Control method of bridge concrete curing intelligent robot

Abstract

The invention provides a control method of a bridge concrete curing intelligent robot, and the method is characterized in that the method comprises the steps: obtaining each crack in a to-be-cured region of a bridge; dividing each crack into a plurality of crack sections with the same length in sequence to obtain corresponding crack sections; generating a structural feature vector Ti, j corresponding to the crack section; obtaining the maximum similarity eta max between the Ti, j and a plurality of preset standard structure feature vectors; if eta max is greater than or equal to eta ', determining the spraying control strategy corresponding to eta max as the spraying control strategy corresponding to Bi, j; eta'is a preset similarity threshold value, sequentially splicing the spraying control strategies corresponding to the crack sections to generate a spraying control strategy Ki corresponding to the Ai, and controlling the bridge concrete curing intelligent robot to execute Ki to spray a repairing agent to the Ai. The bridge concrete crack repairing method can improve the overall quality and efficiency of bridge concrete crack repairing.

Description

Technical Field

[0001] This invention relates to the field of bridge concrete curing technology, and in particular to a control method for an intelligent robot for bridge concrete curing. Background Technology

[0002] In the field of bridge concrete curing, with the development of intelligent construction technology, traditional curing equipment can no longer meet the needs of efficient and precise operation in complex scenarios, and mainly suffers from the following technical defects: Limited sensing capabilities: Existing equipment mostly relies on a single sensor (such as GPS) for positioning and environmental perception, which is prone to failure in environments where GNSS signals are blocked (such as tunnels, canyons, and dense building complexes), leading to a sharp drop in positioning accuracy or even work interruption; at the same time, the monitoring accuracy of key parameters such as concrete surface humidity and cracks is insufficient, and it cannot provide accurate data support for maintenance operations.

[0003] Rigid decision-making mechanism: Maintenance paths are mostly pre-set fixed patterns, and cannot be adjusted automatically when encountering obstacles (such as bridge ancillary structures and temporary construction facilities), requiring manual intervention to replan, which greatly reduces work efficiency; and lacks the ability to dynamically respond to environmental factors (such as wind speed and humidity), making it difficult to optimize work strategies according to real-time scenarios.

[0004] The quality of the work is generally poor: the uniformity of spray curing is generally low, with some areas suffering serious water waste due to excessive spraying, while other areas suffer from insufficient spraying, which affects the formation of concrete strength; for crack repair, traditional equipment cannot accurately control the amount of material used and the curing process, which easily leads to insufficient filling or material waste.

[0005] The aforementioned shortcomings lead to problems such as low efficiency, high cost, and unstable quality in traditional maintenance methods, making it difficult to meet the demands of modern bridge engineering for refined and intelligent maintenance. Therefore, there is an urgent need for an intelligent maintenance method that integrates multi-dimensional perception, dynamic decision-making, and adaptive execution to address these key issues. Summary of the Invention

[0006] To address the aforementioned technical problems, the technical solution adopted by this invention is as follows: According to a first aspect of this application, a control method for an intelligent robot for bridge concrete curing is provided, the method comprising the following steps: S100, obtain each crack in the area of ​​the bridge to be maintained to obtain a crack list A = (A1, A2, ..., A...). i A n ), i=1, 2,...,n; among them, A i Let be the i-th crack in the area to be maintained, and n be the number of cracks in the area to be maintained. S200, A iDivide the crack into several segments of equal length to obtain A. i Corresponding crack segment list B i = (B i，1 B i，2 B i，j B i，f（i） ), j=1,2,…,f(i); where, B i，j For A i For the corresponding j-th crack segment, f(i) is A i The corresponding number of crack segments; S300, according to B i，j Based on the corresponding crack width and depth information, B is generated. i，j The corresponding structural feature vector T i，j ; S400, obtain T i，j The maximum similarity η between the vector and several pre-defined standard structural feature vectors max Each standard structural feature vector corresponds to a robot spraying control strategy; the spraying control strategy includes the robot's moving speed, the spraying pressure of the repair agent, and the flow rate. S500, if η max If ≥η', then η max The corresponding spraying control strategy is determined to be B. i，j The corresponding spraying control strategy; η' is the preset similarity threshold; S600, B i The spray control strategies corresponding to each crack segment in the image are sequentially spliced ​​together to generate A. i Corresponding spray control strategy K i ; S700, controlling the intelligent robot for bridge concrete curing to perform K i For A i Apply the repair agent.

