Method and equipment for analyzing tunneling efficiency of shield tunneling machine based on big data

Abstract

The invention relates to the field of data processing, in particular to a shield tunneling machine tunneling efficiency analysis method and device based on big data, and the method comprises the steps: obtaining geological parameters, and determining the type of a cutting head of a shield tunneling machine and initial tunneling parameters; controlling a shield tunneling machine to perform tunneling and deslagging according to the initial tunneling parameters, and acquiring tunneling data in real time; performing tunneling efficiency evaluation; judging whether an expected standard is reached or not according to a tunneling efficiency evaluation result, and if the expected standard is not reached, updating tunneling parameters; controlling the shield tunneling machine to continue tunneling and deslagging according to the updated tunneling parameters; comparing the tunneling data before and after the tunneling parameters are updated, and analyzing the change condition of the tunneling efficiency; if the speed increasing amplitude is smaller than the preset amplitude, correcting the updated data; and if the speed increasing amplitude is larger than or equal to the preset amplitude, the updated tunneling parameters are continuously used for controlling the shield tunneling machine to continue tunneling and deslagging. Through dynamic adjustment and a real-time feedback mechanism, the tunneling efficiency and the construction precision are improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to a shield machine excavation efficiency analysis method and equipment based on big data. Background Art

[0002] As a key underground space construction equipment, shield machine is widely used in various tunnel projects such as subway, highway, railway, etc. It completes soil cutting and tunnel excavation through the coordinated work of multiple systems such as propulsion system, cutterhead system, slag removal system, segment assembly system and post-supporting system. In particular, the cutterhead system and propulsion system are the parts with the largest energy consumption and are directly responsible for cutting soil and propulsion of the shield machine.

[0003] Due to the complexity and variability of geological conditions, the tunneling process of shield machines is affected by both geological characteristics and construction control parameters. The tunneling efficiency depends not only on the performance of the equipment, but also on the reasonable setting of geological conditions and construction parameters. Improper control of construction parameters may lead to construction delays and safety hazards. Therefore, establishing a scientific and complete evaluation method for shield machine tunneling efficiency and revealing its relationship with control parameters and geological parameters is of great significance for optimizing tunneling control parameters and improving tunneling efficiency. Summary of the invention

[0004] In view of this, the present invention provides a shield machine excavation efficiency analysis method based on big data, the method comprising:

[0005] Obtain geological parameters and determine the shield machine's cutting head type and initial excavation parameters according to the standard correspondence table. The initial excavation parameters include shield machine direction, thrust, cutting head rotation speed, roller speed, and slag discharge speed.

[0006] Controlling the shield machine to perform excavation and slag removal according to the initial excavation parameters, and acquiring excavation data in real time;

[0007] Evaluate excavation efficiency based on excavation data acquired in real time;

[0008] Determine whether the expected standard is met based on the tunneling efficiency evaluation results. If not, update the tunneling parameters.

[0009] Control the shield machine to continue excavating and discharging slag according to the updated excavation parameters, and obtain new excavation data;

[0010] Compare the excavation data before and after the excavation parameters are updated to analyze the changes in excavation efficiency, including the extent of speed improvement;

[0011] If the speed increase is less than the preset speed, the update data is corrected;

[0012] If the speed increase is greater than or equal to the preset speed, the updated tunneling parameters are continued to be used to control the shield machine to continue tunneling and slag discharge.

[0013] Optionally, before determining the cutting head type and initial excavation parameters of the shield machine according to the standard correspondence table, the method further includes:

[0014] Cleaning the geological parameters;

[0015] Perform statistics on geological parameters in the same section and calculate standard deviations and confidence intervals;

[0016] The target geological parameter is determined based on the standard deviation and the confidence interval.

[0017] Optionally, update the excavation parameters, including:

[0018] Calculating parameter update coefficients according to the initial excavation parameters and the current excavation efficiency evaluation results;

[0019] The initial excavation parameters are updated using the update coefficients.

[0020] Optionally, calculating the parameter update coefficient according to the initial excavation parameter and the current excavation efficiency evaluation result includes:

[0021] The initial excavation parameters and the current excavation efficiency evaluation results are input into an update coefficient calculation model, and the update coefficients corresponding to the parameters are output. The update coefficient calculation model is a machine learning regression model trained by historical excavation data, which is used to predict the update coefficients corresponding to the initial excavation parameters and the current excavation efficiency evaluation results.

