Shield tunneling machine cutterhead center area deformation detection system and method

Abstract

The invention belongs to the technical field of shield tunneling machine cutterhead center area deformation detection, and discloses a shield tunneling machine cutterhead center area deformation detection system and method. By establishing a correlation model of dynamic load and static deformation, deformation of the center area of the shield tunneling machine cutterhead is comprehensively evaluated, intelligent deformation monitoring of the center area of the shield tunneling machine cutterhead is achieved, the system integrates multi-dimensional parameters such as vibration spectrum, temperature gradient and stress distribution, a prediction algorithm is established in combination with material fatigue characteristics, and the prediction accuracy is improved. The deformation condition monitoring recognition rate is improved, and the construction safety and the equipment reliability are remarkably improved. According to the method, the deformation area and the risk area are identified based on the correlation model of the dynamic load and the static deformation, the deformation area can be accurately identified, the risk level can be divided, early risk prediction and active maintenance are realized, the limitation that only the deformed state is detected traditionally is broken through, and the perspectiveness and reliability of health management of the cutterhead are remarkably improved.

Description

Technical Field

[0001] The invention belongs to the technical field of shield machine cutter head central area deformation detection, and relates to a shield machine cutter head central area deformation detection system and method. Background Art

[0002] The shield machine is a large-scale engineering machinery used for tunnel construction. It consists of a shield body, a cutterhead, and a cutting system. The cutterhead is located at the front end of the shield machine and is a key component for cutting soil. Various cutters are installed in the center area of ​​the cutterhead. When the shield machine starts to excavate, the cutters in the center area of ​​the cutterhead first contact the stratum and perform initial crushing on the rock and soil in the center of the tunnel. Therefore, the deformation of the center area of ​​the shield machine cutterhead directly affects the efficiency, stability, and safety of the shield machine's excavation work, and its monitoring and research is of great significance.

[0003] There are also technical solutions for the deformation detection of the center area of ​​the shield machine cutterhead in the prior art. For example, the Chinese invention patent application for the shield machine cutterhead deformation detection method and shield machine cutterhead deformation detection system with publication number CN115355812A includes: 1. Hard formation determination: the continuous minimum spacing at a certain point corresponds to the highest hardness area of ​​the face; 2. Roller abnormality identification: when the spacing shows a periodic change synchronized with the cutterhead speed (the phase difference is 1 cycle), the corresponding roller fault is determined. This method provides real-time face hardness distribution and tool status warning through spatiotemporal data analysis, and assists in optimizing excavation parameters and equipment maintenance.

[0004] In addition, a Chinese invention patent application for a shield machine and a detection system for sensing deformation of the central area of ​​the cutter disc of a shield machine with publication number CN113108727B includes real-time monitoring of the deformation of the center of the cutter disc through a telescopic assembly: when the cutter disc is operating normally, the oil outlets of the first and second telescopic parts are isolated from the oil overflow outlets, and the hydraulic oil pressure in the oil storage chamber is stable; if the central area is concave and deformed, the two telescopic parts move toward each other to connect the oil outlets, the hydraulic oil is discharged, and a sudden drop in pressure occurs. The pressure sensor detects the pressure change and triggers an early warning, helping operators to promptly discover and deal with deformation problems to avoid equipment damage.

[0005] Although the above two schemes have proposed some solutions for parameter monitoring and control of the ball making process, they still have certain limitations. For example, on the one hand, the existing technical solutions only rely on dynamic parameter monitoring and ignore the collaborative analysis of static structural degradation, which leads to an increase in the missed detection rate of structural damage; and no correlation model between dynamic load and static deformation has been established, so it is impossible to warn of the cumulative deformation of the steel structure, increase the risk of sudden fracture, and the abnormal detection is relatively delayed.

[0006] On the other hand, existing technical solutions usually only involve the identification of detectable deformation that has already occurred, lack the identification of deformation risk situations, do not monitor early risk factors such as tool wear degree and abnormal stress distribution, and do not combine dynamic data (such as temperature rise, vibration, etc.) to establish a risk prediction model. It is difficult to warn of potential deformation in advance, and measures can only be taken after deformation occurs, and it is impossible to prevent it before it happens. Summary of the invention

[0007] In view of this, in order to solve the problems raised in the above background technology, a system and method for detecting deformation of the central area of ​​the cutter head of a shield machine is proposed.

[0008] The purpose of the present invention can be achieved through the following technical solutions: The first aspect of the present invention provides a shield machine cutter head central area deformation detection system, including: an area division module: based on the three-dimensional structural characteristics of the target shield machine cutter head central area, a geometric grid division algorithm is used to generate an equal-density distributed monitoring area and generate a unique code, and the center point of each monitoring area is used as a monitoring point.