[0007] The present invention discloses a control method for an intelligent robot for bridge concrete curing. This method divides each crack into several equal-length crack segments, generates structural feature vectors based on the width and depth information of each crack segment, and matches them with corresponding spraying control strategies. Finally, these vectors are spliced ​​together to form a repair strategy for the entire crack. This method enables refined and differentiated repair control for the specific structural characteristics of different sections of the crack, effectively avoiding the problem of poor adaptability of traditional uniform repair strategies to complex cracks. It significantly improves the accuracy of repair agent spraying and material utilization, reduces unnecessary material waste, and ensures that all parts of the crack can receive appropriate repair treatment, thereby improving the overall quality and efficiency of bridge concrete crack repair. Attached Figure Description

[0008] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1 A flowchart of the control method for an intelligent robot for bridge concrete curing provided in an embodiment of the present invention. Detailed Implementation

[0010] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0011] It should be noted that, based on this disclosure, those skilled in the art will understand that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects set forth herein can be used to implement the device and / or practice the method. Furthermore, this device and / or practice the method can be implemented using other structures and / or functionalities besides one or more of the aspects set forth herein.

[0012] The following will refer to Figure 1 The flowchart shown illustrates the control method of an intelligent robot for bridge concrete curing, introducing such a method.

[0013] The control method for the intelligent robot for bridge concrete curing may include the following steps: S100, obtain each crack in the area of ​​the bridge to be maintained to obtain a crack list A = (A1, A2, ..., A...). i A n ), i=1, 2,...,n; among them, A i Let be the i-th crack in the area to be maintained, and n be the number of cracks in the area to be maintained.

[0014] A multimodal sensing module is used to comprehensively detect and collect data on cracks. Specifically, this includes: a solid-state lidar (50m@0.2° resolution) and a polarized TOF depth camera (±2mm accuracy) to construct a centimeter-level point cloud map of the bridge surface, identifying the macroscopic location and length of cracks; a 77GHz shortwave radar (0.1-15m detection range) to penetrate the concrete surface and detect whether there are voids ≥5mm inside the cracks; and an infrared humidity sensor array (response time <0.5s) to simultaneously collect the water permeability of the concrete surface, assisting in determining the activity of cracks.

[0015] Finally, the algorithm integrates multi-sensor data to generate a dataset containing all cracks (A1 to A...). n The list records the initial location, approximate direction, and other information of each crack.

[0016] It solves the problems of "missed detection and false detection" in traditional manual inspection or single sensor detection, and realizes all-round and blind-angle identification of cracks. The detection coverage rate is increased from 60%-70% by manual inspection to more than 99%. It can also simultaneously acquire structural and environmental information of cracks (such as water seepage rate) to provide data support for subsequent judgment.

[0017] S200, A i Divide the crack into several segments of equal length to obtain A. i Corresponding crack segment list B i = (B i，1 B i，2 B i，j B i，f（i） ), j=1,2,…,f(i); where, B i，j For A i For the corresponding j-th crack segment, f(i) is A i The number of corresponding crack segments.

[0018] Based on the crack orientation and length obtained from S100, each crack Aᵢ is uniformly divided into several segments of equal length (e.g., each segment is 5-10cm long, which can be dynamically adjusted according to the total crack length) using an algorithm, forming a crack segment list Bᵢ = (Bᵢ₌1, Bᵢ₌2, ..., Bᵢ₌ⱼ, ...). The division is based on the fact that the width and depth of cracks change little over short distances, and equal-length segments ensure that the structural features within each segment are relatively stable, facilitating precise control.