[0022] Optionally, if the speed increase is less than a preset speed, the update data is corrected, including:

[0023] Calculate the error between the actual excavation efficiency and the predicted excavation efficiency;

[0024] The update coefficient is adjusted according to the error and the adjustment factor, wherein the adjustment factor is a proportional coefficient for correcting the error, wherein a'=a+e×b, a' is the adjusted update coefficient, a is the update coefficient, e is the adjustment factor, and b is the error;

[0025] The update data is corrected according to the adjusted update coefficient.

[0026] Optionally, the adjustment factor is calculated from the error, where e 2 =k×b 2 , k is a constant.

[0027] Optionally, if the speed increase is less than a preset speed, the update data is corrected, further comprising:

[0028] The update coefficient is adjusted by using the update coefficient output by the multiple update coefficient calculation model, wherein a'=w1×a1+w2×a2+…w i ×a i , a' is the adjusted update coefficient, w1...w i Calculate the model weights for each update coefficient, a1…a i Calculate the update coefficients of the model output for each update coefficient;

[0029] The update data is corrected according to the adjusted update coefficient.

[0030] Optionally, if the tunneling efficiency of the shield machine still does not reach the preset tunneling efficiency after the updated data is corrected, the mechanical parameters and environmental parameters of the shield machine are obtained for risk assessment.

[0031] Optionally, obtain the mechanical parameters and environmental parameters of the shield machine for risk assessment, including:

[0032] Comparing the mechanical parameter and the environmental parameter with corresponding preset ranges respectively;

[0033] If the mechanical parameter exceeds a preset range, it is determined that a corresponding structure of the shield machine fails;

[0034] If the mechanical parameter does not exceed the preset range and the environmental parameter exceeds the preset range, the excavation strategy is adjusted.

[0035] On the other hand, the present invention also provides a shield machine excavation efficiency analysis device based on big data, which includes: a processor and a memory connected to the processor; wherein the memory stores instructions executable by the processor, and the instructions are executed by the processor so that the processor executes the shield machine excavation efficiency analysis method based on big data.

[0036] The present invention first determines the appropriate cutting head type and initial excavation parameters according to the geological parameters and the standard correspondence table to ensure the pertinence and adaptability of the excavation process. During the excavation process, the excavation data is obtained in real time and the efficiency is evaluated, so that it can be discovered in time whether the excavation efficiency meets the expected standard. If it does not meet the standard, the system will automatically update the excavation parameters and control the shield machine excavation again to optimize the construction efficiency. By comparing the excavation data before and after the update and analyzing the speed improvement, the effect of parameter optimization can be effectively verified. If the improvement is insufficient, the updated data is further corrected to ensure the continuous optimization of the excavation parameters. This dynamic adjustment and real-time feedback mechanism not only improves the excavation efficiency and construction accuracy, but also reduces manual intervention, reduces construction risks, realizes the intelligence and efficiency of the shield machine excavation process, and ultimately improves the overall construction quality and safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0038] Figure 1 A flowchart of a shield machine excavation efficiency analysis method based on big data provided by an embodiment of the present invention;

[0039] Figure 2 A specific flow chart of updating excavation parameters in an embodiment of the present invention;

[0040] Figure 3 A specific flow chart of modifying update data in an embodiment of the present invention;

[0041] Figure 4 Another specific flow chart of correcting update data in an embodiment of the present invention. DETAILED DESCRIPTION

[0042] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0043] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance.

[0044] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, it can also be the internal connection of two components, it can be a wireless connection, or it can be a wired connection. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0045] In addition, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0046] like Figure 1 As shown, an embodiment of the present invention provides a shield machine excavation efficiency analysis method based on big data, comprising:

[0047] S101, obtaining geological parameters, and determining the cutting head type and initial excavation parameters of the shield machine according to a standard corresponding table. The initial excavation parameters include shield machine direction, thrust, cutting head rotation speed, roller speed, and slag discharge speed.

[0048] Geological parameters may include the type, hardness, and water content of rock formations. The original data of the type, hardness, and water content of rock formations are obtained in advance through geological exploration instruments (such as geological radar, drilling equipment, etc.). The standard correspondence table is compiled from historical data and engineering experience, and provides recommended parameters under different geological conditions. The geological parameters are compared with the standard correspondence table to select the initial excavation parameters suitable for the geological conditions.

[0049] S102, controlling the shield machine to perform excavation and slag removal according to the initial excavation parameters, and acquiring excavation data in real time.