[0009] Static monitoring module: Use a 3D laser scanning device to scan the 3D topography data of each monitoring area in real time, compare it with the pre-stored initial 3D data for point cloud registration, use image processing algorithms to achieve quantitative analysis of tool wear and cutter head surface defect assessment, and further output a static anomaly index.

[0010] Dynamic monitoring module: Integrates infrared thermal imaging devices, vibration sensor groups and three-dimensional scanning devices to monitor the temperature rise gradient, vibration spectrum characteristics and shield machine soil discharge morphological parameters of each monitoring area in real time, and calculates the dynamic anomaly index through a multi-source data fusion model.

[0011] Anomaly recognition module: Utilize the distributed stress sensor network embedded in the monitoring points in a dot matrix arrangement to obtain the stress distribution matrix in real time and perform differential calculation with the preset reference stress field matrix, dynamically correct the anomaly judgment threshold, and perform anomaly recognition based on the static anomaly index and dynamic anomaly index.

[0012] Deformation positioning module: locates and outputs deformation areas and risk areas based on anomaly recognition results.

[0013] The second aspect of the present invention provides a method for detecting deformation of the central area of ​​the cutter head of a shield machine, comprising: S1, area division: based on the three-dimensional structural characteristics of the central area of ​​the cutter head of the target shield machine, a geometric grid division algorithm is used to generate monitoring areas with equal density distribution and generate unique codes, and the center point of each monitoring area is used as a monitoring point.

[0014] S2. Static monitoring: Use a 3D laser scanning device to scan the 3D morphology data of each monitoring area in real time, compare it with the pre-stored initial 3D data for point cloud registration, use image processing algorithms to achieve quantitative analysis of tool wear and cutter head surface defect assessment, and further output a static anomaly index.

[0015] S3. Dynamic monitoring: Integrate infrared thermal imaging devices, vibration sensor groups and soil slag image acquisition devices to collect temperature rise gradients, vibration spectrum characteristics and shield machine soil slag discharge images of each monitoring area in real time, and calculate the dynamic anomaly index through a multi-source data fusion model.

[0016] S4. Anomaly identification: A distributed stress sensor network embedded in monitoring points in a dot matrix arrangement is used to obtain the stress distribution matrix in real time and perform differential calculations with the preset reference stress field matrix. The anomaly judgment threshold is dynamically corrected, and anomaly identification is performed based on the static anomaly index and dynamic anomaly index.

[0017] S5. Deformation positioning: locate and output the deformed area and risk area based on the abnormality recognition results.

[0018] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) The present invention establishes a correlation model between dynamic load and static deformation, comprehensively evaluates the deformation of the central area of ​​the shield machine cutter head, and realizes intelligent deformation monitoring of the central area of ​​the shield machine cutter head. The system integrates multi-dimensional parameters such as vibration spectrum, temperature gradient, and stress distribution, and establishes a prediction algorithm based on material fatigue characteristics, thereby improving the deformation monitoring recognition rate and significantly improving construction safety and equipment reliability.

[0019] (2) The present invention identifies deformation areas and risk areas based on a correlation model between dynamic load and static deformation. It can accurately identify deformation areas and classify risk levels, achieve early risk prediction and proactive maintenance, break through the limitation of traditional methods that only detect the deformed state, and significantly improve the foresight and reliability of cutter head health management.

[0020] (3) The present invention constructs a stress distribution matrix and performs differential calculation with a preset reference stress field matrix, dynamically corrects the anomaly judgment threshold, and realizes adaptive optimization of the anomaly judgment threshold, breaking through the rigid detection limitations of the traditional fixed threshold. It can accurately identify hidden structural damage under different geological conditions, and greatly reduce the risk of misjudgment while ensuring detection sensitivity. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0022] Figure 1 It is a schematic diagram of the connection of each module of the system of the present invention.

[0023] Figure 2 It is a schematic diagram of the implementation steps of the method of the present invention.

[0024] Figure 3 A schematic diagram of monitoring area division corresponding to an embodiment provided by the present invention.

[0025] Figure numerals: 1 - cutter head, 2 - monitoring area, 3 - monitoring point. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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.

[0027] See also Figure 1 As shown, the first aspect of the present invention provides a shield machine cutter head central area deformation detection system, including an area division module, a static monitoring module, a dynamic monitoring module, an abnormality identification module and a deformation positioning module, wherein the area division module is respectively connected to the static monitoring module and the dynamic monitoring module, the static monitoring module and the dynamic monitoring module are both connected to the abnormality identification module, and the abnormality identification module is connected to the deformation positioning module.

[0028] The area division module is used based on the three-dimensional structural characteristics of the target shield machine cutter head center area. Figure 3 As shown, a geometric grid partitioning algorithm is used to generate monitoring areas with equal density distribution and generate unique codes, and the center point of each monitoring area is used as a monitoring point.

[0029] It should be noted that, in the actual area division process, each monitoring area may or may not have tool distribution.

