Tunneling parameter adjusting method and system of shield tunneling machine

Abstract

The invention relates to a shield tunneling machine tunneling parameter adjusting method and system, and relates to the technical field of shield construction, and the method comprises the steps: recording propulsion resistance information in a plurality of past historical moments, obtaining a historical propulsion resistance sequence, carrying out the prediction of propulsion resistance at a plurality of future moments, and obtaining a predicted propulsion resistance sequence; optimizing the propulsive force of the shield tunneling machine in order to improve the propulsive speed and the soil layer stability to obtain optimized propulsive force; carrying out fluctuation analysis on the historical propulsion resistance sequence and the predicted propulsion resistance sequence, calculating to obtain minimum fluctuation propulsion resistance and maximum fluctuation propulsion resistance, respectively carrying out propulsion speed analysis and soil layer stability analysis to obtain minimum propulsion speed and minimum soil layer stability information, and carrying out auxiliary optimization on the optimized propulsion force to obtain the optimal propulsion force. And optimal propulsive force is obtained, and tunneling parameters of the shield tunneling machine are adjusted. The technical effect of improving the shield construction efficiency, stability and safety is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of shield construction, and in particular to a shield machine excavation parameter adjustment method and system. Background Art

[0002] During underground tunneling, shield machines face challenges from soil resistance and geological variations. Precise adjustment of tunneling parameters is crucial for both construction efficiency and safety. Traditional tunneling parameter adjustment methods often rely on manual experience or simple rule-based models, lacking the ability to respond to complex soil conditions and dynamic changes in real time. This can lead to slow tunneling speeds or unstable soil, impacting the shield machine's tunneling performance and construction progress.

[0003] Therefore, accurately predicting future thrust resistance based on historical thrust resistance information and engineering characteristics, optimizing the thrust of the shield machine, and combining this with soil stability for auxiliary optimization, has become a pressing technical challenge. Existing technologies suffer from inaccurate adjustment of shield machine excavation parameters, impacting shield construction efficiency and safety. Summary of the Invention

[0004] The present invention aims to solve the technical problem in the prior art that inaccurate shield machine excavation parameter adjustment affects shield construction efficiency and safety, and provides a shield machine excavation parameter adjustment method and system.

[0005] In a first aspect, the present invention provides a method for adjusting shield machine excavation parameters, comprising: during the excavation process of the shield machine, recording propulsion resistance information at multiple historical moments in the past to obtain a historical propulsion resistance sequence; and predicting propulsion resistance at multiple future moments in the future in combination with engineering characteristic information of the excavation project to obtain a predicted propulsion resistance sequence; According to the predicted propulsion resistance sequence, the propulsion force of the shield machine is optimized for the purpose of increasing the propulsion speed and improving the stability of the soil layer to obtain an optimized propulsion force; Performing a fluctuation analysis on the historical advancement resistance sequence and the predicted advancement resistance sequence, and calculating the minimum fluctuation advancement resistance and the maximum fluctuation advancement resistance, respectively performing advancement speed analysis and soil layer stability analysis to obtain minimum advancement speed and minimum soil layer stability information; According to the minimum propulsion speed and the minimum soil layer stability information, the optimized propulsion force is assisted optimized to obtain the optimal propulsion force, and the tunneling parameters of the shield machine are adjusted.

[0006] In a second aspect, the present invention provides a shield machine excavation parameter adjustment system, comprising: a propulsion resistance prediction module for recording propulsion resistance information at multiple historical moments during the excavation process of the shield machine to obtain a historical propulsion resistance sequence, and combining the propulsion resistance information at multiple future moments with engineering characteristic information of the excavation project to obtain a predicted propulsion resistance sequence; A propulsion force optimization module is used to optimize the propulsion force of the shield machine according to the predicted propulsion resistance sequence, with the purpose of increasing the propulsion speed and improving the stability of the soil layer, to obtain an optimized propulsion force; a fluctuation analysis module for performing fluctuation analysis on the historical propulsion resistance sequence and the predicted propulsion resistance sequence, and calculating the minimum fluctuation propulsion resistance and the maximum fluctuation propulsion resistance, and performing propulsion speed analysis and soil layer stability analysis respectively to obtain minimum propulsion speed and minimum soil layer stability information; The auxiliary optimization module is used to perform auxiliary optimization on the optimized propulsion force according to the minimum propulsion speed and the minimum soil layer stability information, obtain the optimal propulsion force, and adjust the tunneling parameters of the shield machine.

[0007] In a third aspect, the present invention provides an electronic device comprising: a memory for storing a computer software program; and a processor for reading and executing the computer software program, thereby implementing the shield machine excavation parameter adjustment method of the first aspect.

[0008] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer software program stored therein. When the computer software program is executed by a processor, the shield machine excavation parameter adjustment method of the first aspect is implemented.

[0009] The shield machine excavation parameter adjustment method of the present invention, by introducing historical propulsion resistance sequences and engineering feature information, and combining a prediction model to accurately predict the propulsion resistance at multiple moments in the future, can accurately predict the propulsion resistance in the future. By analyzing the predicted propulsion resistance sequence, the present invention can optimize the propulsion force of the shield machine, reasonably increase the propulsion speed, and effectively balance the stability of the soil layer. Further, the propulsion force is optimized and adjusted according to the predicted propulsion resistance sequence, thereby improving the propulsion efficiency while ensuring construction safety. In addition, the fluctuation analysis based on historical and predicted propulsion resistance information is combined to further optimize the propulsion force, thereby improving the overall operation efficiency. Through the technical solution of this application, the accuracy and flexibility of the shield machine excavation parameter adjustment are improved, the propulsion force can be adjusted according to the real-time soil layer conditions, the propulsion speed can be optimized, the soil layer during excavation can be guaranteed to be stable, and the safety and efficiency of the shield construction process can be ensured, thereby improving the adaptability of the shield machine under complex geological conditions. Through the adjustment and optimization of the shield machine propulsion force parameters, the technical effect of improving the efficiency, stability and safety of shield construction is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 A schematic diagram of a process for adjusting shield machine excavation parameters according to an embodiment of the present invention; Figure 2 A schematic structural diagram of a shield machine excavation parameter adjustment system provided in an embodiment of the present invention; Figure 3 A functional block diagram of an electronic device provided by an embodiment of the present invention; Figure 4 This is a principle block diagram of the computer-readable storage medium provided by the present invention.

