0072] Further, a generative adversarial network is used to construct a generator and a discriminator to form a transmission data supplement channel. The set of sample transmission data sequences, the set of sample transmission quality influence parameters, and the set of sample shaking time are used as the supervised training data of the generator, and the set of sample generated transmission data sequences is used as the supervised training data of the discriminator to perform the supervised training of the transmission data supplement channel. During the training process, the generator and the discriminator are alternately trained.

[0073] Exemplarily, the generator is responsible for generating a transmission data sequence to compensate for the missing transmission data caused by the influence of data transmission quality. An LSTM network is used, and ReLU is used as the activation function. The discriminator is used to distinguish real data (sample generated transmission data sequence) from generated data (transmission data sequence generated by the generator) to improve the authenticity of the generated transmission data supplement. During the training process, the learning rate is set to 0.0002. The loss function of the generator is the mean square error loss function, and the error between the generated transmission data sequence and the sample generated transmission data sequence is calculated to calculate the loss, and the network parameters are tuned to reduce the loss until convergence, for example, the accuracy rate reaches 95%. The loss of the discriminator is the discrimination error, which is calculated based on the binary cross-entropy loss function, and the network parameters are tuned according to the loss to reduce the loss until convergence, for example, the discrimination error is less than 5%. During the alternating training, after training the generator for one round, the discriminator is trained, and thus the alternating training is completed until both the generator and the discriminator converge, and the trained transmission data supplement channel is obtained.

[0074] Based on the trained transmission data supplement channel, input the current transmission data sequence, transmission quality influence parameters, and swaying time, and supplement and generate the generated transmission data within the future swaying time range to form a generated transmission data sequence, which serves as the transmission data for supplementing the data transmission loss during the swaying time, so as to improve the stability and transmission quality of data transmission.

[0075] Among them, the methods provided in the embodiments of this application are all processed in the lifting machine room after data transmission, which has a large amount of computing power deployment, can execute the above steps, and generate the above-mentioned generated transmission data sequence during data transmission, and then display it to improve the coherence and stability of data transmission in the shaft.

[0076] This method uses a generative adversarial network to supplement data when data is lost and generate transmission data during the period when the cage swaying affects data transmission. This solution can effectively improve the stability and integrity of shaft maintenance data transmission, reduce the impact of cage swaying on data quality, and ensure the safety and efficiency of mine maintenance operations.

[0077] S40: Generate data identification information according to the transmission quality influence parameters, identify the generated transmission data sequence, display the generated data within the swaying time, and continue data signal transmission.

[0078] In the embodiments of this application, when supplementing and generating the generated transmission data sequence, due to the use of the above-mentioned deep learning means for generation, there may be some errors. Therefore, it is necessary to generate data identification information according to the transmission quality influence parameters to identify the generated transmission data sequence, so as to prompt the staff that this part of the generated transmission data sequence is obtained through generation supplement, and prompt the staff of the degree to which the current cage swaying affects data transmission quality, and then take corresponding measures, such as giving an early warning or adjusting the data transmission strategy, etc.

[0079] After identifying the generated transmission data sequence, display it within the swaying time to complete the supplement of data transmission during the swaying period, and after the swaying time, continue data signal transmission, and continue to predict the cage swaying and process data transmission based on the above method.

[0080] Step S40 in the method provided by the embodiments of this application includes:

[0081] Generate data identification information according to the transmission quality influence parameters;

[0082] Use the data identification information to identify the generated transmission data sequence, display the generated data within the swaying time, and continue data signal transmission.

[0083] In the embodiment of the present application, data identification information is generated according to the transmission quality influence parameter. For example, the transmission quality influence parameter is directly used as the data identification information. For example, if the transmission quality influence parameter includes a bit error rate of 3.5%, then for example, "the current data transmission bit error rate is 3.5%, and the transmitted data within 3 seconds is auxiliary generated data" is used as the data identification information for identification.