[0030] The static monitoring module is used to use a three-dimensional laser scanning device to scan the three-dimensional morphological data of each monitoring area in real time, perform point cloud registration and comparison with the pre-stored initial three-dimensional data, use image processing algorithms to achieve quantitative analysis of tool wear and cutter head surface defect evaluation, and further output a static anomaly index.

[0031] It should be explained that the three-dimensional shape data may be three-dimensional contour information.

[0032] In a preferred embodiment of the present invention, the specific analysis process of the tool wear quantitative analysis is as follows: the actual volume of each tool is obtained based on the three-dimensional morphology data of each monitoring area obtained by real-time scanning using a three-dimensional laser scanning device, and then compared with the initial volume of each tool, and combined with the wear analysis to obtain the wear index of each tool.

[0033] Preferably, the wear index analysis method is: calculating the difference between the actual volume of each tool and the initial volume, and then calculating the ratio with the initial volume to obtain the wear index of each tool.

[0034] A number of wear monitoring points are evenly arranged on the surface of each tool to obtain the corresponding monitoring thickness, and then the monitoring thickness of each tool surface corresponding to each wear monitoring point is compared and analyzed to obtain the wear heterogeneity index of each tool.

[0035] Preferably, the wear variability index analysis method is: performing difference calculation on the monitored thickness of each wear monitoring point on the surface of each tool and the corresponding initial monitoring thickness to obtain the wear thickness of each wear monitoring point on the surface of each tool, and then performing mean calculation to obtain the average wear thickness of each tool, performing ratio calculation on the wear thickness of each wear monitoring point on the surface of each tool and the corresponding average wear thickness to obtain the wear deviation of each wear monitoring point of each tool, and then performing mean calculation to obtain the wear variability index of each tool.

[0036] The wear index and wear heterogeneity index of each tool are averaged to obtain the quantitative evaluation index of wear of each tool.

[0037] The projection position of each tool on the cutter disc surface is obtained, one or more monitoring areas to which these projections belong are identified, and a tool list corresponding to each monitoring area is compiled.

[0038] The wear quantitative evaluation index of each tool in each monitoring area is averaged to obtain the tool wear evaluation index of each monitoring area. If there is no tool in a monitoring area, the tool wear evaluation index of the monitoring area is set to 0.

[0039] In a preferred embodiment of the present invention, the specific analysis process of the cutter disc surface defect evaluation is as follows: the three-dimensional morphological data of each monitoring area acquired in real time by a three-dimensional laser scanning device is used to determine the number of surface defects and their actual area in each monitoring area, and then compared with the preset reference defect number and reference defect area, and the mean value is calculated and analyzed to obtain the cutter disc surface defect evaluation index of each monitoring area.

[0040] Exemplarily, the surface defect type may be cracks, wear pits, deformation, peeling, etc.

[0041] Preferably, the specific analysis method of the cutter disc surface defect evaluation index of each monitoring area is as follows: the number of surface defects in each monitoring area is compared with the preset reference defect number to obtain the abnormality of the number of surface defects in each monitoring area, and at the same time, the actual area of ​​each defect in each monitoring area is calculated by ratio with the preset reference defect area, and the average is obtained to obtain the defect area abnormality of each monitoring area.

[0042] The surface defect quantity abnormality and defect area abnormality of each monitoring area are fused and calculated to obtain the cutter head surface defect evaluation index of each monitoring area.

[0043] It should be noted that the above data fusion calculation method can be mean, weighted sum or maximum value, and the specific method can be pre-set based on needs.

[0044] The dynamic monitoring module is used to integrate infrared thermal imaging devices, vibration sensor groups and three-dimensional scanning devices to monitor the temperature rise gradient, vibration spectrum characteristics and shield machine soil and slag discharge morphological parameters of each monitoring area in real time, and calculate the dynamic anomaly index through a multi-source data fusion model.

[0045] In a preferred embodiment of the present invention, the specific analysis method of calculating the dynamic anomaly index through the multi-source data fusion model is as follows: based on the temperature rise gradient, vibration spectrum characteristics and shield machine soil discharge morphology parameters of each monitoring area, the temperature rise anomaly index, vibration frequency anomaly index and shield machine soil discharge morphology anomaly index of each monitoring area are constructed.

[0046] It should be explained that the analysis of the temperature rise gradient, vibration spectrum characteristics and shield machine soil and slag discharge morphological parameters of each monitoring area is mainly to comprehensively and accurately evaluate the working status of the shield machine cutter head center area, discover potential problems in advance, and ensure the safe and efficient operation of the shield machine. The specific reasons are as follows: 1. Temperature rise gradient: It can reflect the wear and friction of the tool. When the tool cuts the soil, the friction heat will increase the local temperature of the cutter head. The abnormal temperature rise gradient means that the tool wear is aggravated and the cutting force is increased. Through analysis, tool problems can be discovered in time, providing a basis for replacement and maintenance. It can also detect the hidden dangers of local faults of the cutter head and assist in judging the changes in geological conditions so as to adjust the construction parameters.