[0011] In the accompanying drawings, the components represented by the reference numerals are as follows: Propulsion resistance prediction module 11, propulsion force optimization module 12, fluctuation analysis module 13, auxiliary optimization module 14, electronic device 100, memory 110, processor 120, first computer program 111, computer-readable storage medium 200, second computer program 211. DETAILED DESCRIPTION

[0012] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0013] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Therefore, a feature specified as "first" or "second" may explicitly or implicitly include one or more of the described features. In the description of this application, "plurality" means two or more, unless otherwise specifically specified.

[0014] In the description of this application, the term "for example" is used to mean "used as an example, illustration or explanation". Any embodiment described as "for example" in this application is not necessarily to be 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 listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art will recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be 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 is consistent with the widest scope consistent with the principles and features disclosed in this application.

[0015] Example 1 like Figure 1 As shown, an embodiment of the present invention provides a shield machine excavation parameter adjustment method, including: S10: During the tunneling process of the shield machine, the propulsion resistance information at multiple historical moments in the past is recorded to obtain a historical propulsion resistance sequence. Combined with the engineering characteristic information of the tunneling project, the propulsion resistance at multiple future moments in the future is predicted to obtain a predicted propulsion resistance sequence. In an embodiment of the present application, during the tunneling process of the shield machine, the propulsion resistance information at multiple historical moments in the past is recorded. By recording the propulsion resistance information at multiple historical moments, the dynamic changes of the propulsion resistance in the tunneling operation can be effectively captured.

[0016] Furthermore, combined with the engineering characteristic information of the tunneling project, the propulsion resistance at multiple moments in the future is accurately predicted, so as to obtain the propulsion resistance at multiple moments in the future as the basis for optimizing and adjusting the propulsion force of the shield machine and obtain the predicted propulsion resistance sequence.

[0017] Step S10 in the method provided in the embodiment of the present application includes: Obtain engineering characteristic information of tunneling projects; During the tunneling process of the shield machine, the propulsion resistance information at multiple historical moments in the past is recorded to obtain the historical propulsion resistance sequence; The historical propulsion resistance sequence is input into a pre-trained propulsion resistance predictor, and the propulsion resistance predictions at the multiple future moments are obtained by prediction output to obtain a predicted propulsion resistance sequence, wherein the propulsion resistance predictor is obtained through supervised training.

[0018] In the present embodiment, engineering characteristic information of the tunneling project is first obtained. The tunneling project is the project currently being excavated by a shield machine, such as a tunnel. This engineering characteristic information includes, for example, soil type (e.g., soft soil, hard rock, sandy soil), soil moisture, soil hardness, and tunnel depth. This engineering characteristic information can reflect the resistance level of the current tunneling project. For example, if the soil type is soft soil, the soil moisture is high, and the soil hardness is low, the tunneling resistance is low.

[0019] During the actual tunneling process of the shield machine, real-time monitoring and recording of thrust resistance data at multiple historical moments are used to construct a historical thrust resistance sequence. This thrust resistance refers to the geological resistance encountered by the shield machine during tunneling, specifically the resistance experienced by the cutterhead during cutting. Pressure sensors are installed on the cutterhead to measure the pressure at contact between the soil and the cutterhead, and thrust resistance data is obtained and recorded. Examples of thrust resistance data include 175kN and 300kN. For example, the thrust resistance is measured every second, and 10 thrust resistance data points within the past 10 seconds are recorded to create a historical thrust resistance sequence.

[0020] Furthermore, future propulsion resistance is predicted based on historical propulsion resistance sequences. These sequences are then fed into a pre-built propulsion resistance predictor. This propulsion resistance predictor is a model trained through supervised learning. By inputting a set of known historical data (including historical propulsion resistance sequences and corresponding engineering characteristics), the predictor is trained to learn the patterns and regularities within these data and predict future propulsion resistance values ​​based on these regularities. The training process for this predictor is conducted using a large set of historical data, optimizing model parameters so that it can accurately output predicted propulsion resistance values ​​at multiple moments in the future when presented with new historical data, thereby forming a predicted propulsion resistance sequence.

[0021] Predicting the thrust resistance sequence not only provides real-time resistance warnings for shield machine operations but also provides a reliable basis for optimizing tunneling parameters. This provides a basis for dynamically adjusting thrust force for the shield machine in real time, thereby improving tunneling efficiency, reducing energy consumption, and ensuring construction safety.

[0022] In this embodiment of the present application, the supervised training step of the propulsion resistance predictor includes: Based on the shield machine excavation data in the historical time, a sample engineering feature information set, a sample historical propulsion resistance series set and a sample predicted propulsion resistance series set are collected; The sample engineering feature information set, the sample historical propulsion resistance sequence set and the sample predicted propulsion resistance sequence set are used as supervised training data to perform supervised training of the propulsion resistance predictor until convergence, and then tested. When the requirements are met, the training is completed.

[0023] In the embodiment of the present application, the supervised training step of the propulsion resistance predictor utilizes historical data to learn and optimize the propulsion resistance predictor, thereby improving its prediction accuracy and reliability in practical applications.

[0024] Specifically, based on the shield machine excavation data recorded over time, a sample engineering feature information set, a sample historical propulsion resistance sequence set, and a sample predicted propulsion resistance sequence set are collected. The sample engineering feature information set includes information such as soil type (e.g., soft soil, sand, hard rock, etc.), soil moisture, soil hardness, and tunnel depth for multiple sample shield machine construction projects over time. The sample historical propulsion resistance sequence set includes multiple sample historical propulsion resistance sequences recorded during the construction of sample shield machine construction projects over time, collected in the same manner as the current historical propulsion resistance sequence. The sample predicted propulsion resistance sequence set includes multiple propulsion resistance sequences formed by propulsion resistance at multiple moments after the multiple sample historical propulsion resistance sequences, labeled as multiple sample predicted propulsion resistance sequences, i.e., the propulsion resistance sequence after each sample historical propulsion resistance sequence.