[0050] The tunneling and slag removal process can: adjust the direction of the shield machine through the navigation system to ensure that it moves forward along the predetermined route; control the thrust through the hydraulic system to overcome the front resistance; adjust the rotation speed of the cutting head to achieve effective crushing of the soil; adjust the roller speed to ensure the smooth advancement of the shield machine. At the same time, start the slag removal equipment, such as screw conveyors and belt conveyors, to ensure that the crushed soil is discharged in time, and monitor and adjust the slag removal speed in real time to keep it matching the tunneling speed.

[0051] S103, evaluating the excavation efficiency based on the excavation data acquired in real time.

[0052] When evaluating efficiency based on real-time excavation data, the main focus is on excavation speed and completion. By monitoring the excavation speed (such as footage per hour) and the actual excavation length completed, and comparing them with the planned target, the progress deviation is calculated. At the same time, the stability of parameters such as thrust, cutting head rotation speed, roller speed and slag discharge speed is analyzed to evaluate the equipment operation status. Combined with these data, the excavation efficiency is comprehensively judged so that parameters can be adjusted in time to optimize the excavation process.

[0053] S104, judging whether the excavation efficiency has reached the expected standard according to the excavation efficiency evaluation result, and if it has not reached the expected standard, updating the excavation parameters.

[0054] The excavation efficiency evaluation result is compared with the expected standard excavation efficiency. If the excavation efficiency evaluation result exceeds the expected standard excavation efficiency, it means that the excavation progress is normal. If the excavation efficiency evaluation result is less than the expected standard excavation efficiency, it means that the excavation progress has not met expectations and the excavation parameters need to be updated.

[0055] S105, controlling the shield machine to continue excavating and discharging slag according to the updated excavation parameters, and obtaining new excavation data.

[0056] The shield machine is controlled to perform excavation and slag discharge according to updated parameters such as shield machine direction, thrust, cutting head rotation speed, roller speed, and slag discharge speed.

[0057] S106, comparing the excavation data before and after the excavation parameters are updated, and analyzing the change in excavation efficiency, including the speed improvement.

[0058] The excavation data before and after the excavation parameters are updated are compared and analyzed, with a focus on the changes in excavation speed. The difference in excavation speed before and after is calculated to measure the extent of improvement in excavation efficiency.

[0059] S107: If the speed increase is less than a preset speed increase, the update data is corrected.

[0060] If the increase in excavation speed after updating the excavation parameters does not reach the preset standard (i.e., less than the preset range), it is considered that the current update optimization effect is not ideal. At this time, it is necessary to correct the update data, adjust the relevant excavation parameters or re-evaluate the update coefficient.

[0061] S108, if the speed increase is greater than or equal to the preset speed, continue to use the updated tunneling parameters to control the shield machine to continue tunneling and slag discharge.

[0062] If the tunneling speed is increased by a preset standard (i.e., greater than or equal to the preset range) after the tunneling parameters are updated, the current update optimization effect is considered significant. At this time, the updated tunneling parameters are continued to be used to control the shield machine for tunneling and slag discharge operations without further correction.

[0063] This embodiment first determines the appropriate cutting head type and initial excavation parameters according to the geological parameters and standard correspondence table to ensure the pertinence and adaptability of the excavation process. During the excavation process, the excavation data is obtained in real time and the efficiency is evaluated, so that it can be discovered in time whether the excavation efficiency meets the expected standard. If it does not meet the standard, the system will automatically update the excavation parameters and control the shield machine excavation again to optimize the construction efficiency. By comparing the excavation data before and after the update and analyzing the speed improvement, the effect of parameter optimization can be effectively verified. If the improvement is insufficient, the updated data is further corrected to ensure the continuous optimization of the excavation parameters. This dynamic adjustment and real-time feedback mechanism not only improves the excavation efficiency and construction accuracy, but also reduces manual intervention, reduces construction risks, realizes the intelligence and efficiency of the shield machine excavation process, and ultimately improves the overall construction quality and safety.

[0064] In one embodiment, in step S101, before determining the cutting head type and initial excavation parameters of the shield machine according to the standard correspondence table, the method further includes:

[0065] Before determining the excavation parameters, it is necessary to pre-process the pre-collected geological parameters, such as using Z-score and other methods to monitor abnormal data. For the detected abnormal values, you can choose to delete or replace them with the mean or median, or use interpolation to correct them.