[0047] 2. Vibration spectrum characteristics: can reflect the operating status of the cutterhead and tool. During normal operation, the vibration frequency of the cutterhead and tool is within a certain range. If the vibration spectrum characteristics are abnormal, it may be that the tool is loose, severely worn, or there is a problem with the cutterhead structure. Analyzing this parameter can timely discover these problems, avoid equipment damage, and ensure construction safety and progress.

[0048] 3. Soil and slag discharge morphology parameters: can be used to determine whether the excavation process is normal. Large fluctuations in soil and slag discharge or abnormal particle size distribution may indicate that there are problems in the cutting and slag discharge of the shield machine, such as poor tool cutting effect and blockage of the slag discharge system. By analyzing these parameters, construction operations can be adjusted in time to ensure smooth excavation work. It also helps to understand the changes in the strata and provide a reference for subsequent construction.

[0049] The abnormal indexes are fused and analyzed to obtain the dynamic abnormal index of each monitoring area, wherein the temperature rise abnormal index and the vibration frequency abnormal index are for the actual situation of each monitoring area, and the shield machine's soil slag discharge morphology abnormal index is for the actual situation of the entire shield machine.

[0050] Preferably, the dynamic abnormality index is analyzed by fusing the temperature rise abnormality index, the vibration frequency abnormality index and the soil and slag discharge morphology abnormality index of each monitoring area to obtain the dynamic abnormality index of each monitoring area.

[0051] It should be noted that the above data fusion calculation method can be mean, weighted sum or maximum value, and the specific method can be pre-set based on needs.

[0052] In a preferred embodiment of the present invention, the specific method of constructing the temperature rise anomaly index, the vibration frequency anomaly index and the shield machine's soil and slag discharge morphology anomaly index of each monitoring area is as follows: the average temperature of adjacent monitoring points is calculated as the reference temperature, and then the temperature of each monitoring point is compared with the reference temperature to analyze and obtain the temperature rise anomaly index of each monitoring area.

[0053] The specific analysis method of the vibration frequency abnormality index refers to the specific analysis method of the temperature rise abnormality index.

[0054] The soil discharge morphological parameters of the shield machine collected by the three-dimensional scanning device are used to evaluate the soil discharge volume and soil particle size distribution of the shield machine's soil discharge process, and then a comparative analysis is performed to obtain the soil discharge morphological abnormality index of the shield machine.

[0055] In a preferred embodiment of the present invention, the specific analysis method for evaluating the soil debris discharge volume and soil debris particle size distribution of the shield machine soil debris discharge process is as follows: extract the shield machine soil debris discharge morphological parameters collected by the three-dimensional scanning device, extract several batches of soil debris discharge data for the soil debris discharge parameters based on unit time intervals, obtain the soil debris volume corresponding to each batch of soil debris discharge data, analyze the soil debris discharge volume of each batch in combination with the soil debris density, and perform fluctuation analysis on the soil debris discharge volume of the batch closest to the current moment and the soil debris discharge volumes of other batches to obtain the evaluation results of the soil debris discharge volume fluctuation.

[0056] Preferably, the discharge amount of each batch of soil residue is calculated by multiplying the volume of each batch of soil residue by the corresponding soil residue density.

[0057] Preferably, the specific analysis method of the soil debris discharge volume fluctuation evaluation result is: based on the shield machine soil debris discharge morphological parameters provided by the three-dimensional scanning device, multiple batches of data extraction and processing are performed within a unit time interval to determine the volume of each batch of soil debris.

[0058] The actual amount of soil slag discharged in each batch is calculated by combining the volume of soil slag in each batch with the density of soil slag.

[0059] The fluctuation analysis of the soil residue discharge of the latest batch was conducted in comparison with the previous batches to obtain the evaluation results of the soil residue discharge fluctuation.

[0060] Preferably, the soil residue discharge amount of each other batch is calculated by difference with the soil residue discharge amount of the previous batch, and then the ratio is calculated with the soil residue discharge amount of the previous batch to obtain the fluctuation of the soil residue discharge amount of each other batch; the soil residue discharge amount of the most recent batch at the current moment is calculated by difference with the soil residue discharge amount of the previous batch, and then the ratio is calculated with the soil residue discharge amount of the previous batch to obtain the fluctuation of the soil residue discharge amount of the current batch.

[0061] The soil slag discharge fluctuation index is calculated by comparing the fluctuation of the current batch of soil slag discharge with the average fluctuation of the soil slag discharge of other batches.