[0025] After collecting the above sample data, the sample engineering feature information set, the sample historical propulsion resistance sequence set, and the sample predicted propulsion resistance sequence set are used as supervised training data. The sample engineering feature information set and the sample historical propulsion resistance sequence set are used as input data, and the sample predicted propulsion resistance sequence set is used as output data for training the propulsion resistance predictor. The supervised training data is divided into a training set and a test set, for example, with a ratio of 4:1 between the training set and the test set. For example, a feedforward neural network in machine learning is used to train the propulsion resistance predictor. The propulsion resistance predictor comprises an input layer, a hidden layer, and an output layer. The number of nodes in the input layer is equal to the number of data points in the engineering feature information and the historical propulsion resistance sequence. For example, if the engineering feature information includes four feature data points and the historical propulsion resistance sequence includes 10 historical propulsion resistances, the number of nodes in the input layer is 10. The hidden layer comprises multiple neural nodes and uses the Reluctant Unit (ReLU) activation function. The number of nodes in the output layer is equal to the number of data points in the predicted propulsion resistance sequence, for example, 10. During training, the input data in the supervised training data is input, processed, and output is transmitted to obtain an output value. The error ratio between the output value and the corresponding sample propulsion resistance sequence in the output data is calculated as the loss. Based on the loss, gradient descent is used to adjust the weights of the multiple neural nodes in the hidden layer to reduce the loss until the loss is less than the required value, for example, the error ratio of all predicted propulsion resistances is less than 5%. Furthermore, iterative training is performed using multiple sets of supervised training data to gradually reduce the error until the error of the propulsion resistance predictor is less than the required value, for example, less than 4%, and the training converges.

[0026] After the training is completed, the propulsion resistance predictor is tested using the test set to obtain its accuracy and determine whether it meets the requirements, for example, whether the accuracy is greater than or equal to 96%. If so, the training is completed.

[0027] Based on the trained propulsion resistance predictor, the current historical propulsion resistance sequence can be input and the predicted propulsion resistance sequence at multiple future moments in the future can be predicted and output.

[0028] S20: Optimizing the propulsion force of the shield machine according to the predicted propulsion resistance sequence, with the goal of increasing propulsion speed and improving soil layer stability, to obtain an optimized propulsion force; In this embodiment, the thrust force of the shield machine is optimized based on the predicted thrust resistance sequence, with the goal of increasing thrust speed and improving soil stability. Excessive thrust force can reduce soil stability, while too little thrust force can reduce thrust speed, affecting construction efficiency. Therefore, a combination of these two optimizations is used to improve construction efficiency and safety when optimizing thrust force.

[0029] Step S20 in the method provided in the embodiment of the present application includes: Acquiring a propulsion force adjustment space of the shield machine, and randomly generating a first propulsion force within the propulsion force adjustment space; Propelling speed prediction and soil layer stability grade prediction are performed based on the first propulsion force and the predicted propulsion resistance sequence to obtain a first predicted propulsion speed sequence and a first predicted soil layer stability grade sequence, wherein a set of sample propulsion forces, a set of sample propulsion resistances, and a set of sample propulsion speeds and a set of sample soil layer stability grades are collected from historical excavation data, and a propulsion prediction channel is trained to perform propulsion prediction; calculating a first propulsion adaptability of the first propulsion force according to the first predicted propulsion speed sequence and the first predicted soil layer stability grade sequence; The shield machine propulsion force is continuously optimized within the propulsion force adjustment space until convergence, and the propulsion force with the maximum propulsion adaptability is output as the optimized propulsion force.

[0030] In the embodiments of this application, propulsion force refers to the force applied by the shield machine's propulsion system (typically powered by a hydraulic system or electric propulsion device) to propel the shield machine forward along the tunnel. A propulsion force adjustment range is defined, namely, the range of adjustable propulsion force for the shield machine. The propulsion force range includes the minimum and maximum available propulsion force, for example, a minimum propulsion force of 50 kN and a maximum propulsion force of 300 kN. Using the minimum and maximum propulsion forces as endpoints, an interval is constructed to obtain the propulsion force adjustment range.

[0031] A first propulsion force is randomly generated in the propulsion force adjustment space, for example, a propulsion force of 100 kN is randomly generated.

[0032] Based on the first propulsion force and the predicted propulsion resistance sequence, propulsion speed prediction and soil layer stability grade prediction are performed to obtain a first predicted propulsion speed sequence and a first predicted soil layer stability grade sequence. Exemplarily, a propulsion prediction channel is trained based on a feedforward neural network in machine learning to perform current propulsion speed prediction and propulsion soil layer stability prediction.

[0033] For example, historical excavation data is collected from different construction projects to determine the thrust force, thrust resistance experienced by the shield machine, and the thrust speed and soil stability grade under the influence of the thrust force and thrust resistance. This yields a set of sample thrust forces, a set of sample thrust resistances, and a set of sample thrust speeds and soil stability grades. The thrust speed is the speed at which the shield machine cutterhead advances during excavation, measured in millimeters per minute (mm / min). For example, a thrust force of 200 kN and a thrust resistance of 175 kN yields a thrust speed of 150 mm / min, which can be measured. The soil stability grade is, for example, 1-5. The larger the grade, the more stable the soil layer. The soil stability grade can be rated by shield construction technicians based on the propulsion force and propulsion resistance. For example, the greater the difference between the propulsion force and the propulsion resistance, the smaller the soil stability grade, and vice versa. For example, when the difference between the propulsion force and the propulsion resistance is 0 or a negative number, the soil stability grade is 5. When the difference is 0~50kN, the soil stability grade is 4. And so on, a sample soil stability grade set is obtained.

[0034] Using a set of sample propulsion forces and a set of sample propulsion resistances as input data, and a set of sample propulsion velocities and a set of sample soil stability grades as output data, a propulsion prediction channel is trained based on a feedforward neural network to perform propulsion prediction. The training method for the propulsion prediction channel is the same as the training process for the propulsion resistance predictor described above, except for the specific training data. The input data is fed into the propulsion prediction channel constructed based on the feedforward network, and the output value is obtained. Based on the percentage difference between the output value and the sample propulsion velocities and sample soil stability grades in the output data, the network parameters are adjusted to reduce the percentage difference until the required accuracy is met. Iterative training is then performed until the prediction accuracy for all sample data meets the required accuracy, for example, greater than 95%, completing the training.

[0035] The current first propulsion force is combined with multiple predicted propulsion resistances in the predicted propulsion resistance sequence and input into the trained propulsion prediction channel to output multiple first predicted propulsion speeds and multiple first predicted soil layer stability levels. Combined with the future moments corresponding to the multiple predicted propulsion resistances, the first predicted propulsion speed sequence and the first predicted soil layer stability level sequence are arranged.

[0036] Based on the first predicted propulsion speed sequence and the first predicted soil layer stability level sequence, a first propulsion fitness is calculated to obtain a first propulsion force for the purpose of increasing propulsion speed and soil layer stability. The magnitude of the first propulsion fitness is positively correlated with the magnitudes of propulsion speed, soil layer stability, etc.