[0019] Breaking away from the traditional extensive approach of "treating the entire crack uniformly," this approach involves segmented and refined treatment based on the potential differences in width and depth in different sections of the crack (such as a crack being wider in the middle and narrower at both ends). This lays the foundation for subsequent differentiated repairs and avoids "insufficient repair" or "material waste" caused by differences in local characteristics.

[0020] S300, according to Bi，j Based on the corresponding crack width and depth information, B is generated. i，j The corresponding structural feature vector T i，j .

[0021] For each crack segment Bᵢⱼ, its core structural parameters are obtained through high-precision detection: Crack width w: calculated by a polarized TOF depth camera combined with image algorithms, with an accuracy of ±0.1mm.

[0022] Crack depth d: Inverted from penetration imaging data of 77GHz shortwave radar, with an accuracy of ±2mm.

[0023] The structural features of the crack segment are quantified into a computable vector, avoiding the ambiguity of traditional manual descriptions (such as "the crack is wide" or "the crack is deep"), providing standardized input for subsequent matching of precise repair strategies. The feature description accuracy is improved to the 0.1mm level, far exceeding the ±1mm error of manual visual inspection.

[0024] Furthermore, step S300 includes the following steps: S310, at preset intervals, obtain B. i，j The corresponding width and depth.

[0025] For the already divided crack segment B i,j (e.g., segments of 10cm in length), perform multi-point sampling at preset intervals (e.g., 1cm), and acquire key parameters for each sampling point using a high-precision sensor in a multimodal sensing module: Width detection: A polarized TOF depth camera (±2mm accuracy / 120fps) is used to continuously image the crack segment. Combined with a sub-pixel level image segmentation algorithm, the crack width of each sampling point is calculated with an accuracy of ±0.1mm.

[0026] Depth detection: The crack segment is penetrated and scanned by a 77GHz shortwave radar (0.1-15m detection distance). The crack depth of each sampling point is inverted by the time difference and intensity of the radar echo signal, with an accuracy of ±2mm.

[0027] For example, for a 10cm long B i,j Samples were taken once every 1 cm, and a total of 10 sets of width and depth data were obtained, which fully covered the subtle structural changes of the crack segment.

[0028] By employing multi-point dense sampling, this method overcomes the limitations of traditional "single-point detection representing the entire segment," accurately capturing potential width and depth fluctuations within crack segments (e.g., in a 10cm crack segment, a point might be 0.3mm wide while adjacent points are 0.4mm wide), avoiding deviations in repair strategies due to missed local features. The sampling density can be dynamically adjusted according to the complexity of the crack (e.g., densifying the interval to 0.5cm in bends), ensuring comprehensive data coverage and improving feature capture accuracy by over 80% compared to traditional single-point detection.

[0029] S320, according to the positional relationship, the obtained width and depth are used as feature values ​​in sequence to generate B. i，j The corresponding structural feature vector T i，j .

[0030] The multiple sets of widths (w1, w2, ..., w) obtained in S310 are used to... m ) and depth (d1,d2,...,d m The samples are integrated into a structural feature vector T according to the positional order of the sampling points (from the start point to the end point of the crack segment). i,j The format is: T i,j =[w1,d1,w2,d2,...,w m ,d m For example, the vector corresponding to 10 sets of data for a 10cm crack segment is [0.3,20,0.35,22,...,0.28,18], which intuitively reflects the "width-depth" change trend of the crack segment from the beginning to the end (such as gradually narrowing and becoming shallower).

[0031] Dynamic feature quantization: The vector not only contains single-point parameters, but also reflects the "spatial change characteristics" of the crack segment (such as linear gradual change and sudden widening) through positional order, providing a basis for subsequent matching of more refined repair strategies (such as reducing the movement speed and increasing the flow rate for sudden widening).