[0084] Alternatively, the data transmission quality influence can be classified according to the bit error rate in the transmission quality influence parameter. For example, a bit error rate within 0-3% has a minor impact, a bit error rate within 3-8% has a general impact, and a bit error rate above 8% has a severe impact. Classification is carried out according to the interval in which the bit error rate in the transmission quality influence parameter falls. For example, "the current data transmission quality has a general impact, and the transmitted data within 3 seconds is auxiliary generated data" is generated as the data identification information.

[0085] Furthermore, the generated transmission data sequence is identified using this data identification information, the generated data is displayed during the shaking time, and data signal transmission continues after the shaking time.

[0086] In the embodiment of the present application, by generating data identification information, the generated data sequence is marked, and data is displayed during the shaking time of the cage, ensuring the continuity and stability of data transmission. At the same time, it can prompt the staff about the amplitude of the impact of cage shaking on data transmission quality, serving as a decision-making reference and enhancing the safety and reliability of mine maintenance.

[0087] A data signal transmission method for shaft maintenance provided by an embodiment of the present invention has at least the following technical effects:

[0088] In the embodiments of the present invention, by predicting the shaking of the cage and supplementing and optimizing data, the stability and reliability of the data transmission for shaft maintenance are improved. Through the first communication device arranged on the headframe and the second communication device arranged on the cage in the shaft, the maintenance data in the shaft is transmitted to obtain a transmission data sequence, and the shaking of the cage is predicted, so as to be able to collect the running state of the cage and the data transmission situation in real time. According to the predicted shaking parameters, the communication deviation angle and the shaking time of the first communication device and the second communication device are obtained, and the influence on the data transmission quality is predicted according to the communication deviation angle to obtain a transmission quality influence parameter, which can accurately analyze the influence of data transmission under different shaking conditions. Further, according to the transmission quality influence parameter, the shaking time, and the transmission data sequence, the transmission data during the shaking time is supplemented to obtain a generated transmission data sequence, and according to the transmission quality influence parameter, data identification information is generated to identify the generated transmission data sequence, and the data signal is transmitted during the shaking time. Through the data identification, the influence situation of data transmission can be known, enabling the receiving end to intelligently adjust the data processing strategy to ensure the timeliness and reliability of the maintenance operation. Through technical means such as cage shaking prediction, communication deviation angle calculation, transmission quality influence assessment, data supplementation, and identification optimization, the present invention realizes the improvement of the stability of shaft maintenance data transmission, reduces the problems of signal attenuation, data loss, and transmission quality degradation caused by cage shaking, and further improves the efficiency and safety of shaft maintenance work.

[0089] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications to these embodiments once they know the basic inventive concept.

[0090] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the present invention and its equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A data signal transmission method for wellbore maintenance, characterized in that: The method comprises: Transmitting maintenance data in the wellbore by means of a first communication device arranged on the derrick and a second communication device arranged in the cage in the wellbore, obtaining a transmission data sequence, performing cage sway prediction, and obtaining predicted sway parameters, wherein the transmission data includes a transmission image; obtaining a communication deviation angle and a sway time between the first communication device and the second communication device based on the predicted sway parameters, and performing a data transmission quality impact prediction based on the communication deviation angle to obtain a transmission quality impact parameter, wherein the communication deviation angle is a maximum sway angle of the cage within the predicted sway parameters; Supplementing the transmission data within the shaking time according to the transmission quality influencing parameter, the shaking time and the transmission data sequence to obtain a transmission data sequence; According to the transmission quality influencing parameter, data identification information is generated, the generated transmission data sequence is identified, the generated data is displayed within the shaking time, and the data signal transmission is continued.