[0037] In the embodiment of the present application, calculating the first propulsion adaptability of the first propulsion force according to the first predicted propulsion speed sequence and the first predicted soil layer stability grade sequence includes: Calculating the mean of the historical propulsion resistance sequence and the predicted propulsion resistance sequence to obtain an average propulsion resistance; Obtaining a propulsion resistance threshold, multiplying the ratio of the average propulsion resistance to the propulsion resistance threshold by a preset stability weight to obtain an adjusted stability weight, and subtracting the adjusted stability weight from 1 to obtain a speed weight; According to the adjusted stability weight and speed weight, the first predicted propulsion speed sequence and the first predicted soil layer stability grade sequence are used to calculate the first propulsion fitness of the first propulsion force, as shown in the following formula: ; Among them, PRA is the promotion fitness, and is the speed weight and the adjustment stability weight, M is the number of multiple future moments, is the predicted propulsion speed at the ith future moment in the predicted propulsion speed sequence, is the preset propulsion speed, To predict the soil stability level at the ith future moment in the soil stability level sequence, It is the preset soil stability grade.

[0038] In the embodiment of the present application, the average of all propulsion resistances in the historical propulsion resistance sequence and the predicted propulsion resistance sequence is calculated to obtain the average propulsion resistance.

[0039] Furthermore, a propulsion resistance threshold is obtained. For example, the propulsion resistance threshold is the average of all propulsion resistances in all construction data records of the shield machine in the historical time, reflecting the average propulsion resistance level of the shield machine.

[0040] The ratio of the average propulsion resistance to the propulsion resistance threshold is multiplied by the preset stability weight to obtain the adjusted stability weight. The preset stability weight is the weight of the importance of soil layer stability in the preset propulsion force adjustment, for example, 0.5. The greater the current average propulsion resistance, the more important the stability of the soil layer is during the propulsion construction. The ratio of the average propulsion resistance to the propulsion resistance threshold is multiplied by the preset stability weight to obtain the adjusted adjusted stability weight. The greater the average propulsion resistance, the greater the preset stability weight, and the greater the importance of soil layer stability in the optimization. For example, if the ratio of the average propulsion resistance to the propulsion resistance threshold is 1.1, the adjusted stability weight is 1.1*0.5=0.55.

[0041] For example, in soft soil layers, lower propulsion resistance may result in a greater weighting of velocity, whereas in hard rock layers, greater propulsion resistance may result in a greater weighting of adjusted stability.

[0042] The speed weight is calculated by subtracting the adjusted stability weight from 1. For example, 1 - 0.55 = 0.45. The speed weight is the weight that accounts for the importance of propulsion speed in propulsion force regulation. The larger the speed weight, the greater its importance in shield machine propulsion force regulation. The sum of the speed weight and the adjusted stability weight is 1.

[0043] According to the adjustment of stability weight and speed weight, the first predicted propulsion speed sequence and the first predicted soil layer stability grade sequence are used to calculate the first propulsion fitness of the first propulsion force, as follows: ; Among them, PRA is the promotion fitness, and is the speed weight and the adjustment stability weight, M is the number of multiple future moments, is the predicted propulsion speed at the ith future moment in the predicted propulsion speed sequence, The preset advancing speed is, for example, the average advancing speed in the historical period, for example, 150 mm / min. To predict the soil stability level at the ith future moment in the soil stability level sequence, is a preset soil layer stability level, for example, an average soil layer stability level, such as 3. The greater the predicted advancing speed in the predicted advancing speed sequence, the greater the predicted soil layer stability level in the predicted soil layer stability level sequence, and the greater the advancing adaptability.

[0044] Furthermore, based on the aforementioned approach, the shield machine's thrust force is optimized within the thrust force adjustment space. Specifically, the thrust force is randomly generated, the thrust speed and soil stability level are predicted, and the thrust fitness is calculated and optimized until convergence occurs. For example, the optimization cycle reaches 100 times. After convergence, the thrust force with the highest thrust fitness is output as the optimized thrust force, completing the optimization.

[0045] Through the above steps, the thrust of the shield machine can be optimized while simultaneously considering the improvement of thrust speed and soil stability. The current thrust resistance level is taken into account, and the weights of thrust speed and soil stability in the configuration optimization are configured to improve the accuracy of thrust optimization and construction safety. During the optimization process, the thrust speed and soil stability level at each future moment have varying degrees of influence on the final thrust fitness. Calculating thrust fitness based on the thrust speed and soil stability level predicted at multiple future moments can consider the thrust situation over a period of time, providing foresight and improving construction stability. Based on the calculated thrust fitness, the thrust of the shield machine is continuously optimized until the optimal thrust configuration is achieved, thereby achieving an efficient and safe excavation process.

[0046] S30: performing a fluctuation analysis on the historical propulsion resistance sequence and the predicted propulsion resistance sequence, and calculating the minimum fluctuation propulsion resistance and the maximum fluctuation propulsion resistance, and performing propulsion speed analysis and soil layer stability analysis respectively to obtain minimum propulsion speed and minimum soil layer stability information; In the embodiment of the present application, due to the changes in the soil structure at different depths during shield construction, the propulsion resistance will fluctuate to a certain extent, resulting in local large or small changes in the propulsion resistance. In the process of adjusting the propulsion resistance, this change needs to be taken into account, and the propulsion force needs to be further adjusted and optimized to avoid a sudden change in the propulsion resistance leading to a decrease in construction speed or a decrease in soil stability, which affects the construction quality.

[0047] Step S30 in the method provided in the embodiment of the present application includes: Randomly selecting a plurality of first random propulsion resistances and a plurality of second random propulsion resistances from the historical propulsion resistance sequence and the predicted propulsion resistance sequence; calculating a deviation amplitude between an average of the plurality of first random propulsion resistances and an average of the plurality of second random propulsion resistances to obtain a first propulsion resistance fluctuation amplitude; Continue to calculate to obtain multiple propulsion resistance fluctuation amplitudes, calculate the average, and obtain the average propulsion resistance fluctuation amplitude; extracting the maximum propulsion resistance and the minimum propulsion resistance in the historical propulsion resistance sequence and the predicted propulsion resistance sequence; Using the average propulsion resistance fluctuation amplitude, positive compensation calculation and negative compensation calculation are performed on the maximum propulsion resistance and the minimum propulsion resistance, respectively, to obtain the maximum fluctuating propulsion resistance and the minimum fluctuating propulsion resistance; The maximum fluctuating propulsion resistance and the minimum fluctuating propulsion resistance are respectively combined with the optimized propulsion force and input into the propulsion prediction channel, and the prediction output obtains the minimum soil layer stability information and the minimum propulsion speed.