[0066] Perform statistics on geological parameters in the same section and calculate standard deviations and confidence intervals;

[0067] The target geological parameter is determined according to the standard deviation and the confidence interval. The stability of the geological condition is evaluated according to the standard deviation, and the mean value within the confidence interval is selected as the target geological parameter.

[0068] This embodiment can significantly improve data quality, accurately evaluate geological conditions, optimize excavation parameters, enhance decision-making confidence, and effectively reduce construction risks by cleaning geological parameter data, statistically analyzing, and determining target geological parameters using standard deviations and confidence intervals, thereby improving construction efficiency and success rate in complex tunnel excavation operations.

[0069] In one embodiment, Figure 2 As shown, in step S104, the excavation parameters are updated, specifically including:

[0070] S1041, calculating the parameter update coefficient according to the initial excavation parameters and the current excavation efficiency evaluation result.

[0071] S1042: Update the initial excavation parameters using the update coefficient.

[0072] Specifically, taking thrust as an example, the initial thrust is q, and the updated thrust becomes q', then q'=a×q, where a is the update coefficient of thrust. The update formula of the remaining initial excavation parameters is the same as that of thrust, except for the different update coefficients.

[0073] For example, the thrust is determined to be 4000 KN according to the standard correspondence table, and the update coefficient of the calculated thrust is 1.25, then the updated thrust q'=4000×1.25=5000 KN.

[0074] This embodiment can effectively improve the excavation efficiency and stability by calculating the update coefficient and updating the initial excavation parameters. The method dynamically adjusts the excavation parameters based on real-time monitoring data, making the excavation process more flexible and adaptable to different geological conditions and excavation environments, thereby reducing resource waste, improving production efficiency, and extending the service life of equipment.

[0075] Furthermore, step S1041 specifically includes:

[0076] The initial excavation parameters and the current excavation efficiency evaluation results are input into an update coefficient calculation model, and the update coefficients corresponding to the parameters are output. The update coefficient calculation model is a machine learning regression model trained by historical excavation data, which is used to predict the update coefficients corresponding to the initial excavation parameters and the current excavation efficiency evaluation results.

[0077] Specifically, the update coefficient calculation model can be a comprehensive model for all initial excavation parameters, or it can be multiple models, that is, one initial excavation parameter corresponds to one model. The update coefficient calculation model can be a linear regression model, a random forest regression model or a deep learning model. The update coefficients obtained from the excavation parameters, excavation efficiency and engineer experience of previous excavation operations are used as training set data to train the model, adjust the model parameters to achieve the minimum prediction error, and use cross-validation technology to optimize the model and prevent overfitting. In actual excavation operations, geological conditions and equipment status often change, which will cause different excavation parameters to produce different excavation efficiencies. This model can update the update coefficients of excavation parameters in real time, adapt to dynamic changes, and improve the stability and efficiency of operations.

[0078] In one embodiment, Figure 3 As shown, step S107 specifically includes:

[0079] S1071a, calculating the error between the actual excavation efficiency and the predicted excavation efficiency.

[0080] S1072a, adjusting the update coefficient according to the error and the adjustment factor, where the adjustment factor is a proportional coefficient for correcting the error, wherein a'=a+e×b, a' is the adjusted update coefficient, a is the update coefficient, e is the adjustment factor, and b is the error;

[0081] S1073a, correcting the update data according to the adjusted update coefficient.

[0082] This embodiment improves the accuracy of data correction by calculating the error between the actual tunneling efficiency and the predicted tunneling efficiency and using the adjustment factor to correct the update coefficient, which helps to timely discover and correct the deviation of the model output and ensure the reliability of the tunneling efficiency analysis, thereby more effectively monitoring and managing the working status and tunneling process of the shield machine.

[0083] Furthermore, the adjustment factor is calculated from the error, where e 2 =k×b 2 , k is a constant.

[0084] Specifically, k is the proportional constant when the square function is applied to the adjustment factor when the error is small or medium, which can control the adjustment amplitude of the update factor when the error is within a certain medium range. The adjustment factor e is calculated by the square function of the error. This method makes the adjustment amplitude of the adjustment factor more sensitive and accurate when the error is within a medium range (i.e., the error value is neither too large nor too small).

[0085] Assuming the actual excavation efficiency is 40 m / h and the preset excavation efficiency is 44 m / h, the error is 4 m / h. Therefore, the error is adjusted using the adjustment factor. If k = 0.1, then e 2 =0.1×4 2 , and we get e≈1.27, a'=1.25+1.27×4=6.33, so the corrected thrust q"=5000×6.33=31650KN.