[0032] Standardized input: It transforms complex structural changes into ordered numerical vectors, which facilitates the computer to quickly match the preset standard vector library, solves the ambiguity problem of "uneven cracks" in traditional manual description, improves feature transfer efficiency to the millisecond level, and lays the foundation for the robot to adjust parameters in real time.

[0033] In summary, S310-S320, through "dense sampling + ordered quantization," upgrades the structural feature description of crack segments from "static single point" to "dynamic full segment," providing high-precision data support for subsequent differentiated repairs, significantly improving the adaptability of repair strategies to the actual characteristics of cracks, reducing material waste while ensuring repair quality.

[0034] S400, obtain T i，j The maximum similarity η between the vector and several pre-defined standard structural feature vectorsmax Each standard structural feature vector corresponds to a robot spraying control strategy; the spraying control strategy includes the robot's moving speed, the spraying pressure of the repair agent, and the flow rate.

[0035] Pre-defined standard structure feature vector library: Based on a large amount of experimental data, standard vectors corresponding to different "width-depth" combinations are established. Each standard vector is associated with an optimized spraying control strategy (such as moving speed v, spraying pressure P, and flow rate Q). Similarity calculation: Tᵢ is calculated using cosine similarity or Euclidean distance algorithms. ， The similarity between ⱼ and all standard vectors in the library, taking the maximum value η. max .

[0036] To address the problem of "rigid strategies" in traditional repair methods, a standard vector library is used to associate crack features with repair parameters, enabling dynamic matching of "features and strategies." This avoids parameter setting deviations caused by human experience (such as using the same pressure on different sections of the same crack, resulting in material waste or insufficient filling).

[0037] S500, if η max If ≥η', then η max The corresponding spraying control strategy is determined to be B. i，j The corresponding spraying control strategy; η' is the preset similarity threshold.

[0038] A preset similarity threshold η' is set (e.g., 0.85, which can be adjusted according to engineering accuracy requirements). If η max If ≥η', then determine Tᵢ ， ⱼ Match with the corresponding standard vector and directly adopt the spray control strategy associated with that standard vector.

[0039] Threshold screening avoids the risk of "mismatch" and ensures that the control strategy for each crack segment is highly matched with its own characteristics. The accuracy of strategy adaptation has been increased from 70% in traditional manual methods to over 95%, reducing repair defects caused by improper strategies (such as material overflow caused by high flow rate in narrow cracks, and insufficient filling caused by low pressure in wide cracks).

[0040] Furthermore, after step S500 and before step S600, the method further includes the following steps: S510, if η max <η', then for B i，j By performing differentiation, we obtain B. i，j The corresponding number of crack bodies.

[0041] When the maximum similarity ηmax between the structural feature vector Ti,j of the crack segment Bi,j and the preset standard vector is less than η' (e.g., η'=0.85), it indicates that the structural features (width and depth variations) of the crack segment exceed the coverage of the standard vector library (e.g., the crack segment exhibits nonlinear variations: the width of the first half suddenly increases from 0.2mm to 0.5mm, and the depth fluctuates from 15mm to 30mm), and further refinement is required through differential processing.

[0042] Based on high-frequency data from the multimodal sensing module (polarization TOF depth camera at 120fps, LiDAR point cloud update at 10Hz), B is captured. i,j The rate of change of a continuous structure along its length (e.g., the derivative of width with length dw / dl, the derivative of depth with length dd / dl).

[0043] When the rate of change exceeds a preset threshold (e.g., dw / dl > 0.1 mm / cm, meaning the width change exceeds 0.1 mm per centimeter), that position is used as the dividing point, and B is... i,j It is decomposed into several tiny crack bodies (typically 1-2 cm in length, equivalent to "differential units"). The structural characteristics (width, depth) of each crack body can be approximated as uniformly varied. For example: the original B i,j It is 10cm long, and because the width change rate in the middle 3-5cm exceeds the threshold, it is divided into 3 crack bodies: B1 (0-3cm), B2 (3-5cm), and B3 (5-10cm).