[0048] In this embodiment, multiple first random propulsion resistances and multiple second random propulsion resistances are randomly selected from the aforementioned historical propulsion resistance sequence and predicted propulsion resistance sequence. The number of the multiple first random propulsion resistances and the number of the multiple second random propulsion resistances are the same, and each randomly selected random propulsion resistance may be a historical propulsion resistance or a predicted propulsion resistance. This allows analysis of propulsion resistance fluctuations at multiple historical moments and multiple future moments.

[0049] Calculate the average of the multiple first random propulsion resistances and the multiple second random propulsion resistances to obtain the average of the first random propulsion resistances and the average of the second random propulsion resistances. Calculate the deviation between the two, for example, by calculating the ratio of the absolute value of the difference between the average of the first random propulsion resistances and the average of the second random propulsion resistances to the average of the second random propulsion resistances, to obtain the first propulsion resistance fluctuation amplitude. A larger first propulsion resistance fluctuation amplitude indicates a greater overall difference between the multiple first random propulsion resistances and the multiple second random propulsion resistances, and a greater propulsion resistance fluctuation amplitude.

[0050] Continue to randomly select multiple groups of first random propulsion resistances and second random propulsion resistances, and calculate the propulsion resistance fluctuation amplitudes in the same way to obtain multiple propulsion resistance fluctuation amplitudes.

[0051] The average of multiple propulsion resistance fluctuations is then calculated to obtain the average propulsion resistance fluctuation. A larger average propulsion resistance fluctuation indicates a greater disparity between the historical propulsion resistance series and the predicted propulsion resistance series. This larger fluctuation indicates a greater potential change in propulsion resistance at multiple future moments.

[0052] The maximum and minimum values ​​of the propulsion resistance in the historical propulsion resistance sequence and the predicted propulsion resistance sequence are extracted to obtain the maximum propulsion resistance and the minimum propulsion resistance.

[0053] The average propulsion resistance fluctuation amplitude is used to perform positive and negative compensation calculations on the maximum and minimum propulsion resistances, respectively. For example, the maximum propulsion resistance is compensated amplified, while the minimum propulsion resistance is compensated reduced. This allows the maximum and minimum fluctuating propulsion resistances to be obtained under extreme conditions where the propulsion resistance may fluctuate over multiple future moments. In this way, the maximum and minimum fluctuating propulsion resistances are obtained.

[0054] Exemplarily, the maximum propulsion resistance is obtained by multiplying the sum of 1 and the average propulsion resistance fluctuation amplitude by the maximum propulsion resistance, and the minimum propulsion resistance is obtained by multiplying the difference between 1 and the average propulsion resistance fluctuation amplitude by the minimum propulsion resistance.

[0055] Furthermore, the maximum fluctuation propulsion resistance and the minimum fluctuation propulsion resistance after fluctuation analysis compensation are respectively combined with the optimized propulsion force obtained by preliminary optimization in step S20 as input data, and input into the propulsion prediction channel in the aforementioned content, and the output is the propulsion speed corresponding to the maximum fluctuation propulsion resistance, that is, the minimum propulsion speed. Among them, the greater the propulsion resistance, the greater the obstruction to the propulsion of the shield machine, and the smaller the propulsion speed. The minimum propulsion speed reflects the minimum propulsion speed under the influence of propulsion resistance fluctuation when the shield is advanced according to the optimized propulsion force. And the minimum fluctuation propulsion resistance after fluctuation analysis compensation is combined with the optimized propulsion force as input data, and input into the propulsion prediction channel in the aforementioned content, and the output is the soil layer stability level corresponding to the minimum fluctuation propulsion resistance, that is, the minimum soil layer stability information. Among them, the smaller the propulsion resistance, the smaller the soil layer stability level, and the minimum soil layer stability information reflects the minimum soil layer stability level under the influence of propulsion resistance fluctuation when the shield is advanced according to the optimized propulsion force.

[0056] By analyzing the fluctuation of the propulsion resistance and compensating the calculation to obtain the maximum and minimum propulsion resistance under the fluctuation condition, the minimum soil layer stability information and minimum propulsion speed during the propulsion under the fluctuation condition are further analyzed to reflect the impact of the propulsion resistance fluctuation on the propulsion stability, which will further serve as the basis for subsequent adjustments to the optimized propulsion force.

[0057] S40: According to the minimum propulsion speed and the minimum soil layer stability information, the optimized propulsion force is assisted optimized to obtain the optimal propulsion force, and the tunneling parameters of the shield machine are adjusted.

[0058] In an embodiment of the present application, the minimum propulsion speed and minimum soil layer stability information caused by the fluctuation of the propulsion resistance are taken into consideration, and the optimized propulsion force optimized in the above content is further optimized and adjusted to reduce the impact of the propulsion speed and soil layer stability caused by the fluctuation of the propulsion resistance.

[0059] Step S40 in the method provided in the embodiment of the present application includes: Obtaining the optimized predicted propulsion speed and the optimized predicted soil layer stability grade of the optimized propulsion force; Calculating a ratio of the minimum propulsion speed to the optimized predicted propulsion speed to obtain a speed attenuation amplitude, and adding 1 to the speed attenuation amplitude to obtain a speed correction coefficient; Calculating the ratio of the minimum soil layer stability information to the optimized predicted soil layer stability grade to obtain a stable attenuation amplitude, and subtracting the stable attenuation amplitude from 1 to obtain a stable correction coefficient; The speed correction coefficient is multiplied by the optimized propulsion force to obtain the speed optimized propulsion force, and the stability correction coefficient is multiplied by the optimized propulsion force to obtain the stability optimized propulsion force; The average of the speed-optimized propulsion force and the stability-optimized propulsion force is calculated to obtain the optimal propulsion force.

[0060] In an embodiment of the present application, the optimized predicted propulsion speed and the optimized predicted soil layer stability level of the optimized propulsion force are obtained. Specifically, the predicted propulsion speed sequence and the predicted soil layer stability level sequence obtained by inputting the optimized propulsion force into the propulsion prediction channel output in combination with multiple predicted propulsion resistances in the predicted propulsion resistance sequence are obtained, and then the mean of the predicted propulsion speed sequence and the predicted soil layer stability level sequence are calculated to obtain the average predicted propulsion speed and the average predicted soil layer stability level of the optimized propulsion force as the optimized predicted propulsion speed and the optimized predicted soil layer stability level.