[0062] It needs further explanation that the construction logic of this step is to accurately measure the stability of shield machine slag discharge by comparing the fluctuation of soil discharge volume of current batches with that of previous batches, so as to provide a basis for judging the operation status of the equipment. When calculating, first find the average value of the fluctuation of soil discharge volume of other batches, which represents the general level of past slag discharge fluctuation. Then compare the fluctuation of soil discharge volume of the current batch with it. The ratio can intuitively reflect the degree of deviation of the current fluctuation from the average level. The larger the ratio, the more abnormal the current fluctuation, indicating that there may be problems with the equipment.

[0063] Based on the point cloud segmentation algorithm, the soil residue is segmented into particles and the volume of each soil residue particle is obtained. Based on this, an equivalent diameter analysis is performed to obtain the equivalent diameter of each soil residue particle, and then a three-dimensional particle size distribution map is constructed. The proportion of soil residue particles in the preset expected particle size range is obtained and a difference analysis is performed with the preset reference expected particle size proportion to obtain the evaluation results of soil residue particle size distribution abnormalities.

[0064] It should be noted that the specific method of performing equivalent diameter analysis is: using the formula The equivalent diameter of each soil slag particle is obtained by analysis ,in represents the volume of each soil particle, Indicates the number of the soil particles. , Indicates the number of soil particles.

[0065] It needs to be explained that the construction principle of the above formula is based on the volume formula of a sphere. For a standard sphere, its volume formula is ,in is the volume of the sphere, is the radius of the sphere. And the diameter of the sphere ,Will Substituting into the volume formula we get , and then transformed to obtain the equivalent diameter analysis formula.

[0066] It should be noted that the reason for using the above formula is that in the shield machine soil slag analysis scenario, the shape of soil slag particles is often irregular, and it is difficult to accurately describe their size using conventional methods. By calculating the equivalent diameter through this formula, irregular soil slag particles can be approximately regarded as spheres, and a unified diameter value can be used to represent their size, which is convenient for subsequent statistical analysis of soil slag particles, such as analyzing soil slag particle size distribution, and then assisting in judging the cutting conditions of the shield machine, formation characteristics, etc.

[0067] Preferably, the specific method of constructing the three-dimensional particle size distribution map is: the equivalent diameter of each soil slag particle is compared with the preset reference distribution diameter. Specifically, if the equivalent diameter of the soil slag particle is smaller than , marking it as , if the equivalent diameter of the soil residue particle is greater than or equal to and less than , marking it as , if the equivalent diameter of the soil residue particle is greater than or equal to and less than , marking it as If the equivalent diameter of the soil residue particle is larger than , marking it as , count the number of soil slag particles of each diameter grade, and calculate the proportion of soil slag particles of each diameter grade by comparing them with the total number of soil slag particles.

[0068] Preferably, the specific analysis method of the evaluation result of the abnormal soil residue particle size distribution is as follows: summing up the proportion of soil residue particles of each diameter grade within the expected particle size range, performing difference calculation between the reference expected particle size proportion and the sum calculation result to obtain the expected particle size proportion failure rate, and then performing ratio calculation with the reference expected particle size proportion to obtain the soil residue particle size distribution abnormality evaluation index.

[0069] For example, if the expected particle size range is , then the diameter grade is and The soil residue particle proportions are summed up and then analyzed for differences with the preset reference expected particle size proportions to obtain the soil residue particle size distribution anomaly evaluation index.

[0070] The anomaly identification module is used to use a distributed stress sensor network embedded in the monitoring points in a dot matrix arrangement to obtain the stress distribution matrix in real time and perform differential calculation with a preset reference stress field matrix, dynamically correct the anomaly judgment threshold, and perform anomaly identification based on static anomaly index and dynamic anomaly index.

[0071] In a preferred embodiment of the present invention, the specific method of dynamically correcting the abnormal judgment threshold is as follows: the stress distribution matrix obtained in real time is differentially calculated with the preset reference stress field matrix to obtain the stress deviation matrix of the central area of ​​the shield machine, and then compared and analyzed with the preset reference stress deviation threshold to obtain the abnormal judgment threshold correction coefficient of each monitoring point.

[0072] Preferably, the stress distribution matrix obtained in real time is subtracted from the corresponding elements of the preset reference stress field. For example, for a two-dimensional stress distribution matrix and a reference stress field matrix, let the stress distribution matrix be , the preset reference stress field is , then the stress deviation matrix C after differential calculation is .

[0073] It should be explained that the stress distribution matrix is ​​a two-dimensional matrix, and its number of rows and columns corresponds to the dot matrix arrangement of stress sensors on the shield machine. Assuming that on the plane of the center area of ​​the cutterhead of the shield machine, the stress sensors are arranged in the form of m rows and n columns, then the stress distribution matrix A is a Each element in the matrix ( , ) represents the stress value measured by the stress sensor located in the i-th row and j-th column.

[0074] Preferably, the analysis method of the abnormality determination threshold correction coefficient is as follows: the stress deviation of each monitoring point is calculated by ratio with a preset reference stress deviation threshold to obtain the abnormality determination threshold correction coefficient of each monitoring point.