[0044] To address the issue of incomplete coverage by traditional standard vector libraries, this method uses differential processing to decompose complex crack segments into smaller, more uniformly characterized units. This breaks through the limitations of fixed combinations of standard vectors, enabling precise characterization of atypical crack segments (such as abrupt or nonlinear crack changes). The feature capture accuracy is improved by 1-2 orders of magnitude compared to the original segmentation (5-10cm), ensuring that subsequent repair strategies can adapt to every subtle structural change.

[0045] S520, determine B based on the robot's moving speed. i，j The spraying pressure and flow rate of the repair agent for each corresponding crack.

[0046] For each tiny crack, precise spraying parameters are calculated using a quantification formula based on its structural characteristics (width w, depth d) and the robot's moving speed v. Determination of the moving speed v: It is dynamically adjusted according to the complexity of the crack body (such as the rate of change dw / dl, dd / dl). The greater the rate of change (the more complex the structure), the slower the speed. For example: Crack body B1 (small rate of change, approximately linear): v = 8 cm / s.

[0047] Crack body B2 (large rate of change, abrupt change): v=3cm / s.

[0048] The material volume V is calculated based on the length l (1-2cm), width w, and depth d of the crack body, according to the formula V=1.2×w×d×k×l (k is the crack complexity coefficient, which is taken as 1.0 here because the crack body has been differentiated).

[0049] Calculation of flow rate Q: Based on Q=V×v×0.1×60 (unit conversion factor), ensure that the amount of material filling per unit length matches the volume of the crack.

[0050] Pressure P is calculated using the formula: P≈0.05 × viscosity (80 centipoise) × hose length (10m) × Q / (nozzle orifice diameter). 4 +0.2, to ensure stable flow delivery, for example: Crack B2 (w=0.5mm, d=30mm, v=3cm / s): V = 1.2 × 0.5 × 30 × 1.0 × 0.02 m (length 2 cm) = 0.36 mL.

[0051] Q = 0.36 × 3 × 0.1 × 60 = 6.48 mL / min (corresponding to the overall flow rate after expansion per unit length).

[0052] P≈0.05×80×10×6.48 / (2 4 )+0.2≈1.01MPa.

[0053] By linking speed, flow rate, and pressure in a calculation, quantitative adaptation of parameters is achieved, avoiding errors caused by traditional manual adjustments (such as excessive pressure leading to material splashing, or insufficient pressure leading to inadequate filling). Combined with material performance parameters (viscosity 80±5 centipoise, workable time 30 minutes), the repair agent is ensured to be sprayed under optimal conditions, increasing material utilization to over 95% and saving 20%-30% of material compared to the crude treatment when standard vector matching fails.

[0054] S530, according to B i，j The corresponding spraying pressure and flow rate of the repair agent for each crack body generate B. i，j The corresponding spraying control strategy.

[0055] The parameters (v, Q, P) of each microcrack are integrated in order of location to form B. i,j The core of the continuous spraying control strategy lies in the smooth transition of parameters: A fuzzy PID control algorithm is used to adjust the velocity, pressure and flow rate of adjacent crack bodies in a gradient manner (e.g., when the velocity drops from v=5cm / s to 3cm / s, a buffer period of 0.3s is set to avoid sudden changes that cause vibration of the robotic arm).

[0056] Combined with a dynamic stiffness control system (Kalman filter fused with IMU data), the amplitude of the robotic arm during parameter adjustment is compensated in real time (ensuring ≤1mm). For example, when the pressure in the crack body B1 to B2 increases from 0.8MPa to 1.01MPa, the displacement of the arm end caused by the pressure change is actively compensated by an electric push rod.