[0061] The ratio of the minimum propulsion speed to the optimized predicted propulsion speed is calculated and used as the speed attenuation amplitude, i.e., the amplitude at which the propulsion speed changes to the minimum due to possible propulsion resistance fluctuations. 1 plus the speed attenuation amplitude is used as the speed correction coefficient.

[0062] Furthermore, the ratio of the minimum soil stability information to the optimized predicted soil stability grade is calculated. This ratio is used as the stability attenuation amplitude, which is the amplitude at which the soil stability grade changes to the minimum due to possible propulsion resistance fluctuations. The stability correction factor is obtained by subtracting the stability attenuation amplitude from 1.

[0063] The speed correction coefficient is multiplied by the optimized propulsion force to amplify the optimized propulsion force to obtain the speed optimized propulsion force, so as to increase the propulsion speed when possible propulsion resistance fluctuation occurs and reduce the propulsion speed reduction caused by the propulsion resistance fluctuation.

[0064] And adopt a stability correction coefficient, multiply it by the optimized propulsion force, reduce the optimized propulsion force, and obtain a stable optimized propulsion force, so as to reduce the propulsion force when possible propulsion resistance fluctuations occur, improve the soil stability level, and reduce the reduction in soil stability caused by propulsion resistance fluctuations.

[0065] During the adjustment process, in addition to improving the propulsion speed and soil stability level under fluctuation conditions, the soil stability level and propulsion speed when no propulsion resistance fluctuation occurs will also be affected. Therefore, the average of the adjusted speed-optimized propulsion force and the stability-optimized propulsion force is calculated, and the speed-optimized propulsion force and the stability-optimized propulsion force adjusted in the two directions are integrated to obtain the optimal propulsion force after the optimal auxiliary optimization processing. This can ensure the propulsion speed and soil stability to the greatest extent when possible propulsion resistance fluctuations occur, thereby improving the stability, efficiency and safety of shield construction.

[0066] The embodiments of the present application achieve the following technical effects through the above specific implementation methods: The shield machine excavation parameter adjustment method of the present invention, by introducing historical propulsion resistance sequences and engineering feature information, and combining a prediction model to accurately predict the propulsion resistance at multiple moments in the future, can accurately predict the propulsion resistance in the future. By analyzing the predicted propulsion resistance sequence, the present invention can optimize the propulsion force of the shield machine, reasonably increase the propulsion speed, and effectively balance the stability of the soil layer. Further, the propulsion force is optimized and adjusted according to the predicted propulsion resistance sequence, thereby improving the propulsion efficiency while ensuring construction safety. In addition, the fluctuation analysis based on historical and predicted propulsion resistance information is combined to further optimize the propulsion force, thereby improving the overall operation efficiency. Through the technical solution of this application, the accuracy and flexibility of the shield machine excavation parameter adjustment are improved, the propulsion force can be adjusted according to the real-time soil layer conditions, the propulsion speed can be optimized, the soil layer during excavation can be guaranteed to be stable, and the safety and efficiency of the shield construction process can be ensured, thereby improving the adaptability of the shield machine under complex geological conditions. Through the adjustment and optimization of the shield machine propulsion force parameters, the technical effect of improving the efficiency, stability and safety of shield construction is achieved.

[0067] Example 2 like Figure 2 As shown, based on the same inventive concept as the shield machine excavation parameter adjustment method provided in Example 1, an embodiment of the present invention provides a shield machine excavation parameter adjustment system, the system comprising: The propulsion resistance prediction module 11 is used to record the propulsion resistance information of multiple historical moments in the past during the tunneling process of the shield machine, obtain a historical propulsion resistance sequence, and predict the propulsion resistance at multiple future moments in the future based on the engineering characteristic information of the tunneling project to obtain a predicted propulsion resistance sequence; A propulsion force optimization module 12 is configured to optimize the propulsion force of the shield machine according to the predicted propulsion resistance sequence, with the goal of increasing propulsion speed and improving soil stability, to obtain an optimized propulsion force; The fluctuation analysis module 13 is used to perform fluctuation analysis on the historical propulsion resistance sequence and the predicted propulsion resistance sequence, calculate the minimum fluctuation propulsion resistance and the maximum fluctuation propulsion resistance, and perform propulsion speed analysis and soil layer stability analysis respectively to obtain the minimum propulsion speed and minimum soil layer stability information; The auxiliary optimization module 14 is used to perform auxiliary optimization on the optimized propulsion force according to the minimum propulsion speed and the minimum soil layer stability information, obtain the optimal propulsion force, and adjust the tunneling parameters of the shield machine.

[0068] Furthermore, the propulsion resistance prediction module 11 is further configured to: Obtain engineering characteristic information of tunneling projects; During the tunneling process of the shield machine, the propulsion resistance information at multiple historical moments in the past is recorded to obtain the historical propulsion resistance sequence; The historical propulsion resistance sequence is input into a pre-trained propulsion resistance predictor, and the propulsion resistance predictions at the multiple future moments are obtained by prediction output to obtain a predicted propulsion resistance sequence, wherein the propulsion resistance predictor is obtained through supervised training.

[0069] The supervised training step of the propulsion resistance predictor includes: Based on the shield machine excavation data in the historical time, a sample engineering feature information set, a sample historical propulsion resistance series set and a sample predicted propulsion resistance series set are collected; The sample engineering feature information set, the sample historical propulsion resistance sequence set and the sample predicted propulsion resistance sequence set are used as supervised training data to perform supervised training of the propulsion resistance predictor until convergence, and then tested. When the requirements are met, the training is completed.

[0070] Furthermore, the propulsion optimization module 12 is further configured to: Acquiring a propulsion force adjustment space of the shield machine, and randomly generating a first propulsion force within the propulsion force adjustment space; Propelling speed prediction and soil layer stability grade prediction are performed based on the first propulsion force and the predicted propulsion resistance sequence to obtain a first predicted propulsion speed sequence and a first predicted soil layer stability grade sequence, wherein a set of sample propulsion forces, a set of sample propulsion resistances, and a set of sample propulsion speeds and a set of sample soil layer stability grades are collected from historical excavation data, and a propulsion prediction channel is trained to perform propulsion prediction; calculating a first propulsion adaptability of the first propulsion force according to the first predicted propulsion speed sequence and the first predicted soil layer stability grade sequence; The shield machine propulsion force is continuously optimized within the propulsion force adjustment space until convergence, and the propulsion force with the maximum propulsion adaptability is output as the optimized propulsion force.