[0075] The corrected abnormality determination threshold of each monitoring area is constructed based on the abnormality determination threshold correction coefficient of each monitoring point and the initially set abnormality determination threshold.

[0076] Preferably, the abnormality determination threshold correction coefficient of each monitoring point is multiplied by the initially set abnormality determination threshold to obtain the corrected abnormality determination threshold of each monitoring area, wherein each monitoring point corresponds to each monitoring area.

[0077] The present invention constructs a stress distribution matrix and performs differential calculation with a preset reference stress field matrix, dynamically corrects the anomaly judgment threshold, realizes adaptive optimization of the anomaly judgment threshold, breaks through the rigid detection limitations of traditional fixed thresholds, and can accurately identify hidden structural damage under different geological conditions, greatly reducing the risk of misjudgment while ensuring detection sensitivity.

[0078] In a preferred embodiment of the present invention, the specific method of performing abnormality identification is as follows: a weighted sum of the static abnormality index and the dynamic abnormality index of each monitoring area is calculated to obtain a comprehensive evaluation abnormality index of each monitoring area.

[0079] It should be noted that the basis for setting the corresponding weights of the static anomaly index and the dynamic anomaly index is as follows: The weights of the static anomaly index and the dynamic anomaly index are set based on many factors. The first is the degree of impact on the safe operation of the cutterhead. If static factors (such as tool wear and surface defects of the cutterhead) pose a great threat to the long-term stable operation of the cutterhead, the weight of the static anomaly index can be increased; conversely, when dynamic factors (such as temperature rise and vibration) have a more direct and significant impact, the dynamic weight is higher. Secondly, considering the reliability and stability of the monitoring data, the weight of the index of accurate and reliable data is appropriately increased. The weight will also be flexibly adjusted in combination with the different construction stages and geological conditions of the shield machine to more accurately evaluate the status of the cutterhead.

[0080] The comprehensive evaluation abnormality index of each monitoring area is compared with the corresponding modified abnormality judgment threshold. If the comprehensive evaluation abnormality index of a monitoring area is greater than the corresponding modified abnormality judgment threshold, it is judged that there is an abnormality in the monitoring area and it is recorded as an abnormal monitoring area. Otherwise, it is judged that there is no abnormality in the monitoring area.

[0081] It should be noted that the present invention realizes intelligent deformation monitoring of the central area of ​​the shield machine cutter head by establishing a correlation model of dynamic load and static deformation and comprehensively evaluating the deformation of the central area of ​​the shield machine cutter head. The system integrates multi-dimensional parameters such as vibration spectrum, temperature gradient, and stress distribution, and establishes a prediction algorithm based on material fatigue characteristics, which improves the deformation monitoring recognition rate and significantly improves construction safety and equipment reliability.

[0082] The deformation positioning module is used to locate and output the deformation area and the risk area.

[0083] In a preferred embodiment of the present invention, the specific method of locating and outputting deformation areas and risk areas is as follows: the comprehensive abnormality index of each abnormal monitoring area is arranged in descending order, and they are classified into deformation areas and risk areas according to the risk index, and the corresponding area numbers are output.

[0084] For example, it is assumed that there are five abnormal monitoring areas in the central area of ​​the shield machine cutter head, namely X1, X2, X3, X4, and X5.

[0085] Extracting the comprehensive evaluation abnormality index: After preliminary analysis and calculation of the static (such as tool wear, surface defects) and dynamic (such as temperature rise, vibration) indicators of each area, the comprehensive evaluation abnormality index of area X1 is 8.5, area X2 is 7.0, area X3 is 8.0, area X4 is 6.5, and area X5 is 9.0.

[0086] Comparison and sorting: After comparing these indices, they are sorted from large to small as follows: X5 (9.0) > X1 (8.5) > X3 (8.0) > X2 (7.0) > X4 (6.5).

[0087] Classification and area determination: If the pre-set comprehensive assessment abnormality index is greater than 8.0, it is a high risk level, and the corresponding area is a risk area; 6.0-8.0 means there is a possibility of deformation, and the corresponding area is a deformation area. Then the X5 and X1 areas are risk areas, and the X2, X3, and X4 areas are deformation areas. Construction personnel can focus on risk areas, give priority to the inspection and maintenance of the X5 and X1 areas, and deal with potential problems in a timely manner.

[0088] It should be noted that the present invention identifies deformation areas and risk areas based on a correlation model of dynamic load and static deformation. It can accurately identify deformation areas and divide risk levels, achieve early risk prediction and proactive maintenance, break through the limitations of traditional methods of only detecting deformed states, and significantly improve the foresight and reliability of cutter disc health management.

[0089] See also Figure 2 As shown, the second aspect of the present invention provides a method for detecting deformation of the central area of ​​the cutter head of a shield machine, comprising: S1, area division: based on the three-dimensional structural characteristics of the central area of ​​the cutter head of the target shield machine, a geometric grid division algorithm is used to generate monitoring areas with equal density distribution and generate unique codes, and the center point of each monitoring area is used as a monitoring point.