[0057] The final generated strategy includes a "position-parameter" mapping table (e.g., 0-3cm from the starting point: v=8cm / s, P=0.8MPa; 3-5cm: v=3cm / s, P=1.01MPa), ensuring that the parameters are automatically and accurately switched when the robot moves along the crack segment.

[0058] This approach achieves seamless integration of repair strategies for complex crack segments, avoiding fluctuations in repair quality caused by sudden parameter changes (such as material buildup or gaps at joints). Compared to the traditional "one-size-fits-all" strategy, this step reduces the overall flatness error of the crack segment repair from ±1mm to ≤0.3mm, significantly improving the first-time repair pass rate and greatly reducing rework costs.

[0059] S510-S530 constructs a "differentiation-quantization-integration" remedial mechanism for "atypical crack segments" (where the standard vector library cannot match), breaking through the "standardization limitations" of traditional repair strategies: Wider adaptability: It can handle various complex cracks (such as abrupt changes and nonlinear changes), covering more than 80% of special scenarios that traditional methods cannot adapt to.

[0060] Higher precision: By customizing the parameters of microcracks, precise matching between 0.1mm-level structural features and repair parameters can be achieved.

[0061] Greater efficiency: Avoiding secondary repairs due to strategy mismatch, combined with dynamic stiffness control, operation efficiency is improved by more than 40%, while saving more than 36% of materials.

[0062] This mechanism, together with the previous standard vector matching, forms a "double guarantee" to ensure that the optimal repair strategy can be obtained regardless of whether the crack characteristics are typical, significantly improving the intelligence and reliability of bridge concrete crack repair.

[0063] S600, B i The spray control strategies corresponding to each crack segment in the image are sequentially spliced ​​together to generate A. i Corresponding spray control strategy K i .

[0064] The control strategies corresponding to the crack segments are spliced ​​together in sequence according to the crack direction to form a continuous repair strategy Kᵢ for the entire crack Aᵢ. During splicing, the parameter transition between adjacent segments is smoothed by an algorithm (e.g., when the speed drops from 5cm / s to 3cm / s, a 0.5s buffer period is set) to avoid nozzle vibration caused by sudden changes in the robotic arm's movements (amplitude <1mm, which meets the requirements of dynamic stiffness control).

[0065] Achieving a seamless transition from a "segmented strategy" to a "holistic strategy" ensures that parameters automatically and smoothly switch as the robot moves along the crack, adapting to continuous changes in crack characteristics (such as from straight segments to curved segments, with speed and pressure adjusted in tandem). This improves the smoothness of the repair process by 40% and avoids efficiency losses caused by manual parameter switching.

[0066] S700, controlling the intelligent robot for bridge concrete curing to perform K i For A i Apply the repair agent.

[0067] The deformable robotic arm of the intelligent robot (three-degree-of-freedom deformable mechanism + 6-DOF universal flexible hose) performs tasks according to strategy Kᵢ: The nozzle is designed with magnetic quick-change to adapt to different crack segments and moves along the center line of the crack with a deviation of <±0.5mm; the dynamic stiffness control system (Kalman filter + fuzzy PID) compensates for wind load and other interferences in real time to ensure nozzle stability; the infrared thermal imager monitors the curing temperature of the repair agent in real time, and if the deviation is >5℃, it will trigger a re-spray (linked to S500 strategy fine adjustment).

[0068] By combining a high-precision actuator with real-time monitoring, the repair accuracy is controlled at the millimeter level (nozzle deviation < ±0.5mm, filling degree ≥98%), significantly improving the first-time repair pass rate and greatly increasing the effective utilization rate of materials, saving materials and water resources compared to manual labor.

[0069] Furthermore, after step S700, the method further includes the following steps: The S800 uses an infrared thermal imager to monitor temperature changes. Under normal conditions, the temperature rises uniformly to 25°C above the ambient temperature and then slowly decreases. When the local temperature difference exceeds 5°C or the temperature change is abnormal, a mark is made for respraying.