[0071] The first propulsion adaptability of the first propulsion force is calculated based on the first predicted propulsion speed sequence and the first predicted soil layer stability grade sequence, including: Calculating the mean of the historical propulsion resistance sequence and the predicted propulsion resistance sequence to obtain an average propulsion resistance; Obtaining a propulsion resistance threshold, multiplying the ratio of the average propulsion resistance to the propulsion resistance threshold by a preset stability weight to obtain an adjusted stability weight, and subtracting the adjusted stability weight from 1 to obtain a speed weight; According to the adjusted stability weight and speed weight, the first predicted propulsion speed sequence and the first predicted soil layer stability grade sequence are used to calculate the first propulsion fitness of the first propulsion force, as shown in the following formula: ; Among them, PRA is the promotion fitness, and is the speed weight and the adjustment stability weight, M is the number of multiple future moments, is the predicted propulsion speed at the ith future moment in the predicted propulsion speed sequence, is the preset propulsion speed, To predict the soil stability level at the ith future moment in the soil stability level sequence, It is the preset soil stability grade.

[0072] Furthermore, the fluctuation analysis module 13 is further configured to: Randomly selecting a plurality of first random propulsion resistances and a plurality of second random propulsion resistances from the historical propulsion resistance sequence and the predicted propulsion resistance sequence; calculating a deviation amplitude between an average of the plurality of first random propulsion resistances and an average of the plurality of second random propulsion resistances to obtain a first propulsion resistance fluctuation amplitude; Continue to calculate to obtain multiple propulsion resistance fluctuation amplitudes, calculate the average, and obtain the average propulsion resistance fluctuation amplitude; extracting the maximum propulsion resistance and the minimum propulsion resistance in the historical propulsion resistance sequence and the predicted propulsion resistance sequence; Using the average propulsion resistance fluctuation amplitude, positive compensation calculation and negative compensation calculation are performed on the maximum propulsion resistance and the minimum propulsion resistance, respectively, to obtain the maximum fluctuating propulsion resistance and the minimum fluctuating propulsion resistance; The maximum fluctuating propulsion resistance and the minimum fluctuating propulsion resistance are respectively combined with the optimized propulsion force and input into the propulsion prediction channel, and the prediction output obtains the minimum soil layer stability information and the minimum propulsion speed.

[0073] Furthermore, the auxiliary optimization module 14 is further configured to: obtain the optimized predicted propulsion speed and the optimized predicted soil stability grade of the optimized propulsion force; Calculating a ratio of the minimum propulsion speed to the optimized predicted propulsion speed to obtain a speed attenuation amplitude, and adding 1 to the speed attenuation amplitude to obtain a speed correction coefficient; Calculating the ratio of the minimum soil layer stability information to the optimized predicted soil layer stability grade to obtain a stable attenuation amplitude, and subtracting the stable attenuation amplitude from 1 to obtain a stable correction coefficient; The speed correction coefficient is multiplied by the optimized propulsion force to obtain the speed optimized propulsion force, and the stability correction coefficient is multiplied by the optimized propulsion force to obtain the stability optimized propulsion force; The average of the speed-optimized propulsion force and the stability-optimized propulsion force is calculated to obtain the optimal propulsion force.

[0074] See also Figure 3 , Figure 3 Schematic diagram of an embodiment of an electronic device provided by an embodiment of the present invention. Figure 3 As shown, an embodiment of the present invention provides an electronic device 100, including a memory 110, a processor 120, and a first computer program 111 stored in the memory 110 and executable on the processor 120. When the processor 120 executes the first computer program 111, the steps of the method in Example 1 are implemented.

[0075] See also Figure 4 , Figure 4 Schematic diagram of an embodiment of a computer-readable storage medium provided in an embodiment of the present invention. Figure 4 As shown, this embodiment provides a computer-readable storage medium 200 on which a second computer program 211 is stored. When the second computer program 211 is executed by a processor, the steps of the method in embodiment 1 are implemented.

[0076] It should be noted that, in the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0077] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0078] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0079] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0080] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0081] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0082] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A method for adjusting tunneling parameters of a shield machine, characterized in that: The method comprises: During the tunneling process of the shield machine, the propulsion resistance information in multiple historical moments in the past is recorded to obtain the historical propulsion resistance sequence. Combined with the engineering characteristic information of the tunneling project, the propulsion resistance at multiple future moments is predicted to obtain the predicted propulsion resistance sequence. According to the predicted propulsion resistance sequence, the propulsion force of the shield machine is optimized for the purpose of increasing the propulsion speed and improving the stability of the soil layer to obtain an optimized propulsion force; Performing fluctuation analysis on the historical advancement resistance sequence and the predicted advancement resistance sequence, and calculating and obtaining the minimum fluctuation advancement resistance and the maximum fluctuation advancement resistance, respectively performing advancement speed analysis and soil layer stability analysis, and obtaining the minimum advancement speed and minimum soil layer stability information; According to the minimum propulsion speed and the minimum soil layer stability information, the optimized propulsion force is auxiliary optimized to obtain the optimal propulsion force, and the tunneling parameters of the shield machine are adjusted.

2. The shield machine excavation parameter adjustment method according to claim 1, characterized in that: During the tunneling process of the shield machine, the propulsion resistance information in multiple historical moments in the past is recorded to obtain the historical propulsion resistance sequence. Combined with the engineering characteristic information of the tunneling project, the propulsion resistance at multiple future moments is predicted to obtain the predicted propulsion resistance sequence, including: Obtain engineering characteristic information of excavation engineering; During the tunneling process of the shield machine, the advancement resistance information in multiple historical moments in the past is recorded to obtain the historical advancement resistance sequence; The historical propulsion resistance sequence is input into a pre-trained propulsion resistance predictor, and the propulsion resistance predictions at the multiple future moments are obtained by prediction output to obtain a predicted propulsion resistance sequence, wherein the propulsion resistance predictor is obtained through supervised training.

3. The shield machine excavation parameter adjustment method according to claim 2, characterized in that: The supervised training steps of the propulsion resistance predictor include: According to the shield machine excavation data in the historical time, the sample engineering characteristic information set, the sample historical advancement resistance sequence set and the sample predicted advancement resistance sequence set are collected; The sample engineering feature information set, the sample historical propulsion resistance sequence set and the sample predicted propulsion resistance sequence set are used as supervised training data to perform supervised training of the propulsion resistance predictor until convergence, and then tested. When the requirements are met, the training is completed.