[0090] S2. Static monitoring: Use a 3D laser scanning device to scan the 3D morphology data of each monitoring area in real time, compare it with the pre-stored initial 3D data for point cloud registration, use image processing algorithms to achieve quantitative analysis of tool wear and cutter head surface defect assessment, and further output a static anomaly index.

[0091] S3. Dynamic monitoring: Integrate infrared thermal imaging devices, vibration sensor groups and soil slag image acquisition devices to collect temperature rise gradients, vibration spectrum characteristics and shield machine soil slag discharge images of each monitoring area in real time, and calculate the dynamic anomaly index through a multi-source data fusion model.

[0092] S4. Anomaly identification: A distributed stress sensor network embedded in monitoring points in a dot matrix arrangement is used to obtain the stress distribution matrix in real time and perform differential calculations with the preset reference stress field matrix. The anomaly judgment threshold is dynamically corrected, and anomaly identification is performed based on the static anomaly index and dynamic anomaly index.

[0093] S5. Deformation positioning: locate and output the deformed area and risk area based on the abnormality recognition results.

[0094] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they shall all fall within the protection scope of the present invention.

Claims

1. A shield machine cutter head center area deformation detection system, characterized in that: include: Area division module: Based on the three-dimensional structural characteristics of the target shield machine cutter head center area, a geometric mesh division algorithm is used to generate monitoring areas with equal density distribution and generate unique codes. The center point of each monitoring area is used as the monitoring point; Static monitoring module: Use a 3D laser scanning device to scan the 3D topography data of each monitoring area in real time, compare the point cloud with the pre-stored initial 3D data, use image processing algorithms to achieve quantitative analysis of tool wear and evaluation of cutter head surface defects, and further output static abnormality index; Dynamic monitoring module: Integrates infrared thermal imaging devices, vibration sensor groups and three-dimensional scanning devices to monitor the temperature rise gradient, vibration spectrum characteristics and shield machine soil discharge morphological parameters of each monitoring area in real time, and calculates the dynamic anomaly index through a multi-source data fusion model; Abnormal identification module: The distributed stress sensor network embedded in the monitoring points in a dot matrix arrangement is used to obtain the stress distribution matrix in real time and perform differential calculation with the preset reference stress field matrix, dynamically correct the abnormal judgment threshold, and perform abnormal identification based on the static abnormal index and dynamic abnormal index; Deformation positioning module: locates and outputs deformation areas and risk areas based on anomaly recognition results.

2. A shield machine cutter head central area deformation detection system as claimed in claim 1, characterized in that: The specific analysis process of the tool wear quantitative analysis is as follows: The actual volume of each tool is obtained based on the three-dimensional topography data of each monitoring area obtained by real-time scanning using a three-dimensional laser scanning device, and then compared with the initial volume of each tool, and the wear index of each tool is obtained in combination with the wear analysis; A number of wear monitoring points are evenly arranged on the surface of each tool to obtain the corresponding monitoring thickness, and then the monitoring thickness of each tool surface corresponding to each wear monitoring point is compared and analyzed to obtain the wear heterogeneity index of each tool; The wear index and wear heterogeneity index of each tool are averaged to obtain the wear quantitative evaluation index of each tool; Obtain the projection position of each tool on the cutter disc surface, identify one or more monitoring areas to which these projections belong, and compile a list of tools corresponding to each monitoring area; The tool wear evaluation index of each monitoring area is obtained by calculating the average of the wear quantitative evaluation index of each tool in each monitoring area.

3. A shield machine cutter head central area deformation detection system as claimed in claim 1, characterized in that: The specific analysis process of the cutter head surface defect evaluation is as follows: The three-dimensional morphological data of each monitoring area obtained in real time by a three-dimensional laser scanning device is used to determine the number of surface defects and their actual area in each monitoring area, which are then compared with the preset reference defect number and reference defect area, and the mean value is calculated and analyzed to obtain the cutter head surface defect evaluation index of each monitoring area.

4. A shield machine cutter head central area deformation detection system as claimed in claim 1, characterized in that: The specific analysis method of calculating the dynamic anomaly index through the multi-source data fusion model is as follows: Based on the temperature rise gradient, vibration spectrum characteristics and shield machine soil discharge morphology parameters of each monitoring area, the temperature rise anomaly index, vibration frequency anomaly index and shield machine soil discharge morphology anomaly index of each monitoring area are constructed; The abnormal indexes are fused and analyzed to obtain the dynamic abnormal index of each monitoring area, wherein the temperature rise abnormal index and the vibration frequency abnormal index are for the actual situation of each monitoring area, and the shield machine's soil slag discharge morphology abnormal index is for the actual situation of the entire shield machine.