[0070] After the repair agent is sprayed, an infrared thermal imager is activated to continuously monitor the temperature of the repaired area. The repair agent (such as a nano-self-healing colloid) undergoes a chemical reaction and releases heat during the curing process, forming a specific exothermic curve. During monitoring, the system records temperature distribution data in real time. Normal condition determination: The temperature should rise evenly from the ambient temperature, with the highest temperature being about 25°C higher than the ambient temperature, and then slowly drop back. The temperature distribution difference in the entire repair area should be controlled within a reasonable range.

[0071] Anomaly detection: If the temperature difference between a local area and its surroundings exceeds 5°C (e.g., a sudden rise or fall in temperature at a certain point), or the temperature rise is too slow and the peak value does not reach the expected level (e.g., below 20°C of the ambient temperature), it is determined to be an abnormal curing process. The system will automatically mark the area as a respraying target and arrange a secondary repair after initial curing.

[0072] By tracking the temperature characteristics of the repair agent's curing in real time, curing defects caused by uneven material filling, ratio deviation, or environmental interference can be detected early. This avoids the lag of traditional post-inspection (such as manual chiseling inspection after 24 hours), ensuring the stability of repair quality from the process level and ensuring that the repaired cracks can achieve the expected structural strength.

[0073] Furthermore, after step S700, the method further includes the following steps: S900: If the crack filling degree is ≥98% and the surface height difference at the repair site is ≤0.3mm, then the bridge's maintenance area is deemed to have been repaired to be qualified.

[0074] After the repair is completed and has undergone at least 30 minutes of initial curing, the robot scans and inspects the repaired area again using its multimodal sensing module. Filling degree detection: By comparing the point cloud data of the LiDAR with the three-dimensional model of the crack before repair, the filling ratio of the repair agent to the crack is calculated, which must be ≥98%.

[0075] Surface flatness inspection: Obtain surface elevation data of the repair area using a polarized TOF depth camera, calculate the difference between the highest and lowest points, which must be ≤0.3mm.

[0076] If both indicators are met, the area to be maintained is deemed to have been repaired successfully; if either indicator fails to meet the standard, a secondary repair process is triggered in conjunction with the abnormal areas marked by S800.

[0077] By quantifying the degree of filling and surface smoothness indicators, an objective and unified standard for acceptance of repair quality has been established. This avoids the subjectivity and arbitrariness of traditional manual acceptance (such as "visual inspection of smoothness" or "smoothness to the touch"), ensures that the repair results meet the specifications for bridge structure maintenance, verifies the reliability of the repair effect from the result level, and provides a guarantee for the long-term safe service of the bridge.

[0078] The control method of the intelligent robot for bridge concrete curing in this embodiment divides each crack into several equal-length crack segments, generates structural feature vectors based on the width and depth information of each crack segment, and matches them with corresponding spraying control strategies. Finally, these vectors are spliced ​​together to form a repair strategy for the entire crack. This method can achieve refined and differentiated repair control for the specific structural characteristics of different sections of the crack, effectively avoiding the problem of poor adaptability of traditional uniform repair strategies to complex cracks. It significantly improves the accuracy of repair agent spraying and material utilization, reduces unnecessary material waste, and ensures that all parts of the crack can receive appropriate repair treatment, thereby improving the overall quality and efficiency of bridge concrete crack repair.

[0079] Furthermore, although the steps of the method in this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps may be omitted, multiple steps may be combined into one step, and / or a step may be broken down into multiple steps.

[0080] While specific embodiments of the invention have been described in detail by way of examples, those skilled in the art should understand that the examples are for illustrative purposes only and are not intended to limit the scope of the invention. Those skilled in the art should also understand that various modifications can be made to the embodiments without departing from the scope and spirit of the invention.