[0086] In addition, if the error is large, a simple square function may not provide sufficient adjustment accuracy and control strength. In order to cope with the challenge of large errors, a nonlinear function can be introduced to adjust the adjustment factor to achieve more precise control. For example, the nonlinear function can be a logarithmic function, an exponential function, and a piecewise function.

[0087] This embodiment calculates the adjustment factor by combining the square function, which can achieve sensitive and precise control of the adjustment factor within the medium error range, ensuring the stability and efficiency of the excavation process. At the same time, for the case of large errors, nonlinear functions (such as logarithmic functions, exponential functions or piecewise functions) are introduced for adjustment, thereby providing more precise control and avoiding excessive or insufficient adjustment. This method improves the optimization effect of the update coefficient and excavation parameters, thereby improving construction efficiency, reducing risks, and ensuring project quality.

[0088] In another embodiment, step S107 further includes:

[0089] S1071b, using the update coefficients output by the multiple update coefficient calculation models to adjust the update coefficients, wherein a'=w1×a1+w2×a2+…w i ×a i , a' is the adjusted update coefficient, w1...w i Calculate the model weights for each update coefficient, a1…a i Calculate the update coefficients for the model outputs for each update coefficient.

[0090] S1072b, correcting the update data according to the adjusted update coefficient.

[0091] Specifically, taking the thrust in the excavation parameters as an example, since the update coefficient calculated in step S1041 is inaccurate, the update coefficient needs to be adjusted. Other types of models (such as time series analysis, expert system, etc.) can be introduced and combined with the output of the main regression model to comprehensively consider the update coefficient prediction results of different models.

[0092] The time series analysis model uses the historical data of the thrust update coefficient (including excavation efficiency and excavation parameters) to train the time series model to capture the time series characteristics of the update coefficient. The time series model can select ARIMA, LSTM, etc.; the expert system model is an expert system model built based on the experience and knowledge of historical engineers. The expert system model can include a rule base, a case base, and a knowledge base for the prediction of the thrust update coefficient. The three update coefficients output by the regression model, the time series analysis model, and the expert system model used in step S1041 are comprehensively processed. Different weights can be given to each model through the weighted average method to obtain the final update coefficient.

[0093] This embodiment can effectively improve the accuracy and reliability of the thrust update coefficient by adjusting the coefficient with errors using the update coefficient output by the multiple update coefficient calculation model. The weights and outputs of each model are comprehensively considered, so that the final adjusted update coefficient reflects the advantages of multiple models and reduces the errors that may be caused by a single model. In addition, by correcting the update data based on the adjusted update coefficient, the adaptability and response speed of the excavation parameters can be further improved, thereby optimizing the excavation efficiency, reducing construction risks, and ensuring the safety and stability of the construction process. This method realizes dynamic adjustment based on data-driven and enhances the system's intelligent decision-making ability in complex environments.

[0094] In one embodiment, if the tunneling efficiency of the shield machine still does not reach the preset tunneling efficiency after the updated data is corrected, the mechanical parameters and environmental parameters of the shield machine are obtained for risk assessment.

[0095] This embodiment comprehensively analyzes the operating status of mechanical equipment and changes in the geological environment, accurately identifies key factors affecting excavation efficiency, evaluates the possibility of failure or risk, and formulates targeted response strategies based on this, such as equipment adjustment, soil improvement or construction process optimization, to ensure the safety and efficiency of the construction process, while providing a scientific basis for subsequent construction plans and equipment maintenance.

[0096] Furthermore, the mechanical parameters and environmental parameters of the shield machine are obtained for risk assessment, including:

[0097] Comparing the mechanical parameter and the environmental parameter with corresponding preset ranges respectively;

[0098] If the mechanical parameter exceeds a preset range, it is determined that a corresponding structure of the shield machine fails;

[0099] If the mechanical parameter does not exceed the preset range and the environmental parameter exceeds the preset range, the excavation strategy is adjusted.

[0100] Specifically, mechanical parameters take tool wear data as an example. If the wear data exceeds the preset range, it means that the tool is severely worn and cannot be restored to normal by adjusting the parameters. In this case, a fault is determined and repair or replacement measures are taken immediately. Environmental parameters take the abnormal rise of groundwater level as an example. If the mechanical parameters are normal but the water level exceeds the preset range, the impact of environmental parameters should be analyzed immediately and the excavation strategy should be adjusted. For example, geological improvement measures or temporary water blocking methods should be taken to adapt to the new engineering environment and ensure the excavation efficiency of the shield machine.