5. A shield machine cutter head central area deformation detection system as claimed in claim 4, characterized in that: The specific method of constructing the temperature rise abnormality index, the vibration frequency abnormality index and the shield machine soil slag discharge form abnormality index of each monitoring area is as follows: Calculate the average temperature of adjacent monitoring points as the reference temperature, then compare the temperature of each monitoring point with the reference temperature to analyze and obtain the temperature rise anomaly index of each monitoring area; The specific analysis method of the vibration frequency abnormality index refers to the specific analysis method of the temperature rise abnormality index; The soil discharge morphological parameters of the shield machine collected by the three-dimensional scanning device are used to evaluate the soil discharge volume and soil particle size distribution of the shield machine's soil discharge process, and then a comparative analysis is performed to obtain the soil discharge morphological abnormality index of the shield machine.

6. A shield machine cutter head central area deformation detection system as claimed in claim 5, characterized in that: The specific analysis method of the evaluation corresponding to the soil slag discharge amount and soil slag particle size distribution during the shield machine soil slag discharge process is as follows: Based on the shield machine soil slag discharge morphological parameters provided by the three-dimensional scanning device, multiple batches of data extraction and processing are performed within a unit time interval to determine the volume of each batch of soil slag; The actual discharge amount of soil slag of each batch is calculated by combining the volume of soil slag of each batch with the density of soil slag; Conduct a fluctuation analysis on the soil slag discharge volume of the latest batch and the previous batches, so as to obtain the evaluation results of the soil slag discharge volume fluctuation; Based on the point cloud segmentation algorithm, the soil residue is segmented into particles and the volume of each soil residue particle is obtained. Based on this, an equivalent diameter analysis is performed to obtain the equivalent diameter of each soil residue particle, and then a three-dimensional particle size distribution map is constructed. The proportion of soil residue particles in the preset expected particle size range is obtained and a difference analysis is performed with the preset reference expected particle size proportion to obtain the evaluation results of soil residue particle size distribution abnormalities.

7. A shield machine cutter head central area deformation detection system as claimed in claim 1, characterized in that: The specific method of dynamically correcting the abnormality determination threshold is as follows: The stress distribution matrix obtained in real time is differentially calculated with the preset reference stress field matrix to obtain the stress deviation matrix of the central area of ​​the shield machine, and then compared with the preset reference stress deviation threshold to obtain the abnormal judgment threshold correction coefficient of each monitoring point; The corrected abnormality determination threshold of each monitoring area is constructed based on the abnormality determination threshold correction coefficient of each monitoring point and the initially set abnormality determination threshold.

8. A shield machine cutter head central area deformation detection system as claimed in claim 1, characterized in that: The specific method of performing abnormal identification is as follows: The weighted sum of the static anomaly index and the dynamic anomaly index of each monitoring area is calculated to obtain the comprehensive evaluation anomaly index of each monitoring area; The comprehensive evaluation abnormality index of each monitoring area is compared with the corresponding modified abnormality judgment threshold. If the comprehensive evaluation abnormality index of a monitoring area is greater than the corresponding modified abnormality judgment threshold, it is judged that there is an abnormality in the monitoring area and it is recorded as an abnormal monitoring area. Otherwise, it is judged that there is no abnormality in the monitoring area.

9. A shield machine cutter head central area deformation detection system as claimed in claim 1, characterized in that: The specific method of locating and outputting the deformation area and risk area is as follows: The comprehensive abnormality index of each abnormal monitoring area is arranged in descending order, and classified into deformation area and risk area according to the risk index, and the corresponding area number is output.

10. A method for detecting deformation of the central area of ​​a shield machine cutterhead, characterized in that: include: S1. Area division: Based on the three-dimensional structural characteristics of the target shield machine cutter head center area, a geometric mesh division algorithm is used to generate monitoring areas with equal density distribution and generate unique codes. The center point of each monitoring area is used as the monitoring point; S2. Static monitoring: Use a 3D laser scanning device to scan the 3D topography data of each monitoring area in real time, compare the point cloud with the pre-stored initial 3D data, use image processing algorithms to achieve quantitative analysis of tool wear and evaluation of cutter head surface defects, and further output static anomaly index; S3, dynamic monitoring: Integrate infrared thermal imaging devices, vibration sensor groups and soil slag image acquisition devices to collect temperature rise gradients, vibration spectrum characteristics and shield machine soil slag discharge images of each monitoring area in real time, and calculate dynamic anomaly index through multi-source data fusion model; S4, abnormality identification: using the distributed stress sensor network embedded in the monitoring points in a dot matrix arrangement to obtain the stress distribution matrix in real time and perform differential calculation with the preset reference stress field matrix, dynamically correct the abnormality judgment threshold, and perform abnormality identification based on the static abnormality index and dynamic abnormality index; S5. Deformation positioning: locate and output the deformed area and risk area based on the abnormality recognition results.

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