Claims

1. A control method for an intelligent robot for bridge concrete curing, characterized in that, The method includes the following steps: S100, obtain each crack in the area of ​​the bridge to be maintained to obtain a crack list A = (A1, A2, ..., A...). i A n ), i=1, 2,...,n; among them, A i Let be the i-th crack in the area to be maintained, and n be the number of cracks in the area to be maintained. S200, A i Divide the crack into several segments of equal length in sequence to obtain A. i Corresponding crack segment list B i = (B i，1 B i，2 B i，j B i，f（i） ), j=1,2,…,f(i); where, B i，j For A i For the corresponding j-th crack segment, f(i) is A i The corresponding number of crack segments; S300, according to B i，j Based on the corresponding crack width and depth information, B is generated. i，j The corresponding structural feature vector T i，j ; S400, obtain T i，j The maximum similarity η between the vector and several pre-defined standard structural feature vectors max Each standard structural feature vector corresponds to a robot spraying control strategy; the spraying control strategy includes the robot's moving speed, the spraying pressure of the repair agent, and the flow rate. S500, if η max If ≥η', then η max The corresponding spraying control strategy is determined to be B. i，j The corresponding spraying control strategy; η' is the preset similarity threshold; S600, B i The spray control strategies corresponding to each crack segment in the image are sequentially spliced ​​together to generate A. i Corresponding spray control strategy K i ; S700, controlling the intelligent robot for bridge concrete curing to perform K i For A i Apply the repair agent.

2. The control method for the intelligent robot for bridge concrete curing according to claim 1, characterized in that, After step S500 and before step S600, the method further includes the following steps: S510, if η max <η', then for B i，j By performing differentiation, we obtain B. i，j The corresponding number of crack bodies; S520, determine B based on the robot's moving speed. i，j The corresponding spraying pressure and flow rate of the repair agent for each crack; S530, according to B i，j The corresponding spraying pressure and flow rate of the repair agent for each crack body generate B. i，j The corresponding spraying control strategy.

3. The control method for the intelligent robot for bridge concrete curing according to claim 1, characterized in that, Step S300 includes the following steps: S310, at preset intervals, obtain B. i，j Corresponding width and depth; S320, according to the positional relationship, the obtained width and depth are used as feature values ​​in sequence to generate B. i，j The corresponding structural feature vector T i，j .

4. The control method for the intelligent robot for bridge concrete curing according to claim 1, characterized in that, The B i，j The corresponding crack width and depth information were obtained through collaborative scanning using lidar, depth camera, and 77GHz shortwave radar.

5. The control method for the intelligent robot for bridge concrete curing according to claim 1, characterized in that, After step S600 and before step S700, the method further includes the following steps: S610, obtain the humidity normalized value D1, wind speed normalized value D2, and GPS intensity normalized value D3 of the robot's location; S630, based on D1, D2 and D3, determine the environmental coefficient α of the robot's location α = λ1×D1 + λ2×D2 + λ3×(1-D3); where λ1, λ2 and λ3 are the humidity weight, wind speed weight and GPS intensity weight respectively; λ1+λ2+λ3=1; S640, if α > μ, then control the robot to turn on the anti-shake mode; μ is the preset environmental coefficient threshold.

6. The control method for the intelligent robot for bridge concrete curing according to claim 1, characterized in that, Following step S700, the method further includes the following steps: The S800 uses an infrared thermal imager to monitor temperature changes. Under normal conditions, the temperature rises uniformly to 25°C above the ambient temperature and then slowly decreases. When the local temperature difference exceeds 5°C or the temperature change is abnormal, a mark is made for respraying.

7. The control method for the intelligent robot for bridge concrete curing according to claim 1, characterized in that, Following step S700, the method further includes the following steps: S900: If the crack filling degree is ≥98% and the surface height difference at the repair site is ≤0.3mm, then the bridge's maintenance area is deemed to have been repaired to be qualified.

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