4. The shield machine excavation parameter adjustment method according to claim 1, characterized in that: According to the predicted propulsion resistance sequence, the propulsion force of the shield machine is optimized for the purpose of increasing the propulsion speed and improving the stability of the soil layer to obtain the optimized propulsion force, including: Acquiring a propulsion force adjustment space of the shield machine, and randomly generating a first propulsion force in the propulsion force adjustment space; According to the first propulsion force and the predicted propulsion resistance sequence, propulsion speed prediction and soil layer stability grade prediction are performed to obtain a first predicted propulsion speed sequence and a first predicted soil layer stability grade sequence, wherein a sample propulsion force set, a sample propulsion resistance set, and a sample propulsion speed and a sample soil layer stability grade set are collected in historical excavation data, a propulsion prediction channel is trained, and propulsion prediction is performed; Calculating a first propulsion fitness of the first propulsion force according to the first predicted propulsion speed sequence and the first predicted soil layer stability grade sequence; Continue to optimize the thrust of the shield machine in the thrust adjustment space until convergence, and output the thrust with the maximum thrust fitness as the optimized thrust.

5. The shield machine excavation parameter adjustment method according to claim 4, characterized in that: Calculating a first propulsion fitness of the first propulsion force according to the first predicted propulsion speed sequence and the first predicted soil layer stability grade sequence includes: Calculating the mean of the historical propulsion resistance sequence and the predicted propulsion resistance sequence to obtain the average propulsion resistance; Acquire a propulsion resistance threshold, multiply the ratio of the average propulsion resistance to the propulsion resistance threshold by a preset stability weight to obtain an adjusted stability weight, and subtract the adjusted stability weight from 1 to obtain a speed weight; According to the adjusted stability weight and speed weight, the first predicted propulsion speed sequence and the first predicted soil layer stability grade sequence are used to calculate the first propulsion fitness of the first propulsion force, as shown in the following formula: ； Among them, PRA is the promotion fitness, and is the speed weight and the adjustment stability weight, M is the number of multiple future moments, is the predicted propulsion speed at the ith future moment in the predicted propulsion speed sequence, For the preset propulsion speed, To predict the soil stability level at the ith future moment in the soil stability level sequence, It is the preset soil stability grade.

6. The shield machine excavation parameter adjustment method according to claim 5, characterized in that: The historical advancement resistance sequence and the predicted advancement resistance sequence are subjected to fluctuation analysis, and the minimum fluctuation advancement resistance and the maximum fluctuation advancement resistance are calculated and obtained. The advancement speed analysis and the soil layer stability analysis are respectively performed to obtain the minimum advancement speed and the minimum soil layer stability information, including: Randomly selecting a plurality of first random propulsion resistances and a plurality of second random propulsion resistances from the historical propulsion resistance sequence and the predicted propulsion resistance sequence; Calculating the deviation amplitude of the mean values ​​of the plurality of first random propulsion resistances and the plurality of second random propulsion resistances to obtain the first propulsion resistance fluctuation amplitude; Continue to calculate to obtain multiple propulsion resistance fluctuation amplitudes, calculate the average, and obtain the average propulsion resistance fluctuation amplitude; Extracting the maximum propulsion resistance and the minimum propulsion resistance in the historical propulsion resistance sequence and the predicted propulsion resistance sequence; Using the average propulsion resistance fluctuation amplitude to perform positive compensation calculation and negative compensation calculation on the maximum propulsion resistance and the minimum propulsion resistance, respectively, to obtain the maximum fluctuating propulsion resistance and the minimum fluctuating propulsion resistance; The maximum fluctuating propulsion resistance and the minimum fluctuating propulsion resistance are respectively input into the propulsion prediction channel in combination with the optimized propulsion force, and the prediction output obtains the minimum soil layer stability information and the minimum propulsion speed.

7. The shield machine excavation parameter adjustment method according to claim 6, characterized in that: According to the minimum propulsion speed and the minimum soil layer stability information, the optimized propulsion force is auxiliary optimized to obtain the optimal propulsion force, including: Obtaining the optimized predicted propulsion speed and the optimized predicted soil stability grade of the optimized propulsion force; Calculating the ratio of the minimum propulsion speed to the optimized predicted propulsion speed to obtain a speed attenuation amplitude, and adding 1 to the speed attenuation amplitude to obtain a speed correction coefficient; Calculating the ratio of the minimum soil layer stability information to the optimized predicted soil layer stability grade to obtain a stable attenuation amplitude, and subtracting the stable attenuation amplitude from 1 to obtain a stable correction coefficient; The speed correction coefficient is multiplied by the optimized propulsion force to obtain the speed optimized propulsion force, and the stability correction coefficient is multiplied by the optimized propulsion force to obtain the stable optimized propulsion force; The average of the speed-optimized propulsion force and the stability-optimized propulsion force is calculated to obtain the optimal propulsion force.

8. A shield machine excavation parameter adjustment system, characterized in that: The system comprises: The propulsion resistance prediction module is used to record the propulsion resistance information at multiple historical moments in the past during the tunneling process of the shield machine, obtain the historical propulsion resistance sequence, and combine the engineering characteristic information of the tunneling project to predict the propulsion resistance at multiple future moments in the future to obtain the predicted propulsion resistance sequence; A propulsion force optimization module is used to optimize the propulsion force of the shield machine according to the predicted propulsion resistance sequence, with the purpose of increasing the propulsion speed and improving the stability of the soil layer, so as to obtain an optimized propulsion force; A fluctuation analysis module is used to perform fluctuation analysis on the historical advancement resistance sequence and the predicted advancement resistance sequence, and calculate the minimum fluctuation advancement resistance and the maximum fluctuation advancement resistance, and respectively perform advancement speed analysis and soil layer stability analysis to obtain minimum advancement speed and minimum soil layer stability information; The auxiliary optimization module is used to assist in optimizing the optimized propulsion force according to the minimum propulsion speed and the minimum soil layer stability information, obtain the optimal propulsion force, and adjust the tunneling parameters of the shield machine.

9. An electronic device, characterized in that: include: Memory for storing computer software programs; A processor is used to read and execute the computer software program, thereby implementing the shield machine excavation parameter adjustment method described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The storage medium stores a computer software program, and when the computer software program is executed by the processor, the shield machine excavation parameter adjustment method according to any one of claims 1 to 7 is implemented.

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