[0101] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, 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 disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0102] 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 flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, 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 processor, 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 flowchart and / or block diagram. 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.

[0103] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate 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 A function specified in one or more boxes.

[0104] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0105] Obviously, the above embodiments are merely examples for the purpose of clear explanation, and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived from these are still within the scope of protection of the invention.

Claims

1. A shield machine excavation efficiency analysis method based on big data, characterized in that: include: Obtain geological parameters and determine the shield machine's cutting head type and initial excavation parameters according to the standard correspondence table. The initial excavation parameters include shield machine direction, thrust, cutting head rotation speed, roller speed, and slag discharge speed. Controlling the shield machine to perform excavation and slag removal according to the initial excavation parameters, and acquiring excavation data in real time; Evaluate excavation efficiency based on excavation data acquired in real time; Determine whether the expected standard is met based on the tunneling efficiency evaluation results. If not, update the tunneling parameters. Control the shield machine to continue excavating and discharging slag according to the updated excavation parameters, and obtain new excavation data; Compare the excavation data before and after the excavation parameters are updated to analyze the changes in excavation efficiency, including the extent of speed improvement; If the speed increase is less than the preset speed, the update data is corrected; If the speed increase is greater than or equal to the preset speed, the updated tunneling parameters are continued to be used to control the shield machine to continue tunneling and slag discharge.

2. The method according to claim 1, characterized in that Before determining the shield machine's cutting head type and initial excavation parameters according to the standard correspondence table, it also includes: cleaning the geological parameters; Perform statistics on geological parameters in the same section and calculate standard deviations and confidence intervals; The target geological parameter is determined based on the standard deviation and the confidence interval.

3. The method according to claim 1, characterized in that Update excavation parameters, including: Calculating parameter update coefficients according to the initial excavation parameters and the current excavation efficiency evaluation results; The initial excavation parameters are updated using the update coefficients.

4. The method according to claim 3, characterized in that: The parameter update coefficient is calculated according to the initial excavation parameters and the current excavation efficiency evaluation result, including: The initial excavation parameters and the current excavation efficiency evaluation results are input into an update coefficient calculation model, and the update coefficients corresponding to the parameters are output. The update coefficient calculation model is a machine learning regression model trained by historical excavation data, which is used to predict the update coefficients corresponding to the initial excavation parameters and the current excavation efficiency evaluation results.

5. The method according to claim 3, characterized in that: If the speed increase is less than the preset speed, the update data is corrected, including: Calculate the error between the actual excavation efficiency and the predicted excavation efficiency; Adjusting the update coefficient according to the error and the adjustment factor, wherein the adjustment factor is a proportional coefficient for correcting the error, wherein a'=a+e×b, a' is the adjusted update coefficient, a is the update coefficient, e is the adjustment factor, and b is the error; The update data is corrected according to the adjusted update coefficient.

6. The method according to claim 5, characterized in that The adjustment factor is calculated from the error, where e 2 =k×b 2 , k is a constant.

7. The method according to claim 3, characterized in that If the speed increase is less than the preset speed, the update data is corrected, further comprising: The update coefficient is adjusted by using the update coefficient output by the multiple update coefficient calculation model, wherein a'=w1×a1+w2×a2+…w i ×a i , a' is the adjusted update coefficient, w1...w i Calculate the model weights for each update coefficient, a1…a i Calculate the update coefficients of the model output for each update coefficient; The update data is corrected according to the adjusted update coefficient.

8. The method according to claim 1, characterized in that If the tunneling efficiency of the shield machine still does not reach the preset tunneling efficiency after the updated data is corrected, the mechanical parameters and environmental parameters of the shield machine are obtained for risk assessment.

9. The method according to claim 8, characterized in that Obtain the mechanical and environmental parameters of the shield machine for risk assessment, including: Comparing the mechanical parameter and the environmental parameter with corresponding preset ranges respectively; If the mechanical parameter exceeds a preset range, it is determined that a corresponding structure of the shield machine fails; If the mechanical parameter does not exceed the preset range and the environmental parameter exceeds the preset range, the excavation strategy is adjusted.

10. A shield machine excavation efficiency analysis device based on big data, characterized in that: include: A processor and a memory connected to the processor; wherein the memory stores instructions executable by the processor, and the instructions are executed by the processor so that the processor executes the shield machine excavation efficiency analysis method based on big data as described in any one of claims 1-9.

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