2. The data signal transmission method for wellbore maintenance according to claim 1, characterized in that: Through the first communication device arranged on the derrick and the second communication device arranged in the cage in the wellbore, the maintenance data in the wellbore is transmitted, the transmission data sequence is obtained, the cage sway is predicted, and the predicted sway parameters are obtained, including: Through the first communication device arranged on the derrick and the second communication device arranged in the cage in the wellbore, the maintenance data in the wellbore is transmitted to the hoist room to obtain a transmission data sequence, wherein each transmission data includes a transmission image and a transmission voice; extracting a transmission image sequence from the transmission data sequence; Cage sway prediction is performed based on the transmitted image sequence to obtain predicted sway parameters.

3. The data signal transmission method for wellbore maintenance according to claim 2, characterized in that: Cage sway prediction is performed based on the transmitted image sequence to obtain predicted sway parameters, including: Based on the historical wellbore maintenance transmission data, a set of sample transmission image sequences is collected. The maximum shaking angle and duration of the cage shaking after different sample transmission image sequences are collected and marked as a sample shaking parameter set. Build a shaking predictor based on convolutional neural network; Using the sample transmission image sequence set and the sample shake parameter set, supervised training and testing are performed on the shake predictor until the accuracy meets the requirement; The transmission image sequence is input into the jitter predictor, and the predicted jitter parameters are obtained by predicting the output.

4. The data signal transmission method for wellbore maintenance according to claim 1, characterized in that: Acquiring a communication deviation angle and a shaking time between the first communication device and the second communication device according to the predicted shaking parameter, including: Obtaining a maximum shaking angle and a continuous shaking time within the predicted shaking parameters; The communication deviation angle between the first communication device and the second communication device is calculated according to the maximum shaking angle, and the continuous shaking time is used as the shaking time.

5. The data signal transmission method for wellbore maintenance according to claim 1, characterized in that: The impact of data transmission quality is predicted based on the communication deviation angle, and the transmission quality impact parameters are obtained, including: Based on the historical data of wellbore data transmission, a set of sample communication deviation angles is collected, and the data transmission quality impact amplitudes under different sample communication deviation angles are collected and marked as a set of sample transmission quality impact parameters; A feedforward neural network is used to construct a data transmission impact predictor; Using the sample communication deviation angle set and the sample transmission quality impact parameter set, supervised training is performed on the data transmission impact predictor until the accuracy meets the requirement; The communication deviation angle is input into the data transmission impact predictor, and the prediction output is used to obtain the transmission quality impact parameter.

6. The data signal transmission method for wellbore maintenance according to claim 1, characterized in that: Supplementing the transmission data within the shaking time according to the transmission quality influencing parameter, the shaking time, and the transmission data sequence to obtain and generate the transmission data sequence includes: Build a supplementary channel for transmitting data; The transmission data sequence, transmission quality influencing parameters and jitter time are input into the transmission data supplement channel to supplement and generate transmission data within the jitter time range in the future, thereby obtaining a transmission data sequence.

7. The data signal transmission method for wellbore maintenance according to claim 6, characterized in that: Construct a supplementary channel for transmitting data, including: According to the local historical maintenance data and historical transmission data of the cage in the wellbore maintenance, a sample transmission data sequence set, a sample transmission quality influencing parameter set, and a sample sloshing time set are collected. In addition, a transmission data sequence within the sample sloshing time after each sample transmission data sequence is collected and marked as a sample generation transmission data sequence set. Generative adversarial networks are used to construct generators and discriminators to obtain a supplementary channel for transmission data. The sample transmission data sequence set, the sample transmission quality influencing parameter set, the sample jitter time set and the sample generation transmission data sequence set are used to perform supervised training on the transmission data supplement channel until the accuracy meets the requirements, wherein the generator and the discriminator are trained alternately.

8. The data signal transmission method for wellbore maintenance according to claim 1, characterized in that: Generating data identification information according to the transmission quality influencing parameter, identifying the generated transmission data sequence, displaying the generated data within the shaking time, and continuing to transmit the data signal, including: generating data identification information according to the transmission quality influencing parameter; The data identification information is used to identify the generated transmission data sequence, the generated data is displayed within the shaking time, and the data signal transmission is continued.

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